Abstract: Microplastics, plastic particles smaller than 5 mm in size, have become a contaminant of priority concern in the environment. Microplastic pollution is a significant environmental challenge, highlighting the need for improved water treatment methods. This study investigates the removal of two fractions of polyurethane microplastics ranging in size from smaller than 100 µm, D1, and in range 200 µm - 500 µm, D2, from aqueous synthetic solutions having a concentration of 0.2 g/L, around 175 NTU. In the first stage of the study, tests were performed to identify the optimal doses of efficient reactive agents for microplastic removal, using the classical method: the Jar test. At this stage, attention was directed towards analyzing the variation of turbidity and their removal efficiency in the presence of classical coagulants such as aluminum sulfate, SA, ferrous sulfate, SF, aluminum polychloride, PA; aloe vera flocculant; and activated carbon, CA of the Norit GAC 830 W type. The classical coagulants such as aluminum sulphate, ferrous sulphate have a good efficiency on microplastic removal, which can provide a residual turbidity in range of 6-10 NTU after a retention time of 50 - 60 minutes. In the second stage of the study, the efficiency of smart decantation-filtration system, DFS, was determined. The efficiency of decanter was studied using Response Surface Methodology (RSM) for identification of the mathematical models necessary to evaluate the effects of key process variables: Flow rate (A), Microplastic size (B), and Aluminum sulphate concentration (C) on microplastic removal efficiency. The sedimentation can raise the optimal value of 98.98 % at the outlet of the decanter. Microplastics in D1 and D2 sized synthetic solutions can be removed from contaminated water by decantation and filtration, the efficiency is around maximum permissible limit, MPL, values of 1 NTU.

Abstract: Background: A wellplanned health workforce is essential for achieving highquality, equitable rehabilitation services. In Kuwait, physiotherapy services have expanded over the past decade, yet little is known about longterm workforce trends, gender and nationality imbalances, or the impact of the COVID19 pandemic. This study provides the first comprehensive 14year analysis of physiotherapy workforce dynamics and service utilisation within Kuwait’s Ministry of Health. Methods: A retrospective descriptive study was conducted using Ministry of Health annual physiotherapy workforce and serviceutilisation reports (2011–2024). Data included workforce size, gender, nationality, rank, educational qualifications, and physiotherapy service activity across specialized and general hospitals. Descriptive statistics, linear regression, and interrupted timeseries (ITS) analyses were used to examine longterm trends and COVID19–related changes. and physiotherapy service activity. Results: The physiotherapy workforce increased from 567 in 2011 to 1,078 in 2024, a rise of 90%. Growth was driven mainly by Kuwaiti and non-Kuwaiti females, resulting in a strongly feminised workforce by 2024. In contrast, Kuwaiti male physiotherapists declined by 28.8%. Workforce density increased modestly from 1.85 to 2.19 physiotherapists per 10,000 population, remaining below international benchmarks. Educational qualifications improved, with PhD-trained physiotherapists increasing from 2 to 23. Interrupted time-series analysis showed a significant pandemic-related decline in service utilisation, particularly in Kuwaiti outpatient activity. Significant post-pandemic recovery was observed in selected service streams. Conclusion: Kuwait’s physiotherapy workforce expanded substantially, but workforce density remained below global benchmarks and gender and nationality imbalances persisted. Future planning should strengthen national recruitment, equitable workforce distribution, and digital and tele-physiotherapy models.

Abstract: In the course of extending the obtained regularities of lattice packings corresponding to layered lattices of the “Lambda” series in dimensions 1–24 to groups of higher dimensions, a hypothesis was formulated about the density function of the densest lattice packing of equal spheres in n−dimensional Euclidean space. The hypothesis can be used as an approximate method for solving the problem of lattice packing of equal spheres in n−dimensional Euclidean space.

