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.

Abstract: Imidacloprid (IMI), the commonly used neonicotinoid pesticide, has emerged as a persistent aquatic contaminant due to its high solubility and stability, posing risks to non-target organisms and ecosystem health. In this study, a MnZnFe₂O₄/SrWO₄ ferrite–tungstate nanocomposite was synthesized via a hydrothermal process and its ability to photocatalytically degrade IMI under UV light was assessed. SEM, XRD and FT-IR were used to characterize the composite to confirm its structural and morphological features. Photocatalytic performance was systematically investigated by examining the effects of operational factors, including initial pollutant concentration, catalyst dosage, pH, and irradiation time. The MnZnFe₂O₄/SrWO₄ nanocomposite exhibited significantly enhanced activity, achieving up to 87% degradation of IMI within 30 minutes at pH 9, outperforming individual components (SrWO₄: 37%; MnZnFe₂O₄: 75%) under identical conditions. The degradation kinetics followed a pseudo-first-order model consistent with the Langmuir–Hinshelwood mechanism. Effective interfacial charge transfer between the ferrite and tungstate phases, which promotes electron-hole recombination and increases the production of reactive species, is responsible for the enhanced performance. Furthermore, the composite demonstrated good stability and reusability across several cycles, indicating its practical applicability. Overall, the results demonstrate the potential of MnZnFe₂O₄/SrWO₄ nanocomposites as efficient and sustainable photocatalysts for removing imidacloprid and similar organic contaminants from aqueous systems.

Abstract: In this work, we describe a new wavelike nonlinear heat conduction model aimed at implementing chiral thermal management and dynamic tunable chiral thermal emission on rotating conductors exposed to a chopped laser beam. We assume the existence of a rotational dynamical thermal Hall effect due to a self-induced out-of-equilibrium Barnett magnetic field, demonstrating that it allows for the transverse deviation of the harmonic heat flux and the modulation of the phase velocity of helical thermal waves propagating on the rotating metallic disks. We introduce a novel dynamic approach to thermoelectricity with complex valued thermal field dependent transport coefficients,deducing then a new dynamic chiral Thomson effect. We show that it is proportional to the angular velocity vector of the rotating disk, providing an estimate of its average Thomson voltage coefficient in the case of a ferromagnetic sample. We exploit then the laser-induced chiral Thomson electric field associated with a time-dependent Barnett magnetic field to enhance dynamic magnetic phase transitions and to tune time dependent Curie temperature fluctuations. We introduce finally a dynamic tunable chiral thermal emissivity dependent on a gauge breaking thermal Poynting vector, outlining its relevance for a novel rotational approach to chiral nonreciprocal photonics.

Abstract: In the historical development of various fields of mathematics, significant advances have occurred in areas such as algebra, abstract algebra, group theory, and numerous other mathematical and scientific domains. Contributions from mathematicians such as Dio- phantus, Goldbach, Euler, Girolamo Cardano, Johannes Kepler, Poncelet, Henri Poincaré, George Cantor, Felix Klein, David Hilbert, and Hermann Weyl have been fundamental, particularly in the pursuit of increasingly complex and deeper structures within geometry and topology. In this work, the division operation in the Alpha group is defined by analogy with the Kronecker tensor product. The representation of quaternion theory, based on De Moivre’s theorem, is employed for the construction of the matrices. The Alpha Group di- vision operation is then applied to analyze the various tensor metrics resulting from plane rotations over the interval from 0 to 2π radians. Since the general transformation kernel of the 4 × 4 matrix is defined within the Alpha group, it is possible to observe the variabil- ity associated with the tangent and cotangent functions that constitute the transformation matrix. The Alpha group, defined through a generalized division operation, thus provides a geometric and topological representation of infinity via the kernel transformation of the 4 × 4 matrix. Ultimately, this work seeks to connect the ideas developed by Poncelet and Cantor regarding the formation of imaginary elements in infinite projections with the con- cept of different types of infinity, as interpreted through the application of group theory.

Abstract: Rotated ship detection in complex synthetic aperture radar (SAR) scenes remains a critical yet challenging task for maritime remote sensing applications. Existing methods are plagued by three core bottlenecks: inconsistent directional responses across multi-scale features, unstable rotation angle regression, and non-uniform supervision quality of positive samples during training, which collectively lead to elevated false alarms, missed detections, and severe localization degradation, especially under high IoU thresholds in complex inshore environments. To address these challenges, we propose CORE-Net, a collaborative optimization framework integrating three dedicated modules in the forward detection stage: a Rotation-Consistent Feature Pyramid (RCFP) to alleviate cross-scale directional mismatch, a Progressive Cascade Rotation Head (PCR Head) to improve progressive angle prediction stability, and an Orientation-Aware Regression Enhancement Unit (OAREU) to strengthen directional geometric representation in regression features, alongside an Uncertainty-Aware Sample Reliability Steering (UARS) module for training-stage optimization to softly downweight the regression contribution of positive samples with high classification confidence but low geometric consistency. Extensive experiments on three public SAR ship detection datasets (RSDD-SAR, SSDD+, and RSAR) demonstrate that the proposed method consistently improves AP50:95 while maintaining high Recall and Precision, validating that joint optimization of feature representation, rotated regression, and sample reliability is an effective strategy to enhance both the robustness and fine-grained localization capability of rotated ship detection in complex SAR scenes.

