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	Three-dimensional B-Process
	digital I-Process
	subtraction I-Process
	angiographic I-Process
	( O
	3D-DSA B-Process
	) O
	images O
	from O
	diagnostic O
	cerebral B-Process
	angiography I-Process
	were O
	obtained O
	at O
	least O
	one O
	day O
	prior O
	to O
	embolization B-Process
	in O
	all O
	patients O
	. O
	The O
	raw O
	data O
	of O
	3D-DSA B-Process
	in O
	a O
	DICOM B-Material
	file I-Material
	were O
	used O
	for O
	creating B-Task
	a I-Task
	3D I-Task
	model I-Task
	of I-Task
	the I-Task
	target I-Task
	vessel I-Task
	segment I-Task
	. O
	These O
	data O
	were O
	converted O
	to O
	standard B-Process
	triangulation I-Process
	language I-Process
	( O
	STL B-Process
	) O
	surface O
	data O
	as O
	an O
	aggregation O
	of O
	fine O
	triangular B-Material
	meshes I-Material
	using O
	3D B-Process
	visualization I-Process
	and O
	measurement B-Process
	software O
	( O
	Amira B-Material
	version I-Material
	X I-Material
	, O
	FEI O
	, O
	Burlington O
	, O
	MA O
	, O
	USA O
	) O
	. O
	An O
	unstructured O
	computational B-Material
	volumetric I-Material
	mesh I-Material
	was O
	constructed O
	from O
	the O
	triangulated B-Material
	surface I-Material
	. O
	Smoothing B-Process
	and O
	remeshing B-Process
	followed O
	as O
	next O
	steps O
	. O
	The O
	STL B-Material
	file I-Material
	was O
	then O
	transferred O
	to O
	a O
	3D B-Material
	printer I-Material
	( O
	OBJET30 B-Material
	Pro I-Material
	; O
	Stratasys O
	Ltd. O
	, O
	Eden O
	Prairie O
	, O
	MN O
	, O
	USA O
	) O
	. O
	The O
	resolution O
	of O
	the O
	build O
	layer O
	was O
	0.028mm O
	, O
	and O
	the O
	3D O
	printed O
	vessel O
	model O
	was O
	produced O
	using O
	acrylic B-Material
	resin I-Material
	( O
	Vero B-Material
	) O
	. O
	Following O
	immersion B-Process
	in I-Process
	water I-Process
	for O
	a O
	few O
	hours O
	, O
	the O
	surface O
	of O
	the O
	3D B-Material
	printed I-Material
	model I-Material
	was O
	smoothed B-Process
	by O
	manually O
	removing O
	spicule O
	. O

	Fig. O
	9 O
	displays O
	the O
	growth O
	of O
	two O
	of O
	the O
	main O
	corrosion B-Material
	products I-Material
	that O
	develop O
	or O
	form O
	on O
	the O
	surface O
	of O
	Cu40Zn B-Material
	with O
	time O
	, O
	hydrozincite B-Material
	( O
	Fig. O
	9a O
	) O
	and O
	Cu2O B-Material
	( O
	Fig. O
	9b O
	) O
	. O
	It O
	should O
	be O
	remembered O
	that O
	both O
	phases O
	were O
	present O
	already O
	from O
	start O
	of O
	the O
	exposure O
	. O
	The O
	data O
	is O
	presented O
	in O
	absorbance B-Process
	units I-Process
	and O
	allows O
	comparisons B-Task
	to I-Task
	be I-Task
	made I-Task
	of I-Task
	the I-Task
	amounts I-Task
	of I-Task
	each I-Task
	species I-Task
	between I-Task
	the I-Task
	two I-Task
	Cu40Zn I-Task
	surfaces I-Task
	investigated I-Task
	, O
	DP B-Material
	and O
	HZ7 B-Material
	. O
	The O
	tendency O
	is O
	very O
	clear O
	that O
	the O
	formation B-Process
	rates I-Process
	of O
	both O
	hydrozincite B-Material
	and O
	cuprite B-Material
	are O
	quite O
	suppressed O
	for O
	Cu40Zn B-Material
	with O
	preformed O
	hydrozincite B-Material
	( O
	HZ7 B-Material
	) O
	compared O
	to O
	the O
	diamond B-Material
	polished I-Material
	surface I-Material
	( O
	DP B-Material
	) O
	. O
	In O
	summary O
	, O
	without O
	being O
	able O
	to O
	consider O
	the O
	formation B-Process
	of I-Process
	simonkolleite I-Process
	, O
	it O
	can O
	be O
	concluded O
	that O
	an O
	increased O
	surface O
	coverage O
	of O
	hydrozincite B-Material
	reduces O
	the O
	initial B-Process
	spreading I-Process
	ability O
	of O
	the O
	NaCl-containing B-Material
	droplets I-Material
	and O
	thereby O
	lowers O
	the O
	overall O
	formation B-Process
	rate I-Process
	of O
	hydrozincite B-Material
	and O
	cuprite B-Material
	. O

	AA B-Material
	2024-T3 I-Material
	aluminium I-Material
	alloy I-Material
	is O
	widely O
	used O
	for O
	aerospace B-Task
	applications I-Task
	due O
	to O
	its O
	high O
	strength O
	to O
	weight O
	ratio O
	and O
	high O
	damage O
	tolerance O
	that O
	result O
	from O
	copper B-Material
	and O
	magnesium B-Material
	as O
	the O
	principal O
	alloying B-Material
	elements I-Material
	and O
	appropriate O
	thermomechanical B-Process
	processing I-Process
	. O
	The O
	microstructure O
	of O
	the O
	alloy B-Material
	is O
	relatively O
	complex O
	and O
	a O
	number O
	of O
	compositionally-distinct B-Process
	phases I-Process
	have O
	been O
	identified O
	[ O
	1 O
	] O
	. O
	Although O
	possessing O
	favourable O
	mechanical O
	properties O
	, O
	the O
	alloy B-Material
	is O
	relatively O
	susceptible O
	to O
	corrosion B-Process
	and O
	generally O
	requires O
	surface B-Process
	treatment I-Process
	in O
	practical O
	applications O
	. O
	The O
	corrosion B-Process
	behaviour O
	of O
	the O
	alloy B-Material
	is O
	particularly O
	affected O
	by O
	the O
	presence O
	of O
	the O
	intermetallic B-Material
	particles I-Material
	due O
	to O
	their O
	differing O
	potentials O
	with O
	respect O
	to O
	the O
	alloy B-Material
	matrix I-Material
	[ O
	2 O
	– O
	9 O
	] O
	. O
	Copper-containing B-Material
	second I-Material
	phase I-Material
	particles I-Material
	at O
	the O
	alloy B-Material
	surface O
	are O
	particularly O
	detrimental O
	to O
	the O
	corrosion B-Process
	resistance I-Process
	as O
	they O
	provide O
	preferential O
	cathodic O
	sites O
	[ O
	2,10 O
	] O
	. O
	One O
	of O
	the O
	principle O
	types O
	of O
	second B-Material
	phase I-Material
	particle I-Material
	that O
	is O
	important O
	to O
	the O
	corrosion B-Process
	behaviour I-Process
	of O
	the O
	alloy O
	is O
	the O
	S B-Material
	phase I-Material
	( O
	Al2CuMg B-Material
	) O
	particle O
	[ O
	1,11 O
	] O
	. O
	Dealloying O
	of O
	S B-Material
	phase I-Material
	particles I-Material
	, O
	which O
	may O
	account O
	for O
	∼ O
	60 O
	% O
	of O
	the O
	constituent B-Material
	particles I-Material
	in O
	AA2024 B-Material
	alloys I-Material
	[ O
	11 O
	] O
	, O
	is O
	commonly O
	observed O
	when O
	the O
	alloy B-Material
	is O
	exposed O
	to O
	an O
	aggressive O
	environment O
	. O
	The O
	particles B-Material
	are O
	considered O
	as O
	important O
	initiation O
	sites O
	for O
	severe O
	localized O
	corrosion B-Process
	in O
	the O
	alloy B-Material
	[ O
	11 O
	– O
	22 O
	] O
	. O
	The O
	dealloying B-Process
	of O
	the O
	S B-Material
	phase I-Material
	particles I-Material
	and O
	the O
	resulting O
	enrichment B-Process
	of O
	copper B-Material
	result O
	in O
	a O
	decrease B-Process
	of I-Process
	the I-Process
	Volta I-Process
	potential I-Process
	with O
	respect O
	to O
	the O
	matrix O
	and O
	hence O
	the O
	dealloyed B-Material
	particles I-Material
	become O
	active O
	cathodic B-Material
	sites I-Material
	[ O
	23 O
	– O
	25 O
	] O
	. O

	Measuring B-Task
	and I-Task
	analysing I-Task
	the I-Task
	hold I-Task
	time I-Task
	of I-Task
	the I-Task
	CPA I-Task
	pill I-Task
	allows O
	the O
	thermal B-Process
	boundary I-Process
	resistance I-Process
	within O
	the O
	pill B-Material
	to O
	be O
	assessed O
	; O
	the O
	thermal B-Process
	boundary I-Process
	dictates O
	the O
	actual O
	temperature B-Process
	of O
	the O
	CPA B-Material
	crystals I-Material
	in O
	comparison O
	to O
	the O
	temperature O
	of O
	the O
	cold B-Material
	finger I-Material
	, O
	which O
	is O
	maintained O
	at O
	a O
	constant O
	temperature O
	by O
	a O
	servo B-Process
	control I-Process
	program I-Process
	. O
	Fig. O
	17 O
	shows O
	the O
	temperature O
	profile O
	during O
	the O
	recycling B-Process
	of O
	the O
	CPA B-Material
	pill I-Material
	and O
	subsequent O
	operation B-Process
	at I-Process
	200mK I-Process
	. O
	During O
	the O
	hold O
	time O
	, O
	the O
	servo B-Process
	control I-Process
	program I-Process
	maintained O
	the O
	CPA B-Material
	pill I-Material
	temperature O
	to O
	within O
	a O
	millikelvin O
	. O
	It O
	is O
	expected O
	that O
	microkelvin B-Process
	stability I-Process
	can O
	be O
	achieved O
	with O
	fast B-Process
	read-out I-Process
	thermometry I-Process
	( O
	which O
	was O
	not O
	available O
	at O
	the O
	time O
	of O
	testing O
	but O
	which O
	will O
	be O
	used O
	for O
	the O
	mKCC B-Task
	) O
	, O
	as O
	this O
	would O
	allow O
	for O
	temperature O
	control O
	on O
	much O
	faster O
	( O
	millisecond O
	) O
	timescales O
	than O
	the O
	current O
	( O
	approximately O
	1s O
	) O
	thermometry O
	readout O
	used O
	. O

	The O
	product B-Process
	change I-Process
	between O
	batches B-Material
	# I-Material
	1 I-Material
	/# I-Material
	2 I-Material
	and O
	the O
	others O
	is O
	the O
	most O
	influential O
	on O
	the O
	test O
	results O
	. O
	The O
	redesign O
	and O
	upgrade O
	to O
	110-nm B-Process
	process I-Process
	technology I-Process
	reduces O
	the O
	pass O
	rate O
	at O
	LNT B-Material
	by O
	approximately O
	half O
	. O
	This O
	is O
	mainly O
	caused O
	by O
	the O
	increased O
	incidence O
	of O
	erase B-Process
	and I-Process
	program I-Process
	timeouts I-Process
	with O
	some O
	contribution O
	from O
	long B-Process
	erase I-Process
	and O
	program B-Process
	times I-Process
	and O
	bit B-Process
	errors I-Process
	. O
	The O
	difference O
	in O
	pass B-Process
	rates I-Process
	at O
	88K O
	between O
	batches B-Material
	# I-Material
	3 I-Material
	/# I-Material
	4 I-Material
	and O
	# B-Material
	5 I-Material
	/# I-Material
	6 I-Material
	, O
	which O
	use O
	the O
	same O
	process B-Process
	technology I-Process
	with O
	the O
	same O
	dimensions O
	, O
	can O
	be O
	explained O
	by O
	the O
	fabrication O
	in O
	different O
	assembly B-Process
	lines I-Process
	, O
	where O
	other O
	processes O
	or O
	base O
	materials O
	may O
	have O
	been O
	changed O
	. O
	This O
	means O
	different O
	tolerances B-Process
	in O
	base O
	materials O
	and O
	production O
	process O
	, O
	which O
	are O
	more O
	pronounced O
	the O
	lower O
	the O
	temperature O
	. O
	Some O
	of O
	the O
	differences O
	of O
	technology O
	scale O
	may O
	reflect O
	shifts O
	in O
	transistor B-Process
	parameters I-Process
	such O
	as O
	transconductance B-Process
	/ I-Process
	gain I-Process
	, O
	threshold B-Process
	voltage I-Process
	, O
	and O
	threshold B-Process
	slope I-Process
	[ O
	7 O
	] O
	. O

	Prior O
	to O
	assembling B-Task
	the I-Task
	miniature I-Task
	ADR I-Task
	, O
	the O
	mKCC B-Material
	MR I-Material
	heat I-Material
	switch I-Material
	could O
	not O
	be O
	fully O
	thermally B-Process
	characterised I-Process
	due O
	to O
	cryostat B-Material
	constraints I-Material
	. O
	However O
	, O
	based O
	on O
	experiments O
	and O
	research O
	conducted O
	at O
	MSSL O
	on O
	a O
	range O
	of O
	tungsten B-Material
	heat I-Material
	switches I-Material
	, O
	the O
	thermal B-Process
	conductivity I-Process
	has O
	been O
	estimated O
	. O
	In O
	Hills O
	et O
	al O
	. O
	[ O
	8 O
	] O
	, O
	an O
	equation O
	is O
	derived O
	which O
	allows O
	the O
	thermal O
	conductivity O
	( O
	κ B-Process
	) O
	below O
	6K O
	to O
	be O
	calculated O
	as O
	a O
	function O
	of O
	magnetic B-Process
	field I-Process
	( O
	B B-Process
	) O
	and O
	temperature O
	( O
	T O
	) O
	( O
	see O
	Eq. O
	( O
	1 O
	)) O
	. O
	To O
	estimate O
	the O
	performance O
	of O
	the O
	mKCC B-Material
	heat I-Material
	switch I-Material
	, O
	the O
	parameters O
	in O
	Eq O
	. O
	( O
	1 O
	) O
	have O
	been O
	taken O
	from O
	the O
	measured O
	thermal B-Process
	conductivity I-Process
	of O
	another O
	MSSL B-Material
	heat I-Material
	switch I-Material
	with O
	the O
	same O
	1.5mm O
	square O
	cross O
	section O
	, O
	a O
	free O
	path O
	length O
	of O
	43cm O
	and O
	a O
	RRR O
	of O
	20,000 O
	; O
	it O
	has O
	been O
	observed O
	from O
	experiments O
	conducted O
	at O
	MSSL O
	that O
	there O
	is O
	little O
	change O
	in O
	the O
	thermal B-Process
	performance I-Process
	for O
	tungsten B-Material
	heat I-Material
	switches I-Material
	with O
	a O
	RRR O
	between O
	20,000 O
	and O
	32,000 O
	( O
	subject O
	of O
	a O
	future O
	publication O
	) O
	and O
	therefore O
	the O
	performance O
	of O
	the O
	20,000 B-Material
	RRR I-Material
	heat I-Material
	switch I-Material
	has O
	been O
	assumed O
	to O
	be O
	a O
	good O
	approximation O
	. O
	Fig. O
	5 O
	gives O
	the O
	calculated O
	thermal B-Process
	conductivity I-Process
	of O
	the O
	mKCC B-Material
	switch I-Material
	at O
	0 O
	, O
	1 O
	, O
	2 O
	and O
	3T O
	based O
	on O
	Eq O
	. O
	( O
	1 O
	) O
	, O
	where O
	the O
	constants O
	b0 O
	, O
	a1 O
	, O
	a2 O
	, O
	a3 O
	, O
	a4 O
	and O
	n O
	have O
	the O
	values O
	0.0328 O
	, O
	1.19 O
	× O
	10 O
	− O
	4 O
	, O
	3.57 O
	× O
	10 O
	− O
	6 O
	, O
	1.36 O
	, O
	0.000968 O
	and O
	1.7 O
	respectively O
	. O
	It O
	should O
	be O
	noted O
	that O
	the O
	calculated O
	thermal B-Process
	conductivity I-Process
	of O
	the O
	mKCC B-Material
	switch I-Material
	presented O
	in O
	Fig. O
	5 O
	has O
	been O
	validated O
	by O
	comparing O
	the O
	experimental O
	results O
	of O
	the O
	miniature B-Material
	ADR I-Material
	with O
	modelled O
	predictions O
	( O
	this O
	is O
	discussed O
	in O
	Section O
	3.3 O
	).( O
	1 O
	) O
	κ O
	( O
	T O
	)= O
	b0T2 O
	+ O
	1a1 O
	+ O
	a2T2T O
	+ O
	Bna3T O
	+ O
	a4T4 O

	An O
	early O
	attempt O
	to O
	combine B-Task
	sets I-Task
	and I-Task
	networks I-Task
	in I-Task
	a I-Task
	single I-Task
	visualization I-Task
	relied O
	on O
	first O
	drawing O
	an O
	Euler B-Process
	diagram I-Process
	then O
	placing O
	a O
	graph O
	inside O
	it O
	[ O
	30 O
	] O
	, O
	however O
	the O
	sets O
	were O
	often O
	visualized O
	with O
	convoluted O
	, O
	difficult O
	to O
	follow O
	curves O
	. O
	In O
	addition O
	, O
	only O
	limited O
	kinds O
	of O
	set O
	data O
	could O
	be O
	shown O
	as O
	the O
	system O
	was O
	limited O
	to O
	well-formed O
	Euler B-Process
	diagrams I-Process
	. O
	Compound B-Material
	graphs I-Material
	can O
	be O
	used O
	to O
	represent O
	restricted O
	kinds O
	of O
	grouped O
	network B-Material
	data I-Material
	[ O
	8 O
	] O
	. O
	Graph B-Material
	clusters I-Material
	are O
	visualized O
	with O
	transparent B-Process
	hulls I-Process
	by O
	Santamaria O
	and O
	Theron O
	[ O
	39 O
	] O
	. O
	However O
	, O
	the O
	technique O
	removes O
	edges O
	from O
	the O
	graph O
	and O
	it O
	is O
	not O
	sufficiently O
	sophisticated O
	for O
	arbitrary O
	overlapping O
	sets O
	. O
	Itoh O
	et O
	al O
	. O
	[ O
	24 O
	] O
	proposed O
	to O
	overlay O
	pie-like O
	glyphs B-Material
	over O
	the O
	nodes O
	in O
	a O
	graph O
	to O
	encode O
	multiple O
	categories O
	. O
	Each O
	set O
	is O
	hence O
	represented O
	using O
	disconnected O
	regions O
	that O
	are O
	linked O
	by O
	having O
	the O
	same O
	colour O
	. O
	This O
	causes O
	difficulties O
	with O
	tasks O
	that O
	involve O
	finding B-Task
	relations I-Task
	between I-Task
	sets I-Task
	such O
	as O
	T1 B-Process
	, I-Process
	T3 I-Process
	and I-Process
	T4 I-Process
	in O
	Section O
	5.3 O
	. O
	A O
	related O
	class O
	of O
	techniques O
	visualize B-Process
	grouping I-Process
	information I-Process
	over I-Process
	graphs I-Process
	using O
	convex B-Process
	hulls I-Process
	, O
	such O
	as O
	Vizster B-Process
	[ O
	22 O
	] O
	. O
	However O
	, O
	they O
	do O
	not O
	support O
	visualizing O
	set O
	overlaps O
	. O

	Moreover O
	, O
	one O
	observes O
	segregation B-Process
	effects I-Process
	by O
	the O
	XRD B-Task
	analysis I-Task
	, O
	which O
	probably O
	took O
	place O
	at O
	high O
	temperature O
	, O
	and O
	were O
	partially O
	quenched B-Process
	to O
	room O
	temperature O
	. O
	The O
	phase O
	analysis O
	showed O
	up O
	to O
	three O
	distinct O
	phases O
	, O
	which O
	should O
	have O
	hence O
	a O
	distinct O
	measurable O
	phase O
	transition O
	temperature O
	, O
	if O
	they O
	crystallise B-Process
	from O
	the O
	liquid B-Material
	on O
	the O
	surface B-Material
	. O
	In O
	the O
	thermograms B-Process
	these O
	effects O
	are O
	not O
	observable O
	as O
	different O
	solidification B-Process
	arrest I-Process
	or O
	clear B-Process
	inflections I-Process
	. O
	The O
	proportion O
	of O
	new O
	appearing O
	phases O
	is O
	small O
	and O
	therefore O
	the O
	latent B-Process
	heat I-Process
	released O
	by O
	this O
	new O
	phase O
	will O
	be O
	also O
	small O
	. O
	The O
	reflected B-Process
	light I-Process
	signal I-Process
	technique I-Process
	only O
	showed O
	one O
	phase B-Process
	change I-Process
	during O
	cooling O
	. O
	As O
	well O
	, O
	the O
	location O
	of O
	this O
	segregation B-Process
	cannot O
	be O
	determined O
	exactly O
	in O
	the O
	molten B-Material
	pool I-Material
	or O
	later O
	in O
	the O
	re-solidified B-Material
	material I-Material
	. O
	At O
	the O
	surface O
	, O
	where O
	the O
	temperature O
	is O
	measured O
	, O
	the O
	material B-Task
	analysis I-Task
	by I-Task
	Raman I-Task
	spectroscopy I-Task
	has O
	not O
	shown O
	signs O
	of O
	segregation B-Process
	, O
	so O
	that O
	also O
	the O
	uncertainties O
	in O
	composition O
	for O
	the O
	phase B-Process
	transition I-Process
	are O
	taken O
	from O
	the O
	uncertainties O
	from O
	the O
	XRD B-Task
	analysis I-Task
	for O
	the O
	most O
	abundant O
	phase O
	at O
	each O
	composition O
	in O
	re-solidified B-Material
	material I-Material
	. O

	Myocardial B-Process
	electrical I-Process
	propagation I-Process
	can O
	be O
	simulated O
	using O
	the O
	monodomain B-Material
	or I-Material
	bidomain I-Material
	PDEs I-Material
	[ O
	5,6 O
	] O
	. O
	Due O
	to O
	its O
	capacity O
	to O
	represent O
	complex O
	geometries B-Material
	with O
	ease O
	, O
	approximations O
	are O
	often O
	obtained O
	using O
	the O
	finite B-Process
	element I-Process
	method I-Process
	( O
	FEM B-Process
	) O
	to O
	discretise O
	the O
	PDEs B-Material
	in O
	space O
	on O
	realistic O
	cardiac B-Material
	geometry I-Material
	meshes I-Material
	; O
	this O
	results O
	in O
	very O
	large O
	( O
	up O
	to O
	forty-million O
	degrees O
	of O
	freedom O
	( O
	DOF O
	) O
	for O
	human B-Task
	heart I-Task
	geometries I-Task
	) O
	systems B-Process
	of I-Process
	linear I-Process
	equations I-Process
	which O
	must O
	be O
	solved O
	many O
	thousands O
	of O
	times O
	over O
	the O
	course O
	of O
	even O
	a O
	short O
	simulation B-Process
	. O
	Thus O
	, O
	they O
	are O
	extremely O
	computationally O
	demanding O
	, O
	presenting O
	taxing O
	problems O
	even O
	to O
	high-end B-Process
	supercomputing I-Process
	resources I-Process
	. O
	This O
	computational O
	demand O
	means O
	that O
	effort O
	has O
	been O
	invested O
	in O
	developing B-Task
	efficient I-Task
	solution I-Task
	techniques I-Task
	, O
	including O
	work O
	on O
	preconditioning B-Task
	, O
	parallelisation B-Task
	and O
	adaptivity B-Task
	in I-Task
	space I-Task
	and I-Task
	time I-Task
	[ O
	7 O
	– O
	12 O
	] O
	. O
	In O
	this O
	study O
	, O
	we O
	investigate B-Task
	the I-Task
	potential I-Task
	of I-Task
	reducing I-Task
	the I-Task
	number I-Task
	of I-Task
	DOF I-Task
	by O
	using O
	a O
	high-order B-Process
	polynomial I-Process
	FEM I-Process
	[ O
	13 O
	– O
	15 O
	] O
	to O
	approximate O
	the O
	monodomain B-Material
	PDE I-Material
	in O
	space O
	, O
	with O
	the O
	goal O
	of O
	significantly B-Task
	improving I-Task
	simulation I-Task
	efficiency I-Task
	over O
	the O
	piecewise-linear B-Process
	FEM I-Process
	approach O
	commonly O
	used O
	in O
	the O
	field O
	[ O
	16 O
	– O
	19 O
	] O
	. O
	For O
	schemes O
	where O
	the O
	polynomial O
	degree O
	p O
	of O
	the O
	elements O
	is O
	adjusted O
	according O
	to O
	the O
	error O
	in O
	the O
	approximation O
	, O
	this O
	is O
	known O
	as O
	the O
	finite B-Process
	element I-Process
	p-version I-Process
	. O
	In O
	the O
	work O
	presented O
	here O
	, O
	we O
	work O
	with O
	schemes O
	which O
	keep O
	p O
	fixed O
	. O

	In O
	this O
	work O
	we O
	develop B-Task
	a I-Task
	new I-Task
	approach I-Task
	to I-Task
	DEA I-Task
	suitable I-Task
	for I-Task
	modelling I-Task
	three-dimensional I-Task
	problems I-Task
	. O
	The O
	present O
	DEA B-Process
	methods I-Process
	rely O
	on O
	the O
	fact O
	that O
	one O
	can O
	easily O
	parametrise B-Process
	the I-Process
	boundary I-Process
	of I-Process
	the I-Process
	region I-Process
	being O
	modelled O
	, O
	and O
	then O
	apply O
	an O
	orthonormal B-Process
	basis I-Process
	approximation I-Process
	over O
	the O
	resulting O
	boundary B-Material
	phase I-Material
	space I-Material
	coordinate I-Material
	system I-Material
	. O
	In O
	two O
	dimensions O
	this O
	is O
	simple O
	as O
	the O
	boundary O
	may O
	be O
	parametrised O
	along O
	its O
	arc-length B-Material
	and O
	the O
	associated O
	momentum B-Material
	( I-Material
	or I-Material
	direction I-Material
	) I-Material
	coordinate I-Material
	taken O
	tangential O
	to O
	the O
	boundary O
	. O
	The O
	basis O
	can O
	be O
	any O
	suitable O
	( O
	scaled O
	) O
	univariate B-Material
	basis I-Material
	in O
	both O
	position O
	and O
	momentum O
	, O
	such O
	as O
	a O
	Fourier B-Material
	basis I-Material
	[ O
	8 O
	] O
	or O
	Chebyshev B-Material
	polynomials I-Material
	[ O
	9 O
	] O
	. O
	Defining O
	a O
	suitable O
	parametrisation O
	for O
	the O
	spatial O
	coordinate O
	in O
	three-dimensions O
	becomes O
	much O
	more O
	difficult O
	. O
	In O
	momentum O
	space O
	spherical B-Process
	polar I-Process
	coordinates I-Process
	may O
	be O
	employed O
	and O
	so O
	these O
	problems O
	do O
	not O
	arise O
	. O

	We O
	order B-Task
	the I-Task
	discrete I-Task
	unknowns I-Task
	so O
	that O
	the O
	vector B-Material
	of I-Material
	unknowns I-Material
	, O
	xPS B-Material
	=[ I-Material
	X,L I-Material
	] I-Material
	, O
	contains O
	the O
	nx O
	unknown O
	nodal B-Material
	coordinates I-Material
	, O
	followed O
	by O
	the O
	nb O
	unknown O
	discrete O
	Lagrange B-Material
	multipliers I-Material
	. O
	The O
	linear O
	systems O
	to O
	be O
	solved O
	in O
	the O
	course O
	of O
	the O
	Newton-based B-Process
	solution I-Process
	of O
	Eq O
	. O
	( O
	10 O
	) O
	, O
	subject O
	to O
	the O
	displacement B-Material
	constraint I-Material
	( O
	9 O
	) O
	, O
	then O
	have O
	saddle-point B-Process
	structure I-Process
	,( O
	15 O
	) O
	where O
	E O
	is O
	the O
	tangent B-Process
	stiffness I-Process
	matrix I-Process
	of O
	the O
	unconstrained B-Material
	pseudo-solid I-Material
	problem I-Material
	, O
	and O
	the O
	two B-Material
	off-diagonal I-Material
	blocks I-Material
	Cxl B-Material
	and O
	Clx B-Material
	= I-Material
	CxlT I-Material
	arise O
	through O
	the O
	imposition O
	of O
	the O
	displacement O
	constraint O
	by O
	the O
	Lagrange B-Material
	multipliers I-Material
	. O
	We O
	refer O
	to O
	[ O
	34 O
	] O
	for O
	the O
	proof O
	of O
	the O
	LBB B-Material
	stability O
	of O
	this O
	discretisation B-Process
	; O
	see O
	also O
	[ O
	35,36 O
	] O
	for O
	a O
	discussion O
	of O
	the O
	LBB B-Material
	stability O
	of O
	the O
	Lagrange-multiplier-based B-Process
	imposition I-Process
	of O
	Dirichlet B-Process
	boundary I-Process
	conditions I-Process
	in O
	related O
	problems O
	. O
	We O
	note O
	that O
	during O
	the O
	first O
	step O
	of O
	the O
	Newton B-Process
	iteration I-Process
	, O
	E O
	is O
	symmetric O
	positive O
	definite O
	since O
	it O
	represents O
	the O
	tangent B-Material
	stiffness I-Material
	matrix I-Material
	relative O
	to O
	the O
	system O
	’s O
	equilibrium B-Process
	configuration I-Process
	. O

	Inequality B-Process
	( O
	22 O
	) O
	indicates O
	that O
	the O
	maximum-norm B-Material
	is O
	the O
	loosest O
	among O
	all O
	p-norms B-Material
	. O
	Fortunately O
	, O
	this O
	loosest O
	constraint O
	would O
	not O
	seriously O
	affect O
	the O
	accuracy O
	since O
	the O
	value O
	of O
	\|\| B-Material
	y I-Material
	\|\|∞ I-Material
	is O
	comparable O
	to O
	that O
	of O
	the O
	2-norm B-Material
	and O
	1-norm B-Material
	. O
	The O
	maximum-norm B-Material
	provides O
	us O
	with O
	the O
	largest O
	number O
	of O
	possible O
	solutions O
	under O
	a O
	given O
	error O
	limitation O
	[ O
	24 O
	] O
	. O
	This O
	would O
	greatly O
	enhance O
	the O
	possibility O
	of O
	finding O
	a O
	group O
	of O
	optimized B-Material
	coefficients I-Material
	when O
	scanning O
	a O
	vast B-Process
	solution I-Process
	set I-Process
	. O
	On O
	the O
	other O
	hand O
	, O
	checking O
	the O
	maximum B-Material
	deviation I-Material
	sounds O
	more O
	reasonable O
	than O
	checking O
	the O
	“ O
	distance O
	” O
	between O
	the O
	accurate O
	and O
	approximated O
	wave O
	numbers O
	since O
	it O
	is O
	not O
	working O
	in O
	the O
	space O
	domain O
	. O
	Therefore O
	, O
	we O
	chose O
	the O
	maximum-norm B-Material
	as O
	our O
	criterion O
	for O
	designing O
	the O
	objective B-Material
	functions I-Material
	to O
	extend B-Task
	the I-Task
	accurate I-Task
	wave I-Task
	number I-Task
	coverage I-Task
	as O
	widely O
	as O
	possible O
	. O

	Similar O
	numerical B-Material
	oscillations I-Material
	to O
	those O
	described O
	above O
	also O
	emerge O
	in O
	the O
	ISPM B-Material
	when O
	utilising O
	classical O
	IBM B-Process
	kernels I-Process
	due O
	to O
	their O
	lack O
	of O
	regularity O
	( O
	with O
	discontinuous O
	second O
	derivatives O
	) O
	. O
	Furthermore O
	, O
	it O
	is O
	important O
	to O
	remark O
	that O
	the O
	immersed O
	structure O
	stresses O
	are O
	captured O
	in O
	the O
	Lagrangian B-Process
	description I-Process
	and O
	hence O
	, O
	in O
	order O
	to O
	compute O
	them O
	accurately O
	, O
	it O
	is O
	important O
	to O
	ensure O
	that O
	these O
	spurious O
	oscillations B-Material
	are O
	not O
	introduced O
	via O
	the O
	kernel B-Process
	interpolation I-Process
	functions I-Process
	. O
	In O
	this O
	paper O
	, O
	the O
	authors O
	have O
	specifically O
	designed O
	a B-Task
	new I-Task
	family I-Task
	of I-Task
	kernel I-Task
	functions I-Task
	which I-Task
	do I-Task
	not I-Task
	introduce I-Task
	these I-Task
	spurious I-Task
	oscillations I-Task
	. O
	The O
	kernel O
	functions O
	are O
	obtained O
	by O
	taking O
	into O
	account O
	discrete O
	reproducibility O
	conditions O
	as O
	originally O
	introduced O
	by O
	Peskin O
	[ O
	14 O
	] O
	( O
	in O
	our O
	case O
	, O
	tailor-made O
	for O
	Cartesian B-Process
	staggered I-Process
	grids I-Process
	) O
	and O
	regularity O
	requirements O
	to O
	prevent O
	the O
	appearance O
	of O
	spurious O
	oscillations B-Material
	when O
	computing B-Process
	derivatives I-Process
	. O
	A O
	Maple B-Task
	computer I-Task
	program I-Task
	has O
	been O
	developed O
	to O
	obtain B-Task
	explicit I-Task
	expressions I-Task
	for I-Task
	the I-Task
	new I-Task
	kernels I-Task
	. O

	Contact B-Process
	methods I-Process
	have O
	been O
	developed O
	and O
	used O
	in O
	Lagrangian O
	staggered-grid B-Material
	hydrodynamic I-Material
	( O
	SGH B-Material
	) O
	calculations O
	for O
	many O
	years O
	. O
	Early O
	examples O
	of O
	contact B-Process
	methods I-Process
	are O
	discussed O
	in O
	Wilkins O
	[ O
	37 O
	] O
	and O
	Cherry O
	et O
	al O
	. O
	[ O
	7 O
	] O
	. O
	Hallquist O
	et O
	al O
	. O
	[ O
	17 O
	] O
	provides O
	an O
	overview O
	of O
	multiple O
	contact B-Process
	algorithms I-Process
	used O
	in O
	various O
	Lagrangian B-Process
	SGH I-Process
	codes O
	dating O
	back O
	to O
	HEMP B-Process
	[ O
	37 O
	] O
	. O
	Of O
	particular O
	interest O
	, O
	Hallquist O
	et O
	al O
	. O
	[ O
	17 O
	] O
	describes O
	the O
	contact B-Process
	surface I-Process
	scheme I-Process
	used O
	in O
	TOODY B-Process
	[ O
	31 O
	] O
	and O
	later O
	implemented O
	in O
	DYNA2D B-Process
	[ O
	36 O
	] O
	. O
	The O
	contact B-Process
	method I-Process
	of O
	TOODY B-Process
	uses O
	a O
	master B-Process
	– I-Process
	slave I-Process
	approach I-Process
	. O
	The O
	goal O
	of O
	this O
	approach O
	is O
	to O
	treat O
	the O
	nodes B-Material
	on O
	the O
	contact B-Material
	surface I-Material
	in O
	a O
	manner O
	similar O
	to O
	an O
	internal B-Material
	node I-Material
	. O
	The O
	physical O
	properties O
	of O
	the O
	slave B-Material
	surface I-Material
	are O
	interpolated O
	to O
	a O
	ghost B-Material
	mesh I-Material
	( O
	termed O
	phony B-Material
	elements I-Material
	in O
	[ O
	17 O
	]) O
	that O
	overlays O
	the O
	slave B-Material
	zones I-Material
	. O
	The O
	physical O
	properties O
	are O
	interpolated O
	from O
	the O
	slave B-Material
	surface I-Material
	to O
	the O
	ghost B-Material
	zones I-Material
	using O
	surface O
	area O
	weights O
	. O
	The O
	surface O
	area O
	weights O
	are O
	equal O
	to O
	the O
	ratio O
	of O
	the O
	ghost B-Material
	zone I-Material
	surface I-Material
	area I-Material
	to O
	the O
	surface O
	area O
	of O
	the O
	master B-Material
	surface I-Material
	. O
	The O
	contact B-Task
	surface I-Task
	method I-Task
	for O
	nodal-based B-Process
	Lagrangian I-Process
	cell-centered I-Process
	hydrodynamics I-Process
	( O
	CCH B-Material
	) O
	presented O
	in O
	this O
	paper O
	will O
	use O
	surface O
	area O
	weights O
	similar O
	in O
	concept O
	to O
	those O
	in O
	TOODY B-Process
	. O
	Following O
	the O
	area B-Process
	fraction I-Process
	approach I-Process
	of O
	TOODY B-Process
	may O
	seem O
	retrospective O
	; O
	however O
	, O
	using O
	surface O
	area O
	weights O
	naturally O
	extends O
	to O
	the O
	new O
	CCH B-Material
	methods O
	that O
	solve B-Task
	a I-Task
	Riemann-like I-Task
	problem I-Task
	at I-Task
	the I-Task
	node I-Task
	of I-Task
	a I-Task
	zone I-Task
	[ O
	10,24,25,3 O
	] O
	. O

