bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sleep and health-related quality of life in women following a cancer diagnosis: results from the Women’s Wellness after Cancer Program in Australia
Authors: Edmed Shannon L. , Huda M. Mamun , Smith Simon S. , Seib Charrlotte , Porter-Steele Janine , Anderson Debra , McCarthy Alexandra L. 
Published: November 2022

Abstract
Purpose
Sleep disturbance after cancer treatment could compromise recovery. This paper examined the associations between post-treatment sleep problems and health-related quality of life (HRQoL), and the effectiveness of an e-enabled lifestyle intervention on sleep outcomes.

Methods
The Women’s Wellness after Cancer Program (WWACP) was examined in a single blinded, multi-centre randomised controlled trial. Data were collected from 351 women (Mage = 53.2, SD = 8.8; intervention n = 175, control group n = 176) who had completed surgery, chemotherapy and/or radiotherapy for breast, gynaecological or blood cancers within the previous 24 months. Participants completed the Pittsburgh Sleep Quality Index (PSQI) at baseline (prior to intervention randomisation), and at 12 and 24 weeks later. Sociodemographic information, menopausal symptoms (Greene Climacteric Scale) and HRQoL (36-Item Short Form Health Survey; SF-36) were also collected. Linear panel regression was used to examine the association between sleep variables and SF36 Physical Component Summary (PCS) and Mental Component Summary (MCS) scores. A difference-in-difference regression model approach was used to examine the intervention effect on the sleep outcomes.

Results
After adjustment for potential confounders, the sleep variables (except sleep duration) significantly predicted physical, but not mental, HRQoL. There was no statistically significant effect of the intervention on sleep outcomes at 12 or 24 weeks.

Conclusion
Women who have completed treatment for cancer experience sleep problems that are associated with decreased physical HRQoL. Improving sleep through targeted interventions should improve their physical HRQoL. Improved targeting of the sleep components of the WWACP should be explored.
An exploration of the use of photobiomodulation for management of oral mucositis in children and young people undergoing cancer treatment in the UK
Authors: Heggie Claudia , Gray-Burrows Kara A. , Day Peter F. , Phillips Bob 
Published: November 2022

Abstract
Purpose
Oral mucositis affects up to 80% of children and young people (CYP) receiving chemotherapy. This can result in pain, reduced oral intake and, in severe cases, hospitalisation for parental nutrition and pain relief. Photobiomodulation is recommended by multiple bodies for mucositis management for those undergoing cancer treatments. The current use of photobiomodulation within the UK, and the barriers and facilitators to implementation is unknown.

Method
An online mixed-methods survey was administered to representatives from the Children’s Cancer and Leukaemia Group (CCLG) between October 2021 and March 2022. This explored: use of photobiomodulation, planned future use, barriers and facilitators to implementation and dental assessment. Quantitative data underwent descriptive statistics. Barriers and facilitators to the implementation of photobiomodulation were analysed using the Theoretical Domains Framework (TDF).

Results
All UK CCLG centres responded (n = 20, a response rate of 100%). Two units in Scotland were delivering photobiomodulation. A further four units were planning to implement a service. Most units, 65% (n = 13) utilised specialist Paediatric Dentistry services for dental assessment. In the TDF analysis, five domains were most frequently populated: knowledge, skills, environmental context and resources, social influences, and social/professional role and identity.

