bluuebunny/crossref_metadata_2025_split huggingface dataset:
Does generativity matter? A meta-analysis on individual work outcomes
Authors: Wiktorowicz Justyna , Warwas Izabela , Turek Dariusz , Kuchciak Iwa 
Published: September 2022

AbstractOngoing demographic changes and global population ageing require organisations to pay special attention to their employment policies. With working life extension and age management increasingly included in discussions about reactive versus proactive personnel policies, the term ‘generativity’ gains special importance as an approach to managing a generationally diverse workforce. Generativity can be understood as an attitude of openness towards the younger generations that focuses on exchanging values, knowledge, and experiences with them. It is a source of positive emotions and better social relationships, personal fulfilment, good energy, and aliveness. In the paper, generativity is discussed in the framework of two theories: the socio-emotional selectivity theory (SST) and successful ageing theory (SOC). The aim of this paper is to assess the relationship between generativity and individual work outcomes. We considered both in-role and extra-role outcomes analysed in the job context. Meta-analysis is conducted of studies that investigate generativity and its relationships with motivational outcomes (job satisfaction, engagement, work motivation, affective commitment, self-efficacy), cognitive outcomes (attitudes toward retirement, career success, self-control), personal outcomes (wellbeing, health, job strain), relational outcomes and extra-role behaviours (organisational citizenship behaviour and sustainable behaviour). The analysis examines 65 independent samples that included 30,540 individuals, and considers the role of three moderators—the cultural context, the measurement method and age. It demonstrates that generativity has significant and positive motivational, cognitive and extra-role behaviour outcomes for workers and that it improves their well-being.
Anglonormativity in Norwegian language education policy and in the educational trajectories of immigrant adolescents
Authors: Beiler Ingrid Rodrick 
Published: September 2022

AbstractIn the Nordic countries, policy debates about English often highlight the threat of domain loss for national languages, but the high status of English may also have a differential impact on people in Nordic societies. This article investigates a policy gap in Norwegian upper secondary education, whereby an advanced English subject requirement may hinder graduation for immigrant adolescents with little previous English instruction, despite English not being the medium of education in Norway. The aim of the study is to examine the impact of the upper secondary English requirement and of sheltered instruction as a local policy solution for such students. I use nexus analysis (Hult, 2015) to analyze ethnographic data from one upper secondary school that created an ad hoc sheltered English class. Data include field notes, classroom video and audio recordings, language portraits, and interviews with one school leader, one teacher, and six students. I draw on decolonial theory (e.g., García et al., 2021; Santos, 2007), notably Anglonormativity (McKinney, 2017), to trace discursive, interactional, and personal policy scales. I found that the sheltered class reflected discourses of integration and Anglonormativity, but nonetheless offered greater affordances for participation than a mainstream English class. Furthermore, comparing the emphasis on English remediation with students’ broader repertoires surfaced possibilities for reframing students as resourceful multilingual learners. I discuss policy options that might better address underlying issues of epistemic justice, compared to solutions limited to increasing students’ proficiency in languages of power like English.
Distinct-Cluster Tree-Child Phylogenetic Networks and Possible Uses to Study Polyploidy
Authors: Willson Stephen J. 
Published: September 2022

AbstractAs phylogenetic networks become more widely studied and the networks grow larger, it may be useful to “simplify” such networks into especially tractable networks. Recent results have found methods to simplify networks into normal networks. By definition, normal networks contain no redundant arcs. Nevertheless, there may be redundant arcs in networks where speciation events involving allopolyploidy occur. It is therefore desirable to find a different tractable class of networks that may contain redundant arcs. This paper proposes distinct-cluster tree-child networks as such a class, here abbreviated as DCTC networks. They are shown to have a number of useful properties, such as quadratic growth of the number of vertices with the number of leaves. A DCTC network is shown to be essentially a normal network to which some redundant arcs may have been added without losing the tree-child property. Every phylogenetic network can be simplified into a DCTC network depending only on the structure of the original network. There is always a CSD map from the original network to the resulting DCTC network. As a result, the simplified network can readily be interpreted via a “wired lift” in which the original network is redrawn with each arc represented in one of two ways.
4D Flow MRI in the portal venous system: imaging and analysis methods, and clinical applications
Authors: Hyodo Ryota , Takehara Yasuo , Naganawa Shinji 
Published: September 2022