Abstract: Background/Objectives: The objective of the present study was to determine the association between blood cadmium (Cd) and lead (Pb) levels and survival of the patients with kidney cancer. In this prospective study, we analyzed 272 consecutive, unselected kidney cancer patients and assessed their 8-year survival in relation to Cd and Pb levels. Methods: Cd and Pb concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). Patients were categorized into four groups according to the quartile distribution of Cd and Pb levels, ranked in ascending order. Multivariable models were adjusted for covariates including age at diagnosis, sex, smoking status, type of surgery, histopathological classification and blood levels of selenium, zinc, copper, iodine, cadmium and lead. Results: We observed no association between blood Cd and Pb levels and all-cause mortality in patients with kidney cancer. Conclusions: To our knowledge, this study is the first to investigate the relationship between blood levels of cadmium and lead and kidney cancer survival.

Abstract: Palms of the genus Ceroxylon constitute a key component of Andean tropical montane forests; however, their internal structural integrity has been scarcely studied in Peru. The present study assessed the internal structural condition of natural populations within the Private Conservation Area (PCA) Bosque de Palmeras of the Taulía-Molinopampa Peasant Community using sonic tomography, a non-invasive technique for detecting cavities and decay in the stipe. A total of 64 individuals distributed across four zones with differing degrees of anthropogenic disturbance — passive recovery, reforestation, active cattle ranching, and mixed forest — were analyzed, generating 256 tomograms at four vertical levels. Results revealed high levels of structural deterioration across all zones (59.8%–67.9%), with the greatest affectation recorded in the active cattle ranching zone, and significant differences among zones (Kruskal–Wallis, p < 0.05). A significant positive correlation was found between diameter at breast height (DBH) and structural damage (ρ = 0.298; p = 0.0168), whereas altitude showed no significant association (p = 0.7462). Structural deterioration exhibited a heterogeneous distribution both vertically and among individuals. Taken together, these findings indicate that anthropogenic activities increase structural deterioration and compromise the stability of Ceroxylon populations, and confirm the potential of sonic tomography as an effective tool for the monitoring and conservation of Andean palms.

Abstract: This study investigates the influence of various land use systems (LUSs) on soil physico-chemical properties, nutrient dynamics, and soil organic carbon (SOC) stocks in the Central Plain Zone of Uttar Pradesh, India. Soil samples were collected from six distinct LUSs, i.e., fallow, crop-based, horticulture-based, forest-based, vegetable-based, and barren land, and analyzed across three depth intervals (0–15 cm, 15–30 cm, and 30–60 cm). Soil pH increased steadily with depth, ranging from 7.43 to 8.58 at the surface layer to 7.55 to 10.32 in deeper layers. Horticulture-based LUSs recorded the lowest pH, while barren lands had the highest. Electrical conductivity (EC) also rose with depth, ranging from 0.12 to 3.63 Mgm-1, at the surface to subsoil layers, all below critical salinity thresholds. Soil organic carbon (SOC) content decreased with increasing soil depth across all land-use systems. Among the studied systems, horticulture-based land use recorded the highest SOC content (0.77%), whereas barren land showed the lowest SOC content (0.21%).”Due to greater organic matter inputs and reduced disturbances, horticultural systems also exhibited significantly higher levels of macronutrients (N: 17.98 kgha⁻¹, P: 330.45 kgha-1, K: 374.81 kgha⁻¹, S: 84.33 mgha⁻¹) and micronutrients (Fe: 164.12 mgha⁻¹, Mn: 60.89 mgha⁻¹, Cu: 2.85 mgha⁻¹, Zn: 1.80 mgha⁻¹). Bulk density increased slightly with depth (1.46–1.63 Mgm-³), while soil moisture content remained relatively stable (43.43% to 42.31%) with moderate variability (CV: 24–27%). The mean total SOC stock was 10.77 t C ha⁻¹, ranging from 5.44 to 14.46 tCha⁻¹. Microbial properties also varied among land uses: dehydrogenase activity (DEA), an indicator of microbial functionality, peaked in vegetable-based systems (30.54 µgTPF g⁻¹), whereas microbial biomass carbon (MBC) was highest in forest-based systems (184.83 µg g⁻¹). Correlation and regression analyses revealed a strong positive relationship between SOC and nutrient availability, with the highest correlation observed for Zn (R² = 0.99), followed by N (R² = 0.83) and K (R² = 0.75). Overall, barren lands showed the poorest soil quality indicators, while horticulture-based systems consistently demonstrated superior soil fertility and carbon sequestration potential. These findings emphasize the critical role of land use management in regulating soil fertility, SOC dynamics, and the long-term sustainability of agro-ecosystems in the region.