Conclusion
Photobiomodulation was only available in Scotland in two children’s cancer units. Lack of knowledge and skills, and insufficient environmental resources were identified as barriers. Collaboration with paediatric dental services was identified as a facilitator. The establishment of a national network of Paediatric Dentists and Oncologists would promote collaboration to standardise protocols and to address the identified barriers to wider implementation of photobiomodulation.
A cross-diffusion system obtained via (convex) relaxation in the JKO scheme
Authors: Ducasse Romain , Santambrogio Filippo , Yoldaş Havva 
Published: November 2022

AbstractIn this paper, we start from a very natural system of cross-diffusion equations, which can be seen as a gradient flow for the Wasserstein distance of a certain functional. Unfortunately, the cross-diffusion system is not well-posed, as a consequence of the fact that the underlying functional is not lower semi-continuous. We then consider the relaxation of the functional, and prove existence of a solution in a suitable sense for the gradient flow of (the relaxed functional). This gradient flow has also a cross-diffusion structure, but the mixture between two different regimes, that are determined by the relaxation, makes this study non-trivial.
On torsional rigidity and ground-state energy of compact quantum graphs
Authors: Mugnolo Delio , Plümer Marvin 
Published: November 2022

AbstractWe develop the theory of torsional rigidity—a quantity routinely considered for Dirichlet Laplacians on bounded planar domains—for Laplacians on metric graphs with at least one Dirichlet vertex. Using a variational characterization that goes back to Pólya, we develop surgical principles that, in turn, allow us to prove isoperimetric-type inequalities: we can hence compare the torsional rigidity of general metric graphs with that of intervals of the same total length. In the spirit of the Kohler-Jobin inequality, we also derive sharp bounds on the ground-state energy of a quantum graph in terms of its torsional rigidity: this is particularly attractive since computing the torsional rigidity reduces to inverting a matrix whose size is the number of the graph’s vertices and is, thus, much easier than computing eigenvalues.
Sulfonated graphene nanomaterials for membrane antifouling, pollutant removal, and production of chemicals from biomass: a review
Authors: Tawfik Ahmed , Eraky Mohamed , Khalil Mohamed N. , Osman Ahmed I. , Rooney David W. 
Published: November 2022

AbstractWater pollution and the unsustainable use of fossil fuel derivatives require advanced catalytic methods to clean waters and to produce fine chemicals from modern biomass. Classical homogeneous catalysts such as sulfuric, phosphoric, and hydrochloric acid are highly corrosive and non-recyclable, whereas heterogeneous catalysts appear promising for lignocellulosic waste depolymerization, pollutant degradation, and membrane antifouling. Here, we review the use of sulfonated graphene and sulfonated graphene oxide nanomaterials for improving membranes, pollutant adsorption and degradation, depolymerization of lignocellulosic waste, liquefaction of biomass, and production of fine chemicals. We also discuss the economy of oil production from biomass. Sulfonated graphene and sulfonated graphene oxide display an unusual large theoretical specific surface area of 2630 m2/g, allowing the reactants to easily enter the internal surface of graphene nanosheets and to reach active acid sites. Sulfonated graphene oxide is hydrophobic and has hydrophilic groups, such as hydroxyl, carboxyl, and epoxy, thus creating cavities on the graphene nanosheet’s surface. The adsorption capacity approached 2.3–2.4 mmol per gram for naphthalene and 1-naphthol. Concerning membranes, we observe an improvement of hydrophilicity, salt rejection, water flux, antifouling properties, and pollutant removal. The nanomaterials can be reused several times without losing catalytic activity due to the high stability originating from the stable carbon–sulfur bond between graphene and the sulfonic group.
Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery
Authors: Keil Tim , Kleikamp Hendrik , Lorentzen Rolf J. , Oguntola Micheal B. , Ohlberger Mario 
Published: November 2022

AbstractIn this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we particularly focus on polymer flooding. The computational approach is based on an adaptive training procedure of a neural network that directly approximates an input-output map of the underlying PDE-constrained optimization problem. The training process thereby focuses on the construction of an accurate surrogate model solely related to the optimization path of an outer iterative optimization loop. True evaluations of the objective function are used to finally obtain certified results. Numerical experiments are given to evaluate the accuracy and efficiency of the approach for a heterogeneous five-spot benchmark problem.
Combining adaptive expertise and (critically) reflective practice to support the development of knowledge, skill, and society
Authors: Ng Stella L , Forsey Jacquelin , Boyd Victoria A , Friesen Farah , Langlois Sylvia , Ladonna Kori , Mylopoulos Maria , Steenhof Naomi 
Published: November 2022

