Search Results (8,510)

Environmental innovation represents a pivotal pathway toward achieving energy efficiency improvements, carbon footprint reduction, and ecological sustainability enhancement. The research investigates Chinese manufacturing enterprises listed on domestic stock exchanges throughout 2011–2023. The analytical framework utilizes count-based regression methodologies to explore how R&D investment intensity influences eco-innovation capabilities. Results demonstrate curvilinear associations linking R&D expenditure levels with both substantive and strategic environmental innovation achievements across industrial firms. This outcome successfully passed the turning-point test. Environmental oversight and financial incentives produce divergent moderating influences on innovation trajectories. Regulatory frameworks generate restrictive impacts through narrowing optimal investment ranges and dampening peak innovation outputs, whereas fiscal support mechanisms foster expansive effects via broadening resource availability and amplifying achievement levels. Cross-sectional examination uncovers substantial variations among ownership categories and geographical locations. State-owned enterprises demonstrate significantly lower optimal R&D intensity thresholds. Private firms require substantially elevated thresholds for optimal performance. Inland territories manifest unbalanced innovation dynamics. Coastal areas exhibit symmetric innovation patterns. The research enriches empirical knowledge in eco-innovation studies while offering context-specific strategic insights. The findings establish theoretical foundations and practical guidance for policy architects designing integrated environmental management systems that enhance innovation capabilities. Full article

Water quality evaluation is a crucial component of water source management and pollution prevention, essential for achieving regional water safety and sustainable development. The spatial distribution and trends of major water pollutants in Wan’an Reservoir were analyzed. Subsequently, a fuzzy membership model was employed to develop a comprehensive water quality evaluation method. This approach assessed spatial variations in water quality across the upper, middle, and lower reaches of the reservoir, identifying key factors influencing water quality. The results indicate that water quality in Wan’an Reservoir, primarily characterized by total nitrogen, was poor. Notably, 50% of the sampling points in the main stream were identified as highly polluted, with the highest exceedance rate observed in the middle reaches of the tributaries. Sampling points classified as Class I were predominantly located in the upper reaches, where water quality benefitted from clean incoming water and minimal disturbance. In contrast, the lower reaches experienced more severe pollution due to the cumulative effects of domestic sewage, industrial wastewater, and agricultural runoff. These findings are crucial for developing effective water environmental protection strategies and promoting the sustainable utilization and protection of water resources. Full article

Background/Objectives: Metabolic syndrome (MetS) comprises a cluster of cardiometabolic risk factors that vary dynamically under environmental and behavioral influences. Although there are data suggesting seasonal variability in individual metabolic components, few studies have comprehensively assessed MetS as a composite condition across seasons using a large, nationally representative population. In this study, we aimed to evaluate the seasonal and monthly patterns of MetS prevalence and component burden, with a focus on sex-specific differences. Methods: We analyzed 5,507,251 health screening records from 2,057,897 Korean adults aged ≥40 years between 2013 and 2022, obtained from the National Health Insurance Service database. Seasons were categorized as: spring (March–May), summer (June–August), fall (September–November), and winter (December–February). Trends in MetS prevalence and its components were evaluated monthly and seasonally, stratified by sex. Results: MetS prevalence significantly varied by season in both sexes (p < 0.001), ranging from 30.2% to 34.5% in men and from 21.5% to 25.5% in women. Among men, a U-shaped pattern was observed, with the lowest prevalence during summer and a progressive increase through winter. Women showed a steady decline in prevalence from January to September, followed by a slight rebound. Winter was associated with increased odds of MetS in both sexes. A significant interaction between sex and season (p for interaction <0.001) indicated the presence of sex-specific temporal patterns. Conclusions: This nationwide study revealed clear seasonal variation in MetS prevalence and component burden, with sex-specific patterns. These findings highlight the importance of incorporating seasonality and sex in cardiometabolic risk assessments and public health interventions. Full article

