bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design Calculation of Deflection Curves of I‐Section Steel Beam With Longitudinally Profiled Flanges Under Concentrated Load
Authors: Yang Dahai , Wang Siyuan , Xiuli Du , Wang Zhitian 
Published: February 2025

ABSTRACTWelded I‐beams with longitudinally profiled (LP) flanges (where LP steel plates are utilized as flanges) can be used to achieve structural optimization. However, its variable stiffness property poses a challenge for deformation design, which requires the formulation of computational equations. In this paper, the theoretical solution of the deflection curve under concentrated load at any position is derived using the unit load method and the mathematical integration method. The accuracy is verified by comparing with the finite element (FE) model. Further, a coefficient is introduced on the basis of the traditional flexural curve expression, and a simple design formula for the flexural curve is proposed by combining the location and coordinates of the load action, and the fitting coefficient is determined by fitting a nonlinear surface to a large number of theoretical results. The results show that the bending deformation MRE corrected by the bending deformation correction coefficient β is only −2.0%, which shows that the bending deformation correction coefficient has good accuracy and simplicity. The welded I‐beam with LP flanges elastic deformation design formulas established in this paper can be applied to the actual construction design of building and bridge structures, which will also promote the application of such advanced steel products.
Wind Power Plant Site Selection Using Fuzzy AHP and Weighted Fuzzy Overlay
Authors: Döker Mehmet Fatih , Özdoruk Esra , Kırlangıçoğlu Cem , Minaei Masoud 
Published: February 2025

ABSTRACTThe availability of energy is a fundamental prerequisite for technological and economic advancement. Although the majority of contemporary energy requirements are fulfilled by fossil fuels, the transition towards sustainable and efficient energy sources is gathering pace. Wind energy, a renewable energy source, has the potential to significantly contribute to energy independence. The objective of this study is to ascertain the most appropriate locations for the construction of wind power plants in Sakarya Province, thereby facilitating energy independence through an enhanced energy supply. The study identified economic, environmental, and physical criteria and excluded areas and conducted analyses using multicriteria decision‐making methods. The relative importance weights of the criteria were calculated using the fuzzy analytic hierarchy process, and the results obtained were realistic. This was achieved using the weighted fuzzy modeling method. The analysis, which focused on Sakarya Province, revealed that the Kaynarca, Hendek, Söğütlü, Adapazarı, Akyazı, Taraklı, Geyve, and Pamukova districts possess considerable potential for wind energy. The results demonstrate that renewable sources currently account for only 21% of Sakarya's electricity demand. However, the installation of 315 wind turbines has the potential to meet this demand entirely through renewable sources. The estimated cost of these turbines is approximately $1.26 billion, with a required area of 693 ha. The identification of a total of 7541 ha of suitable land indicates that there is sufficient land available for investment. It is recommended that future studies analyze investment and maintenance costs and electricity demand based on population projections.
How Community Members Engage With Wildlife—A Psychological Typology With Implications for Policy Making
Authors: Romero‐de‐Diego Cristina , Mills Morena , Witt Bradd , Dean Angela 
Published: February 2025

ABSTRACTCommunity perceptions influence wildlife‐related management and policy efforts. However, there remains limited research into population‐level social and psychological aspects driving engagement with wildlife and how this shapes support for broader wildlife management approaches. Here we draw on Australian national data to develop a typology of community engagement with wildlife based on cognitive, affective, and behavioral factors, and how they shape support for wildlife management policies. We identified seven groups reflecting different patterns of engagement with wildlife, ranging from Champions to Disengaged. Importantly, respondents who were members of more engaged groups were also more likely to exhibit polarized views toward wildlife management strategies that involve culling or translocating wildlife. Our results suggest that wildlife managers and decision‐makers should not assume that support for wildlife always translates into support for wildlife management or conservation policies. We discuss approaches to communications and conflict management for diverse social groups.
Digitalization, Social Change, and Crime Trends: A Literature Review to Build a Conjecture
Authors: Miró-Llinares Fernando , Aebi Marcelo F. 
Published: February 2025