Abstract: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary views of brain activity, capturing neural dynamics across temporal and spatial scales. Integrating these modalities offers a powerful approach for studying brain function, yet remains fundamentally challenging due to differences in measurement mechanisms, temporal resolution, and neurovascular coupling. At its core, EEG–fMRI fusion can be viewed as an inverse problem: the goal is to recover latent neural processes that are only partially observed through electrophysiological and hemodynamic signals. Here, we review data-driven fusion methods developed between 2000 and 2025, focusing on approaches that aim to identify shared neural representations across modalities. We organize the existing methods according to the fusion strategy (symmetric vs. asymmetric), the methodological objective (factorization vs. translation), and the modeling assumptions (linear vs. non-linear), and discuss commonly-used evaluation metrics and visualization strategies. We further examine evaluation strategies, highlighting the lack of a universal validation standard and the challenges of interpreting latent multimodal components. Across neurological, psychiatric, and cognitive applications, EEG-fMRI fusion has revealed distributed network dynamics that are not accessible through unimodal analyses. However, key challenges remain, including temporal misalignment, noise-induced coupling, and model-dependent interpretation. We discuss emerging directions such as nonlinear modeling, flexible coupling frameworks, and large-scale group-level fusion, which may enable more robust and interpretable multimodal integration. Together, this review reframes EEG-fMRI fusion as a problem of latent neural inference and outlines a path toward more principled, scalable, and biologically grounded approaches for understanding brain function and dysfunction.

Abstract:

Copy-move forgery in biomedical research images threatens scientific integrity, yet automated pixel-level localisation remains challenging due to low-contrast textures and small duplicated regions. We propose a segmentation pipeline that pairs a frozen DINOv2-base vision transformer (86M parameters) with a lightweight 3.4M-parameter convolutional decoder. Training proceeds in two stages: decoder warmup at learning rate $10^{-5}$ with the backbone fully frozen, followed by joint fine-tuning of the last twelve transformer blocks at $5 \times 10^{-7}$, yielding a $20\times$ learning rate ratio that preserves pretrained features while adapting to biomedical imagery. At inference, flip-based test-time augmentation, gradient-enhanced adaptive thresholding ($\alpha = 0.45$), and grid-searched area/probability gating ($A_{\min} \in [200,400]$, $p_{\min} \in [0.20,0.30]$) convert probability maps into binary masks. Evaluated on the Recod.ai/LUC benchmark derived from over 2{,}000 retracted papers, the method achieves a validation F1 of 0.563 on a 1{,}027-image held-out split. Comparative studies with representative existing approaches, spanning rule-based, CNN-based, and hybrid multi-component strategies, show that the proposed pipeline provides a stronger balance of localisation accuracy, architectural simplicity, and reproducibility than existing methods for biomedical copy-move forgery detection.

Abstract: Rekindling attention has been directed towards phage therapy, a technique that utilizes phages to eradicate bacterial infections by targeting specific bacteria. This review delves into the interplay between bacteriophages and antibiotics, with a focus on the emerging concept of phage-antibiotic synergism (PAS). The escalating threat of antibiotic resistance and its advancing mechanisms for resistance development underscore the imperative need for alternative approaches to treat life-threatening infections. Consideration of bacteriophages that can specifically target and eliminate particular bacteria is gaining prominence for the improved treatment of infections. Moreover, the combination of both phages and antibiotics is viewed as a more efficient means of achieving treatment objectives. The observed synergistic effects in phage-antibiotic therapies showcase proficiency in antimicrobial activity, leading to a reduction in the development of microbial resistance towards antibiotics by providing an alternative way for bacterial eradication. Key findings from recent reviews emphasize the diverse mechanisms underlying phage-antibiotic synergism, including bacterial morphological changes, interference with biofilm formation, efflux pumps, increased cell wall permeability, and the potential to overcome antibiotic-resistant strains. Furthermore, this review paper elaborates on the significance of understanding the interaction between phages and antibiotics to enhance therapeutic outcomes along with an effort to shed light on the merits and demerits of this combined therapy. This comprehensive understanding can pave the way for an innovative approach to infectious disease treatment and management.