AbstractAdaptive expertise (AE) and reflective practice (RP), two influential and resonant theories of professional expertise and practice in their own right, may further benefit health professions education if carefully combined. The current societal and systemic context is primed for both AE and RP. Both bodies of work position practitioners as agentive, learning continually and thoughtfully throughout their careers, particularly in order to manage unprecedented situations well. Similar on the surface, the roots and practices of AE and RP diverge at key junctures and we will focus on RP’s movement toward critically reflective practice. The roots of AE and RP, and how they relate to or diverge from present-day applications matter because in health professions education, as in all education, paradigmatic mixing should be undertaken purposefully. This paper will explore the need for AE and RP, their shared commitments, distinctive histories, pedagogical possibilities both individually and combined, and next steps for maximizing their potential to positively impact the field. We argue that this exploration is urgently needed because both AE and RP hold much promise for improving health care and yet employing them optimally—whether alone or together—requires understanding and intent. We build an interprofessional education case situated in long-term care, throughout the paper, to demonstrate the potential that AE and RP might offer to health professions education individually and combined. This exploration comes just in time. Within the realities of uncertain practice emphasized by the pandemic, practitioners were also called to act in response to complex and urgent social movements. A combined AE and RP approach, with focus on critically reflective practice in particular, would potentially prepare professionals to respond effectively, compassionately, and equitably to future health and social crises and challenges.
Constructing legitimacy for technologies developed in response to environmental regulation: the case of ammonia emission-reducing technology for the Flemish intensive livestock industry
Authors: van der Velden Daniel , Dessein Joost , Klerkx Laurens , Debruyne Lies 
Published: November 2022

AbstractThis study is focused on unsustainable agri-food systems, especially intensive livestock farming and its resulting environmental harms. Specifically we focus on the development of technologies that seek to mitigate these environmental harms. These technologies are generally developed as incremental innovations in response to government regulation. Critics of these technological solutions allege that these developments legitimate unsustainable food production systems and are incapable of supporting agri-food systems transformation. At the same time, technology developers and other actors seek to present these technologies as the legitimate solution to agri-environmental harms. Our study seeks to explore the perceptions and constructions of legitimacy for technologies that are developed to reduce ammonia emissions in intensive livestock farming in Flanders (Belgium). We use a qualitative case study, employing semi-structured interviews and workshops, with technology developers of ammonia-emission reducing technologies and stakeholders in the intensive livestock farming industry in Flanders. What our study shows is that technologies developed to reduce emissions are dependent on regulative legitimacy. The normative and cognitive legitimacy of these technologies is lacking, both due to ties to the intensive livestock industry and due to uncertainty over the performance of these technologies. With the delegitimation of intensive livestock farming, the legitimacy of these technologies is also under threat. In response, technology developers are looking to (re-)construct this legitimacy through knowledge claims over the performance of their technologies. We show several ways for other actors to deal with this, centred on either re-legitimising technologies to maintain the status quo, or to contest these knowledge claims and use them to disrupt path dependencies.
Not by demography alone: Neanderthal extinction and null hypotheses in paleoanthropological explanation
Authors: Currie Adrian , Meneganzin Andra 
Published: November 2022

AbstractNeanderthal extinction is a matter of intense debate. It has been suggested that demography (as opposed to environment or competition) could alone provide a sufficient explanation for the phenomenon. We argue that demography cannot be a ‘stand-alone’ or ‘alternative’ explanation of token extinctions as demographic features are entangled with competitive and environmental factors, and further because demography should not be conflated with neutrality.
Tubulin Cytoskeleton in Neurodegenerative Diseases–not Only Primary Tubulinopathies
Authors: Cyske Zuzanna , Gaffke Lidia , Pierzynowska Karolina , Węgrzyn Grzegorz 
Published: November 2022

