Search Results (8,608)

Coal mining can cause the rupture of the overlying strata, and the energy released by large-scale fractures can therefore induce earthquake disasters, which in turn can cause more secondary disasters. In the past 50 years, countless earthquakes induced by coal mining have been reported. In this paper, the main factors relating to the mining-induced seismicity, including the mechanical properties, geometry of the space, excavation advance, and excavation rate, are investigated using both experimental and numerical methods. The sensitivity of these factors behaves differently with regard to the stress distribution and failure mode. Space geometry and excavation advances have the highest impact on the surface settlement and the failure, while the excavation rate in practical engineering projects has the least impact on the failure mode. The numerical study coincides well with the experimental observation. The result indicates that the mechanical properties given by the geological survey report can be effectively used to assess the risk of mining-induced seismicity, and the proper adjustment of the tunnel geometry can largely reduce the surface settlement and improve the safety of mining. Full article

A new generation of compact radon detectors with high sensitivity and fine spatial resolution (1–2 cm scale) was used to investigate indoor radon distribution and identify potential entry pathways. Solid-state nuclear track detectors (Kodak-Pathe LR-115 type II, Dosirad, France), combined with activated carbon fabric (ACC-5092-10), enabled sensitive, spatially resolved radon measurements. Two case studies were conducted: Case 1 involves a room with elevated radon levels suspected to originate from the floor. Case 2 involves a house with persistently high indoor radon concentrations despite active basement ventilation. In the first case, radon emission from the floor was found to be highly inhomogeneous, with concentrations varying by more than a factor of four. In the second, unexpectedly high radon levels were detected at electrical switches and outlets on walls in the living space, suggesting radon transport through wall voids and entry via non-hermetic electrical fittings. These novel detectors facilitate fine-scale mapping of indoor radon concentrations, revealing ingress routes that were previously undetectable. Their use can significantly enhance radon diagnostics and support the development of more effective mitigation strategies. Full article

In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article

Introduction: The increasing cultural diversity of patients poses new challenges for nurses. Cultural competence, especially knowledge of the cultural determinants of health and illness, is an important element of professionalism in nursing care. The aim of this study was to analyse nurses’ self-assessment of cultural knowledge, with a focus on the six dimensions of the Giger and Davidhizar model, prior to formal training in this area. Methods: A triangulation method combining qualitative and quantitative analysis was used. The analysis included 353 statements from 36 master’s student nurses. Data were coded according to six cultural phenomena: biological factors, communication, space, time, social structure, and environmental control. Content analysis, ANOVA, Spearman’s rank correlation, and cluster analysis (k-means) were conducted. Results: The most frequently identified that categories were environmental control (34%), communication (20%), and social structure (16%). Significant knowledge gaps were identified in the areas of non-verbal communication, biological differences, and understanding space in a cultural context. Three cultural knowledge profiles of the female participants were distinguished: pragmatic, socio-reflective, and critical–experiential. Conclusions: The cultural knowledge of the participants was fragmented and simplified. The results indicate the need to personalise cultural learning and to take into account nurses’ level of readiness and experience profile. The study highlights the importance of the systematic development of reflective and contextual cultural knowledge as a foundation for competent care. Full article

With the global aging population on the rise, identifying environmental factors that modulate immunosenescence is critical for health interventions. While urban green spaces are known to confer health benefits, the long-term effects of forest exposure on immunosenescence remain unclear. This study investigated the differential impacts of urban forest versus urban environments on immunosenescence using a D-galactose-induced murine model. Mice were assigned to urban or forest environments for 8 weeks, with serum cytokines (IL-2, IL-6, TNF-α, IFN-γ), T-cell subsets, and organ indices analyzed. Forest environments exhibited significantly higher humidity and negative air ion concentrations alongside lower noise levels compared to urban settings. Aged forest-exposed mice showed attenuated immunosenescence markers, including significantly lower IL-6 levels (p < 0.01) and improved thymic indices, suggesting urban forest environments may mitigate immune decline. These findings highlight the potential of urban forests in promoting healthy aging, advocating for their integration into urban planning. Further human studies are warranted to translate these findings into public health strategies. Full article

