bluuebunny/crossref_metadata_2025 huggingface dataset:
A Single Physical Exercise Imitating Muscle Activity During an Epileptic Seizure Causes an Increase of MMP-9 Serum Level but Does Not Increase S100B
Authors: Sielczak Jan Karol , Kurkowska-Jastrzębska Iwona , Cudna Agnieszka , Krawczyk Maciej
Published: November 2024

Abstract: The aim of the study was to evaluate two widely studied biomarkers: MMP-9 and S100B in blood serum in healthy people after physical exercise mimicking physical activity during bi-lateral tonic-clonic seizures. Both biomarkers have been shown to increase after seizures, and their serum levels tend to be higher in epilepsy patients compared to individuals without epi-lepsy.
To achieve this goal, we have developed two sets of exercises, each consisting of a tonic contrac-tion phase and a phase of alternating muscle work, lasting a total of approximately 5 minutes. Both sets of motor exercises were performed in the same group of healthy volunteers 6 months apart. Serum samples were collected before exercise, 3 hours after exercise, and the next day (time points 0, 3 hours, and 24 hours).
Our results demonstrated that both sets of motor exercises led to a similar increase in MMP-9 levels, while neither while neither affected S100B levels. No significant differences in MMP-9 levels were observed due to muscle failure. We propose that the increase in MMP-9 is primarily induced by muscle contraction and additional mechanisms observed during exercise, such as neutrophil degranulation. S100B appears to be a promising biomarker in epilepsy, as it is not induced by physical activity but does increase following seizures.
Exploring the Impact of Cybersecurity Threats on Small and Medium Enterprises' Performance: A Case Study of Kajiado County, Kenya
Authors: Neyole Jacob , Minado Okwiri Saad , Mapema Nelson
Published: November 2024

Cybersecurity threats pose a significant challenge to the operational resilience and sustainability of Small and Medium Enterprises (SMEs) globally, with distinct implications for regional business ecosystems. This research presents an in-depth investigation into the impact of cybersecurity threats on SMEs within the unique context of Kajiado County, Kenya. Against the backdrop of an increasingly digitized business environment, this study aims to comprehensively assess the challenges SMEs face in mitigating cyber risks and their consequent impact on performance. The research employed a mixed methods approach, combining quantitative and qualitative analyses to elucidate the multifaceted nature of cybersecurity challenges encountered by SMEs in Kajiado County. Utilizing interviews, and document analysis, the study gathered empirical data to identify prevalent cybersecurity threats, their frequency, and the subsequent repercussions on SMEs' operational efficiency, financial stability, and overall performance metrics. Initial findings reveal a diverse spectrum of cybersecurity threats SMEs face in Kajiado County, ranging from phishing attacks and data breaches to ransomware incidents. These threats significantly impede business operations, resulting in financial losses, service delivery disruptions, and customer trust erosion. Moreover, the study examined the cybersecurity measures adopted by SMEs, highlighting the challenges in implementing effective preventive strategies within resource-constrained environments. Notably, the research addresses the identified gap by offering a nuanced understanding of the interplay between cybersecurity threats and SME performance in Kajiado County. The study intends to propose tailored and pragmatic recommendations to fortify SMEs' cybersecurity resilience by contextualizing global best practices within the regional business landscape. The anticipated outcomes of this research endeavor extend beyond academic discourse, aiming to provide actionable insights to SME stakeholders, policymakers, and industry practitioners. The findings aspire to empower SMEs in Kajiado County to enhance their cybersecurity posture, enabling them to navigate the evolving threat landscape more effectively and bolster their long-term sustainability within the digital economy.
A Review of Current Explainable Artificial Intelligence Forms
Authors: Salih Ahmed M
Published: November 2024

Artificial intelligence techniques including machine learning models have shown success in variety of domains. This is more evident with complex models including deep learning. However, such success accompanied by vagueness around how the models work, the internal mechanism and making a decision. Explainable Artificial intelligence (XAI) emerged as a new field of research to uncover the mystery around how the complex models work. The ultimate aim of XAI is to make the complex models more transparent, trustworthy and understandable even by lay-persons with no technical background. XAI could come in different forms including heatmaps applied to images, significant concepts to the model when making a decision, informative features with tabular data, one-feature effect on the outcome, fuzzy logic rules, textual explanation through images captioning, uncertainty quantification and much more. There are several factors affect the XAI forms including the used data and the model. This paper is dedicated to review and group the current XAI methods in the literature based on the outcome form. In addition, the paper discusses the XAI groups, how they work, strengths and weaknesses. Our paper shows that although e XAI methods have been used extensively within the research context, their employability in real life problems especially in sensitive domains might be unreasonable in the current stage.
Arabic Temporal Common Sense Understanding
Authors: Alqifari Reem , Al-Khalifa Hend , O'Keefe Simon
Published: November 2024