Abstract
This chapter examines the complex relationship between digitalization and crime trends through a comprehensive literature review, developing a conjecture about how digital transformation has influenced criminal behavior since the 1990s. The authors challenge existing narratives by arguing that digitalization’s impact on crime operates primarily through changes in routine activities and lifestyle patterns, particularly among young people, rather than through direct technological effects. The chapter identifies and addresses three key misunderstandings in previous research: the conflation of cybercrime with digitalization effects, temporal inconsistencies in trend analysis, and oversimplified cause-and-effect relationships. Drawing on routine activity theory and recent research on youth crime trends across multiple countries, the authors propose that digitalization’s primary impact on crime operates through its reduction of unstructured socialization among young people, who increasingly spend time at home engaged in digital activities rather than in potentially criminogenic public spaces. This mechanism helps explain the pronounced decline in youth crime observed across many Western countries since the 1990s. The chapter synthesizes evidence from studies on various aspects of youth behavior, including alcohol consumption, social interaction patterns, and criminal activity, to support this conjecture. The authors argue that while digitalization may not be the sole cause of crime reduction, its role as a significant contributing factor has been systematically underappreciated in criminological research. The chapter concludes by calling for more nuanced research examining the specific mechanisms through which technological change affects criminal opportunities and behavior patterns, particularly among young people.
Crime Opportunities, Lockdowns, and Online Video Games: The Digital Leisure Hypothesis (and More on the Impact of Digitalization on Crime Trends)
Authors: Miró-Llinares Fernando 
Published: February 2025

Abstract
This chapter examines the complex relationship between digital leisure activities and crime patterns through the development and testing of a "digital leisure hypothesis." Drawing on routine activity theory and empirical data from multiple sources, the author argues that increased engagement with digital entertainment, particularly among youth, has contributed to significant shifts in criminal opportunities by altering how people spend their time. The study presents evidence of strong negative correlations between time spent on digital leisure activities and youth crime rates, particularly for offenses like vandalism and burglary. Using data from the American Time Use Survey and Bureau of Justice Statistics (1994-2016), the research demonstrates that higher rates of digitalization are associated with lower arrest rates, with effects being particularly pronounced among young people. The chapter also explores how the COVID-19 pandemic has accelerated these trends by forcing rapid adoption of digital activities across all age groups. The author proposes a dual-effect model where digitalization simultaneously reduces opportunities for traditional street crime by keeping people indoors while creating new opportunities for cybercrime through increased online activity. This is supported by empirical analysis showing exponential growth in cyber-victimization correlating with increased digital technology adoption. The findings suggest that the relationship between digital leisure and crime is not a simple displacement effect, but rather reflects fundamental changes in routine activities and criminal opportunities. The chapter concludes by considering how these trends might evolve in a post-pandemic world, arguing that understanding the relationship between digital leisure and crime patterns will become increasingly crucial for criminological theory and crime prevention strategies.
Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers
Authors: Chen Yile , Lee Kyoungyeul , Woo Junwoo , Kim Dong-wook , Keum Changwon , Babbi Giulia , Casadio Rita , Martelli Pier Luigi , Savojardo Castrense , Manfredi Matteo , Shen Yang , Sun Yuanfei , Katsonis Panagiotis , Lichtarge Olivier , Pejaver Vikas , Seward David J. , Kamandula Akash , Bakolitsa Constantina , Brenner Steven E. , Radivojac Predrag , O’Donnell-Luria Anne , Mooney Sean D. , Jain Shantanu 
Published: February 2025

Abstract
Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.
Mathematics education for sustainable futures: a strengths-based survey of the field to invite further research action
Authors: Makramalla Mariam , Coles Alf , le Roux Kate , Wagner David 
Published: February 2025

Abstract
In this survey, we introduce and prompt an article collection—"Mathematics Education for Sustainable Futures"—which we are guest editing. The collection will comprise original research articles, written and published over a 2-year period. Recognising the collection title as making reference to what is an emerging area of research, in this introduction, we survey work that has been done. In the spirit of opening and inviting new questions and research directions, we focus on what it means to take mathematics education for sustainable futures seriously, for how we practise and imagine mathematics education, including established topics such as curriculum, knowledge, pedagogy, teacher education, language, modelling, and technology. We structure our review around eight invitations (here arranged into six sections) in the call for collection articles. Each section ends with further invitations for potential authors, or others, wanting to locate and chart their own work in the space. We end drawing out two themes that resonate across the invitations: attention to who and what is marginalised and the importance of a deep reflexivity in our choice/use of concepts. This text is part of the article collection entitled “Mathematics Education for Sustainable Futures” (available at https://link.springer.com/collections/acebaagbha).
Aspect-based sentiment classification of user reviews to understand customer satisfaction of e-commerce platforms
Authors: Davoodi Laleh , Mezei József , Heikkilä Markku 
Published: February 2025