AbstractThus far, ultrasound, CT, and 2D cine phase-contrast MRI has been adopted to evaluate blood flow and vascular morphology in the portal venous system; however, all these techniques have some shortcomings, such as limited field of view and difficulty in accurately evaluating blood flow. A new imaging technique, namely 3D cine phase-contrast (4D Flow) MRI, can acquire blood flow data of the entire abdomen at once and in a time-resolved manner, allowing visual, quantitative, and comprehensive assessment of blood flow in the portal venous system. In addition, a retrospective blood flow analysis, i.e., "retrospective flowmetry," is possible. Although the development of 4D Flow MRI for the portal system has been delayed compared to that for the arterial system owing to the lower flow velocity of the portal venous system and the presence of respiratory artifacts, several useful reports have recently been published as the technology has advanced. In the first part of this narrative review article, technical considerations of image acquisition and analysis methods of 4D Flow MRI for the portal venous system and the validations of their results are described. In the second part, the current clinical application of 4D Flow MRI for the portal venous system is reviewed.
Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis
Authors: Berrone Stefano , Canuto Claudio , Pintore Moreno 
Published: September 2022

AbstractWe consider the discretization of elliptic boundary-value problems by variational physics-informed neural networks (VPINNs), in which test functions are continuous, piecewise linear functions on a triangulation of the domain. We define an a posteriori error estimator, made of a residual-type term, a loss-function term, and data oscillation terms. We prove that the estimator is both reliable and efficient in controlling the energy norm of the error between the exact and VPINN solutions. Numerical results are in excellent agreement with the theoretical predictions.
Sustainable water tariffs and inequality in post-drought Cape Town: exploring perceptions of fairness
Authors: Enqvist Johan , van Oyen Wessel 
Published: September 2022

AbstractFair allocation of diminishing natural resources is increasingly central to sustainability. This includes the allocation of costs related to providing access, such as dams, pipes and pumps delivering clean water. Water tariffs are often designed to both recover these costs, meet social needs of water services to the poor, and incentivise conservation in dry times. However, strained public finances, prolonged droughts and economic inequality can undermine these goals and force prioritisations that many see as unfair. This happened in Cape Town, South Africa, during its 2015–2018 water crisis. This study investigates what residents in three different socioeconomic contexts view as fair water tariffs 1 year after the crisis. Using Q method, we describe five distinct perspectives on fairness: ‘the Insurer’, ‘the Individualist’, ’the Bureaucrat’, ‘the Humanitarian’, and ‘the Prepper’. These, we argue, can help distinguish between different ideas of what fairness implies, and what is required to promote it. We exemplify this by examining how viewpoints might have been shaped by specific communities’ experiences during and after the apartheid state’s discriminatory segregation policies. Using distributive, procedural and interactional interpretations of fairness, we discuss how the complex layers of poverty, inequality, mistrust, privilege and discrimination might produce different experiences and ideas of who should pay for and benefit from water services. Using these insights, we also reflect on the merits of tariffs that emphasise cost recovery and resource conservation over social needs, and the risks this poses for growing informal settlements in climate-stressed cities of the global South.
Graphical abstract
Using Q method, we identified five distinct perspectives with different views on what constitutes a fair water tariff.
Bayesians Still Don’t Learn from Conditionals
Authors: Günther Mario , Trpin Borut 
Published: September 2022

AbstractOne of the open questions in Bayesian epistemology is how to rationally learn from indicative conditionals (Douven, 2016). Eva et al. (Mind 129(514):461–508, 2020) propose a strategy to resolve this question. They claim that their strategy provides a “uniquely rational response to any given learning scenario”. We show that their updating strategy is neither very general nor always rational. Even worse, we generalize their strategy and show that it still fails. Bad news for the Bayesians.
Cerebellar Prediction and Feeding Behaviour
Authors: Iosif Cristiana I. , Bashir Zafar I. , Apps Richard , Pickford Jasmine 
Published: September 2022

Abstract
Given the importance of the cerebellum in controlling movements, it might be expected that its main role in eating would be the control of motor elements such as chewing and swallowing. Whilst such functions are clearly important, there is more to eating than these actions, and more to the cerebellum than motor control. This review will present evidence that the cerebellum contributes to homeostatic, motor, rewarding and affective aspects of food consumption.Prediction and feedback underlie many elements of eating, as food consumption is influenced by expectation. For example, circadian clocks cause hunger in anticipation of a meal, and food consumption causes feedback signals which induce satiety. Similarly, the sight and smell of food generate an expectation of what that food will taste like, and its actual taste will generate an internal reward value which will be compared to that expectation. Cerebellar learning is widely thought to involve feed-forward predictions to compare expected outcomes to sensory feedback. We therefore propose that the overarching role of the cerebellum in eating is to respond to prediction errors arising across the homeostatic, motor, cognitive, and affective domains.
Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects
Authors: Sarker Iqbal H. 
Published: September 2022

