bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contact formalism for dissipative mechanical systems on Lie algebroids
Authors: Anahory Simoes Alexandre , Colombo Leonardo , de León Manuel , Salgado Modesto , Souto Silvia 
Published: February 2025

Abstract
In this paper, we introduce a geometric description of contact Lagrangian and Hamiltonian systems on Lie algebroids in the framework of contact geometry, using the theory of prolongations. We discuss the relation between Lagrangian and Hamiltonian settings through a convenient notion of Legendre transformation. We also discuss the Hamilton-Jacobi problem in this framework and introduce the notion of a Legendrian Lie subalgebroid of a contact Lie algebroid.
Innovating Challenges and Experiences in Emory Health AI Bias Datathon: Experience Report
Authors: Paddo Atika Rahman , Purkayastha Saptarshi , Newsome Janice , Trivedi Hari , Gichoya Judy Wawira 
Published: February 2025

Abstract
This paper presents an in-depth analysis of the Emory Health AI (Artificial Intelligence) Bias Datathon held in August 2023, providing insights into the experiences gained during the event. The datathon, focusing on health-related issues, attracted diverse participants, including professionals, researchers, and students from various backgrounds. The paper discusses the preparation, organization, and execution of the datathon, detailing the registration process, team formulation, dataset creation, and logistical aspects. We also explore the achievements and personal experiences of participants, highlighting their resilience, dedication, and innovative contributions. The findings include a breakdown of participant demographics, responses to post-event surveys, and participant backgrounds. Observing the trends, we believe the lessons learned, and the overall impact of the Emory Health AI Bias Datathon on the participants and the field of health data science will contribute significantly in organizing future datathons.
Action detection of objects devices using deep learning in IoT applications
Authors: Rustemli Sabir , Alani Ahmed Yaseen Bishree , Şahin Gökhan , van Sark Wilfried 
Published: February 2025

Abstract
Internet of Things (IoT) technology is the communication and communication of smart technological devices with each other. However, with the development of the Internet of Things (IoT), the number of smart applications and interconnected devices is increasing day by day. Deep Learning (DL) method has become necessary to process the large amount of raw data collected and to further improve intelligence and application capabilities. It is seen that the majority of researchers focus on action detection. Standard Deep Learning techniques are difficult to use in IoT devices as Deep Learning applications require high CPU, RAM and storage. In this study, an action detection technique has been developed directly on the edge device by enabling the use of deep learning techniques in IoT devices. This technique, as a representation of neural networks, divides it into on-board computers. Visual action detection is one of the critical components of a smart city. High processing capacity and storage requirements severely limit comprehensive and precise monitoring within the IoT and edge computing framework. The structure proposed in this paper suggests the deployment of micro deep learning algorithms to the latest IoT and embedded devices, including the utilisation of minimal computing resources such as processor, power and memory, with a contribution to IoT and embedded device activities in action detection. The systematic analysis shows that many IoT devices can be applied to the proposed optimisation design. The proposed model is much smaller in size than existing models.
Detailed controls on biomineralization in an adult echinoderm: skeletal carbonate mineralogy of the New Zealand sand dollar (Fellaster zelandiae)
Authors: Dixon-Anderson Ian S. , Smith Abigail M. 
Published: February 2025

