Search Results (2,235)

Comprehensive monitoring of gas extraction parameters is crucial for the safe production of coal mines. However, it is a challenge to collect the overall gas drainage network parameters with limited sensors due to technical and econoincorporating mic constraints. To address this issue, a nonlinear model for gas confluence structure is construed for the conservation of mass, energy, and gas state properties. Considering exogenous variables such as frictional loss correction coefficient (α) and air leakage resistance coefficient (β), as well as the iterative structure of drainage networks, a hybrid PSO–Newton algorithm framework is designed. This framework realizes iterative solutions for multi confluence structures by combining global optimization (PSO) and local nonlinear solving (Newton’s method). A case study using historical monitoring data from the 11,306 working face of S Coal Mine was conducted to evaluate the proposed algorithm at both branch and drill field scale. The results show that key parameters such as gas flow velocity, concentration, and density align with actual observation trends, with most deviations within 10%, verifying the accuracy and effectiveness of the algorithm. A deviation comparison between the standalone Newton’s method and the PSO–Newton algorithm further demonstrates the stability of the latter. By enabling the derivation of comprehensive network parameters from limited monitoring data, this study provides strong support for the intelligent management of coal mine gas extraction. Full article

Background/Objectives: Findings on the association between metabolic syndrome (MetS) and coffee consumption are conflicting. Methods: This cross-sectional study included a large Taiwanese cohort and aimed to investigate associations between coffee consumption and the risk of MetS and individual components of MetS. Data of 27,119 participants (17,530 females and 9589 males; mean age 55.0 ± 10.3 years) were obtained from the Taiwan Biobank from July 2011 to November 2019. Associations among coffee consumption (type, intake and frequency) with MetS and its components were examined with multivariable logistic regression analysis, which included the significant variables in univariable analysis. Coffee consumption was assessed according to frequency, type and intake. Results: The results showed an association between coffee consumption and a lower risk of MetS (odds ratio [OR], 0.875; p < 0.001). Significant associations were found between the consumption of black coffee (OR, 0.848; p < 0.001) and coffee with milk (OR, 0.848; p = 0.001) with a low risk of MetS, while coffee with creamer was not. Daily consumption of one or two cups (237–474 mL) (OR, 0.805; p < 0.001 and 0.887; p = 0.001, respectively) was significantly associated with a low prevalence of MetS, whereas daily consumption of three or more cups was not. In addition, the participants who drank coffee every day (OR, 0.811; p < 0.001) were significantly associated with a low prevalence of MetS, whereas those who only drank coffee weekly or monthly were not. Further, significant associations were found between coffee consumption with lower risks of hypertriglyceridemia (OR, 0.844; p < 0.001) and low high-density lipoprotein cholesterolemia (OR, 0.836; p < 0.001) but not with abdominal obesity, hyperglycemia or high blood pressure. Conclusions: The regular consumption of black coffee or coffee with milk was linked to a low prevalence of MetS and certain components. Longitudinal studies are warranted to confirm these findings and elucidate the underlying mechanisms. Full article

The heat island effect and air stagnation issues caused by high-density built-up areas are becoming increasingly severe. Optimising urban ventilation structures and establishing ventilation corridors have become key approaches to improving the urban thermal environment and enhancing liveability. However, traditional methods for constructing ventilation corridors often rely on empirical weighting or linear models, which struggle to accurately reveal the resistance coefficients of resistance indicators and fail to reflect the threshold at which indicators transition between positive and negative impacts. Consequently, this study employs Shanghai, China, as a case study, integrating machine learning models with the minimum cost path (MCR) model. Key variables were screened through multiple linear regression and variance inflation factor (VIF) analysis. Subsequently, machine learning models were compared to select the optimal model, with parameter optimisation conducted using Optuna, followed by computational implementation. The results indicate that built environment factors (such as building height, shape complexity, and road density) exert a significantly greater influence on ventilation potential than natural green space factors. By introducing the SHAP method, the positive and negative effects of each indicator on the ventilation environment and their threshold relationships were revealed. Negative indicators were converted into ventilation resistance factors to construct a resistance surface. Building upon this, cold and heat sources were identified using LST, NPP, and population density data. The MCR model was then employed to calculate the minimum resistance paths from cold to heat sources, forming an urban ventilation corridor network. The results indicate that primary corridors align with prevailing wind directions, following urban rivers and low-density green spaces. This study reveals the nonlinear effects of building and green space elements on ventilation systems, proposing machine learning-based optimisation strategies for ventilation corridors. It provides quantitative decision support for mitigating the urban heat island effect and enhancing city livability. Full article

