bluuebunny/crossref_metadata_2025_split huggingface dataset:
Addressing Health Disparities in Violence
Authors: Castater Christine , Jaeger Briana Woods , Hampton-Anderson Joya , Smith Randi N. 
Published: April 2024

Abstract
Purpose of Review
Healthcare disparities exist throughout the medical field and are highly pronounced concerning violence in the Black community. Addressing it involves not just an understanding of the problem, but the ability to enact widespread interventions at multiple levels, particularly the systemic level.

Recent Findings
Historically, investigations into the drivers of firearm violence have been largely at the individual or interpersonal level, but recent research has centered social determinants of health and structural racism as potential drivers of disparities in firearm violence. Understanding these potential contributors of these disparities allows for targeted solutions to combat violence and promote health equity.

Summary
A full investigation into the drivers of disparities is the key to successful implementation of public health strategies that promote health equity.
Visualizing Self: Altruism is (Un)Affected by Field Versus Observer Representations of Self
Authors: Weathers Shelby E. , Saven Helena Q. , Apicella Coren L. 
Published: April 2024

Abstract
Purpose
This study examines the impact of first-person versus third-person mental visualizations of self on prosocial behavior, building on research that links perspective-taking to differences in moral judgments, agency, and self-awareness. A first-person perspective of self typically enhances feelings of agency, personal responsibility, and empathy, which has been hypothesized to lead to greater helping. However, a third-person perspective of self may heighten self-awareness, potentially leading to a greater focus on reputation management and consequently, helping.

Methods
In two preregistered experiments we test the impact of perspective taking of self on altruistic behavior. Experiment One (n = 599) manipulates generalized perspective taking of self during memory recall and assesses its effect on the amount of time individuals engage in a charitable activity. Experiment Two (n = 271) extends this investigation to explore how targeted perspective taking of self while visualizing a future volunteer activity influences intention to volunteer and actual volunteering.

Results
Across both experiments we found no evidence of an effect of perspective taking on altruistic behavior.

Conclusion
Our results contrast with previous research suggesting that differences in mental visualizations of self influence prosocial behaviors. These findings underscore the complexity of this research area and call for a deeper examination of the theoretical frameworks and methodology used in studies.
Quantitative Insight into the Compressive Strain Rate Sensitivity of Polylactic Acid, Acrylonitrile Butadiene Styrene, Polyamide 12, and Polypropylene in Material Extrusion Additive Manufacturing
Authors: Vidakis N. , Petousis M. , Ntintakis I. , David C. , Sagris D. , Mountakis N. , Moutsopoulou A. 
Published: April 2024

AbstractHerein, a research and engineering gap, i.e., the quantitative determination of the effects of the compressive loading rate on the engineering response of the most popular polymers in Material Extrusion (MEX) Additive Manufacturing (AM) is successfully filled out. PLA (Polylactic Acid), ABS (Acrylonitrile Butadiene Styrene), PP (Polypropylene), and PA12 (Polyamide 12) raw powders were evaluated and melt-extruded to produce fully documented filaments for 3D printing. Compressive specimens after the ASTM-D695 standard were then fabricated with MEX AM. The compressive tests were carried out in pure quasi-static conditions of the test standard (1.3 mm/min) and in accelerated loading rates of 50, 100, 150, and 200 mm/min respectively per polymer. The experimental and evaluation course proved differences in engineering responses among different polymers, in terms of compressive strength, elasticity modulus, toughness, and strain rate sensitivity index. A common finding was that the increase in the strain rate increased the mechanical response of the polymeric parts. The increase in the compressive strength reached 25% between the lowest and the highest strain rates the parts were tested for most polymers. Remarkable variations of deformation and fracture modes were also observed and documented. The current research yielded results with valuable predictive capacity for modeling and engineering modeling, which hold engineering and industrial merit.
Sensemaking Process of Parents of Children with Autism Spectrum Disorder: Identification of Specific Clusters of “Sense of Grip”
Authors: De Luca Picione Raffaele , Freda Maria Francesca , Bianco Roberto , Capelli Francesca , De Fortuna Angelo Maria , Dioni Barbara , Fante Chiara , Raffin Cinzia , Rollo Simone , Troisi Gina , Sandomenico Carmelo , Musetti Alessandro 
Published: April 2024