	Three O
	Runge B-Process
	– I-Process
	Kutta I-Process
	IMEX I-Process
	schemes I-Process
	were O
	tested O
	by O
	Ullrich O
	and O
	Jablonowski O
	[ O
	23 O
	] O
	for O
	the O
	HEVI B-Process
	solution I-Process
	of O
	the O
	equations O
	governing O
	atmospheric B-Process
	motion I-Process
	. O
	They O
	tested O
	the O
	ARS B-Process
	( I-Process
	2,3,2 I-Process
	) I-Process
	scheme I-Process
	of O
	Ascher O
	et O
	al O
	. O
	[ O
	1 O
	] O
	and O
	also O
	suggested O
	the O
	less O
	computationally O
	expensive O
	but O
	nearly O
	as O
	accurate O
	Strang B-Process
	carryover I-Process
	scheme I-Process
	. O
	This O
	involves O
	Strang B-Process
	splitting I-Process
	but O
	the O
	first O
	implicit O
	stage O
	is O
	cleverly O
	re-used O
	from O
	the O
	final O
	implicit O
	stage O
	of O
	the O
	previous O
	time-step O
	and O
	so O
	there O
	is O
	only O
	one O
	implicit O
	solution O
	per O
	time-step O
	. O
	Another O
	novel O
	approach O
	taken O
	by O
	Ullrich O
	and O
	Jablonowski O
	[ O
	23 O
	] O
	is O
	to O
	use O
	a O
	Rosenbrock B-Process
	solution I-Process
	in O
	order O
	to O
	treat O
	all O
	of O
	the O
	vertical O
	terms O
	implicitly O
	rather O
	than O
	just O
	the O
	terms O
	involved O
	in O
	wave B-Process
	propagation I-Process
	. O
	A O
	Rosenbrock B-Process
	solution I-Process
	is O
	one B-Process
	iteration I-Process
	of I-Process
	a I-Process
	Newton I-Process
	solver I-Process
	. O
	This O
	circumvents O
	the O
	time-step B-Process
	restriction I-Process
	associated O
	with O
	vertical B-Process
	advection I-Process
	at O
	the O
	cost O
	of O
	slowing B-Process
	the I-Process
	vertical I-Process
	advection I-Process
	. O

	After O
	all O
	micro O
	elements O
	reach O
	a O
	relaxed O
	steady-state O
	, O
	measurements O
	are O
	obtained O
	using O
	a O
	cumulative B-Process
	averaging I-Process
	technique I-Process
	to O
	reduce B-Task
	noise I-Task
	. O
	Each O
	micro O
	element O
	is O
	divided B-Process
	into I-Process
	spatially-oriented I-Process
	bins I-Process
	in O
	the O
	y-direction O
	in O
	order O
	to O
	resolve B-Process
	the I-Process
	velocity I-Process
	and I-Process
	shear-stress I-Process
	profiles I-Process
	. O
	Velocity O
	in O
	each O
	bin O
	is O
	measured O
	using O
	the O
	Cumulative B-Process
	Averaging I-Process
	Method I-Process
	( O
	CAM B-Process
	) O
	[ O
	24 O
	] O
	, O
	while O
	the O
	stress B-Process
	tensor I-Process
	field I-Process
	is O
	measured O
	using O
	the O
	Irving B-Process
	– I-Process
	Kirkwood I-Process
	relationship I-Process
	[ O
	25 O
	] O
	. O
	A O
	least-squares B-Process
	polynomial I-Process
	fit I-Process
	to O
	the O
	data O
	is O
	performed O
	, O
	which O
	helps O
	reduce B-Task
	noise I-Task
	further O
	. O
	The O
	fit O
	produces O
	a O
	continuous B-Process
	function I-Process
	that O
	avoids O
	stability B-Process
	issues I-Process
	arising O
	from O
	supplying B-Process
	highly I-Process
	fluctuating I-Process
	data I-Process
	to I-Process
	the I-Process
	macro I-Process
	solver I-Process
	. O
	A O
	least-squares B-Process
	fit I-Process
	is O
	applied O
	to O
	an O
	Nth B-Material
	order I-Material
	polynomial I-Material
	for O
	the O
	velocity O
	profile O
	in O
	the O
	core O
	region O
	, O
	and O
	an O
	Mth B-Material
	order I-Material
	polynomial I-Material
	for O
	the O
	velocity O
	profile O
	in O
	the O
	constrained O
	region O
	:( O
	16 O
	)〈 O
	ui,core O
	〉=∑ O
	k O
	= O
	1Nbk,iyi′ O
	( O
	N O
	− O
	k O
	) O
	, O
	for O
	0 O
	⩽ O
	yi′ O
	⩽ O
	hcore O
	, O
	and O
	( O
	17 O
	)〈 O
	ui,cs O
	〉=∑ O
	k O
	= O
	1Mck,iyi O
	″( O
	M O
	− O
	k O
	) O
	, O
	for O
	0 O
	⩽ O
	yi O
	″⩽ O
	hcs O
	, O
	where O
	bk,i O
	and O
	ck,i O
	are O
	the O
	coefficients O
	of O
	the O
	polynomials B-Material
	used O
	in O
	the O
	core O
	micro O
	region O
	and O
	constrained O
	region O
	respectively O
	. O
	An O
	estimate O
	of O
	the O
	new B-Material
	slip I-Material
	velocity I-Material
	uB O
	for O
	input O
	to O
	the O
	macro B-Material
	solution I-Material
	( O
	6 O
	) O
	is O
	taken O
	directly O
	from O
	the O
	compressed B-Material
	wall I-Material
	micro-element I-Material
	solution I-Material
	( O
	16 O
	) O
	, O
	at O
	yi′ O
	= O
	0 O
	. O

	It O
	is O
	interesting O
	to O
	quantify B-Task
	the I-Task
	effects I-Task
	of I-Task
	the I-Task
	Schmidt I-Task
	number I-Task
	and I-Task
	the I-Task
	chemical I-Task
	reaction I-Task
	rate I-Task
	on O
	the O
	bulk-mean B-Material
	concentration I-Material
	of I-Material
	B I-Material
	in O
	water O
	. O
	The O
	data O
	could O
	present O
	important O
	information O
	on O
	evaluating O
	the O
	environmental O
	impacts O
	of O
	the O
	degradation B-Material
	product I-Material
	of O
	B O
	, O
	as O
	well O
	as O
	acidification B-Process
	of I-Process
	water I-Process
	by O
	the O
	chemical B-Process
	reaction I-Process
	. O
	Here O
	, O
	the O
	bulk-mean B-Material
	concentration I-Material
	of I-Material
	B I-Material
	is O
	defined O
	by O
	( O
	24 O
	) O
	CB O
	⁎¯=∫ O
	01 O
	〈 O
	CB O
	⁎〉( O
	z O
	⁎) O
	dz O
	⁎ O
	Fig. O
	15 O
	depicts O
	the O
	effect O
	of O
	the O
	Schmidt B-Material
	and O
	the O
	chemical B-Process
	reaction I-Process
	rate I-Process
	on O
	the O
	bulk-mean B-Material
	concentration I-Material
	CB I-Material
	⁎¯ I-Material
	. O
	It O
	is O
	worth O
	to O
	mention O
	here O
	that O
	the O
	bulk-mean B-Material
	concentration I-Material
	of I-Material
	B I-Material
	reaches O
	approximately O
	0.6 O
	as O
	the O
	chemical B-Process
	reaction I-Process
	rate I-Process
	and O
	the O
	Schmidt B-Material
	number I-Material
	increase O
	to O
	infinite O
	, O
	and O
	the O
	concentration O
	is O
	smaller O
	than O
	the O
	equilibrium B-Material
	concentration I-Material
	of I-Material
	A I-Material
	at O
	the O
	interface O
	. O
	This O
	figure O
	indicates O
	that O
	progress O
	of O
	the O
	chemical B-Process
	reaction I-Process
	is O
	somewhat O
	interfered O
	by O
	turbulent B-Process
	mixing I-Process
	in O
	water O
	, O
	and O
	the O
	efficiency O
	of O
	the O
	chemical B-Process
	reaction I-Process
	is O
	up O
	to O
	approximately O
	60 O
	% O
	. O
	The O
	efficiency O
	of O
	the O
	chemical O
	reaction O
	in O
	water O
	will O
	be O
	a O
	function O
	of O
	the O
	Reynolds B-Material
	number I-Material
	of O
	the O
	water O
	flow O
	, O
	and O
	the O
	efficiency O
	could O
	increase O
	as O
	the O
	Reynolds O
	number O
	increases O
	. O
	We O
	need O
	an O
	extensive O
	investigation O
	on O
	the O
	efficiency O
	of O
	the O
	aquarium B-Process
	chemical I-Process
	reaction I-Process
	in O
	the O
	near O
	future O
	to O
	extend O
	the O
	results O
	of O
	this O
	study O
	further O
	to O
	establish O
	practical B-Process
	modelling I-Process
	for O
	the O
	gas B-Process
	exchange I-Process
	between O
	air O
	and O
	water O
	. O

	Numerical B-Task
	simulation I-Task
	of O
	the O
	gas B-Process
	flow I-Process
	through O
	such O
	non-trivial O
	internal O
	geometries O
	is O
	, O
	however O
	, O
	extremely O
	challenging O
	. O
	This O
	is O
	because O
	conventional B-Process
	continuum I-Process
	fluid I-Process
	dynamics I-Process
	, O
	which O
	assumes O
	that O
	locally O
	a O
	gas O
	is O
	close O
	to O
	a O
	state O
	of O
	thermodynamic B-Process
	equilibrium I-Process
	, O
	becomes O
	invalid O
	or O
	inaccurate O
	as O
	the O
	smallest O
	characteristic O
	scale O
	of O
	the O
	geometry O
	( O
	e.g. O
	the O
	channel B-Material
	height I-Material
	) O
	approaches O
	the O
	mean O
	distance O
	between O
	molecular B-Process
	collisions I-Process
	, O
	λ O
	[ O
	1 O
	] O
	. O
	An O
	accurate B-Process
	and I-Process
	flexible I-Process
	modelling I-Process
	alternative I-Process
	for O
	these O
	cases O
	is O
	the O
	direct B-Process
	simulation I-Process
	Monte I-Process
	Carlo I-Process
	method I-Process
	( O
	DSMC B-Process
	) O
	[ O
	2 O
	] O
	. O
	However O
	, O
	DSMC O
	can O
	be O
	prohibitively O
	expensive O
	for O
	internal-flow B-Process
	applications I-Process
	, O
	which O
	typically O
	have O
	a O
	geometry O
	of O
	high-aspect B-Material
	ratio I-Material
	( O
	i.e. O
	are O
	extremely O
	long O
	, O
	relative O
	to O
	their O
	cross-section O
	) O
	. O
	The O
	high-aspect O
	ratio O
	creates O
	a O
	formidable O
	multiscale B-Task
	problem I-Task
	: O
	processes O
	need O
	to O
	be O
	resolved O
	occurring O
	over O
	the O
	smallest O
	characteristic O
	scale O
	of O
	the O
	geometry O
	( O
	e.g. O
	a O
	channel O
	ʼs O
	height O
	) O
	, O
	as O
	well O
	as O
	over O
	the O
	largest O
	characteristic O
	scale O
	of O
	the O
	geometry O
	( O
	e.g. O
	the O
	length O
	of O
	a O
	long B-Process
	channel I-Process
	network I-Process
	) O
	, O
	simultaneously O
	. O

	The O
	test O
	cases O
	confirm O
	that O
	the O
	high-order B-Process
	discretisation I-Process
	retains O
	exponential B-Process
	convergence I-Process
	properties I-Process
	with O
	increasing O
	geometric O
	and O
	expansion O
	polynomial O
	order O
	if O
	both O
	the O
	solution B-Material
	and O
	true B-Material
	surface I-Material
	are O
	smooth O
	. O
	Errors O
	are O
	found O
	to O
	saturate O
	when O
	the O
	geometric O
	errors O
	, O
	due O
	to O
	the O
	parametrisation B-Process
	of I-Process
	the I-Process
	surface I-Process
	elements I-Process
	, O
	begin O
	to O
	dominate O
	the O
	temporal O
	and O
	spatial O
	discretisation O
	errors O
	. O
	For O
	the O
	smooth B-Material
	solutions I-Material
	considered O
	as O
	test O
	cases O
	, O
	the O
	results O
	show O
	that O
	this O
	dominance O
	of O
	geometric O
	errors O
	quickly O
	limits O
	the O
	effectiveness O
	of O
	further O
	increases O
	in O
	the O
	number O
	of O
	degrees O
	of O
	freedom O
	, O
	either O
	through O
	mesh B-Process
	refinement I-Process
	or O
	higher B-Process
	solution I-Process
	polynomial I-Process
	orders I-Process
	. O
	Increasing O
	the O
	order O
	of O
	the O
	geometry B-Process
	parametrisation I-Process
	reduces O
	the O
	geometric B-Process
	error I-Process
	. O
	The O
	analytic B-Task
	test I-Task
	cases I-Task
	presented O
	here O
	use O
	a O
	coarse B-Material
	curvilinear I-Material
	mesh I-Material
	; O
	for O
	applications O
	, O
	meshes B-Material
	are O
	typically O
	more O
	refined O
	in O
	order O
	to O
	capture B-Process
	features I-Process
	in I-Process
	the I-Process
	solution I-Process
	and O
	so O
	will O
	better O
	capture B-Process
	the I-Process
	geometry I-Process
	and O
	consequently O
	reduce B-Process
	this I-Process
	lower I-Process
	bound I-Process
	on O
	the O
	solution B-Material
	error O
	. O
	If O
	the O
	solution O
	is O
	not O
	smooth O
	, O
	we O
	do O
	not O
	expect O
	to O
	see O
	rapid O
	convergence O
	. O
	In O
	the O
	case O
	that O
	the O
	solution O
	is O
	smooth O
	, O
	but O
	the O
	true B-Material
	surface I-Material
	is O
	not O
	, O
	then O
	exponential B-Process
	convergence I-Process
	with O
	P B-Material
	and O
	Pg B-Material
	can O
	only O
	be O
	achieved O
	if O
	, O
	and O
	only O
	if O
	, O
	the O
	discontinuities B-Process
	are I-Process
	aligned I-Process
	with I-Process
	element I-Process
	boundaries I-Process
	. O
	However O
	, O
	if O
	discontinuities O
	lie O
	within O
	an O
	element O
	, O
	convergence O
	will O
	be O
	limited O
	by O
	the O
	geometric B-Process
	approximation I-Process
	, O
	since O
	the O
	true B-Material
	surface I-Material
	cannot O
	be O
	captured O
	. O
	In O
	the O
	cardiac B-Task
	problem I-Task
	, O
	we O
	consider O
	both O
	the O
	true B-Material
	surface I-Material
	and O
	solution B-Material
	to O
	be O
	smooth O
	. O

	DPD B-Process
	was O
	first O
	proposed O
	in O
	order O
	to O
	recover O
	the O
	properties O
	of O
	isotropy B-Process
	( O
	and O
	Galilean B-Process
	invariance I-Process
	) O
	that O
	were O
	broken O
	in O
	the O
	so-called O
	lattice-gas B-Process
	automata I-Process
	method O
	[ O
	5 O
	] O
	. O
	In O
	DPD B-Process
	, O
	each O
	body O
	is O
	regarded O
	as O
	a O
	coarse-grained B-Material
	particle I-Material
	. O
	These O
	particles B-Material
	interact O
	in O
	a O
	soft O
	( O
	and O
	short-ranged O
	) O
	potential O
	, O
	allowing O
	larger O
	integration O
	timesteps O
	than O
	would O
	be O
	possible O
	in O
	MD B-Process
	, O
	while O
	simultaneously O
	decreasing O
	the O
	number O
	of O
	degrees O
	of O
	freedom O
	required O
	. O
	As O
	in O
	Langevin B-Process
	dynamics I-Process
	, O
	a O
	thermostat B-Process
	consisting O
	of O
	well-balanced O
	damping O
	and O
	stochastic O
	terms O
	is O
	applied O
	to O
	each O
	particle O
	. O
	However O
	, O
	unlike O
	in O
	Langevin B-Process
	dynamics I-Process
	, O
	both O
	terms O
	are O
	pairwise O
	and O
	the O
	damping O
	term O
	is O
	based O
	on O
	relative O
	velocities O
	, O
	leading O
	to O
	the O
	conservation O
	of O
	both O
	the O
	angular B-Process
	momentum I-Process
	and O
	the O
	linear B-Process
	momentum I-Process
	. O
	The O
	property O
	of O
	Galilean B-Process
	invariance I-Process
	( O
	i.e. O
	, O
	the O
	dependence B-Process
	on I-Process
	the I-Process
	relative I-Process
	velocity I-Process
	) O
	makes O
	DPD B-Process
	a O
	profile-unbiased B-Process
	thermostat I-Process
	( O
	PUT B-Process
	) O
	[ O
	6,7 O
	] O
	by O
	construction O
	and O
	thus O
	it O
	is O
	an O
	ideal O
	thermostat O
	for O
	nonequilibrium B-Task
	molecular I-Task
	dynamics I-Task
	( I-Task
	NEMD I-Task
	) I-Task
	[ O
	8 O
	] O
	. O
	The O
	momentum O
	is O
	expected O
	to O
	propagate O
	locally O
	( O
	while O
	global O
	momentum O
	is O
	conserved O
	) O
	and O
	thus O
	the O
	correct O
	hydrodynamics O
	is O
	expected O
	in O
	DPD B-Process
	[ O
	8 O
	] O
	, O
	as O
	demonstrated O
	previously O
	in O
	[ O
	9 O
	] O
	. O
	Due O
	to O
	the O
	aforementioned O
	properties O
	, O
	DPD O
	has O
	been O
	widely O
	used O
	to O
	recover B-Task
	thermodynamic I-Task
	, I-Task
	dynamical I-Task
	, I-Task
	and I-Task
	rheological I-Task
	properties I-Task
	of O
	complex B-Material
	fluids I-Material
	, O
	with O
	applications O
	in O
	polymer B-Material
	solutions I-Material
	[ O
	10 O
	] O
	, O
	colloidal B-Material
	suspensions I-Material
	[ O
	11 O
	] O
	, O
	multiphase B-Material
	flows I-Material
	[ O
	12 O
	] O
	, O
	and O
	biological B-Material
	systems I-Material
	[ O
	13 O
	] O
	. O
	DPD B-Process
	has O
	been O
	compared O
	with O
	Langevin B-Process
	dynamics I-Process
	for O
	out-of-equilibrium O
	simulations O
	of O
	polymeric B-Process
	systems I-Process
	in O
	[ O
	14 O
	] O
	, O
	where O
	as O
	expected O
	the O
	correct O
	dynamic B-Process
	fluctuations I-Process
	of O
	the O
	polymers B-Material
	were O
	obtained O
	with O
	the O
	former O
	but O
	not O
	with O
	the O
	latter O
	. O

	Copper-catalyzed B-Material
	Huisgen I-Material
	cycloadditions I-Material
	have O
	been O
	recently O
	extensively O
	studied O
	by O
	polymer O
	chemists O
	for O
	the O
	synthesis B-Task
	of I-Task
	functional I-Task
	polymers I-Task
	( O
	either O
	end-functional O
	or O
	side-functional O
	) O
	. O
	The O
	post-functionalization O
	of O
	synthetic B-Material
	polymers I-Material
	is O
	an O
	important O
	feature O
	of O
	macromolecular B-Task
	engineering I-Task
	as O
	many O
	polymerization B-Process
	mechanisms O
	are O
	rather O
	sensitive O
	to O
	the O
	presence O
	of O
	bulky O
	or O
	functional O
	groups O
	. O
	For O
	example O
	, O
	a O
	wide O
	variety O
	of O
	telechelic B-Material
	polymers I-Material
	( O
	i.e. O
	polymers B-Material
	with I-Material
	defined I-Material
	chain-ends I-Material
	) O
	can O
	be O
	efficiently O
	prepared O
	using O
	a O
	combination O
	of O
	atom B-Process
	transfer I-Process
	radical I-Process
	polymerization I-Process
	( O
	ATRP B-Process
	) O
	and O
	CuAAC B-Process
	. O
	This O
	strategy O
	was O
	independently O
	reported O
	in O
	early O
	2005 O
	by O
	van O
	Hest O
	and O
	Opsteen O
	[ O
	31 O
	] O
	, O
	Lutz O
	et O
	al O
	. O
	[ O
	32 O
	] O
	, O
	and O
	Matyjaszewski O
	et O
	al O
	. O
	[ O
	33 O
	] O
	. O
	Such O
	step O
	was O
	important O
	since O
	ATRP B-Process
	is O
	a O
	very O
	popular O
	polymerization B-Process
	method O
	in O
	modern O
	materials O
	science O
	[ O
	34,35 O
	] O
	. O
	Indeed O
	, O
	ATRP B-Process
	is O
	a O
	facile O
	technique O
	, O
	which O
	allows O
	the O
	preparation B-Process
	of I-Process
	well-defined I-Process
	polymers I-Process
	with O
	narrow O
	molecular O
	weight O
	distribution O
	, O
	predictable O
	chain O
	length O
	, O
	controlled O
	microstructure O
	, O
	defined O
	chain-ends O
	and O
	controlled O
	architecture O
	[ O
	36 O
	– O
	41 O
	] O
	. O
	However O
	, O
	the O
	range O
	of O
	possibilities O
	of O
	ATRP B-Process
	can O
	be O
	further O
	broadened O
	by O
	CuAAC B-Process
	. O
	For O
	instance O
	, O
	the O
	ω-bromine B-Material
	chain-ends I-Material
	of I-Material
	polymers I-Material
	prepared O
	by O
	ATRP B-Process
	can O
	be O
	transformed O
	into O
	azides B-Material
	by O
	nucleophilic B-Process
	substitution I-Process
	and O
	subsequently O
	reacted O
	with O
	functional O
	alkynes B-Material
	( O
	Scheme O
	3 O
	) O
	[ O
	32 O
	] O
	. O
	Due O
	to O
	the O
	very O
	high O
	chemoselectivity O
	of O
	CuAAC B-Process
	, O
	this O
	method O
	is O
	highly O
	modular O
	and O
	may O
	be O
	used O
	to O
	synthesize O
	a O
	wide O
	range O
	of O
	ω-functional B-Material
	polymers I-Material
	. O
	Moreover O
	, O
	the O
	formed O
	triazole B-Material
	rings I-Material
	are O
	not O
	“ O
	passive O
	” O
	spacers O
	but O
	interesting O
	functions O
	exhibiting O
	H-bonds B-Process
	capability O
	, O
	aromaticity O
	and O
	rigidity O
	. O

	The O
	viscoelastic B-Process
	behavior I-Process
	of O
	elastomers B-Material
	containing O
	small O
	amounts O
	of O
	unattached O
	chains O
	has O
	been O
	investigated O
	to O
	characterize B-Task
	the I-Task
	dynamics I-Task
	of I-Task
	the I-Task
	polymer I-Task
	chains I-Task
	trapped O
	in O
	fixed O
	networks O
	[ O
	66 O
	– O
	68 O
	] O
	. O
	Polymer B-Material
	chains I-Material
	trapped O
	in O
	fixed O
	networks O
	constitute O
	a O
	simpler O
	system O
	for O
	the O
	study O
	of O
	the O
	polymer B-Material
	chain I-Material
	dynamics O
	than O
	the O
	corresponding O
	uncrosslinked O
	polymer B-Material
	melts I-Material
	. O
	This O
	is O
	because O
	the O
	complicated O
	effect O
	of O
	the O
	motion O
	of O
	the O
	surrounding O
	chains O
	on O
	the O
	dynamics O
	of O
	the O
	probe B-Material
	chain I-Material
	– O
	called O
	“ O
	constraint B-Process
	release I-Process
	” O
	[ O
	69 O
	] O
	– O
	is O
	absent O
	in O
	the O
	fixed O
	network O
	systems O
	. O
	Most O
	of O
	the O
	earlier O
	studies O
	employed O
	randomly O
	crosslinked B-Material
	elastomers I-Material
	as O
	host O
	networks O
	. O
	In O
	this O
	case O
	, O
	precise O
	control B-Task
	of I-Task
	the I-Task
	mesh I-Task
	size I-Task
	of O
	the O
	host O
	networks O
	is O
	not O
	possible O
	, O
	and O
	the O
	mesh O
	size O
	has O
	a O
	broad O
	distribution O
	. O
	The O
	end-linking O
	systems O
	give O
	the O
	host O
	networks O
	a O
	more O
	uniform O
	mesh O
	size O
	, O
	and O
	they O
	can O
	control O
	the O
	mesh O
	size O
	by O
	the O
	size O
	of O
	the O
	precursor B-Material
	chains I-Material
	. O
	We O
	investigated O
	the O
	dynamic O
	viscoelasticity O
	of O
	end-linked O
	PDMS B-Material
	elastomers I-Material
	containing O
	unattached O
	linear O
	PDMS O
	as O
	functions O
	of O
	the O
	size O
	of O
	the O
	unattached B-Material
	chains I-Material
	( O
	Mg B-Material
	) O
	and O
	the O
	network B-Material
	mesh I-Material
	( O
	Mx B-Material
	) O
	( O
	Fig. O
	9a O
	) O
	[ O
	70 O
	] O
	. O
	We O
	employed O
	two O
	types O
	of O
	host O
	networks O
	with O
	Mx B-Process
	> I-Process
	Me I-Process
	and O
	Mx B-Process
	< I-Process
	Me I-Process
	where O
	Me O
	(≈ O
	10,000 O
	for O
	PDMS B-Material
	) O
	is O
	the O
	molecular O
	mass O
	between O
	adjacent O
	entanglements O
	in O
	the O
	molten O
	state O
	. O
	The O
	Mx B-Process
	> I-Process
	Me I-Process
	and O
	Mx B-Process
	< I-Process
	Me I-Process
	networks O
	( O
	designated O
	as O
	NL B-Process
	and O
	NS B-Process
	, O
	respectively O
	) O
	were O
	designed O
	by O
	end-linking O
	the O
	long O
	( O
	Mn O
	= O
	84,000 O
	) O
	and O
	short O
	precursor O
	chains O
	( O
	Mn O
	= O
	4,550 O
	) O
	, O
	respectively O
	. O
	The O
	mesh B-Material
	of O
	the O
	NL O
	networks O
	is O
	dominated O
	by O
	trapped O
	entanglements O
	, O
	while O
	that O
	of O
	the O
	NS O
	network O
	is O
	governed O
	by O
	chemical O
	cross-links O
	. O

	When O
	incompatible O
	three O
	component O
	polymer B-Material
	chains I-Material
	are O
	tethered O
	at O
	a O
	junction O
	point O
	, O
	the O
	resultant O
	star B-Material
	molecules I-Material
	of O
	the O
	ABC O
	type O
	are O
	in O
	a O
	very O
	frustrated O
	field O
	in O
	bulk O
	. O
	That O
	is O
	, O
	their O
	junction O
	points O
	cannot O
	be O
	aligned O
	on O
	two-dimensional O
	planes O
	but O
	on O
	one-dimensional O
	lines O
	, O
	as O
	schematically O
	shown O
	in O
	Fig. O
	1. O
	Furthermore O
	, O
	when O
	the O
	chain O
	length O
	difference O
	is O
	not O
	so O
	large O
	, O
	the O
	array O
	of O
	junction O
	points O
	tends O
	to O
	be O
	straight O
	and O
	long O
	one O
	. O
	Consequently O
	each O
	domain O
	with O
	mesoscopic O
	sizes O
	becomes O
	cylinders B-Material
	, O
	and O
	their O
	cross O
	sections O
	could O
	be O
	conformed O
	by O
	polygons B-Material
	[ O
	28,29 O
	] O
	. O
	This O
	is O
	because O
	three B-Material
	interfaces I-Material
	, I-Material
	A I-Material
	/ I-Material
	B I-Material
	, I-Material
	B I-Material
	/ I-Material
	C I-Material
	and I-Material
	C I-Material
	/ I-Material
	A I-Material
	are O
	likely O
	to O
	be O
	flat O
	since O
	there O
	exist O
	no O
	junction O
	points O
	at O
	interfaces O
	and O
	therefore O
	chain B-Process
	entropy I-Process
	contribution O
	to O
	the O
	free O
	energy O
	of O
	structure O
	formation O
	is O
	considerably O
	small O
	comparing O
	with O
	regular O
	block O
	and O
	graft O
	copolymer O
	systems O
	. O
	As O
	a O
	matter O
	of O
	fact O
	, O
	Dotera O
	predicted O
	several O
	tiling O
	patterns O
	by O
	the O
	diagonal B-Process
	bond I-Process
	method I-Process
	, O
	a O
	new O
	Monte B-Process
	Carlo I-Process
	Simulation I-Process
	[ O
	30 O
	] O
	, O
	while O
	Gemma O
	and O
	Dotera O
	pointed O
	out O
	that O
	only O
	three O
	regular O
	tilings O
	, O
	i.e. O
	, O
	( O
	6.6.6 O
	) O
	, O
	( O
	4.8.8 O
	) O
	and O
	( O
	4.6.12 O
	) O
	are O
	permitted O
	for O
	three-branched B-Material
	molecules I-Material
	proposed O
	as O
	the O
	“ O
	even B-Process
	polygon I-Process
	theorem I-Process
	” O
	[ O
	31 O
	] O
	. O

	A O
	living O
	polymerization B-Process
	is O
	a O
	reaction B-Process
	without O
	transfer O
	and O
	termination B-Process
	reactions O
	that O
	can O
	proceed O
	up O
	to O
	complete O
	monomer B-Process
	conversion I-Process
	. O
	In O
	addition O
	, O
	when O
	initiation O
	is O
	quantitative O
	and O
	fast O
	compared O
	to O
	the O
	propagation B-Process
	reaction O
	, O
	polymers B-Material
	with O
	precisely O
	controlled O
	chain O
	length O
	and O
	narrow O
	molar O
	mass O
	distribution O
	can O
	be O
	obtained O
	. O
	In O
	the O
	case O
	of O
	an O
	industrial B-Task
	styrene I-Task
	polymerization I-Task
	this O
	would O
	permit O
	to O
	avoid O
	any O
	specific O
	washing B-Process
	or O
	degassing B-Process
	steps O
	, O
	which O
	are O
	necessary O
	in O
	the O
	radical O
	process O
	to O
	remove O
	residual O
	monomer O
	and O
	low B-Material
	molar I-Material
	mass I-Material
	oligomers I-Material
	. O
	Since O
	head-to-head O
	defects O
	along O
	the O
	chains O
	are O
	absent O
	, O
	anionic B-Material
	polystyrene I-Material
	would O
	exhibit O
	also O
	a O
	better O
	thermal O
	stability O
	than O
	radical O
	one O
	. O
	Therefore O
	, O
	production B-Task
	of I-Task
	anionic I-Task
	polystyrene I-Task
	( I-Task
	PS I-Task
	) I-Task
	would O
	be O
	of O
	interest O
	if O
	the O
	conditions O
	required O
	to O
	control O
	the O
	polymerization B-Process
	could O
	be O
	adapted O
	to O
	the O
	market O
	and O
	be O
	able O
	to O
	compete O
	economically O
	with O
	industrial O
	radical O
	processes O
	. O
	The O
	use O
	of O
	organic B-Material
	solvents I-Material
	and O
	of O
	expensive O
	alkyllithium B-Process
	initiators I-Process
	, O
	as O
	well O
	as O
	the O
	relatively O
	low O
	reaction O
	temperatures O
	required O
	, O
	was O
	some O
	important O
	limitation O
	to O
	overcome O
	. O
	The O
	possibilities O
	to O
	achieve O
	a O
	quantitative O
	living-like O
	anionic B-Process
	polymerization I-Process
	of I-Process
	styrene I-Process
	in O
	the O
	absence O
	of O
	solvent B-Material
	and O
	at O
	elevated O
	temperature O
	, O
	using O
	inexpensive O
	initiating O
	systems O
	, O
	were O
	the O
	main O
	targets O
	identified O
	to O
	tremendously O
	decrease O
	the O
	cost O
	of O
	the O
	anionic O
	process O
	. O
	This O
	implied O
	at O
	first O
	to O
	control O
	the O
	reactivity O
	and O
	stability O
	of O
	initiating O
	and O
	propagating B-Process
	active I-Process
	species I-Process
	in O
	such O
	unusual O
	operating O
	conditions O
	. O

	A O
	hydroxyl-functionalized O
	poly O
	( O
	butylene O
	succinate O
	) O
	based O
	polyester B-Material
	was O
	prepared O
	by O
	conventional O
	polycondensation B-Process
	of O
	benzyl-protected O
	dimethyl B-Material
	malonate I-Material
	and O
	1,4-butanediol B-Material
	( O
	Scheme O
	2 O
	( O
	a O
	)) O
	[ O
	24a O
	] O
	. O
	Yao O
	et O
	al. O
	reported O
	on O
	the O
	direct O
	polycondensation B-Process
	of O
	l-lactic B-Material
	acid I-Material
	and O
	citric B-Material
	acid I-Material
	with O
	the O
	formation O
	of O
	poly B-Material
	[( I-Material
	l-lactic I-Material
	acid I-Material
	)- I-Material
	co I-Material
	-( I-Material
	citric I-Material
	acid I-Material
	)] I-Material
	, O
	obtaining O
	a O
	polyester B-Material
	oligomer I-Material
	with O
	both O
	pendant O
	carboxylic O
	and O
	hydroxyl O
	groups O
	[ O
	24b O
	] O
	. O
	This O
	PLCA B-Material
	oligomer I-Material
	was O
	reacted O
	with O
	dihydroxylated B-Material
	PLLA I-Material
	as O
	a O
	macromonomer O
	, O
	yielding O
	a O
	PLCA B-Material
	– I-Material
	PLLA I-Material
	multiblock I-Material
	copolymer I-Material
	as O
	shown O
	in O
	Scheme O
	2 O
	( O
	b O
	) O
	. O
	While O
	lipases B-Material
	have O
	been O
	investigated O
	for O
	the O
	ring-opening B-Process
	polymerization I-Process
	( O
	ROP B-Process
	) O
	of O
	cyclic B-Material
	ester I-Material
	monomers I-Material
	[ O
	25,26 O
	] O
	, O
	they O
	have O
	also O
	been O
	used O
	for O
	the O
	preparation O
	of O
	polyesters B-Material
	by O
	polycondensation B-Process
	reactions O
	. O
	The O
	advantage O
	of O
	this O
	technique O
	is O
	that O
	these O
	enzyme-catalyzed B-Process
	reactions O
	proceed O
	without O
	protection O
	of O
	the O
	pendant O
	functional O
	groups O
	. O
	In O
	this O
	field O
	, O
	hydroxyl-bearing B-Material
	polyesters I-Material
	have O
	been O
	synthesized O
	by O
	the O
	copolymerization B-Process
	of O
	divinyl B-Material
	adipate I-Material
	with O
	various O
	triols B-Material
	( O
	e.g. O
	glycerol B-Material
	, O
	1,2,4-butanetriol B-Material
	) O
	as O
	represented O
	in O
	Scheme O
	2 O
	( O
	c O
	) O
	[ O
	27 O
	] O
	and O
	by O
	copolymerizations B-Process
	of O
	1,8-octanediol B-Material
	with O
	adipic B-Material
	acid I-Material
	and O
	several O
	alditols B-Material
	[ O
	28 O
	] O
	. O
	Very O
	recently O
	, O
	several O
	α-hydroxy B-Material
	acids I-Material
	derived O
	from O
	amino B-Material
	acids I-Material
	were O
	homo O
	- O
	and O
	copolymerized O
	with O
	lactic B-Material
	acid I-Material
	by O
	polycondensation B-Process
	in O
	bulk O
	without O
	protected O
	monomers B-Material
	( O
	Scheme O
	2 O
	( O
	d O
	)) O
	[ O
	29 O
	] O
	. O
	Biodegradable B-Material
	polyesters I-Material
	with O
	various O
	pendant O
	groups O
	were O
	obtained O
	, O
	although O
	the O
	molecular O
	weights O
	remained O
	low O
	( O
	1000 O
	– O
	3000gmol O
	− O
	1 O
	) O
	. O

