bluuebunny/crossref_metadata_2025 huggingface dataset:
AI-Driven Routing: Transforming Network Efficiency and Resilience<span class="EOP SCXW51976932 BCX0" data-ccp-props="{"134233117":false,"134233118":false,"134245418":true,"134245529":true,"335551550":2,"335551620":2,"335559685":0,"335559731":0,"335559738":480,"335559739":120,"469777462":[216],"469777927":[0],"469777928":[2]}">
Authors: Bhattacharjee Indraneel
Published: February 2025

The exponential growth of global data traffic has underscored the limitations of traditional routing protocols such as BGP, OSPF, and MPLS, which rely on predefined policies and reactive measures. While BGP dynamically adapts to network changes, its convergence time and path selection mechanisms often lead to suboptimal routing, increased latency, and inefficient bandwidth utilization. As network infrastructures scale, these approaches struggle to adapt dynamically, resulting in suboptimal routing decisions, increased latency, and inefficient bandwidth utilization. This paper introduces AI-driven routing protocols that leverage machine learning (ML) and software-defined networking (SDN) to optimize real-time network traffic. I propose an autonomous routing framework integrating reinforcement learning-based path optimization, predictive congestion control, and self-healing network mechanisms to enhance network performance by up to 200%. My experimental simulations in Mininet and GNS3 demonstrate a 35% reduction in packet loss, 40% improvement in traffic predictability, and sub-second failure recovery. Additionally, I discuss the scalability of AI-enhanced routing in real-world network infrastructures, emphasizing its applicability in 5G/6G networks, cloud environments, and enterprise SDN deployments.
A Rare Thermochromic Zwitterionic Nickel(II) Complex of the Bulky Alpha Ligand 1,2-Bis-(di-tbutylphosphinomethyl)benzene
Authors: Butler Ian R. , Horton Peter Neil , Coles Simon J. , Clegg William , Fortune Kevin M. , Drew Michael G. B. , Suwada Kamil
Published: February 2025

The reaction of the bulky ligand 1,2-bis-(di-tert-butylphosphinomethyl)benzene with [Ni(DME)Cl2] affords the blue Zwitterionic complex containing a phosphonium group and an anionic nickel trichloride. The neutral and stable complex is of interest as it shows an apparently Zwitterionic structure, one counter-anion to the Ni(II) being delocalised into the backbone of the ligand. This complex decomposes in alcohols such as methanol and turns yellow. A discussion of the mechanism leading to the observed product is presented. In dimethylformamide, however, the complex exhibits thermochromic properties: an ambient temperature pale blue solution changes colour reversibly to yellow on cooling. The structures of the previously prepared molybdenum complex, [1,2-(C6H4-CH2PtBu2)2Mo(CO)4] and the diphosphine sulfide 1,2-(C6H4-CH2P(S)tBu2)2 are described for comparative purposes.
Chromotropism of Iron(II) Complexes with Non-Symmetric Heterocyclic Ligands: Polarity Sensing in Aqueous Urea Solutions
Authors: Papadakis Raffaello
Published: February 2025

In this work, two pentacyanoferrate(II) complexes are synthesized and characterized, and their intense solvatochromic properties are examined in various solvents and solvent mixtures. These complexes feature ligands with flexible xylylene bridges and distinct heterocycles: one combining 4-dimethylaminopyridine (DMAP) with 4,4’-bipyridine, and the other isoquinoline with 4,4’-bipyridine. This structural diversity leads to intriguing solvatochromic behavior, enabling various intermolecular interactions and motifs in solution among the solvents and the complexes acting as solutes. The determined dipole moment changes between ground and excited states of both compounds highlight their sensitivity to small changes in dipolarity within polar media, such as water and polar organic solvents and mixtures thereof. Linear solvation energy relationships are employed to investigate their dependencies on solvent polarity and their responsiveness to different media. Additionally, the solvatochromic aptitude of the molecules examined is utilized to determine the polarity properties of aqueous solutions containing the bio-based molecule urea, a major player in bio-based resins and adhesives and other imminent applications.
Emerging Trends and Challenges in Artificial Intelligence: A Research Perspective
Authors: Bollikonda Tejaswini , Bura Chiranjeevi
Published: February 2025

Artificial Intelligence (AI) has undergone rapid advancements over the last decade, impacting diverse domains such as healthcare, finance, cybersecurity, and industrial automation. This paper explores emerging trends in AI, including the integration of Explainable AI (XAI), Edge AI, federated learning, and next-generation computing paradigms. The study discusses challenges such as ethical concerns, interpretability, security risks, and computational overhead, while also highlighting key innovations driving AI evolution. Our proposed architecture aims to enhance trustworthiness in AI-driven applications through a hybrid approach leveraging XAI and federated learning. Experimental results demonstrate the efficacy of this architecture in balancing accuracy, interpretability, and data privacy, making it suitable for real-world AI applications.
The Effect of Polyethylene Terephthalate Nanoplastics on Amyloid-β Peptide Fibrillation
Authors: Bashirova Narmin , Schölzel Franziska , Hornig Dominik , Scheidt Holger A. , Krueger Martin , Salvan Georgeta , Huster Daniel , Matysik Joerg , Alia A
Published: February 2025