Natural Language Understanding (NLU) includes temporal text understanding, which can be complex and encompasses temporal common sense understanding. There are many challenges in comprehending common sense within a text. Currently, there are a limited number of datasets containing temporal common sense in English, and there is an absence of such datasets specifically for the Arabic language. In this study, an Arabic dataset was constructed based on an available English dataset. This dataset is considered a valuable resource for the Arabic community. Consequently, different multilingual pretrained language models (PLMs) were applied to both the English and new Arabic datasets. Based on this, the effectiveness of these models in Arabic and English is compared and discussed. After analyzing the errors, a new categorization of errors was proposed. Finally, the ability of the PLMs to understand the input text and predict temporal features was evaluated. Through this detailed categorization of errors and classification of temporal elements, this study establishes a comprehensive framework aimed at clarifying the specific challenges encountered by PLMs in temporal common sense understanding (TCU). This methodology underscores the urgent need for further research on PLMs’ capabilities for TCU tasks.
Enhancing Mobile Ad Hoc Networks (MANETs) with Software-Defined Networking (SDN): A Balanced Approach
Authors: Piroddi Andrea , Fonti Riccardo
Published: November 2024

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks, characterized by their dynamic topologies and node mobility. In the era of cutting-edge technologies, integrating Software-Defined Networking (SDN) with MANETs offers a promising solution to manage these challenges more efficiently. This paper presents a balanced discussion of MANETs and SDN, demonstrating how SDN principles, such as centralized control and network virtualization, can optimize MANET performance in terms of scalability, cost-efficiency, and security. A mathematical model is developed to analyze CAPEX, OPEX, and network efficiency.
How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?
Authors: Saoji Aniket A , Sheikh Bilal A , Bertsch Natasha J , Goulson Kayla R , Graham Madison K , McDonald Elizabeth A , Bross Abigail E , Vaisberg Jonathan M , Kühnel Volker , Voss Solveig C , Qian Jinyu , Hogan Cynthia H , DeJong Melissa D
Published: November 2024

Background/Objectives
Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition in their best hearing aid condition, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) based noise reduction on cochlear implant evaluation.

Methods
Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratio (SNR) through one loudspeaker. Sentence recognition scores were measured for 10 (5-bilateral, 5-unilateral) hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility.

Results
The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. This improvement might make it difficult for some patients to achieve the
Classical and Differential Topological Approaches to Gravitational Fields and Cosmic Singularities
Authors: Montgomery Richard Murdoch
Published: November 2024

This paper explores the relationships between differential topology and general relativity, examining the classical framework's applicability in modelling gravitational fields and the limits encountered with topological defects such as cosmic strings. By illustrating smooth metric structures, holonomy, and geodesic completeness, we reveal the foundational role of differential topology in describing spacetime curvature without quantum corrections. The Einstein field equations and global topological invariants are discussed in contexts where continuous manifolds hold, while highlighting how phenomena like cosmic strings challenge these assumptions. We propose that such topological defects introduce singular behaviours where traditional differential topology reaches its boundaries, potentially necessitating quantum gravitational models for a complete description. Visualizations of Schwarzschild curvature, holonomy in spherical surfaces, and geodesic paths provide insights into gravitational field variability and topological constraints in classical models, underscoring the mathematical and physical principles at play.This paper explores the relationships between differential topology and general relativity, examining the classical framework's applicability in modelling gravitational fields and the limits encountered with topological defects such as cosmic strings. By illustrating smooth metric structures, holonomy, and geodesic completeness, we reveal the foundational role of differential topology in describing spacetime curvature without quantum corrections. The Einstein field equations and global topological invariants are discussed in contexts where continuous manifolds hold, while highlighting how phenomena like cosmic strings challenge these assumptions. We propose that such topological defects introduce singular behaviours where traditional differential topology reaches its boundaries, potentially necessitating quantum gravitational models for a complete description. Visualizations of Schwarzschild curvature, holonomy in spherical surfaces, and geodesic paths provide insights into gravitational field variability and topological constraints in classical models, underscoring the mathematical and physical principles at play.
Modern Hypoglycemic Agents and Their Perioperative Implications
Authors: Goudra Basavana , Green Michael
Published: November 2024