Background: Claustrophobia is defined as the fear of enclosed spaces, and it is a rather common specific phobia. Although the Claustrophobia Questionnaire (CLQ) is a valid questionnaire to measure claustrophobia, there have been no studies validating this tool in Greek. Thus, our aim was to translate and validate the CLQ in Greek. Methods: We applied the forward–backward translation method to translate the English CLQ into Greek. We conducted confirmatory factor analysis (CFA) to examine the two-factor model of the CLQ. We examined the convergent and divergent validity of the Greek CLQ by using the Fear Survey Schedule-III (FSS-III-CL), the NEO Five-Factor Inventory (NEO-FFI-NL-N), and the Spielberger’s State-Trait Anxiety Inventory (STAI). We examined the convergent validity of the Greek CLQ by calculating Pearson’s correlation coefficient between the CLQ scores and scores on FSS-III-CL, NEO-FFI-NL-N, STAI-S (state anxiety), and STAI-T (trait anxiety). We examined the divergent validity of the Greek CLQ using the Fisher r-to-z transformation. To further evaluate the discriminant validity of the CLQ, we calculated the average variance extracted (AVE) score and the Composite Reliability (CR) score. We calculated the intraclass correlation coefficient (ICC) and Cronbach’s alpha to assess the reliability of the Greek CLQ. Results: Our CFA confirmed the two-factor model of the CLQ since all the model fit indices were very good. Standardized regression weights between the 26 items of the CLQ and the two factors ranged from 0.559 to 0.854. The convergent validity of the Greek CLQ was very good since it correlated strongly with the FSS-III-CL and moderately with the NEO-FFI-NL-N and the STAI. Additionally, the Greek CLQ correlated more highly with the FSS-III-CL than with the NEO-FFI-NL-N and the STAI, indicating very good divergent validity. The AVE for the suffocation factor was 0.573, while for the restriction factor, it was 0.543, which are both higher than the acceptable value of 0.50. Moreover, the CR score for the suffocation factor was 0.949, while for the restriction factor, it was 0.954. The reliability of the Greek CLQ was excellent since the ICC in test–retest study was 0.986 and the Cronbach’s alpha was 0.956. Conclusions: The Greek version of the CLQ is a reliable and valid tool to measure levels of claustrophobia among individuals. Full article

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

To study the generation and dissipation process of agglomerate fog in mountainous expressways and deeply understand the hazard mechanisms of agglomerate fog sections in mountainous expressways, based on the analysis of the geographical location characteristics of mountainous expressways and the spatial and temporal distribution characteristics of agglomerate fog, the airspace constitutive model of agglomerate fog in mountainous expressways was constructed based on Newton constitutive theory. Firstly, the properties of the Newtonian fluid and cluster fog were compared and analyzed, and the influence mechanism of environmental factors such as the altitude difference, topography, water system, valley effect, and vegetation on the generation and dissipation of agglomerate fog in mountainous expressways was analyzed. Based on Newton’s constitutive theory, the constitutive model of temperature, humidity, wind speed, and agglomerate fog points in the foggy airspace of the mountainous expressway was established. Then, the time and spatial distribution of fog in Chongqing and Guizhou from 2021 to 2023 were analyzed. Finally, the model was verified by using the meteorological data and fog warning data of Liupanshui City, Guizhou Province in 2023. The results show that the foggy airspace of mountainous expressways can be defined as “the space occupied by the agglomerate fog that occurs above the mountain expressway”; The temporal and spatial distribution of foggy airspace on expressways in mountainous areas is closely related to the topography, water system, vegetation distribution, and local microclimate formed by thermal radiation. The horizontal and vertical movements of the atmosphere have little influence on the foggy airspace on expressways in mountainous areas. The specific manifestation of time distribution is that the occurrence of agglomerate fog is concentrated from November to April of the following year, and the daily occurrence time is mainly concentrated between 4:00–8:00 and 18:00–22:00. The calculation results of the foggy airspace constitutive model of the expressway in the mountainous area show that when there is low surface radiation or no surface radiation, the fogging value range is [90, 100], and the fogging value range is [50, 70] when there is high surface radiation (>200), and there is generally no fog in other intervals. The research results can provide a theoretical basis for traffic safety management and control of mountainous expressway fog sections. Full article