AbstractDue to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today’s cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.
A novel dynamic reference point model for preference-based evolutionary multiobjective optimization
Authors: Lin Xin , Luo Wenjian , Gu Naijie , Zhang Qingfu 
Published: September 2022

AbstractIn the field of preference-based evolutionary multiobjective optimization, optimization algorithms are required to search for the Pareto optimal solutions preferred by the decision-maker (DM). The reference point is a type of techniques that effectively describe the preferences of DM. So far, the reference point is either static or interactive with the evolutionary process. However, the existing reference point techniques do not cover all application scenarios. A novel case, i.e., the reference point changes over time due to the environment change, has not been considered. This paper focuses on the multiobjective optimization problems with dynamic preferences of the DM. First, we propose a change model of the reference point to simulate the change of the preference by the DM over time. Then, a dynamic preference-based multiobjective evolutionary algorithm framework with a clonal selection algorithm (ĝa-NSCSA) and a genetic algorithm (ĝa-NSGA-II) is designed to solve such kind of optimization problems. In addition, in terms of practical applications, the experiments on the portfolio optimization problems with the dynamic reference point model are tested. Experimental results on the benchmark problems and the practical applications show that ĝa-NSCSA exhibits better performance among the compared optimization algorithms.
Monodomain Liquid Crystals of Two-Dimensional Sheets by Boundary-Free Sheargraphy
Authors: Cao Min , Liu Senping , Zhu Qingli , Wang Ya , Ma Jingyu , Li Zeshen , Chang Dan , Zhu Enhui , Ming Xin , Puchtler Florian , Breu Josef , Wu Ziliang , Liu Yingjun , Jiang Yanqiu , Xu Zhen , Gao Chao 
Published: September 2022

AbstractEliminating topological defects to achieve monodomain liquid crystals is highly significant for the fundamental studies of soft matter and building long-range ordered materials. However, liquid crystals are metastable and sensitive to external stimuli, such as flow, confinement, and electromagnetic fields, which cause their intrinsic polycrystallinity and topological defects. Here, we achieve the monodomain liquid crystals of graphene oxide over 30 cm through boundary-free sheargraphy. The obtained monodomain liquid crystals exhibit large-area uniform alignment of sheets, which has the same optical polarized angle and intensity. The monodomain liquid crystals provide bidirectionally ordered skeletons, which can be applied as lightweight thermal management materials with bidirectionally high thermal and electrical conductivity. Furthermore, we extend the controllable topology of two-dimensional colloids by introducing singularities and disclinations in monodomain liquid crystals. Topological structures with defect strength from − 2 to + 2 were realized. This work provides a facile methodology to study the structural order of soft matter at a macroscopic level, facilitating the fabrication of metamaterials with tunable and highly anisotropic architectures.
Inflammatory Biomarkers in Elite Cross-Country Skiers After a Competition Season: A Case–Control Study
Authors: Luoto Raakel , Ruuskanen Olli , Ihalainen Johanna K. , Pekkala Satu , Hintikka Jukka , Kanerva Noora , Waris Matti , Heinonen Olli J. , Valtonen Maarit 
Published: September 2022

Abstract
Purpose
Whether elite athletes, who have been exposed to vigorous-intensity exercise combined with other stressors, have elevated systemic low-grade inflammation, remains largely unclear. To address this question, we studied the levels of six inflammatory cytokines as potential biomarkers of a low-grade inflammatory state in elite athletes after an 11-month training and competition season.

Methods
We collected sera from 27 Finnish elite cross-country skiers and 27 gender- and age-matched, moderately-exercising controls. The serum concentrations of C-reactive protein (CRP), interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), glycoprotein acetyls (GlycA), interleukin 10 (IL-10), and interferon gamma (IFN-γ) cytokines were quantified as surrogate markers of low-grade inflammation.

Results
The athletes were found to have significantly lower concentrations of CRP (P = 0.0232) and higher concentrations of IL-10, TNF-α, and IFN-γ (P = 0.0097, P = 0.0256, and P = 0.0185, respectively) than the controls. No significant differences between athletes and controls were detected in the concentrations of IL-6 and GlycA. The inflammatory score (IS) did not differ significantly between athletes and controls.