	Despite O
	the O
	loss O
	of O
	directed O
	, O
	self-complementary O
	hydrogen B-Process
	bonding I-Process
	through O
	alkylation B-Process
	of O
	the O
	imidazole O
	ring O
	, O
	electrostatic B-Process
	aggregation I-Process
	of O
	imidazolium B-Material
	salts I-Material
	is O
	a O
	tunable O
	, O
	self-assembly O
	process O
	, O
	which O
	is O
	instrumental O
	to O
	several O
	applications O
	. O
	Imidazolium B-Material
	salts I-Material
	are O
	used O
	to O
	extract O
	metal B-Material
	ions I-Material
	from O
	aqueous B-Material
	solutions I-Material
	and O
	coat O
	metal B-Material
	nanoparticles I-Material
	[ O
	15 O
	] O
	, O
	dissolve O
	carbohydrates B-Material
	[ O
	16 O
	] O
	, O
	and O
	create O
	polyelectrolyte B-Process
	brushes I-Process
	on O
	surfaces O
	[ O
	17 O
	] O
	. O
	For O
	example O
	, O
	atom B-Process
	transfer I-Process
	radical I-Process
	polymerization I-Process
	( O
	ATRP B-Process
	) O
	was O
	used O
	to O
	graft O
	poly B-Material
	( I-Material
	1-ethyl I-Material
	3 I-Material
	-( I-Material
	2-methacryloyloxy I-Material
	ethyl I-Material
	) I-Material
	imidazolium I-Material
	chloride I-Material
	) O
	brushes O
	onto O
	gold B-Material
	surfaces I-Material
	[ O
	17 O
	] O
	. O
	One O
	of O
	the O
	imidazolium B-Material
	salt I-Material
	’s O
	most O
	promising O
	attributes O
	is O
	its O
	antimicrobial B-Process
	action I-Process
	[ O
	12,18 O
	] O
	and O
	molecular O
	self-assembly O
	into O
	liquid B-Material
	crystals I-Material
	[ O
	19,20 O
	] O
	. O
	1-Alkyl-3-methylimidazolium B-Material
	chlorides I-Material
	and O
	bromides B-Material
	, O
	1-alkyl-2-methyl-3-hydroxyethylimidazolium B-Material
	chlorides I-Material
	, O
	and O
	N-alkyl-N-hydroxyethylpyrrolidinonium B-Material
	, O
	for O
	example O
	, O
	all O
	exhibit O
	strong O
	biocidal B-Process
	activity I-Process
	[ O
	18 O
	] O
	. O
	Hydrogels B-Material
	form I-Material
	from O
	polymerized B-Material
	methylimidazolium-based I-Material
	ionic I-Material
	liquids I-Material
	with O
	acryloyl B-Material
	groups I-Material
	; O
	the O
	polymer B-Material
	self-assembles O
	into O
	organized B-Material
	lamellae I-Material
	with O
	unique O
	swelling O
	properties O
	, O
	leading O
	to O
	bioactive B-Task
	applications I-Task
	[ O
	19 O
	] O
	. O
	Other O
	bioactive O
	applications O
	for O
	imidazolium B-Material
	salts I-Material
	include O
	antiarrhythmics B-Material
	[ O
	21 O
	] O
	, O
	anti-metastic B-Material
	agents I-Material
	[ O
	22,23 O
	] O
	, O
	and O
	imidazolium-based B-Material
	steroids I-Material
	[ O
	24 O
	] O
	. O
	Separation B-Process
	applications I-Process
	include O
	efficient B-Process
	absorption I-Process
	of I-Process
	CO2 I-Process
	[ O
	25 O
	] O
	. O
	Imidazolium B-Material
	salts I-Material
	enhance O
	vesicle B-Process
	formation I-Process
	as O
	imidazolium B-Material
	surfactants I-Material
	[ O
	26 O
	] O
	, O
	and O
	they O
	also O
	find O
	application O
	in O
	polymeric B-Process
	actuators I-Process
	[ O
	27 O
	] O
	. O

	Although O
	the O
	basic O
	mechanisms O
	of O
	the O
	AD B-Process
	process O
	are O
	reasonably O
	well O
	understood O
	, O
	it O
	has O
	not O
	proved O
	simple O
	to O
	apply O
	existing O
	theories O
	to O
	the O
	interpretation O
	of O
	experimental O
	data O
	. O
	What O
	is O
	needed O
	is O
	a O
	combination O
	of O
	the O
	AD O
	theory O
	and O
	the O
	electronic O
	structure O
	of O
	realistic O
	systems O
	, O
	including O
	surface B-Material
	defects I-Material
	and O
	adsorbed B-Material
	species I-Material
	. O
	Such O
	electronic O
	structure O
	calculations O
	are O
	still O
	complex O
	and O
	time-consuming O
	. O
	In O
	many O
	cases O
	, O
	especially O
	for O
	insulating O
	surfaces O
	, O
	attempts O
	to O
	model O
	MIES B-Material
	spectra I-Material
	use O
	simple O
	or O
	intuitive O
	models O
	. O
	In O
	Refs O
	. O
	[ O
	4,6,23 O
	] O
	it O
	is O
	assumed O
	that O
	the O
	main O
	transition O
	mechanism O
	is O
	Auger B-Process
	de-excitation I-Process
	, O
	and O
	the O
	MIES B-Material
	spectra I-Material
	have O
	been O
	simulated O
	by O
	the O
	surface O
	density B-Process
	of I-Process
	states I-Process
	( O
	DOS B-Process
	) O
	projected O
	on O
	the O
	surface B-Material
	oxygen I-Material
	ions I-Material
	of O
	the O
	uppermost O
	surface O
	layer O
	using O
	a O
	Hartree B-Process
	– I-Process
	Fock I-Process
	method I-Process
	( O
	the O
	crystal B-Process
	code I-Process
	[ O
	24,25 O
	]) O
	and O
	a O
	density B-Process
	functional I-Process
	theory I-Process
	( O
	DFT B-Process
	) O
	method O
	( O
	the O
	cetep B-Process
	code I-Process
	[ O
	26 O
	]) O
	. O
	The O
	effect O
	of O
	the O
	overlap O
	between O
	the O
	surface O
	and O
	He O
	( O
	1s O
	) O
	wavefunctions O
	was O
	taken O
	into O
	account O
	only O
	approximately O
	by O
	applying O
	an O
	additional O
	z-dependent O
	exponential O
	factor O
	to O
	the O
	surface O
	DOS B-Process
	. O
	Other O
	workers O
	[ O
	5,6 O
	] O
	estimated O
	the O
	AD B-Process
	transition I-Process
	probability O
	using O
	a O
	DOS B-Process
	projected O
	on O
	to O
	the O
	projectile O
	1s O
	atomic O
	orbital O
	. O
	However O
	, O
	they O
	were O
	not O
	able O
	to O
	use O
	state-of-the-art O
	methods O
	for O
	the O
	surface O
	electronic O
	structure O
	. O
	Yet O
	the O
	success O
	of O
	the O
	simplified O
	treatments O
	[ O
	4 O
	– O
	6 O
	] O
	, O
	especially O
	for O
	MIES B-Material
	features O
	such O
	as O
	relative O
	energies O
	of O
	the O
	different O
	peaks O
	, O
	suggests O
	that O
	real O
	spectra O
	are O
	indeed O
	related O
	to O
	the O
	projection O
	of O
	the O
	surface O
	DOS B-Process
	on O
	to O
	the O
	projectile O
	orbital O
	. O

	In O
	this O
	section O
	, O
	we O
	use O
	the O
	terrain B-Process
	data I-Process
	processing I-Process
	as O
	an O
	example O
	to O
	describe B-Task
	the I-Task
	geodetic I-Task
	data I-Task
	transformation I-Task
	method I-Task
	. O
	Since O
	Google O
	Maps O
	/ O
	Earth O
	server O
	only O
	gives O
	the O
	terrain O
	data O
	in O
	graphical O
	display O
	, O
	we O
	have O
	to O
	get O
	terrain B-Material
	digital I-Material
	data I-Material
	from O
	other O
	sources O
	. O
	The O
	fine-resolution B-Process
	( I-Process
	3 I-Process
	″ I-Process
	or I-Process
	finer I-Process
	) I-Process
	terrain I-Process
	data I-Process
	bases I-Process
	such O
	as O
	SRTM B-Process
	( O
	Shuttle B-Process
	Radar I-Process
	Topographical I-Process
	Mission I-Process
	) O
	or O
	USGS O
	's O
	DEM B-Process
	( O
	Digital B-Process
	Elevation I-Process
	Model I-Process
	) O
	data O
	are O
	necessary O
	. O
	Moreover O
	, O
	since O
	3DWF O
	is O
	used O
	to O
	model O
	the O
	fine-scale B-Process
	( I-Process
	meters I-Process
	up I-Process
	to I-Process
	100m I-Process
	) I-Process
	atmospheric I-Process
	flow I-Process
	, O
	it O
	needs O
	fine O
	resolution O
	terrain O
	data O
	. O
	In O
	this O
	project O
	, O
	we O
	use O
	the O
	terrain O
	elevation O
	data O
	set O
	from O
	SRTM B-Process
	( O
	Farr O
	et O
	al O
	. O
	2007 O
	) O
	with O
	3-arcsecond O
	(~ O
	90m O
	resolution O
	at O
	the O
	equator O
	) O
	resolution O
	. O
	The O
	data O
	covers O
	the O
	land O
	area O
	, O
	nearly O
	global O
	from O
	56S O
	to O
	60N O
	latitudes O
	. O
	We O
	use O
	the O
	processed O
	version O
	4 O
	SRTM O
	data O
	set O
	as O
	described O
	in O
	Gamache O
	( O
	2005 O
	) O
	in O
	which O
	some O
	of O
	the O
	missing O
	data O
	holes O
	were O
	filled O
	. O
	The O
	original O
	data O
	is O
	organized O
	in O
	WGS84 B-Material
	( O
	World B-Material
	Geodetic I-Material
	System I-Material
	84 I-Material
	) O
	geodetic O
	coordinate O
	system O
	. O
	When O
	the O
	data O
	are O
	applied O
	to O
	the O
	3DWF B-Process
	model O
	, O
	they O
	are O
	transformed O
	to O
	the O
	local O
	East B-Material
	, I-Material
	North I-Material
	and I-Material
	Up I-Material
	( O
	ENU B-Material
	) O
	coordinate O
	( O
	see O
	Fig. O
	3 O
	) O
	. O
	Since O
	the O
	3DWF B-Process
	is O
	a O
	fine O
	scale O
	wind O
	model O
	and O
	its O
	entire O
	model O
	domain O
	is O
	not O
	intended O
	to O
	be O
	larger O
	than O
	20 O
	× O
	20km O
	, O
	this O
	Cartesian B-Material
	coordinate I-Material
	system I-Material
	is O
	a O
	good O
	choice O
	with O
	very O
	little O
	distortion O
	due O
	to O
	the O
	curvature O
	of O
	the O
	Earth O
	's O
	surface O
	. O
	The O
	transformation O
	from O
	the O
	WGS84 O
	data O
	to O
	the O
	ENU O
	coordinate O
	is O
	performed O
	as O
	follows O
	( O
	Fukushima O
	, O
	2006 O
	; O
	Featherstone O
	and O
	Claessens O
	, O
	2008 O
	) O
	. O

	Apache B-Process
	Pig I-Process
	is O
	a O
	platform O
	for O
	creating O
	MapReduce B-Process
	workflows I-Process
	with O
	Hadoop B-Material
	. O
	These O
	workflows O
	are O
	expressed O
	as O
	directed B-Material
	acyclic I-Material
	graphs I-Material
	( O
	DAGs B-Material
	) O
	of O
	tasks O
	that O
	exist O
	at O
	a O
	conceptually O
	higher O
	level O
	than O
	their O
	implementations O
	as O
	series O
	of O
	MapReduce B-Process
	jobs O
	. O
	Pig B-Process
	Latin I-Process
	is O
	the O
	procedural O
	language O
	used O
	for O
	building O
	these O
	workflows O
	, O
	providing O
	syntax O
	similar O
	to O
	the O
	declarative O
	SQL B-Process
	commonly O
	used O
	for O
	relational O
	database O
	systems O
	. O
	In O
	addition O
	to O
	standard O
	SQL B-Process
	operations I-Process
	, O
	Pig B-Process
	can O
	be O
	extended O
	with O
	user-defined B-Process
	functions I-Process
	( O
	UDFs B-Process
	) O
	commonly O
	written O
	in O
	Java O
	. O
	We O
	adopted O
	Pig B-Process
	for O
	our O
	implementation O
	of O
	the O
	correlator O
	to O
	speed O
	up O
	development O
	time O
	, O
	allow O
	for O
	ad O
	hoc O
	workflow O
	changes O
	, O
	and O
	to O
	embrace O
	the O
	Hadoop B-Material
	community O
	׳ O
	s O
	migration O
	away O
	from O
	MapReduce B-Process
	towards O
	more O
	generalized O
	DAG B-Task
	processing I-Task
	( O
	Mayer O
	, O
	2013 O
	) O
	. O
	Specifically O
	, O
	in O
	the O
	event O
	that O
	future O
	versions O
	of O
	Hadoop B-Material
	are O
	optimized O
	to O
	support O
	paradigms O
	other O
	than O
	MapReduce B-Process
	, O
	Pig B-Material
	scripts I-Material
	could O
	take O
	advantage O
	of O
	these O
	advances O
	without O
	recoding O
	, O
	whereas O
	explicit O
	Java B-Process
	MapReduce I-Process
	jobs O
	would O
	need O
	to O
	be O
	rewritten O
	. O

	The B-Task
	threshold I-Task
	values I-Task
	for I-Task
	removing I-Task
	large I-Task
	caters I-Task
	were I-Task
	determined I-Task
	by O
	examining O
	the O
	craters O
	within O
	the O
	study O
	area O
	, O
	referencing O
	previous O
	studies O
	( O
	Molloy O
	and O
	Stepinski O
	, O
	2007 O
	) O
	, O
	and O
	some O
	trial O
	and O
	error O
	. O
	After O
	the O
	parameter O
	values O
	are O
	determined O
	, O
	the O
	rest O
	of O
	the O
	process O
	is O
	automated O
	. O
	However O
	, O
	we O
	do O
	anticipate O
	some O
	minimum O
	manual B-Process
	editing I-Process
	may O
	be O
	needed O
	in O
	some O
	complicated O
	terrains O
	when O
	apply O
	it O
	to O
	all O
	of O
	Mars O
	. O
	To O
	minimize O
	the O
	distortion O
	resulted O
	from O
	map O
	projection O
	on O
	global O
	datasets O
	, O
	we O
	will O
	choose O
	an O
	equal O
	area O
	projection O
	by O
	evaluating O
	the O
	options O
	suggested O
	in O
	Steinwand O
	et O
	al O
	. O
	( O
	1995 O
	) O
	or O
	conduct O
	geodesic O
	area O
	calculation O
	using O
	software O
	such O
	as O
	“ O
	Tools B-Process
	for I-Process
	Graphics I-Process
	and I-Process
	Shapes I-Process
	” O
	( O
	http O
	:// O
	www.jennessent.com O
	/ O
	arcgis O
	/ O
	shapes O
	_ O
	graphics.htm O
	) O
	Although O
	post-formational O
	modification O
	to O
	the O
	valleys O
	may O
	be O
	minimum O
	( O
	Williams O
	and O
	Phillips O
	, O
	2001 O
	) O
	, O
	there O
	may O
	nonetheless O
	be O
	modifications O
	such O
	as O
	eolian O
	fill O
	and O
	mass O
	wasting O
	( O
	e.g. O
	, O
	Grant O
	et O
	al. O
	, O
	2008 O
	) O
	. O
	Thus O
	the O
	volume O
	estimates O
	derived O
	with O
	PBTH B-Process
	method I-Process
	represents O
	a O
	lower O
	bound O
	. O
	Comparing O
	the O
	estimates O
	from O
	MOLA B-Material
	and O
	HRSC B-Material
	data O
	reveals O
	that O
	MOLA B-Material
	estimate O
	is O
	about O
	91 O
	% O
	of O
	HRSC B-Material
	value O
	. O
	However O
	, O
	MOLA B-Material
	has O
	global O
	coverage O
	whereas O
	HRSC B-Material
	does O
	not O
	. O
	Therefore O
	, O
	for O
	areas O
	where O
	there O
	is O
	only O
	MOLA B-Material
	coverage O
	, O
	the O
	estimate O
	may O
	be O
	scaled O
	upward O
	by O
	1.1 O
	times O
	. O
	The O
	algorithm O
	has O
	been O
	tested O
	on O
	DEMs B-Material
	with O
	various O
	resolutions O
	( O
	2 O
	m O
	for O
	simulated O
	DEM B-Material
	, O
	75m O
	for O
	HRSC B-Material
	, O
	and O
	463m O
	for O
	MOLA B-Material
	) O
	. O
	It O
	can O
	certainly O
	be O
	applied O
	to O
	higher O
	resolution O
	DEMs B-Material
	for O
	Mars O
	when O
	they O
	become O
	available O
	, O
	but O
	the O
	threshold O
	values O
	will O
	need O
	to O
	be O
	adjusted O
	. O

	This O
	research O
	traces O
	the O
	implementation O
	of O
	an O
	information O
	system O
	in O
	the O
	form O
	of O
	ERP B-Material
	modules I-Material
	covering O
	tenant O
	and O
	contract O
	management O
	in O
	a O
	Chinese O
	service O
	company O
	. O
	Misalignments O
	between O
	the O
	ERP B-Process
	system I-Process
	specification O
	and O
	user O
	needs O
	led O
	to O
	the O
	adoption O
	of O
	informal O
	processes O
	within O
	the O
	organisation O
	. O
	These O
	processes O
	are O
	facilitated O
	within O
	an O
	informal O
	organisational O
	structure O
	and O
	are O
	based O
	on O
	human O
	interactions O
	undertaken O
	within O
	the O
	formal O
	organisation O
	. O
	Rather O
	than O
	to O
	attempt O
	to O
	suppress O
	the O
	emergence O
	of O
	the O
	informal O
	organisation O
	the O
	company O
	decided O
	to O
	channel O
	the O
	energies O
	of O
	staff O
	involved O
	in O
	informal O
	processes O
	towards O
	organisational O
	goals O
	. O
	The O
	company O
	achieved O
	this O
	by O
	harnessing O
	the O
	capabilities O
	of O
	what O
	we O
	term O
	a O
	hybrid B-Process
	ERP I-Process
	system I-Process
	, O
	combining O
	the O
	functionality O
	of O
	a O
	traditional O
	( O
	formal O
	) O
	ERP B-Process
	installation I-Process
	with O
	the O
	capabilities O
	of O
	Enterprise B-Process
	Social I-Process
	Software I-Process
	( O
	ESS B-Process
	) O
	. O
	However O
	the O
	company O
	recognised O
	that O
	the O
	successful O
	operation O
	of O
	the O
	hybrid O
	ERP B-Process
	system I-Process
	would O
	require O
	a O
	number O
	of O
	changes O
	in O
	organisational O
	design O
	in O
	areas O
	such O
	as O
	reporting O
	structures O
	and O
	communication O
	channels O
	. O
	A O
	narrative O
	provided O
	by O
	interviews O
	with O
	company O
	personnel O
	is O
	thematised O
	around O
	the O
	formal O
	and O
	informal O
	characteristics O
	of O
	the O
	organisation O
	as O
	defined O
	in O
	the O
	literature O
	. O
	This O
	leads O
	to O
	a O
	definition O
	of O
	the O
	characteristics O
	of O
	the O
	hybrid O
	organisation O
	and O
	strategies O
	for O
	enabling O
	a O
	hybrid O
	organisation O
	, O
	facilitated O
	by O
	a O
	hybrid B-Process
	ERP I-Process
	system I-Process
	, O
	which O
	directs O
	formal O
	and O
	informal O
	behaviour O
	towards O
	organisational O
	goals O
	and O
	provides O
	a O
	template O
	for O
	future O
	hybrid O
	implementations O
	. O

	We O
	addressed O
	the O
	question O
	whether B-Task
	carbohydrate I-Task
	coupling I-Task
	increased I-Task
	antigen I-Task
	uptake I-Task
	by O
	DCs O
	via O
	C-type B-Process
	lectin I-Process
	receptor I-Process
	targeting I-Process
	. O
	Therefore O
	, O
	the O
	antigens B-Material
	were O
	labeled O
	with O
	pHrodo B-Material
	Red I-Material
	dye I-Material
	( O
	Invitrogen B-Material
	) O
	, O
	a O
	dye O
	that O
	specifically O
	fluoresces B-Process
	as O
	pH O
	decreases O
	from O
	neutral O
	to O
	acidic O
	, O
	as O
	provided O
	in O
	endosomes B-Material
	/ O
	lysosomes B-Material
	of O
	cells O
	. O
	In O
	vitro O
	characterization O
	of O
	the O
	cellular O
	uptake O
	of O
	neoglycocomplexes B-Material
	using O
	bone B-Material
	marrow I-Material
	derived I-Material
	dendritic I-Material
	cells I-Material
	( O
	BMDCs B-Material
	) O
	demonstrated O
	superior O
	ingestion B-Process
	of O
	mannan-conjugates B-Material
	MN I-Material
	– I-Material
	Ova I-Material
	and I-Material
	MN I-Material
	– I-Material
	Pap I-Material
	( O
	Supplementary O
	Fig. O
	S4A-D,F O
	) O
	. O
	This O
	was O
	confirmed O
	in O
	vivo O
	by O
	intradermal B-Task
	needle-injection I-Task
	of O
	labeled O
	antigen O
	into O
	the O
	ear O
	pinnae O
	of O
	mice O
	. O
	Antigen B-Material
	uptake O
	and O
	transport O
	to O
	the O
	ear O
	dLNs O
	were O
	measured O
	after O
	24h O
	by O
	FACS B-Process
	analysis I-Process
	. O
	DCs O
	in O
	cervical O
	LNs O
	were O
	identified O
	according O
	to O
	their O
	high O
	expression O
	of O
	MHC O
	class O
	II O
	( O
	Fig. O
	3A O
	) O
	and O
	additionally O
	characterized O
	by O
	CD8α B-Material
	, O
	CD11b B-Material
	, O
	and O
	CD11c B-Material
	expression O
	and O
	uptake O
	of O
	pHrodo-labeled B-Material
	antigen I-Material
	( O
	Fig. O
	3B O
	, O
	D O
	– O
	F O
	) O
	. O
	The O
	results O
	showed O
	significantly O
	elevated O
	numbers O
	of O
	pHrodo B-Material
	+ I-Material
	MHCIIhigh I-Material
	DCs I-Material
	for O
	mannan O
	conjugates O
	MN B-Material
	– I-Material
	Ova I-Material
	and O
	MN B-Material
	– I-Material
	Pap I-Material
	( O
	and O
	to O
	a O
	lesser O
	degree O
	for O
	MD B-Material
	– I-Material
	Pap I-Material
	) O
	in O
	comparison O
	to O
	the O
	unmodified O
	antigens B-Material
	( O
	Fig. O
	3C O
	) O
	. O
	Both O
	carbohydrates B-Material
	targeted O
	antigen B-Material
	preferentially O
	to O
	CD8α B-Material
	− I-Material
	DCs I-Material
	, O
	as O
	indicated O
	by O
	an O
	increase O
	in O
	CD8α B-Material
	−/ I-Material
	pHrodo I-Material
	+ I-Material
	DCs I-Material
	compared O
	to O
	unmodified O
	antigens B-Material
	( O
	Fig. O
	3E O
	and O
	F O
	) O
	. O
	Nevertheless O
	, O
	whether O
	the O
	antigens O
	were O
	taken O
	up O
	in O
	situ O
	by O
	dermal O
	DCs O
	or O
	by O
	LN O
	resident O
	APCs O
	via O
	the O
	afferent O
	lymphatics O
	could O
	not O
	be O
	fully O
	elucidated O
	. O
	Histology O
	revealed O
	that O
	antigen-loaded B-Material
	cells I-Material
	in O
	the O
	dLNs O
	were O
	already O
	present O
	30min O
	after O
	intradermal B-Process
	injection I-Process
	( O
	Supplementary O
	Fig. O
	S4G O
	) O
	, O
	suggesting O
	both O
	mechanisms O
	. O

	The O
	mesoporous B-Material
	silica I-Material
	particles O
	were O
	prepared O
	by O
	the O
	surfactant B-Process
	self-assembly I-Process
	method O
	described O
	previously O
	[ O
	18,24 O
	] O
	. O
	Briefly O
	, O
	a O
	homogeneous B-Material
	solution I-Material
	of O
	the O
	soluble B-Material
	silica I-Material
	precursor I-Material
	, O
	tetraethylorthosilicate B-Material
	( O
	TEOS B-Material
	; O
	Sigma-Aldrich O
	Corp. O
	, O
	St. O
	Louis O
	, O
	MO O
	) O
	, O
	and O
	hydrochloric B-Material
	acid I-Material
	was O
	mixed O
	in O
	ethanol B-Material
	and O
	water B-Material
	. I-Material
	A O
	surfactant B-Material
	, O
	cetyltrimethylammonium B-Material
	bromide I-Material
	( O
	CTAB B-Material
	; O
	Sigma-Aldrich O
	Corp. O
	, O
	St. O
	Louis O
	, O
	MO O
	) O
	, O
	with O
	an O
	initial O
	concentration O
	much O
	less O
	than O
	the O
	critical O
	micelle O
	concentration O
	was O
	added O
	to O
	lower O
	the O
	surface O
	tension O
	of O
	the O
	liquid O
	mixture O
	and O
	act O
	as O
	the O
	mesoporous B-Material
	structure-directing I-Material
	template I-Material
	. O
	Aerosol B-Material
	solutions I-Material
	of O
	soluble B-Material
	silica I-Material
	plus O
	surfactant B-Material
	were O
	then O
	generated O
	with O
	nitrogen B-Process
	as O
	a O
	carrier O
	atomizing B-Material
	gas I-Material
	using O
	a O
	commercially O
	available O
	atomizer B-Process
	( O
	Model O
	9392A O
	, O
	TSI O
	, O
	Inc. O
	, O
	St. O
	Paul O
	, O
	MN O
	) O
	. O
	The O
	aerosol B-Material
	droplets I-Material
	were O
	solidified O
	in O
	a O
	tube B-Process
	furnace I-Process
	at O
	400 O
	° O
	C O
	until O
	dry O
	. O
	Once O
	dried O
	, O
	a O
	durapore B-Material
	membrane I-Material
	filter I-Material
	, O
	kept O
	at O
	80 O
	° O
	C O
	, O
	was O
	used O
	to O
	collect O
	the O
	particles O
	. O
	As O
	a O
	final O
	step O
	, O
	the O
	surfactant B-Material
	was O
	removed O
	at O
	400 O
	° O
	C O
	for O
	5h O
	via O
	calcination B-Process
	. O
	The O
	surface O
	of O
	the O
	mesoporous B-Material
	silica I-Material
	core I-Material
	in O
	these O
	studies O
	was O
	chemically O
	modified O
	with O
	10wt. O
	% O
	or O
	15wt. O
	% O
	by O
	aminopropyltriethoxysilane B-Material
	( O
	APTES B-Material
	; O
	Sigma-Aldrich O
	Corp. O
	, O
	St. O
	Louis O
	, O
	MO O
	) O
	conducted O
	identically O
	as O
	previously O
	described O
	[ O
	17 O
	] O
	to O
	create O
	a O
	positive O
	surface O
	charge O
	to O
	increase O
	loading O
	efficiency O
	of O
	negatively O
	charged O
	cargo O
	. O
	Further O
	, O
	Liu O
	and O
	colleagues O
	report O
	the O
	colloidal O
	stability O
	of O
	these O
	protocells B-Material
	with O
	lipid O
	bilayers O
	, O
	excess O
	amount O
	of O
	liposomes B-Material
	( O
	50μg O
	liposomes O
	per O
	0.5mg O
	silica O
	were O
	used O
	[ O
	18 O
	]) O
	. O

	Ultrasound B-Process
	( O
	US B-Process
	) O
	can O
	initiate O
	the O
	release O
	of O
	drugs O
	from O
	liposomes B-Material
	via O
	an O
	event O
	called O
	inertial B-Task
	cavitation I-Task
	, O
	whereby O
	the O
	rarefactional O
	phase O
	of O
	an O
	ultrasound O
	wave O
	causes O
	the O
	expansion O
	of O
	a O
	gas B-Material
	bubble I-Material
	followed O
	by O
	a O
	violent O
	collapse O
	due O
	to O
	the O
	inertia O
	of O
	the O
	surrounding O
	media O
	. O
	This O
	collapse O
	creates O
	shock O
	waves O
	which O
	can O
	disrupt O
	the O
	stability O
	of O
	co-localised O
	liposomal O
	drug O
	carriers O
	. O
	To O
	date O
	, O
	studies O
	have O
	concentrated O
	on O
	the O
	use O
	of O
	low O
	frequency O
	or O
	high O
	intensity O
	US B-Process
	to O
	generate O
	gas B-Material
	bubbles I-Material
	in O
	situ O
	, O
	and O
	most O
	recently O
	such O
	parameters O
	have O
	been O
	used O
	to O
	achieve O
	a O
	variable O
	level O
	of O
	triggered O
	drug O
	release O
	following O
	an O
	intratumoral B-Process
	injection I-Process
	of O
	liposomes O
	[ O
	14 O
	] O
	. O
	However O
	, O
	concerns O
	persist O
	over O
	the O
	damage O
	to O
	non-target O
	tissue O
	that O
	such O
	US B-Process
	exposure O
	parameters O
	may O
	cause O
	and O
	whether O
	ultimately O
	they O
	will O
	be O
	widely O
	clinically O
	applicable O
	. O
	An O
	alternative O
	strategy O
	is O
	to O
	utilise O
	high-frequency O
	US O
	pulses O
	at O
	pressures O
	in O
	the O
	diagnostic O
	range O
	in O
	the O
	presence O
	of O
	pre-existing O
	gas O
	bubbles O
	. O
	This O
	provides O
	an O
	inertial B-Task
	cavitation I-Task
	stimulus O
	for O
	drug O
	release O
	using O
	safe O
	, O
	clinically O
	achievable O
	US B-Process
	exposure O
	conditions O
	and O
	approved O
	US B-Material
	contrast I-Material
	agents I-Material
	[ O
	15 O
	] O
	. O
	Indeed O
	, O
	in O
	the O
	context O
	of O
	improving O
	the O
	delivery O
	of O
	therapeutics O
	such O
	as O
	oncolytic B-Material
	viruses I-Material
	, O
	this O
	approach O
	has O
	already O
	shown O
	great O
	promise O
	[ O
	16 O
	] O
	. O
	A O
	further O
	advantage O
	of O
	this O
	approach O
	is O
	that O
	US-induced B-Process
	cavitation I-Process
	events O
	produce O
	distinct O
	acoustic O
	emissions O
	that O
	can O
	be O
	recorded O
	and O
	characterised O
	providing O
	non-invasive O
	feedback O
	, O
	a O
	feature O
	which O
	has O
	proven O
	useful O
	in O
	ablative O
	US B-Process
	applications O
	[ O
	17 O
	– O
	19 O
	] O
	. O

	The O
	most O
	widely O
	used O
	ion O
	source O
	in O
	FIB B-Process
	instruments I-Process
	is O
	a O
	gallium B-Material
	( O
	Ga B-Material
	) O
	liquid O
	metal O
	ion O
	source O
	( O
	LMIS O
	) O
	[ O
	1 O
	] O
	. O
	Gallium B-Material
	is O
	attractive O
	as O
	an O
	ion O
	source O
	because O
	of O
	its O
	low O
	melting O
	temperature O
	( O
	29.8 O
	° O
	C O
	at O
	standard O
	atmospheric O
	pressure O
	[ O
	4 O
	]) O
	and O
	its O
	low O
	volatility O
	[ O
	1 O
	] O
	. O
	However O
	, O
	some O
	materials O
	show O
	sensitivity O
	to O
	the O
	Ga B-Process
	ion I-Process
	beam I-Process
	. O
	This O
	sensitivity O
	is O
	manifested O
	as O
	changes O
	in O
	the O
	structure O
	and O
	chemical O
	composition O
	of O
	the O
	starting O
	material O
	upon O
	exposure O
	to O
	the O
	Ga O
	ion O
	beam O
	[ O
	5 O
	] O
	. O
	Group O
	III O
	– O
	V O
	compound O
	semiconductors B-Process
	are O
	one O
	class O
	of O
	materials O
	that O
	show O
	such O
	sensitivity O
	. O
	Cryo-FIB B-Task
	milling I-Task
	has O
	recently O
	been O
	reported O
	to O
	suppress O
	the O
	reactions O
	between O
	the O
	Ga B-Process
	ion I-Process
	beam I-Process
	and O
	III B-Material
	– I-Material
	V I-Material
	materials I-Material
	[ O
	6 O
	] O
	. O
	The O
	suggested O
	advantage O
	of O
	cryo-FIB B-Task
	milling I-Task
	over O
	room O
	temperature O
	milling O
	of O
	Group B-Material
	III I-Material
	– I-Material
	V I-Material
	materials I-Material
	is O
	appealing O
	, O
	given O
	the O
	variety O
	of O
	present O
	and O
	potential O
	future O
	applications O
	for O
	these O
	materials O
	( O
	e.g. O
	, O
	as O
	electronic O
	or O
	photonic O
	devices O
	given O
	the O
	favorable O
	electron O
	transport O
	and O
	direct O
	band O
	gap O
	properties O
	associated O
	with O
	several O
	III O
	– O
	V O
	semiconductor B-Process
	systems O
	) O
	. O

	According O
	to O
	the O
	ellipsometric B-Process
	spectra I-Process
	, O
	optical B-Process
	constants I-Process
	and O
	other O
	physical O
	parameters O
	can O
	be O
	extracted O
	by O
	an O
	appropriate O
	fitting O
	model O
	. O
	In O
	order O
	to O
	estimate O
	the O
	optical B-Process
	constants I-Process
	/ I-Process
	dielectric I-Process
	functions O
	of O
	Ni-doped B-Material
	TiO2 I-Material
	films I-Material
	, O
	a O
	three-phase B-Process
	layered I-Process
	system I-Process
	( O
	air B-Process
	/ I-Process
	film I-Process
	/ I-Process
	substrate I-Process
	) O
	[ O
	15 O
	] O
	was O
	utilized O
	to O
	study O
	the O
	ellipsometric O
	spectra O
	. O
	TiO2 O
	belongs O
	to O
	the O
	wide O
	band O
	gap O
	semiconductors B-Process
	. O
	Considering O
	the O
	contribution O
	of O
	the O
	M0 O
	type O
	critical O
	point O
	with O
	the O
	lowest O
	three O
	dimensions O
	, O
	its O
	dielectric O
	function O
	can O
	be O
	calculated O
	by O
	Adachi B-Process
	's I-Process
	model I-Process
	[ O
	15,22,23 O
	] O
	: O
	ε B-Process
	( I-Process
	Ε I-Process
	)= I-Process
	ε I-Process
	∞+{ I-Process
	A0 I-Process
	[ I-Process
	2 I-Process
	−( I-Process
	1 I-Process
	+ I-Process
	χ0 I-Process
	) I-Process
	1 I-Process
	/ I-Process
	2 I-Process
	−( I-Process
	1 I-Process
	− I-Process
	χ0 I-Process
	) I-Process
	1 I-Process
	/ I-Process
	2 I-Process
	]}/( I-Process
	EOBG2 I-Process
	/ I-Process
	3χ02 I-Process
	) I-Process
	. O
	In O
	the O
	model O
	, O
	E B-Process
	is O
	the O
	incident B-Process
	photon I-Process
	energy I-Process
	, O
	ε B-Process
	∞ I-Process
	is O
	the O
	high-frequency B-Process
	dielectric I-Process
	constant I-Process
	, O
	χ0 B-Process
	=( I-Process
	E I-Process
	+ I-Process
	iΓ I-Process
	) I-Process
	, O
	EOBG B-Process
	is O
	the O
	optical O
	gap O
	energy O
	, O
	and O
	A0 B-Process
	and I-Process
	Γ I-Process
	are O
	the O
	strength B-Process
	and I-Process
	broadening I-Process
	parameters I-Process
	of O
	the O
	EOBG B-Process
	transition O
	, O
	respectively O
	. O
	As O
	an O
	example O
	, O
	the O
	experimental O
	SE B-Process
	of O
	the O
	film O
	TN1 O
	at O
	an O
	incident O
	angle O
	70 O
	° O
	by O
	dot O
	scatter O
	is O
	shown O
	in O
	Fig. O
	4. O
	The O
	Fabry B-Material
	– I-Material
	Pérot I-Material
	interference I-Material
	oscillations O
	due O
	to O
	multiple O
	reflections O
	within O
	the O
	film O
	have O
	been O
	found O
	in O
	the O
	photon O
	energy O
	from O
	1.5eV O
	to O
	3.5eV O
	( O
	354nm O
	– O
	826nm O
	) O
	, O
	which O
	indicates O
	that O
	the O
	films O
	are O
	transparent O
	in O
	this O
	region O
	. O
	Note O
	that O
	a O
	good O
	agreement O
	of O
	the O
	experimental O
	and O
	calculated O
	spectra O
	is O
	attained O
	in O
	the O
	whole O
	measured O
	photon O
	energy O
	range O
	. O
	The O
	fitting O
	thickness O
	for O
	film O
	TN2 O
	is O
	159nm O
	, O
	which O
	is O
	very O
	near O
	to O
	the O
	value O
	obtained O
	by O
	SEM B-Process
	( O
	see O
	Fig. O
	1 O
	( O
	b O
	)) O
	. O