There are far more options to treat diabetes now, than about 15-20 years ago. Among the newer agents, glucagon-like peptide 1 receptor agonists (GLP-1RA), dipeptidyl peptidase-4 inhibitors (DPP-4i) and sodium-glucose cotransporter 2 inhibitors (SGLT-2i), are in common use. While their benefits extend far beyond the management of hyperglycemia, some pose concerns, and challenges to the anesthesia providers. Retained gastric contents due to slow gastric emptying is a significant drawback of GLP-1RA. It benefits the patient in losing weight; however, increases the risk of aspiration and can surprise an unsuspecting anesthesia provider. National associations have issued statements in an effort the mitigate the risk. Recommendations range from withholding of GLP-1RA for a predefined period of time, gastric ultrasound to evaluate the gastric contents and modification of anesthesia management especially with regards to the airway and cancellation of a scheduled (elective) surgery/procedure. SGLT-2i are known to increase the risk of euglycemic ketoacidosis. Duration of preprocedural withholding of SGLT-2i varies with the drug. Use of both GLP-1RA and SGLT-2i extends beyond the treatment of diabetes. While SGLT-2i are being extensively used to treat obesity, they are also used in the management of heart failure. A new insulin formulation of basal insulin that can be administered once weekly to achieve similar blood sugar control as once daily formulation has become available in some countries. Labelled as icodec, this is as yet not approved by the FDA because of severe hypoglycemia concerns. Imeglimin is a variant of metformin with unique mechanisms of action. It is approved for clinical use for type 2 diabetes in Japan and India. Another, as yet investigational product is IcoSema, a once-weekly combination of insulin icodec plus semaglutide. A discussion of all aspects of these drugs is warranted.
Enhancing Lamb Weight Increase Through Genetic and Environmental Strategies in Arid Environments
Authors: Al-Najjar Khaled , Yasin Abdulmounem O. Al , Alshdaifat Mustafa M. , Dayoub Moammar , Raheem Dele
Published: November 2024

Sheep farming thrives in diverse climates, with the Awassi breed well-suited for arid environments, though it faces challenges that necessitate selective breeding and innovative management to improve productivity and weight performance. This study, conducted in 2023 at Al-Fjaj Station in Jordan, analyzed the weights of Awassi sheep managed under a semi-intensive system, examining 2,263 records to evaluate the environmental and genetic factors influencing growth and utilizing statistical models to estimate heritability and breeding values. Variance analysis revealed that birth type, lamb sex, parity, and ewe age at lambing significantly influence birth, weaning, 6-month, and annual weights, with single-born lambs generally heavier than twins, males surpassing females, and younger ewes producing lighter lambs at birth that catch up in weight later. Lamb weights are correlated positively across various stages, especially between weaning, six-month, and yearling weights, while the influence of birth weight on later weights is comparatively weaker. Analysis of Variance (Type 3) indicates that rams have the greatest influence on lamb weights at all ages, while ewes primarily affect early weight, and genetic factors play a moderate role in weight variation, with maternal effects being more significant than paternal influences. There are significant positive genetic correlations among lamb weight traits, indicating that improvement in one trait can benefit others, while mean breeding values decrease with age due to increasing environmental influences, suggesting the use of index selection to enhance multiple traits in breeding programs. High correlation coefficients between breeding values and mean weights of Awassi sheep indicate that higher breeding values for specific weight traits are associated with greater actual weights, demonstrating that selection based on breeding values can effectively enhance genetic potential and predict future performance. The genetic performance and growth traits of rams' progeny indicate that selecting rams with higher breeding values can enhance weight gain in future generations, while mean weights serve as performance benchmarks to help breeders improve flock genetics and profitability. In conclusion, this study identifies key factors influencing lamb weights, such as birth type, lamb sex, ewe parity, and ewe age, emphasizing the importance of single lambs and males in achieving higher growth, while suggesting that an index selection approach combining genetic merit with environmental factors is vital for optimizing sustainable lamb production in arid environments.
Recent Progress in Plastic Transformation and Depolymerization Techniques
Authors: de Souza Acácio Silva , Ferreira Patricia Garcia , de Jesus Iva Souza , de Oliveira Rafael Portugal Rizzo Franco , de Carvalho Alcione Silva , Futuro Debora Omena , Ferreira Vitor Francisco
Published: November 2024

Plastic polymers derived from petroleum exhibit diverse physical properties, ranging from flexible to rigid, permeable to impermeable, and hydrophilic to hydrophobic. However, when discarded improperly, they become a significant problem. Plastic pollution poses a threat to the planet, affecting various ecosystems, including terrestrial environments, rivers, lakes, oceans, and underground freshwater sources. These ecosystems are polluted with ropes, fishing nets, syringes, plastic bottles, and garbage bags. This issue is closely linked to the incredible versatility of these materials, whose continuous growth in production is driven by their market value. The current linear production and consumption of plastics are unsustainable and result in excessive waste. This waste is poorly managed, leading to pollution and carbon dioxide emissions that undermine global climate goals and the Sustainable Development Goals (SDGs). While some macroplastics are mechanically recycled, others are incinerated, and a significant portion ends up in the environment. Strategies for transforming and depolymerizing plastics offer potential pathways for future reuse. In this article, we explore the latest alternatives for the transformation and depolymerization of macroplastics through processes such as pyrolysis and hydrogenolysis.
Outcomes and Prognosis of COVID-19-Induced ARDS Patients Treated with Prolonged VV-ECMO: A Retrospective Multicenter Study
Authors: Bitan Amram , Sagie Nitzan , Ilgiyaev Eduard , Stavi Dekel , Makhoul Maged , Soroksky Arie , Kasif Yigal , Novack Victor , Galante Ori
Published: November 2024