Exposure of organisms to nanoplastics (NPs) is inevitable given their global abundance and environmental persistence. Polyethylene terephthalate (PET) is a common plastic used in a wide range of products, including clothing and food and beverage packaging. Recent studies suggest that NPs can cross the blood-brain barrier and cause potential neurotoxicity. It is widely known that aggregation of amyloid beta (Aβ) peptides in the brain is a pathological hallmark of Alzheimer's disease (AD). While the impact of nanoplastics such as polystyrene (PS) on amyloid aggregation has been studied, the effects of PET NPs remain unexplored. In this study, we examined the effect of PET NPs of different sizes (PET50 nm and PET140 nm) and concentrations (0, 10, 50 and 100 ppm) on the fibrillation of Aβ1-40. Our results showed that the presence of PET50 nm as well as PET140 nm decreased the lag phase of the fibrillation processes in a dose- and size-dependent manner from 6.7 ± 0.08 h for Aβ in the absence of PET (Aβcontrol) to 3.1 ± 0.03 h for PET50 nm and 3.8 ± 0.06 h for PET140 nm. CD spectroscopy showed that PET50nm significantly impacts the structural composition of Aβ aggregates. A significant rise in antiparallel β-sheet content and β-turn structure and a substantial reduction in other structures were observed in presence of 100 ppm PET50 nm. These changes indicate that higher concentrations (100 ppm) of PET50 nm promote more rigid and uniform peptide aggregats. Although PET50 nm NPs influence the kinetics of aggregation and secondary structure, the overall morphology of the resulting fibrils remains largely unaltered, as seen by transmission electron microscopy. Also the local cross-β structure of the fibrils was not affected by the presence of PET50 nm NPs during fibrillation, as confirmed by 13C solid-state NMR spectroscopy. Overall, these findings show that PET NPs accelerate amyloid fibril formation and alter the secondary structure of Aβ fibrils. These results also indicate that the accumulation of PET-NPs in the brain may facilitate the progression of various neurodegenerative diseases, including Alzheimer's disease.
A Randomized, Placebo-Controlled, Double-Blind Trial to Assess the Effects of Apocynum venetum L. (A. venetum) Venetron® on Sleep and Stress in Those Expressing Feelings of Anxiety
Authors: White Kaitlyn P. , Hewlings Susan , Moseley Megan , Moseley Megan , D'Adamo Christopher R. , Colwell Christopher S. , Chen Jeff , Pauli Emily K.
Published: February 2025

Background/Objectives: Anxiety and stress are interrelated and connected to reduced health-related quality of life. Botanicals such as Apocynum venetum L. (A. venetum) have been shown to improve health outcomes. No human studies have been conducted in a diverse large group of healthy adults in the US. The purpose of this randomized, double-blind, placebo-controlled study was to evaluate the effects of Venetron® Rafuma (A. venetum leaf extract) on self-reported anxiety levels and overall health outcomes compared to placebo. Methods: Healthy adults (N = 476) seeking improvement in self-reported anxiety and stress were randomly assigned to receive 50 mg of A. venetum (n = 234) or placebo (n = 242) for 6 weeks. Feelings of anxiety were assessed at baseline and weekly using Patient-Reported Outcomes Measurement Information System (PROMIS™) Anxiety 8A. Perceived stress, sleep quality, and cognitive function were evaluated at baseline and weekly using validated assessments. A linear mixed-effects regression model was used to compare the change in health outcome scores between active and placebo groups. Results: There was a significant difference between the groups in the rate of improvement of perceived stress and sleep disturbance. The active group was significantly more likely to experience a Minimal Clinically Important Difference (MCID) in their perceived stress and marginally significantly more likely to experience an MCID in their feelings of anxiety. Participants who reported experiencing side effects did not significantly differ between arms. Conclusion: Venetron® may be safe and effective therapy for stress and sleep disturbance among those suffering from feelings of anxiety.
Impact of Technical Snow Production Process on Bacterial Community Composition, Antibacterial Resistance Genes and Antibiotic Input—A Double Effect of Inevitable
Authors: Stankiewicz Klaudia , Bulanda Klaudia , Prajsnar Justyna , Lenart-Boroń Anna
Published: February 2025

Although climate warming-induced reduction in snow cover makes technical snowmaking inevitable in moderate climatic regions, it causes hydrological, health-related and environmental concerns. This study analyzed culturable bacteriological indicators of water quality, presence and concentration of antimicrobials, genes determining bacterial antibiotic resistance (ARGs) and next-generation sequencing-based bacterial community composition and diversity in resource water and technical snow of five ski resorts. Numbers of culturable bacteria (E. coli, fecal enterococci, Salmonella, Staphylococcus) and prevalence of most ARGs (extended-spectrum beta-lactamase, macrolide–lincosamide–streptogramin B, streptomycin phosphotransferase, tetracycline efflux pump and sulfamethoxazole resistance determinant) decreased during snowmaking. Concentration of antimicrobial agents changed irregularly, e.g. ofloxacin (with the highest overall concentration) and erythromycin (most frequently detected) dropped through snowmaking process, while cefoxitin concentration exceeded LOQ only in technical snow. Finally, bacterial community composition altered during technical snowmaking due to advantage of certain strains over others in e.g. survivability in freezing temperatures or in the presence of antimicrobial agents. Some of these taxa were important for agriculture (Arthrobacter), or were potential pathogens to humans (Glutamicibacter), animals and plants (Flavobacteriaceae). With still not elucidated impact of technical snowmaking on the surrounding environment, in bacteriological, antibiotic and ARG contamination, or bacterial community alterations, further research is necessary.
LSEWOA: An Enhanced Whale Optimization Algorithm with Multi-strategy for Numerical And Engineering Design Optimization Problems
Authors: Wei Junhao , Gu Yanzhao , Yan Yuzheng , Li Zikun , Lu Baili , Pan Shirou , Cheong Ngai
Published: February 2025