	Fig. O
	7 O
	shows O
	the O
	relationship O
	between O
	the O
	testing B-Process
	time I-Process
	and O
	friction B-Process
	coefficients I-Process
	of O
	various O
	samples O
	under O
	dry O
	conditions O
	. O
	There O
	exist O
	running O
	in O
	and O
	steady O
	wear O
	period O
	in O
	the O
	wear B-Process
	process I-Process
	of O
	uncoated B-Material
	AZ31 I-Material
	and O
	anodizing B-Material
	coating I-Material
	without I-Material
	Al2O3 I-Material
	nanoparticles I-Material
	while O
	there O
	has O
	a O
	steady O
	wear O
	period O
	only O
	in O
	the O
	wear B-Process
	process I-Process
	of O
	composite B-Material
	anodizing I-Material
	coating I-Material
	with O
	Al2O3 B-Material
	nanoparticles I-Material
	. O
	At O
	the O
	same O
	time O
	, O
	the O
	addition O
	of O
	nano-particles B-Material
	to O
	electrolyte B-Material
	led O
	to O
	reduction B-Process
	of I-Process
	friction I-Process
	coefficient I-Process
	. O
	The O
	friction O
	coefficient O
	of O
	composite B-Process
	coating I-Process
	is O
	relatively O
	lower O
	and O
	more O
	stable O
	than O
	what O
	has O
	been O
	reported O
	in O
	literature O
	[ O
	24,25 O
	] O
	for O
	anodizing B-Material
	coatings I-Material
	. O
	This O
	may O
	be O
	caused O
	by O
	“ B-Process
	rolling I-Process
	effect I-Process
	” I-Process
	made O
	by O
	Al2O3 B-Material
	nanoparticles I-Material
	on O
	the O
	surface O
	of O
	oxide O
	coating O
	. O
	Spherical B-Material
	nanoparticles I-Material
	change O
	sliding O
	into O
	rolling O
	, O
	which O
	reduce B-Process
	friction I-Process
	, O
	making O
	the O
	friction B-Process
	coefficient I-Process
	becomes O
	more O
	stable O
	. O
	The O
	friction O
	coefficient O
	of O
	anodizing B-Material
	coating I-Material
	without I-Material
	Al2O3 I-Material
	nanoparticles I-Material
	has O
	large O
	fluctuation O
	maybe O
	for O
	the O
	damage O
	of O
	coating O
	. O
	In O
	contrast O
	to O
	the O
	uncoated B-Material
	AZ31 I-Material
	magnesium I-Material
	alloy I-Material
	, O
	the O
	anodizing B-Material
	coatings I-Material
	show O
	slightly O
	lower O
	friction B-Process
	coefficient I-Process
	. O
	This O
	can O
	be O
	attributed O
	to O
	their O
	higher O
	load-bearing O
	capacity O
	for O
	high O
	hardness O
	. O

	Functionally B-Material
	Graded I-Material
	Materials I-Material
	( O
	FGMs B-Material
	) O
	, O
	described O
	in O
	detail O
	by O
	Suresh O
	and O
	Mortensen O
	[ O
	1 O
	] O
	, O
	are O
	a O
	type O
	of O
	heterogeneous B-Material
	composite I-Material
	materials I-Material
	exhibiting O
	gradual O
	variation O
	in O
	volume O
	fraction O
	of O
	their O
	constituents O
	from O
	one O
	surface O
	of O
	the O
	material O
	to O
	the O
	other O
	, O
	resulting O
	in O
	properties O
	which O
	vary O
	continuously O
	across O
	the O
	material O
	. O
	The O
	idea O
	of O
	a O
	Functionally B-Material
	Graded I-Material
	Material I-Material
	is O
	not O
	a O
	new O
	one O
	, O
	there O
	are O
	in O
	fact O
	many O
	natural O
	materials O
	which O
	exhibit O
	this O
	property O
	. O
	Study B-Task
	of I-Task
	bone I-Task
	, I-Task
	shell I-Task
	, I-Task
	balsawood I-Task
	and I-Task
	bamboo I-Task
	shows O
	that O
	they O
	are O
	all O
	graded O
	with O
	their O
	greatest O
	strength O
	on O
	the O
	outside O
	, O
	in O
	areas O
	where O
	the O
	greatest O
	protection O
	is O
	required O
	. O
	However O
	it O
	was O
	not O
	until O
	the O
	1980s O
	in O
	Japan O
	[ O
	2 O
	] O
	that O
	the O
	idea O
	of O
	a O
	Functionally B-Material
	Graded I-Material
	Material I-Material
	was O
	actively O
	researched O
	in O
	order O
	to O
	gain O
	advances O
	in O
	heat B-Material
	resistant I-Material
	materials I-Material
	for O
	use O
	in O
	aerospace B-Task
	and O
	nuclear B-Task
	fission I-Task
	reactors I-Task
	. O

	Recently O
	together O
	with O
	structural B-Process
	efficiency I-Process
	, O
	passenger B-Task
	safety I-Task
	is O
	also O
	an O
	important O
	issue O
	in O
	application O
	of O
	material O
	to O
	transportation B-Process
	industries I-Process
	. O
	Hence O
	, O
	the O
	crashworthiness B-Material
	parameters I-Material
	are O
	introducing O
	to O
	predict O
	the O
	capability O
	of O
	structure O
	to O
	prevent B-Task
	the I-Task
	massive I-Task
	damage I-Task
	and O
	protect B-Task
	the I-Task
	passenger I-Task
	in O
	the O
	event O
	of O
	a O
	crash O
	. O
	Crashworthiness B-Task
	parameters I-Task
	for O
	various O
	thin-walled O
	tubes O
	made O
	from O
	metal B-Material
	or O
	fibre B-Material
	/ I-Material
	resin I-Material
	composites I-Material
	in O
	different O
	geometries O
	have O
	been O
	studied O
	. O
	A O
	critical O
	difference O
	of O
	tubular B-Process
	composites I-Process
	failure I-Process
	modes O
	compared O
	with O
	metallic O
	is O
	the O
	brittle B-Process
	collapse I-Process
	. O
	In O
	addition O
	, O
	in O
	composites B-Material
	, O
	tubular B-Material
	failure I-Material
	modes I-Material
	are O
	involved O
	with O
	micro-cracking B-Process
	development I-Process
	, O
	delamination B-Process
	, O
	fibre B-Process
	breakage I-Process
	, O
	etc. O
	, O
	instead O
	of O
	plastic B-Process
	deformation I-Process
	. O
	Implementation B-Task
	of I-Task
	composite I-Task
	materials I-Task
	in O
	the O
	field O
	of O
	crashworthiness O
	is O
	attributed O
	to O
	Hull O
	, O
	who O
	in O
	80s O
	and O
	90s O
	of O
	the O
	last O
	century O
	studied O
	extensively O
	the O
	crushing B-Process
	behaviour I-Process
	of O
	fibre B-Material
	reinforced I-Material
	composite I-Material
	material I-Material
	. O
	He O
	found O
	that O
	the O
	composite B-Material
	materials I-Material
	absorbed O
	high O
	energy O
	in O
	the O
	face O
	of O
	the O
	fracture B-Process
	surface I-Process
	energy I-Process
	mechanism O
	rather O
	than O
	plastic B-Process
	deformation I-Process
	as O
	observed O
	for O
	metals B-Material
	[ O
	1 O
	] O
	. O
	This O
	observation O
	has O
	inspired O
	others O
	to O
	further O
	investigation O
	about O
	crashworthiness B-Process
	characteristics I-Process
	of O
	composite B-Material
	materials I-Material
	. O
	Studies O
	have O
	examined O
	the O
	axial B-Process
	crushing I-Process
	behaviour I-Process
	of O
	fibre-reinforced B-Material
	tubes I-Material
	[ O
	2 O
	] O
	, O
	fibreglass B-Material
	tubes I-Material
	[ O
	3,4 O
	] O
	, O
	PVC B-Material
	tubes I-Material
	[ O
	5 O
	] O
	and O
	carbon B-Material
	fibre I-Material
	reinforced I-Material
	plastic I-Material
	( O
	CFRP B-Material
	) O
	tubes O
	[ O
	6 O
	] O
	. O

	Nanoparticle B-Process
	Tracking I-Process
	Analysis I-Process
	( O
	NTA B-Process
	) O
	has O
	been O
	applied O
	to O
	characterising O
	soot B-Material
	agglomerates I-Material
	of O
	particles O
	and O
	compared O
	with O
	Transmission B-Process
	Electron I-Process
	Microscoscopy I-Process
	( O
	TEM B-Process
	) O
	. O
	Soot B-Material
	nanoparticles I-Material
	were O
	extracted O
	from O
	used O
	oil O
	drawn O
	from O
	the O
	sump O
	of O
	a O
	light O
	duty O
	automotive O
	diesel O
	engine O
	. O
	The O
	samples O
	were O
	prepared O
	for O
	analysis O
	by O
	diluting O
	with O
	heptane B-Material
	. O
	Individual O
	tracking O
	of O
	soot B-Material
	agglomerates I-Material
	allows O
	for O
	size B-Process
	distribution I-Process
	analysis I-Process
	. O
	The O
	size O
	of O
	soot O
	was O
	compared O
	with O
	length O
	measurements O
	of O
	projected O
	two-dimensional O
	TEM B-Process
	images O
	of O
	agglomerates B-Material
	. O
	Both O
	the O
	techniques O
	show O
	that O
	soot-in-oil O
	exists O
	as O
	agglomerates O
	with O
	average O
	size O
	of O
	120nm O
	. O
	NTA B-Process
	is O
	able O
	to O
	measure O
	particles O
	in O
	polydisperse B-Material
	solutions I-Material
	and O
	reports O
	the O
	size O
	and O
	volume O
	distribution O
	of O
	soot-in-oil B-Material
	aggregates I-Material
	; O
	it O
	has O
	the O
	advantages O
	of O
	being O
	fast O
	and O
	relatively O
	low O
	cost O
	if O
	compared O
	with O
	TEM.Nanoparticle O
	Tracking O
	Analysis O
	( O
	NTA O
	) O
	has O
	been O
	applied O
	to O
	characterising O
	soot O
	agglomerates O
	of O
	particles O
	and O
	compared O
	with O
	Transmission O
	Electron O
	Microscoscopy O
	( O
	TEM B-Process
	) O
	. O
	Soot O
	nanoparticles O
	were O
	extracted O
	from O
	used O
	oil O
	drawn O
	from O
	the O
	sump O
	of O
	a O
	light O
	duty O
	automotive O
	diesel O
	engine O
	. O
	The O
	samples O
	were O
	prepared O
	for O
	analysis O
	by O
	diluting O
	with O
	heptane O
	. O
	Individual O
	tracking O
	of O
	soot O
	agglomerates O
	allows O
	for O
	size O
	distribution O
	analysis O
	. O
	The O
	size O
	of O
	soot O
	was O
	compared O
	with O
	length O
	measurements O
	of O
	projected O
	two-dimensional O
	TEM O
	images O
	of O
	agglomerates O
	. O
	Both O
	the O
	techniques O
	show O
	that O
	soot-in-oil O
	exists O
	as O
	agglomerates O
	with O
	average O
	size O
	of O
	120nm O
	. O
	NTA O
	is O
	able O
	to O
	measure O
	particles O
	in O
	polydisperse O
	solutions O
	and O
	reports O
	the O
	size O
	and O
	volume O
	distribution O
	of O
	soot-in-oil O
	aggregates O
	; O
	it O
	has O
	the O
	advantages O
	of O
	being O
	fast O
	and O
	relatively O
	low O
	cost O
	if O
	compared O
	with O
	TEM O
	. O

	Fig. O
	11 O
	shows O
	the O
	wear-mode B-Material
	map I-Material
	of O
	RH O
	ceramics O
	, O
	in O
	which O
	the O
	early-stage O
	friction O
	coefficients O
	and O
	the O
	surface O
	roughness O
	of O
	the O
	pure O
	surface O
	were O
	chosen O
	. O
	The O
	value O
	of O
	the O
	fracture O
	toughness O
	of O
	RH O
	ceramics O
	was O
	calculated O
	based O
	on O
	the O
	reference O
	data O
	in O
	other O
	literature O
	[ O
	22 O
	] O
	. O
	The O
	Sc O
	of O
	RH O
	ceramics O
	was O
	smaller O
	than O
	Sc,critical O
	under O
	all O
	tested O
	conditions O
	during O
	the O
	initial O
	stage O
	of O
	friction O
	. O
	Thus O
	, O
	the O
	initial O
	wear O
	mode O
	of O
	RH O
	ceramics O
	was O
	powder B-Process
	formation I-Process
	or O
	plowing B-Process
	. O
	In O
	addition O
	, O
	powder B-Process
	formation I-Process
	and O
	plowing B-Process
	can O
	be O
	distinguished O
	using O
	a O
	dimensionless B-Material
	parameter I-Material
	( O
	Sc O
	⁎) O
	and O
	a O
	critical O
	parameter O
	( O
	Sc,critical O
	⁎).( O
	3 O
	) O
	Sc O
	⁎= O
	HvRmaxKIc O
	( O
	4 O
	) O
	Sc,critical O
	⁎= O
	51 O
	+ O
	10μwhere O
	Hv O
	is O
	the O
	Vickers O
	hardness O
	of O
	RH O
	ceramics O
	[ O
	Pa O
	] O
	. O
	The O
	initial O
	wear O
	mode O
	of O
	RH O
	ceramics O
	was O
	determined O
	as O
	powder O
	formation O
	under O
	all O
	tested O
	conditions O
	, O
	as O
	demonstrated O
	in O
	Fig. O
	12 O
	( O
	a O
	) O
	. O
	Furthermore O
	, O
	the O
	wear-mode O
	map O
	at O
	2 O
	× O
	104 O
	cycles O
	was O
	constructed O
	, O
	as O
	shown O
	in O
	Fig. O
	12 O
	( O
	b O
	) O
	. O
	In O
	the O
	map O
	, O
	all O
	plots O
	moved O
	near O
	the O
	transition O
	curve O
	to O
	plowing O
	. O
	In O
	particular O
	, O
	the O
	plots O
	for O
	RH O
	ceramics O
	sliding O
	against O
	stainless O
	steel O
	or O
	Al2O3 O
	balls O
	were O
	nearer O
	than O
	SiC O
	or O
	Si3N4 O
	balls O
	. O
	Therefore O
	, O
	RH O
	ceramics O
	sliding O
	against O
	SiC O
	and O
	Si3N4 O
	balls O
	showed O
	relatively O
	higher O
	wear O
	than O
	the O
	other O
	counterpart O
	materials O
	. O
	Nevertheless O
	, O
	these O
	results O
	from O
	the O
	wear-mode O
	maps O
	indicated O
	that O
	the O
	wear O
	mode O
	of O
	RH O
	ceramics O
	was O
	powder O
	formation O
	accompanied O
	with O
	microcracks O
	under O
	all O
	tested O
	conditions O
	in O
	this O
	study O
	, O
	resulting O
	in O
	low O
	wear O
	(< O
	5 O
	× O
	10 O
	− O
	9mm2 O
	/ O
	N O
	) O
	. O
	Indeed O
	, O
	the O
	observation O
	of O
	the O
	worn O
	surfaces O
	revealed O
	that O
	the O
	catastrophic O
	wear O
	of O
	RH O
	ceramics O
	accompanied O
	by O
	large O
	brittle O
	fracture O
	was O
	prevented O
	overall O
	, O
	as O
	shown O
	in O
	Fig. O
	13 O
	. O

	The O
	lateral B-Task
	force I-Task
	, I-Task
	Q I-Task
	, I-Task
	is I-Task
	measured I-Task
	and I-Task
	recorded I-Task
	throughout O
	the O
	entire O
	test O
	by O
	a O
	piezoelectric B-Process
	load I-Process
	cell I-Process
	which O
	is O
	connected O
	to O
	the O
	quasi-stationary O
	LSMB B-Process
	. O
	The O
	LSMB O
	is O
	mounted O
	on O
	flexures B-Material
	which O
	provide O
	flexibility O
	in O
	the O
	horizontal O
	direction O
	so O
	that O
	the O
	majority O
	of O
	the O
	lateral O
	force O
	is O
	transmitted O
	though O
	the O
	much O
	stiffer O
	load O
	path O
	which O
	contains O
	the O
	load B-Process
	cell I-Process
	as O
	shown O
	in O
	Fig. O
	2. O
	Both O
	displacement B-Process
	and I-Process
	load I-Process
	sensors I-Process
	have O
	been O
	calibrated O
	( O
	both O
	externally O
	and O
	in-situ O
	) O
	in O
	static O
	conditions O
	. O
	The O
	load O
	and O
	displacement O
	signals O
	are O
	sampled O
	at O
	a O
	rate O
	of O
	two O
	hundred O
	measurements O
	per O
	fretting O
	cycle O
	at O
	all O
	fretting O
	frequencies O
	, O
	with O
	these O
	data O
	being O
	used O
	to O
	generate O
	fretting B-Material
	loops I-Material
	. I-Material
	The O
	loops O
	were O
	used O
	to O
	derive O
	the O
	contact O
	slip O
	amplitude O
	and O
	the O
	energy O
	coefficient O
	of O
	friction O
	in O
	each O
	cycle O
	according O
	to O
	the O
	method O
	suggested O
	by O
	Fouvry O
	et O
	al O
	. O
	[ O
	17 O
	] O
	. O
	Average O
	values O
	for O
	these O
	were O
	calculated O
	for O
	each O
	test O
	( O
	the O
	average O
	coefficient O
	of O
	friction O
	included O
	values O
	associated O
	with O
	the O
	initial O
	transients O
	in O
	the O
	tests O
	as O
	suggested O
	by O
	Hirsch O
	and O
	Neu O
	[ O
	18 O
	]) O
	. O

	We O
	have O
	developed O
	the O
	theory O
	of O
	electrons B-Process
	carrying I-Process
	quantized I-Process
	orbital I-Process
	angular I-Process
	momentum I-Process
	. I-Process
	To O
	make O
	connection O
	to O
	realistic O
	situations O
	, O
	we O
	considered O
	a O
	plane B-Material
	wave I-Material
	moving O
	along O
	the O
	optic O
	axis O
	of O
	a O
	lens B-Material
	system I-Material
	, O
	intercepted O
	by O
	a O
	round O
	, O
	centered O
	aperture.88In O
	the O
	experiment O
	, O
	this O
	aperture O
	carries O
	the O
	holographic O
	mask O
	. O
	It O
	turns O
	out O
	that O
	the O
	movement O
	along O
	the O
	optic O
	axis O
	can O
	be O
	separated O
	off O
	; O
	the O
	reduced O
	Schrödinger O
	equation O
	operating O
	in O
	the O
	plane O
	of O
	the O
	aperture O
	can O
	be O
	mapped O
	onto O
	Bessel O
	's O
	differential O
	equation O
	. O
	The O
	ensuing O
	eigenfunctions O
	fall O
	into O
	families O
	with O
	discrete O
	orbital O
	angular O
	momentum O
	ℏm O
	along O
	the O
	optic O
	axis O
	where O
	m O
	is O
	a O
	magnetic O
	quantum O
	number O
	. O
	Those O
	vortices O
	can O
	be O
	produced O
	by O
	matching O
	a O
	plane O
	wave O
	after O
	passage O
	through O
	a O
	holographic O
	mask O
	with O
	a O
	fork O
	dislocation O
	to O
	the O
	eigenfunctions O
	of O
	the O
	cylindrical O
	problem O
	. O
	Vortices O
	can O
	be O
	focussed O
	by O
	magnetic O
	lenses O
	into O
	volcano-like O
	charge O
	distributions O
	with O
	very O
	narrow O
	angular O
	divergence O
	, O
	resembling O
	loop O
	currents O
	in O
	the O
	diffraction O
	plane O
	. O
	Inclusion O
	of O
	spherical O
	aberration O
	changes O
	the O
	ringlike O
	shape O
	but O
	does O
	not O
	destroy O
	the O
	central O
	zero O
	intensity O
	of O
	vortices O
	with O
	m O
	≠ O
	0 O
	. O
	Partial O
	coherence O
	of O
	the O
	incident O
	wave O
	leads O
	to O
	a O
	rise O
	of O
	the O
	central O
	intensity O
	minimum O
	. O
	It O
	is O
	shown O
	that O
	a O
	very O
	small O
	source O
	angle O
	( O
	i.e. O
	a O
	very O
	high O
	coherence O
	) O
	is O
	necessary O
	so O
	as O
	to O
	keep O
	the O
	volcano O
	structure O
	intact O
	. O
	Their O
	small O
	angular O
	width O
	in O
	the O
	far O
	field O
	may O
	allow O
	the O
	creation O
	of O
	nm-sized O
	or O
	smaller O
	electron O
	vortices O
	but O
	the O
	demand O
	for O
	extremely O
	high O
	coherence O
	of O
	the O
	source O
	poses O
	a O
	serious O
	difficulty O
	. O

	Some O
	methods O
	use O
	1D B-Material
	radial I-Material
	profiles I-Material
	obtained O
	from O
	circular B-Process
	averaging I-Process
	of O
	2D B-Process
	experimental I-Process
	PSD I-Process
	[ O
	4,8,11 O
	] O
	or O
	by O
	elliptical B-Process
	averaging I-Process
	[ O
	17 O
	] O
	. O
	An O
	inadequacy O
	of O
	circular B-Process
	averaging I-Process
	is O
	that O
	it O
	neglects O
	astigmatism B-Process
	. O
	Astigmatism B-Process
	distorts O
	the O
	circular O
	shape O
	of O
	the O
	Thon B-Material
	rings I-Material
	and O
	thus O
	decreases O
	their O
	modulation O
	depth O
	in O
	the O
	obtained O
	1D B-Material
	profile I-Material
	. O
	A O
	few O
	algorithms O
	that O
	consider O
	astigmatism O
	involve O
	concepts O
	such O
	as O
	dividing O
	the O
	PSD B-Process
	into O
	sectors O
	where O
	Thon B-Material
	rings I-Material
	are O
	approximated O
	by O
	circular O
	arcs O
	[ O
	15,21 O
	] O
	, O
	applying O
	Canny B-Process
	edge I-Process
	detection I-Process
	to O
	find O
	the O
	rings O
	[ O
	17 O
	] O
	prior O
	to O
	elliptical B-Process
	averaging I-Process
	, O
	determining O
	the O
	relationship O
	between O
	the O
	1D O
	circular O
	averages O
	with O
	and O
	without O
	astigmatism B-Process
	[ O
	22 O
	] O
	, O
	or O
	using O
	a O
	brute-force O
	scan O
	of O
	a O
	database O
	containing O
	precalculated O
	patterns O
	as O
	in O
	ATLAS B-Process
	[ O
	23 O
	] O
	. O
	Some O
	other O
	approaches O
	for O
	estimating O
	CTF B-Process
	parameters O
	do O
	a O
	fully O
	2D B-Process
	PSD I-Process
	optimization I-Process
	[ O
	12,14,18,20 O
	] O
	but O
	they O
	usually O
	regulate O
	and O
	fit O
	numerous O
	parameters O
	by O
	an O
	extensive O
	search O
	that O
	does O
	not O
	guarantee O
	convergence O
	. O
	Furthermore O
	, O
	only O
	a O
	few O
	schemes O
	that O
	were O
	developed O
	for O
	defocus O
	estimation O
	provide O
	an O
	error O
	analysis O
	[ O
	23,24 O
	] O
	. O

	Traditionally O
	, O
	archaeologists O
	have O
	recorded B-Task
	sites I-Task
	and I-Task
	artefacts I-Task
	via O
	a O
	combination O
	of O
	ordinary B-Material
	still I-Material
	photographs I-Material
	, O
	2D B-Material
	line I-Material
	drawings I-Material
	and O
	occasional B-Material
	cross-sections I-Material
	. O
	Given O
	these O
	constraints O
	, O
	the O
	attractions O
	of O
	3D B-Process
	models I-Process
	have O
	been O
	obvious O
	for O
	some O
	time O
	, O
	with O
	digital B-Process
	photogrammetry I-Process
	and O
	laser B-Process
	scanners I-Process
	offering O
	two O
	well-known O
	methods O
	for O
	data B-Task
	capture I-Task
	at I-Task
	close I-Task
	range I-Task
	( O
	e.g. O
	Bates O
	et O
	al. O
	, O
	2010 O
	; O
	Hess O
	and O
	Robson O
	, O
	2010 O
	) O
	. O
	The O
	highest O
	specification O
	laser B-Process
	scanners I-Process
	still O
	boast O
	better O
	positional O
	accuracy O
	and O
	greater O
	true O
	colour O
	fidelity O
	than O
	SfM B-Process
	– I-Process
	MVS I-Process
	methods I-Process
	( O
	James O
	and O
	Robson O
	, O
	2012 O
	) O
	, O
	but O
	the O
	latter O
	produce O
	very O
	good O
	quality O
	models O
	nonetheless O
	and O
	have O
	many O
	unique O
	selling O
	points O
	. O
	Unlike O
	traditional B-Process
	digital I-Process
	photogrammetry I-Process
	, O
	little O
	or O
	no O
	prior O
	control B-Process
	of I-Process
	camera I-Process
	position I-Process
	is O
	necessary O
	, O
	and O
	unlike O
	laser B-Process
	scanning I-Process
	, O
	no O
	major O
	equipment O
	costs O
	or O
	setup O
	are O
	involved O
	. O
	However O
	, O
	the O
	key O
	attraction O
	of O
	SfM B-Process
	– I-Process
	MVS I-Process
	is O
	that O
	the O
	required O
	input O
	can O
	be O
	taken O
	by O
	anyone O
	with O
	a O
	digital B-Material
	camera I-Material
	and O
	modest O
	prior B-Process
	training I-Process
	about I-Process
	the I-Process
	required I-Process
	number I-Process
	and I-Process
	overlap I-Process
	of I-Process
	photographs I-Process
	. O
	A O
	whole O
	series O
	of O
	traditional O
	bottlenecks O
	are O
	thereby O
	removed O
	from O
	the O
	recording B-Process
	process I-Process
	and O
	large O
	numbers O
	of O
	archaeological B-Task
	landscapes I-Task
	, O
	sites B-Task
	or O
	artefacts B-Task
	can O
	now O
	be O
	captured O
	rapidly O
	, O
	in O
	the O
	field O
	, O
	in O
	the O
	laboratory O
	or O
	in O
	the O
	museum O
	. O
	Fig. O
	2a O
	– O
	c O
	shows O
	examples O
	of O
	terracotta B-Process
	warrior I-Process
	models I-Process
	for O
	which O
	the O
	level O
	of O
	surface O
	detail O
	is O
	considerable O
	. O

	Recent O
	astronomical B-Task
	observations I-Task
	of O
	high B-Material
	redshift I-Material
	type I-Material
	Ia I-Material
	supernovae I-Material
	performed O
	by O
	two O
	groups O
	[ O
	1 O
	– O
	3 O
	] O
	as O
	well O
	as O
	the O
	power B-Task
	spectrum I-Task
	of I-Task
	the I-Task
	cosmic I-Task
	microwave I-Task
	background I-Task
	radiation I-Task
	obtained O
	by O
	the O
	BOOMERANG B-Process
	[ O
	4 O
	] O
	and O
	MAXIMA-1 B-Process
	[ O
	5 O
	] O
	experiments O
	seem O
	to O
	indicate O
	that O
	at O
	present O
	the O
	Universe O
	is O
	in O
	a O
	state O
	of O
	accelerated O
	expansion O
	. O
	If O
	one O
	analyzes O
	these O
	data O
	within O
	the O
	Friedmann B-Process
	– I-Process
	Robertson I-Process
	– I-Process
	Walker I-Process
	( I-Process
	FRW I-Process
	) I-Process
	standard I-Process
	model I-Process
	of O
	cosmology B-Task
	their O
	most O
	natural O
	interpretation O
	is O
	that O
	the O
	Universe O
	is O
	spatially O
	flat O
	and O
	that O
	the O
	( O
	baryonic O
	plus O
	dark O
	) O
	matter B-Process
	density I-Process
	ρ B-Process
	is O
	about O
	one O
	third O
	of O
	the O
	critical B-Process
	density I-Process
	ρcrit B-Process
	. O
	Most O
	interestingly O
	, O
	the O
	dominant O
	contribution O
	to O
	the O
	energy O
	density O
	is O
	provided O
	by O
	the O
	cosmological B-Material
	constant I-Material
	Λ B-Material
	. O
	The O
	vacuum B-Process
	energy I-Process
	density I-Process
	( O
	1.1 O
	) O
	ρΛ B-Process
	≡ I-Process
	Λ I-Process
	/( I-Process
	8πG I-Process
	) I-Process
	is O
	about O
	twice O
	as O
	large O
	as O
	ρ B-Process
	, O
	i.e. O
	, O
	about O
	two O
	thirds O
	of O
	the O
	critical B-Process
	density I-Process
	. O
	With O
	ΩM O
	≡ O
	ρ O
	/ O
	ρcrit O
	, O
	ΩΛ O
	≡ O
	ρΛ O
	/ O
	ρcrit O
	and O
	Ωtot O
	≡ O
	ΩM O
	+ O
	ΩΛ O
	: O
	( O
	1.2 O
	) O
	ΩM O
	≈ O
	1 O
	/ O
	3,ΩΛ O
	≈ O
	2 O
	/ O
	3,Ωtot O
	≈ O
	1 O
	. O
	This O
	implies O
	that O
	the O
	deceleration B-Process
	parameter I-Process
	q B-Process
	is O
	approximately O
	− O
	1 O
	/ O
	2 O
	. O
	While O
	originally O
	the O
	cosmological B-Task
	constant I-Task
	problem I-Task
	[ O
	6 O
	] O
	was O
	related O
	to O
	the O
	question B-Task
	why I-Task
	Λ I-Task
	is I-Task
	so I-Task
	unnaturally I-Task
	small I-Task
	, O
	the O
	discovery O
	of O
	the O
	important O
	role O
	played O
	by O
	ρΛ B-Material
	has O
	shifted O
	the O
	emphasis O
	toward O
	the O
	“ O
	coincidence B-Task
	problem I-Task
	” O
	, O
	the O
	question O
	why B-Task
	ρ I-Task
	and I-Task
	ρΛ I-Task
	happen I-Task
	to I-Task
	be I-Task
	of I-Task
	the I-Task
	same I-Task
	order I-Task
	of I-Task
	magnitude I-Task
	precisely I-Task
	at I-Task
	this I-Task
	very I-Task
	moment I-Task
	[ O
	7 O
	] O
	. O

	First O
	results O
	from O
	RHIC O
	on O
	charged B-Task
	multiplicities I-Task
	, O
	evolution B-Task
	of I-Task
	multiplicities I-Task
	with I-Task
	centrality I-Task
	, O
	particle B-Task
	ratios I-Task
	and O
	transverse B-Task
	momentum I-Task
	distributions I-Task
	in O
	central O
	and O
	minimum O
	bias O
	collisions O
	, O
	are O
	analyzed O
	in O
	a O
	string B-Process
	model I-Process
	which O
	includes O
	hard B-Material
	collisions I-Material
	, O
	collectivity B-Material
	in I-Material
	the I-Material
	initial I-Material
	state I-Material
	considered O
	as O
	string O
	fusion O
	, O
	and O
	rescattering B-Material
	of I-Material
	the I-Material
	produced I-Material
	secondaries I-Material
	. O
	Multiplicities B-Task
	and O
	their B-Task
	evolution I-Task
	with O
	centrality O
	are O
	successfully O
	reproduced O
	. O
	Transverse B-Process
	momentum I-Process
	distributions I-Process
	in O
	the O
	model O
	show O
	a O
	larger O
	pT-tail O
	than O
	experimental O
	data O
	, O
	disagreement O
	which O
	grows O
	with O
	increasing O
	centrality O
	. O
	Discrepancies B-Process
	with I-Process
	particle I-Process
	ratios I-Process
	appear O
	and O
	are O
	examined O
	comparing O
	with O
	previous O
	features O
	of O
	the O
	model O
	at O
	SPS O
	. O

	In O
	this O
	section O
	we O
	wish O
	to O
	calculate B-Task
	the I-Task
	cross I-Task
	section I-Task
	for I-Task
	the I-Task
	absorption I-Task
	of I-Task
	massless I-Task
	scalars I-Task
	by O
	the O
	self-dual B-Process
	string I-Process
	in O
	the O
	world O
	volume O
	of O
	the O
	M-theory B-Material
	five-brane I-Material
	. O
	We O
	will O
	adopt O
	an O
	entirely B-Process
	world I-Process
	volume I-Process
	approach I-Process
	similar O
	to O
	that O
	of O
	[ O
	21 O
	– O
	23 O
	] O
	. O
	We O
	begin O
	by O
	writing O
	the O
	equation O
	satisfied O
	by O
	the O
	s-wave B-Material
	with O
	energy O
	ω O
	, O
	φ O
	( O
	r,t O
	)= O
	φ O
	( O
	r O
	) O
	eiωt O
	, O
	of O
	the O
	linear B-Process
	fluctuations I-Process
	of O
	the O
	four O
	overall O
	transverse O
	scalars O
	about O
	the O
	self-dual O
	string O
	, O
	( O
	it O
	is O
	known O
	that O
	there O
	are O
	problems O
	when O
	one O
	considers O
	higher B-Process
	angular I-Process
	momentum I-Process
	modes I-Process
	[ O
	23 O
	] O
	, O
	one O
	must O
	take O
	care O
	with O
	the O
	validity O
	of O
	the O
	linearized B-Process
	approximation I-Process
	, O
	this O
	is O
	discussed O
	in O
	[ O
	13 O
	]) O
	: O
	( O
	15 O
	) O
	ρ O
	− O
	3ddρρ3ddρ O
	+ O
	1 O
	+ O
	R6ω6ρ6φ O
	( O
	ρ O
	)= O
	0 O
	, O
	where O
	ρ O
	= O
	rω O
	, O
	R O
	= O
	Q1 O
	/ O
	3ℓp O
	. O
	Note O
	, O
	as O
	pointed O
	out O
	by O
	[ O
	11 O
	] O
	world B-Process
	volume I-Process
	solitons I-Process
	have O
	a O
	much O
	sharper O
	potential O
	than O
	the O
	Coulomb O
	type O
	potential O
	typical O
	of O
	brane B-Process
	solutions I-Process
	in O
	supergravity O
	; O
	thus O
	this O
	scattering O
	is O
	different O
	to O
	that O
	of O
	the O
	string O
	in O
	six-dimensional O
	supergravity O
	. O
	Nevertheless O
	, O
	for O
	small B-Task
	ωR I-Task
	one O
	may O
	solve O
	this O
	problem O
	by O
	matching O
	an O
	approximate B-Process
	solution I-Process
	in O
	the O
	inner O
	region O
	to O
	an O
	approximate O
	solution O
	in O
	the O
	outer O
	region O
	; O
	this O
	follows O
	closely O
	the O
	supergravity B-Process
	calculation I-Process
	[ O
	24 O
	] O
	. O