AbstractAutism spectrum disorder (ASD) represents a pervasive neurodevelopmental disorder characterized by persistent atypicalities in social communication and social interactions across different domains of life, often with restricted, repetitive, stereotyped patterns of behaviors, interests, and activities. A large corpus of previous research shows that parental involvement in caregiving is perceived as stressful and challenging, in many cases resulting in poorer parental quality of life. The authors present a quali-quantitative mixed method research on sensemaking process of 34 parents (16 mothers and 18 fathers) of children with ASD. The interviews were collected and analyzed according to the “Sense of Grip” (SoG) methodology. SoG indeed refers to the sensemaking processes that a person realizes to manage the relationship with diseases or disorders by organizing several degrees of flexibility, differentiation, and adaptation to the variability of experiences and contexts of life. This construct of health psychology—semiotically and clinically oriented—is based on specific narrative functions (i.e., temporal, integrative, emotional, sharing, and agentive functions). The cluster analysis procedure allowed to identify three different SoG profiles: (a) enterprising (parents who are able to construct flexible and dynamic familiar routines), (b) opposing (parents who experience the ASD disorder as a deep unacceptable crisis, hindering the rest of familiar and professional activities), and (c) condescending (parents who organize family life completely on adherence to medical prescriptions and care protocols). These profiles show interesting implications for clinical practice and support as showing specific aspects of treating, managing, and sharing children conditions within family and relational contexts of life.
Optimization of the QuEChERS Extraction of Mycotoxins in Maize Using Response Surface Methodology (RSM)
Authors: Mbisana Mesha , Mogopodi Dikabo , Tshepho Rebagamang , Chibua Inonge , Nkoane Bonang 
Published: April 2024

AbstractSeveral methods have been developed for the analysis and detection of mycotoxins in food; however, most do not make use of critical statistics and mathematical tools for precise optimization. This study developed, optimized, and validated a modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction procedure for the extraction of multiple mycotoxins in maize and subsequent validation using liquid chromatography tandem mass spectrometry (LC–MS/MS). Central composite design (CCD) was used to optimize extraction conditions. Data analysis of full factorial screening experiments revealed that MeCN (%), FA (%), and extraction time significantly affected the mycotoxins recovery. Assessment of the statistical significance of the generated model using analysis of variance (ANOVA), coefficient tables, and surface plots showed the relative interactions of factors and the adequacy of the model. Thus, P values from the lack of fit (LOF) test ranged from 0.137– 0.467 and a composite desirability function of 0.91 was obtained. Using the optimum extraction conditions of 0.1% (v/v) FA in 80.2% MeCN for 74 min, 10 mycotoxins were effectively extracted with satisfactory recoveries (85–114%), coefficients of regression (R2 > 0.98), coefficients of variation (CVs < 15%), limit of quantifications (LOQs) (0.33–60.45 µg/kg), and other associated method validation parameters. The method validation was carried out according to Commission Implementing Regulation 2021/808 and Commission Regulation (EC) No 401/2006 of 23 February 2006. Application of this method to 20 maize samples collected from markets in Botswana showed detectable mycotoxins in 13 samples, with 2 exceeding the European Union (EU) maximum aflatoxin B1 (AFB1) limit, suggesting potential exposure to high levels of toxic mycotoxins in Botswana.
Performing Sentiment Analysis Using Natural Language Models for Urban Policymaking: An analysis of Twitter Data in Brussels
Authors: Tori Floriano , Tori Sara , Keseru Imre , Ginis Vincent 
Published: April 2024

Abstract
Objectives
Mobility is a core challenge to transition towards sustainability. Cities are, therefore, rethinking their mobility to reduce negative externalities such as (greenhouse) gas emissions or congestion. When trying to implement sustainable urban mobility plans, there is often resistance from citizens. This can indicate a disconnect between the public and policymakers due to a lack of participation, coupled with the fact that current data-collection methods often used (such as travel surveys) are limited in scope. Advances in big data analysis and user-generated content provide opportunities to gain deeper insights into citizens' perceptions of mobility policy changes. This paper explores how sentiment analysis through deep learning can be used in transport planning.