The Whale Optimization Algorithm (WOA) is a bio-inspired metaheuristic algorithm known for its simple structure and ease of implementation. However, WOA suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. In this paper, we proposed an enhanced whale optimization algorithm with multi-strategy (LSEWOA). LSEWOA employs Good Nodes Set Initialization to generate uniformly distributed whale individuals, a newly designed Leader-Followers Search-for-Prey Strategy, a Spiral-based Encircling Prey strategy inspired by the concept of Spiral flight, and an Enhanced Spiral Updating Strategy. Additionally, we redesigned the update mechanism for parameter a to better balance exploration and exploitation. The effectiveness of the proposed LSEWOA was evaluated using CEC2005, and the impact of each improvement strategy was analyzed. We also performed a quantitative analysis of LSEWOA and compare it with other state-of-art metaheuristic algorithms. Finally, we applied LSEWOA to nine engineering design optimization problems to verify its capability in solving real-world optimization challenges. Experimental results demonstrated that LSEWOA outperformed better than other algorithms and successfully addressed the shortcomings of the classic WOA.
Race/Ethnicity and Clinical Outcomes in Hypertrophic Cardiomyopathy: Insights from a Latin American Center
Authors: Lopez Juan David , Tovar Vanessa , Giraldo Hoover Leon , Naranjo Maria Camila , Florez Noel , Olaya Pastor , Carrillo Diana , Gomez Juan Esteban
Published: February 2025

Background: In hypertrophic cardiomyopathy (HCM), the association of race/ethnicity with disease expression and clinical outcomes is not well characterized, most studies evaluating ethnic disparities in this pathology are derived from registries in North America and Europe, the purpose of this study is to assess the possible association of race/ethnicity with different relevant clinical outcomes in a university hospital in Colombia; Methods: This was an analytical cross-sectional study of patients with HCM between January 2011 and June 2024, patient data were analyzed from an institutional cardiomyopathy registry, two groups were defined for analysis, five clinical outcomes were defined for comparative analysis. Results: A total of 97 patients were included, 19.5% self-identified as Afro-Colombian, the average age was 55.8 years, the median left ventricular ejection fraction (LVEF) was 65%, no statistically significant differences were identified in other echocardiographic characteristics, the most commonly observed hypertrophic pattern was asymmetric septal hypertrophy in both groups, a trend toward a higher proportion of late gadolinium enhancement >15% was observed in the Afro-Colombian group, and no significant differences were found in implantable cardioverter-defibrillator placement, septal reduction techniques, ventricular arrhythmia, or atrial fibrillation.; Conclusions: This study provides valuable insights into hypertrophic cardiomyopathy (HCM) in an underrepresented population, highlighting potential ethnic disparities in disease presentation and management
Innovative Virtual Reality Teaching for the Sustainable Development of Vocational High School Students: A Case Study of Hair Braiding
Authors: Chiang Sumei , Chiang Daihua , Chang Shao-Hsun , Yao Kai-Chao
Published: February 2025

This study applies the "Flow Theory" and the "Extended Technology Acceptance Model" (ETAM) to examine the effectiveness of virtual reality (VR) immersive learning in vocational high schools (VHS), focusing on hairdressing education. It investigates how ETAM influences students' learning attitudes and the sustainability of vocational training. The research involved 1,190 students from three VHS in Nantou and Changhua, Taiwan, who participated in "VR Basic Hairstyling Design" and "VR Bridal Styling" courses. Data were analyzed using SPSS 22.0 and Smart PLS. Key findings include: (1) ETAM's path coefficients were significant, confirming its validity for VHS; (2) Students' "perceived usefulness" and "perceived ease of use" significantly impacted their "attitude towards use" of VR learning; (3) "Flow experience" significantly influenced "attitude towards use" and "behavioral intention"; (4) "Flow experience" partially mediated the relationship between "perceived usefulness" and "attitude towards use." These results highlight ETAM's applicability in VR hairdressing education and demonstrate that innovative VR teaching positively contributes to the sustainable development of vocational beauty education.
Conversational AI in Pediatric Mental Health: A Scoping Review of Applications, Evidence, and Special Considerations
Authors: Mansoor Masab , Hamide Ali , Tran Tyler
Published: February 2025