Background: Predicting whether extracorporeal membrane oxygenation (ECMO) treatment duration affects prognosis is important both medically and economically. We conducted a retrospective, multicenter study to better understand the outcomes of patients treated with VV ECMO for a prolonged duration, analyzing data from the Israel ECMO registry. The study included all adult patients treated with VV-ECMO due to COVID-19-induced respiratory failure. The primary outcomes were survival rates up to 180 days from cannulation. Results: One hundred and eighty-eight patients were included in the study. The median age was 50 years (IQR 42, 50) and 69% were male. Patients were mechanically ventilated for a median of 2.5 days before cannulation (IQR 0.5, 5). The mean ECMO support duration was 29.9 days, with a maximal duration of 189.9 days. Survival rate for 180 days was 56%. We found no change in survival for patients on ECMO for 14, 28, or 56 days. Every day of mechanical ventilation before cannulation correlated with an 11% greater risk for prolonged ECMO treatment (p=0.01). Conclusions: For COVID-19-induced ARDS patients treated with VV-ECMO, ECMO treatment duration did not affect mortality. The longer the duration of mechanical ventilation before ECMO cannulation, the higher the risk for prolonged ECMO treatment.
Wavelet Transform-Based Feature Extraction for Face Kinship Verification
Authors: El Ouanas Belabbaci , Khammari Mohammed , Akram Abderraouf Gharbi , Mohamed Rafik Aymene Berkani , Ammar Chouchane
Published: November 2024

Kinship verification using facial images has become an important area of research within computer vision. This paper presents a robust kinship verification system that leverages Histograms of a Two-Dimensional Discrete Wavelet Transform (Hist-2D-DWT) to improve recognition accuracy. The system extracts planar features by decomposing images into four coefficients—approximation, horizontal, vertical, and diagonal—via discrete wavelet transform. Comprehensive experiments conducted on the TSKinFace and Cornell KinFace datasets validate the method’s effectiveness, achieving accuracies of 93.46% and 89.61%, respectively.
Impact of Problematic TikTok Use on Mental Health: A Systematic Review and Meta-Analysis
Authors: Galanis Petros , Katsiroumpa Aglaia , Katsiroumpa Zoe , Mangoulia Polyxeni , Gallos Parisis , Moisoglou Ioannis , Koukia Evmorfia
Published: November 2024

TikTok has rapidly become one of the most popular social media platforms, and, thus, scholars should pay attention to its association with users’ mental health. Our aim was to synthesize and evaluate the association between problematic TikTok use and mental health. We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines in our review. The review protocol was registered with PROSPERO (CRD42024582054). We searched PubMed, Scopus, Web of Science, PsycINFO, ProQuest, and CINAHL until September 02, 2024. We identified 16 studies with 15,821 individuals. All studies were cross-sectional and were conducted after 2019. Our meta-analysis showed a statistically significant positive association between TikTok use and depression (β = 0.321, 95% confidence interval [CI]: 0.261 to 0.381, p < 0.001), and anxiety (β = 0.406, 95% CI: 0.279 to 0.533, p < 0.001). Also, we found a positive association between TikTok use and body image issues, poor sleep, anger, distress intolerance, narcissism, and stress. Our findings suggest that problematic TikTok use has a negative impact on several mental health issues. Given the high levels of TikTok use especially among young adults, our findings are essential to further enhance our understanding of the impact of TikTok use on mental health. Finally, there is a need for further studies of better quality to assess the impact of problematic TikTok use on mental health in a more valid way.
Darwinian Evolution as a Product of the Second Law: Biotic Entropy Production in the Universe
Authors: Lineweaver Charles H.
Published: November 2024

There is much confusion about the apparent opposition between Darwinian evolution and the second law of thermodynamics. Entropy and entropy production play a more important role in the origin of life and Darwinian evolution than is generally recognized. Here I argue that (i) Darwinian evolution is a product of the second law, in that life forms are far from equilibrium dissipative systems that have come into existence because of gradients in the universe, and that (ii) life enhances the 2nd law by increasing entropy production. Since the 2nd law involves entropy increase, we go beyond it with an hypothesis that involves the increase of entropy production. Gould proposed a wall of minimal complexity to explain an apparent increase in biological complexity. I show that a tendency for entropy production to increase offers a better explanation for the simultaneous increase of both biological complexity and simplicity. I review a simple universe-in-a-cup-of-coffee model and propose an alternative operational definition of complexity that it is proportional to entropy production. Finally, I sketch the cosmic history of entropy production and use it to evaluate the effectiveness of the hypothesis that there is a tendency for entropy production to increase.
Real-world Clinical Utility of a Methylated DNA Biomarker Assay on Samples Collected with a Swallowable Capsule-balloon for Detection of Barrett’s Esophagus (BE)
Authors: Lister Dan , Fine Andy , Maheshwari Shail , Bradley Paul S. , Lister Beth , Lee Victoria T. , DeGuzman Brian J. , Verma Suman , Aklog Lishan
Published: November 2024