Abstract

Fellaster zelandiae, a sand dollar endemic to Aotearoa New Zealand, follows other echinoderms in producing Mg-calcite. Their skeletons, however, show mineralogical variation at different levels of scale: nanostructure, body part, individual, and population. Atomic-level imaging highlighted differences in skeletal ultrastructure with varying levels of consistency in elemental composition. Teeth, the deepest internal skeletal structures in the individual and vital for feeding, showed the greatest compositional variation at the nanoscale, whereas tests and spines were both more consistent in Mg concentrations. Mg incorporation is, approximately, a function of proximity to seawater, with levels highest in layers further away from the marine environment. Body part variation within individuals of a populations was relatively low (Maximum SD
x̄
= ± 0.19 wt% MgCO3, n = 9) while average variation was ± 0.14 wt% MgCO3 (n = 670), reflecting genetic variability. Population variation across a range of latitudes indicated both well-known and novel environmental influences. Skeletal mineralogy in a population in Tauranga, North Island, New Zealand at 38°S (mean = 8.5, SD = 0.07, n = 50) is significantly different (p < 0.0001) from a population in Timaru, South Island, New Zealand at 44°S (mean = 8.2, SD = 0.07, n = 62). Populations across the country showed that external parts (spines) were most affected by temperature and classical environmental factors, while internal parts (Aristotle’s lanterns) were not swayed by abiotic factors. Intermediate structures (tests) were unexpectedly influenced by wave energy, where increases in Mg content among populations was correlated to higher wave-energy beaches. While intrinsic, phylogenetic, and extrinsic factors can individually influence skeletal carbonate mineralogy, these data show that accounting for the cumulative individual- and population-level factors affecting mineralogy provides an extremely nuanced understanding of biomineralization within a single species.
Strengthening local siting authority for utility-scale solar: effects on regulatory decisions and public opposition
Authors: Hess David J. , Seo Hayoung , Belletto Kaelee 
Published: February 2025

Abstract
As governments have implemented climate mitigation policy, the construction of utility-scale renewable energy has led to increased opposition. Some governments have responded by preempting local authority over siting decisions, whereas others have opted to strengthen it. In the US, the state of Ohio is the leading example of the latter. Its enabling law for increased local authority is also part of a broader policy push by the conservative-controlled government to support continued use of fossil fuels. Based on the analysis of regulatory and media documents for 55 cases of proposed utility-scale solar farms in the state, the study examines the motivations and outcomes of the state’s local authority law. First, the study analyzes concerns expressed in local government resolutions since the state government implemented the law. Concerns with preserving the county’s rural character and with potential negative economic effects are the most salient, followed by specific site-related issues. Second, the study analyzes how the regulatory agency responded to the law by shifting its decision-making criteria to strengthen its consideration of local opposition. Third, the study quantifies the volume of opposition with two measures that show how the law increased opposition from local governments and the public. In summary, the law has been an effective instrument not only to undermine new utility-scale, renewable energy development but also to increase local opposition. Recommendations are developed for improving community acceptance of controversial siting proposals, no matter what decision a government makes on the preemption versus local siting authority issue.
Optimizing medical data classification: integrating hybrid fuzzy joint mutual information with binary Cheetah optimizer algorithm
Authors: Hegazy Ah. E. , Hafiz B. , Makhlouf M. A. , Salem Omar A. M. 
Published: February 2025

Abstract
Traditional classification algorithms struggle with the high dimensionality of medical data, resulting in reduced performance in tasks like disease diagnosis. Feature selection (FS) has emerged as a crucial preprocessing step to mitigate these challenges by extracting relevant features and improving classification accuracy. This paper proposes a hybrid FS method, FJMIBCOA, which integrates Fuzzy Joint Mutual Information (FJMI) as a filter measure and Binary Cheetah Optimizer Algorithm (BCOA) as a wrapper method. Unlike existing hybrid FS methods, the proposed method employs FJMI to address uncertainty in feature relationships, providing several advantages such as handling both discrete and continuous features, accommodating linear and non-linear relationships, noise robustness and effectively utilizing intra- and inter-class information. It also employs BCOA as a wrapper method, requiring a few parameters, minimizing computational overhead and enhancing classification robustness, making it an efficient and adaptable solution for FS in complex medical datasets. The proposed method is validated on 23 medical datasets and 14 high-dimensional microarray datasets, demonstrating excellent performance in terms of fitness value, accuracy and feature size. FJMIBCOA surpasses existing methods in medical datasets by achieving higher accuracy in 78.26% of datasets while reducing the feature size by 84.79%. Similarly, in microarray datasets, it improves accuracy in 78.58% of datasets with an impressive 95.08% reduction in feature size. Furthermore, FJMIBCOA achieves superior accuracy in 60% of datasets while selecting fewer features in 78.57% of datasets as compared to previous studies. Statistical testing indicates that FJMIBCOA outperforms other methods significantly. The proposed method enhances diagnosis accuracy and minimizes medical testing requirements, making it suitable for real-world, high-dimensional datasets and decision-making in medical data analysis. The findings from gene expression analysis emphasize the biological significance of the top selected genes, providing new insights into their potential roles in disease progression.
Smart factory concepts and their fitness to the plastics processing industry: a critical review
Authors: Bibow Pascal , Sapel Patrick , Hopmann Christian 
Published: February 2025