The aim of the study was to determine the fluctuations of selected physiological parameters in young Lipizzan stallions (n = 10) during the initial phase of their training as indicators of adaptation to a graded exercise load and stress exposure. For this purpose, four exercise tests (ExT) with lunging were carried out over a period of one year. Physiological parameters (gait speed, heart and respiratory rate (HR and RR), rectal and body surface temperature (RT and BST), and cortisol and lactate concentration (CORT and LAC)) were measured before and after training. In all ExT, gait speeds increased (p < 0.001) during the transitions from walk to trot and canter, followed by a significant (p < 0.001) increase in HR, RT, BST, and CORT, but not LAC values. However, the gate speed has no influence on the measured parameters. The highest BST values and corresponding warming were measured in the cranial region, followed by the caudal and distal body regions. The values of the measured variables remained within the ranges for warm-blooded horses, indicating adequate adaptation of the stallions to the applied stress level, but their variations could depend on air temperature or humidity. The results presented contribute to the knowledge of the complex physiological processes that occur in horses during exercise and point to the importance of environmental factors for adaptation to exercise. Full article

The construction industry is the biggest consumer of raw materials, and there is growing pressure for this industry to reduce its environmental footprint through the adoption of sustainable solutions. Waste plastic in a recycled form can be used to produce valuable products that can decrease dependence on natural resources. Despite the growing trend of exploring the potential of recycled plastics in construction through composite manufacturing and nonstructural products, to date no scientific data is available about converting waste plastic into recycled plastic to manufacture interlocking hollow blocks (IHBs) for construction. Thus, the current study intended to fill this gap by investigating the dynamic, mechanical, and physicochemical properties of engineered IHBs made out of recycled plastic. Engineered IHBs are able to self-center via controlled tolerance to lateral displacement, which makes their design novel. High-density polyethylene (HDPE) waste was considered due to its anticipated material properties and abundance in daily-use household products. Mechanical recycling coupled with extrusion-based pressurized filling was adopted to manufacture IHBs. Various configurations of IHBs and prism samples were tested for compression and shear strength, and forensic tests were conducted to study the physicochemical changes in the recycled plastic. In addition, to obtain better dynamic properties for energy dissipation, the compressive strength of the IHBs was 30.99 MPa, while the compressive strength of the prisms was 34.23 MPa. These values are far beyond the masonry strength requirements in applicable codes across the globe. In-plane shear strength was greater than out-of-plane shear strength, as anticipated. Microstructure analysis showed fibrous surfaces with good resistance and enclosed unburnt impurities. The extrusion process resulted in the elimination of contaminants and impurities, with limited variation in thermal stability. Overall, the outcomes are favorable for potential use in house construction due to sufficient masonry strength and negligible environmental concerns. Full article

Pervious concrete is a promising sustainable pavement material for sponge city construction. The incorporation of Steel Slag Aggregate (SSA) as a substitute for natural aggregates has the double role of clean production with significant economic and environmental benefits. While the strength and permeability, known as two critical design parameters of pervious concrete, are closely linked to its porosity, there is limited research on the influence of the porosity on the mechanical properties of pervious concrete. In this paper, both experimental and numerical investigations were performed, focusing on the influence of target porosity on the strength and permeability of pervious concrete with and without SSA. Three different target porosities (15%, 20%, and 25%), five distinct water-to-cement (w/c) ratios (0.25, 0.28, 0.30, 0.33, and 0.35), and five SSA replacement ratios (0, 25%, 50%, 75%, and 100%) were considered in this study. A two-dimensional (2D) finite-element (FE) model was developed, with which the failure mode and the strength variation of pervious concrete under different target porosities were analyzed and verified with the experimental results. The results showed that the porosity had a significant influence on both the strength and permeability of pervious concrete, while the influence of the w/c ratio is marginal. There existed an optimal w/c ratio of 0.3, for which pervious concrete with porosities of 15%, 20%, and 25% achieved 28-day compressive strengths of 27.8, 20.6, and 15.6 MPa and permeability coefficients of 0.32, 0.58, and 1.02 cm/s, respectively. Specifically, at the lowest porosity of 15%, the replacement of 100% SSA resulted in the largest improvement in the compressive strength up to 37.86%. Based on the regression analysis, a series of empirical equations correlating the porosity, strength and permeability of pervious concrete was formulated and validated against the experimental data. The findings presented herein are expected to provide references to the practical evaluation of the optimal mix proportion of previous concrete, considering specific and demanding engineering requirements. Full article