Background: Barrett’s Esophagus (BE) is the only known precursor for esophageal adenocarcinoma (EAC). Patients with multiple risk factors for BE/EAC are recommended for screening, however few eligible patients undergo evaluation by endoscopy. EsoGuard® (EG) is a commercially available biomarker assay used to analyze esophageal cells collected non-endoscopically with EsoCheck® (EC), for qualitative detection of BE/EAC. This study evaluates real-world clinical utility of EG on cells collected with EC in patients defined by U.S. gastroenterology societies to be at-risk for BE and EAC. Methods: This multi-center, observational CLinical Utility of EsoGuard (CLUE) study enrolled screening-eligible patients as defined by the American College of Gastroenterology (ACG) and the American Gastroenterological Association (AGA). Clinical utility was evaluated by the provider decision impact of EG, and additionally by assessing patient compliance outcomes with recommended follow-up testing. Results: 551 patients were enrolled, with a mean age of 62.0 ± 12.4 years and 56.1% (309/551) meeting ACG guideline criteria for BE screening. EC cell collection was successful in 97.1% (535/551), among which EG positivity rate was 27.3% (n = 146). The provider decision impact was high with 100% of EG positive patients being referred for esophagogastroduodenoscopy (EGD), while 98% of EG negative patients were not referred. Among EG positive patients, the overall compliance with follow-up EGD was 85.4%. Conclusions: Combining EC non-endoscopic esophageal cell collection with the EG biomarker assay is effective in guiding provider decision-making for detection of BE and EAC. Patients with positive EG results demonstrate high compliance with recommended follow-up EGD.
Chebyshev Polynomials in the Physics of the One-Dimensional Finite-Size Ising Model: An Alternative View and Some New Results
Authors: Tonchev Nicholay , Dantchev Daniel
Published: November 2024

For studying of the finite-size behavior of the Ising model under different boundary conditions we propose an alternative to the standard transfer matrix technique approach based on Abel\`{e}s theorem and Chebyshev polynomials. Using it we easily reproduce the known for periodic boundary conditions results for Lee-Yang zeros, the exact position space renormalization group transformation, etc., and extend them deriving new results for antiperiodic boundary conditions. Note that in the latter case one has a nontrivial order-parameter profile, which we also calculate, where the average value of a given spin depends on the distance from the seam with opposite bond in the system. It is interesting to stress that under both boundary conditions, the one-dimensional case exhibits Schottky anomaly.
A Computational Model of Hybrid Trunk-Like Robots for Synergy Formation in Anticipation of Physical Interaction
Authors: Morasso Pietro Giovanni
Published: November 2024

Trunk-like robots have attracted a lot of attention in the community of researchers interested in the general field of bio-inspired soft robotics, because trunk-like soft arms may exhibit high dexterity and adaptability very similar to the elephants and potentially quite superior to traditional articulated manipulators. In view of practical applications, the integration of a soft hydrostatic segment with a hard-articulated segment, i.e. a hybrid kinematic structure similar to the elephant’s body, is probably the best design framework. It is proposed that this integration should occur at the conceptual/cognitive level before being implemented in specific soft technologies, including the related control paradigms. The proposed modeling approach is based on the Passive Motion Paradigm (PMP), originally conceived for addressing the degrees of freedom problem of highly-redundant, articulated structures. It is shown that this approach can be naturally extended from highly-redundant to hyper-redundant structures, including hybrid structures that include a hard and a soft component. The PMP model is force-based, not motion-based and is characterized by two main computational modules: the Jacobian matrix of the hybrid kinematic chain and a Compliance matrix that maps generalized force fields into coordinated gestures of the whole body-model. It is shown how the modulation of the compliance matrix can be used for the synergy formation process, that coordinates the hyper-redundant nature of the hybrid body-model and, at the same time, for the preparation of the trunk-tip in view of a stable physical interaction of the body with the environment, in agreement with the general impedance-control concept.
IoT Based Sensing for Assessing Ambient Environmental Conditions and Air Quality Influences on Avian Vocal Behavior and Diversity
Authors: Iqbal Mazhar , Wijeratne Lakitha Omal Harindha , Waczak John , Dewage Prabuddha M.H. , Balagopal Gokul , Lary David J.
Published: November 2024