Abstract: Feline Panleukopenia Virus (FPV) is a highly contagious and systemic virus that is environmentally stable, targets leukocytes, and affects cats of all ages. Within the United States, particularly in the population-dense downstate region of New York, no molecular surveillance or strain isolation has been conducted for FPV. The main goal of this study is to conduct molecular and serological surveillance of FPV among some shelter cats in this area and to do isolation and genome mining of some of the detected isolates. We used samples (swabs and sera) from 109 shelter cats by qPCR and immunofluorescent assay (IFA) respectively. Our results show that 25.0% (29/108) of the cats tested were FPV positive by qPCR, and 39.4% (43/109) of the cats tested FPV positive by IFA. FPV Viral isolation and identification were conducted using Madin-Darby Canine Kidney (MDCK) cells. Next Generation Sequencing (NGS) confirmed the presence of a novel FPV isolate (Accession: PZ251627) circulating within the tested shelter cat population. Phylogenetic analysis showed that the sequence of the reported FPV isolate had the highest full-length sequence similarity with isolates MH165482 at 99.3%, MN127781 at 99.2%, and MH165481 at 99.2%. Additionally, phylogenetic analysis of the VP2 genome sequence showed the following highest sequence similarities; OQ615264 at 99.2%, KT899746 at 99.1%, and PVMRFD at 99.3%. The following substitutions were noted in comparison of the isolate to the reference sequence (MN45165): Ile101Thr and Glu411Ala. Inverse distance weighting (IDW) interpolation indicated the presence of a higher occurrence of FPV-positive cats in the western region of Downstate New York, encompassing the areas of the 5 boroughs of NYC and Nassau County. Continued FPV surveillance in cats in this region is highly recommended.

Abstract: Background: Adipose tissue expansion in obesity is accompanied by extracellular matrix (ECM) remodelling, regulated by matrix metalloproteinases (MMPs). Visceral adipose tissue (VAT) is metabolically more active than subcutaneous adipose tissue (SAT). However, depot-specific differences in proteolytic activity and protein glycooxi-dation remain incompletely characterized. Methods: In this case-control study, we assessed the activity of six matrix metallo-proteinases (MMP-1, -2, -7, -9, -11, -13) using a fluorescence resonance energy transfer (FRET) assay and quantified advanced glycation and glycooxidation-related markers in paired VAT, SAT and plasma samples obtained from 40 patients with obesity and 21 non-obese controls. Results: The activities of all assessed MMPs were greater in patients with obesity than in the control group (p < 0.01 for all MMPs). Direct tissue-compartment comparisons showed that MMP activity and glycooxidation-related markers were most pronounced in VAT, with markedly higher values in obese individuals compared with controls. In VAT of obese individuals, median MMP activity was approximately 50–60% higher compared with controls. Amyloid cross-β-structure, vesperlysine and pentosidine were significantly elevated in VAT in obesity, whereas plasma levels were markedly lower and showed limited group differences. No significant differences were observed between obese par-ticipants with and without metabolic syndrome. Conclusions: Obesity is associated with a depot-specific molecular profile charac-terized by enhanced proteolytic and glycooxidative activity predominantly within vis-ceral adipose tissue. These findings highlight the importance of tissue-compartment–specific assessment in obesity.

Abstract: After leakage from buried hydrogen-blended natural gas pipelines, gas may seep through soil into enclosed spaces and form buoyancy-driven non-uniform combustible clouds. The effect of ignition delay on such clouds remains insufficiently understood, especially regarding the relationship between visible flame behavior and local thermal response. In this study, 44 soil-seepage combustion experiments were conducted in a 1.5 m × 1.5 m × 1.5 m enclosure. Methane and hydrogen concentrations at three heights, flame evolution, and transient temperatures were measured using gas sensors, high-speed imaging, and thermocouples. The ignition delay ranged from 27 s to 5429 s, with hydrogen blending ratios of 10–30 vol% and ignition positions at the floor, middle, and ceiling. The results show that longer ignition delays generally weakened visible flame luminosity and propagation extent. However, the peak temperature measured at the central thermocouple did not decrease accordingly. For the long-delay subset with td &gt; 307 s, the central peak temperature increased with ignition delay, with R² = 0.74. Concentration measurements indicate that preferential hydrogen migration and slower methane redistribution continuously reconfigured the local flammability state before ignition. These findings suggest that, in enclosed soil-seepage HBNG scenarios, prolonged ignition delay may weaken visible flames but does not necessarily reduce local thermal exposure.

