dataesr/scientific-paragraphs-categorization huggingface dataset:
However, consecutive model verification failed, allowing us to notice that the default rule implied that SERCA should be always TRUE for Calcium_ER to be TRUE. The rule was then corrected to take into account the fact that Calcium_ER should stay TRUE whenever it would reach this value in absence of IP3R1. In the first Jupyter notebook provided as Supplementary Material, we include all the code enabling the verification of our final model, which encompasses 36 unit tests split in four test suites. On a MacBook Pro using macOS 10. 13 High Sierra, with a 2. 3 GHz Intel Core i7 and 16GB 1600 MHz DDR3, all the tests were run in 87s. The four test suites cover the most complex parts of the model, some of them particularly difficult to define. These suites use submodels, whose delineation was guided by known pathways and practical knowledge gained by the modeler during the assembly of the molecular map.
Moreover, the largest bicycle parking in the world was recently opened in Utrecht, showing the city's high cycling ambitions in urban planning  (GemeenteUtrecht, 2019). However, as a result of a constantly increasing number of cyclist, the city faces serious issues of cycling congestion and dangerous traffic situations  (Van Duppen & Spierings, 2013). In Utrecht, both the municipal and the provincial office of Utrecht took part in the Fietstelweek. The Fietstelweek annually collected (local) data on cycling routes and infrastructure during one particular week. Cyclists actively participated in the data collection process by installing a free application on their mobile phone that automatically collected information on their cycling behaviour (including speed, hours and location). The data was subsequently analysed with the use of CyclePrint, an analytical tool developed by the NHTV university of applied sciences that processed, analysed and visualized the collected data  (Cycleprint.
Data collection and analysis Data collection took place between December 2017 and December 2018 and consisted of project documents, websites and grey literature and was complemented with semi-structured interviews and site visits All interviews were conducted face-to-face during which notes were taken. Site visits involved a combination of observing the initiatives in practice, attending meetings with case study actors and informal interactions with case study actors. Table  2  provides a brief overview of our key data sources. Our data analysis followed a three-step approach. Specifically, we used  Manders et al. (2018)  adjusted typology of expectations (Table  1 ) as a frame of reference in our efforts to disentangle the different expectations and the knowledge claims embedded therein, that underpin our smart mobility projects.
Concurrently, BikePredict made a number of efficiency-oriented claims for Keolis as operator of the bike-sharing network, namely to improve general bicycle availability because the algorithm enables more efficient bicycle allocation to stations by the operator; to increase overall use of the system through improved user travel experience and safety, and to reduce the amount of kilometres run by operator trucks and therefore costs based on the algorithm's calculations  (Cherrier, 2016). These expectations were based on three project-level knowledge claims, namely conducting large-scale, real-time and predictive data collection on the location of bicycles and the condition of these bicycles; building a database of users' rental behaviour; and developing an optimised management infrastructure based on the collected data.
This data-based infrastructure was expected to anticipate the optimum number of bicycles to be removed or added for the system's rental locations, and to identify broken bicycles. In essence, BikePredict thus claimed to rationalise the management of urban services through a computational approach to user's behaviours. QUCIT's algorithm was considered of crucial importance to do so: our respondent from Keolis explained that they already collected large amounts of data before they started collaborating with QUCIT, but Keolis did not know how to "make them talk". They only started understanding the bicycle movements they observed and impacts of different variables on bicycle use during the BikePredict project. At a societal-level, BikePredict is advertised on the QuCit website as a tool to enhance the liveability of Bordeaux by making soft and active transport modes more attractive in a city currently dominated with cars.
Enacting knowledge claims in practice The BikePredict project reached some uncontested achievements in practice, especially with regard to project-level expectations: it produced real-time and predictive information about bicycle locations and rental behaviours, which was used for a well-performing infrastructure management for bicycle redistribution and maintenance. In practice, BikePredict was able to analyse large sets of data and visualise the results efficiently, and predicted station needs for re-balancing 12 h ahead accurately  (Cherrier, 2016). 3 This performance in practicerather than a knowledge claim-based expectationwas an important source of legitimacy for the bikesharing network as a whole among users renting bicycles, who were supplied with relevant, real-time, information on bicycle availability at desired hours.
