Search Results (21,580)

Accurately determining the thermal transmittance (U-value) of glazing systems plays a pivotal role in building energy conservation. This study establishes an explicit analytical model and conducts a systematic parametric analysis to elucidate the heat transfer mechanisms and key influencing factors governing the U-values of both single-pane and insulating glass. Based on fundamental thermodynamic principles and blackbody radiation laws, numerical iterative models are developed and validated against WINDOW and Fluent software simulations, with deviations consistently below 3.8%. A comprehensive parametric analysis quantifies the effects of glass thickness, cavity width, surface emissivity, and indoor/outdoor heat transfer coefficients. The results reveal that: (1) while U-values decrease approximately linearly with increasing glass thickness, they exhibit a non-monotonic relationship with cavity width, identifying an optimal cavity width of approximately 16 mm for air-filled insulating glass units; (2) surface emissivity exerts the most significant influence on the U-value, with cavity-facing surfaces demonstrating the greatest sensitivity (up to 81% variation), whereas outdoor surface emissivity shows negligible impact; (3) the U-value displays greater sensitivity to variations in the indoor heat transfer coefficient compared to outdoor conditions. Based on the parametric analysis under standard winter conditions, a preliminary design hierarchy is proposed for energy optimization: prioritize Low-E coatings on cavity surfaces, followed by cavity width optimization near 16 mm, and finally consider increasing glass thickness. The validated models and quantitative insights establish a benchmark calculation method for U-value analysis. These findings offer theoretical guidance and a prioritized optimization pathway for the preliminary design of energy-efficient glazing systems, particularly under standard winter conditions. Full article

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

Crop production, nitrogen use efficiency (NUE), and environmental footprint are not only of great significance for ensuring food security, but also serve as key determinants for achieving the integrated governance of agricultural development and environmental protection. However, Iran is currently facing challenges such as production in an arid climate and on degraded land, low NUE, and associated ecological and environmental pollution. Current agricultural nitrogen (N) management research is mostly limited to single crops or dimensions, leaving a gap in integrated multi-crop, multi-dimensional spatiotemporal analyses and grid-scale high-resolution spatial assessments of regional heterogeneity. Therefore, from the perspectives of food, resources, and the environment, this study systematically assessed the sown area, yield, N application rate, NUE, N surplus, and greenhouse gas emissions (GHG emissions) of six major crops (wheat, rice, barley, maize, sugarcane, and cotton) in Iran for the years 2000, 2010, and 2020. The aim was to assess the current status and spatiotemporal evolution of cropland N management in Iran. The results of this study indicate that the total N application rate in Iranian cropland exhibited an overall upward trend from 2000 to 2020, increasing from 1.095 × 10⁶ t to 1.1937 × 10⁶ t over this period. The NUE improved in some regions but remained generally low, increasing from 31.7% to 41.8%. Provinces in northern and southern Iran were characterized by high N application rates, low NUE (20–40%), substantial N surplus accumulation, and high GHG emissions. The multi-dimensional comprehensive assessment framework proposed in this study provides a scientific basis for N management in regions aiming for coordinated governance of food security and the ecological environment. Full article

Background/Objectives: Light-chain cast nephropathy remains a major cause of morbidity in newly diagnosed multiple myeloma (MM), and rapidly reducing circulating free light chains (FLCs) is considered essential for renal recovery and survival. Methods: We conducted a single-center retrospective study evaluating high- and medium-cut-off hemodialysis (HCO/MCO HD) in newly diagnosed MM patients presenting with acute kidney injury (AKI). Consecutive patients treated at the University Medical Center Ljubljana between 1 January 2020 and 31 December 2023 were included. As per institutional protocols, HCO/MCO HD- and myeloma-directed therapy were initiated on the day of diagnosis. Primary endpoints were the magnitude of FLC reduction, renal and hematologic responses at three months, and overall survival. Results: The median FLC concentration at presentation was 9630 mg/L. FLC levels declined rapidly after HCO/MCO HD initiation, reaching 2400 mg/L by day 7, 1083 mg/L by day 14, and 370 mg/L by day 30. MCO HD achieved kapa FLC clearance comparable to HCO HD for the lambda isotype. Despite a median of only four HCO/MCO-HD sessions, the reduction in FLC was rapid, with an additional decline observed over time, while the median FLC concentration fell below 500 mg/L. At three months, the overall hematologic response was 87%, including very good partial response or better in 35% of patients, and renal response in 79% of patients. Achieving a ≥70% FLC reduction by day 7 was associated with superior outcomes, including markedly longer median overall survival (82.5 vs. 23.2 months). Conclusions: Our data show that HCO/MCO-HD treatment alongside anti-myeloma therapy achieves sustained FLC reduction in newly diagnosed MM with AKI and early FLC reduction is highlighted as a key determinant of survival. Full article