This paper proposes an applied framework for cyberattack and anomaly detection in resource-constrained embedded/IoT environments by combining signal-processing feature construction with supervised and unsupervised AI (Artificial Intelligence) models. The workflow covers dataset preparation and normalization, correlation-driven feature analysis, and compact representations via PCA (Principal Component Analysis), followed by classification and anomaly scoring. In addition to the original UNSW-NB15 (University of New South Wales—Network-Based Dataset 2015) traffic features, Fourier-domain descriptors, wavelet-domain descriptors, and Kalman-based smoothing/innovation features are considered to improve robustness under variability and measurement noise. Detection performance is assessed using classical and ensemble learning methods (SVM (Support Vector Machines), RF (Random Forest), XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine)), unsupervised baselines (K-Means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise)), and DL (Deep-Learning) anomaly detectors based on Autoencoder reconstruction and GAN (Generative Adversarial Network)-based scoring. Experimental results on UNSW-NB15 indicate that ensemble-based models provide the strongest overall detection performance, while the signal-processing augmentation and PCA-based compactness support efficient deployment in embedded contexts. The findings confirm that integrating lightweight signal processing with AI-driven models enables effective and adaptable identification of malicious network traffic supporting deployment-oriented embedded cybersecurity and motivating future real-time validation on edge hardware. Full article

Background/Objectives: The transition to higher education is often accompanied by lifestyle changes that may influence sleep habits, physical activity, and social media use, with potential consequences for physical and mental health. Methods: A quantitative, cross-sectional, descriptive, and correlational study was conducted using an online questionnaire administered between April and May 2024. The sample included 201 participants (123 students and 78 teaching/non-teaching staff). Data were collected using the Mental Health Inventory-5 (MHI-5), Social Media Addiction Scale (SMAS), Global Physical Activity Questionnaire (GPAQ), and Pittsburgh Sleep Quality Index (PSQI). Descriptive statistics and Spearman correlation analyses were performed. Results: Students presented lower mental health scores compared to staff members. Sleep quality indicators, particularly reduced sleep efficiency and increased use of sleep medication, were significantly associated with poorer mental health. Correlations between physical activity, social media use, sleep quality, and mental health were generally weak, suggesting that these domains contribute independently to perceived well-being. Staff members showed slightly higher levels of social media addictive behaviors, while students reported shorter sleep duration and greater emotional variability. Conclusions: The findings indicate that students presented lower mental health scores and poorer sleep indicators compared to staff members. Sleep quality—particularly sleep duration, efficiency, and use of sleep medication—showed the most consistent associations with mental health, while physical activity and social media use demonstrated weaker relationships. These results highlight the relevance of targeted sleep-focused interventions within higher education settings, especially for students in low-density regions. Full article

As bearings are critical mechanical components, their actual operating conditions exhibit notable dynamic complexity. Multiple factors—including rotational speed fluctuations, sudden load changes, and environmental disturbances—interact in a strongly coupled fashion. This imposes severe challenges on traditional fault diagnosis methods, such as limited interpretability, weak adaptive capacity, and elevated misjudgment rates. Therefore, this paper proposes an Interval Belief Rule Base model integrated with an attention mechanism (IBRB-a) under variable operating conditions. The proposed model combines expert knowledge’s ability to quantify uncertainty with a data-driven adaptation mechanism, thereby addressing the challenge of variable operating conditions in complex industrial systems. First, a novel interval rule construction method is incorporated into the traditional IBRB model, and kernel density estimation (KDE) is employed to select reference values. Second, during the model reasoning process, a two-stage fusion strategy based on Evidential Reasoning (ER) is adopted: progressive information fusion is implemented via the ER analysis algorithm and the ER rule algorithm, which effectively mitigates the interval uncertainty under variable operating conditions. Finally, the constrained projected covariance matrix adaptive evolution strategy (P-CMA-ES) is employed to optimize the model. Furthermore, experimental validation under variable operating conditions is conducted via Case Western Reserve University and Southeast University bearing datasets. The effectiveness and generalizability of the proposed method are validated by the experimental result. Full article