AbstractNeurodegenerative diseases represent a large group of disorders characterized by gradual loss of neurons and functions of the central nervous systems. Their course is usually severe, leading to high morbidity and subsequent inability of patients to independent functioning. Vast majority of neurodegenerative diseases is currently untreatable, and only some symptomatic drugs are available which efficacy is usually very limited. To develop novel therapies for this group of diseases, it is crucial to understand their pathogenesis and to recognize factors which can influence the disease course. One of cellular structures which dysfunction appears to be relatively poorly understood in the light of neurodegenerative diseases is tubulin cytoskeleton. On the other hand, its changes, both structural and functional, can considerably influence cell physiology, leading to pathological processes occurring also in neurons. In this review, we summarize and discuss dysfunctions of tubulin cytoskeleton in various neurodegenerative diseases different than primary tubulinopathies (caused by mutations in genes encoding the components of the tubulin cytoskeleton), especially Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, prion diseases, and neuronopathic mucopolysaccharidoses. It is also proposed that correction of these disorders might attenuate the progress of specific diseases, thus, finding newly recognized molecular targets for potential drugs might become possible.
An Alternative Bootstrap for Proxy Vector Autoregressions
Authors: Bruns Martin , Lütkepohl Helmut 
Published: November 2022

AbstractWe propose a new bootstrap algorithm for inference for impulse responses in structural vector autoregressive models identified with an external proxy variable. Simulations show that the new bootstrap algorithm provides confidence intervals for impulse responses which often have more precise coverage than and similar length to the competing moving-block bootstrap intervals. An empirical example shows how the new bootstrap algorithm can be applied in the context of identifying monetary policy shocks.
Extended loneliness. When hyperconnectivity makes us feel alone
Authors: Candiotto Laura 
Published: November 2022

AbstractIn this paper, I analyse a specific kind of loneliness that can be experienced in the networked life, namely “extended loneliness”. I claim that loneliness—conceived of as stemming from a lack of satisfying relationships to others—can arise from an abundance of connections in the online sphere. Extended loneliness, in these cases, does not result from a lack of connections to other people. On the contrary, it consists in the complex affective experience of both lacking and longing for meaningful relationships while being connected to many people online. The recursive interaction with a digital assistant in a smart flat is my key example for defining the contours of this specific kind of loneliness that emerges when hyperconnectivity becomes pervasive in the user’s daily-life. Drawing on Sherry Turkle’s work and employing the conceptual framework of the extended mind, I analyse the specific characteristics of extended loneliness and explore its phenomenology.
Automating anticorruption?
Authors: Ceva Emanuela , Jiménez María Carolina 
Published: November 2022

AbstractThe paper explores some normative challenges concerning the integration of Machine Learning (ML) algorithms into anticorruption in public institutions. The challenges emerge from the tensions between an approach treating ML algorithms as allies to an exclusively legalistic conception of anticorruption and an approach seeing them within an institutional ethics of office accountability. We explore two main challenges. One concerns the variable opacity of some ML algorithms, which may affect public officeholders’ capacity to account for institutional processes relying upon ML techniques. The other pinpoints the risk that automating certain institutional processes may weaken officeholders’ direct engagement to take forward-looking responsibility for the working of their institution. We discuss why both challenges matter to see how ML algorithms may enhance (and not hinder) institutional answerability practices.
Conditions for finiteness and bounds on moments of record values from iid continuous life distributions
Authors: Rychlik Tomasz , Szymkowiak Magdalena 
Published: November 2022

AbstractWe consider the standard andkth record values arising in sequences of independent identically distributed continuous and positive random variables with finite expectations. We determine necessary and sufficient conditions on the type of recordk, its numbernand moment orderrso that therth moment of thenvalue ofkth record is finite for every parent distribution. Under the conditions we present the optimal upper bounds on these moments expressed in the scale units being the respective powers of the first population moment. The theoretical results are illustrated by some numerical evaluations.
Asymptotic behavior of an intrinsic rank-based estimator of the Pickands dependence function constructed from B-splines
Authors: Bücher Axel , Genest Christian , Lockhart Richard A. , Nešlehová Johanna G. 
Published: November 2022