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

Municipal solid waste (MSW) generation is a global problem affecting the environment and public health. The current landfill’s useful life is reaching its end, making new site selection a priority to guarantee proper MSW management. This research evaluated the suitability of the metropolitan area of the municipalities of Chihuahua, Aldama, and Aquiles Serdan, using Spatial Decision Support Systems (SDSS) integrated with Multi-criteria Decision-making (MCDM) and hierarchical analysis, and Geographic Information Systems (GIS) to determine potential sites for new Metropolitan landfill development in a semi-arid region. Results showed that 44.7% of the areas studied present a high suitability level, while 29.52% corresponds to a very high suitability level. These areas are located mainly in the north and center zones of the Chihuahua and Aldama municipalities, with some isolated areas in Aquiles Serdan. The key selection criteria were airport distance, land slope, and proximity to the intermunicipal boundary, which enabled the identification of sites with lower environmental impact and greater technical and economic feasibility. This study demonstrates that SDSS and GIS are efficient tools for identifying potential landfill sites. The results highlight the importance of integrating technical, environmental, and social criteria into MSW management planning to achieve sustainable, efficient management in the region. Full article

To address the difficulty of directionally cutting thick, hard key strata in gob-side entry retaining using conventional blasting or hydraulic fracturing, this paper proposes a high-pressure water-jet slotting-induced pre-cracked weakening belt (PCWB) roof-cutting technology. Several finite-length PCWBs are arranged within the key stratum and designed to coalesce into a plane, inducing through-going roof failure along a pre-determined path. A fixed–fixed key strata beam model combined with linear elastic fracture mechanics shows that the double-belt configuration forces the bending moment and shear force to concentrate in a thin rock bridge, where bending and shear stresses are amplified by about 1.5–2.8 times and 1.2–1.7 times, respectively, for 2–4 m thick key strata, providing a mechanical basis for preferential tensile–shear failure. Two-dimensional RFPA2D simulations reveal “width-dominated, length-assisted” control of cutting performance and identify an optimal weakening belt geometry of about 400 mm in width and 200 mm in length. Three-dimensional numerical modeling of parallel slot pairs indicates that intra-pair spacing of about 40 mm produces a continuous, directional weakening belt, whereas smaller or larger spacing causes, respectively, destructive interference or loss of connectivity. High-pressure water-jet tests (320 MPa, 0.33 mm nozzle, 1.30 mm/s traverse speed) on limestone blocks confirm that single slots can penetrate the full thickness and that cracks from adjacent slots coalesce through the rock bridge, forming a wide, straight fracture band. Field application in the Dongjiang Mine (3.5 m limestone key stratum, ~400 m depth) shows that the first weighting is advanced from the 7th to the 3rd day, peak support resistance is reduced from 8.8 to 7.4 MPa, and periodic weighting becomes more frequent and smoother. The PCWB technology is therefore suitable for panels with 2–4 m thick hard key strata at similar depths, offering precise key stratum severance, active stress relief, and safe, controllable construction. Full article