Three-dimensional (3D) modeling technologies are increasingly vital in civil engineering, providing precise digital representations of infrastructure for analysis, supervision, and planning. This study presents a comparative assessment of Neural Radiance Fields (NeRFs) and digital photogrammetry using a real-world case study involving a terrace at the Civil Engineering School of the Pontificia Universidad Católica de Valparaíso. The comparison is motivated by the operational complexity of image acquisition campaigns, where large image datasets increase flight time, fieldwork effort, and survey costs. Both techniques were evaluated across varying levels of data availability to analyze reconstruction behavior under progressively constrained image acquisition conditions, rather than to propose new algorithms. NeRF and photogrammetry were compared based on visual quality, point cloud density, geometric accuracy, and processing time. Results indicate that NeRF delivers fast, photorealistic outputs even with reduced image input, enabling efficient coverage with fewer images, while photogrammetry remains superior in metric accuracy and structural completeness. The study concludes by proposing an application-oriented evaluation framework and potential hybrid workflows to guide the selection of 3D modeling technologies based on specific engineering objectives, survey design constraints, and resource availability while also highlighting how AI-based reconstruction methods can support emerging digital workflows in infrastructure monitoring under variable or limited data conditions. Full article

Background/Objectives: The present study aimed to examine how obesity and its distribution influence the relationship between healthy lifestyle habits and cardiometabolic health indicators in adolescents. Methods: This cross-sectional study included 340 adolescents (54.8% female; mean age, 16.6 ± 1.0 years) from Brazil. The cardiometabolic variables included systolic (SBP) and diastolic blood pressure (DBP), high-sensitivity C-reactive protein (CRP), and markers of lipid and glucose metabolism. Information on regular physical activity, healthy diet, reduced alcohol consumption, and non-smoking was collected via a self-reported questionnaire. Body mass index, waist circumference, and skinfold measurements were assessed to determine general obesity, abdominal obesity, and excess body fat, respectively. Multiple linear regression, adjusted for confounding factors, was employed for the analysis. Results: The adoption of ≥3 healthy lifestyle habits was directly associated with high-density lipoprotein cholesterol (up to 1.2 mg/dL) and inversely associated with triglycerides (up to −0.11 p.p.). Engaging in multiple healthy lifestyle habits was inversely associated with SBP among adolescents with general (p = 0.018) and central obesity (p = 0.004). Furthermore, the adoption of multiple healthy lifestyle habits was inversely associated with CRP in adolescents with central obesity (p = 0.037). Conclusions: Even in adolescents with obesity, it is speculated that the adoption of healthy habits may contribute to a reduction in cardiometabolic risk, given the inverse association with SBP in those with general and central obesity and the inverse association with CRP in adolescents with central obesity. Full article

The sustainable management of Northern China’s vulnerable agro-pastoral ecotone requires a clearer understanding of how tillage systems affect crop productivity through local soil-climate interactions. Therefore, this study was conducted to quantify and compare the long term effects of different tillage practices on soil hydrothermal regimes, resource use efficiency, and maize yield stability in a semi-arid agroecosystem. A long term five-year field experiment with maize was conducted in this ecotone to assess three tillage methods: no tillage (NT), deep ploughing (DP), and conventional rotary tillage (RT). Seasonal monitoring included soil moisture, temperature, bulk density, and straw cover. Analyses focused on soil water use efficiency (WUE), the production efficiency per soil thermal unit (PEsoil), and pathways affecting theoretical calculated yield. Results show that relative to RT and DP, NT consistently elevated soil water content within the 0–30 cm profile during the growing season, with the most marked increases from pre-sowing to the V12 stage. This water-conserving effect was stronger in wet years, highlighting the role of precipitation in NT’s performance. DP also retained more soil water than RT, particularly in deeper layers, though its effect was less pronounced than NT’s. Regarding temperature, NT lowered the daily mean soil temperature and accumulated growing degree days (GDD) in early growth phases, a result of residue cover buffering thermal changes. Despite reduced heat accumulation, NT achieved the greatest efficiencies for both heat and water use (PEsoil and WUE), showing increases of 62.03% and 16.64% over RT, respectively, without yield penalty. Key mechanisms include permanent straw mulch under NT, which curtails evaporation, promotes water infiltration, and stabilizes soil structure, thereby modulating hydrothermal dynamics. Structural equation modeling indicated that soil water content, ear number per hectare, and hundred-kernel weight directly and positively determined final yield. Tillage methods exerted indirect effects on yield by modifying soil physical traits and microclimatic conditions. In this semi-arid setting, both NT and DP outperformed RT in conserving soil water, moderating soil temperature, and boosting resource use efficiency. These practices present viable strategies for strengthening crop resilience and sustaining productivity amid climatic variability. Full article