This study introduces a new approach to monitoring avian diversity and vocal behavior, as well as assessing their responses to environmental factors using IoT-based sensors. We utilized BirdNet, a deep learning model for identifying bird species by their vocalizations, to data from our MINTS-AI environmental sensors deployed across Dallas, Texas. The study investigates how ambient temperature, humidity, light intensity, and particulate matter concentrations affect bird behavior. The results show a positive correlation between bird diversity and temperature during winter, with a Pearson coefficients of 0.65 (2023) and 0.80 (2024), and R2 values of 0.43 and 0.64, respectively. In contrast, during summer, a negative correlation was found with Pearson coefficients of -0.63 (2023) and -0.34 (2024), with corresponding R2 values of 0.40 and 0.11. Machine learning models further highlighted species such as the Northern Mockingbird and Mississippi Kite as particularly sensitive to temperature changes. Humidity analysis revealed significant correlations of vocal activity for species like the Canada Goose and House Finch, indicating that vocal activity may depend on moisture levels. Light intensity also showed strong influences on species such as the Northern Mockingbird and Scissor-tailed Flycatcher, linking variations in light to vocal behavior. However, no significant relationship was found between particulate matter (PM2.5) concentrations and bird behavior. This methodology offers a new way to explore the effects of climate change on bird behavior over time, given adequate long-term data. These findings emphasize the important role of environmental changes in shaping bird populations and their ecological interactions.
Data-Driven Optimization for Low-Carbon Prefabricated Components Production Based on Intelligent Algorithms
Authors: Ho Chun-Ling , Wang Chien-Chih , Shenjun Qi , Zichen Zhang
Published: November 2024

The global industries are gradually moving towards the development of low-carbon, however, construction industry has significant energy consumption and carbon emissions. To making the promotion of industrialized construction with low-carbon and energy-efficient, prefabricated buildings are one of important ways to achieve environmental sustainability. This study first investigated the development and status of prefabricated concrete component factories in Fujian Province about regional distribution and production conditions and collecting data of carbon emissions, time and costs to create a multi-objective optimization model for intelligent and collaborative production. All while keeping costs as low as possible, using ant colony algorithms meets low-carbon and promotes sustainable development of prefabricated buildings. With the number of iterations, the overall cost shows a downward during the optimization process. According to optimization modes, the result of slabs indicates the minimum production cost (5.7023 million with carbon emissions 1154.85 tons), and a cost difference of 10000 can result in a maximum carbon emissions difference of 50 tons while minimizing costs to be the primary goal. Compared with general production, the optimization of collaborative production reduces both costs and carbon emissions. Meanwhile, focused on normal and rush mod, the cost reduced more than 20% can be down to 50% on carbon emissions. Therefore, effectively reducing carbon emissions in the component production will contribute to the sustainability of the construction industry.
Genome Language Modeling (Glm): A Beginner’s Cheat Sheet
Authors: Tyagi Navya , Vahab Naima , Tyagi Sonika
Published: November 2024

Combining genomics with digital healthcare information is set to transform per- sonalized medicine. However, this integration is challenging due to the differing nature of the data modalities. The large size of the genome makes it impossible to store it as part of the standard electronic health record (EHR) system. Rep- resenting the genome as a condensed representation containing biomarkers and usable features is required to make the genome interoperable with EHR data. This systematic review examines both conventional and state-of-the-art methods for genome language modeling (GLM), which involves representing and extract- ing features from genomic sequences. Feature extraction is an essential step for applying machine learning (ML) models to large genomic datasets, especially within integrated workflows. We first provide a step-by-step guide to various genomic sequence pre-processing and representation techniques. Then we explore feature extraction methods including tokenization, and transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss ML applications in genomics, focusing on classification, prediction, and language processing algorithms. Additionally, we explore the role of GLM in func- tional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers (BERT), enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.
Laponite vs. Montmorillonite as Eugenol Nanocarriers for Low Density Polyethylene Active Packaging Films
Authors: Kechagias Achilleas , Salmas Constantinos E. , Chalmpes Nikolaos , Leontiou Areti A. , Karakassides Michael A. , Giannelis Emmanuel P. , Giannakas Aris E.
Published: November 2024

This study investigates the use of two nanoclays, Laponite (Lap) and Montmorillonite (Mt), as eugenol slow release carriers for enhancing the functionality of low-density polyethylene (LDPE) active packaging films. Utilizing a vacuum-assisted adsorption method, eugenol was successfully intercalated into Lap and Mt nanoclays, forming EG@Lap and EG@Mt nanohybrids. The nanohybrids were subsequently incorporated into LDPE films at varying concentrations (5, 10, and 15 wt.%). Extensive characterization by a number of analytical techniques confirmed effective integration and dispersion of the nanohybrids within the LDPE matrix. EG@Lap and EG@Mt nanohybrids enhanced the films’ antioxidant and barrier properties, with EG@Lap demonstrating higher eugenol release rates, antioxidant activity, and barrier effectiveness. Packaging tests on minced pork revealed that the LDPE/15EG@Lap films extended shelf life by approximately two days more compared to the LDPE/15EG@Mt films, due to the higher eugenol release. These findings suggest that EG@Lap-based films offer improved active packaging capabilities, with potential applications in a number of areas where extending the freshness of perishable foods is needed.
Integrative Multi-Omics Approach in Vascular Ehlers-Danlos Syndrome: Further Insights into the Disease Mechanisms by Proteomic Analysis of Patient Dermal Fibroblasts
Authors: Chiarelli Nicola , Cinquina Valeria , Zoppi Nicoletta , Bertini Valeria , Maddaluno Marianna , De Leonibus Chiara , Settembre Carmine , Venturini Marina , Colombi Marina , Ritelli Marco
Published: November 2024

