bluuebunny/crossref_metadata_2025_split huggingface dataset:
The direct sum of q-matroids
Authors: Ceria Michela , Jurrius Relinde 
Published: February 2024

AbstractFor classical matroids, the direct sum is one of the most straightforward methods to make a new matroid out of existing ones. This paper defines a direct sum for q-matroids, the q-analogue of matroids. This is a lot less straightforward than in the classical case, as we will try to convince the reader. With the use of submodular functions and the q-analogue of matroid union we come to a definition of the direct sum of q-matroids. As a motivation for this definition, we show it has some desirable properties.
Academic Integrity Training Module for Academic Stakeholders: IEPAR Framework
Authors: Khan Zeenath Reza 
Published: February 2024

AbstractThe global surge in academic misconduct during the COVID-19 pandemic, exacerbated by remote teaching and online assessment, necessitates a comprehensive understanding of the multidimensional aspects and stakeholders' perspectives associated with this issue. This paper addresses the prevalent use of answer-providing sites and other types of academic misconduct, underscoring the challenge of detecting all or most of the student misconduct. Exploring factors such as faculty inexperience in remote teaching and assessment, the paper advocates for proactive measures to preserve integrity in education. Emphasizing the need for a culture of integrity beyond traditional classrooms, the paper reviews existing models, then details steps to create a framework using the International Labour Organization (ILO)’s TREE training method. It presents the IEPAR framework (Inspiration, Education, Pedagogical considerations, Assessment design, Response and Restorative practice), and assesses its effectiveness. Incorporating faculty feedback, the paper concludes with evidence-based findings, positioning the IEPAR framework as a robust approach for addressing academic misconduct and fostering a culture of academic integrity in higher education through responsible training of all stakeholders.
Waterfowl Eggshell Refines Palaeoenvironmental Reconstruction and Supports Multi-species Niche Construction at the Pleistocene-Holocene Transition in the Levant
Authors: Yeomans Lisa , Codlin Maria C. , Mazzucato Camilla , Dal Bello Federica , Demarchi Beatrice 
Published: February 2024

AbstractUtilising multiple lines of evidence for palaeoenvironmental reconstruction improves our understanding of the past landscapes in which human populations interacted with other species. Illuminating such processes is key for a nuanced understanding of fundamental transitions in human history, such as the shift from hunting and gathering to farming, and allows us to move beyond simple deterministic interpretations of climate-driven innovation. Avifaunal remains provide detailed indications of complex multi-species interactions at the local scale. They allow us to infer relationships between human and non-human animals, but also to reconstruct their niche, because many bird species are sensitive to specific ecological conditions and will often relocate and change their breeding patterns. In this paper, we illustrate how novel evidence that waterfowl reproduced at Levantine wetlands, which we obtained through biomolecular archaeology, together with modern ornithological data reveals conditions of wetlands that are conducive for breeding waterfowl. By understanding the interplay between wetland productivity cycles and waterfowl ecology, we argue that human modifications to the environment could have promoted wetland productivity inviting waterfowl to remain year-round. Within this landscape of “mutual ecologies”, the feedback resulting from the agency of all species is involved in the construction of the human niche.
Self-Reported Adverse Childhood Experiences and Risk for Internalizing and Externalizing Difficulties among Adolescent Custodial Grandchildren
Authors: Smith Gregory C. , Dolbin-MacNab Megan , Infurna Frank J. , Crowley Daniel M. , Castro Saul , Musil Carol , Webster Britney 
Published: February 2024

AbstractDespite custodial grandchildren’s (CG) traumatic histories and risk for psychological difficulties, knowledge is scant regarding the frequencies, types, and consequences of adverse childhood experiences (ACEs) they have encountered. We examined self-reported ACEs via online surveys with 342 CG (ages 12 to 18) who were recruited to participate in an RCT of a social intelligence training program. ACEs were assessed by 14 widely used items, and risk for internalizing (ID) and externalizing (ED) difficulties were measured using 80th percentile cut-offs on the Strengths and Difficulties Questionnaire. Classification and regression tree analyses included all 14 ACEs (along with CG gender and age) as predictors of ID and ED risk separately. Given possible comorbidity, analyses were run with and without the other risk type as a predictor. Less than 9% of CG self-reported no ACEs, 48.6% reported two to five ACEs, and 30.5% reported ≥6. Irrespective of ED risk, bullying from peers strongly predicted ID risk. ED risk was peak among CG who also had risk for ID. Without ID risk as a predictor, ED risk was highest among CG who were emotionally abused, not lived with a substance abuser, and encountered neighborhood violence. The frequency and types of ACEs observed were alarmingly higher than those among the general population, suggesting that many CG have histories of trauma and household dysfunction. That a small number of ACEs among the 14 studied here were significant predictors of ID and ED risk challenges the widespread belief of a cumulative dose ACE effect.
The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives
Authors: Cascella Marco , Semeraro Federico , Montomoli Jonathan , Bellini Valentina , Piazza Ornella , Bignami Elena 
Published: February 2024