Understanding the social drivers of pro-environmental behavior in urban forests and green spaces is critical for addressing sustainability challenges. Subjective norms serve as a key pathway through which social expectations influence individuals’ behavioral intentions. Despite mixed findings in the literature regarding the impact of subjective norms on individuals’ intentions, there is a research gap about the determinants of this construct. This study was conducted to explore how social expectations shape perceived subjective norms among visitors of urban forests. A theoretical model was developed with subjective norms at its center, incorporating their predictors including social identity, media influence, interpersonal influence, and institutional trust, personal norms as a mediator, and behavioral intention as the outcome variable. Using structural equation modeling, data was collected and analyzed from a sample of visitors of urban forests in Tehran, Iran. The results revealed that subjective norms play a central mediating role in linking external social factors to behavioral intention. Social identity emerged as the strongest predictor of subjective norms, followed by media and interpersonal influence, while institutional trust had no significant effect. Subjective norms significantly influenced both personal norms and intentions, and personal norms also directly predicted intention. The model explained 50.9% of the variance in subjective norms and 39.0% in behavioral intention, highlighting its relatively high explanatory power. These findings underscore the importance of social context and internalized norms in shaping sustainable behavior. Policy and managerial implications suggest that strategies should prioritize community-based identity reinforcement, media engagement, and peer influence over top-down institutional messaging. This study contributes to environmental psychology and the behavior change literature by offering an integrated, empirically validated model. It also provides practical guidance for designing interventions that target both social and moral dimensions of environmental action. Full article

This study aims to clarify the nonlinear pressure loss patterns of the pneumatic system in a pneumatic seeder under varying pipeline structures and airflow parameters, and to develop a rapid prediction equation for the main pipe’s pressure loss. The studied multi-branch pipeline system consists of a main pipe, a header, and ten branch pipes. The main pipe is vertically installed at the center of the header in a straight-line configuration. The ten branch pipes are symmetrically and evenly spaced along the axial direction of the header, distributed on both sides of the main pipe. The outlet directions of the branch pipes are arranged in a 180° orientation opposite to the inlet direction of the main pipe, forming a symmetric multi-branch configuration. Firstly, this study investigated the flow characteristics within the multi-branch pipeline of the pneumatic system and elaborated on the mechanism of flow division in the pipeline. The key geometric factors affecting airflow were identified. Secondly, from a microscopic perspective, CFD simulations were employed to analyze the fundamental causes of pressure loss in the multi-branch pipeline system. Finally, from a macroscopic perspective, a dimensional analysis method was used to establish an empirical equation describing the relationship between the pressure loss (P) and several influencing factors, including the air density (ρ), air’s dynamic viscosity (μ), closed-end length of the header (Δl), branch pipe 1’s flow rate (Q), main pipe’s inner diameter (D), header’s inner diameter (γ), branch pipe’s inner diameter (d), and the spacing of the branch pipe (δ). The results of the bench tests indicate that when 0.0018 m³·s⁻¹ ≤ Q ≤ 0.0045 m³·s⁻¹, 0.0272 m < d ≤ 0.036 m, 0.225 m < δ ≤ 0.26 m, 0.057 m ≤ γ ≤ 0.0814 m, and 0.0426 m ≤ D ≤ 0.0536 m, the prediction accuracy of the empirical equation can be controlled within 10%. Therefore, the equation provides a reference for the structural design and optimization of pneumatic seeders’ multi-branch pipelines. Full article

Objective: This study aims to examine the anterior chamber structure and related factors in 8-year-old children based on data from The Yamanashi Adjunct Study of the Japan Environment and Children’s Study (JECS). Methods: A total of 709 children aged 8 years (350 boys and 359 girls) who participated in the JECS Adjunct Study were included. The right eyes were primarily used for measurements. Optical Coherence Tomography (OCT) was utilized to scan the anterior chambers of the participants’ eyes. The following parameters were measured: Angle Opening Distance (AOD500, 750), Trabecular Iris Space Area (TISA500, 750), Anterior Chamber Angle (ACA500, 750), Peripheral Iris Thickness (IT500, 750), and Peripheral Corneal Thickness (PCT500, 750). The relationships between anterior chamber structure, axial length (AL), spherical equivalent (SE), logMAR (without correction), and body height were analyzed. Results: A significant negative correlation was found between SE and ACA (500: coefficient = −0.19; 750: −0.24), AOD (500: −0.19; 750: −0.24), and TISA (500: −0.17; 750: −0.23) (p < 0.001). Conversely, a significant positive correlation was observed between AL and ACA (500: 0.22; 750: 0.26), AOD (500: 0.25; 750: 0.30), and TISA (500: 0.24; 750: 0.29) (p < 0.001). Boys exhibited a longer AL (boys: girls = 23.30 ± 0.76 mm; girls = 22.79 ± 0.72 mm) and greater CT (500: boys = 812.82 ± 51.94 mm; girls = 784.48 ± 51.81 mm; 750: boys = 776.01 ± 48.64 mm; girls = 751.34 ± 49.63 mm) compared to girls (p < 0.001) despite no significant difference in body height. CT and IT showed no correlation with AL or SE, and visual acuity had minimal correlation with IT and CT. Conclusions: In our cohort of eight-year-old children, the anterior chamber angle structure correlates with ocular structures and refractive error, revealing notable differences between boys and girls. Full article