Abstract
A key feature for implementing Industry 4.0 in practice is the Smart Factory. Although there has been much research on this buzzword, it can be observed that there is a need for a distinct definition. Furthermore, differentiation to other terms and paradigms, e.g., Cyber-Physical-Production-Systems (CPPS), Industrial-Internet-Of-Things (IIoT), or Industry 4.0, can hardly be found. To overcome these issues, the term "Smart Factory" was defined in the context of comparable terms and paradigms. Therefore, a literature research on 175 scientific contributions was performed and clustered into three categories regarding their scope, i.e., general literature review, conceptual work, and application-oriented case studies. Subsequently, a categorization of these contributions to their content either into terms and definitions, general discussion on challenges and chances, application-oriented engineering trends and technologies, resulting requirements and restrictions, and security and safety issues follows. As a result, three main pillars of Smart Factory objectives were determined, namely data analytics, automation, and modular structures. Finally, the readiness of the plastics processing industry in these pillars is discussed to transfer Smart Factory concepts into practical use and state high fitness, e.g., in terms of data acquisition and communication standards. This contribution supports researchers and practitioners in achieving a common understanding of the term "Smart Factory" and its specifications, providing them with a framework of technological objectives to offer industrial companies the right solutions for a comprehensive Smart Factory implementation. Furthermore, the categorization of the reviewed literature serves as a foundation for subsequent research within specific areas of interest by differentiating well-elaborated topics from scientific white spots.
An Actual Discussion of Einstein’s Early Fusion Picture for Photons and Classical Maxwell Fields
Authors: Rieckers Alfred 
Published: February 2025

Abstract
This is the first part of a pair of companion papers devoted to an analysis of Einstein’s fusion picture for light-quanta (subsequently also referred to as “photons”) with classical electromagnetic fields that he formulated in 1909 via a purely verbal description. There, the overlap of many of the hypothetical local force fields, surrounding the photonic energy points, should produce the well–known classical Maxwell fields. We claim that, alone from such a logical connection, that apparently did not receive sufficient attention in the literature, one may draw essential conclusions on the photon states. In the present paper, we give a historical overview of Einstein’s photon conceptions until 1927. We extract hints from his formulations, that lead from his local force fields to Schrödinger wave functions, which reproduce the conserved integrals for energy, momentum, and angular momentum of Maxwell theory in form of Hilbert space expectations. Also a preliminary multi–photon theory, including Glauber states, is shown to be motivated by Einstein’s composite photons with possibly (incoherently!) overlapping force fields. In the resulting Fock space theory, gained by symmetrizing the many–photon states the goal of a fusion theory is, however, not realizable. In the subsequent companion paper “On sectorial photon theories reconstructing Einstein’s early fusion picture for light” (referred to as Rieckers 2025) our conclusions are extended to photon states that surpass Fock space theory by describing actual infinitely many photons. These are shown to cover also classical light states in terms of collective structures. Appealing to the more recent mathematical tools of convex state space approach and algebraic quantum field theory, we suggest there mathematical realizations of Einstein’s fusion program.
A generalized disjunctive programming model for the static bike sharing rebalancing problem with demand intervals
Authors: Ikonen Teemu J. , Heljanko Keijo , Harjunkoski Iiro 
Published: February 2025