Abstract
Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research for applications in diagnostic and clinical decision support, as well as patient triage. Therefore, LLMs can be used for multiple tasks within patient care, research, and education. Throughout 2023, there has been an escalation in the release of LLMs, some of which are applicable in the healthcare domain. This remarkable output is largely the effect of the customization of pre-trained models for applications like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As healthcare professionals, we recognize the imperative to stay at the forefront of knowledge. However, keeping abreast of the rapid evolution of this technology is practically unattainable, and, above all, understanding its potential applications and limitations remains a subject of ongoing debate. Consequently, this article aims to provide a succinct overview of the recently released LLMs, emphasizing their potential use in the field of medicine. Perspectives for a more extensive range of safe and effective applications are also discussed. The upcoming evolutionary leap involves the transition from an AI-powered model primarily designed for answering medical questions to a more versatile and practical tool for healthcare providers such as generalist biomedical AI systems for multimodal-based calibrated decision-making processes. On the other hand, the development of more accurate virtual clinical partners could enhance patient engagement, offering personalized support, and improving chronic disease management.
Touch as a Stress Buffer? Gender Differences in Subjective and Physiological Responses to Partner and Stranger Touch
Authors: Debrot Anik , Stellar Jennifer E. , Dan-Glauser Elise , Klumb Petra L. 
Published: February 2024

AbstractInterpersonal touch buffers against stress under challenging conditions, but this effect depends on familiarity. People benefit from receiving touch from their romantic partners, but the results are less consistent in the context of receiving touch from an opposite-gender stranger. We propose that there may be important gender differences in how people respond to touch from opposite-gender strangers. Specifically, we propose that touch from an opposite-gender stranger may only have stress-buffering effects for men, not women. Stress was induced as participants took part in an emotion recognition task in which they received false failure feedback while being touched by a romantic partner or stranger. We measured subjective and physiological markers of stress (i.e., reduced heart rate variability) throughout the experiment. Neither stranger’s nor partner’s touch had any effect on subjective or physiological markers of stress for men. Women, however, subjectively experienced a stress-buffering effect of partner and stranger touch, but showed increased physiological markers of stress when receiving touch from an opposite-gender stranger. These results highlight the importance of considering gender when investigating touch as a stress buffer.
Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems
Authors: Sonntag Konstantin , Peitz Sebastian 
Published: February 2024

AbstractWe derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent.
Fabrication of multi-walled carbon nanotube/polycaprolactone scaffolds for possible application in tissue engineering
Authors: Peñaranda-Armbrecht Jose , Correa-Quiceno Mario Jose , Caicedo-Angulo Julio Cesar , Neuta-Arciniegas Paola Andrea , Gutiérrez-Montes José Óscar 
Published: February 2024

AbstractFlexible scaffolds made of polycaprolactone (PCL), and multi-walled carbon nanotubes (MWCNTs) were created and chemically verified through RAMAN. The most suitable coupling percentages for PCL were 1%, 3%, and 5% MWCNT. The scaffolds were very stable with high hardness values (10–12 GPa). The PCL/MWCNT 5% scaffold was the most flexible and least resistant, with adequate porosity, pore size, and interconnectivity for cell anchoring. Moreover, the scaffold exhibited low cytotoxicity with a cell viability of 90%. These flexible PCL/MWCNT scaffolds were developed to support tissue engineering by providing a mechanical response with flexibility and elasticity like the surrounding tissue.
Optimizing Postpartum Care in Rural Communities: Insights from Women in Arizona and Implications for Policy
Authors: Okechukwu Abidemi , Magrath Priscilla , Alaofe Halimatou , Farland Leslie V. , Abraham Ivo , Marrero David G. , Celaya Martin , Ehiri John 
Published: February 2024

Abstract
Objectives
Optimal postpartum care promotes healthcare utilization and outcomes. This qualitative study investigated the experiences and perceived needs for postpartum care among women in rural communities in Arizona, United States.