Abstract
Making use of user-generated content, in particular customer reviews, is an essential means for companies to gather information about customers. In order to utilize reviews in improving customer satisfaction, companies need to identify the important components of their business that are discussed by the customers. In this article, we propose to use aspect-based sentiment analysis for reviews focusing on e-commerce platforms. Fourteen aspects were extracted from the literature and empirical analysis of 3500 randomly selected reviews from the Trustpilot platform. A unique dataset was created by manually annotating the reviews and assigning a sentiment for each aspect-review pair. Aspect-based sentiment classification was performed using some of the most recent machine learning models, with RoBERTa identified as the best-performing model, achieving over 90% accuracy. We also demonstrate how insights generated from the sentiment scores can assist companies in improving their service and increasing customer satisfaction.
Evaluating Fire Severity in Electric Vehicles and Internal Combustion Engine Vehicles: A Statistical Approach to Heat Release Rates
Authors: Mohd Tohir Mohd Zahirasri , Martín-Gómez César 
Published: February 2025

Abstract
This study provides a comprehensive statistical analysis of heat release rate (HRR) profiles in electric vehicles (EVs) and internal combustion engine (ICE) vehicles, addressing fire safety challenges in performance-based design. Using experimental data, key parameters such as peak heat release rate (PHRR), time to peak heat release rate (TPHRR), total heat released (THR), and growth coefficients were analysed. Results reveal that EVs, exhibit distinct fire dynamics, often displaying higher PHRR values than ICE vehicles, which highlights the potential for greater fire intensity and growth rates in EV fires. A design fire model was constructed based on this analysis, offering fire engineers a probabilistic alternative to conventional deterministic approaches for simulating vehicle fire scenarios in various infrastructural contexts. This probabilistic approach provides a more flexible framework for decision-making in fire risk assessments. Additionally, the study observed a correlation between larger battery sizes and increased fire severity in EVs, though this should be interpreted cautiously given the limited dataset. This work highlights the importance of adapting fire safety standards to keep pace with advancements in vehicle technology, especially with the growing prevalence of EVs. Future research should aim to expand the dataset with more diverse experiments to enhance the robustness of design fire models, supporting the development of tailored fire safety strategies for different vehicle types across various environments.
A Systematic Literature Review of Research-based Interventions and Strategies for Students with Disabilities in STEM and STEAM Education
Authors: Ariza Jonathan Álvarez , Hernández Hernández Carola 
Published: February 2025

Abstract
Statistical studies performed mainly in the U.S. have depicted that students with disabilities (SWDs) are excluded from the educational process and are prone to several gaps and barriers in terms of special accommodations, learning opportunities, and socio-emotional support in Science, Technology, Engineering, Mathematics (STEM)-Science, Technology, Engineering, Arts, Mathematics (STEAM) education. To clarify this, we conducted a systematic literature review focused on interventions and strategies in STEM and STEAM education for SWDs based on 263 studies retrieved from the databases SCOPUS, Web of Science (WoS), and ERIC from 2013–2024. The studies cover proposals from early childhood to tertiary education. After the screening and appraisal stages, 39 interventions with 21 strategies were identified. The outcomes mostly reveal the following: (1) The studies are mainly focused on the U.S. and in students with autism, learning disabilities, or behavioral disorders between low and medium severity levels. (2) Interventions for autistic students use robotics and coding to foster cognitive, social, and communicative skills. (3) Interventions for deaf or hard-of-hearing students focus on creating a science identity and the issues with non-standardized STEM concepts in American sign language (ASL); in contrast, visually impaired students focus on assistive technologies and the accessibility of educational materials. (4) Little attention has been paid to other disabilities apart from autism, intellectual or learning ones, as well as the perspective of educators to support SWDs in classrooms. And (5) New machine learning, metaverse, and AI models are being used to assess the cognitive-emotional states of the SWDs. The conclusions and insights derived from this study can help educators and researchers to create new methodologies or strategies that sustain SWDs in STEM-STEAM education.
Computing Approximate Global Symmetry of Complex Networks with Application to Brain Lateral Symmetry
Authors: Pidnebesna Anna , Hartman David , Pokorná Aneta , Straka Matěj , Hlinka Jaroslav 
Published: February 2025