Abstract
Solving a static bike sharing rebalancing problem requires finding the minimum-cost route for rebalancing vehicles, subject to meeting the demand at the bike sharing stations of the system. In this work, we study the variant of the problem where the demand is specified by intervals, which adds flexibility to the routing of the rebalancing vehicles. We propose a generalized disjunctive programming (GDP) model to represent the problem and its reformulation into a mixed-integer linear programming (MILP) model. We use demand splitting to duplicate stations that require multiple visits. The model is designed for single-vehicle routing but can be used jointly with a clustering approach proposed in the literature for multi-vehicle routing. The model can solve to optimality 86.8% of benchmark instances with 70 stations within two hours of computing time. On test cases derived from real process data, the model is more than two orders of magnitude faster than the reference model in the literature.
Effects of Sacha Inchi (Plukenetia volubilis L.) Oil Supplementation on Hyperglycaemia, Hypertension and Hyperlipidaemia (3Hs) Patients: A Preliminary Human Trial
Authors: Mhd Rodzi Nur Anis Raihana , Mohd Sopian Mastura , Lee Lai Kuan 
Published: February 2025

Abstract
Sacha Inchi oil (SIO), derived from seeds of Plukenetia volubilis L., is a novel plant source of unsaturated fatty acids, highly regarded for its health benefits. The rich fractions of polyunsaturated fatty acids have shown potential in improving or reducing the impact of hyperglycaemia, hypertension and hyperlipidaemia (3Hs) for both in vivo and preliminary human studies. This study aimed to investigate the effects of SIO supplementation on glycaemic, lipid and blood pressures outcomes of patients with 3Hs. A randomised, double-blind, placebo-controlled human clinical trial was conducted to investigate the effects of SIO supplementation in fifty-four (n = 54) 3Hs patients. Assessments of glycaemic control, blood pressure and lipid profiles were conducted and compared at baseline and week-12 after 3Hs patients were randomised into SIO group (n = 27) (received 1000 mg of SIO soft gel per day) or the placebo group (n = 27) (received 1000 mg of corn oil), with prescribed medications. The compliancy and tolerability of supplementation were assessed concurrently. The SIO group demonstrated lower systolic (-8.6 mmHg, p = 0.004) and diastolic blood pressure (BP) (-7.0 mmHg, p = 0.004), total cholesterol (TC) (-0.6 mmol/L, p = 0.024), low density lipoprotein-cholesterol (LDL-C) (-0.3 mmol/L, p = 0.036) and high density lipoprotein-cholesterol (HDL-C) (p = 0.048) but no effect on glycaemic markers. No clinical adverse events were reported, and the side effects of SIO supplementation were self-limiting and minimal. SIO supplementation showed the potential to alleviate BPs, reduced both TC and LDL-C, and improved HDL-C levels. SIO may offer a simple, novel complementary medicine for the management of 3Hs.
Exploring Teachers’ Professional Development on Socioscientific Issues and Model-Based Learning: A Multiple Case Study
Authors: Durak Benzegül , Topçu Mustafa Sami 
Published: February 2025

Abstract
This study proposes a three-phase professional development (PD) program for middle school science teachers and aims to investigate their engagement with the Socioscientific Issue and Model-Based Learning (SIMBL) framework. It follows a multiple-case study design, and the main data sources of the study are semi-structured interviews, while the secondary data sources include developed SIMBL units, field notes of the PD program, classroom observations of the lessons, and unstructured phone calls/messages. The semi-structured interviews comprise pre- and post-PD program interviews as well as post-implementation interviews. Employing the Interconnected Model of Professional Growth framework for data analysis, teachers are categorized into three distinct groups: proactive, collaborative, and teacher-led. Findings highlight how teacher beliefs and prior experiences shape their ability to integrate SIMBL into classroom practices. The study emphasizes the critical role of sustained PD, reflective practices, and alignment with teacher beliefs in facilitating the effective implementation of innovative teaching frameworks. Ultimately, the SIMBL framework was found to enhance student engagement with socioscientific issues by fostering critical thinking, systems-based reasoning, and media literacy, contributing to science education.
‘Burned out’ millennials! Can job crafting promote employee well-being in the SMEs?
Authors: Badri Siti Khadijah Zainal , Ngo Michelle She Min , Yusof Junaidah 
Published: February 2025