Pozzolanic concrete demonstrates intricate, highly nonlinear material interactions that pose significant challenges for the accurate prediction of compressive strength (CS). This study introduces a novel, interpretable ensemble machine learning (ML) framework for predicting CS based on 759 mixture records encompassing cement, aggregates, supplementary cementitious materials (pozzolans), water/binder (W/B), superplasticizer, water, and curing age. Descriptive analysis and ANOVA were used to identify key predictors, followed by an 80/20 train–test split with 10-fold cross-validation to ensure robust and generalizable modeling. To further enhance model reliability, 5% of outliers were removed using an isolation forest algorithm, after which data were normalized and ensemble hyperparameters optimized. Among the evaluated models, the extra trees algorithm with standard scaling demonstrated the most stable generalization, achieving a coefficient of determination (R²) of 0.978 and a root mean square error (RMSE) of 4.197 MPa on the test set, and R² = 0.966 (RMSE = 5.053 MPa) under 10-fold cross-validation. Feature importance, SHAP, and partial dependence analyses consistently demonstrated that W/B, curing age, and cement are the principal determinants of CS. Finally, multi-objective optimization generated high-strength, low-impact mixtures, confirming the framework’s effectiveness as a transparent decision-support tool for performance- and sustainability-oriented pozzolanic concrete design. This study is novel in combining interpretable ensemble ML with multi-objective optimization to simultaneously achieve precise CS prediction and the formulation of sustainable, performance-optimized pozzolanic concrete mixtures. Full article

The soundscape serves as a critical determinant of the quality of urban wetland parks. This study employs a mixed-methods approach to comprehensively evaluate wetland soundscapes. First, field investigations combining sound level measurements and questionnaire surveys were conducted in Aixi Lake Wetland Park to analyze the spatiotemporal characteristics of the soundscape. Second, laboratory-based physiological tracking (using wearable sensors) and cognitive tests (Sustained Attention to Response Task, SART) were utilized to experimentally quantify the restorative benefits of typical soundscapes. The findings reveal that: (1) sound level indicators and sound harmonious degree in urban wetland parks exhibit significant spatiotemporal characteristics and distributional variations; (2) a marked competitive effect among biological, geophysical, and human activity sounds is observed in their spatial distribution; sound harmonious degree demonstrates significant spatial autocorrelation in both global and local models; (3) different sound sources possess varying restorative potentials, with bird song showing the highest restorative effect; the SHDs of biological and geophony, along with L_Aeq, are key factors affecting PRSS; (4) a positive correlation exists between L_Aeq and the PRSS up to 56.4 dB, beyond which PRSS declines with increasing L_Aeq; (5) at the physiological level, short-term exposure to urban wetland park soundscapes can rapidly alleviate stress, with the most pronounced restorative effects occurring within the first 60 s; and (6) in terms of attention, soundscape stimulation reduces SART response times and improves response speed, while bird song from treetops and musical sounds further decrease response errors. Full article

Salicylic acid (SA) is a key endogenous regulator involved in plant defense responses to biotic and abiotic stresses. The increasing resistance of pathogens to chemical plant protection products and growing environmental restrictions have intensified the search for alternative strategies to enhance plant health and stress tolerance. Among these strategies, the induction of natural defense mechanisms, in which SA plays a central signaling role, has gained particular attention. This review summarizes current knowledge on the role of SA in shaping plant resistance to environmental factors. The fundamental mechanisms of plant defense, including innate immunity, induced systemic resistance (ISR), and systemic acquired resistance (SAR), are discussed, with emphasis on the signaling function of SA and its interaction with other phytohormones, especially jasmonic acid and ethylene. The role of SA in regulating physiological processes associated with stress tolerance, such as antioxidant system activity, photosynthesis, plant growth, and senescence, is highlighted. The review of research results indicates that appropriately selected doses and timing of SA treatments can enhance resistance to selected pathogens and improve plant tolerance to adverse environmental conditions. However, treatment effectiveness depends on multiple factors, particularly SA concentration and plant–pathogen interactions. Salicylic acid is a promising component of integrated and sustainable plant protection strategies. Further research, especially under field conditions, is necessary to optimize its practical use and fully determine its potential in modern agriculture. Full article

Central configurations are fundamental equilibrium solutions of the Newtonian n-body problem and play a key role in understanding the structure and dynamics of gravitational systems. However, the classification and enumeration of such configurations remain incomplete in the four-body case, particularly for symmetric configurations. In this work, we develop a framework for determining and classifying symmetric four-body Dziobek configurations. The method allows the explicit determination of the number of admissible configurations directly from the mass parameters, without requiring prior knowledge of their geometric structure. Combined with previously established semi-analytical relations, this approach provides a systematic characterization of symmetric configurations in terms of mass ratios. As a physically relevant application, we apply the framework to the Earth–Moon system and determine the possible symmetric four-body central configurations involving Earth- and Moon-mass bodies and an additional object of arbitrary mass. We identify both isolated configurations and continuous families of equilibrium solutions, extending the concept of libration points to the four-body problem. The presented semi-analytical approach contributes to the understanding of equilibrium structures in multi-body gravitational systems and provides a foundation for further studies in celestial mechanics, planetary dynamics, and spacecraft motion in complex gravitational environments. Full article