	We O
	consider B-Task
	cosmological I-Task
	consequences I-Task
	of I-Task
	a I-Task
	conformal-invariant I-Task
	formulation I-Task
	of I-Task
	Einstein I-Task
	's I-Task
	General I-Task
	Relativity I-Task
	where O
	instead O
	of O
	the O
	scale B-Material
	factor I-Material
	of O
	the O
	spatial O
	metrics O
	in O
	the O
	action O
	functional O
	a O
	massless B-Material
	scalar I-Material
	( O
	dilaton B-Material
	) O
	field O
	occurs O
	which O
	scales O
	all O
	masses B-Material
	including O
	the O
	Planck B-Material
	mass I-Material
	. O
	Instead O
	of O
	the O
	expansion B-Process
	of I-Process
	the I-Process
	universe I-Process
	we O
	obtain O
	the O
	Hoyle B-Process
	– I-Process
	Narlikar I-Process
	type I-Process
	of I-Process
	mass I-Process
	evolution I-Process
	, O
	where O
	the O
	temperature B-Process
	history I-Process
	of I-Process
	the I-Process
	universe I-Process
	is O
	replaced O
	by O
	the O
	mass B-Process
	history I-Process
	. O
	We O
	show O
	that O
	this O
	conformal-invariant B-Process
	cosmological I-Process
	model I-Process
	gives O
	a O
	satisfactory O
	description O
	of O
	the O
	new O
	supernova B-Material
	Ia I-Material
	data I-Material
	for O
	the O
	effective B-Process
	magnitude I-Process
	– I-Process
	redshift I-Process
	relation I-Process
	without O
	a O
	cosmological B-Material
	constant I-Material
	and O
	make O
	a O
	prediction O
	for O
	the O
	high-redshift B-Process
	behavior I-Process
	which O
	deviates O
	from O
	that O
	of O
	standard B-Task
	cosmology I-Task
	for O
	z O
	> O
	1.7 O
	. O

	Production B-Task
	of I-Task
	charmonium I-Task
	states I-Task
	J I-Task
	/ I-Task
	ψ I-Task
	and I-Task
	ψ′ I-Task
	in I-Task
	nucleus I-Task
	– I-Task
	nucleus I-Task
	collisions I-Task
	has O
	been O
	studied O
	at O
	CERN O
	SPS O
	over O
	the O
	previous O
	15 O
	years O
	by O
	the O
	NA38 O
	and O
	NA50 O
	Collaborations O
	. O
	This O
	experimental B-Task
	program I-Task
	was O
	mainly O
	motivated O
	by O
	the O
	suggestion O
	[ O
	1 O
	] O
	to O
	use B-Task
	the I-Task
	J I-Task
	/ I-Task
	ψ I-Task
	as I-Task
	a I-Task
	probe I-Task
	of I-Task
	the I-Task
	state I-Task
	of I-Task
	matter I-Task
	created I-Task
	at I-Task
	the I-Task
	early I-Task
	stage I-Task
	of I-Task
	the I-Task
	collision I-Task
	. O
	The O
	original B-Material
	picture I-Material
	[ O
	1 O
	] O
	( O
	see O
	also O
	[ O
	2 O
	] O
	for O
	a O
	modern O
	review O
	) O
	assumes O
	that O
	charmonia B-Material
	are O
	created O
	exclusively O
	at O
	the O
	initial O
	stage O
	of O
	the O
	reaction O
	in O
	primary B-Process
	nucleon I-Process
	– I-Process
	nucleon I-Process
	collisions I-Process
	. O
	During O
	the O
	subsequent B-Process
	evolution I-Process
	of I-Process
	the I-Process
	system I-Process
	, I-Process
	the O
	number O
	of O
	hidden B-Material
	charm I-Material
	mesons I-Material
	is O
	reduced O
	because O
	of O
	: O
	( O
	a O
	) O
	absorption B-Process
	of I-Process
	pre-resonance I-Process
	charmonium I-Process
	states I-Process
	by I-Process
	nuclear I-Process
	nucleons I-Process
	( O
	normal B-Process
	nuclear I-Process
	suppression I-Process
	) O
	, O
	( O
	b O
	) O
	interactions B-Process
	of I-Process
	charmonia I-Process
	with I-Process
	secondary I-Process
	hadrons I-Process
	( O
	comovers B-Process
	) O
	, O
	( O
	c O
	) O
	dissociation B-Process
	of I-Process
	cc I-Process
	̄ I-Process
	bound I-Process
	states I-Process
	in I-Process
	deconfined I-Process
	medium I-Process
	( O
	anomalous B-Process
	suppression I-Process
	) O
	. O
	It O
	was O
	found O
	[ O
	3 O
	] O
	that O
	J B-Process
	/ I-Process
	ψ I-Process
	suppression I-Process
	with O
	respect O
	to O
	Drell B-Material
	– I-Material
	Yan I-Material
	muon I-Material
	pairs I-Material
	measured O
	in O
	proton B-Material
	– I-Material
	nucleus I-Material
	and O
	nucleus B-Process
	– I-Process
	nucleus I-Process
	collisions I-Process
	with O
	light B-Material
	projectiles I-Material
	can O
	be O
	explained O
	by O
	the O
	so-called O
	“ O
	normal O
	” O
	( O
	due O
	to O
	sweeping B-Material
	nucleons I-Material
	) O
	nuclear B-Process
	suppression I-Process
	alone O
	. O
	In O
	contrast O
	, O
	the O
	NA50 B-Task
	experiment I-Task
	with O
	a O
	heavy B-Material
	projectile I-Material
	and I-Material
	target I-Material
	( O
	Pb B-Material
	+ I-Material
	Pb I-Material
	) O
	revealed O
	essentially O
	stronger O
	J B-Material
	/ I-Material
	ψ I-Material
	suppression O
	for O
	central B-Process
	collisions I-Process
	[ O
	4 O
	– O
	7 O
	] O
	. O
	This O
	anomalous O
	J B-Process
	/ I-Process
	ψ I-Process
	suppression I-Process
	was O
	attributed O
	to O
	formation B-Process
	of I-Process
	quark I-Process
	– I-Process
	gluon I-Process
	plasma I-Process
	( O
	QGP B-Material
	) O
	[ O
	7 O
	] O
	, O
	but O
	a O
	comover B-Process
	scenario I-Process
	cannot O
	be O
	excluded O
	[ O
	8 O
	] O
	. O

	Brodsky O
	and O
	Lepage O
	[ O
	8 O
	] O
	have O
	proposed O
	a O
	formula B-Task
	for I-Task
	meson I-Task
	pair I-Task
	production I-Task
	which O
	looks O
	similar O
	to O
	( O
	25 O
	) O
	, O
	except O
	for O
	a O
	different O
	charge O
	factor O
	and O
	the O
	appearance O
	of O
	the O
	timelike O
	electromagnetic B-Material
	meson I-Material
	form O
	factor O
	instead O
	of O
	the O
	annihilation O
	form O
	factor O
	R O
	( O
	s O
	) O
	. O
	This O
	formula B-Task
	was O
	obtained O
	from O
	the O
	leading-twist O
	result O
	by O
	neglecting B-Process
	part I-Process
	of I-Process
	the I-Process
	amplitudes I-Process
	with I-Process
	opposite I-Process
	photon I-Process
	helicities I-Process
	. O
	As O
	has O
	been O
	pointed O
	out O
	in O
	[ O
	9 O
	] O
	, O
	this O
	part O
	is O
	however O
	not O
	approximately O
	independent O
	of O
	the O
	pion O
	distribution O
	amplitude O
	and O
	not O
	generically O
	small O
	. O
	We O
	also O
	remark O
	that O
	the O
	appearance O
	of O
	Fπ O
	( O
	s O
	) O
	in O
	the O
	γγ O
	→ O
	π O
	+ O
	π O
	− O
	amplitude O
	is O
	no O
	longer O
	observed O
	if O
	corrections O
	from O
	partonic B-Process
	transverse I-Process
	momentum I-Process
	in O
	the O
	hard B-Process
	scattering I-Process
	process I-Process
	are O
	taken O
	into O
	account O
	, O
	and O
	that O
	these O
	corrections O
	are O
	not O
	numerically O
	small O
	for O
	the O
	values O
	of O
	s O
	we O
	are O
	dealing O
	with O
	[ O
	13 O
	] O
	. O
	Notice O
	further O
	that O
	two-photon B-Process
	annihilation I-Process
	produces O
	two O
	pions O
	in O
	a O
	C-even O
	state O
	, O
	whereas O
	the O
	electromagnetic O
	form O
	factor O
	projects O
	on O
	the O
	C-odd O
	state O
	of O
	a O
	pion O
	pair O
	. O
	In O
	contrast O
	, O
	our O
	annihilation O
	form O
	factor O
	R2π O
	( O
	s O
	) O
	is O
	C-even O
	as O
	discussed O
	after O
	( O
	24 O
	) O
	. O
	Finally O
	, O
	due O
	to O
	a O
	particular O
	charge O
	factor O
	, O
	the O
	Brodsky O
	– O
	Lepage O
	formula O
	leads O
	to O
	a O
	vanishing O
	cross O
	section O
	for O
	γγ O
	annihilation O
	into O
	pairs O
	of O
	neutral O
	pseudoscalars O
	. O

	Since O
	perturbative B-Process
	expansion I-Process
	is O
	used O
	, O
	it O
	is O
	impossible O
	to O
	find O
	the O
	exact B-Process
	bounds I-Process
	; O
	instead O
	, O
	one O
	can O
	derive O
	tree-level B-Process
	unitarity I-Process
	bounds I-Process
	or O
	loop-improved B-Process
	unitarity I-Process
	bounds I-Process
	. O
	In O
	this O
	study O
	, O
	we O
	will O
	use O
	unitarity O
	bounds O
	coming O
	from O
	a O
	tree-level B-Task
	analysis I-Task
	[ O
	20 O
	] O
	. O
	This O
	tree B-Task
	level I-Task
	analysis I-Task
	is O
	derived O
	with O
	the O
	help O
	of O
	the O
	equivalence B-Process
	theorem I-Process
	[ O
	21 O
	] O
	, O
	which O
	itself O
	is O
	a O
	high-energy B-Process
	approximation I-Process
	where O
	it O
	is O
	assumed O
	that O
	the O
	energy O
	scale O
	is O
	much O
	larger O
	than O
	the O
	Z0 O
	and O
	W O
	± O
	gauge-boson B-Material
	masses I-Material
	. O
	We O
	will O
	consider O
	here O
	this O
	“ O
	high-energy O
	” O
	hypothesis O
	that O
	both O
	the O
	equivalence B-Process
	theorem I-Process
	and O
	the O
	decoupling B-Process
	regime I-Process
	are O
	well O
	settled O
	, O
	but O
	in O
	such O
	a O
	way O
	that O
	the O
	unitarity B-Process
	constraint I-Process
	is O
	also O
	fulfilled O
	. O
	Our O
	purpose O
	is O
	to O
	investigate B-Task
	the I-Task
	quantum I-Task
	effects I-Task
	in I-Task
	the I-Task
	decays I-Task
	of I-Task
	the I-Task
	light I-Task
	CP-even I-Task
	Higgs I-Task
	boson I-Task
	h0 I-Task
	, O
	especially O
	looking B-Task
	for I-Task
	sizeable I-Task
	differences I-Task
	with I-Task
	respect I-Task
	to I-Task
	the I-Task
	SM I-Task
	in I-Task
	the I-Task
	decoupling I-Task
	regime I-Task
	. O

	In O
	the O
	bag B-Process
	model I-Process
	and O
	in O
	linear O
	or O
	harmonic O
	oscillator B-Process
	confining I-Process
	potentials I-Process
	, O
	the O
	first O
	excited O
	S-state O
	lies O
	above O
	the O
	lowest O
	P-state O
	, O
	making O
	the O
	predicted O
	Roper O
	mass O
	heavier O
	than O
	the O
	lightest O
	negative O
	parity O
	baryon O
	mass O
	. O
	Pairwise B-Process
	spin-dependent I-Process
	interactions I-Process
	must O
	reverse O
	the O
	level B-Process
	ordering I-Process
	. O
	As O
	mentioned O
	earlier O
	, O
	color-spin B-Process
	interactions I-Process
	fail O
	in O
	this O
	regard O
	[ O
	29 O
	] O
	, O
	while O
	flavor-spin B-Process
	interactions I-Process
	produce O
	the O
	desired O
	effect O
	. O
	Since O
	the O
	q3 B-Process
	color I-Process
	wave I-Process
	function I-Process
	is O
	antisymmetric O
	, O
	the O
	flavor-spin-orbital B-Process
	wave I-Process
	function I-Process
	is O
	totally O
	symmetric O
	. O
	For O
	all O
	quarks B-Material
	in O
	an O
	S-state O
	, O
	the O
	flavor-spin B-Process
	wave I-Process
	function I-Process
	is O
	totally O
	symmetric O
	all O
	by O
	itself O
	and O
	leads O
	to O
	the O
	most O
	attractive O
	flavor-spin B-Process
	interaction I-Process
	. O
	If O
	one O
	quark B-Material
	is O
	in O
	a O
	P-state O
	, O
	the O
	orbital B-Process
	wave I-Process
	function I-Process
	is O
	mixed O
	symmetry O
	and O
	so O
	is O
	the O
	flavor-spin B-Process
	wave I-Process
	function I-Process
	, O
	and O
	the O
	flavor-spin B-Process
	interaction I-Process
	is O
	a O
	less O
	attractive O
	. O
	In O
	the O
	SU O
	( O
	3 O
	) O
	F O
	symmetric O
	case O
	, O
	Eq O
	. O
	( O
	1 O
	) O
	, O
	one O
	obtains O
	mass B-Process
	splittings I-Process
	( O
	2 O
	) O
	ΔMχ O
	=− O
	14Cχ,N O
	( O
	939 O
	) O
	, O
	N O
	∗( O
	1440 O
	),− O
	4Cχ,Δ O
	( O
	1232 O
	),− O
	2Cχ,N O
	∗( O
	1535 O
	) O
	. O
	Here O
	we O
	have O
	approximated O
	the O
	N O
	∗( O
	1535 O
	) O
	as O
	a O
	state O
	with O
	total O
	quark B-Material
	spin-1 O
	/ O
	2 O
	. O

	The O
	measurements O
	presented O
	here O
	provide O
	evidence O
	for O
	the O
	existence O
	of O
	di-cluster B-Material
	structures I-Material
	in O
	10 O
	– O
	12,14Be O
	. O
	Certainly O
	, O
	if O
	the O
	breakup B-Process
	process I-Process
	samples O
	the O
	overlap O
	between O
	the O
	wavefunctions B-Process
	of I-Process
	the I-Process
	ground I-Process
	state I-Process
	and I-Process
	the I-Process
	excited I-Process
	states I-Process
	, O
	the O
	first-chance B-Process
	cluster I-Process
	breakup I-Process
	cross-sections I-Process
	, O
	shown O
	in O
	Fig. O
	4 O
	( O
	a O
	) O
	, O
	indicate O
	that O
	the O
	xHe B-Material
	+ I-Material
	A I-Material
	− I-Material
	xHe I-Material
	cluster I-Material
	structure I-Material
	does O
	not O
	decrease O
	over O
	the O
	mass O
	range O
	A O
	= O
	10 O
	, O
	12 O
	and O
	14 O
	. O
	Given O
	also O
	that O
	the O
	decay B-Process
	energy I-Process
	threshold O
	increases O
	with O
	mass O
	number O
	, O
	the O
	present O
	data O
	may O
	even O
	indicate O
	a O
	slight O
	increase O
	in O
	clustering O
	. O
	The O
	breakup B-Process
	cross-sections I-Process
	also O
	appear O
	to O
	demonstrate O
	that O
	these O
	nuclei B-Material
	possess O
	a O
	stronger O
	structural O
	overlap O
	with O
	an O
	α O
	– O
	Xn O
	– O
	α O
	configuration O
	, O
	although O
	the O
	reaction B-Process
	mechanics I-Process
	by O
	which O
	this O
	final O
	state O
	is O
	reached O
	may O
	be O
	complex O
	. O
	That O
	is O
	to O
	say O
	that O
	the O
	dominant B-Process
	structural I-Process
	mode I-Process
	of O
	the O
	neutron B-Material
	rich I-Material
	isotopes I-Material
	may O
	be O
	identified O
	with O
	two O
	alpha-particles B-Material
	plus O
	valence B-Material
	neutrons I-Material
	. O
	These O
	comprehensive B-Task
	measurements I-Task
	of I-Task
	the I-Task
	neutron-removal I-Task
	and I-Task
	cluster I-Task
	breakup I-Task
	for O
	the O
	first O
	time O
	provide O
	experimental O
	data O
	whereby O
	the O
	structure O
	of O
	the O
	most O
	neutron-rich B-Material
	Be I-Material
	isotopes I-Material
	can O
	be O
	modeled O
	via O
	their O
	reactions O
	. O

	Let O
	us O
	now O
	consider O
	the O
	case O
	of O
	a O
	beta-beam B-Material
	source I-Material
	. O
	Similarly O
	to O
	the O
	case O
	of O
	a O
	static B-Material
	tritium I-Material
	source I-Material
	, O
	an O
	advantage O
	of O
	the O
	beta-beams B-Process
	is O
	that O
	the O
	neutrino B-Process
	fluxes I-Process
	can O
	be O
	very O
	accurately O
	calculated O
	. O
	Fig. O
	3 O
	shows O
	the O
	electron B-Process
	– I-Process
	neutrino I-Process
	scattering I-Process
	events O
	in O
	the O
	range O
	of O
	0.1 O
	MeV O
	to O
	1 O
	MeV O
	and O
	1 O
	keV O
	to O
	10 O
	keV O
	, O
	respectively O
	. O
	( O
	In O
	Fig. O
	3 O
	( O
	b O
	) O
	we O
	have O
	rounded O
	to O
	the O
	nearest O
	integer O
	number O
	of O
	counts O
	. O
	) O
	The O
	shape O
	of O
	the O
	flux-averaged O
	cross O
	sections O
	is O
	very O
	similar O
	to O
	the O
	reactor B-Material
	case I-Material
	as O
	reflected O
	in O
	the O
	event O
	rates O
	shown O
	in O
	the O
	figures O
	. O
	As O
	can O
	be O
	seen O
	, O
	by O
	measuring O
	electron B-Material
	recoils I-Material
	in O
	the O
	keV O
	range O
	with O
	a O
	beta-beam B-Material
	source I-Material
	one O
	could O
	, O
	with O
	a O
	sufficiently O
	strong O
	source O
	, O
	have O
	a O
	very O
	clear O
	signature O
	for O
	a O
	neutrino B-Process
	magnetic I-Process
	moment I-Process
	of O
	5 O
	× O
	10 O
	− O
	11μB O
	. O
	These O
	figures O
	are O
	for O
	Helium-6 B-Material
	ions I-Material
	, O
	however O
	, O
	similar O
	results O
	can O
	be O
	obtained O
	using O
	neutrinos B-Material
	from O
	18Ne O
	. O
	The O
	results O
	shown O
	are O
	obtained O
	for O
	an O
	intensity O
	of O
	1015 O
	ν O
	/ O
	s O
	( O
	i.e. O
	, O
	1015 O
	ions O
	/ O
	s O
	) O
	. O
	If O
	there O
	is O
	no O
	magnetic O
	moment O
	, O
	this O
	intensity O
	will O
	produce O
	about O
	170 O
	events O
	in O
	the O
	0.1 O
	MeV O
	to O
	1 O
	MeV O
	range O
	per O
	year O
	and O
	3 O
	events O
	in O
	the O
	1 O
	keV O
	to O
	10 O
	keV O
	range O
	per O
	year O
	. O
	These O
	numbers O
	increase O
	to O
	210 O
	and O
	55 O
	, O
	respectively O
	, O
	in O
	the O
	case O
	of O
	a O
	magnetic O
	moment O
	of O
	5 O
	× O
	10 O
	− O
	11μB O
	. O

	Each O
	hit O
	position O
	inside O
	the O
	drift B-Material
	chambers I-Material
	was O
	calculated O
	from O
	the O
	drift O
	time O
	digitized O
	by O
	a O
	flash B-Material
	analog-to-digital I-Material
	converter I-Material
	. O
	The O
	calculation O
	was O
	carried O
	out O
	based O
	on O
	a O
	relation B-Process
	between I-Process
	the I-Process
	hit I-Process
	position I-Process
	and I-Process
	the I-Process
	drift I-Process
	time I-Process
	( O
	x B-Process
	– I-Process
	t I-Process
	relation I-Process
	) O
	. O
	The O
	x O
	– O
	t O
	relation O
	was O
	precisely O
	calculated O
	by O
	a O
	drift B-Material
	chamber I-Material
	simulation I-Material
	package I-Material
	, O
	GARFIELD B-Material
	[ O
	20 O
	] O
	, O
	and O
	a O
	gas B-Material
	property I-Material
	simulation I-Material
	package I-Material
	, O
	MAGBOLTZ B-Material
	[ O
	21 O
	] O
	. O
	Although O
	the O
	chambers B-Material
	were O
	constructed O
	carefully O
	with O
	a O
	tolerance O
	of O
	100 O
	μm O
	, O
	there O
	was O
	a O
	small O
	position O
	deviation O
	of O
	wires O
	and O
	field-shaping O
	patterns O
	, O
	which O
	could O
	locally O
	modify O
	the O
	electric O
	field O
	. O
	In O
	order O
	to O
	take O
	account O
	of O
	the O
	limited O
	accuracy O
	in O
	the O
	chamber O
	manufacturing O
	, O
	a O
	correction O
	was O
	commonly O
	applied O
	to O
	the O
	calculated O
	x B-Process
	– I-Process
	t I-Process
	relation I-Process
	throughout O
	the O
	experiments O
	. O
	The O
	correction O
	was O
	obtained O
	to O
	minimize O
	the O
	χ2 O
	in O
	the O
	fitting O
	of O
	straight O
	tracks O
	of O
	clean O
	muon O
	events O
	observed O
	on O
	the O
	ground O
	without O
	magnetic B-Process
	field I-Process
	. O
	The O
	correction O
	was O
	as O
	small O
	as O
	expected O
	from O
	the O
	accuracy O
	in O
	the O
	chamber B-Material
	manufacturing O
	. O
	During O
	the O
	observations O
	, O
	the O
	x B-Process
	– I-Process
	t I-Process
	relation I-Process
	was O
	affected O
	by O
	the O
	variation O
	in O
	the O
	pressure O
	and O
	temperature O
	of O
	the O
	chamber B-Material
	gas I-Material
	. O
	In O
	order O
	to O
	take O
	account O
	of O
	these O
	time-dependent O
	variations O
	, O
	the O
	x B-Process
	– I-Process
	t I-Process
	relation I-Process
	was O
	calibrated O
	for O
	each O
	data-taking O
	run O
	. O
	Especially O
	in O
	calibrating O
	the O
	x O
	– O
	t O
	relation O
	of O
	ODCs B-Material
	, O
	an O
	absolute O
	reference O
	positions O
	were O
	provided O
	by O
	SciFi O
	, O
	which O
	are O
	not O
	affected O
	by O
	the O
	variation O
	in O
	the O
	pressure O
	nor O
	temperature O
	. O

	We O
	define B-Task
	a I-Task
	new I-Task
	multispecies I-Task
	model I-Task
	of I-Task
	Calogero I-Task
	type I-Task
	in I-Task
	D I-Task
	dimensions I-Task
	with O
	harmonic B-Process
	, I-Process
	two-body I-Process
	and I-Process
	three-body I-Process
	interactions I-Process
	. O
	Using O
	the O
	underlying O
	conformal B-Process
	SU I-Process
	( I-Process
	1,1 I-Process
	) I-Process
	algebra I-Process
	, O
	we O
	indicate O
	how O
	to O
	find O
	the O
	complete O
	set O
	of O
	the O
	states O
	in O
	Bargmann O
	– O
	Fock O
	space O
	. O
	There O
	are O
	towers O
	of O
	states O
	, O
	with O
	equidistant B-Process
	energy I-Process
	spectra I-Process
	in O
	each O
	tower O
	. O
	We O
	explicitely O
	construct B-Process
	all I-Process
	polynomial I-Process
	eigenstates I-Process
	, O
	namely O
	the O
	center-of-mass O
	states O
	and O
	global O
	dilatation O
	modes O
	, O
	and O
	find B-Process
	their I-Process
	corresponding I-Process
	eigenenergies I-Process
	. O
	We O
	also O
	construct B-Process
	ladder I-Process
	operators I-Process
	for O
	these O
	global O
	collective O
	states O
	. O
	Analysing B-Task
	corresponding I-Task
	Fock I-Task
	space I-Task
	, O
	we O
	detect O
	the O
	universal O
	critical O
	point O
	at O
	which O
	the O
	model O
	exhibits O
	singular O
	behavior O
	. O
	The O
	above O
	results O
	are O
	universal O
	for O
	all O
	systems O
	with O
	underlying O
	conformal O
	SU O
	( O
	1,1 O
	) O
	symmetry O
	. O

	The O
	expression O
	for O
	Pc B-Material
	is O
	also O
	easily O
	found O
	in O
	the O
	same O
	basis O
	, O
	where O
	it O
	becomes O
	apparent O
	that O
	the O
	dynamics O
	of O
	conversion B-Process
	in I-Process
	matter I-Process
	depends O
	only O
	on O
	the O
	relative B-Process
	orientation I-Process
	of I-Process
	the I-Process
	eigenstates I-Process
	of O
	the O
	vacuum B-Process
	and O
	matter B-Process
	Hamiltonians I-Process
	. O
	This O
	allows O
	to O
	directly O
	apply O
	the O
	known O
	analytical B-Process
	solutions I-Process
	for I-Process
	Pc I-Process
	, O
	and O
	, O
	upon O
	rotating O
	back O
	, O
	obtain O
	a O
	generalization B-Process
	of I-Process
	these I-Process
	results I-Process
	to I-Process
	the I-Process
	NSI I-Process
	case I-Process
	. O
	For O
	example O
	, O
	the O
	answer O
	for O
	the O
	infinite O
	exponential O
	profile O
	[ O
	18,19 O
	] O
	A O
	∝ O
	exp O
	(− O
	r O
	/ O
	r0 O
	) O
	becomes O
	Pc O
	= O
	exp O
	[ O
	γ O
	( O
	1 O
	− O
	cos2θrel O
	)/ O
	2 O
	]− O
	1exp O
	( O
	γ O
	)− O
	1 O
	, O
	where O
	γ O
	≡ O
	4πr0Δ O
	= O
	πr0Δm2 O
	/ O
	Eν O
	. O
	We O
	further O
	observe O
	that O
	since O
	γ O
	⪢ O
	1 O
	the O
	adiabaticity B-Process
	violation I-Process
	occurs O
	only O
	when O
	\| O
	θ O
	− O
	α O
	\|⪡ O
	1 O
	and O
	φ O
	≃ O
	π O
	/ O
	2 O
	, O
	which O
	is O
	the O
	analogue O
	of O
	the O
	small-angle O
	MSW O
	[ O
	10,20 O
	] O
	effect O
	in O
	the O
	rotated O
	basis O
	. O
	The O
	“ O
	resonant O
	” O
	region O
	in O
	the O
	Sun O
	where O
	level O
	jumping O
	can O
	take O
	place O
	is O
	narrow O
	, O
	defined O
	by O
	A O
	≃ O
	Δ O
	[ O
	21 O
	] O
	. O
	A O
	neutrino B-Material
	produced O
	at O
	a O
	lower O
	density O
	evolves O
	adiabatically O
	, O
	while O
	a O
	neutrino O
	produced O
	at O
	a O
	higher O
	density O
	may O
	undergo O
	level O
	crossing O
	. O
	The O
	probability O
	Pc B-Material
	in O
	the O
	latter O
	case O
	is O
	given O
	to O
	a O
	very O
	good O
	accuracy O
	by O
	the O
	formula O
	for O
	the O
	linear O
	profile O
	, O
	with O
	an O
	appropriate O
	gradient O
	taken O
	along O
	the O
	neutrino B-Material
	trajectory O
	, O
	( O
	12 O
	) O
	Pc O
	≃ O
	Θ O
	( O
	A O
	− O
	Δ O
	) O
	e O
	− O
	γ O
	( O
	cos2θrel O
	+ O
	1 O
	)/ O
	2 O
	, O
	where O
	Θ O
	( O
	x O
	) O
	is O
	the O
	step O
	function O
	, O
	Θ O
	( O
	x O
	)= O
	1 O
	for O
	x O
	> O
	0 O
	and O
	Θ O
	( O
	x O
	)= O
	0 O
	otherwise O
	. O
	We O
	emphasize O
	that O
	our O
	results O
	differ O
	from O
	the O
	similar O
	ones O
	given O
	in O
	[ O
	5,22 O
	] O
	in O
	three O
	important O
	respects O
	: O
	( O
	i O
	) O
	they O
	are O
	valid O
	for O
	all O
	, O
	not O
	just O
	small O
	values O
	of O
	α O
	( O
	which O
	is O
	essential O
	for O
	our O
	application O
	) O
	, O
	( O
	ii O
	) O
	they O
	include O
	the O
	angle O
	φ O
	, O
	and O
	( O
	iii O
	) O
	the O
	argument O
	of O
	the O
	Θ O
	function O
	does O
	not O
	contain O
	cos2θ O
	, O
	as O
	follows O
	from O
	[ O
	21 O
	] O
	. O
	We O
	stress O
	that O
	for O
	large O
	values O
	of O
	α O
	and O
	φ O
	≃ O
	π O
	/ O
	2 O
	adiabaticity O
	is O
	violated O
	for O
	large O
	values O
	of O
	θ O
	. O

	One O
	major O
	goal O
	of O
	current O
	nuclear B-Task
	physics I-Task
	is O
	the O
	observation B-Task
	of I-Task
	at I-Task
	least I-Task
	partial I-Task
	restoration I-Task
	of I-Task
	chiral I-Task
	symmetry I-Task
	. O
	Since O
	the O
	chiral B-Material
	order I-Material
	parameter I-Material
	〈 O
	q B-Material
	̄ I-Material
	q I-Material
	〉 O
	is O
	expected O
	to O
	decrease O
	by O
	about O
	30 O
	% O
	already O
	at O
	normal O
	nuclear B-Material
	matter I-Material
	density O
	[ O
	1 O
	– O
	4 O
	] O
	, O
	any O
	in-medium O
	change O
	due O
	to O
	the O
	dropping O
	quark B-Material
	condensate I-Material
	should O
	in O
	principle O
	be O
	observable O
	in O
	photonuclear B-Process
	reactions I-Process
	. O
	The O
	conjecture O
	that O
	such O
	a O
	partial B-Process
	restoration I-Process
	of I-Process
	chiral I-Process
	symmetry I-Process
	causes O
	a O
	softening O
	and O
	narrowing O
	of O
	the O
	σ B-Material
	meson I-Material
	as O
	the O
	chiral B-Material
	partner I-Material
	of O
	the O
	pion B-Material
	in O
	the O
	nuclear B-Material
	medium I-Material
	[ O
	5,6 O
	] O
	has O
	led O
	to O
	the O
	idea O
	of O
	measuring B-Process
	the I-Process
	π0π0 I-Process
	invariant I-Process
	mass I-Process
	distribution I-Process
	near O
	the O
	2π O
	threshold O
	in O
	photon B-Material
	induced O
	reactions O
	on O
	nuclei B-Material
	[ O
	7 O
	] O
	. O
	In O
	contrast O
	to O
	its O
	questionable O
	nature O
	as O
	a O
	proper O
	quasiparticle B-Material
	in O
	vacuum O
	, O
	the O
	σ B-Material
	meson I-Material
	might O
	develop O
	a O
	much O
	narrower O
	peak O
	at O
	finite O
	baryon O
	density O
	due O
	to O
	phase-space O
	suppression O
	for O
	the O
	σ B-Process
	→ I-Process
	ππ I-Process
	decay I-Process
	, O
	hence O
	making O
	it O
	possible O
	to O
	explore O
	its O
	properties O
	when O
	embedded O
	in O
	a O
	nuclear B-Process
	many-body I-Process
	system I-Process
	[ O
	8 O
	– O
	11 O
	] O
	. O
	Measuring B-Process
	a I-Process
	threshold I-Process
	enhancement I-Process
	of I-Process
	the I-Process
	π0π0 I-Process
	invariant I-Process
	mass I-Process
	spectrum I-Process
	might O
	serve O
	as O
	a O
	signal O
	for O
	the O
	partial B-Task
	restoration I-Task
	of I-Task
	chiral I-Task
	symmetry I-Task
	inside O
	nuclei B-Material
	and O
	, O
	therefore O
	, O
	give O
	information O
	about O
	one O
	of O
	the O
	most O
	fundamental O
	features O
	of O
	QCD B-Material
	. O

	An O
	OPE B-Material
	of O
	VQCD B-Material
	( I-Material
	r I-Material
	) I-Material
	was O
	developed O
	in O
	[ O
	3 O
	] O
	. O
	In O
	this O
	and O
	the O
	next O
	paragraph O
	, O
	we O
	review O
	the O
	content O
	of O
	that O
	paper O
	relevant O
	to O
	our O
	analysis O
	. O
	Within O
	this O
	framework O
	, O
	short-distance O
	contributions O
	are O
	contained O
	in O
	the O
	potentials O
	, O
	which O
	are O
	in O
	fact O
	the O
	Wilson O
	coefficients O
	, O
	while O
	non-perturbative O
	contributions O
	are O
	contained O
	in O
	the O
	matrix O
	elements O
	that O
	are O
	organized O
	in O
	multipole B-Process
	expansion I-Process
	in O
	r O
	→ O
	at O
	r O
	≪ O
	ΛQCD O
	− O
	1 O
	. O
	The O
	following O
	relation O
	was O
	derived O
	: O
	( O
	16 O
	) O
	VQCD O
	( O
	r O
	)= O
	VS O
	( O
	r O
	)+ O
	δEUS O
	( O
	r O
	),( O
	17 O
	) O
	δEUS O
	=− O
	ig2TFNC O
	∫ O
	0 O
	∞ O
	dte O
	− O
	iΔV O
	( O
	r O
	) O
	t O
	×〈 O
	r O
	→⋅ O
	E O
	→ O
	a O
	( O
	t O
	) O
	φadj O
	( O
	t,0 O
	) O
	abr O
	→⋅ O
	E O
	→ O
	b O
	( O
	0 O
	)〉+ O
	O O
	( O
	r3 O
	) O
	. O
	VS O
	( O
	r O
	) O
	denotes O
	the O
	singlet O
	potential O
	. O
	δEUS O
	( O
	r O
	) O
	denotes O
	the O
	non-perturbative O
	contribution O
	to O
	the O
	QCD B-Material
	potential I-Material
	, O
	which O
	starts O
	at O
	O O
	( O
	ΛQCD3r2 O
	) O
	in O
	the O
	multipole B-Process
	expansion I-Process
	. O
	ΔV O
	( O
	r O
	)= O
	VO O
	( O
	r O
	)− O
	VS O
	( O
	r O
	) O
	denotes O
	the O
	difference O
	between O
	the O
	octet O
	and O
	singlet O
	potentials O
	; O
	see O
	[ O
	3 O
	] O
	for O
	details O
	. O
	Intuitively O
	VS O
	( O
	r O
	) O
	corresponds O
	to O
	VUV O
	( O
	r O
	; O
	μf O
	) O
	and O
	δEUS O
	( O
	r O
	) O
	to O
	VIR O
	( O
	r O
	; O
	μf O
	) O
	. O
	We O
	adopt O
	dimensional B-Process
	regularization I-Process
	in O
	our O
	analysis O
	; O
	we O
	also O
	refer O
	to O
	hard B-Process
	cutoff I-Process
	schemes I-Process
	when O
	discussing O
	conceptual O
	aspects O
	. O