Abstract
Although job crafting is a proactive initiative to help organisations withstand dynamic business landscapes, little is known about its impact on employees’ burnout experience. Using structural equation modelling on a survey sample of 352 millennials working full-time in small and medium-sized enterprises (SMEs), this study examines millennials’ burnout experience through job crafting. It also examines the mediating role of job satisfaction in the job crafting–burnout linkage. Findings suggest that millennials in SMEs perceived two job crafting aspects, increasing structural resources and increasing social resources, help to boost their overall satisfaction at work. With none of the job crafting aspects found to have a direct link with millennial burnout, this study signifies the practicality of job crafting as a strategy to improve employee work experience instead of triggering burnout. Further results also reveal that millennials’ overall experience in terms of job satisfaction is the underlying reason that explains the link between job crafting and burnout. One practical implication drawn from this study includes the potential use of job crafting to improve millennials’ work experience in SMEs. Besides, this study highlights the importance of smaller companies in establishing more structural clarity and social resources to support millennials’ overall satisfaction and well-being at work.
Burden of Respiratory Syncytial Virus Disease in Adults with Asthma and Chronic Obstructive Pulmonary Disease: A Systematic Literature Review
Authors: Penders Yolanda , Brusselle Guy , Falsey Ann R. , Rohde Gernot , Betancur Estefania , Guardado Maria Elena , Agudelo Juan Luis Ramirez , Saeedi Pouya , Harrington Lauriane , Michaud Jean-Philippe 
Published: February 2025

Abstract

Purpose of Review
Accumulating data indicate that asthma and chronic obstructive pulmonary disease (COPD) increase the risk of severe respiratory syncytial virus (RSV) infection. This systematic literature review assessed the burden of RSV disease among adults ≥ 18 years with asthma or COPD.

Recent Findings
Data on the prevalence of asthma or COPD among RSV-infected adults, RSV-related hospitalizations, complications, and mortality were collected from studies published between January 1, 2000 and November 28, 2023 in PubMed, Embase, and grey literature. All extracted data were analyzed descriptively. Pooled estimates of asthma or COPD prevalence among RSV-infected adults were calculated from generalized linear mixed effects model meta-analyses. Forty studies were included. The prevalence of asthma and COPD among RSV-infected adults was high, especially in inpatient settings with pooled estimates (95% confidence interval) of 19.3% (15.0–24.6) for asthma and 30.8% (26.1–36.0) for COPD. Adults with asthma or COPD were more likely to be hospitalized following RSV infection than those without these conditions. The incidence rate ratios of hospitalization were 2.0–3.6 (crude) and 6.7–8.2 (adjusted) for asthma and 3.2–13.4 (crude) and 9.6–9.7 (adjusted) for COPD. The most frequently reported RSV-related complications were exacerbation of asthma (up to 64.9%) and COPD (up to ≥ 83.0%). In-hospital case fatality rates were 2.6–4.3% (asthma) and 2.8–17.8% (COPD).

Summary
These comprehensive data showing a high RSV disease burden in adults with asthma or COPD can be used to inform policy decisions around RSV vaccines and improve preventive care in this high-risk population.

Graphical Abstract
Dermatologic Lesions with Eosinophilia in the Head and Neck
Authors: Danielson David T. , Lagerstrom Ian , Wary Zachary , Auerbach Aaron , Cassarino David S. 
Published: February 2025

Abstract

Background
Dermatologic lesions with notable eosinophilic infiltration of the head and neck region represent a diverse group of conditions, ranging from benign to malignant proliferations.

Methods
We performed a comprehensive literature review focusing on head and neck dermatologic conditions that commonly present with a prominent eosinophilic infiltrate.

Results
This review provides an overview of common entities showing prominent associated eosinophilic inflammatory infiltrates in this region, including epithelioid hemangioma, eosinophilic cellulitis (Wells syndrome), eosinophilic folliculitis, eosinophilic granulomatosis with polyangiitis (Churg-Strauss syndrome), granuloma faciale, and Langerhans cell histiocytosis (LCH).

Conclusion
Eosinophils play a key role in the pathogenesis of these disorders, although the exact mechanisms remain poorly understood. Accurate diagnosis is crucial for differentiating these conditions, as they can share similar histologic features. This review aims to enhance understanding of these eosinophilic dermatologic conditions, improving diagnostic accuracy and treatment strategies.