Background/Objectives: Mental health disorders among children and adoles-cents represent a significant global health challenge, with approximately 50% of condi-tions emerging before age 14. Despite substantial investment in services, persistent barriers including provider shortages, stigma, and accessibility issues limit effective care delivery. This scoping review examines the emerging application of conversational ar-tificial intelligence (AI) in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental con-siderations required for implementation. Methods: Following established scoping review methodology, we searched multiple electronic databases (PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus) for literature published between January 2010 and February 2025 that addressed conversational AI applications relevant to pedi-atric mental health. We employed a narrative synthesis approach with thematic analysis to organize findings across technological approaches, therapeutic applications, devel-opmental considerations, implementation contexts, and ethical frameworks. Results: The review identified promising applications for conversational AI in pediatric mental health, particularly for common conditions like anxiety and depression, psychoeducation, skills practice, and bridging to traditional care. However, most robust empirical research has focused on adult populations, with pediatric applications only beginning to receive dedicated investigation. Key therapeutic mechanisms identified include reduced barriers to self-disclosure, cognitive change, emotional validation, and behavioral activation. Developmental considerations emerged as fundamental challenges, necessitating age-appropriate adaptations across cognitive, emotional, linguistic, and ethical dimen-sions rather than simple modifications of adult-oriented systems. Conclusions: Conver-sational AI has potential to address significant unmet needs in pediatric mental health as a complement to, rather than replacement for, human-delivered care. Future research should prioritize developmental validation, longitudinal outcomes, implementation science, safety monitoring, and equity-focused design. Interdisciplinary collaboration involving children and families is essential to ensure these technologies effectively ad-dress the unique mental health needs of young people while mitigating potential risks.
The Ethical Workplace and Sustainability: Understanding Employee Green Behavior Through a Moderated Mediation Framework
Authors: Sarwar Usman , Baig Waqas , Rahi Samar , Sattar Sonia
Published: February 2025

The premise of this research is to investigate the influence of ethical climate on the environmentally responsible behavior of employees within the hospitality sector in Pakistan. This study aims to further investigate the concept of regulated mediation by exploring the relationship between environmental information and motivational states. The study's methodology, approach, and design were carefully considered and implemented. The data was gathered from a sample of 290 front-line managers in the hospitality sector of Pakistan, employing a research instrument. Structural Equation Modelling (SEM) was employed to test the hypothesis. (1) The study results indicate a significant relationship between ethical climate and employees' green behavior. (2) Furthermore, the findings indicate that motivational states partially mediate the association between organizational climate and green behavior. (3) Additionally, the results clearly indicate that the influence of ethical climate on motivational states is moderated by environmental knowledge. The present study has significant significance for managers operating within the hospitality sector of Pakistan, as it highlights the relevance of organizational climate in enhancing their environmentally friendly practices. The present study adds to the current knowledge base by investigating the mediating influence of motivational states and the moderating impact of environmental awareness on the association between ethical climate and employees' green behavior within the hotel industry.
Designing and Adapting IoTs for the Next-Generation Smart Systems
Authors: Akujuobi Cajetan M.
Published: February 2025

The Internet of Things (IoT) is a network of physical objects embedded with sensors and other technologies that allow them to connect and exchange data. The IoT is a link between objects in the real world and the virtual world, thereby enabling anytime, anywhere connectivity for anything. This paper explores what it will take for IoT to meet the demands of next-generation intelligent systems in terms of design and adaptability. The possible challenges are also explored, and recommendations necessary to meet those challenges are made as well.
Technical Trends, Radical Innovation, and the Economics of Sustainable Industrial-Scale Electric Heating for Energy Efficiency and Water Savings.
Authors: Vissa A A , Sekhar J A
Published: February 2025

The energy efficiencies of various heating methods commonly used across industrial sectors are explored. The fossil fuel combustion heat sources typically used for industrial heating account for significant atmospheric pollution. The impact of fossil fuel-enabled industrial heating on the tropospheric CO2 levels is discussed. The electrification of industrial heating can significantly mitigate climate change. Advances in alternative sustainable heating processes are driven by improved energy conversion, precision control of processes because of electrification, and the related financial payback period. These advancements are related to improved materials, process optimization, and waste heat utilization practices at higher temperatures. The technical challenges of large-scale, heavy-duty electric process heating are addressed through novel innovations discussed in the article. Electrification and energy efficiency improvements have led to savings in the amount of water used for industrial electric steam generators that replace pressure boilers, thus further adding to the sustainability benefits of electrification of industrial heating devices. The financial payback calculations for energy-optimized processes are illustrated with examples. The article additionally examines the role of futuristic radical technical innovation as a sustainable pathway that can significantly reduce energy use without compromising performance objectives. The possibility of a new paradigm for self-organization across a process and the final use objectives is briefly explored as a possibility for sustainable innovations in thermal engineering and materials development.
The New Generalized Odd Median Based Unit Rayleigh with a New Shape Oscillating Hazard Rate Function
Authors: Attia Iman
Published: February 2025