	It O
	has O
	recently O
	been O
	demonstrated O
	[ O
	15 O
	] O
	( O
	see O
	also O
	[ O
	13 O
	] O
	and O
	references O
	therein O
	) O
	that O
	for O
	a O
	self-dual O
	background O
	the O
	two-loop B-Material
	QED I-Material
	effective O
	action O
	takes O
	a O
	remarkably O
	simple O
	form O
	that O
	is O
	very O
	similar O
	to O
	the O
	one-loop B-Process
	action I-Process
	in O
	the O
	same O
	background O
	. O
	There O
	are O
	expectations O
	that O
	this O
	similarity O
	persists O
	at O
	higher B-Process
	loops I-Process
	, O
	and O
	therefore O
	there O
	should O
	be O
	some O
	remarkable O
	structure O
	encoded O
	in O
	the O
	all-loop B-Process
	effective I-Process
	action I-Process
	for O
	gauge O
	theories O
	. O
	In O
	the O
	supersymmetric O
	case O
	, O
	one O
	has O
	to O
	replace O
	the O
	requirement O
	of O
	self-duality O
	by O
	that O
	of O
	relaxed O
	super O
	self-duality O
	[ O
	16 O
	] O
	in O
	order O
	to O
	arrive O
	at O
	conclusions O
	similar O
	to O
	those O
	given O
	in O
	[ O
	15 O
	] O
	. O
	Further O
	progress O
	in O
	this O
	direction O
	may O
	be O
	achieved O
	through O
	the O
	analysis B-Task
	of I-Task
	N I-Task
	= I-Task
	2 I-Task
	covariant I-Task
	supergraphs I-Task
	. O
	Finally O
	, O
	we O
	believe O
	that O
	the O
	results O
	of O
	this O
	Letter O
	may O
	be O
	helpful O
	in O
	the O
	context O
	of O
	the O
	conjectured O
	correspondence O
	[ O
	17 O
	– O
	19 O
	] O
	between O
	the O
	D3-brane B-Process
	action I-Process
	in O
	AdS5 B-Material
	× I-Material
	S5 I-Material
	and O
	the O
	low-energy O
	action O
	for O
	N O
	= O
	4 O
	SU B-Material
	( I-Material
	N I-Material
	) I-Material
	SYM I-Material
	on O
	its O
	Coulomb B-Material
	branch I-Material
	, O
	with O
	the O
	gauge B-Material
	group I-Material
	SU B-Material
	( I-Material
	N I-Material
	) I-Material
	spontaneously O
	broken O
	to O
	SU B-Material
	( I-Material
	N I-Material
	− I-Material
	1 I-Material
	)× I-Material
	U I-Material
	( I-Material
	1 I-Material
	) I-Material
	. O
	There O
	have O
	appeared O
	two O
	independent O
	F6 B-Task
	tests I-Task
	of I-Task
	this I-Task
	conjecture I-Task
	[ O
	19,20 O
	] O
	, O
	with O
	conflicting O
	conclusions O
	. O
	The O
	approach O
	advocated O
	here O
	provides O
	the O
	opportunity O
	for O
	a O
	further O
	test O
	. O

	The O
	Substrate B-Task
	Induced I-Task
	Coagulation I-Task
	( O
	SIC B-Task
	) O
	coating B-Process
	process O
	provides O
	a O
	self O
	assembled O
	and O
	almost O
	binder O
	free O
	coating O
	with O
	small O
	particles O
	. O
	Most O
	research O
	so O
	far O
	has O
	been O
	used O
	to O
	coat O
	a O
	variety O
	of O
	surfaces O
	with O
	highly O
	conductive O
	carbon B-Material
	blacks I-Material
	[ O
	34,35,36 O
	] O
	. O
	Layers O
	deposited O
	by O
	this O
	technique O
	have O
	been O
	used O
	in O
	electromagnetic B-Process
	wave I-Process
	shielding I-Process
	, O
	in O
	the O
	metallization B-Process
	process O
	of O
	through-holes O
	in O
	printed B-Material
	wiring I-Material
	boards I-Material
	, O
	and O
	in O
	the O
	manufacture O
	of O
	conducting B-Material
	polymers I-Material
	( O
	such O
	as O
	Teflon B-Material
	) O
	[ O
	37,38,39 O
	] O
	. O
	An O
	advantage O
	of O
	this O
	dip-coating B-Process
	process O
	is O
	that O
	it O
	can O
	be O
	used O
	for O
	any O
	kind O
	of O
	surface O
	, O
	provided O
	the O
	substrate O
	is O
	stable O
	in O
	water O
	and O
	that O
	the O
	particles O
	used O
	for O
	the O
	coating O
	form O
	a O
	meta-stable B-Process
	dispersion I-Process
	. O
	Recently O
	, O
	a O
	non-aqueous O
	SIC B-Process
	coating I-Process
	process O
	of O
	carbon B-Material
	black I-Material
	was O
	developed O
	by O
	investigating O
	the O
	stabilities O
	of O
	non-aqueous O
	dispersions O
	[ O
	36 O
	] O
	. O
	These O
	dispersions O
	were O
	used O
	to O
	prepare O
	LiCoO2-composite B-Process
	electrodes I-Process
	for O
	Li-ion B-Process
	batteries I-Process
	with O
	an O
	improved O
	conductivity O
	while O
	keeping O
	the O
	content O
	of O
	active O
	battery O
	material O
	high O
	[ O
	35 O
	] O
	. O

	This O
	paper O
	proposes O
	a O
	sentence B-Process
	stress I-Process
	feedback I-Process
	system I-Process
	in O
	which O
	sentence O
	stress O
	prediction O
	, O
	detection O
	, O
	and O
	feedback O
	provision O
	models O
	are O
	combined O
	. O
	This O
	system O
	provides B-Task
	non-native I-Task
	learners I-Task
	with I-Task
	feedback I-Task
	on I-Task
	sentence I-Task
	stress I-Task
	errors I-Task
	so O
	that O
	they O
	can O
	improve O
	their O
	English O
	rhythm O
	and O
	fluency O
	in O
	a O
	self-study O
	setting O
	. O
	The O
	sentence O
	stress O
	feedback O
	system O
	was O
	devised O
	to O
	predict B-Task
	and I-Task
	detect I-Task
	the I-Task
	sentence I-Task
	stress I-Task
	of O
	any O
	practice O
	sentence O
	. O
	The O
	accuracy O
	of O
	the O
	prediction B-Process
	and I-Process
	detection I-Process
	models I-Process
	was O
	96.6 O
	% O
	and O
	84.1 O
	% O
	, O
	respectively O
	. O
	The O
	stress B-Process
	feedback I-Process
	provision I-Process
	model I-Process
	offers O
	positive O
	or O
	negative O
	stress O
	feedback O
	for O
	each O
	spoken O
	word O
	by O
	comparing O
	the O
	probability O
	of O
	the O
	predicted O
	stress O
	pattern O
	with O
	that O
	of O
	the O
	detected O
	stress O
	pattern O
	. O
	In O
	an O
	experiment O
	that O
	evaluated O
	the O
	educational O
	effect O
	of O
	the O
	proposed O
	system O
	incorporated O
	in O
	our O
	CALL B-Material
	system I-Material
	, O
	significant O
	improvements O
	in O
	accentedness O
	and O
	rhythm O
	were O
	seen O
	with O
	the O
	students O
	who O
	trained O
	with O
	our O
	system O
	but O
	not O
	with O
	those O
	in O
	the O
	control O
	group O
	. O

	Plastically O
	deformed O
	MGs B-Material
	develop O
	inhomogeneity O
	and O
	show O
	harder O
	and O
	softer O
	regions O
	[ O
	16 O
	] O
	. O
	While O
	this O
	could O
	in O
	principle O
	be O
	associated O
	with O
	a O
	BE O
	according O
	to O
	the O
	composite O
	model O
	, O
	a O
	MG B-Material
	provides O
	no O
	basis O
	for O
	a O
	dislocation-based O
	theory O
	. O
	The O
	search B-Task
	for I-Task
	a I-Task
	BE I-Task
	in I-Task
	plastic I-Task
	flow I-Task
	is O
	hindered O
	by O
	the O
	softening O
	of O
	MGs B-Material
	associated O
	with O
	shear-banding B-Process
	( O
	in O
	contrast O
	to O
	the O
	work-hardening O
	familiar O
	in O
	conventional O
	alloys O
	) O
	. O
	Anelastic B-Process
	deformation I-Process
	is O
	, O
	however O
	, O
	of O
	interest O
	as O
	its O
	time-dependence O
	must O
	relate O
	to O
	relaxation O
	processes O
	in O
	the O
	MG O
	structure O
	that O
	in O
	turn O
	should O
	be O
	connected O
	to O
	the O
	onset O
	of O
	plasticity O
	. O
	In O
	particular O
	, O
	anelasticity O
	may O
	offer O
	a O
	way O
	to O
	study O
	the O
	operation O
	of O
	the O
	shear B-Process
	transformation I-Process
	zones I-Process
	( O
	STZs B-Process
	[ O
	17 O
	]) O
	often O
	used O
	to O
	interpret O
	the O
	deformation O
	of O
	MGs B-Material
	. O
	Fujita O
	et O
	al. O
	have O
	used O
	torsion O
	tests O
	to O
	observe B-Task
	anelasticity I-Task
	in I-Task
	MGs I-Task
	loaded O
	( O
	at O
	maximum O
	, O
	on O
	the O
	cylindrical O
	sample O
	surface O
	) O
	to O
	30 O
	% O
	, O
	16 O
	% O
	and O
	just O
	4 O
	% O
	of O
	the O
	shear O
	yield O
	stress O
	τy O
	[ O
	18 O
	] O
	. O
	In O
	the O
	present O
	work O
	we O
	apply O
	torsion O
	to O
	MG B-Material
	samples O
	to O
	reach O
	stresses O
	up O
	to O
	24 O
	% O
	of O
	τy O
	, O
	and O
	for O
	the O
	first O
	time O
	in O
	the O
	elastic O
	regime O
	investigate O
	the O
	effects O
	of O
	torque O
	reversal O
	. O

	SPS B-Material
	has O
	been O
	utilized O
	in O
	several O
	studies O
	to O
	retain B-Process
	the I-Process
	nanostructure I-Process
	of I-Process
	aluminum I-Process
	alloy I-Process
	powders I-Process
	during I-Process
	consolidation I-Process
	. O
	Ye O
	et O
	al. O
	investigated B-Task
	the I-Task
	effect I-Task
	of I-Task
	processing I-Task
	of I-Task
	cryomilled I-Task
	Al I-Task
	5083 I-Task
	powder I-Task
	via O
	SPS B-Process
	[ O
	13 O
	] O
	. O
	X-ray B-Process
	Diffraction I-Process
	( I-Process
	XRD I-Process
	) I-Process
	grain I-Process
	size I-Process
	calculations I-Process
	before O
	and O
	after O
	SPS B-Process
	showed O
	that O
	the O
	average O
	grain O
	size O
	of O
	the O
	alloy B-Material
	only O
	increased O
	from O
	25nm O
	to O
	50nm O
	( O
	from O
	powder O
	to O
	bulk O
	state O
	) O
	. O
	Subsequently O
	, O
	the O
	hardness O
	values O
	obtained O
	through O
	nanoindentation B-Process
	for O
	specimens O
	of O
	AA5083 B-Material
	produced O
	via O
	SPS B-Material
	were O
	highly O
	improved O
	in O
	comparison O
	to O
	conventional O
	sintering O
	methods O
	were O
	grain O
	coarsening O
	takes O
	place O
	on O
	a O
	larger O
	scale O
	. O
	In O
	another O
	study O
	the O
	combination B-Process
	of I-Process
	cryomilling I-Process
	and I-Process
	SPS I-Process
	of I-Process
	AA-5356 I-Process
	/ I-Process
	B4C I-Process
	nanocomposites I-Process
	powder I-Process
	was O
	found O
	to O
	largely O
	improve O
	the O
	microhardness O
	and O
	flexural O
	strengths O
	of O
	the O
	bulk B-Material
	nanocomposite I-Material
	. O
	Rana O
	et O
	al O
	. O
	[ O
	14 O
	] O
	investigated B-Task
	the I-Task
	effect I-Task
	of I-Task
	SPS I-Task
	on I-Task
	mechanically I-Task
	milled I-Task
	AA6061 I-Task
	( I-Task
	Al I-Task
	– I-Task
	Mg I-Task
	– I-Task
	Si I-Task
	) I-Task
	micro-alloy I-Task
	powder I-Task
	. O
	The O
	average O
	grain O
	size O
	after O
	20h O
	of O
	milling O
	was O
	∼ O
	35nm O
	and O
	increased O
	to O
	only O
	∼ O
	85nm O
	after O
	processing O
	with O
	SPS B-Material
	at O
	500 O
	° O
	C O
	. O
	Microhardness O
	and O
	compressive O
	tests O
	were O
	carried O
	out O
	on O
	the O
	consolidated O
	near O
	full O
	density O
	specimens O
	of O
	both O
	unmilled B-Material
	and I-Material
	milled I-Material
	powders I-Material
	and O
	the O
	results O
	showed O
	significant O
	increase O
	in O
	both O
	hardness O
	and O
	compressive O
	strengths O
	for O
	the O
	milled B-Material
	nanocrystalline I-Material
	powders I-Material
	as O
	a O
	result O
	of O
	the O
	very O
	fine O
	grain O
	size O
	. O

	A O
	principle O
	of O
	high-throughput B-Task
	materials I-Task
	science I-Task
	is O
	that O
	one O
	does O
	not O
	know O
	a O
	priori O
	where O
	the O
	value O
	of O
	the O
	data O
	lies O
	for O
	any O
	specific O
	application O
	. O
	Trends O
	and O
	insights O
	are O
	deduced O
	a O
	posteriori O
	. O
	This O
	requires O
	efficient B-Task
	interfaces I-Task
	to I-Task
	interrogate I-Task
	available I-Task
	data I-Task
	on I-Task
	various I-Task
	levels I-Task
	. O
	We O
	have O
	developed O
	a O
	simple O
	WEB-based B-Process
	API I-Process
	to O
	greatly O
	improve B-Task
	the I-Task
	accessibility I-Task
	and I-Task
	utility I-Task
	of I-Task
	the I-Task
	AFLOWLIB I-Task
	database I-Task
	[ O
	14 O
	] O
	to O
	the O
	scientific O
	community O
	. O
	Through O
	it O
	, O
	the O
	client O
	can O
	access O
	calculated O
	physical B-Material
	properties I-Material
	( O
	thermodynamic B-Material
	, I-Material
	crystallographic I-Material
	, I-Material
	or I-Material
	mechanical I-Material
	properties I-Material
	) O
	, O
	as O
	well O
	as O
	simulation B-Material
	provenance I-Material
	and O
	runtime B-Material
	properties I-Material
	of O
	the O
	included O
	systems O
	. O
	The O
	data O
	may O
	be O
	used O
	directly O
	( O
	e.g. O
	, O
	to O
	browse B-Process
	a I-Process
	class I-Process
	of I-Process
	materials I-Process
	with I-Process
	a I-Process
	desired I-Process
	property I-Process
	) O
	or O
	integrated O
	into O
	higher B-Process
	level I-Process
	work-flows I-Process
	. O
	The O
	interface B-Process
	also O
	allows O
	for O
	the O
	sharing B-Process
	of I-Process
	updates I-Process
	of I-Process
	data I-Process
	used O
	in O
	previous O
	published O
	works O
	, O
	e.g. O
	, O
	previously O
	calculated O
	alloy B-Material
	phase I-Material
	diagrams I-Material
	[ O
	19 O
	– O
	31 O
	] O
	, O
	thus O
	the O
	database O
	can O
	be O
	expanded O
	systematically O
	. O

	The O
	Discrete B-Process
	Element I-Process
	Method I-Process
	applied O
	to O
	spheres B-Material
	is O
	well O
	established O
	as O
	a O
	reasonably O
	realistic O
	tool O
	, O
	in O
	a O
	wide O
	range O
	of O
	engineering O
	disciplines O
	, O
	for O
	modelling B-Task
	packing I-Task
	and I-Task
	flow I-Task
	of I-Task
	granular I-Task
	materials I-Task
	; O
	Asmar O
	et O
	al O
	. O
	[ O
	8 O
	] O
	describes O
	the O
	fundamentals O
	of O
	this O
	method O
	as O
	applied O
	by O
	code O
	developed O
	in-house O
	at O
	Nottingham O
	; O
	since O
	these O
	are O
	widely O
	documented O
	the O
	details O
	are O
	not O
	reproduced O
	here O
	, O
	simply O
	a O
	summary O
	. O
	It O
	applies O
	an O
	explicit B-Process
	time I-Process
	stepping I-Process
	approach I-Process
	to O
	numerically B-Process
	integrate I-Process
	the O
	translational B-Process
	and I-Process
	rotational I-Process
	motion I-Process
	of O
	each O
	particle O
	from O
	the O
	resulting O
	forces B-Process
	and O
	moments B-Process
	acting O
	on O
	them O
	at O
	each O
	timestep O
	. O
	The O
	inter-particle B-Material
	and I-Material
	particle I-Material
	wall I-Material
	contacts I-Material
	are O
	modelled O
	using O
	the O
	linear B-Process
	spring I-Process
	– I-Process
	dashpot I-Process
	– I-Process
	slider I-Process
	analogy I-Process
	. O
	Contact B-Process
	forces I-Process
	are O
	modelled O
	in O
	the O
	normal O
	and O
	tangential O
	directions O
	with O
	respect O
	to O
	the O
	line O
	connecting O
	the O
	particles B-Material
	centres I-Material
	. O
	Particle B-Material
	elastic I-Material
	stiffness O
	is O
	set O
	so O
	sphere B-Process
	“ I-Process
	overlap I-Process
	” I-Process
	is O
	not O
	significant O
	and O
	moderate O
	contact B-Process
	damping I-Process
	is O
	applied O
	. O
	Particle B-Process
	cohesion I-Process
	can O
	also O
	be O
	modelled O
	but O
	is O
	assumed O
	to O
	be O
	negligible O
	in O
	the O
	current O
	study O
	. O
	The O
	translational B-Process
	and I-Process
	rotational I-Process
	motion I-Process
	of O
	each O
	particle B-Material
	is O
	modelled O
	using O
	a O
	half B-Process
	step I-Process
	leap-frog I-Process
	Verlet I-Process
	numerical I-Process
	integration I-Process
	scheme I-Process
	to O
	update O
	particle B-Material
	positions I-Material
	and O
	velocities B-Material
	. O
	Near-neighbour B-Process
	lists I-Process
	are O
	used O
	to O
	increase O
	the O
	computational O
	efficiency O
	of O
	determining O
	particle B-Material
	contacts I-Material
	and O
	a O
	zoning B-Process
	method I-Process
	is O
	used O
	each O
	time O
	the O
	list O
	is O
	composed O
	; O
	that O
	is O
	the O
	system O
	is O
	divided O
	into O
	cubic O
	regions O
	, O
	each O
	particle B-Material
	centre I-Material
	is O
	within O
	one O
	zone O
	, O
	and O
	potential O
	contacting B-Material
	particles I-Material
	are O
	within O
	the O
	same O
	or O
	next-door O
	neighbour O
	zones O
	. O
	Full O
	details O
	are O
	given O
	in O
	Asmar O
	et O
	al O
	. O
	[ O
	8 O
	] O
	. O

	In O
	this O
	paper O
	, O
	crystal B-Process
	plasticity I-Process
	model I-Process
	, O
	in O
	combination O
	with O
	XFEM B-Process
	, O
	has O
	been O
	applied O
	to O
	study B-Task
	cyclic I-Task
	deformation I-Task
	and I-Task
	fatigue I-Task
	crack I-Task
	growth I-Task
	in O
	a O
	nickel-based B-Material
	superalloy I-Material
	LSHR I-Material
	( O
	Low B-Material
	Solvus I-Material
	High I-Material
	Refractory I-Material
	) O
	at O
	high O
	temperature O
	. O
	The O
	first O
	objective O
	of O
	this O
	research O
	was O
	to O
	develop B-Task
	and I-Task
	evaluate I-Task
	a I-Task
	RVE-based I-Task
	finite I-Task
	element I-Task
	model I-Task
	with O
	the O
	incorporation O
	of O
	a O
	realistic O
	material O
	microstructure O
	. O
	The O
	second O
	objective O
	of O
	this O
	work O
	was O
	to O
	determine B-Task
	the I-Task
	parameters I-Task
	of I-Task
	a I-Task
	crystal I-Task
	plasticity I-Task
	constitutive I-Task
	model I-Task
	to O
	describe O
	the O
	cyclic B-Process
	deformation I-Process
	behaviour O
	of O
	the O
	material O
	by O
	using O
	a O
	user-defined B-Material
	material I-Material
	subroutine I-Material
	( O
	UMAT B-Material
	) O
	interfaced O
	with O
	the O
	finite O
	element O
	package O
	ABAQUS B-Material
	. O
	The O
	model O
	parameters O
	were O
	calibrated O
	from O
	extensive O
	finite B-Process
	element I-Process
	analyses I-Process
	to O
	fit O
	the O
	monotonic B-Material
	, I-Material
	stress I-Material
	relaxation I-Material
	and I-Material
	cyclic I-Material
	test I-Material
	data I-Material
	. O
	The O
	third O
	objective O
	was O
	to O
	predict B-Task
	crack I-Task
	growth I-Task
	by O
	combining O
	the O
	XFEM B-Process
	technique I-Process
	and O
	the O
	calibrated B-Process
	crystal I-Process
	plasticity I-Process
	UMAT I-Process
	, O
	for O
	which O
	accumulated O
	plastic B-Process
	strain I-Process
	was O
	used O
	as O
	the O
	fracture B-Process
	criterion I-Process
	. O

	This O
	paper O
	has O
	highlighted B-Task
	a I-Task
	band I-Task
	of I-Task
	frequencies I-Task
	, O
	outside O
	the O
	conventional O
	operation O
	range O
	, O
	and O
	close O
	to O
	electrical B-Process
	resonance I-Process
	of I-Process
	an I-Process
	eddy I-Process
	current I-Process
	probe I-Process
	, O
	where O
	the O
	magnitude O
	of O
	impedance B-Material
	SNR I-Material
	reaches O
	a O
	peak O
	. O
	The O
	SNR O
	of O
	scans O
	of O
	three O
	slots O
	of O
	varying O
	depth O
	were O
	enhanced O
	by O
	a O
	factor O
	of O
	up O
	to O
	3.7 O
	, O
	from O
	the O
	SNR O
	measured O
	at O
	1MHz O
	. O
	This O
	is O
	a O
	result O
	of O
	a O
	defect-decoupling B-Process
	resonance-shift I-Process
	effect I-Process
	and O
	is O
	referred O
	to O
	as O
	the O
	near B-Process
	electrical I-Process
	resonance I-Process
	signal I-Process
	enhancement I-Process
	( O
	NERSE B-Process
	) O
	phenomenon O
	. O
	NERSE O
	frequency O
	operation O
	has O
	significant O
	potential O
	for O
	ECT B-Process
	inspection I-Process
	, O
	and O
	opens O
	up O
	a O
	range O
	of O
	investigative O
	possibilities O
	. O
	Within O
	this O
	investigation O
	, O
	only O
	the O
	magnitude O
	of O
	the O
	electrical B-Material
	impedance I-Material
	has O
	been O
	analyzed O
	. O
	An O
	immediate O
	extension O
	of O
	this O
	investigation O
	will O
	be O
	to O
	consider O
	phase O
	information O
	, O
	and O
	determine O
	whether O
	a O
	similar O
	exploitable O
	NERSE B-Process
	effect O
	exists O
	. O

	There O
	are O
	a O
	number O
	of O
	avenues O
	to O
	explore O
	for O
	future O
	work O
	, O
	in O
	particular O
	the O
	use B-Task
	of I-Task
	other I-Task
	time I-Task
	– I-Task
	frequency I-Task
	analysis I-Task
	methods I-Task
	. O
	The O
	STFT B-Process
	spectrogram I-Process
	was O
	utilised O
	here O
	, O
	as O
	it O
	is O
	the O
	simplest O
	to O
	implement O
	. O
	Whilst O
	all O
	of O
	the O
	echoes O
	could O
	be O
	clearly O
	resolved O
	in O
	both O
	time O
	and O
	frequency O
	, O
	the O
	spectrogram O
	suffers O
	from O
	a O
	fixed B-Material
	resolution I-Material
	, O
	i.e. O
	an O
	increase O
	of O
	time B-Material
	resolution I-Material
	necessarily O
	leads O
	to O
	a O
	decrease O
	in O
	frequency B-Material
	resolution I-Material
	. O
	Other O
	methods O
	of O
	time B-Process
	– I-Process
	frequency I-Process
	analysis I-Process
	, O
	such O
	as O
	discrete B-Process
	wavelet I-Process
	analysis I-Process
	, O
	benefit O
	from O
	advantage O
	of O
	multi-resolution B-Process
	analysis I-Process
	, O
	which O
	offers O
	improved O
	temporal B-Material
	resolution I-Material
	of O
	the O
	high O
	frequency O
	components O
	, O
	and O
	frequency B-Material
	resolution I-Material
	of O
	the O
	low B-Material
	frequency I-Material
	components I-Material
	[ O
	25,18,19 O
	] O
	. O
	Also O
	, O
	whilst O
	the O
	current O
	work O
	has O
	utilised O
	SH B-Material
	waves I-Material
	that O
	are O
	generated O
	by O
	EMATs B-Process
	, O
	the O
	physics O
	that O
	describes O
	the O
	pulsed B-Material
	array I-Material
	system I-Material
	is O
	universal O
	to O
	other O
	types O
	of O
	waves O
	. O
	Future O
	work O
	will O
	include O
	demonstrating B-Task
	this I-Task
	phenomenon I-Task
	with I-Task
	a I-Task
	number I-Task
	of I-Task
	other I-Task
	systems I-Task
	, O
	for O
	example O
	using O
	longitudinal B-Material
	ultrasonic I-Material
	waves I-Material
	or O
	electromagnetic B-Material
	waves I-Material
	. O

	Global B-Process
	optimisation I-Process
	algorithms I-Process
	are O
	used O
	in O
	this O
	study O
	to O
	solve B-Task
	the I-Task
	optimisation I-Task
	problem I-Task
	as O
	they O
	are O
	known O
	to O
	be O
	efficient O
	in O
	incorporating B-Process
	statistical I-Process
	information I-Process
	and O
	dealing O
	with O
	complicated O
	objective B-Process
	functions I-Process
	that O
	have O
	multiple O
	local B-Material
	minima I-Material
	/ I-Material
	maxima I-Material
	. O
	The O
	genetic B-Process
	algorithm I-Process
	( O
	GA B-Process
	) O
	is O
	such O
	a O
	global B-Process
	optimisation I-Process
	technique I-Process
	that O
	mimics B-Process
	biological I-Process
	evolution I-Process
	processes I-Process
	and O
	is O
	used O
	in O
	this O
	particular O
	study O
	. O
	The O
	algorithm O
	starts O
	with O
	a O
	random B-Process
	selection I-Process
	of I-Process
	a I-Process
	population I-Process
	from O
	the O
	decision B-Material
	variable I-Material
	domain I-Material
	( O
	X B-Material
	) O
	. O
	The O
	genetic B-Process
	algorithm I-Process
	repeatedly O
	modifies O
	this O
	population O
	. O
	At O
	each O
	step O
	, O
	the O
	algorithm O
	selects B-Process
	a I-Process
	group I-Process
	of I-Process
	individual I-Process
	values I-Process
	from O
	the O
	population B-Material
	( O
	parent B-Material
	) O
	which O
	are O
	evolved O
	through O
	crossover B-Process
	or O
	mutation B-Process
	to O
	produce O
	members O
	of O
	the O
	next O
	generation O
	. O
	This O
	process O
	is O
	repeated O
	for O
	several O
	generations O
	until O
	an O
	optimum O
	solution O
	is O
	reached O
	. O
	See O
	[ O
	19 O
	] O
	for O
	a O
	fuller O
	description O
	of O
	the O
	GA B-Process
	. O

	In O
	the O
	Total B-Process
	Focusing I-Process
	Method I-Process
	( O
	TFM B-Process
	) O
	the O
	beam O
	is O
	synthetically O
	focused O
	at O
	every O
	point O
	in O
	the O
	target O
	region O
	[ O
	7 O
	] O
	as O
	follows O
	. O
	After O
	obtaining O
	the O
	FMC B-Material
	data I-Material
	, O
	the O
	target O
	region O
	, O
	which O
	is O
	in O
	the O
	x B-Material
	– I-Material
	z I-Material
	plane I-Material
	in O
	2D O
	( O
	Fig. O
	1 O
	) O
	, O
	is O
	discretized B-Process
	into I-Process
	a I-Process
	grid I-Process
	. O
	The O
	signals O
	from O
	all O
	elements O
	in O
	the O
	array O
	are O
	then O
	summed B-Process
	to O
	synthesize B-Process
	a I-Process
	focus I-Process
	at I-Process
	every I-Process
	point I-Process
	in I-Process
	this I-Process
	grid I-Process
	. O
	Linear B-Process
	interpolation I-Process
	of O
	the O
	time O
	domain O
	signals O
	is O
	necessary O
	since O
	they O
	are O
	discretely B-Process
	sampled I-Process
	. O
	The O
	intensity O
	of O
	the O
	TFM B-Material
	image I-Material
	ITFM O
	at O
	any O
	point O
	( O
	x,z O
	) O
	is O
	given O
	by O
	:( O
	10 O
	) O
	ITFM O
	( O
	x,z O
	)=\|∑ O
	HTR O
	( O
	1c O
	(( O
	xT O
	− O
	x O
	) O
	2 O
	+ O
	z2 O
	+( O
	xR O
	− O
	x O
	) O
	2 O
	+ O
	z2 O
	))\| O
	forallT,Rwhere O
	HTR O
	( O
	t O
	) O
	is O
	the O
	Hilbert B-Process
	transform I-Process
	of O
	a O
	signal O
	uTR O
	( O
	t O
	) O
	in O
	the O
	FMC B-Material
	data I-Material
	, O
	xT O
	is O
	the O
	x-position O
	of O
	the O
	transmitting B-Material
	element I-Material
	( O
	T B-Material
	) O
	and O
	xR O
	is O
	the O
	x-position O
	of O
	the O
	receiving B-Material
	element I-Material
	( O
	R B-Material
	) O
	. O
	Note O
	that O
	the O
	z-position O
	of O
	all O
	elements O
	is O
	zero O
	( O
	Fig. O
	3a O
	) O
	. O
	The O
	summation B-Process
	is O
	carried O
	out O
	for O
	all O
	possible O
	transmitter B-Material
	– I-Material
	receiver I-Material
	pairs I-Material
	and O
	therefore O
	uses O
	all O
	the O
	information O
	captured O
	with O
	FMC B-Material
	. O
	This O
	algorithm O
	is O
	referred O
	to O
	as O
	‘ O
	conventional O
	TFM’ O
	in O
	this O
	paper O
	. O

	It O
	is O
	known O
	that O
	as O
	the O
	temperature O
	of O
	the O
	sample O
	rises O
	, O
	the O
	Lorentz B-Process
	mechanism I-Process
	remains O
	dominant O
	until O
	Tc O
	of O
	steel B-Material
	is O
	reached O
	( O
	770 O
	° O
	C O
	for O
	a O
	low B-Material
	carbon I-Material
	steel I-Material
	) O
	, O
	when O
	the O
	magnetostrictive B-Process
	mechanism I-Process
	becomes O
	more O
	efficient O
	[ O
	15 O
	] O
	. O
	Previously O
	this O
	has O
	been O
	thought O
	due O
	to O
	a O
	thin O
	ferromagnetic B-Material
	oxide I-Material
	layer O
	on O
	the O
	sample O
	surface O
	, O
	the O
	surface O
	being O
	cooler O
	than O
	the O
	bulk O
	of O
	the O
	material O
	[ O
	16,17 O
	] O
	. O
	This O
	layer O
	concentrates O
	the O
	magnetic B-Material
	field I-Material
	, O
	increasing O
	generation B-Process
	efficiency I-Process
	. O
	Recent O
	studies O
	also O
	show O
	that O
	rearrangement O
	of O
	the O
	magnetic O
	moments O
	from O
	ordered O
	domains O
	to O
	a O
	disordered O
	state O
	at O
	a O
	magnetic B-Process
	phase I-Process
	transition I-Process
	lowers O
	the O
	magnetostrictive O
	constant O
	. O
	This O
	ferromagnetic B-Process
	to I-Process
	paramagnetic I-Process
	transition I-Process
	is O
	accompanied O
	by O
	large O
	changes O
	in O
	the O
	efficiency O
	of O
	electromagnetic B-Process
	ultrasound I-Process
	generation I-Process
	leading O
	to O
	the O
	use O
	of O
	EMATs B-Material
	as O
	a O
	method O
	of O
	studying B-Task
	phase I-Task
	transitions I-Task
	in O
	magnetic B-Material
	alloys I-Material
	[ O
	18 O
	] O
	. O

	Shear B-Material
	horizontal I-Material
	( I-Material
	SH I-Material
	) I-Material
	ultrasound I-Material
	waves I-Material
	are O
	guided B-Material
	waves I-Material
	( O
	they O
	have O
	propagation B-Process
	properties O
	affected O
	by O
	the O
	geometry O
	of O
	the O
	propagation O
	medium O
	) O
	, O
	with O
	symmetric O
	and O
	anti-symmetric O
	modes O
	; O
	phase O
	and O
	group O
	speeds O
	are O
	dependent O
	on O
	frequency O
	, O
	sample O
	thickness O
	, O
	and O
	the O
	bulk B-Process
	shear I-Process
	wave I-Process
	speed I-Process
	[ O
	11,12 O
	] O
	. O
	The O
	properties O
	of O
	the O
	different O
	modes O
	can O
	be O
	very O
	useful O
	, O
	such O
	as O
	in O
	thickness O
	measurement O
	[ O
	13 O
	] O
	, O
	but O
	in O
	this O
	case O
	they O
	are O
	a O
	complication O
	. O
	SH0 B-Material
	has O
	a O
	thickness O
	independent O
	speed O
	, O
	equal O
	to O
	the O
	shear B-Process
	wave I-Process
	speed I-Process
	, O
	and O
	is O
	non-dispersive O
	( O
	the O
	phase B-Process
	and I-Process
	group I-Process
	speed I-Process
	are O
	equal O
	to O
	the O
	shear B-Process
	wave I-Process
	speed I-Process
	for O
	all O
	frequencies O
	) O
	. O
	The O
	oscillation B-Process
	direction O
	of O
	SH B-Process
	ultrasound I-Process
	is O
	in O
	the O
	plane O
	of O
	the O
	surface O
	where O
	the O
	wave B-Material
	was O
	generated O
	, O
	and O
	perpendicular O
	to O
	the O
	propagation B-Process
	direction O
	, O
	as O
	shown O
	in O
	Fig. O
	1 O
	, O
	with O
	respect O
	to O
	a O
	reference O
	interface O
	, O
	which O
	is O
	typically O
	a O
	sample O
	surface O
	. O
	Under O
	certain O
	conditions O
	, O
	such O
	as O
	over O
	short O
	propagation O
	distances O
	, O
	SH B-Material
	waves I-Material
	can O
	be O
	treated O
	as O
	bulk B-Material
	waves I-Material
	. O