Urban parks play a vital role in mitigating the urban heat island effect and enhancing urban climate resilience. However, quantitative characterization of park cooling effects and the synergistic mechanisms among multiple factors remains limited. Focusing on the central urban area of Nanjing, a typical high-density subtropical city, this study analyzes Landsat 8/9 imagery from 2022 to 2025. The inflection point method was used to quantify three core indicators—cooling intensity, cooling distance, and cooling efficiency—while Pearson correlation analysis was applied to identify key drivers and examine synergistic relationships. The results show that (1) urban parks exhibit a “central aggregation–peripheral diffusion” pattern, which corresponds to pronounced spatial variability in the thermal environment; (2) park cooling effects display strong spatiotemporal heterogeneity, with notable interannual fluctuations in cooling intensity and a relatively stable cooling distance of approximately 400–500 m; and (3) cooling performance is primarily governed by tri-factor synergy among park size, vegetation characteristics, and surrounding urban environmental conditions. Park size largely determines the cooling extent, whereas underlying surface properties and building density regulate or constrain cooling. These findings clarify quantitative patterns and composite drivers of park cooling in high-density cities and provide evidence to support climate-adaptive green space planning and urban heat mitigation strategies. Full article

The increasing penetration of renewable energy has led to a decrease in system inertia, challenging grid stability and frequency regulation. This paper presents a dynamic estimation framework for load-side virtual inertia, supported with high-power-density electrical double-layer supercapacitors (EDLCs). By leveraging the fast response and high power density of EDLCs, the proposed method enables the real-time emulation of demand-side inertial behavior, enhancing frequency support capabilities. A hybrid estimation algorithm has been developed that combines demand forecasting and adaptive filtering to track virtual inertia parameters under varying load conditions. Simulation results, based on a 150 kVA distributed system with 27% renewable penetration and 33% demand variability, demonstrate the effectiveness of the approach in improving transient stability and mitigating frequency deviations within ±0.1 Hz. The integration of ESS-based support offers a scalable and energy-efficient solution for future smart grids, ensuring operational reliability under real-world variability. Full article

We introduce the Onco-Hem Connectome (OHC), a patient similarity network (PSN) designed to organize real-world hemato-oncology inpatients by exploratory phenotypes with potential clinical utility. Background: Polypharmacy and drug–drug interactions (DDIs) are pervasive in hemato-oncology and vary with comorbidity and treatment intensity. Methods: We retrospectively analyzed a 2023 single-center cohort of 298 patients (1158 hospital episodes). Standardized feature vectors combined demographics, comorbidity (Charlson, Elixhauser), comorbidity polypharmacy score (CPS), aggregate DDI severity score (ADSS), diagnoses, and drug exposures. Cosine similarity defined edges (threshold ≥ 0.6) to build an undirected PSN; communities were detected with modularity-based clustering and profiled by drugs, diagnosis codes, and canonical chemotherapy regimens. Results: The OHC comprised 295 nodes and 4179 edges (density 0.096, modularity Q = 0.433), yielding five communities. Communities differed in comorbidity burden (Kruskal–Wallis ${\epsilon }^2$: Charlson 0.428, Elixhauser 0.650, age 0.125, all FDR-adjusted p < 0.001) but not in utilization (LOS, episodes) after FDR (${\epsilon }^2$ ≈ 0.006–0.010). Drug enrichment (e.g., enoxaparin $\Delta$ = +0.13 in Community 2; vinblastine $\Delta$ = +0.09 in Community 3) and principal diagnoses (e.g., C90.0 23%, C91.1 15%, C83.3 15% in Community 1) supported distinct clinical phenotypes. Robustness analyses showed block-equalized features preserved communities (ARI 0.946; NMI 0.941). Community drug signatures and regimen signals aligned with diagnosis patterns, reflecting the integration of resource-use variables in the feature design. Conclusions: The Onco-Hem Connectome yields interpretable, phenotype-level insights that can inform supportive care bundles, DDI-aware prescribing, and stewardship, and it provides a foundation for phenotype-specific risk models (e.g., prolonged stay, infection, high-DDI episodes) in hemato-oncology. Full article