Methods
We conducted in-depth interviews with thirty childbearing women and analyzed the transcripts using reflexive thematic analysis to gauge their experiences, needs, and factors affecting postpartum healthcare utilization.

Results
Experiences during childbirth and multiple structural factors, including transportation, childcare services, financial constraints, and social support, played crucial roles in postpartum care utilization for childbearing people in rural communities. Access to comprehensive health information and community-level support systems were perceived as critical for optimizing postpartum care and utilization.

Conclusions for Practice
This study provides valuable insights for policymakers, healthcare providers, and community stakeholders in enhancing postpartum care services for individuals in rural communities in the United States.
MGSGA: Multi-grained and Semantic-Guided Alignment for Text-Video Retrieval
Authors: Wu Xiaoyu , Qian Jiayao , Yang Lulu 
Published: February 2024

AbstractIn the text-video retrieval task, the objective is to calculate the similarity between a text and a video, and rank the relevant candidates higher. Most existing methods only consider the text-video semantic alignment in the global view. But using mean-pooling to obtain global semantics and simply aligning text and video in the global view may lead to semantic bias. In addition, some methods utilize offline object detectors or sentence parsers to obtain entity-level information in text and video and achieve local alignment. However, inaccurate detection introduces possible errors and such approaches prevent models from being trained end-to-end for retrieval. To overcome these limitations, we propose multi-grained and semantic-guided alignment for text-video retrieval in this paper, which can achieve fine-grained alignment based on video frames and text words, local alignment based on semantic centers, and global alignment. Specially, we explore summary semantics of text and video to guide the local alignment based on semantic centers for we believe that the importance of each semantic center is determined by summary semantics. We evaluate our approach on four benchmark datasets of MSRVTT, MSVD, ActivityNet Captions, and DiDeMo, achieving better performance than most existing methods.
Combining Swin Transformer and Attention-Weighted Fusion for Scene Text Detection
Authors: Li Xianguo , Yao Xingchen , Liu Yi 
Published: February 2024

AbstractThe existing text detection algorithms based on Convolutional Neural Networks (CNN) commonly have the problems of insufficient receptive fields and inadequate extraction of spatial positional information, which limit their ability to detect large-scale variation text instances, long-distance and wide-spaced text instances as well as effectively distinguish complex background textures. To address the above problems, in this paper, a scene text detection algorithm combining Swin Transformer and attention-weighted fusion is proposed. Firstly, an attention-weighted fusion (AWF) module is proposed, which embeds a modified coordinate attention module (CAM) in the feature pyramid network (FPN). This module learns spatial positional weights of foreground information in different-scale features while suppressing redundant background information. As a result, the fused features are more focused on the text regions, enhancing the localization ability for text regions and boundaries. Secondly, the window-based self-attention mechanism of the Swin Transformer is utilized to achieve global feature perception on the fused features of the pyramid network. This compensates for the insufficient receptive fields of CNN and enhances the representation capability of global contextual features, thereby further improving the performance of text detection. Experimental results demonstrate that the proposed algorithm achieves competitive performance on three public datasets, namely ICDAR2015, MSRA-TD500, and Total-Text, with F-measure reaching 87.9%, 91.4%, and 86.7%, respectively. Code is available at: https://github.com/xgli411/ST-AWFNet.
Few-Shot Semantic Segmentation via Mask Aggregation
Authors: Ao Wei , Zheng Shunyi , Meng Yan , Yang Yang 
Published: February 2024

AbstractFew-shot semantic segmentation aims to recognize novel classes with only very few labelled data. This challenging task requires mining of the correlation between the query image and the support images. Previous works have typically regarded it as a pixel-wise classification problem. Therefore, various models have been designed to explore the correlation of pixels between the query image and the support images. However, they focus only on pixel-wise correspondence and ignore the overall correlation of objects. In this paper, we introduce a mask-based classification method for addressing this problem. The mask aggregation network, which is a simple mask classification model, is proposed to simultaneously generate a fixed number of masks and their probabilities of being targets. Then, the final segmentation result is obtained by aggregating all the masks according to their locations. Experiments on both the PASCAL-$$5^i$$