4 Similarly, initial resistance from the workers in charge of maintaining and redistributing the bicycles gradually reduced after discussions between these workers and QuCit software developers resulted in an adjusted design and interface of the BikePredict that better met the needs of the workers. The predictive dimension was equally appreciated by Keolis: their head of marketing particularly stressed that "the prediction, that we tested, has an incredible reliability"  (Jacot, 2017: translation authors). Keolis presented BikePredict as a successful smart mobility experiment fulfilling its function-level expectations in the forms of e. g. increased user-experience but also, an overall expansion of bike-sharing in Bordeaux area. In this context, the bike-sharing network and BikePredict were associated with increased bicycle use by the BikePredict coalition (34 % since 2014 according to the city of Bordeaux lis  (Barthélémy, 2018) ).
The results show that this availability is predicted with an accuracy per hour of 97%, according to Cherrier, CEO of QuCit. 4 The BikePredict system and its algorithm are invisible to the end-users of the bike-sharing network. Hence, the QuCit system only added to the legitimacy base of the bike-sharing network as a whole for this group. cycling was presented as being beneficial for society: "through congestion easing, emissions and noise reduction, public health and infrastructure cost savings, cycling benefits even those who don't practice". The website of the Fietstelweek stated: "the Netherlands is cycling country par excellence, which is positive given that cycling is sustainable and healthy"  (Fietstelweek. nl, 2017a). Interestingly, the societal-level expectation as voiced on the website, were not about promoting cycling, but addressed the barriers that emerge from rising number of cyclists (and the promise of big data in doing so).
A typical argument that we came across in our interviews, was the disconnection of, on the one hand, having a high-quality, and wide-spread cycling infrastructure and, on the other hand, having so little data on cycling behaviour. Indeed, the Fietstelweek's project-level claims of large data collection, data analytics and visualisation were backed by these arguments of 'non-knowing'. In particular, the use of mobile phones to collect data, thus linking the non-smart bicycle with a common, smart application, was perceived as an innovative way to collect cycling data. Indeed, as we show in our theoretical section, hypes commonly fetishize newness and favour it a-priori over the advancement of prevailing solutions  (O'Leary, 2008). With regard to actor-induced legitimacy, the legitimacy of the Fietstelweek expectations was further amassed through a process that was similar to the BikePredict case: by building a heterogeneous coalition of actors that brought together the required capacities to develop and legitimise the Fietstelweek project.
One respondent, however, argued that the data could be used to justify infrastructural changes in hindsight, and illustrated this with an example from the City of Amsterdam. The city used CyclePrint's data to demonstrate that the opening of a cycling tunnel had affected the city's cycling behaviour in a positive way: the route through this tunnel was clearly preferred over other routes. Similar to our BikePredict case such realisation of the project's knowledge claims in practice was an important source of legitimacy for their decisions among urban planners and thereby in Better management of increasing numbers of cyclists turn legitimised the Fietstelweek project.
For example, in the BikePredict case, we witnessed disconnections between the computational and operational logic of project-and function-level expectations and the larger, societal benefits expected at the societal-level. And, where the Fietstelweek voiced expectations of being able to deal with increased number of cyclists and associated dangerous and unfavourable situations, these expectations were closely intertwined with knowledge claims on project level, here large-scale data collection. However, during the process of actualisation, interwoven smart mobility expectations disentangled. Data tools and analytics (part of project-level expectations) were presented as a sine qua non to deal with cycling challenges in the city in both cases: issues with the bike-sharing network in the BikePredict case; and the unforeseen obstacles faced by cyclists who made use of the current infrastructure in increasing numbers in the Fietstelweek.
We particularly observed that actors within these coalitions took on different roles and responsibilities than those which are traditionally considered legitimate. Indeed, the formation of heterogeneous coalitions around urban mobility challenges is not specifictosmart urban mobility in itself, but the shifts in roles that different actors play within smart mobility coalitions canat least in partbe attributed to smart. The idea that data collection and use are politically neutral activities with rightful authority allowed both public and private actors to move into terrain that is sometimes far beyond their usual activities. For example, Bordeaux Metropolis' alliance with Keolis and BikePredict shows how a public authority delegates the design and management of public services to private companies rather than procuring clearly pre-specified products and services.