Microplastics (MPs) are emerging pollutants with widespread global distribution, continuously accumulating in soils and posing risks of cross-media pollution. Current soil MP detection methods lack unified standards, suffering from high inter-laboratory variability and cost, which become key bottlenecks limiting data comparability and global microplastics pollution control. Here, we systematically reviewed soil MPs studies (2020–2024) and based on stepwise verification, we established a standardized, reproducible detection method: soil samples were dried at 80 °C for 12 h; density separation was performed in Erlenmeyer flasks with decantation, 10 s glass rod stirring, and 12 h settling, repeated five times; digestion was conducted using a 1:2 volume ratio of H₂O₂ to supernatant at 80 °C for 8 h; and MPs were quantified via stereo-microscopy combined with ImageJ. It should be noted that the use of NaCl limits the recovery of high-density polymers (e.g., PVC, PET), and the minimum detectable particle size is approximately 127 µm. The method was validated in sandy, loam, and clay soils, achieving an average recovery rate of 96.4%, with a processing time of 68 h and a cost of USD 9.77 per sample. In contrast to previous fragmented, non-standardized protocols, this workflow synergistically optimizes high recovery efficiency, cost-effectiveness, and broad applicability, offering a low-cost, efficient, and widely applicable approach for soil MPs monitoring, supporting data comparability across studies and contributing to global pollution assessment and the United Nations 2030 Sustainable Development Goals. Full article

Background: Systemic bone loss, particularly osteoporosis, and periodontal disease are highly prevalent chronic conditions that share common risk factors and biological pathways. Increasing evidence suggests a bidirectional relationship between these conditions; however, findings remain heterogeneous and evolving. Objective: This review aims to evaluate and update current evidence on the bidirectional association between systemic bone loss and periodontal disease, with emphasis on underlying mechanisms and clinical implications. Methods: A narrative review of the literature was conducted using major electronic databases, focusing on human studies evaluating the relationship between osteoporosis or systemic bone loss and periodontal disease. Relevant experimental, clinical, and epidemiological studies were included. Results: Most studies support an association between reduced bone mineral density and increased severity of periodontal disease, including greater alveolar bone loss and attachment loss. Conversely, periodontal inflammation may contribute to systemic bone remodeling through inflammatory mediators. However, variability in study design, diagnostic criteria, and confounding factors limits definitive conclusions. Conclusions: Current evidence supports a bidirectional association between systemic bone loss and periodontal disease. Greater interdisciplinary awareness is warranted, and future well-designed longitudinal studies are needed to clarify causality and inform preventive and therapeutic strategies. Full article

In this study, the optical gain characteristics of a green laser sample based on a III-Nitride InGaN single-quantum-well structure were investigated. The Green gap phenomenon, caused by bandgap fluctuations due to inhomogeneous indium composition and the quantum-confined Stark effect (QCSE), has been a major obstacle in achieving high efficiency and high output in green-light-emitting devices. To address these issues, a sample grown on a (0001)-oriented GaN substrate with a single-quantum-well active layer was fabricated to suppress In composition non-uniformity and enhance the overlap of electron and hole wavefunctions. The optical gain behavior was analyzed using the Variable Stripe Length Method (VSLM) under various excitation densities and stripe lengths ($L_{cav}$). The results showed that as the stripe length increased, the spectral linewidth decreased and stimulated emission occurred at lower excitation densities. However, excessive cavity length led to gain saturation and a red shift in the peak wavelength due to Joule heating effects. These findings provide essential insights for determining the optimal cavity length in laser diode fabrication and are expected to serve as fundamental guidelines for improving the efficiency and output power of III-Nitride-based green laser diodes. Full article