Resource-exhausted cities have long played a key role in national energy development. Urban renewal projects, such as the renovation of old underground commercial spaces, can improve urban vitality and promote sustainable development. However, in resource-based cities, traditional industries dominate, while new industries such as modern commerce develop slowly. This results in low economic dynamism and weak motivation for urban development. To address this issue, we propose a systematic method for analyzing construction risks during the decision-making stage of renovation projects. The method includes three steps: risk value assessment, risk factor identification, and risk weight calculation. First, unlike previous studies that only used SWOT for risk factor analysis, we also applied it for project value assessment. Then, using the Work Breakdown Structure–Risk Breakdown Structure framework method (WBS-RBS), we identified specific risk sources by analyzing key construction technologies throughout the entire lifecycle of the renovation project. Finally, to enhance expert consensus, we proposed an improved Delphi–Analytic Hierarchy Process method (Delphi–AHP) to calculate risk indicator weights for different construction phases. The risk analysis covered all lifecycle stages of the renovation and upgrading project. The results show that in the Fushun city renovation case study, the established framework—consisting of five first-level indicators and twenty s-level indicators—enables analysis of renovation projects. Among these, management factors and human factors were identified as the most critical, with weights of 0.3608 and 0.2017, respectively. The proposed method provides a structured approach to evaluating renovation risks, taking into account the specific characteristics of construction work. This can serve as a useful reference for ensuring safe and efficient implementation of underground commercial space renovation projects in resource-exhausted cities. Full article

Carbon (C) sequestration and nitrogen (N) and phosphorus (P) accumulation in urban forest green spaces are significant for global climate regulation and alleviating nutrient pollution. However, the effects of management and conservation practices across different urban forest vegetation types on soil C, N, and P contents and stoichiometric ratios remain largely unexplored. We selected forest soils from Guangzhou, a major Metropolis in China, as our study area. Soil samples were collected from two urban secondary forests that naturally regenerated after disturbance (108 samples) and six urban forest parks primarily composed of artificially planted woody plant communities (72 samples). We employed mixed linear models and variance partitioning to analyze and compare soil C, N, and P contents and their stoichiometry and its main driving factors beneath suburban forests and urban park vegetation. These results exhibited that soil pH and bulk density in urban parks were higher than those in suburban forests, whereas soil water content, maximum storage capacity, and capillary porosity were higher in urban forests than in urban parks. Soil C, N, and P contents and their stoichiometry (except for N:P ratio) were significantly higher in suburban forests than in urban parks. Multiple analyzes showed that soil pH had the most pronounced negative influence on soil C, N, C:N, C:P, and N:P, but the strongest positive influence on soil P in urban parks. Soil water content had the strongest positive effect on soil C, N, P, C:N, and C:P, while soil N:P was primarily influenced by the positive effect of soil non-capillary porosity in suburban forests. Overall, our study emphasizes that suburban forests outperform urban parks in terms of carbon and nutrient accumulation, and urban green space management should focus particularly on the impact of soil pH and moisture content on soil C, N, and P contents and their stoichiometry. Full article

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access. Full article

High-viscosity stratified strata, characterized by complex geotechnical properties such as strong cohesion, low permeability, and pronounced layered structures, exhibit significant lateral friction resistance and high-end resistance during steel sheet pile installation. These factors substantially increase construction difficulty and may even cause structural damage. This study addresses two critical mechanical challenges during vibratory pile driving in Fujian Province’s hydraulic engineering project: prolonged high-frequency driving durations, and severe U-shaped steel sheet pile head damage in high-viscosity stratified soils. Employing the Coupled Eulerian–Lagrangian (CEL) numerical method, a systematic investigation was conducted into the penetration resistance, stress distribution, and damage patterns during vibratory pile driving under varying conditions of cohesive soil layer thickness, predrilled hole spacing, and aperture dimensions. The correlation between pile stress and penetration depth was established, with the influence mechanisms of key factors on driving-induced damage in high-viscosity stratified strata under multi-factor coupling effects elucidated. Finally, the feasibility of predrilling techniques for resistance reduction was explored. This study applies the damage prediction model based on the CEL method to U-shaped sheet piles in high-viscosity stratified formations, solving the problem of mesh distortion in traditional finite element methods. The findings provide scientific guidance for steel sheet pile construction in high-viscosity stratified formations, offering significant implications for enhancing construction efficiency, ensuring operational safety, and reducing costs in such challenging geological conditions. Full article