Background: The 'cycle of care' (COC) pay for performance (PFP) programme, introduced in 2015, has resourced Irish GPs to provide structured care to PCRS eligible patients with type 2 diabetes mellitus (T2DM). Aim: To investigate the effect of COC on management processes. Design & setting: Cross-sectional observational study undertaken with two points of comparison (2014 and 2017) in participating practices (Republic of Ireland general practices), with comparator data from the United Kingdom National Diabetes Audit (UKNDA) 2015-2016. Method: Invitations to participate were sent to practices using a discussion forum for Health One clinical software. Participating practices provided data on the processes of care in the management of patients with T2DM. Data on PCRS eligible patients was extracted from the electronic medical record system of participating practices using secure customised software.
Descriptive analysis, using IBM SPSS Statistics for Windows (version 25), was performed. Results: Of 250 practices invited, 41 practices participated (16. 4%), yielding data from 3146 patients. There were substantial improvements in the rates of recording of glycosylated haemoglobin ([HbA1c]  53. 1%-98. 3%), total cholesterol ([TC] 59. 2%-98. 8%), urinary albumin:creatinine ratio ([ACR] 9. 9%-42. 3%), blood pressure ([BP] 61. 4%-98. 2%), and body-mass index ([BMI] 39. 8%-97. 4%) from 2014 to 2017. For the first time, rates of retinopathy screening (76. 3%), foot review (64. 9%), and influenza immunisation (69. 9%) were recorded. Comparison of 2017 data with UKNDA 2015-2016 was broadly similar.
Participants and recruitment In 2015 there were 2932 GPs and 1700 practice nurses working in 1734 practices in the Republic of Ireland, approximately 94% of which are computerised. 20 It is estimated that Health One clinical software is used by approximately 400 of these practices. 21 Approximately 250 of these use a discussion forum for the software. The percentage of these practices that are registered to provide COC services to their patients is not known. In 2019 in the Republic of Ireland, approximately 2215 GP practices were registered with the Irish PCRS to provide COC services to their patients. 22  letter of invitation to participate in the study was circulated to all practices using the discussion forum in mid-March 2018. Follow-up letters were issued 1 and 2 months later.
Data extraction The study instrument was designed by 2 GPs on the research team to extract the relevant information at the two time points from practices using Health One clinical software. It was initially piloted in six GP practices and was subsequently adapted and refined, based on feedback from the participants. Three types of data were collected: 1. Demographic data on participating GPs and their practices. This included the age of the senior GP lead in the practice, the sex of all participating GPs, and the location of the practice, from which the area deprivation score was calculated. This allowed comparison of data with a nationally representative sample (Supplementary Table  2 ); 2. Demographic data on patients with T2DM. This included the age and sex of patients, and allowed the authors to compare results with those of the UKNDA 2015-2016  18  (Supplementary Table  3 ).
Summary To the authors' knowledge, this is the first study to examine the effect of the COC initiative on the standard of care of patients with T2DM in Irish general practice. Results demonstrate a substantial improvement in the processes of care since the introduction of the programme, such that it is now directly comparable with the UK system, which has been much longer established. As well as improving the rates of recording of important clinical and biochemical parameters, other equally important parameters (such as foot care, influenza vaccination, and retinopathy screening) started to be measured, or those measurements began to be recorded in a manner that allowed subsequent population analysis. Such parameters may have been recorded in the past as free text in the patient's file. Demonstrated improvements in recording essential data as part of care for this sample of patients eligible under the PCRS with T2DM provided by general practice teams is an important improvement in care provided, as is improvement in targets achieved, particularly in TC and BP, but not in HbA1c, levels.
Strengths and limitations Strengths of the present study include the use of appropriate methodology, delivering clear results in a very important area of care. The authors used an automated data collection process where the data was gathered directly from practice computer systems. The characteristics of the sample of GPs studied were similar to those of a nationally representative sample, making the data generalisable. All patients involved in the programme were analysed. The demographic characteristics of the study patient population was very similar to that of an Irish and UK population with T2DM, with which comparison was made. Results provide a baseline for ongoing monitoring of the COC. Limitations were that the authors only have data from 2 years, and that the low rate of recording in 2014 limits comparisons of all outcomes over time.
Other limitations were that PCRS-ineligible patients in the practices who were not covered by the COC were not studied, and at the time of the study only 45. 5% of patients with T2DM were covered by the COC. This was an observational study with no control group of practices that did not become involved in the COC programme. The study outcomes were limited to the processes of care and measurements, and did not include long-term morbidity and mortality indicators. Another limitation was that the authors only invited practices that used Health One clinical software to participate using a discussion forum. The response rate of 41 practices was only 16. 4% of those eligible to participate.