Abstract: As key lifting equipment in construction engineering, tower cranes (TCs) play a critical role in prefabricated buildings (PBs). However, current construction scheduling relies primarily on manual observation by operators and assistants and their experience to perform repetitive tasks, resulting in inefficiency, tediousness, and safety hazards. To enhance lean construction and management efficiency in PBs, this study proposes a scheduling model that comprehensively considers the initial hook position and the specific locations of prefabricated component (PC) supply and demand points. The model is then solved using particle swarm optimization (PSO). Optimization results clearly show that the operational times of two TCs are reduced by 23.94% and 12.16%, respectively, while their daily operating costs decrease by ¥207.29 and ¥293.96. Moreover, the overall construction cost of the PBs is lowered by 8.0%. These findings clearly demonstrate the effectiveness of the proposed model in significantly improving construction efficiency and promoting lean management in PBs.

Abstract: This study explores how a sustainability induction module for staff and students can meaningfully operationalise multiple Sustainability Development Goals (SDGs) across a higher education institution (HEI). The paper examines tensions between comprehensive SDG integration and efforts to cultivate whole-institution sustainability culture. Using Sterling’s transformational framework, we analyse how staff and students engaged with module content spanning diverse SDGs, including Indigenous land management (SDG 15), ethical consumption (SDG 12), modern slavery (SDG 8), governance (SDG 16) and community engagement (SDG 17). Findings reveal how staff and students experience the parallels between working across SDGs and learning about sustainable actions within personal, organisational, and community contexts of HEIs. While participants appreciated the interconnectedness of sustainability challenges, they also highlighted difficulties associated with the breadth and complexity of addressing multiple SDGs within a single induction experience. This research advances understanding of how transition-oriented learning spaces that are situated between individual and institutional development and those involving affective, cognitive, and intentional dimensions of change, can support HEIs in progressing the 2030 Agenda. At the same time, it identifies key pedagogical challenges in designing induction modules that integrate multiple SDGs in practice.

Abstract: Background/Objectives: Handgrip strength is a widely used indicator of physical function that is associated with various health outcomes of older adults. However, the relationship between lifestyle factors and handgrip strength, as well as the age-associated relationship between them, remains insufficiently understood. This study examined age-adjusted associations between multiple lifestyle factors and handgrip strength among older women. Methods: During this cross-sectional study of 2,206 older women, handgrip strength was categorized into low, middle, and high tertiles. Lifestyle factors such as dietary status, exercise frequency, sleep quality, social interaction, and outing frequency were assessed using a questionnaire. Group differences were evaluated using an analysis of variance and chi-square tests. An analysis of covariance was performed to examine associations between lifestyle factors and handgrip strength after adjusting for age. Results: Participants in the high handgrip strength tertile were younger and more likely to report favorable lifestyle behaviors. After adjusting for age, dietary status (p = 0.024), social interaction (p = 0.001), and outing frequency (p = 0.017) remained significantly associated with handgrip strength. In contrast, sleep quality (p = 0.073) and exercise frequency (p=0.060) were not significantly associated with handgrip strength after age adjustment. A clear dose–response relationship was observed between lifestyle scores and handgrip strength. Conclusions: Among older women, dietary status, social interaction, and outing frequency were independently associated with handgrip strength, even after accounting for age. These findings suggest that multidimensional lifestyle factors, particularly those related to nutrition and social engagement, may contribute to maintaining physical function in older adults.

Abstract: Data leakage represents a critical methodological challenge in machine learning–based predictive modeling, as it can inflate performance estimates and lead to misleading interpretations. In higher education contexts, where predictive models increasingly support institutional decision-making, the temporal and structural conditions under which predictions are generated and evaluated are often insufficiently specified. This study conceptualizes predictive modeling as a temporally formalized decision task and identifies four core design conditions: explicit specification of the prediction cutoff, temporal restriction of the information set, consistent definition of the at-risk popu-lation, and temporally coherent validation. The empirical analysis combines a structu-red review of recent dropout prediction studies with a controlled experimental de-monstration based on longitudinal student data. The review shows that the joint for-malization of these conditions remains uncommon, with many models relying on ret-rospective and temporally unspecified configurations. The experimental results de-monstrate that improper validation in longitudinal data structures can produce systematic performance inflation, particularly through identity leakage, and that mo-dels with higher representational capacity exploit such leakage more effectively. These findings indicate that predictive performance cannot be interpreted independently of the temporal and structural definition of the prediction task. The proposed framework provides a methodological basis for evaluating predictive models in higher education and other domains where decisions depend on temporally grounded predictions.