This study examined the spatial distribution and environmental determinants of hairtail (Trichiurus lepturus) catch volumes in the southern coastal waters of South Korea, employing a Spatial Durbin Model (SDM) based on grid-level data collected from 2020 to 2022. Key explanatory variables included chlorophyll-a concentration, dissolved oxygen, salinity, sea surface temperature, and fishing effort. Spatial autocorrelation was confirmed through Moran’s I test, justifying the application of a spatial econometric framework. Among the environmental factors, salinity exhibited the strongest positive direct effect on catch volumes, whereas dissolved oxygen consistently showed a negative effect. Chlorophyll-a concentration exhibited significant positive effects both within local grids and in neighboring areas. Sea surface temperature also had a modest but significant direct effect on catch volumes. Additionally, higher fishing effort was associated with increased catch volumes, emphasizing the spatial impact of human activities on fishery resources. These findings reveal that hairtail tend to aggregate in high-salinity, low-oxygen environments and respond to seasonal oceanographic variations. Overall, the results highlight the value of spatial econometric models in fisheries research by revealing how environmental and anthropogenic factors influence fish catch through both direct and indirect effects. The spatial framework offers deeper insight into the mechanisms driving hairtail distribution, particularly in ecologically complex regions like the Jeju Strait. Full article

Scene text understanding, serving as a cornerstone technology for autonomous navigation, document digitization, and accessibility tools, has witnessed a paradigm shift from traditional methods relying on handcrafted features and multi-stage processing pipelines to contemporary deep learning frameworks capable of learning hierarchical representations directly from raw image inputs. This survey distinctly categorizes modern scene text recognition (STR) methodologies into three principal paradigms: two-stage detection frameworks that employ region proposal networks for precise text localization, single-stage detectors designed to optimize computational efficiency, and specialized architectures tailored to handle arbitrarily shaped text through geometric-aware modeling techniques. Concurrently, an in-depth analysis of text recognition paradigms elucidates the evolutionary trajectory from connectionist temporal classification (CTC) and sequence-to-sequence models to transformer-based architectures, which excel in contextual modeling and demonstrate superior performance. In contrast to prior surveys, this work uniquely emphasizes several key differences and contributions. Firstly, it provides a comprehensive and systematic taxonomy of STR methods, explicitly highlighting the trade-offs between detection accuracy, computational efficiency, and geometric adaptability across different paradigms. Secondly, it delves into the nuances of text recognition, illustrating how transformer-based models have revolutionized the field by capturing long-range dependencies and contextual information, thereby addressing challenges in recognizing complex text layouts and multilingual scripts. Furthermore, the survey pioneers the exploration of critical research frontiers, such as multilingual text adaptation, enhancing model robustness against environmental variations (e.g., lighting conditions, occlusions), and devising data-efficient learning strategies to mitigate the dependency on large-scale annotated datasets. By synthesizing insights from technical advancements across 28 benchmark datasets and standardized evaluation protocols, this study offers researchers a holistic perspective on the current state-of-the-art, persistent challenges, and promising avenues for future research, with the ultimate goal of achieving human-level scene text comprehension. Full article

Walnut anthracnose (Colletotrichum gloeosporioides and C. siamense) and brown spot (Alternaria alternata) cause severe yield losses globally. Conventional fungicides face the challenges of pathogen resistance and environmental toxicity. This study evaluates 4-propylphenol, a plant-derived phenolic compound, as an eco-friendly alternative against key fungal pathogens of walnut. In vitro assays determined EC₅₀ values against target pathogens (29.11–31.89 mg·L⁻¹) via mycelial growth inhibition and conidial germination suppression (EC₅₀ = 55.04–71.85 mg·L⁻¹). Mechanistic analyses confirmed membrane disruption through propidium iodide staining (9.5-to-14.0-fold fluorescence intensity increase), DNA leakage (77.82–85.15% at 250 mg·L⁻¹), and protein efflux (58.10–66.49%). In field trials, we implemented a phenology-driven strategy: 100 mg·L⁻¹ ground/canopy spray at flowering to reduce primary inoculum, followed by 400 mg·L⁻¹ canopy application at fruiting. This protocol achieved 86.67% control efficacy against disease complexes with negligible phytotoxicity (SPAD variation < 5%). 4-propylphenol provides a sustainable solution through membrane-targeting action, effectively overcoming fungicide resistance in woody crops. Full article