Materials and Methods
In this research, we analyse the sentiments of citizens towards recent changes in mobility policy in Brussels, Belgium, through Twitter data. We analyse 1998 tweets about changing mobility policy in Brussels between July 18th, 2019 (forming of the last Brussels regional government), and December 31st, 2022 (starting date of the analyses). For our analysis, we employ two pre-trained language models: XLM-T and GPT4.

Results
Our results show that the sentiment with regard to the new mobility interventions is, as reflected by Twitter posts, not overwhelmingly negative. Furthermore, we find that the performance scores of XLM-T when no domain-specific fine-tuning has occurred (zero-shot evaluation) is fairly low (0.48). Once the model is trained on our domain-specific data, it reaches an accuracy of 0.67. When using GPT4, the model reaches an accuracy of 0.66. Additionally, GPT4 seems better suited at identifying mismatched tweets, i.e. tweets using vocabulary that has a different sentiment than the one the tweets expresses (e.g., sarcasm). This might indicate that large language models might be better suited to obtain implicit sentiments expressed in a text.

Conclusions
From a machine learning perspective, our experiments highlight the difficulty of recognising contextual sentiment (in this case, a sentiment towards changes in mobility policies), which may differ from the sentiment reflected in the vocabulary used. This is especially important if these two sentiments do not correspond, a problem both models struggled with. Additionally, we show that GPT4 can provide additional information when performing sentiment analysis by prompting it to attribute scores to texts. This paper opens new perspectives on understanding and addressing public sentiment in urban mobility policies. The advancements in language models, and the effective integration of user-generated content, can provide policymakers with a more comprehensive understanding of public sentiment, facilitating the implementation of certain policies.

Clinical Relevance
None.
Dominance of transposable element-related satDNAs results in great complexity of “satDNA library” and invokes the extension towards “repetitive DNA library”
Authors: Tunjić-Cvitanić Monika , García-Souto Daniel , Pasantes Juan J. , Šatović-Vukšić Eva 
Published: April 2024

AbstractResearch on bivalves is fast-growing, including genome-wide analyses and genome sequencing. Several characteristics qualify oysters as a valuable model to explore repetitive DNA sequences and their genome organization. Here we characterize the satellitomes of five species in the family Ostreidae (Crassostrea angulata, C. virginica, C. hongkongensis, C. ariakensis, Ostrea edulis), revealing a substantial number of satellite DNAs (satDNAs) per genome (ranging between 33 and 61) and peculiarities in the composition of their satellitomes. Numerous satDNAs were either associated to or derived from transposable elements, displaying a scarcity of transposable element-unrelated satDNAs in these genomes. Due to the non-conventional satellitome constitution and dominance of Helitron-associated satDNAs, comparative satellitomics demanded more in-depth analyses than standardly employed. Comparative analyses (including C. gigas, the first bivalve species with a defined satellitome) revealed that 13 satDNAs occur in all six oyster genomes, with Cg170/HindIII satDNA being the most abundant in all of them. Evaluating the “satDNA library model” highlighted the necessity to adjust this term when studying tandem repeat evolution in organisms with such satellitomes. When repetitive sequences with potential variation in the organizational form and repeat-type affiliation are examined across related species, the introduction of the terms “TE library” and “repetitive DNA library” becomes essential.
Identifying influential climatic factors for urban risk studies in rapidly urbanizing Region
Authors: Mangal Saloni , Kumar Deepak , Dhupper Renu , Kumari Maya , Gupta Anil Kumar 
Published: April 2024