In this paper, the author presents the generalized form of the Median-Based Unit Rayleigh (MBUR) distribution, a novel statistical distribution that is specifically defined within the interval (0, 1) expressing oscillating hazard rate function. This generalization adds a new parameter to the MBUR distribution that significantly addresses the unique characteristics of data represented as ratios and proportions, which are commonly encountered in various fields of research. The establishment of this generalization aims to deepen our understanding of these phenomena by providing a robust framework for analysis. The paper offers a thorough and meticulous derivation of the probability density function (PDF) for the MBUR distribution, illuminating each phase of the process with clarity and precision. It delves deep into the intricacies of the MBUR distribution's properties, presenting a rigorous examination of the accompanying functions that are vital for robust statistical evaluation. These functions—comprising the cumulative distribution function (CDF), survival function, hazard rate and reversed hazard rate function. The paper discusses real data analysis and how the generalization improves such analysis.
How to Improve Mental Health in the Older Adults Through AI‐Enhanced Physical Activity: An Emerging Research Topic
Authors: Fang Wen , Fan Sijing , Zheng Hongyun , Fang Zijian , You Yanwei , Yin Bo , Ye Xinming , Li Baixia
Published: February 2025

Introduction: With the global rise in elderly populations, the demand for healthcare and long-term care services has grown, particularly for addressing mental health. While AI has shown promise for health management of older adults, its effects on physical activity, mental health, and their associations remain largely unexamined.

Objective: This study aimed to investigate the role of AI in improving mental health among older adults through the promotion of physical activity and elucidating the underlying mechanisms.

Methods: Bibliometric methods, including CiteSpace and VOSviewer, were employed to analyze 1,831 publications, identifying trends, collaborations, hotspots, and potential applications in this domain.

Results: AI interventions significantly enhanced mental health in older adults by promoting physical activity. Prominent mechanisms included tailored psychological support, cognitive rehabilitation, and targeted cardiovascular and musculoskeletal interventions. These mechanisms drove better cognitive function, emotional regulation, and physical resilience. These interventions were supported in their effectiveness by biomarkers of BDNF, serotonin, and increased physical activity levels.

Conclusion: The present study combines insights on AI, physical activity, and mental health to facilitate emerging research opportunities and practical applications. It highlights the importance of AI for individualized exercise interventions, smart health monitoring, and cognitive and emotional rehabilitation. The insights provide an important compass for scholars and practitioners to harness the power of AI to meet the mental health needs of aging societies by systematically mapping the global research landscape that jointly features the changing demographic profile of older persons and anticipated solutions through the use of AI technologies, delivering evidence-based measures.
The Analysis of the Glycosyltransferase Activity Gene Family in Gossypium hirsutum and Functional Verification of GTs Conferring Resistance to Verticillium Wilt
Authors: Zhang Mingli , Zhu Fang , Sun Guo , Mi Yingjie , Zhang Xuekun , Zhao Sifeng , Yu Yu , Xi Hui
Published: February 2025

Glycosyltransferases (GTs) play an important role in plant growth and development, and responses to biotic and abiotic stresses. However, the function of GTs gene family in cotton resistance to Verticillium wilt is limited. In the present study, transcriptome analysis revealed eight GTs upregulated in susceptible cotton varieties and downregulated in resistant cotton varieties during early Verticillium dahliae inoculation, indicating which were involved in regulating the infection of V. dahliae in cotton. Promoter analysis revealed a high prevalence of MeJA (methyl jasmonate) and ABA (abscisic acid)-related cis-acting elements among these GTs. Genome-wide and location analysis of the homologous genes showed that these GTs were relatively conserved in evolution. Furthermore, a Virus-Induced Gene Silencing (VIGS) experiment results demonstrated a reduction in disease resistance after GhGT61 silencing. These insights not only deepen our understanding of the GT family's role in cotton but also provide a foundation for future research on the disease resistance mechanisms of these genes.
Open Government Data Topic Modelling and Taxonomy Development
Authors: Ferencek Aljaž , Kljajić Borštnar Mirjana
Published: February 2025

The expectations for the (re)use of Open Government Data (OGD) are high. However, measuring its impact remains challenging, as its effects are not solely economic, but also long-term and spread across multiple domains. To accurately assess these impacts, we must first understand where they occur. This research presents a structured approach to developing a taxonomy for Open Government Data (OGD) impact areas using machine learning-driven topic modeling and iterative taxonomy refinement. By analyzing a dataset of 697 OGD use cases, we employed various machine learning techniques—including Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP) - to extract thematic categories and construct a structured taxonomy. The final taxonomy comprises seven high-level dimensions: Society, Health, Infrastructure, Education, Innovation, Governance, and Environment, each with specific subdomains and characteristics. Our findings reveal that OGD’s impact extends beyond governance and transparency, influencing sectors such as education, sustainability, and public services. Compared to previous research that rely on predefined classifications or manual taxonomies, our approach provides a scalable and data-driven methodology for categorizing OGD impact areas. However, the study has certain limitations, including a relatively small dataset, brief use cases, and the inherent subjectivity of taxonomic classification, which requires further validation by domain experts. This research contributes to the systematic assessment of OGD initiatives and provides a foundational framework for policymakers and researchers aiming to maximize the benefits of open data.
The Persistence of Memory: Behavioral Analysis and Arm Usage of a 9-Armed Octopus vulgaris
Authors: Soule Sam Ellington , Cabanellas-Reboredo Miguel , González Ángel Francisco , Juijn Hidde , Hernández-Urcera Jorge
Published: February 2025