Gender-based and sexual violence are major public health concerns, and norms about consent are central to their prevention. This study examines how attitudes toward sexual consent relate to digital sexual consent and to the occasional feeling of distrust in public consent campaigns and institutions. We conducted a cross-sectional online survey embedded in the evaluation of a municipal consent campaign in Zaragoza (Spain). Adults (N = 404; 56.7% women) completed a 14-item short version of the Sexual Consent Scale–Revised, two items on digital sexual consent, and three items on institutional reluctance (perceived “sermonizing” tone, distrust in effectiveness, and lack of personal identification with the message). Correlation and multiple regression models with robust standard errors were estimated, controlling for gender, age, education, income, relationship status, and social media use. Attitudes toward sexual consent were strongly and positively associated with digital sexual consent. Gender was the most consistent sociodemographic correlate: men showed less egalitarian attitudes than women across all consent measurements. Institutional reluctance was systematically related to less supportive consent attitudes: perceiving institutional messages as exaggerated or personally irrelevant predicted lower support for sexual and digital consent norms, whereas trust in the campaign’s effectiveness was associated with more egalitarian attitudes. The findings support the continuity between sexual and digital consent and highlight gender and institutional trust as key determinants for the prevention of gender-based and sexual violence. Public health and social policies should integrate digital consent into consent education and co-design campaigns that minimize defensive reactions and rebuild trust in institutions. Full article

The article provides a comprehensive review of empirical studies on consumer attitudes, motivations, and behaviors in the functional food market. The main objective of this study is to identify groups of determinants and to update and systematize current knowledge on the influence of various factors on consumer purchasing decisions in this market. Based on an analysis of international research published between 2004 and 2025, four key groups of determinants were identified: (1) health- and trust-related factors, (2) cognitive and psychological factors, (3) perceptual and product-related factors, and (4) socio-demographic and segmentation factors. The analysis confirms that purchasing decisions in this product category are complex and multidimensional. They result from the interaction between rational factors (health-related and cognitive) and emotional-symbolic factors (psychological and sensory). The strongest predictors of functional food acceptance include perceived health benefits, trust in producers and information sources, sensory attractiveness, and product naturalness. Socio-demographic characteristics, such as age, education level, and income, further differentiate purchasing intentions and behaviors. Overall, the findings highlight the need for further comparative and cross-cultural research, as cultural and economic conditions may significantly shape consumer decisions across markets. The results obtained have both theoretical and practical implications. They contribute to a better understanding of consumer decision-making processes and emphasize the importance of promoting health awareness. Full article

This study investigates the residual stresses developed during the curing process of polymer fiber-reinforced composites and their influence on fracture behavior, particularly the initiation and propagation of interlaminar cracks. The main objective is to quantify how different curing histories, including incomplete cure, alter the spatial distribution of residual stresses and, in turn, affect the mode-I fracture response of carbon-fiber/epoxy laminates. A transient thermal–structural finite element framework incorporating an autocatalytic cure kinetics model was used to simulate the curing process and predict residual stress development in a unidirectional carbon-fiber/epoxy laminate with an edge crack, considering thermal, chemical, and geometric effects. The cure model was calibrated using isothermal differential scanning calorimetry data to determine the degree of cure under different thermal conditions. The key novelty of this work is the integration of a validated cure-kinetics-based curing simulation with fracture analysis, enabling direct correlation of thermal history and degree of cure with spatially varying residual stresses at the crack front and their effect on fracture toughness. Numerical load–displacement predictions were compared with double cantilever beam experimental results and showed good agreement for the curing profiles examined. The results demonstrate that residual stresses generated by different cure cycles, including hold conditions and incomplete curing, significantly influence fracture toughness. In particular, the incomplete-cure profile produced an approximately 40% reduction in toughness compared with profiles that achieved complete cure, highlighting the importance of cure history in determining final structural performance. Full article