AbstractSevere weather events, such as heat waves, floods, pollution, and health threats, are becoming more common in metropolitan places across the world. Overcrowding, poor infrastructure, and fast, unsustainable urbanization are some of the problems that India faces, and the country is also susceptible to natural disasters. This research analyzes climatic variables affecting urban hazards in Bangalore (also known as Bengaluru) via a thorough review. Heat waves, urban floods, heat islands, and drought were identified in 156 qualifying publications using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. Contributing variables were also considered. City development and urbanization were key to changing climate and increasing urban dangers. While long-term climatic variable distribution is uneven, warming is evident. The report promotes strong urban planning techniques, comprehensive policies, more green areas, and sustainable development beyond short-term heat response programs to boost urban climate resilience. This study shows how climate, land use, and urban dangers are interconnected. Future studies may benefit by categorizing urban risk studies and identifying climatic factors.
Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm
Authors: Pandya Sundaram B. , Kalita Kanak , Jangir Pradeep , Ghadai Ranjan Kumar , Abualigah Laith 
Published: April 2024

AbstractThis research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed the Multi-Objective Geometric Mean Optimizer (MOGMO). MOGMO melds the traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions through offspring creation and selection. A Crowding Distance (CD) coupled with an Information Feedback Mechanism (IFM) selection strategy is employed to maintain and amplify the convergence and diversity of potential solutions. MOGMO efficacy and capabilities are assessed using thirty notable case studies. This encompasses nineteen multi-objective benchmark problems without constraints, six with constraints and five multi-objective engineering design challenges. Based on the optimization results, the proposed MOGMO is better 54.83% in terms of GD, 64.51% in terms of IGD, 67.74% in terms of SP, 70.96% in terms of SD, 64.51% in terms of HV and 77.41% in terms of RT. Therefore, MOGMO has a better convergence and diversity for solving un-constraint, constraint and real-world application. Statistical outcomes from MOGMO are compared with those from Multi-Objective Equilibrium Optimizer (MOEO), Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D), Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Multi-Verse Optimization (MOMVO) and Multi-Objective Plasma Generation Optimizer (MOPGO) algorithms, utilizing identical performance measures. This comparison reveals that MOGMO consistently exhibits robustness and excels in addressing an array of multi-objective challenges. The MOGMO source code is available at https://github.com/kanak02/MOGMO.
Finding Discriminative Subsequences Via a Coverage Measure and Mutual Information Selection Strategy for Multi-Class Time Series Classification
Authors: Yang Jun , Jing Siyuan 
Published: April 2024

AbstractTime series classification (TSC) has attracted considerable attention from the data mining community over the past decades. One of the effective ways to handle this task is to find discriminative subsequences in time series to train a classifier. Obviously, how to measure the discriminative power of subsequences and find the optimal combination of subsequences is crucial to the accuracy of TSC. In this paper, we introduce a new method, CRMI, to find high-quality discriminative subsequences for multi-class time series classification (MC-TSC). Different from existing methods, there are two significant innovations in the work. At first, we propose a novel measure, named coverage ratio, to evaluate the discriminative power of a subsequence based on a coverage matrix which is figured out by the clustering technique. Second, a heuristic algorithm based on mutual information (MI) is proposed to find the optimal combination of subsequence candidates. The calculation of MI is also based on the coverage matrix. Extensive experiments were conducted on 54 UCR time series datasets with at least 3 categories, and the results show that (1) the proposed algorithm achieves the highest average accuracy and outperforms most of the existing shapelet-based TSC algorithms; (2) compared with existing methods, the proposed algorithm performs better on datasets with a large number of categories.
Creating data-driven products and services in industry 4.0: a case study on companies in the German machine and tool industry
Authors: Permin Eike , Castillo Lina 
Published: April 2024