Abstract: Affected persons supporting substance-dependent individuals during COVID-19 needed innovative communication strategies to facilitate their well-being in a scenario of limited access to physical services. This study explored the lived experiences of affected persons assisting substance-dependent individuals during COVID-19 to highlight the perceived benefits of a support framework that could sustain practices beyond the pandemic. This descriptive phenomenological study examined how affected persons developed a sense of coherence while supporting individuals with substance-use disorders, emphasizing health promotion practices. Health promotion is rooted in social support, which enhances subjective well-being. The study drew on Antonovsky’s Sense of Coherence theory, focusing on factors that enable individuals to remain healthy despite stressors. A heterogeneous purposive convenience sample of 26 participants was used, with data saturation achieved. Telephonic interviews lasting up to 45 minutes were conducted using a pretested schedule, followed by open coding. Findings indicate that practical support and resource exchange foster a global life orientation, enabling individuals to perceive their environment as understandable, manageable, and meaningful while addressing substance use. Key factors included social support networks, family bonds, self-care, identity, and relationships. Participants reported positive experiences and sustained actions promoting health, often driven by caregiving, personal growth, and future aspirations.

Abstract: Depression is a widespread mental health disorder with significant personal and societal impacts, yet its early detection remains challenging due to reliance on subjective clinical assessments. Advances in wearable technologies, particularly actigraphy, enable continuous and objective monitoring of behavioral patterns, offering new opportunities for data-driven mental health analysis. In this study, we propose a novel deep learning framework based on a CNN–BiLSTM architecture with an attention mechanism for automated depression detection using Internet of Medical Things (IoMT)-based actigraphy signals. The model effectively captures local temporal patterns and long-range dependencies, while the attention mechanism enhances interpretability by emphasizing clinically relevant time segments. To improve robustness, the framework incorporates preprocessing techniques to address missing data through augmentation and class imbalance using SMOTE. Time-series features are extracted using TSFRESH to capture statistical, temporal, and spectral characteristics, followed by Recursive Feature Elimination (RFE) for feature optimization. These features are then used for classification within the proposed architecture. Experimental results demonstrate that the model achieves superior performance, with an accuracy of 89.93%, along with strong sensitivity, specificity, F1-score, and AUC. These findings highlight the effectiveness of the proposed approach as a scalable, non-invasive solution for early depression detection.

Abstract: In recent years, the wars have gradually increased the risk of marine oil spill accidents. Marine oil spill monitoring becomes more and more important for preventing marine oil pollutions. Chinese Zhuhai-1 satellite can capture abundant spectral reflectance signals. It is a significant way of detecting marine oil spills. Most of the traditional oil spill detection methods only used a small amount of spectral information. It made it difficult identify oil spills accurately from the inhomogeneous marine environment. In order to mine the key differential spectral information of oil slicks, inspired by the encoding method of spectral DNA, an advanced spectral DNA encoding (ASDE) strategy was proposed to describe the spectral details in Zhuhai-1 images. On this basis, two kinds of key spectral information extraction methods were proposed to mine the spectral genes of oil slicks. Finally, the extracted spectral genes were used to detect the marine oil spills. Three Zhuhai-1 satellite images were used to validate the performance of the proposed method based on ASDE strategy. The experimental results indicated that the proposed method could precisely describe the spectral differences of oil slicks and sea-water in Zhuhai-1 images. In addition, the extracted spectral genes could detect marine oil spills correctly.