	Volume B-Task
	EM I-Task
	can O
	be O
	performed O
	using O
	transmission B-Process
	or O
	scanning B-Material
	electron I-Material
	microscopes I-Material
	. O
	Each O
	approach O
	has O
	its O
	own O
	strengths O
	and O
	weaknesses O
	, O
	and O
	the O
	choice O
	is O
	dependant O
	on O
	the O
	required O
	lateral O
	( O
	x O
	, O
	y O
	) O
	and O
	axial O
	( O
	z O
	) O
	resolution O
	, O
	and O
	the O
	size O
	of O
	the O
	structure O
	of O
	interest O
	. O
	Historically O
	, O
	transmission B-Material
	electron I-Material
	microscopy I-Material
	( O
	TEM B-Material
	) O
	was O
	the O
	tool O
	of O
	choice O
	for O
	ultrastructural B-Task
	examination I-Task
	of I-Task
	biomedical I-Task
	specimens I-Task
	at O
	sub-nanometer O
	resolution O
	. O
	However O
	, O
	for O
	many O
	cell B-Task
	biology I-Task
	studies I-Task
	structural O
	resolution O
	is O
	actually O
	limited O
	by O
	the O
	deposition B-Process
	of I-Process
	heavy I-Process
	metals I-Process
	onto O
	membranes O
	during O
	sample O
	preparation O
	. O
	In O
	addition O
	, O
	voxel O
	dimensions O
	may O
	only O
	need O
	to O
	be O
	half O
	that O
	of O
	the O
	smallest O
	expected O
	feature O
	of O
	interest O
	( O
	Briggman O
	and O
	Bock O
	, O
	2012 O
	) O
	. O
	Advances O
	in O
	scanning B-Process
	electron I-Process
	microscopy I-Process
	( I-Process
	SEM I-Process
	) I-Process
	technology I-Process
	are O
	now O
	driving O
	a O
	paradigm O
	shift O
	in O
	electron B-Task
	imaging I-Task
	. O
	SEMs B-Process
	with O
	field B-Material
	emission I-Material
	electron I-Material
	sources I-Material
	and O
	high B-Material
	efficiency I-Material
	electron I-Material
	detectors I-Material
	can O
	achieve O
	lateral O
	resolutions O
	in O
	the O
	order O
	of O
	3nm O
	, O
	allowing O
	visualisation B-Task
	of I-Task
	structures I-Task
	such O
	as O
	synaptic B-Material
	vesicles I-Material
	and O
	membranes B-Material
	( O
	De O
	Winter O
	et O
	al. O
	, O
	2009 O
	; O
	Knott O
	et O
	al. O
	, O
	2008 O
	; O
	Vihinen O
	et O
	al. O
	, O
	2013 O
	; O
	Villinger O
	et O
	al. O
	, O
	2012 O
	) O
	, O
	though O
	resolving B-Task
	individual I-Task
	leaflets I-Task
	of I-Task
	membrane I-Task
	bilayers I-Task
	remains O
	a O
	challenge O
	( O
	Vihinen O
	et O
	al. O
	, O
	2013 O
	) O
	. O
	The O
	use O
	of O
	low B-Material
	beam I-Material
	energies I-Material
	also O
	limits O
	the O
	interaction B-Process
	volume I-Process
	, O
	enhancing B-Process
	axial I-Process
	resolution I-Process
	( O
	Hennig O
	and O
	Denk O
	, O
	2007 O
	) O
	. O
	In O
	this O
	review O
	, O
	volume B-Task
	imaging I-Task
	in O
	both O
	transmission B-Process
	and I-Process
	scanning I-Process
	EMs I-Process
	will O
	be O
	explored O
	, O
	moving O
	from O
	traditional B-Process
	manual I-Process
	techniques I-Process
	, O
	through O
	to O
	the O
	latest B-Process
	systems I-Process
	where O
	aspects O
	of O
	both O
	sample B-Task
	preparation I-Task
	and O
	imaging B-Task
	have O
	been O
	automated O
	. O

	In O
	this O
	paper O
	, O
	we O
	present O
	our O
	experimental B-Task
	observations I-Task
	on I-Task
	how I-Task
	solvents I-Task
	can I-Task
	vary I-Task
	the I-Task
	TPA I-Task
	and I-Task
	TPF I-Task
	properties I-Task
	of I-Task
	fluorescent I-Task
	rhodamine I-Task
	( I-Task
	Rh I-Task
	) I-Task
	dyes I-Task
	Rh6G B-Material
	, O
	RhB B-Material
	and O
	Rh101 B-Material
	. O
	Rhodamines B-Material
	are O
	well-known O
	xanthenes B-Material
	dyes I-Material
	, O
	which O
	have O
	been O
	extensively O
	used O
	for O
	many O
	widespread O
	applications O
	in O
	single-molecule B-Process
	detection I-Process
	[ O
	24 O
	] O
	, O
	DNA-sequence B-Process
	determination I-Process
	[ O
	25 O
	] O
	, O
	fluorescence B-Process
	labelling I-Process
	[ O
	26 O
	] O
	, O
	etc O
	. O
	due O
	to O
	their O
	strong O
	fluorescence O
	over O
	the O
	visible O
	spectral O
	region O
	. O
	Molecular O
	geometries O
	of O
	rhodamine B-Material
	dyes I-Material
	are O
	well-known O
	[ O
	27,28 O
	] O
	and O
	indicate O
	that O
	all O
	the O
	structures O
	are O
	non-centrosymmetric O
	. O
	In O
	general O
	, O
	for O
	centrosymmetric B-Material
	molecules I-Material
	, O
	TPA B-Process
	is O
	forbidden O
	when O
	tuned O
	to O
	the O
	transitions O
	at O
	one-half O
	of O
	the O
	excitation O
	frequencies O
	. O
	However O
	, O
	for O
	non-centrosymmetric B-Material
	molecules I-Material
	due O
	to O
	symmetry B-Process
	relaxations I-Process
	, O
	the O
	single-photon B-Process
	absorption I-Process
	( O
	SPA B-Process
	) O
	peaks O
	and O
	TPA B-Process
	peaks I-Process
	may O
	coincide O
	. O
	So O
	we O
	set O
	our O
	primary O
	aim O
	to O
	find O
	the O
	effect O
	of O
	solvent O
	polarity O
	on O
	the O
	correlation O
	of O
	SPA B-Process
	and O
	TPA B-Process
	peaks O
	for O
	all O
	the O
	dyes B-Material
	. O

	The O
	other O
	methods O
	for O
	enhancement B-Task
	of I-Task
	photocatalytic I-Task
	activity I-Task
	are O
	grafting O
	co-catalysts B-Material
	. O
	There O
	are O
	two O
	kinds O
	of O
	co-catalysts O
	in O
	terms O
	of O
	its O
	function O
	: O
	one O
	is O
	for O
	separation B-Process
	of I-Process
	electrons I-Process
	and O
	the O
	other O
	is O
	for O
	separation B-Process
	of I-Process
	holes I-Process
	. O
	The O
	former O
	representative O
	co-catalysts B-Material
	are O
	Pt B-Material
	, O
	Fe3 B-Material
	+ I-Material
	, O
	and O
	Cu2 B-Material
	+ I-Material
	[ O
	9 O
	– O
	12 O
	] O
	. O
	It O
	was O
	reported O
	that O
	Fe3 B-Material
	+ I-Material
	and O
	Cu2 B-Material
	+ I-Material
	were O
	grafted O
	as O
	amorphous B-Material
	oxide I-Material
	cluster I-Material
	[ O
	9,10 O
	] O
	, O
	and O
	reduced O
	into O
	Fe2 B-Material
	+ I-Material
	and O
	Cu B-Material
	+ I-Material
	by O
	receiving O
	one O
	electron B-Material
	, O
	respectively O
	[ O
	11,12 O
	] O
	. O
	The O
	reduced B-Material
	metal I-Material
	oxide I-Material
	cluster I-Material
	with O
	reduced B-Material
	ions I-Material
	could O
	return O
	into O
	the O
	original O
	state O
	by O
	giving O
	more O
	than O
	one O
	electron B-Material
	to O
	molecular O
	oxygen B-Material
	. O
	The O
	latter O
	ones O
	are O
	CoOx B-Material
	, O
	CoPi B-Material
	( O
	CoPOx B-Material
	) O
	, O
	IrOx B-Material
	, O
	and O
	RuOx B-Material
	which O
	are O
	used O
	for O
	water B-Process
	oxidation I-Process
	, O
	among O
	which O
	CoPi B-Material
	is O
	reported O
	to O
	be O
	the O
	most O
	effective O
	co-catalyst B-Material
	for O
	water B-Process
	oxidation I-Process
	[ O
	13 O
	] O
	. O
	However O
	, O
	there O
	were O
	few O
	reports O
	concerning O
	co-grafting B-Process
	effects O
	on O
	photocatalytic B-Process
	activity I-Process
	especially O
	in O
	gaseous B-Process
	phase I-Process
	. O
	We O
	expected O
	that O
	by O
	co-grafting B-Process
	of O
	both O
	co-catalysts B-Material
	for O
	separations O
	of O
	electrons B-Material
	and O
	holes B-Material
	, O
	photocatalytic B-Process
	activity I-Process
	in O
	gaseous B-Process
	phase I-Process
	would O
	be O
	further O
	enhanced O
	. O
	Moreover O
	, O
	complex O
	of O
	BiVO4 B-Material
	with O
	the O
	other O
	materials O
	of O
	p-type B-Material
	semiconductor I-Material
	is O
	also O
	effective O
	for O
	enhancing O
	photocatalytic B-Process
	activity I-Process
	. O

	An O
	obvious O
	metric O
	to O
	measure O
	the O
	monitoring O
	performance O
	between O
	the O
	different O
	conditions O
	would O
	be O
	to O
	compare B-Task
	how I-Task
	many I-Task
	clicks I-Task
	the I-Task
	users I-Task
	made I-Task
	in I-Task
	average I-Task
	for I-Task
	each I-Task
	condition I-Task
	. O
	Furthermore O
	of O
	interest O
	are O
	the O
	buffer B-Material
	values O
	of O
	the O
	respective O
	buffers B-Material
	at O
	the O
	time O
	of O
	the O
	user O
	's O
	interaction O
	with O
	the O
	simulation B-Process
	( O
	e.g. O
	, O
	the O
	input B-Material
	buffer I-Material
	of O
	a O
	certain O
	machine O
	at O
	the O
	time O
	of O
	refilling O
	it O
	) O
	. O
	A O
	relatively O
	high O
	average O
	buffer O
	value O
	can O
	e.g. O
	signify O
	that O
	the O
	users O
	do O
	not O
	trust O
	that O
	the O
	respective O
	mode O
	of O
	process O
	monitoring O
	conveys O
	the O
	need O
	for O
	interaction O
	in O
	time O
	, O
	leading O
	the O
	users O
	to O
	switching O
	their O
	attention O
	to O
	the O
	process O
	simulation O
	in O
	regular O
	intervals O
	, O
	and O
	performing O
	interactions O
	just O
	in O
	case O
	. O
	A O
	low O
	average O
	buffer O
	can O
	, O
	on O
	the O
	other O
	hand O
	, O
	signify O
	that O
	the O
	users O
	rely O
	on O
	the O
	respective O
	conditions’ O
	ability O
	to O
	signal O
	interaction O
	needs O
	. O
	On O
	the O
	other O
	hand O
	, O
	if O
	e.g. O
	an O
	input O
	buffer O
	had O
	already O
	been O
	completely O
	depleted O
	at O
	the O
	time O
	of O
	intervention O
	, O
	this O
	may O
	signify O
	that O
	the O
	respective O
	condition O
	has O
	failed O
	to O
	inform O
	the O
	users O
	in O
	time O
	. O
	In O
	many O
	cases O
	, O
	participants O
	used O
	double O
	clicks O
	for O
	their O
	interactions O
	, O
	while O
	a O
	single O
	click O
	would O
	have O
	been O
	sufficient O
	, O
	a O
	fact O
	that O
	was O
	perhaps O
	not O
	communicated O
	clearly O
	enough O
	to O
	the O
	participants O
	. O
	Therefore O
	, O
	if O
	several O
	clicks O
	were O
	performed O
	directly O
	one O
	after O
	another O
	, O
	only O
	the O
	first O
	click O
	was O
	taken O
	into O
	account O
	. O

	The O
	first-principles B-Task
	calculations I-Task
	are O
	performed O
	using O
	the O
	Cambridge B-Material
	Serial I-Material
	Total I-Material
	Energy I-Material
	Package I-Material
	( O
	CASTEP B-Material
	) O
	[ O
	21 O
	] O
	which O
	implements O
	the O
	plane-wave B-Process
	pseudopotential I-Process
	DFT I-Process
	method I-Process
	. O
	The O
	exchange B-Process
	correlation I-Process
	functional I-Process
	is O
	approximated O
	using O
	the O
	generalized B-Process
	gradient I-Process
	approximation I-Process
	( O
	PBE-GGA B-Process
	) O
	[ O
	22 O
	] O
	, O
	and O
	the O
	electron B-Material
	– O
	ion B-Material
	interactions O
	are O
	described O
	by O
	Vanderbilt-type B-Process
	ultrasoft I-Process
	pseudopotentials I-Process
	[ O
	23 O
	] O
	. O
	The O
	plane B-Material
	wave I-Material
	basis O
	set O
	is O
	truncated O
	at O
	a O
	cutoff O
	of O
	400eV O
	, O
	and O
	the O
	Brillouin-zone B-Process
	sampling I-Process
	was O
	performed O
	using O
	the O
	Monkhorst-Pack B-Process
	scheme I-Process
	with O
	a O
	k-point B-Process
	spacing I-Process
	in O
	reciprocal O
	space O
	of O
	0.04Å O
	− O
	1 O
	. O
	Tests O
	show O
	that O
	these O
	computational O
	parameters O
	give O
	results O
	that O
	are O
	sufficiently O
	accurate O
	for O
	present O
	purposes O
	. O
	The O
	ferromagnetism B-Process
	of O
	nickel B-Material
	is O
	accounted O
	for O
	by O
	performing O
	all O
	calculations O
	using O
	spin B-Process
	polarization I-Process
	, O
	starting O
	at O
	a O
	ferromagnetic O
	initial O
	configuration O
	and O
	relaxing O
	towards O
	its O
	ground O
	state O
	. O
	However O
	, O
	for O
	all O
	compositions O
	considered O
	, O
	the O
	ground O
	state O
	electronic O
	structure O
	of O
	each O
	alloy B-Material
	is O
	found O
	to O
	exhibit O
	only O
	very O
	weak O
	ferromagnetism B-Process
	, O
	and O
	the O
	effect O
	is O
	not O
	thought O
	to O
	influence O
	their O
	phase O
	stability O
	. O
	Table O
	1 O
	shows O
	the O
	calculated O
	equilibrium O
	lattice O
	constants O
	of O
	the O
	η O
	phase O
	at O
	various O
	Ti B-Material
	concentrations O
	, O
	using O
	partially O
	ordered O
	ηP B-Material
	structures I-Material
	. O
	The O
	change O
	in O
	lattice O
	constant O
	upon O
	Ti B-Process
	alloying I-Process
	is O
	relatively O
	small O
	, O
	but O
	can O
	be O
	related O
	to O
	the O
	∼ O
	10 O
	% O
	larger O
	covalent O
	radius O
	of O
	Ti O
	. O
	The O
	calculated O
	lattice O
	constants O
	are O
	in O
	good O
	agreement O
	with O
	the O
	experimental O
	values O
	, O
	which O
	relate O
	to O
	an O
	alloy B-Material
	with O
	a O
	Al B-Material
	/ O
	Ti B-Material
	ratio O
	of O
	∼ O
	2.75 O
	. O

	When O
	we O
	formulate O
	the O
	downscaling O
	problem O
	as O
	a O
	multi-objective B-Process
	optimization I-Process
	problem O
	, O
	we O
	face O
	, O
	however O
	, O
	the O
	following O
	problems O
	. O
	Minimizing O
	the O
	sum O
	of O
	different O
	objectives O
	is O
	problematic O
	, O
	since O
	they O
	may O
	have O
	different O
	units O
	and O
	ranges O
	. O
	Even O
	with O
	an O
	appropriate O
	scaling B-Process
	procedure I-Process
	there O
	is O
	a O
	risk O
	of O
	treating O
	the O
	objectives O
	unequally O
	or O
	getting O
	trapped O
	in O
	a O
	local O
	minimum O
	. O
	Firstly O
	, O
	we O
	can O
	never O
	know O
	, O
	what O
	is O
	the O
	minimum O
	value O
	of O
	each O
	objective O
	that O
	can O
	be O
	achieved O
	by O
	the O
	regression B-Process
	. O
	Thus O
	, O
	designing O
	an O
	appropriate O
	scaling B-Process
	procedure I-Process
	is O
	difficult O
	and O
	one O
	would O
	need O
	to O
	decide O
	on O
	the O
	relative O
	importance O
	of O
	the O
	different O
	objectives O
	in O
	advance O
	. O
	Secondly O
	, O
	adding O
	multiple O
	, O
	conflicting O
	objectives O
	very O
	likely O
	results O
	in O
	a O
	fitness O
	function O
	with O
	multiple O
	local O
	minima O
	, O
	which O
	makes O
	optimization B-Process
	more O
	difficult O
	. O
	To O
	avoid O
	these O
	problems O
	, O
	we O
	have O
	implemented O
	fitness O
	calculation O
	according O
	to O
	the O
	Strength B-Process
	Pareto I-Process
	Evolutionary I-Process
	Algorithm I-Process
	( O
	SPEA B-Process
	) O
	by O
	Zitzler O
	and O
	Thiele O
	( O
	1999 O
	) O
	, O
	instead O
	of O
	using B-Process
	a I-Process
	single I-Process
	( I-Process
	weighted I-Process
	) I-Process
	fitness I-Process
	or I-Process
	cost I-Process
	function I-Process
	. O
	Approaches O
	for O
	multi-objective B-Process
	optimization I-Process
	like O
	SPEA B-Process
	are O
	widely O
	used O
	in O
	evolutionary B-Task
	computation I-Task
	. O
	In O
	SPEA B-Process
	the O
	fitness B-Process
	calculation I-Process
	during O
	the O
	fitting O
	procedure O
	is O
	based O
	on O
	an O
	intercomparison O
	of O
	the O
	different O
	models O
	. O
	Further O
	, O
	a O
	finite O
	set O
	of O
	so O
	called O
	Pareto B-Process
	optimal I-Process
	models I-Process
	( O
	downscaling B-Process
	rules I-Process
	) O
	is O
	returned O
	. O

	The O
	main O
	objective O
	of O
	this O
	manuscript O
	is O
	to O
	present B-Task
	and I-Task
	discuss I-Task
	the I-Task
	application I-Task
	of I-Task
	SLAMM I-Task
	to I-Task
	the I-Task
	New I-Task
	York I-Task
	coast I-Task
	. O
	Although O
	the O
	base O
	analysis O
	considers O
	a O
	range O
	of O
	different O
	possible O
	SLR B-Process
	scenarios O
	, O
	the O
	effects O
	of O
	various O
	sources O
	of O
	uncertainties O
	such O
	as O
	input O
	parameters O
	and O
	driving O
	data O
	are O
	not O
	accounted O
	for O
	. O
	In O
	addition O
	, O
	refined O
	and O
	site-specific O
	data O
	are O
	often O
	not O
	available O
	requiring O
	the O
	use O
	of O
	regional O
	data O
	collected O
	from O
	literature O
	and O
	professional O
	judgement O
	in O
	order O
	to O
	run O
	the O
	model O
	. O
	To O
	ignore O
	the O
	effects O
	of O
	these O
	uncertainties O
	on O
	predictions O
	may O
	make O
	interpretation O
	of O
	the O
	results O
	and O
	subsequent O
	decision O
	making O
	misleading O
	since O
	the O
	likelihood O
	and O
	probabilities O
	of O
	predicted O
	outcomes O
	would O
	be O
	unknown O
	. O
	A O
	unique O
	capability O
	of O
	the O
	current O
	version O
	of O
	SLAMM B-Process
	is O
	the O
	ability O
	to O
	aggregate O
	multiple O
	types O
	of O
	input-data O
	uncertainty O
	to O
	create O
	outputs O
	accompanied O
	by O
	probability O
	statements O
	and O
	confidence O
	intervals O
	. O
	Uncertainty O
	in O
	elevation O
	data O
	layers O
	have O
	been O
	considered O
	by O
	several O
	modeling O
	groups O
	to O
	various O
	extents O
	( O
	Gesch O
	, O
	2009 O
	; O
	Gilmer O
	and O
	Ferdaña O
	, O
	2012 O
	; O
	Schmid O
	et O
	al. O
	, O
	2014 O
	) O
	. O
	However O
	, O
	to O
	the O
	best O
	of O
	our O
	knowledge O
	, O
	no O
	other O
	marsh B-Material
	migration I-Material
	model I-Material
	simultaneously O
	accounts O
	for O
	the O
	combined O
	effects O
	of O
	uncertainty O
	in O
	spatial B-Process
	inputs I-Process
	( O
	DEM B-Process
	, O
	VDATUM B-Process
	, O
	etc. O
	) O
	and O
	parameter B-Process
	choices I-Process
	( O
	accretion B-Process
	rates I-Process
	, O
	tide B-Process
	ranges I-Process
	, O
	etc. O
	) O
	on O
	landcover B-Process
	projections I-Process
	. O
	This O
	added O
	feature O
	of O
	SLAMM B-Process
	allows O
	results O
	to O
	be O
	evaluated O
	in O
	terms O
	of O
	their O
	likelihood O
	of O
	occurrence O
	with O
	respect O
	to O
	input-data O
	and O
	parameter O
	uncertainties O
	. O
	Further O
	, O
	by O
	assigning O
	wide O
	ranges O
	of O
	uncertainty O
	when O
	appropriate O
	, O
	it O
	permits O
	the O
	production O
	of O
	meaningful O
	projections O
	in O
	areas O
	where O
	high-quality B-Material
	local I-Material
	data I-Material
	are O
	not O
	available O
	. O

	Using O
	measured O
	data O
	from O
	two O
	arable O
	sites O
	in O
	the O
	UK O
	we O
	have O
	shown O
	that O
	lags O
	can O
	have O
	significant O
	impact O
	on O
	model B-Task
	evaluation I-Task
	and O
	can O
	affect O
	both O
	the O
	level O
	of O
	correlation O
	between O
	measured O
	and O
	simulated O
	data O
	and O
	the O
	magnitude O
	of O
	the O
	sums O
	of O
	the O
	residuals O
	. O
	Also O
	, O
	we O
	used O
	the O
	division O
	of O
	MSE B-Process
	to O
	three O
	constituent B-Process
	statistics I-Process
	( O
	SB B-Process
	, O
	SDSD B-Process
	and O
	LCS B-Process
	) O
	to O
	show O
	how O
	the O
	level O
	of O
	correlation O
	can O
	affect O
	the O
	sum O
	of O
	residuals O
	. O
	By O
	dividing O
	the O
	algorithm-predicted O
	series O
	of O
	lag O
	values O
	into O
	monthly O
	sets O
	and O
	examining O
	the O
	frequency O
	distribution O
	of O
	the O
	lags O
	, O
	certain O
	patterns O
	in O
	these O
	temporally O
	patchy O
	series O
	have O
	been O
	identified O
	. O
	A O
	challenging O
	task O
	in O
	relation O
	to O
	time O
	lags O
	between O
	observed O
	and O
	simulated O
	daily O
	data O
	, O
	is O
	to O
	determine O
	their O
	cause O
	. O
	This O
	task O
	becomes O
	more O
	difficult O
	for O
	model O
	outputs O
	such O
	as O
	soil O
	N2O B-Material
	emissions I-Material
	that O
	are O
	driven O
	by O
	various O
	interacting O
	variables O
	. O
	Even O
	more O
	so O
	, O
	because O
	the O
	measured O
	N2O B-Material
	datasets I-Material
	and O
	the O
	measured O
	datasets O
	of O
	their O
	drivers O
	( O
	e.g. O
	soil O
	moisture O
	, O
	soil O
	N O
	content O
	) O
	cover O
	small O
	time O
	periods O
	, O
	they O
	are O
	not O
	continuous O
	and O
	can O
	vary O
	widely O
	in O
	size O
	. O
	In O
	this O
	study O
	we O
	implemented O
	the O
	algorithm O
	using O
	measured B-Material
	and I-Material
	simulated I-Material
	data I-Material
	for I-Material
	soil I-Material
	moisture I-Material
	( O
	first O
	and O
	second O
	example O
	) O
	and O
	soil B-Material
	mineral I-Material
	N I-Material
	( O
	second O
	example O
	) O
	, O
	and O
	compared O
	its O
	results O
	with O
	the O
	respective O
	results O
	for O
	N2O B-Material
	. O
	In O
	our O
	first O
	example O
	, O
	we O
	showed O
	that O
	the O
	estimated O
	lags O
	in O
	N2O B-Task
	prediction I-Task
	are O
	related O
	to O
	the O
	lags O
	in O
	soil B-Task
	moisture I-Task
	prediction I-Task
	in O
	a O
	way O
	that O
	changes O
	gradually O
	through O
	time O
	. O
	In O
	our O
	second O
	example O
	, O
	the O
	lags O
	in O
	N2O B-Task
	prediction I-Task
	were O
	explained O
	by O
	the O
	lags O
	in O
	soil B-Material
	moisture I-Material
	and O
	soil B-Task
	mineral I-Task
	N I-Task
	prediction I-Task
	, O
	with O
	which O
	they O
	had O
	a O
	positive O
	relationship O
	. O

	In O
	representing B-Task
	wetland-river I-Task
	interactions I-Task
	involving I-Task
	GIWs I-Task
	, O
	many O
	models O
	assume O
	that O
	the O
	wetland O
	can O
	discharge O
	into O
	a O
	river O
	but O
	cannot O
	receive O
	overbank O
	flows O
	from O
	it O
	. O
	In O
	such O
	models O
	, O
	the O
	volume O
	of O
	water O
	( O
	or O
	water O
	level O
	elevation O
	) O
	in O
	a O
	wetland O
	and O
	its O
	corresponding O
	threshold O
	value O
	( O
	predominantly O
	controlled O
	by O
	outlet B-Process
	elevation I-Process
	) O
	are O
	the O
	prime O
	determinants O
	of O
	wetland O
	outflow O
	( O
	Feng O
	et O
	al. O
	, O
	2012 O
	; O
	Hammer O
	and O
	Kadlec O
	, O
	1986 O
	; O
	Johnson O
	et O
	al. O
	, O
	2010 O
	; O
	Kadlec O
	and O
	Wallace O
	, O
	2009 O
	; O
	Powell O
	et O
	al. O
	, O
	2008 O
	; O
	Voldseth O
	et O
	al. O
	, O
	2007 O
	; O
	Wen O
	et O
	al. O
	, O
	2013 O
	; O
	Zhang O
	and O
	Mitsch O
	, O
	2005 O
	) O
	. O
	However O
	, O
	in O
	regions O
	characterised O
	by O
	widespread O
	riparian O
	wetlands O
	that O
	are O
	hydraulically O
	connected O
	with O
	adjacent O
	rivers O
	, O
	wetland-river O
	interaction O
	is O
	likely O
	to O
	be O
	bidirectional O
	. O
	Such O
	interactions B-Task
	should I-Task
	be I-Task
	quantified I-Task
	according I-Task
	to I-Task
	hydraulic I-Task
	principles I-Task
	involving O
	relative B-Material
	river I-Material
	and I-Material
	wetland I-Material
	water I-Material
	level I-Material
	elevations I-Material
	as O
	well O
	as O
	the O
	properties B-Material
	of I-Material
	the I-Material
	connection I-Material
	between I-Material
	the I-Material
	two I-Material
	( O
	Kouwen O
	, O
	2013 O
	; O
	Liu O
	et O
	al. O
	, O
	2008 O
	; O
	Min O
	et O
	al. O
	, O
	2010 O
	; O
	Nyarko O
	, O
	2007 O
	; O
	Restrepo O
	et O
	al. O
	, O
	1998 O
	) O
	. O
	In O
	the O
	WATFLOOD B-Process
	model O
	, O
	for O
	instance O
	, O
	riparian B-Task
	wetland-river I-Task
	interaction I-Task
	is I-Task
	modelled I-Task
	using O
	the O
	principle B-Material
	of I-Material
	Dupuit-Forchheimer I-Material
	lateral I-Material
	/ I-Material
	radial I-Material
	groundwater I-Material
	flow I-Material
	( O
	Kouwen O
	, O
	2013 O
	) O
	. O
	Since O
	exchange O
	between O
	riparian O
	wetlands O
	and O
	rivers O
	can O
	occur O
	over O
	the O
	surface O
	and O
	/ O
	or O
	through O
	the O
	subsurface O
	, O
	Restrepo O
	et O
	al O
	. O
	( O
	1998 O
	) O
	incorporated O
	an O
	equivalent B-Process
	transmissivity I-Process
	expression I-Process
	, O
	obtained O
	for O
	wetland B-Material
	vegetation I-Material
	and O
	the O
	subsurface B-Material
	soil I-Material
	, O
	into O
	the O
	Darcy B-Process
	flow I-Process
	equation I-Process
	of O
	the O
	MODFLOW B-Process
	model O
	. O

	Typical O
	physically-based B-Process
	2D I-Process
	flood I-Process
	models I-Process
	solve B-Task
	the I-Task
	Shallow I-Task
	Water I-Task
	Equations I-Task
	( I-Task
	SWEs I-Task
	) I-Task
	, O
	requiring O
	high O
	computational O
	resources O
	. O
	Many O
	of O
	these O
	models O
	have O
	been O
	developed O
	to O
	obtain O
	better O
	performance O
	, O
	while O
	maintaining O
	the O
	required O
	accuracy O
	, O
	by O
	reducing B-Process
	the I-Process
	complexity I-Process
	of I-Process
	the I-Process
	SWEs I-Process
	. O
	This O
	reduction O
	is O
	usually O
	achieved O
	by O
	approximating O
	or O
	neglecting O
	less O
	significant O
	terms O
	of O
	the O
	equations O
	( O
	Hunter O
	et O
	al. O
	, O
	2007 O
	; O
	Yen O
	and O
	Tsai O
	, O
	2001 O
	) O
	. O
	The O
	JFLOW B-Process
	model O
	( O
	Bradbrook O
	et O
	al. O
	, O
	2004 O
	) O
	, O
	Urban B-Process
	Inundation I-Process
	Model I-Process
	( O
	UIM B-Process
	) O
	( O
	Chen O
	et O
	al. O
	, O
	2007 O
	) O
	, O
	and O
	the O
	diffusive O
	version O
	of O
	LISFLOOD-FP B-Process
	( O
	Hunter O
	et O
	al. O
	, O
	2005 O
	) O
	solve O
	the O
	2D B-Process
	diffusion I-Process
	wave I-Process
	equations I-Process
	that O
	neglect O
	the O
	inertial O
	( O
	local O
	acceleration O
	) O
	and O
	advection O
	( O
	convective O
	acceleration O
	) O
	terms O
	( O
	Yen O
	and O
	Tsai O
	, O
	2001 O
	) O
	. O
	The O
	inertial O
	version O
	of O
	LISFLOOD-FP B-Process
	( O
	Bates O
	et O
	al. O
	, O
	2010 O
	) O
	solves O
	the O
	SWEs B-Process
	without O
	the O
	advection O
	term O
	. O
	In O
	either O
	version O
	of O
	LISFLOOD-FP B-Process
	the O
	flow O
	is O
	decoupled O
	in O
	the O
	Cartesian B-Process
	directions I-Process
	. O
	Other O
	models O
	use O
	the O
	full O
	SWEs B-Process
	but O
	focus O
	on O
	the O
	use O
	of O
	multi B-Material
	resolution I-Material
	grids I-Material
	or I-Material
	irregular I-Material
	mesh I-Material
	, O
	like O
	InfoWorks B-Material
	ICM I-Material
	( O
	Innovyze O
	, O
	2012 O
	) O
	and O
	MIKE B-Material
	FLOOD I-Material
	( O
	DHI O
	Software O
	, O
	2014 O
	; O
	Hénonin O
	et O
	al. O
	, O
	2013 O
	) O
	. O
	These O
	last O
	two O
	models O
	are O
	commercial O
	packages O
	, O
	and O
	the O
	code O
	applied O
	in O
	the O
	optimisation O
	techniques O
	is O
	not O
	in O
	the O
	public O
	domain O
	. O

	The O
	purported O
	advantages O
	of O
	EMR B-Task
	implementation I-Task
	in O
	urban O
	slums O
	are O
	widely O
	promoted O
	. O
	Increasingly O
	capable O
	health B-Task
	information I-Task
	systems I-Task
	could O
	facilitate O
	communication O
	, O
	help O
	coordinate O
	care O
	, O
	and O
	improve O
	the O
	continuity O
	of O
	care O
	in O
	disadvantaged O
	communities O
	like O
	Kibera O
	. O
	However O
	, O
	available O
	systems O
	may O
	not O
	have O
	the O
	ability O
	to O
	simplify O
	care O
	or O
	improve O
	efficiency O
	where O
	funding O
	and O
	human O
	resources O
	are O
	scarce O
	, O
	infrastructure O
	is O
	unreliable O
	and O
	health O
	data O
	demands O
	are O
	opportunistic O
	, O
	not O
	strategic O
	. O
	This O
	study O
	described O
	perceptions O
	of O
	local O
	EMR B-Process
	stakeholders O
	in O
	two O
	urban O
	slum O
	clinics O
	. O
	They O
	shared O
	many O
	observations O
	that O
	may O
	be O
	important O
	for O
	other O
	EMR O
	initiatives O
	to O
	heed O
	, O
	and O
	worried O
	most O
	about O
	the O
	sustainability O
	of O
	EMR O
	initiatives O
	in O
	like O
	communities O
	. O
	The O
	future O
	for O
	EMR O
	use O
	in O
	urban O
	slums O
	is O
	promising O
	. O
	Innovative O
	new O
	technologies O
	, O
	such O
	as O
	mobile B-Material
	applications I-Material
	and O
	point-of-care O
	laboratory O
	tests O
	, O
	could O
	extend O
	the O
	reach O
	of O
	EMRs B-Process
	where O
	infrastructure O
	is O
	wanting O
	. O
	New O
	cloud-based B-Process
	EMR I-Process
	ecosystems I-Process
	, O
	where O
	data O
	is O
	collected O
	and O
	stored O
	centrally O
	could O
	leverage O
	cell O
	phone O
	networks O
	to O
	promote O
	more O
	health O
	information O
	sharing O
	, O
	coordination O
	of O
	care O
	and O
	ultimately O
	better O
	outcomes O
	for O
	vulnerable O
	populations.Summary O
	pointsWhat O
	was O
	already O
	known O
	on O
	the O
	topic O
	?• O
	Rapid O
	urbanization O
	is O
	associated O
	with O
	growth O
	in O
	the O
	number O
	and O
	size O
	of O
	urban O
	slums O
	and O
	an O
	associated O
	rise O
	in O
	the O
	burden O
	of O
	disease O
	, O
	further O
	worsening O
	an O
	already O
	fragmented O
	and O
	inefficient O
	health O
	care O
	system O
	. O

	As O
	future O
	work O
	on O
	the O
	protocol B-Task
	, O
	we O
	would O
	promote O
	two O
	items O
	. O
	Firstly O
	, O
	the O
	two O
	mobility B-Process
	models I-Process
	that O
	we O
	have O
	considered O
	in O
	this O
	work O
	propose O
	possible O
	way O
	to O
	capture O
	social O
	context O
	in O
	the O
	way O
	nodes B-Material
	move O
	in O
	the O
	physical B-Material
	space I-Material
	, O
	yet O
	still O
	potentially O
	allowing O
	nodes B-Material
	to O
	explore O
	the O
	geographical B-Material
	regions I-Material
	considered O
	in O
	its O
	entirety O
	. O
	Further O
	insights O
	to O
	the O
	performance B-Material
	potential I-Material
	could O
	be O
	given O
	through O
	the O
	assessment O
	of O
	the O
	protocol O
	with O
	other O
	mobilities O
	that O
	can O
	extend B-Process
	the I-Process
	physical I-Process
	region I-Process
	of I-Process
	movement I-Process
	as O
	well O
	as O
	impose B-Process
	potential I-Process
	restrictions I-Process
	on O
	the O
	nodes B-Material
	mobility O
	, O
	for O
	example O
	by O
	forcing B-Process
	similar I-Process
	nodes I-Process
	to I-Process
	move I-Process
	within I-Process
	specifically I-Process
	defined I-Process
	areas I-Process
	. O
	Secondly O
	, O
	the O
	different O
	forwarding B-Process
	modes I-Process
	introduced O
	in O
	Section O
	3.3 O
	express O
	different O
	levels O
	of O
	cooperation O
	across O
	the O
	network B-Process
	. O
	The O
	push-community B-Process
	mode I-Process
	, O
	for O
	example O
	, O
	is O
	a O
	form O
	of O
	interest-community O
	selfishness O
	and O
	assumes O
	reciprocation O
	in O
	the O
	nodes’ O
	behaviour O
	. O
	The O
	vulnerability O
	( O
	resp. O
	resilience O
	) O
	of O
	the O
	protocol O
	to O
	different O
	instances O
	of O
	node O
	misbehaviours O
	is O
	a O
	research O
	item O
	worth O
	exploring O
	. O