Anatomical abnormalities in octopuses, whose behavior is facilitated by flexible neuron-rich arms, offer insights into life histories and the neurological implications of understudied conditions such as bifurcation. Although documentation is scarce, here we present in-situ videos of 9-armed O. vulgaris with a functional bifurcated R1 arm. Analysis using RDAs and GLMs investigated the impact of the bifurcated arm on behavior and examined changes during growth. Analysis revealed a differential usage of between the bifurcated arms in addition to an initial specialization of the bifurcated arms for actions below the body, decreasing overtime as the arms grew. Further, bifurcated and regrown arms were utilized more in safe behaviors than risky ones, with more severely injured arms showing a higher frequency of use in safe behaviors. These findings contribute to the growing knowledge of arm usage in octopuses, suggesting that arm bifurcation may lead to branchial neural differentiation and potentially indicate post-traumatic associated in O. vulgaris.
Evaluation of Performance on Spiral Fluidic Sprinkler Using Different Nozzle Sizes Under Indoor Conditions
Authors: Lewballah Joseph Kwame , Zhu Xingye , Fordjour Alexander , Yao Simin
Published: February 2025

Sprinkler system performance enhancement has been a key area of research due to concerns about water shortages and rising energy costs. This study evaluated the hydraulic performance of the newly designed Spiral Fluidic Sprinkler (SFS) with various nozzles under different operating pressures. MATLAB software was used to simulate sprinkler uniformities under various operating pressures and the droplet diameter, velocity, and kinetic energy, was measured using a 2DVD video raindrop spectrometer. Results indicated that larger nozzles generally improved application uniformity and efficiency. Specifically, the application rates at distances from the sprinkler showed a reduction at smaller nozzle sizes and higher pressures, with larger nozzles maintaining better uniformity. The coefficient of variation (CV) was lower with the 4 mm nozzle compared to the 3 mm nozzle, suggesting more consistent water distribution. The average droplet size varied from 0.1 to 4.0 mm, with the majority of droplets smaller than 3 mm. The SFS demonstrated significant potential for enhancing water application efficiency, offering a means to conserve water while maintaining or improving agricultural productivity. Operating at lower pressures showed promising water savings, making the system suitable for use in varying field conditions, where uniformity and water conservation are critical.
Voltage Tunable Spoof Surface Plasmon Polariton Waveguide Loaded with Ferroelectric Resonators
Authors: Shen Jiaxiong , Lei Shun , Hu Mingzhe , Zhou Chaobiao , Luo Shengyun , Wang Chuanbin
Published: February 2025

A real-time tunable planar plasmonic waveguide based on voltage-adjustable ferroelectric resonator is designed and investigated. The laminated ferroelectric compound resonator is composed of a ferroelectric layer Ba0.85Ca0.15Zr0.9Ti0.1O3 (BCZT), a PCB layer as well as a localized spoof plasmonic metal layer, where the BCZT layer is benefit for enhancing the voltage tunability in the spoof surface plasmon polaritons (SSPPs) waveguide. The simulated results show that the tuning range of the notch in the transmission curve, generated by the coupling between ferroelectric compound resonator and the plasmonic waveguide, can achieve a variation of up to 8.8% thanks for the large tunability value in the BCZT ferroelectric layer. In addition, the notches are consisted by Fano resonant, whose generation mechanism is elaborately discussed in terms of the temporal coupled mode theory.
Training University Psychology Students to Teach Multiple Skills to Children with Autism Spectrum Disorder
Authors: Matos Daniel Carvalho de , Brito Ryan Matos e Silva Moura de , Silva Fabrício Brito , Costa Juliana Ribeiro Rabelo , Bagaiolo Leila , Pacífico Claudia Romano , Soares Pollianna Galvão
Published: February 2025

Training people interested in implementing Applied Behavior Analysis (ABA) interventions to children with autism spectrum disorder (ASD) is important to promote skill gains. A recommended training package is called behavioral skills training (BST), which involves four components (didactic instruction, modeling, role play, and performance feedback). Background/Objectives: The purpose was to assess the effects of BST on the accurate teaching of multiple repertoires via DTT by six Psychology university students to a confederate and six children diagnosed with ASD. Generalization and maintenance assessments were conducted. Results: Through the research conditions, all university participants were able to teach ten different repertoires with high integrity level to the children. In addition, across four months after training, all participants maintained high teaching integrity levels while teaching skills to the children related to their individualized curriculum goals. Each child accumulated over 1000 correct responses across several sessions. The university participants rated their training with the highest possible score in a social validity assessment. Conclusions: BST successfully trained Psychology university students to accurately teach multiple skills via DTT to children with ASD and involved long lasting effects. Limitations and new avenues for research were discussed.
Quantification of Peptides in Food Hydrolysate from Vicia faba
Authors: Manguy Jean , Papoutsidakis Georgios I , Doyle Ben , Trajkovic Sanja
Published: February 2025