Conclusion
The results of this study argued against the hypothesis of a significant chronic low-grade inflammation in response to prolonged high-performance exercise among elite endurance athletes.
Medical students’ learning approaches and examination outcomes: longitudinal and cross-sectional studies in a problem-based system
Authors: Maudsley Gillian 
Published: September 2022

AbstractEntwistle learning approaches are an evidence-based lens for analysing and improving student learning. Quantifying potential effects on attainment and in specific medical curriculum types merits further attention. This study aimed to explore medical students’ learning approaches in an integrated, problem-based curriculum, namely their validity, reliability, distribution, and how they change with student progression; their association with satisfaction; their association with cumulative attainment (examinations). Within the pragmatism paradigm, two series of mixed-methods questionnaires were analysed multi-cross-sectionally and longitudinally. Of seven surveys of Liverpool medical students (n ~ 115 to 201 responders, postal) and one of prospective medical students (n ~ 968 responders, on-campus), six included Entwistle 18-item Short RASI—Revised Approaches to Studying Inventory and six included ‘satisfaction’ items. Comparing four entry-cohorts, three academic years (9-year period), four levels (year-groups), and follow-ups allowed: cross-tabulation or correlation of learning approaches with demography, satisfaction, and attainment; principal components analysis of learning approaches; and multiple regression on attainment. Relatively high deep and strategic approach and relatively low surface approach prevailed, with strategic approach predominating overall, and deep and strategic approach waning and surface approach increasing from pre-admission to mid-Year 5. In multivariable analysis, deep approach remained associated with sustained (cumulative) high attainment and surface approach was inversely associated with passing Year 1 examinations first time (adjusted odds ratio = 0.89, p = 0.008), while higher ‘satisfaction’ was associated with higher strategic and lower surface approach but not with attainment. This study illuminates difficulties in maintaining cohesive active learning systems while promoting deep approach, attainment, and satisfaction and dissuading surface approach.
Investigation the performance of PV solar cells in extremely hot environments
Authors: Hassan Mohamed K. , Alqurashi Ibrahim M. , Salama Ahmed E. , Mohamed Ahmed F. 
Published: September 2022

AbstractManufacturers typically define photovoltaic (PV) modules under conventional test settings of 1000 W/m2 at 25 °C, which may not be possible anywhere in the globe, because high ambient temperature is one of the most critical factors affecting photovoltaic solar cell efficiency. In this study, we will investigate the ambient temperature as well as the open circuit voltage, output power, short circuit current, and efficiency in hot regions such as Mecca (Makkah). It also discusses the correlations between PV efficiency, solar radiation, and ambient temperature. On the other hand, the addition of a layer of copper sulphate at a concentration of 1% is investigated. The use of a copper sulphate layer enhanced the PV characteristic in cases where the PV cell temperature was reduced by around (0.3–34.6 °C), while also increasing the open circuit voltage, output power, efficiency, and fill factor.
Internationalization as a Leap of Faith: Arbitration Reforms in China and the Challenges of Implementation
Authors: Huang Kai-Shen 
Published: September 2022

AbstractRecent years have witnessed an increasing trend in Chinese arbitration reform that emulates international norms and practices. This article examines some of these key reform measures and major challenges to their implementation. It explores in both legal and practical terms why most of these reform techniques may remain largely ineffective, showing that engaging in international norms and standards in China can be highly challenging due to their potential illegality, the general lack of institutional capacity to sustain them, and the conflicts of local ideas about the purposes of arbitration. It is thus doubtful whether commitment to satisfying the formal requirements prescribed by the legal reforms would often prevail. When it does, it is questionable whether this form of commitment would become prevalent and how it could proceed in a sustainable and coherent manner from a practical perspective.
Regulatory role of JMJD6 in placental development
Authors: Shen Xiaoli , De Geyter Christian , Zhang Hong , Huang Guoning 
Published: September 2022

AbstractCorrect placental development and function are critical to both the mother's and the foetus' health during pregnancy. Placental function depends on the correct development of the vascular network, which requires proper angiogenesis. Impaired angiogenesis in the placenta can induce foetal growth restriction, preeclampsia, and even foetal death. Placental angiogenesis is finely controlled by ubiquitous and pregnancy-specific angiogenic factors. Jumonji domain-containing protein 6 (JMJD6) is a Fe (II)- and 2-oxoglutarate (2OG)-dependent oxygenase that catalyses arginine demethylation and lysine hydroxylation of histone and non-histone peptides. JMJD6 has been implicated in embryonic development, cellular proliferation and migration, self-tolerance induction in the thymus, and adipocyte differentiation. In this review we present JMJD6's structure and activity, as well as its role in angiogenesis, oxygen sensing, and adverse pregnancy outcomes related to placental development. Understanding the interaction between JMJD6 and other placental factors may identify potential therapeutic targets for correcting abnormal placental angiogenesis and function.