Dominant mutations in COL3A1 are known to cause vascular Ehlers-Danlos syndrome (vEDS) by impairing extracellular matrix (ECM) homeostasis. This disruption leads to the fragility of soft connective tissues and a significantly increased risk of life-threatening arterial and organ ruptures. Currently, treatments for vEDS are primarily symptomatic, largely due to a limited understanding of its underlying pathobiology and molecular mechanisms. In this study, we conducted a com-prehensive analysis of the intracellular proteome of vEDS fibroblasts, integrating these findings with our previous transcriptome results to identify key molecular pathways that drive the disease. Additionally, we explored the therapeutic potential of inhibiting miR-29b-3p as a proof of concept. Our integrative multi-omics analysis revealed complex pathological networks, emphasizing the critical role of miRNAs, particularly miR-29b-3p, in impairing ECM organization, autophagy, and cellular stress responses, all of which contribute to the pathogenesis of vEDS. Notably. the inhi-bition of miR-29b-3p in vEDS fibroblasts resulted in the upregulation of several differentially expressed target genes involved in these critical processes, as well as increased protein expression of essential ECM components, such as collagen types V and I. These changes suggest potential therapeutic benefits aimed at improving ECM integrity and restoring intracellular homeostasis. Overall, these insights not only enhance our understanding of the complex biological mechanisms underlying vEDS but also provide a solid foundation for future research focused on developing targeted treatment strategies to address this life-threatening disorder effectively.
Deep Learning-Driven Skin Cancer Classification via CNN Features and Random Forest Classifier
Authors: El Ouanas Belabbaci , Mohamed Rafik Aymene Berkani , Akram Abderraouf Gharbi , Ammar Chouchane , Ouamane Abdelmalik
Published: November 2024

Skin cancer is one of the most common and potentially deadly forms of cancer, making early and accurate detection crucial for effective treatment. In this context, leveraging advanced machine learning techniques can significantly enhance diagnostic accuracy and efficiency. This paper presents an intelligent system for monitoring skin cancer, leveraging a deep feature extraction approach based on Convolutional Neural Networks (CNNs) combined with a Random Forest (RF) classifier. The system processes skin cancer images to extract features using three pre-trained CNN models: ResNet18, Darknet19, and MobileNetv2, which are then used to train an RF classifier for accurate skin cancer detection. The proposed model is designed for edge deployment, making it suitable for real-time applications. Experiments were conducted using the "Skin Cancer: Malignant vs. Benign" dataset, specifically curated for skin cancer classification. The results demonstrate the effectiveness of the proposed system, achieving an accuracy of 82.85%, with a precision of 82.69%, a sensitivity of 82.86%, a specificity of 82.86%, and an F1-Score of 82.75%.
An Interactive Knowledge Platform for Promoting Sustainable Tourism and Other SDGs
Authors: Chowdhury Gobinda , Kwiecien Kanyarat , Chitiyaphol Jaturong , Chowdhury Sudatta
Published: November 2024

The interactive knowledge platform, FaST, can be used by general public, various government and non-government agencies, and businesses for the advancement of the local economy in the community, creating jobs for community members, increasing income potentials, and thus alleviating poverty; and provide enhanced educational opportunities, and can also indirectly benefit to improve health and other prospects.
Validity and Reliability of Wind Speed Calculated by Notio in Comparison with a Hot-Wire Anemometer
Authors: Ordiñana-Pérez Manuel , Mateo Manuel , Roldan Ainoa , Barranco-Gil David , Zabala Mikel , Blasco-Lafarga Cristina
Published: November 2024

(1) Background: Notio calculates coefficient of drag area (CDA) considering dynamic pressure data calculated by an integrated Pitot-static tube. Therefore, this study aimed to evaluate the validity and reliability of Pitot-static tube calculations through wind speed (WS) data, against a hot-wire anemometer (HWA). (2) A total of 60 recordings were made, lasting 30 seconds each, in a closed-circuit wind tunnel at 4 different WS (≈30 to ≈60km/h), and at five different yaw angles (0º to 20º). (3) Results: Notio showed WS 6.44% higher than HWA. The calibration process recommended by the Notio manufacturer reduced the differences to a non-significant 0.76%. Comparison of the WS of Notio calibrated and HWA only showed significant differences in the WS group of ≈60 km/h. There were no significant differences in the comparison of yaw angles groups. The reliability of Notio was worse than that of the HWA. (4) Conclusions: Notio calibrated at a speed close to its use, allows reliable and accurate calculation of WS over a wide range of yaw angles under controlled wind tunnel conditions without the presence of a cyclist and bike. However, due to the influence of WS on aerodynamic drag, small errors in WS could translate into considerable values of CDA for cycling performance.
Scientometric Insights into Rechargeable Solid-State Battery Developments
Authors: Bridgelall Raj
Published: November 2024