Abstract: Natural selection encodes learned information in the genome. Learned solutions may be tuned specifically to past challenges, failing in altered environments. Or solutions can be general, capturing the essential structure of the challenge and performing well across variations within the abstract class. For example, a neural system might recognize the exact outlines of a rattlesnake but not other snakes, or it might recognize the essence of snakeness. The problem of how a system generalizes is a fundamental aspect of evolvability, the ability of a system to learn broad solutions to novel challenges. In recent years, machine learning has significantly advanced our understanding of when systems generalize their learned solutions and how they accomplish such generalization. One surprising discovery overturned conventional wisdom about learning. Large systems, with more adjustable parameters than the dimensions of the incoming data, do not merely memorize the data patterns in the way suggested by traditional theory. Instead, systems with more parameters generalize better than smaller systems. Because natural selection is a learning algorithm, the new theory of generalization applies to biological evolution. Specifically, increasing regulatory complexity and parameterization associates with increasing evolvability for the discovery of general solutions. This link between genomic complexity and generalization may have been a primary driving force in evolutionary history.

Abstract: Background/Objectives: Indigenous children in La Guajira, Colombia, live in a context of structural vulnerability that may compromise growth and nutritional status. This study aimed to characterize anthropometric patterns and longitudinal nutritional changes in Wayúu children under five years of age. Methods: We conducted a prospective cohort study in 398 children from 27 Wayúu communities in Manaure, La Guajira, over an 8-month period. Anthropometric measurements were obtained by pediatricians and classified using standard indicators based on WHO growth references. A descriptive and bivariate analysis was performed for the full sample, and longitudinal changes were evaluated in a follow-up subgroup. Results: At baseline, 92.46% of children presented at least one nutritional alteration, and 89.95% had malnutrition or developmental delay. Stunting was the most frequent condition (89.95%), whereas acute malnutrition was less common. In the longitudinal subgroup, 41.67% of children worsened in at least one indicator, with a significant increase in nutritional risk over time. Older children showed worse weight-for-age and height-for-age indicators than younger children, while no significant differences were observed by sex. Conclusions: Wayúu children under five years in Manaure show a pattern dominated by chronic, symmetrical growth impairment with worsening anthropometric trajectories over time. These findings highlight the need for sustained, culturally adapted, and multisectoral strategies to prevent and manage childhood malnutrition in Indigenous populations.

Abstract: Accurate product-to-catalog invoice matching is a foundational internal control critical to financial oversight and audit quality, yet it is often bottlenecked by inconsistent vendor descriptions. Traditional rule-based matching fails to address this "long tail" of supplier heterogeneity, leading to costly manual reconciliation. This study presents an end-to-end system for automated invoice reconciliation. We introduce a novel “augment-both-sides” strategy: catalog entries are proactively enriched with LLM-generated keywords and synonyms before vectorization, while incoming invoice line items undergo query expansion to bridge the semantic gap between vendor terminology and master data. A final LLM-based reranker applies context-aware judgment to produce highly accurate Top-3 match candidates. We evaluate this system using three diverse entity resolution benchmark datasets, Abt-Buy, Amazon-Google and Walmart-Amazon, structured to simulate real-world ERP environments. The system achieves a Top-3 Recall of 93.14% to 97.96% across all domains, effectively narrowing the search space for accounting and auditing professionals from thousands of SKUs to a precise set of candidates. These results demonstrate that the architecture functions as a highly reliable intelligent decision aid, standardizing complex reconciliations, and structuring the reconciliation task for subsequent human verification.

Abstract: Output-based indicators in entrepreneurial ecosystem governance systematically misclassify pre-threshold structural progress as policy failure, because feedback dynamics produce no immediate output signal. This study examines how institutional coordination shapes those dynamics. Using system dynamics modelling, we construct a three-stock model (active startups, entrepreneurial capabilities, and institutional support). Calibration is performed via structured expert elicitation using the Repertory Grid Technique (RGT), enabling institutionally grounded parameter estimation where comparable time-series data are unavailable. Three policy scenarios — fragmented support, financial intensification without coordination, and coordinated early intervention — are simulated for Mexico and the United Kingdom. Resource intensification alone yields only temporary gains when feedback structures remain fragmented. Coordinated intervention activates reinforcing feedback among all three stocks, enabling self-sustaining growth beyond a critical coordination threshold. The United Kingdom crosses this threshold earlier due to stronger baseline conditions; Mexico responds later but with larger proportional gains. The model provides a feedback-structural diagnostic that distinguishes pre-threshold structural assembly from genuine stagnation, with direct implications for the design of evaluation frameworks in fragile institutional contexts. RGT demonstrates potential as a calibration strategy for feedback models in data-sparse settings.