AbstractA bivariate extreme-value copula is characterized by its Pickands dependence function, i.e., a convex function defined on the unit interval satisfying boundary conditions. This paper investigates the large-sample behavior of a nonparametric estimator of this function due to Cormier et al. (Extremes 17:633–659, 2014). These authors showed how to construct this estimator through constrained quadratic median B-spline smoothing of pairs of pseudo-observations derived from a random sample. Their estimator is shown here to exist whatever the order $$m \ge 3$$

m
≥
3

of the B-spline basis, and its consistency is established under minimal conditions. The large-sample distribution of this estimator is also determined under the additional assumption that the underlying Pickands dependence function is a B-spline of given order with a known set of knots.
Elementary School Students’ Length Estimation Skills—Analyzing a Multidimensional Construct in a Cross-Country Study
Authors: Hoth Jessica , Heinze Aiso , Huang Hsin-Mei E. , Weiher Dana Farina , Niedermeyer Inga , Ruwisch Silke 
Published: November 2022

Abstract
Measurement estimation skills are relevant for our everyday life and should be addressed in the mathematics classroom. Although students’ measurement estimation skills were examined in several empirical studies, a structure model to describe these skills is still missing. The current research aimed at analyzing the structure of length estimation skills as one basic measurement estimation skill. Taking into account the different kinds of estimation situations, a test on length estimation skills was developed and administered in two contrasting samples of 3rd- and 4th-grade students from Germany and Taiwan in two studies. Study 1 (N = 248) followed an exploratory approach to analyze the structure of length estimation skills (research question 1). Exploratory factor analysis suggested a 3-dimensional model of length estimation skills, dividing the dimensions (1) estimating lengths of small objects, (2) estimating lengths of objects that are not small but touchable, and (3) estimating lengths of objects that are not small and not touchable. This 3-dimensional structure could be confirmed using confirmatory factor analyses in study 2 (N = 903). In addition, the test on students’ length estimation skills proved to be partially measurement invariant across the two groups (research question 2). The cross-national comparison (research question 3) revealed differences between Taiwanese and German students’ length estimation skills that correspond to the educational traditions in each country. Taiwanese students reached better results on the dimensions small as well as not small and touchable while German students were ahead in the dimension not small and not touchable.
Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning
Authors: Greis Noel P. , Nogueira Monica L. , Bhattacharya Sambit , Spooner Catherine , Schmitz Tony 
Published: November 2022

AbstractPhysics-guided machine learning (PGML) offers a new approach to stability modeling during machining that leverages experimental data generated during the machining process while incorporating decades of theoretical process modeling efforts. This approach addresses specific limitations of machine learning models and physics-based models individually. Data-driven machine learning models are typically black box models that do not provide deep insight into the underlying physics and do not reflect physical constraints for the modeled system, sometimes yielding solutions that violate physical laws or operational constraints. In addition, acquiring the large amounts of manufacturing data needed for machine learning modeling can be costly. On the other hand, many physical processes are not completely understood by domain experts and have a high degree of uncertainty. Physics-based models must make simplifying assumptions that can compromise prediction accuracy. This research explores whether data generated by an uncertain physics-based milling stability model that is used to train a physics-guided machine learning stability model, and then updated with measured data, domain knowledge, and theory-based knowledge provides a useful approximation to the unknown true stability model for a specific set of factory operating conditions. Four novel strategies for updating the machine learning model with experimental data are explored. These updating strategies differ in their assumptions about and implementation of the type of physics-based knowledge included in the PGML model. Using a simulation experiment, these strategies achieve useful approximations of the underlying true stability model while reducing the number of experimental measurements required for model update.