Abstract
The symmetry of complex networks is a global property that has recently gained attention since MacArthur et al. 2008 showed that many real-world networks contain a considerable number of symmetries. These authors work with a very strict symmetry definition based on the network’s automorphism detecting mostly local symmetries in complex networks. The potential problem with this approach is that even a slight change in the graph’s structure can remove or create some symmetry. Recently, Liu (2020) proposed to use an approximate automorphism instead of strict automorphism. This method can discover symmetries in the network while accepting some minor imperfections in their structure. The proposed numerical method, however, exhibits some performance problems and has some limitations while it assumes the absence of fixed points and thus concentrates only on global symmetries. In this work, we exploit alternative approaches recently developed for treating the Graph Matching Problem and propose a method, which we will refer to as Quadratic Symmetry Approximator (QSA), to address the aforementioned shortcomings. To test our method, we propose a set of random graph models suitable for assessing a wide family of approximate symmetry algorithms. Although our modified method can potentially be applied to all types of symmetries, in the current work we perform optimization and testing oriented towards more global symmetries motivated by testing on the human brain.
Ferric Ammonium Citrate Reduces Claudin-5 Abundance and Function in Primary Mouse Brain Endothelial Cells
Authors: Runwal Pranav , Pyun Jae , Newman Stephanie A. , Mawal Celeste , Bush Ashley I. , Koehn Liam M. , Nicolazzo Joseph A. 
Published: February 2025

Abstract

Background
Iron overload is implicated in many neurodegenerative diseases, where there is also blood–brain barrier (BBB) dysfunction. As there is a growing interest in the role of iron in modulating key BBB proteins, this study assessed the effect of iron on the expression and function of P-glycoprotein (P-gp), breast cancer resistance protein (BCRP) and claudin-5 in primary mouse brain endothelial cells (MBECs) and their abundance in mouse brain microvessel-enriched membrane fractions (MVEFs).

Methods
Following a 48 h treatment with ferric ammonium citrate (FAC, 250 µM), MBEC protein abundance (P-gp, BCRP and claudin-5) and mRNA (abcb1a, abcg2, and cldn5) were assessed by western blotting and RT-qPCR, respectively. Protein function was evaluated by assessing transport of substrates 3H-digoxin (P-gp), 3H-prazosin (BCRP) and 14C-sucrose (paracellular permeability). C57BL/6 mice received iron dextran (100 mg/kg, intraperitoneally) over 4 weeks, and MVEF protein abundance and iron levels (in MVEFs and plasma) were quantified via western blotting and inductively coupled plasma-mass spectrometry (ICP-MS), respectively.

Results
FAC treatment reduced P-gp protein by 50% and abcb1a mRNA by 43%, without affecting 3H-digoxin transport. FAC did not alter BCRP protein or function, but decreased abcg2 mRNA by 59%. FAC reduced claudin-5 protein and cldn5 mRNA by 65% and 70%, respectively, resulting in a 200% increase in 14C-sucrose permeability. In vivo, iron dextran treatment significantly elevated plasma iron levels (2.2-fold) but did not affect brain MVEF iron content or alter P-gp, BCRP or claudin-5 protein abundance.

Conclusions
Iron overload modulates BBB transporters and junction proteins in vitro, highlighting potential implications for CNS drug delivery in neurodegenerative diseases.
Experimental florivory and its effects on pollinators of Opuntia cantabrigiensis Lynch (Cactaceae)
Authors: Cárdenas-Ramos Diana , Mandujano María C. 
Published: February 2025

Abstract
Florivores modify the attributes of flowers while feeding, that affects the behavior and frequency of pollinators that visit flowers and setting of fruits and seed as a result of low pollen deposition. We determined the effect of experimental florivory on pollinators visitations, fruit-set, and seed-set of Opuntia cantabrigiensis. Four treatments were applied on intact flowers during reproductive season: 1) removal of 50% of the perianth, 2) emasculated flower, 3) flower without stigma, perianth and stamens (stigma-perianth) and 4) intact flower-control; selected treatments mimic the types of florivory observed in the population. The frequency of visitation, activity and taxonomic identity of the pollinator were recorded. We found that frequency of pollinators decreased with damage to the flower, with the stigma-perianth treatment receiving the fewest visits. The most frequent pollinators were the solitary bees Macrotera sinaloana, Diadasia rinconis, and Lithurgopsis apicalis (Hymenoptera) that collect rewards and perch on the stigma of flowers. Pollinators responded differently to florivory; Ashmeadiella opuntiae avoided flowers with perianth damage, possibly associating it with a lack of rewards, while D. rinconis prefered such flowers, the removal of this structure facilitated access to floral rewards. Flowers in the stigma-perianth treatment produced seedless fruit. Intact flowers had higher seed production than those with perianth damage or emasculation due to low pollinator visitation and pollen deposition. Opuntia cantabrigiensis is a xenogamous species with dependence on biotic vectors, therefore the damage caused by florivores reduces the visit of their pollinators and its reproductive success, which could negatively affect the population persistence.