≤ c 2 f ). The low cut at 100 Hz minimizes low-frequency disturbance in both the DOA and DOD estimation, respectively. DODs and DOAs become θ S (t) = θS (t) θS (t) , with θS (t) = F L P ∑ p=1 F 100-1. 4k h p,0 (t) 2 θ p , ( 6 ) θ R (t) = θR (t) θR (t) , with θR (t) = F L F 100-2. 5k P ∑ p=1 h p,0 (t) F 100-2. 5k P ∑ p=1 h p,XYZ (t) , ( 7 ) where θ p indicates the direction of the p-th loudspeaker, P = 6 is the number of array loudspeakers, h p,0 the RIR between the p-th loudspeaker and the W channel of the ST450 array, • is the norm operator, and h p,XYZ are the first-order channels of the ST450 microphone array. Both the DOA and DOD are computed using a zero-phase band limitation (e. g. , by MATLAB's filtfilt with a 4th-order Butterworth band pass) denoted by F f l -f u and a zero-phase temporal smoothing F L of the resulting estimates using a moving-average Hann window in the interval [-L/2; L/2] for L = 32.
The processing in the Beam Encoding stage is independent of the auralization technique. In the IKO Control stage the (N B + 1) 2 channels are mapped to 20 loudspeaker signals of the IKO using the frequency-dependent IKO Filters. A measurement based approach for designing the multiple-input multiple-output (MIMO) IKO control filters is presented in  [40]. It is based on laser Doppler vibrometry measurements and allows for control of side-lobe suppression and excursion-limiting filter design. The designed beam patterns were verified by far-field extrapolated measurements from a surrounding microphone array. The IKO's beamforming can be analyzed by using the open source tool balloon_holo, which is part of IEM's Open Data Project (https: //opendata. iem. at/projects/dirpat/). All underlying measurements (laser Doppler vibrometry and pressure of a surrounding microphone array) as well as the corresponding IKO Filters can be found online (https://phaidra.
PIV DOA estimation (for omnidirectional source) 14 [b,a] = butter(4,[100 2500]/(fs/2)); % bandpass with fl = 100Hz and fh = 1. 5kHz 15 xbp = filtfilt(b,a,sum(x,3)); ix = xbp(:,1). * xbp(:,2); ix = circshift(fftfilt(win _ dirfluct,ix),-floor(Lsmooth _ dirfluct/2)); iy = xbp(:,1). * xbp(:,3); iy = circshift(fftfilt(win _ dirfluct,iy),-floor(Lsmooth _ dirfluct/2)); iz = xbp(:,1). * xbp(:,4); iz = circshift(fftfilt(win _ dirfluct,iz),-floor(Lsmooth _ dirfluct/2)); azi = atan2(iy, ix); zen = atan2(sqrt(ix. ^2+iy. ^2),iz); 20 Ydoa = getSH(N,[azi,zen],'real'); % from https://github. com/polarch/Spherical-Harmonic-Transform/ 21 22 %% rE-magnitude-based DOD estimation (for omnidirectional receiver) 23 [b,a] = butter(4,[100 1400]/(fs/2)); % bandpass with fl = 100Hz and fh = 1. 4kHz 24 xbp = filtfilt(b,a,squeeze(x(:,1,:))); 25 U = [ 1 0 0; 0 1 0; -1 0 0;.
% 6 array loudspeaker direction vectors (transposed) -1 0; 0 0 -1; 0 0 1]; = xbp. ^2; E = circshift(fftfilt(win _ dirfluct,E),-floor(Lsmooth _ dirfluct/2)); 28 rE = E * U; 29 azi = atan2(rE(:,2), rE(:,1)); zen = atan2(sqrt(sum(rE(:,1:2). ^2,2)),rE(:,3)); 30 Ydod = getSH(N,[azi,zen],'real'); % from https://github. com/polarch/Spherical-Harmonic-Transform/ 31 = (sum(x(:,1,:),3). * Ydoa). * reshape(Ydod,[size(Ydod,1) 1 size(Ydod,2)]); 34 = thirdoctave _ filter _ bank _ linph(Nfft,fs); 37 x _ c = zeros(Nfft,(N+1)^2, (N+1)^2); % corrected upscaled ARIR 38 x _ c(1:size(x,1),1,1) = x(:,1); 39 for k = 1:size(H,2) 40 xthird0 = ifft(fft(x(:,1,1),Nfft). * H(:,k)); 41 xthird0rms = sqrt(circshift(fftfilt(win _ specdecay,xthird0. ^2),-Lsmooth _ specdecay/2)); nidx _ rcv = nrcv^2+(1:2 * nrcv+1); 44 for nsrc = 1:N.