The hydrolysis of raw food sources by commercially available food-grade enzymes releases thousands of peptides. The full characterization of bioactive hydrolysates requires robust methods to identify and quantify key peptides in these food sources. For this purpose, the absolute quantification of specific peptides, part of a complex peptide network, is necessary. Protein quantification with synthetic tryptic peptides as internal standards is a well-known approach, yet the quantification of non-tryptic peptides contained in food hydrolysates is still largely unaddressed. Similarly, data analyses focus on proteomics applications, thus adding challenges to the study of specific peptides of interest. This paper presents an in-sample calibration curve methodology for the identification of 3 non-tryptic peptides present in a Vicia faba food hydrolysate (PeptiStrong™: NPN_1) using heavy synthetic peptides as both calibrants and internal standards.
Advancing Financial Risk Management: AI-Powered Credit Risk Assessment through Financial Feature Analysis and Human-Centric Decision-Making.
Authors: De Silva Chameera
Published: February 2025

The financial sector operates on a delicate balance of continuous cash flows between firms and customers, inherently exposing itself to credit risk. Effec- tive and precise credit risk assessment is paramount for maintaining finan- cial stability and mitigating potential losses. Leveraging the rapid advance- ments in artificial intelligence (AI) and machine learning (ML), this study proposes an automated, real-time credit risk evaluation framework. Utiliz- ing the World Bank Global Findex dataset, encompassing transactional data from 123 countries, the research focuses on extracting and analyzing financial features critical for risk prediction. A suite of machine learning algorithms, including Support Vector Machines (SVM), Logistic Regression, and Decision Trees, is implemented to model credit risk. These models achieve remarkable performance, with accuracy reaching 99% and an F1-score of 1.00, underscor- ing their robustness in identifying at-risk profiles. Interpretability is ensured using Explainable AI (XAI) tools, highlighting key financial features such as debit card usage, mobile banking adoption, and deposit patterns as the most influential predictors of credit risk. This study emphasizes the integration of domain expertise with ML models, advocating for a human-in-the-loop approach to ensure reliability and trust in automated systems. By focus- ing on model interpretability and transparency, the proposed solution aligns with regulatory requirements and enhances decision-making processes in the financial sector. This work highlights the transformative potential of AI in credit risk management, offering a scalable, data-driven framework for global financial institutions.
Soil Indicators of Terroir and Its Importance for Adaptive and Sustainable Viticulture
Authors: Lisetskii Fedor , Poletaev Arseniy , Zelenskaya Evgenia
Published: February 2025

The comparison of equally important prerequisites of viticulture development (climatope and edaphotope) for its two historical regions has shown that both in the antique era and nowadays less favorable conditions of temperate climate (riskier in terms of frost danger) and poorer biogeochemical properties of soils and rocks in the steppe region compared to the conditions of the sub-Mediterranean region had a decisive influence on the volume and quality of wine products. This is supported by historical sources on local consumption of produced wine and imports of higher quality wine from Sub-Mediterranean vineyards (Tauric Chersonesos). Both regions of the Western Crimea are characterized by active bioaccumulation of iron, copper and manganese in the humus horizon of soils, which create a specific regional geochemical background with the prospect of approaching the ecological threshold of danger during long-term viticulture. The specific features of modern agricultural technology of viticulture lead to high concentrations of elements such as copper, vanadium, chromium and nickel, which can exceed the MPC levels 20-25 years after the vineyard is planted, earlier than other elements. This makes it possible to identify this list of heavy metals as a priority in the agroecological monitoring system for vineyard lands and sets the task of adjusting current agricultural technologies for sustainable viticultural practices in the specific soil and climatic conditions. The authors see prospects for further studies not only in improving methods for assessment of components of a wine terroir, especially biogeochemical parameters of soils and vine plants in a particular geographical area, but also in the technology for obtaining coupled integral estimates both in relation to edaphotope (stagnant environment (soil, parent and underlying rock) transformed by anthropogenic activity) and main products of viticulture and wine materials.
Numerical Investigation of the Effects of Fuel Injection Pressure and Nozzle Hole Diameter on Natural Gas-Diesel Dual-Fuel Combustion Characteristics
Authors: Durmaz Murat , Ergin Selma
Published: February 2025

Natural gas-diesel dual-fuel (NDDF) engines can reduce harmful emissions while maintaining diesel-like efficiency. However, under low-load conditions, they suffer from high methane (CH4) emissions, reduced combustion stability, and lower thermal efficiency. To address and improve these issues, this study numerically investigates the effects of injection pressure (32, 50, 90, and 126 MPa) and nozzle hole diameter (NHD, 110-230 μm) on dual-fuel combustion. 25%, 50%, and 75% natural gas energy fraction (NGEF) conditions are simulated for dual-fuel cases, and fully diesel-fueled conditions are also studied. Results at 50% and 75% NGEF indicate that increasing injection pressure significantly improves thermal efficiency while reducing CH4 and soot emissions. Furthermore, at 75% NGEF, NHD reduction from 230 to 150 μm provides more stable combustion rates, higher thermal efficiency and lower CH4 emissions. At 75% NGEF, the combination of 126 MPa injection pressure and 150 μm NHD reduces CH4 emissions by 77% and increases thermal efficiency by 9.8% compared to the baseline case (32 MPa and 230 μm). This study demonstrates that optimal combinations of injection pressure and NHD can significantly improve low-load issues in NDDF engines.
Complex Network Method for Inferring Well Interconnectivity in Hydrocarbon Reservoirs
Authors: Mayoral-Villa Margarita , Godinez Francisco , Gonzalez-Guevara Jose Antonio , Klapp Jaime , Guzmán Vázquez Enrique
Published: February 2025