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and COCO-$$20^i$$

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i

datasets show that our method performs comparably to the state-of-the-art pixel-based methods. This competitive performance demonstrates the potential of mask classification as an alternative baseline method for few-shot semantic segmentation.
Sufficient access? Activity participation, perceived accessibility and transport-related social exclusion across spatial contexts
Authors: Pot Felix Johan , Heinen Eva , Tillema Taede 
Published: February 2024

AbstractPromoting social inclusion through facilitating the participation in social and economic activities is a central goal of land-use and transport planning. This study examines the relationship between activity participation and experiences of transport-related social exclusion across different spatial accessibility levels in the Netherlands. Using perceived accessibility as an indicator of the benefits derived from spatial opportunities, this paper reveals a weak and non-linear connection between activity participation and satisfaction with accessibility, which becomes negligible at higher participation levels. Even when individuals have low engagement levels, they often report high perceived accessibility, indicating voluntary non-participation. In rural areas with limited local opportunities, constrained participation is more prominent, especially for those with limited mobility options. The weak correlations between participation and experienced benefits from accessibility across diverse spatial contexts emphasize the importance of considering perceived accessibility alongside spatial and activity data in normative debates on determining the sufficiency of accessibility.
The impact of the COVID-19 pandemic on treatment for domestic violence injuries: evidence from medical claims
Authors: Gilbert Lauren , Parker Susan , Schechter Lauren 
Published: February 2024

AbstractPrevious studies have observed heterogeneous changes in domestic violence-related 911 calls, police incident reports, and arrests at the onset of the COVID-19 pandemic. In this study, we use a large-scale medical claims database with coverage of over 100 million patients to examine the impact on domestic violence victims’ use of emergency department care for their injuries in the early weeks of the pandemic compared to the corresponding weeks in previous years. We find a 23–35% decrease in utilization of emergency medical services by domestic violence victims between March and June of 2020. Based on this finding, it is essential to use caution when using medical claims to measure domestic violence in future research covering this time period. Decreases in care utilization also have important implications for the detection, screening, and treatment of domestic violence injuries during future public health crises.
How does team learning from failure facilitate new product performance? The double-edged moderating effect of collective efficacy
Authors: Tao Xiangming , Wang Catherine L. , Robson Paul J. A. , Hughes Mathew 
Published: February 2024

Abstract
Learning from failure can foster innovation, but how a new product development (NPD) team’s learning from failure affects new product performance requires more insights. In particular, the question remains on how collective efficacy, which discerns team members’ belief to achieve desired goals, affects team learning from failure towards improving new product performance. Using social cognitive theory complemented by sensemaking and attribution theories, we examine the effects of NPD teams’ (experiential and vicarious) learning from failure on new product performance and the moderating effects of collective efficacy on these relationships. With survey data collected from 398 responses within 152 NPD teams in Chinese high-tech small and medium-sized enterprises, we find that both experiential and vicarious learning from failure enhance new product performance in terms of speed to market and product innovativeness. Further, as collective efficacy increases, the positive effect of experiential learning from failure on speed to market is strengthened. However, the positive effect of vicarious learning from failure on product innovativeness is weakened. Our results suggest that NPD teams can benefit from experiential and vicarious learning from failure to improve new product performance but must pay attention to the double-edged effect of collective efficacy.
Scientific explanation as a guide to ground
Authors: Kortabarria Markel , Giannotti Joaquim 
Published: February 2024

AbstractGround is all the rage in contemporary metaphysics. But what is its nature? Some metaphysicians defend what we could call, following Skiles and Trogdon (Philos Stud 178(12):4083-4098, 2021), the inheritance view: it is because constitutive forms of metaphysical explanation are such-and-such that we should believe that ground is so-and-so. However, many putative instances of inheritance are not primarily motivated by scientific considerations. This limitation is harmless if one thinks that ground and science are best kept apart. Contrary to this view, we believe that ground is a highly serviceable tool for investigating metaphysical areas of science. In this paper, we defend a naturalistic version of the inheritance view which takes constitutive scientific explanation as a better guide to ground. After illustrating the approach and its merits, we discuss some implications of the emerging scientific conception for the theory of ground at large.