12 13 %% 26 0 27 E 32 %% SRD Upscaling 33 x 35 %% Spectral Decay Correction 36 H 1213262732333536 PIV DOA estimation (for omnidirectional source) 14 [b,a] = butter(4,[100 2500]/(fs/2)); % bandpass with fl = 100Hz and fh = 1. 5kHz 15 xbp = filtfilt(b,a,sum(x,3)); ix = xbp(:,1). * xbp(:,2); ix = circshift(fftfilt(win _ dirfluct,ix),-floor(Lsmooth _ dirfluct/2)); iy = xbp(:,1). * xbp(:,3); iy = circshift(fftfilt(win _ dirfluct,iy),-floor(Lsmooth _ dirfluct/2)); iz = xbp(:,1). * xbp(:,4); iz = circshift(fftfilt(win _ dirfluct,iz),-floor(Lsmooth _ dirfluct/2)); azi = atan2(iy, ix); zen = atan2(sqrt(ix. ^2+iy. ^2),iz); 20 Ydoa = getSH(N,[azi,zen],'real'); % from https://github. com/polarch/Spherical-Harmonic-Transform/ 21 22 %% rE-magnitude-based DOD estimation (for omnidirectional receiver) 23 [b,a] = butter(4,[100 1400]/(fs/2)); % bandpass with fl = 100Hz and fh = 1.
4kHz 24 xbp = filtfilt(b,a,squeeze(x(:,1,:))); 25 U = [ 1 0 0; 0 1 0; -1 0 0;. % 6 array loudspeaker direction vectors (transposed) -1 0; 0 0 -1; 0 0 1]; = xbp. ^2; E = circshift(fftfilt(win _ dirfluct,E),-floor(Lsmooth _ dirfluct/2)); 28 rE = E * U; 29 azi = atan2(rE(:,2), rE(:,1)); zen = atan2(sqrt(sum(rE(:,1:2). ^2,2)),rE(:,3)); 30 Ydod = getSH(N,[azi,zen],'real'); % from https://github. com/polarch/Spherical-Harmonic-Transform/ 31 = (sum(x(:,1,:),3). * Ydoa). * reshape(Ydod,[size(Ydod,1) 1 size(Ydod,2)]); 34 = thirdoctave _ filter _ bank _ linph(Nfft,fs); 37 x _ c = zeros(Nfft,(N+1)^2, (N+1)^2); % corrected upscaled ARIR 38 x _ c(1:size(x,1),1,1) = x(:,1); 39 for k = 1:size(H,2) 40 xthird0 = ifft(fft(x(:,1,1),Nfft). * H(:,k)); 41 xthird0rms = sqrt(circshift(fftfilt(win _ specdecay,xthird0.
This paper introduces the ForestValue research project CLICKdesign -delivering fingertip knowledge to enable service life performance specification of wood. The consortium is working on a primary innovation to move from the complex, fragmented and general to the easy-to-use, consolidated and specific by provision of a digital tool for specifiers. Other materials tackle this and provide designers and architects with software. The specification of performance of wood products is complex and fraught with inconsistency and requires use of multiple platforms for data, experience, standards and national recommendations. CLICKdesign will provide a tool that has within it the decades of research, the complexity of the standards specification systems and the variation of approach due to tradition, materials and culture across Europe and beyond.
-Providing a CLICKdesign tool that utilises European standards, the latest research findings and wood databases such as the IRG Durability Database (http://www. irgwp. com/durability) and puts it at the fingertips of specifiers and users. This will increase market confidence with users for selecting wood as a reliable product and enhance an optimised performance of timber in the built environment. -Features wholly not integrated into specification now. The significant variations due to climate change present changes to exposure dose for wood products and the distribution and locations of wood destroying organisms e. g. termite risk zones in Europe. -Inspiring new wood and wood-based products using the tool, supported by training outreach, and creating business opportunities for industries to innovate.