AbstractDuring the past ten years, lots of new data-driven products and services for tools, machinery and equipment have been developed. While several new players from other industries gained a certain market share, plant and machinery producers also started to enhance their portfolio to take on new data-driven products and services because of the technological changes in Industry 4.0. As a first part of the research, an extensive market study was carried out to analyze how many German companies already offer data-based products and services in addition to their core machines and understand what kind of offerings they make. To classify these offerings, a scheme based on established Industry 4.0 maturity models was developed. In brief, the market for data-driven products and services is still developing, with few technology leaders and fast movers taking the largest share. While the market study gave an overview of what was on offer, the second part of this contribution analyzes how the fast movers with a high level of Industry 4.0 maturity conducted their data-driven services and products. Thus, these few companies were analyzed in more detail, based on public material as well as subsequent expert interviews. Most fast movers in this study relied on the same patterns and approaches, especially when looking at organizational issues such as customer-driven innovation, agile organization of operations, mixed teams, partnering and portfolio enhancement.
Aerothermal and aerodynamic characteristics of reusable hypersonic vehicles with heat transfer minimised sweepback
Authors: Shilwant R.B. , Mahulikar S.P. 
Published: April 2024

AbstractEstablishing aerothermal criteria for swept leading-edge hypersonic vehicle design is the predominant purpose of this work. This study is focused on two different configurations of vehicles based on the swept-back angle (
$\varLambda$
) viz. minimum drag (
$\varLambda$
Drag-min), and minimum heat transfer to vehicle (
$\varLambda$
HT-min). Maximum wall temperatures obtained from the simulation performed in ANSYS 2020 with the k-epsilon turbulence model are 1,013 and 970K for
$\varLambda$
Drag-min and
$\varLambda$
HT-min, respectively. These temperatures are used to obtain the corresponding thicknesses of thermal protection systems to maintain inner wall temperature at 323K. Further study is divided into two cases depending on the direction of thickness of thermal protection system with respect to vehicle body. For constant payload capacity, the direction of thickness is outside; whereas for constant overall volume case, direction of thickness is inside. For constant payload volume case, the percentage weight reduction of thermal protection system is 4.8%. For constant overall volume case, the percentage payload capacity increases with design at
$\varLambda$
HT-min by 4.04% in addition to thermal protection system weight reduction. The lift-induced drag on vehicles with design at
$\varLambda$
HT-min is significantly reduced for both cases, by 47.68% (for constant payload volume) and 45.27% (for constant overall volume).
Piecing together ‘big pictures’ with social network analysis and digital tools
Authors: Kaye Aleksandra 
Published: April 2024

AbstractWhen considering the ‘big picture’ in the history of science, what or who is considered important depends upon the focal point of the analysis. Social network analysis, equipped with digital methods, offers historians a way to help generate alternative perspectives for analysis by revealing elusive patterns obscured by the apparent ‘centre/periphery’ dichotomy or ‘great-men’ narratives. The presented methods are focused on studying connections, relationships and structural characteristics in networks and can thus help bridge global and local perspectives and suggest new historical lines of argument. This point is illustrated with a case study of Polish oil prospectors working in Argentina in the 1880s, which problematizes the narrative of the formation of transnational networks based on this mineral resource. The article discusses the place of digital methods within historical research, arguing that good skills in the interpretation and communication of outputs produced through digital research methods, coupled with familiarity with their associated theories, strengths and weaknesses, are indispensable.
On the Hausdorff dimension of invariant measures of piecewise smooth circle homeomorphisms
Authors: TRUJILLO FRANK 
Published: April 2024

AbstractWe show that, generically, the unique invariant measure of a sufficiently regular piecewise smooth circle homeomorphism with irrational rotation number and zero mean nonlinearity (e.g. piecewise linear) has zero Hausdorff dimension. To encode this generic condition, we consider piecewise smooth homeomorphisms as generalized interval exchange transformations (GIETs) of the interval and rely on the notion of combinatorial rotation number for GIETs, which can be seen as an extension of the classical notion of rotation number for circle homeomorphisms to the GIET setting.