	The O
	proposed O
	multihop B-Process
	routing I-Process
	protocol I-Process
	, O
	PHASeR B-Process
	, O
	applies O
	the O
	technique O
	of O
	blind O
	forwarding O
	in O
	a O
	MWSN B-Material
	, O
	which O
	increases O
	the O
	reliability O
	of O
	data O
	delivery O
	through O
	its O
	inherent O
	use O
	of O
	multiple O
	routes O
	. O
	This O
	approach O
	requires O
	a O
	gradient B-Process
	metric I-Process
	to O
	be O
	continuously O
	maintained O
	, O
	which O
	is O
	problematic O
	in O
	a O
	dynamic O
	topology O
	. O
	The O
	literature O
	commonly O
	uses O
	either O
	flooding O
	or O
	location O
	awareness O
	, O
	however O
	flooding O
	creates O
	large O
	amounts O
	of O
	overhead O
	and O
	location O
	determination O
	schemes O
	can O
	often O
	be O
	inaccurate O
	, O
	power O
	hungry O
	and O
	create O
	the O
	issue O
	of O
	the O
	dead O
	end O
	problem O
	. O
	PHASeR B-Process
	uses O
	a O
	novel O
	method O
	of O
	gradient B-Process
	maintenance I-Process
	in O
	a O
	mobile B-Material
	network I-Material
	, O
	which O
	requires O
	the O
	proactive O
	sharing O
	of O
	only O
	local O
	topology O
	information O
	. O
	This O
	is O
	facilitated O
	by O
	a O
	global O
	TDMA B-Process
	( O
	time B-Process
	division I-Process
	multiple I-Process
	access I-Process
	) O
	MAC B-Process
	( O
	medium B-Process
	access I-Process
	control I-Process
	) O
	layer O
	and O
	further O
	reduces O
	the O
	amount O
	of O
	overhead O
	, O
	which O
	in O
	turn O
	will O
	decrease O
	packet B-Material
	latency I-Material
	. O
	PHASeR B-Process
	is O
	also O
	set O
	apart O
	by O
	its O
	use O
	of O
	encapsulation B-Process
	, O
	which O
	allows O
	data O
	from O
	multiple O
	nodes B-Material
	to O
	be O
	transmitted O
	in O
	the O
	same O
	packet O
	in O
	order O
	to O
	handle O
	high O
	volumes O
	of O
	traffic O
	. O
	It O
	utilises O
	node B-Process
	cooperation I-Process
	to O
	create O
	a O
	robust O
	multipath O
	routing O
	solution O
	. O
	As O
	such O
	, O
	the O
	contribution O
	of O
	this O
	paper O
	is O
	a O
	cross-layer B-Process
	routing I-Process
	protocol I-Process
	for O
	MWSNs B-Material
	that O
	can O
	handle O
	the O
	constant O
	flow O
	of O
	data O
	from O
	sensors B-Material
	in O
	highly O
	mobile O
	situations O
	. O

	Superconductivity B-Process
	in O
	actinides B-Material
	was O
	first O
	observed O
	in O
	thorium B-Material
	metal I-Material
	in O
	1929 O
	[ O
	7 O
	] O
	, O
	then O
	in O
	elemental O
	uranium B-Material
	in O
	1942 O
	[ O
	8 O
	] O
	, O
	and O
	in O
	uranium B-Material
	compounds I-Material
	in O
	1958 O
	[ O
	9 O
	] O
	. O
	A O
	new O
	class O
	of O
	uranium B-Material
	superconductors I-Material
	emerged O
	in O
	the O
	1980 O
	's O
	with O
	the O
	discovery O
	of O
	uranium B-Material
	heavy I-Material
	fermion I-Material
	superconductors I-Material
	[ O
	10 O
	] O
	. O
	Further O
	surprises O
	came O
	at O
	the O
	beginning O
	of O
	the O
	century O
	with O
	the O
	discovery O
	of O
	ferromagnetic B-Material
	superconductors I-Material
	in O
	uranium B-Material
	systems O
	[ O
	11 O
	] O
	and O
	the O
	first O
	observation O
	of O
	superconductivity B-Process
	in O
	plutonium B-Material
	[ O
	12 O
	] O
	and O
	neptunium B-Material
	[ O
	13 O
	] O
	compounds O
	. O
	The O
	actinides B-Material
	( O
	or O
	actinoids B-Material
	) O
	are O
	located O
	at O
	the O
	end O
	of O
	the O
	periodic B-Process
	table I-Process
	( O
	N O
	= O
	89 O
	( O
	Ac B-Material
	) O
	or O
	90 O
	( O
	Th B-Material
	) O
	to O
	103 O
	( O
	Lr B-Material
	)) O
	. O
	Transuranium B-Material
	elements I-Material
	( O
	or O
	transuranics B-Material
	) O
	are O
	the O
	chemical O
	elements O
	with O
	atomic O
	number O
	( O
	Z O
	) O
	greater O
	than O
	92 O
	( O
	uranium B-Material
	) O
	and O
	due O
	to O
	their O
	short O
	half-life O
	on O
	a O
	geological O
	timescale O
	, O
	they O
	are O
	essentially O
	synthetic B-Material
	elements I-Material
	. O
	Above O
	Z O
	= O
	103 O
	( O
	Lr B-Material
	) O
	, O
	one O
	talks O
	about O
	transactinides B-Material
	( O
	or O
	superactinides B-Material
	) O
	elements O
	. O
	These O
	latter O
	elements O
	have O
	extremely O
	short O
	half-lives O
	and O
	no O
	macroscopic O
	quantity O
	is O
	available O
	for O
	the O
	study B-Task
	of I-Task
	condensed-matter I-Task
	properties I-Task
	. O

	PV B-Task
	cells I-Task
	are O
	one O
	of O
	the O
	most O
	promising O
	technologies O
	for O
	conversion B-Process
	of I-Process
	incident I-Process
	solar I-Process
	radiation I-Process
	into I-Process
	electric I-Process
	power I-Process
	. O
	However O
	, O
	this O
	technology O
	is O
	still O
	far O
	from O
	being O
	able O
	to O
	compete O
	with O
	fossil B-Process
	fuel-based I-Process
	energy I-Process
	conversion I-Process
	technologies O
	because O
	of O
	its O
	relatively O
	low O
	efficiency O
	and O
	energy O
	density O
	. O
	Theoretically O
	, O
	there O
	are O
	three O
	unavoidable O
	losses O
	that O
	limit O
	the O
	solar B-Process
	conversion I-Process
	efficiency O
	of O
	a O
	device O
	with O
	a O
	single O
	absorption B-Process
	threshold O
	or O
	band O
	gap O
	Eg O
	: O
	( O
	1 O
	) O
	incomplete B-Process
	absorption I-Process
	, O
	where O
	photons B-Material
	with O
	energies O
	below O
	Eg O
	are O
	not O
	absorbed O
	; O
	( O
	2 O
	) O
	thermalization B-Process
	or O
	carrier B-Process
	cooling I-Process
	, O
	where O
	solar B-Material
	photons I-Material
	with O
	sufficient O
	energy O
	generate O
	electron-hole B-Material
	pairs I-Material
	and O
	then O
	immediately O
	lose O
	almost O
	all O
	energy O
	in O
	excess O
	of O
	Eg O
	in O
	the O
	form O
	of O
	heat O
	; O
	and O
	( O
	3 O
	) O
	radiative B-Process
	recombination I-Process
	, O
	where O
	a O
	small O
	fraction O
	of O
	the O
	excited O
	states O
	radioactively O
	recombine O
	with O
	the O
	ground O
	state O
	at O
	the O
	maximum O
	power O
	output O
	( O
	Hanna O
	& O
	Nozik O
	, O
	2006 O
	; O
	Henry O
	, O
	1980 O
	) O
	. O
	Taking O
	an O
	air O
	mass O
	of O
	1.5 O
	as O
	an O
	example O
	, O
	for O
	different O
	band O
	gap O
	Eg O
	these O
	three O
	losses O
	can O
	be O
	calculated O
	and O
	the O
	results O
	are O
	indicated O
	by O
	areas O
	S1 O
	, O
	S2 O
	, O
	and O
	S3 O
	in O
	Fig. O
	1. O
	Note O
	that O
	the O
	area O
	under O
	the O
	outer O
	curve O
	is O
	the O
	solar B-Process
	power I-Process
	per O
	unit O
	area O
	, O
	and O
	that O
	only O
	S4 O
	can O
	be O
	delivered O
	to O
	the O
	load O
	. O

	Xylanases B-Material
	have O
	potential O
	applications O
	in O
	various O
	fields O
	. O
	Some O
	of O
	the O
	important O
	applications O
	are O
	as O
	fallows O
	. O
	Xylanases O
	are O
	used O
	as O
	bleaching B-Material
	agent I-Material
	in O
	the O
	pulp B-Task
	and I-Task
	paper I-Task
	industry I-Task
	. O
	Mostly O
	they O
	are O
	used O
	to O
	hydrolyzed B-Process
	the I-Process
	xylan I-Process
	component I-Process
	from O
	wood B-Material
	which O
	facilitate O
	in O
	removal B-Process
	of I-Process
	lignin I-Process
	( O
	Viikari O
	, O
	Kantelinen O
	, O
	Buchert O
	, O
	& O
	Puls O
	, O
	1994 O
	) O
	. O
	It O
	also O
	helps O
	in O
	brightening B-Process
	of I-Process
	the I-Process
	pulp I-Process
	to O
	avoid O
	the O
	chlorine B-Process
	free I-Process
	bleaching I-Process
	operations I-Process
	( O
	Paice O
	, O
	Jurasek O
	, O
	Ho O
	, O
	Bourbonnais O
	, O
	& O
	Archibald O
	, O
	1989 O
	) O
	. O
	In O
	bakeries O
	the O
	xylanase B-Material
	act O
	on O
	the O
	gluten O
	fraction O
	of O
	the O
	dough O
	and O
	help O
	in O
	the O
	even B-Process
	redistribution I-Process
	of I-Process
	the I-Process
	water I-Process
	content I-Process
	of I-Process
	the I-Process
	bread I-Process
	( O
	Wong O
	& O
	Saddler O
	, O
	1992 O
	) O
	. O
	Xylanases B-Material
	also O
	have O
	potential O
	application O
	in O
	animal B-Task
	feed I-Task
	industry I-Task
	. O
	They O
	are O
	used O
	for O
	the O
	hydrolysis B-Task
	of I-Task
	non-starchy I-Task
	polysaccharides I-Task
	such O
	as O
	arabinoxylan B-Material
	in O
	monogastric O
	diets O
	( O
	Walsh O
	, O
	Power O
	, O
	& O
	Headon O
	, O
	1993 O
	) O
	. O
	Xylanases B-Material
	also O
	play O
	a O
	key O
	role O
	in O
	the O
	maceration O
	of O
	vegetable O
	matter O
	( O
	Beck O
	& O
	Scoot O
	, O
	1974 O
	) O
	, O
	protoplastation O
	of O
	plant O
	cells O
	, O
	clarification O
	of O
	juices O
	and O
	wine O
	( O
	Biely O
	, O
	1985 O
	) O
	liquefaction O
	of O
	coffee O
	mucilage O
	for O
	making O
	liquid O
	coffee O
	, O
	recovery O
	of O
	oil O
	from O
	subterranian O
	mines O
	, O
	extraction O
	of O
	flavors O
	and O
	pigments O
	, O
	plant O
	oils O
	and O
	starch O
	( O
	McCleary O
	, O
	1986 O
	) O
	and O
	to O
	improve O
	the O
	efficiency O
	of O
	agricultural O
	silage O
	production O
	( O
	Wong O
	& O
	Saddler O
	, O
	1992 O
	) O
	. O

	ObjectiveElectrically O
	evoked O
	auditory O
	steady-state O
	responses O
	( O
	EASSRs O
	) O
	are O
	neural O
	potentials O
	measured O
	in O
	the O
	electroencephalogram O
	( O
	EEG O
	) O
	in O
	response O
	to O
	periodic O
	pulse O
	trains O
	presented O
	, O
	for O
	example O
	, O
	through O
	a O
	cochlear O
	implant O
	( O
	CI O
	) O
	. O
	EASSRs O
	could O
	potentially O
	be O
	used O
	for O
	objective O
	CI O
	fitting O
	. O
	However O
	, O
	EEG O
	signals O
	are O
	contaminated O
	with O
	electrical O
	CI O
	artifacts O
	. O
	In O
	this O
	paper O
	, O
	we O
	characterized O
	the O
	CI O
	artifacts O
	for O
	monopolar O
	mode O
	stimulation O
	and O
	evaluated O
	at O
	which O
	pulse O
	rate O
	, O
	linear O
	interpolation O
	over O
	the O
	signal O
	part O
	contaminated O
	with O
	CI O
	artifact O
	is O
	successful.MethodsCI O
	artifacts O
	were O
	characterized O
	by O
	means O
	of O
	their O
	amplitude O
	growth O
	functions O
	and O
	duration.ResultsCI O
	artifact O
	durations O
	were O
	between O
	0.7 O
	and O
	1.7ms O
	, O
	at O
	contralateral O
	recording O
	electrodes O
	. O
	At O
	ipsilateral O
	recording O
	electrodes O
	, O
	CI O
	artifact O
	durations O
	are O
	range O
	from O
	0.7 O
	to O
	larger O
	than O
	2ms.ConclusionAt O
	contralateral O
	recording O
	electrodes O
	, O
	the O
	artifact O
	was O
	shorter O
	than O
	the O
	interpulse O
	interval O
	across O
	subjects O
	for O
	500pps O
	, O
	which O
	was O
	not O
	always O
	the O
	case O
	for O
	900pps.SignificanceCI O
	artifact-free O
	EASSRs O
	are O
	crucial O
	for O
	reliable O
	CI O
	fitting O
	and O
	neuroscience O
	research O
	. O
	The O
	CI O
	artifact O
	has O
	been O
	characterized O
	and O
	linear O
	interpolation O
	allows O
	to O
	remove O
	it O
	at O
	contralateral O
	recording O
	electrodes O
	for O
	stimulation O
	at O
	500pps O
	. O

	One O
	way O
	to O
	enforce O
	this O
	ratio O
	is O
	to O
	use O
	a O
	probabilistic B-Process
	, I-Process
	‘ I-Process
	roulette I-Process
	wheel’ I-Process
	style I-Process
	lane B-Task
	selection I-Task
	policy I-Task
	. O
	VISSIM B-Process
	, O
	along O
	with O
	most O
	simulation B-Process
	toolkits I-Process
	, O
	offers O
	methods O
	to O
	specify O
	probabilistic B-Process
	routing I-Process
	whereby O
	a O
	defined O
	percentage O
	of O
	vehicles O
	are O
	sent O
	down O
	unique O
	routes O
	. O
	This O
	is O
	a O
	piecewise B-Process
	technique I-Process
	that O
	can O
	be O
	reapplied O
	at O
	various O
	locations O
	around O
	a O
	simulation B-Process
	. O
	While O
	these O
	methods O
	are O
	attractive O
	from O
	a O
	calibration O
	perspective O
	as O
	exact O
	representations O
	of O
	existing O
	statistics O
	can O
	be O
	ensured O
	, O
	the O
	process O
	is O
	an O
	unrealistic O
	one O
	as O
	it O
	assumes O
	that O
	drivers B-Material
	make O
	probabilistic O
	decisions O
	at O
	precise O
	locations O
	. O
	So O
	in O
	this O
	case O
	when O
	a O
	vehicle B-Material
	arrives O
	at O
	a O
	point O
	prior O
	to O
	the O
	weighbridges B-Material
	it O
	is O
	allocated O
	one O
	of O
	the O
	lanes O
	based O
	on O
	the O
	respective O
	probabilities O
	. O
	It O
	turns O
	out O
	that O
	this O
	method O
	leads O
	to O
	significant O
	variations O
	in O
	trip O
	times O
	depending O
	on O
	the O
	initial O
	random O
	number O
	seed O
	, O
	this O
	can O
	be O
	seen O
	in O
	a O
	graphic O
	of O
	the O
	key O
	areas O
	of O
	the O
	simulation O
	for O
	the O
	2 O
	different O
	runs O
	( O
	Fig. O
	7 O
	) O
	. O
	One O
	of O
	the O
	benefits O
	of O
	graphical B-Process
	microsimulation I-Process
	is O
	that O
	the O
	2D B-Process
	and I-Process
	3D I-Process
	simulations I-Process
	help O
	the O
	researcher O
	to O
	visualise O
	a O
	new O
	scheme O
	and O
	its O
	potential O
	benefits O
	but O
	also O
	to O
	highlight O
	unrealistic O
	behaviour O
	. O
	Fig. O
	7 O
	shows O
	the O
	congestion O
	at O
	the O
	decision O
	point O
	for O
	2 O
	different O
	runs O
	. O
	Using O
	probabilistic B-Process
	routing I-Process
	to O
	enforce O
	correct O
	routing O
	percentages O
	is O
	a O
	clear O
	case O
	of O
	overcalibration O
	affecting O
	simulation B-Process
	brittleness O
	. O

	A O
	few O
	studies O
	within O
	the O
	physiological O
	domain O
	are O
	of O
	special O
	relevance O
	to O
	this O
	work O
	. O
	These O
	include O
	a O
	performance O
	analysis O
	of O
	a O
	blood-flow B-Process
	LB I-Process
	solver I-Process
	using O
	a O
	range O
	of O
	sparse O
	and O
	non-sparse O
	geometries O
	[ O
	21 O
	] O
	and O
	a O
	performance B-Process
	prediction I-Process
	model I-Process
	for O
	lattice-Boltzmann B-Process
	solvers I-Process
	[ O
	22,23 O
	] O
	. O
	This O
	performance B-Process
	prediction I-Process
	model I-Process
	can O
	be O
	applied O
	largely O
	to O
	our O
	HemeLB B-Process
	application I-Process
	, O
	although O
	HemeLB O
	uses O
	a O
	different O
	decomposition O
	technique O
	and O
	performs O
	real-time O
	rendering O
	and O
	visualisation O
	tasks O
	during O
	the O
	LB B-Process
	simulations I-Process
	. O
	Mazzeo O
	and O
	Coveney O
	[ O
	1 O
	] O
	studied O
	the O
	scalability O
	of O
	an O
	earlier O
	version O
	of O
	HemeLB B-Process
	. O
	However O
	, O
	the O
	current O
	performance O
	characteristics O
	of O
	HemeLB O
	are O
	substantially O
	enhanced O
	due O
	to O
	numerous O
	subsequent O
	advances O
	in O
	the O
	code O
	, O
	amongst O
	others O
	: O
	an O
	improved O
	hierarchical O
	, O
	compressed O
	file O
	format O
	; O
	the O
	use O
	of O
	ParMETIS B-Material
	to O
	ensure O
	good O
	load-balance O
	; O
	the O
	coalesced B-Process
	communication I-Process
	patterns I-Process
	to O
	reduce O
	the O
	overhead O
	of O
	rendering B-Process
	; O
	use O
	of O
	compile-time B-Process
	polymorphism I-Process
	to O
	avoid O
	virtual O
	function O
	calls O
	in O
	inner O
	loops O
	. O

	Although O
	mean-field B-Process
	models I-Process
	have O
	been O
	used O
	in O
	all O
	these O
	settings O
	, O
	little O
	analysis O
	has O
	been O
	done O
	on O
	their O
	behaviour O
	as O
	spatially B-Task
	extended I-Task
	dynamical I-Task
	systems I-Task
	. O
	In O
	part O
	, O
	this O
	is O
	due O
	to O
	their O
	staggering O
	complexity O
	. O
	The O
	Liley B-Process
	model I-Process
	[ O
	15 O
	] O
	considered O
	here O
	, O
	for O
	instance O
	, O
	consists O
	of O
	fourteen O
	coupled O
	Partial B-Material
	Differential I-Material
	Equations I-Material
	( O
	PDEs B-Material
	) O
	with O
	strong O
	nonlinearities O
	, O
	imposed O
	by O
	coupling O
	between O
	the O
	mean O
	membrane B-Material
	potentials I-Material
	and O
	the O
	mean O
	synaptic B-Material
	inputs I-Material
	. O
	The O
	model O
	can O
	be O
	reduced O
	to O
	a O
	system O
	of O
	Ordinary B-Material
	Differential I-Material
	Equations I-Material
	( O
	ODEs B-Material
	) O
	by O
	considering O
	only O
	spatially O
	homogeneous O
	solutions O
	, O
	and O
	the O
	resulting O
	system O
	has O
	been O
	examined O
	in O
	detail O
	using O
	numerical B-Process
	bifurcation I-Process
	analysis I-Process
	( O
	see O
	[ O
	16 O
	] O
	and O
	references O
	therein O
	) O
	. O
	In O
	order O
	to O
	compute O
	equilibria O
	, O
	periodic O
	orbits O
	and O
	such O
	objects O
	for O
	the O
	PDE B-Material
	model I-Material
	, O
	we O
	need O
	a O
	flexible O
	, O
	stable O
	simulation O
	code O
	for O
	the O
	model O
	and O
	its O
	linearization O
	that O
	can O
	run O
	in O
	parallel O
	to O
	scale O
	up O
	to O
	a O
	domain O
	size O
	of O
	about O
	2500cm2 O
	, O
	the O
	size O
	of O
	a O
	full-grown O
	human O
	cortex O
	. O
	We O
	also O
	need O
	efficient B-Task
	, I-Task
	iterative I-Task
	solvers I-Task
	for I-Task
	linear I-Task
	problems I-Task
	with I-Task
	large I-Task
	, I-Task
	sparse I-Task
	matrices I-Task
	. O
	In O
	this O
	paper O
	, O
	we O
	will O
	show O
	that O
	all O
	this O
	can O
	be O
	accomplished O
	in O
	the O
	open-source O
	software O
	package O
	PETSc B-Material
	[ O
	17 O
	] O
	. O
	Our O
	implementation O
	consists O
	of O
	a O
	number O
	of O
	functions O
	in O
	C O
	that O
	are O
	available O
	publicly O
	[ O
	18 O
	] O
	. O

	While O
	virtualization B-Process
	technologies I-Process
	certainly O
	reduce O
	the O
	complexity O
	of O
	using O
	a O
	system O
	, O
	and O
	especially O
	when O
	working O
	across O
	multiple O
	heterogeneous O
	computing O
	environments O
	, O
	they O
	are O
	not O
	widely O
	deployed O
	in O
	high B-Process
	performance I-Process
	computing I-Process
	scenarios O
	. O
	As O
	its O
	name O
	suggest O
	, O
	HPC B-Process
	seeks O
	to O
	obtain O
	maximum O
	performance O
	from O
	computing O
	platforms O
	. O
	Extra O
	software O
	layers O
	impact O
	detrimentally O
	on O
	performance O
	, O
	meaning O
	that O
	in O
	HPC O
	scenarios O
	users O
	typically O
	run O
	the O
	applications B-Material
	as O
	close O
	to O
	the O
	‘ O
	bare O
	metal’ O
	as O
	possible O
	. O
	In O
	addition O
	to O
	the O
	performance O
	degradation O
	introduced O
	by O
	virtualization B-Process
	technologies I-Process
	, O
	choosing O
	what O
	details O
	to O
	abstract O
	in O
	a O
	virtualized B-Material
	interface I-Material
	is O
	itself O
	very O
	important O
	. O
	Grid B-Process
	and I-Process
	cloud I-Process
	computing I-Process
	support O
	different O
	interaction B-Process
	models I-Process
	. O
	In O
	grid B-Process
	computing I-Process
	, O
	the O
	user O
	interacts O
	with O
	an O
	individual O
	resource O
	( O
	or O
	sometimes O
	a O
	broker B-Material
	) O
	in O
	order O
	to O
	launch O
	jobs O
	into O
	a O
	queuing B-Process
	system I-Process
	. O
	In O
	cloud B-Process
	computing I-Process
	, O
	users O
	interact O
	with O
	a O
	virtual B-Material
	server I-Material
	, O
	in O
	effect O
	putting O
	them O
	in O
	control O
	of O
	their O
	own O
	complete O
	operating O
	system O
	. O
	Both O
	of O
	these O
	interaction B-Process
	models I-Process
	put O
	the O
	onus O
	on O
	the O
	user O
	to O
	understand O
	very O
	specific O
	details O
	of O
	the O
	system O
	that O
	they O
	are O
	dealing O
	with O
	, O
	making O
	life O
	difficult O
	for O
	the O
	end O
	user O
	, O
	typically O
	a O
	scientist O
	who O
	wants O
	to O
	progress O
	his O
	or O
	her O
	scientific O
	investigations O
	without O
	any O
	specific O
	usability O
	hurdles O
	obstructing O
	the O
	pathway O
	. O

	FabHemeLB B-Material
	is O
	a O
	Python B-Material
	tool I-Material
	which O
	helps O
	automate B-Task
	the I-Task
	construction I-Task
	and I-Task
	management I-Task
	of I-Task
	ensemble I-Task
	simulation I-Task
	workflows I-Task
	. O
	FabHemeLB B-Material
	is O
	an O
	extended O
	version O
	of O
	FabSim B-Material
	[ O
	27 O
	] O
	configured O
	to O
	handle O
	HemeLB B-Material
	operations O
	. O
	Both O
	FabSim B-Material
	and O
	FabHemeLB B-Material
	help O
	to O
	automate B-Task
	application I-Task
	deployment I-Task
	, I-Task
	execution I-Task
	and I-Task
	data I-Task
	analysis I-Task
	on I-Task
	remote I-Task
	resources I-Task
	. O
	FabHemeLB B-Material
	can O
	be O
	used O
	to O
	compile O
	and O
	build O
	HemeLB B-Material
	on O
	any O
	remote O
	resource O
	, O
	to O
	reuse O
	machine-specific O
	configurations O
	, O
	and O
	to O
	organize B-Process
	and I-Process
	curate I-Process
	simulation I-Process
	data I-Process
	. O
	It O
	can O
	also O
	submit O
	HemeLB B-Material
	jobs O
	to O
	a O
	remote O
	resource O
	specifying O
	the O
	number O
	of O
	cores O
	and O
	the O
	wall O
	clock O
	time O
	limit O
	for O
	completing O
	a O
	simulation B-Process
	. O
	The O
	tool O
	is O
	also O
	able O
	to O
	monitor B-Process
	the I-Process
	queue I-Process
	status I-Process
	on I-Process
	remote I-Process
	resources I-Process
	, O
	fetch B-Process
	results I-Process
	of I-Process
	completed I-Process
	jobs I-Process
	, O
	and O
	can O
	conveniently O
	combine B-Process
	functionalities I-Process
	into I-Process
	single I-Process
	one-line I-Process
	commands I-Process
	. O
	In O
	general O
	, O
	the O
	FabHemeLB B-Material
	commands O
	have O
	the O
	following O
	structure O
	: O

	In O
	this O
	paper O
	, O
	an O
	implementation O
	of O
	a O
	LBP B-Material
	( O
	local B-Material
	binary I-Material
	pattern I-Material
	) O
	based O
	fast O
	face B-Task
	recognition I-Task
	system O
	on O
	symbian B-Material
	platform I-Material
	is O
	presented O
	. O
	First O
	, O
	face O
	in O
	picture O
	taken O
	from O
	camera O
	is O
	detected O
	using O
	AdaBoost B-Process
	algorithm I-Process
	. O
	Second O
	, O
	the O
	pre-processing O
	of O
	the O
	face O
	is O
	done O
	, O
	including O
	eye B-Task
	location I-Task
	, I-Task
	geometric I-Task
	normalization I-Task
	, I-Task
	illumination I-Task
	normalization I-Task
	. O
	During O
	the O
	face B-Task
	preprocessing I-Task
	, O
	a O
	rapid O
	eye B-Task
	location I-Task
	method O
	named O
	ER B-Process
	( O
	Eyeball B-Process
	Search I-Process
	) O
	is O
	proposed O
	and O
	implemented O
	. O
	Last O
	, O
	the O
	improved O
	LBP B-Material
	is O
	adopted O
	for O
	recognition B-Task
	. O
	Although O
	the O
	computational O
	capability O
	of O
	the O
	symbian B-Material
	platform I-Material
	is O
	limited O
	, O
	the O
	experimental O
	results O
	show O
	good O
	performance O
	for O
	recognition O
	rate O
	and O
	time O
	. O
	in O
	pressIn O
	this O
	paper O
	, O
	an O
	implementation O
	of O
	a O
	LBP B-Material
	( O
	local B-Material
	binary I-Material
	pattern I-Material
	) O
	based O
	fast O
	face O
	recognition O
	system O
	on O
	symbian B-Material
	platform I-Material
	is O
	presented O
	. O
	First O
	, O
	face O
	in O
	picture O
	taken O
	from O
	camera O
	is O
	detected O
	using O
	AdaBoost B-Process
	algorithm I-Process
	. O
	Second O
	, O
	the O
	pre-processing B-Task
	of I-Task
	the I-Task
	face I-Task
	is O
	done O
	, O
	including O
	eye B-Task
	location I-Task
	, O
	geometric B-Task
	normalization I-Task
	, O
	illumination B-Task
	normalization I-Task
	. O
	During O
	the O
	face O
	preprocessing B-Process
	, O
	a O
	rapid O
	eye B-Process
	location I-Process
	method I-Process
	named O
	ER B-Process
	( O
	Eyeball B-Process
	Search I-Process
	) O
	is O
	proposed O
	and O
	implemented O
	. O
	Last O
	, O
	the O
	improved O
	LBP B-Material
	is O
	adopted O
	for O
	recognition B-Process
	. O
	Although O
	the O
	computational O
	capability O
	of O
	the O
	symbian B-Material
	platform I-Material
	is O
	limited O
	, O
	the O
	experimental O
	results O
	show O
	good O
	performance O
	for O
	recognition B-Task
	rate O
	and O
	time O
	. O
	in O
	press O

	A O
	sentence B-Task
	alignment I-Task
	model O
	based O
	on O
	combined O
	clues O
	and O
	Kernel B-Process
	Extensional I-Process
	Matrix I-Process
	Matching I-Process
	( O
	KEMM B-Process
	) O
	method O
	is O
	proposed O
	. O
	In O
	this O
	model O
	, O
	a O
	similarity B-Material
	matrix I-Material
	for O
	sentence B-Task
	aligning I-Task
	is O
	formed O
	by O
	the O
	similarities O
	of O
	bilingual O
	sentences O
	calculated O
	by O
	the O
	combined O
	clues O
	, O
	such O
	as O
	lexicon O
	, O
	morphology O
	, O
	length O
	and O
	special O
	symbols O
	, O
	etc. O
	; O
	then O
	this O
	similarity B-Material
	matrix I-Material
	is O
	used O
	to O
	construct B-Process
	a I-Process
	select I-Process
	matrix I-Process
	for O
	sentence B-Task
	aligning I-Task
	; O
	finally O
	, O
	obtains O
	the O
	sentence O
	alignments O
	by O
	KEMM B-Process
	. O
	Experimental O
	results O
	illustrated O
	that O
	our O
	model O
	outperforms O
	over O
	the O
	Gale B-Process
	's I-Process
	system I-Process
	on O
	handling O
	any O
	types O
	of O
	sentence O
	alignments O
	, O
	with O
	30 O
	% O
	total O
	sentence B-Task
	alignment I-Task
	error O
	rate O
	decreasing O
	. O

	In O
	this O
	paper O
	a O
	comparison B-Task
	between I-Task
	two I-Task
	popular I-Task
	feature I-Task
	extraction I-Task
	methods I-Task
	is O
	presented O
	. O
	Scale-invariant B-Process
	feature I-Process
	transform I-Process
	( O
	or O
	SIFT B-Process
	) O
	is O
	the O
	first O
	method O
	. O
	The O
	Speeded B-Process
	up I-Process
	robust I-Process
	features I-Process
	( O
	or O
	SURF B-Process
	) O
	is O
	presented O
	as O
	second O
	. O
	These O
	two O
	methods O
	are O
	tested O
	on O
	set O
	of O
	depth B-Material
	maps I-Material
	. O
	Ten O
	defined O
	gestures O
	of O
	left O
	hand O
	are O
	in O
	these O
	depth O
	maps O
	. O
	The O
	Microsoft B-Process
	Kinect I-Process
	camera I-Process
	is O
	used O
	for O
	capturing O
	the O
	images O
	[ O
	1 O
	] O
	. O
	The O
	Support B-Process
	vector I-Process
	machine I-Process
	( O
	or O
	SVM B-Process
	) O
	is O
	used O
	as O
	classification B-Process
	method O
	. O
	The O
	results O
	are O
	accuracy O
	of O
	SVM B-Process
	prediction O
	on O
	selected O
	images O
	. O

	This O
	figure O
	demonstrates O
	that O
	changes B-Process
	in I-Process
	the I-Process
	measure I-Process
	of I-Process
	bitumen I-Process
	content I-Process
	create O
	sizable O
	differences B-Process
	in I-Process
	the I-Process
	stiffness I-Process
	modulus I-Process
	of I-Process
	asphaltic I-Process
	samples I-Process
	that O
	include O
	waste B-Material
	glass I-Material
	cullet I-Material
	. O
	As O
	the O
	percentage O
	of O
	glass O
	increases O
	, O
	the O
	measure O
	of O
	the O
	stiffness B-Process
	modulus I-Process
	of O
	modified B-Material
	asphalt I-Material
	increases O
	too O
	. O
	But O
	with O
	pass O
	of O
	optimum O
	measure O
	of O
	glass B-Material
	the O
	stiffness B-Process
	modulus I-Process
	of O
	asphaltic B-Material
	samples I-Material
	decrease O
	. O
	This O
	trend O
	in O
	total O
	of O
	percentages O
	of O
	bitumen B-Material
	content O
	is O
	existing O
	. O
	Due O
	to O
	that O
	waste B-Material
	glass I-Material
	cullet I-Material
	has O
	no O
	suction O
	; O
	the O
	trend O
	does O
	not O
	extend O
	to O
	measuring O
	the O
	stiffness B-Process
	modulus I-Process
	of O
	asphaltic B-Material
	samples I-Material
	including O
	waste B-Material
	glass I-Material
	cullet I-Material
	with O
	different O
	percentage O
	of O
	bitumen B-Material
	content O
	. O
	Glass B-Material
	particles I-Material
	do O
	not O
	absorb O
	any O
	bituminous B-Material
	material I-Material
	, O
	so O
	it O
	is O
	necessary O
	to O
	decrease O
	the O
	bitumen B-Material
	content O
	with O
	the O
	addition O
	of O
	glass B-Material
	cullet I-Material
	. O
	According O
	to O
	Fig. O
	2 O
	and O
	the O
	results O
	of O
	the O
	Marshall B-Process
	tests I-Process
	, O
	the O
	optimum O
	bitumen B-Material
	measures O
	decrease O
	significantly O
	in O
	samples O
	that O
	include O
	higher O
	percentages O
	of O
	waste B-Material
	glass I-Material
	cullet I-Material
	. O
	As O
	the O
	percentage O
	of O
	optimum O
	bitumen B-Material
	content O
	is O
	1 O
	% O
	more O
	in O
	samples O
	without O
	waste B-Material
	glass I-Material
	cullet I-Material
	in O
	comparison O
	with O
	saphaltic B-Material
	samples I-Material
	that O
	include O
	20 O
	% O
	waste B-Material
	glass I-Material
	cullet I-Material
	. O
	The O
	stiffness B-Process
	modulus I-Process
	of O
	asphaltic B-Material
	samples I-Material
	that O
	include O
	waste B-Material
	glass I-Material
	cullet I-Material
	increased O
	due O
	to O
	additional O
	interlocking B-Process
	between O
	the O
	aggregate O
	and O
	the O
	angularity O
	of O
	particles O
	of O
	glass B-Material
	cullet I-Material
	content O
	. O
	The O
	increase O
	in O
	the O
	intrusive O
	friction O
	angle O
	because O
	of O
	the O
	glass B-Material
	particles’ I-Material
	increased O
	angularity O
	is O
	the O
	main O
	reason O
	for O
	the O
	addition O
	of O
	the O
	stiffness B-Process
	modulus I-Process
	of O
	asphaltic B-Material
	samples I-Material
	that O
	include O
	waste B-Material
	glass I-Material
	cullet I-Material
	. O
	But O
	as O
	the O
	percentage O
	of O
	glass O
	content O
	reaches O
	greater O
	than O
	15 O
	% O
	, O
	the O
	particles’ O
	abundance O
	cause O
	slip O
	these O
	particles B-Material
	on O
	together O
	. O
	The O
	stiffness B-Process
	modulus I-Process
	of O
	samples O
	decreases O
	as O
	the O
	percentage O
	of O
	glass B-Material
	cullet I-Material
	increases O
	. O
	The O
	variations O
	in O
	the O
	stiffness B-Process
	modulus I-Process
	of O
	asphaltic B-Material
	samples I-Material
	that O
	include O
	different O
	percentages O
	of O
	waste B-Material
	glass I-Material
	cullet I-Material
	at O
	different O
	temperature O
	are O
	shown O
	in O
	Fig. O
	3 O
	. O