This study presents an ad hoc machine-learning approach to uncover dynamical correlations among production and injection wells in naturally fractured reservoirs. The method is an application of the Visibility Graph Analysis (VGA) technique introduced by Lacassa et al. (2008) to analyze the state of an oil field. Due to its statistical nature, this technique relies on the available field information and historical production logs, makes no assumptions, and does not require specific information about the reservoir’s properties. We assume that all inter-well’s dynamic details are fully encoded in the historical logs. Thus, all production-injection data is transformed into a set of complex multiplex networks that take advantage of the VGA technique at the method’s core. These sets of networks map the production rates onto a ‘state’ image of the well system at a particular time. One well’s influence on another is then established by assessing the time development of the respective states, thereby effectively capturing the long and short-term dependencies and revealing the evolution of the well’s ‘hidden’ interconnections. This methodology was applied to a dataset (whose logs span sixty years) belonging to an active oilfield in Mexico. Our results show that the transitions between the field’s states provide crucial insights into the flow pathways within the reservoir. Moreover, they give a clear picture of how the operation of certain wells influences the production of other wells (e.g., when the production of oil in a well increases, decreases, or when a well switches from a production to an injection operation).
Electrochemical Behavior of Some Cinchona Alkaloids by Using Screen-Printed Electrodes
Authors: Palombi Laura , Caruso Tonino
Published: February 2025

An effective deposition of cinchonine layer on platinum metal surface can be easily achieved by the cathodic reduction of cinchonine hydrochloride methanolic solution at a controlled potential of -220 mV vs. the silver standard electrode (SSE). A coated screen-printed platinum electrode has proven suitable for cinchonine determination in water, urine and serum at ug L-1 level concentrations by differential pulse voltammetry in phosphate buffer solution (pH=7.0). The limits of detection (LOD) and the limits of quantitation (LOQ) are 0.6 ug L-1 and 1.8 ug L-1 respectively.
Curious Dichotomies of Apolipoprotein E Function in Alzheimer’s Disease and Cancer—One Explanatory Mechanism of Inverse Disease Associations?
Authors: Perks Claire M. , Barker Rachel M. , Alhadrami Mai , Alkahtani Omar , Gill Emily , Grishaw Mary , Harland Abigail J. , Henley Peter , Li Haonan , O'Sullivan Ellie , Stone Gideon , Su Xiaoyu , Kehoe Patrick G.
Published: February 2025

An apparent "inverse" relationship exists between two seemingly unconnected conditions: Alzheimer's disease (AD) and cancer, despite sharing similar risk factors, like increased age and obesity. AD is associated with amyloid beta (Aβ) plaques and neurofibrillary tau tangles that cause neural degeneration; cancer, in contrast, is characterised by enhanced cell survival and proliferation. Apolipoprotein E (ApoE) is the main lipoprotein found in the central nervous system and via its high affinity with lipoprotein receptors plays a critical role in cholesterol transport and uptake. ApoE has 3 protein isoforms: ApoE E2, ApoE E3, and ApoE E4, respectively encoded for by 3 allelic variants of APOE (ε2, ε3 and ε4). This review examines the characteristics and function of ApoE described in both AD and cancer, to assimilate evidence for its potential contribution to mechanisms that may underly the reported inverse association between the two conditions. Of the genetic risk factors relevant to most cases of AD, the most well-known with the strongest contribution to risk is APOE, specifically the ε4 variant, whereas for cancer risk, APOE has not featured as a significant genetic contributor to risk. Yet, at the protein level in both conditions, ApoE contributes to disease pathology via affecting lipid physiology and transport. In AD, Aβ-dependent and independent interactions have been suggested, whereas in cancer, ApoE plays a role in immunoregulation. Understanding the mechanism of action of ApoE in these diametrically opposed diseases, may enable differential targeting of therapeutics, to provide a beneficial outcome for both.
Development of Microfabrication Flow for Surfaced Micromachined Accelerometer
Authors: Jadoon Nabeel Ahmad Khan , Puventhiram Vaigunthan , Mosa Mayada Ahmed Hassan , Sharma Ashutosh , Wang Kaying
Published: February 2025

In this study, we present the microfabrication process for a surface micromachined accelerometer, aimed at addressing the demand for advanced rehabilitation tools in healthcare. Our overall solution offers an efficient range of motion (ROM) monitoring system to assess upper limb motion, leveraging off-the-shelf components and IoT technology. We focus on the fabrication of a compact wearable prototype, incorporat- ing inertial sensors and a suitable microcontroller for precise angle measurements. This research holds promise for enhancing patient outcomes in physical therapy by providing accurate monitoring and customized rehabilitation exercises through the proposed sensor-measurements in this report.