Solid-state batteries (SSBs) offer significant improvements in safety, energy density, and cycle life over conventional lithium-ion batteries, with promising applications in electric vehicles and grid storage due to their non-flammable electrolytes and high-capacity lithium metal anodes. However, challenges such as interfacial resistance, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This study conducts a comprehensive scientometric analysis, examining 131 peer-reviewed SSB research articles from IEEE Xplore and Web of Science databases to identify key thematic areas and bibliometric patterns driving SSB advancements. Through a detailed analysis of thematic keywords and publication trends, this study identifies innovations in high-ionic-conductivity solid electrolytes and advanced cathode materials, which have significantly impacted performance gains and are critical to commercializing SSB technology. The findings provide actionable insights for researchers and industry stakeholders, specifically in the areas of interfacial engineering and manufacturability, where gaps in long-term stability and scalable production continue to hinder widespread adoption of SSBs. The study reveals key advances in electrolyte interface stability and ion transport mechanisms, identifying how solid-state electrolyte modifications and cathode coating methods improve charge cycling and reduce dendrite formation, particularly for high-energy-density applications. By mapping publication growth and clustering research themes, this study highlights high-impact areas such as cycling stability and ionic conductivity, providing a roadmap for targeted research efforts and strategic investments to advance SSB technology for critical applications in transportation and storage. The insights from this analysis guide researchers toward impactful areas, such as electrolyte optimization and scalable production, and provide industry leaders with strategies for accelerating SSB commercialization to extend electric vehicle range, enhance grid storage, and improve overall energy efficiency.
Sustainability in Small-Scale Coffee Farming: Agricultural Practices, Women's Role and Climate Change Adaptation along the Buffer Zone of Mt. Hamiguitan, Davao Oriental, Philippines
Authors: Nemenzo-Calica Phoebe , Pateño-Bongas Henzel , Magallones Maria Odessa G. , Clapano Misael B.
Published: November 2024

To promote a sustainable coffee industry, Davao Oriental has branded its coffee in association with Mt. Hamiguitan. This study evaluated the smallholder coffee farmers’ practices along the Mt. Hamiguitan buffer zone, highlighting women’s roles, and their climate change awareness and adaptation strategies. Fifty-seven purposively selected farmers (28 females, 29 males) were interviewed using a validated semi-structured questionnaire. Data were analyzed using ARCGIS software, thematic analysis, and descriptive statistics. Most farmers (70%) were old (>51) with over 40 years of experience (51%). Non-participation in farmers’ associations (73.7%) surpassed active membership (26.3%), with only 14% attending training. Most farmers (65%) intercropped 500–600 Robusta coffee trees/ha, with many farms established in the 1980s (36%). Farmers per-formed clearing (82%) and pruning (94%) of their coffee. Women played a vital role, especially during drying and harvesting. Challenges faced by farmers included long distances to buyers (91%), low prices (84%), poor farm-to-market roads (83%), and a lack of buyers (39%). All respondents acknowledged climate change and employed adaptation strategies such as intercropping, pruning, using ipil-ipil for shade, and utilizing nets. Recommendations for fostering sustainable practices while improving livelihoods include joining cooperatives, providing training for enhanced climate change adaptation strategies, and increasing women's participation in coffee production.
Leveraging LLMs in Tourism: A Comparative Study of the latest GPT Omni models and BERT NLP for Customer Review Classification and Sentiment Analysis
Authors: Roumeliotis Konstantinos I. , Nikolaos Tselikas D. , Dimitrios Nasiopoulos K.
Published: November 2024

In today's rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer satisfaction, enabling businesses to gain a competitive edge. This study undertakes a comparative analysis between traditional natural language processing (NLP) models, like BERT, and advanced large language models (LLMs), specifically GPT-4 Omni and GPT-4o Mini, both pre- and post-fine-tuning with few-shot learning. By leveraging an extensive dataset of hotel reviews, we evaluate the effectiveness of these models in predicting star ratings based on review content. The findings demonstrate that the GPT-4 Omni family significantly outperforms the BERT model, achieving an accuracy of 67%, compared to BERT's 60.6%. GPT-4o, in particular, excelled in accuracy and contextual understanding, showcasing the superiority of advanced LLMs over traditional NLP methods. This research underscores the potential of using sophisticated review evaluation systems in the hospitality industry and positions GPT-4o as a transformative tool for sentiment analysis. It marks a new era in automating and interpreting customer feedback with unprecedented precision.