Against the backdrop of increasingly severe global carbon emissions and China’s commitment to achieving carbon peaking by 2030, accelerating the transition to a low-carbon economy has become an urgent priority. As fundamental microeconomic entities, enterprises play a crucial role in the national governance of carbon emissions. This study uses panel data on Chinese A share listed companies from 2019 to 2023 and employs fixed effects models that control for firm, year, and industry effect to analyze how ESG performance influences carbon emissions and through which mechanism. The findings indicate that improvements in ESG ratings significantly reduce firms’ carbon emissions. This effect operates primarily through the following two channels: (1) promoting green technological innovation, thereby enhancing environmental performance, and (2) increasing the attention of financial analysts, which strengthens external monitoring. The heterogeneity analysis further reveals that the mitigating effect of ESG improvement on carbon emissions is more pronounced in firms with a lower proportion of institutional ownership, while this effect is relatively weaker in firms with higher institutional ownership. This suggests that in contexts where institutional investors hold a smaller share, firms may place greater emphasis on the policy pressure and social responsibility expectations associated with ESG performance, thereby exhibiting stronger commitment to emission reduction actions. In contrast, in firms dominated by institutional investors, the implementation of ESG policy objectives may be partially compromised due to the investors’ short-term profit orientation. This study provides empirical evidence for firms to fulfill their environmental and social responsibilities and offers actionable insights for investors aiming to promote sustainable development. From a policy perspective, the findings also offer theoretical support for developing differentiated regulatory strategies based on variations in ownership and shareholding structures. Full article

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis. Full article

The current–voltage (I-V) characteristic provides essential performance parameters of a solar cell, influenced by temperature and solar radiation. The efficiency of a solar cell is sensitive to variations in these conditions. This study electrically characterized a polycrystalline silicon solar cell in a solar simulator chamber at temperatures of 25–55 °C and irradiance levels of 600–1000 W/m². The acquired data were used to train and evaluate neural network models to predict the I-V characteristics of a polycrystalline silicon solar cell. Two recurrent neural network architectures were tested: LSTM and the GRU model. The performance of the model was assessed using MAE, RMSE, and R². The GRU model achieved the results, with MAE = $2.813\times {10}^{-3}$, RMSE = $5.790\times {10}^{-3}$, and R² = 0.9844, similar to LSTM (MAE = $2.6613\times {10}^{-3}$, RMSE = $5.858\times {10}^{-3}$, R² = 0.9840). These findings highlight the GRU network as the most efficient approach for modeling solar cell behavior under varying environmental conditions. Full article

Digital livestock farming faces a critical deployment challenge: bridging the gap between cutting-edge AI algorithms and practical implementation in resource-constrained agricultural environments. While deep learning models demonstrate exceptional accuracy in laboratory settings, their translation to operational farm systems remains limited by computational constraints, connectivity issues, and user accessibility barriers. Dairy DigiD addresses these challenges through a novel edge-cloud AI framework integrating YOLOv11 object detection with DenseNet121 physiological classification for cattle monitoring. The system employs YOLOv11-nano architecture optimized through INT8 quantization (achieving 73% model compression with <1% accuracy degradation) and TensorRT acceleration, enabling 24 FPS real-time inference on NVIDIA Jetson edge devices while maintaining 94.2% classification accuracy. Our key innovation lies in intelligent confidence-based offloading: routine detections execute locally at the edge, while ambiguous cases trigger cloud processing for enhanced accuracy. An entropy-based active learning pipeline using Roboflow reduces the annotation overhead by 65% while preserving 97% of the model performance. The Gradio interface democratizes system access, reducing technician training requirements by 84%. Comprehensive validation across ten commercial dairy farms in Atlantic Canada demonstrates robust performance under diverse environmental conditions (seasonal, lighting, weather variations). The framework achieves mAP@50 of 0.947 with balanced precision-recall across four physiological classes, while consuming 18% less energy than baseline implementations through attention-based optimization. Rather than proposing novel algorithms, this work contributes a systems-level integration methodology that transforms research-grade AI into deployable agricultural solutions. Our open-source framework provides a replicable blueprint for precision livestock farming adoption, addressing practical barriers that have historically limited AI deployment in agricultural settings. Full article

As mental health issues such as stress, anxiety, and depression become increasingly prevalent in urban populations, there is a critical need to embed restorative functions into the built environment. Urban parks, as integral components of ecological infrastructure, play a vital role in promoting psychological well-being. This study explores how diverse park environments facilitate mental health recovery through multi-sensory engagement, using integrated psychophysiological assessments in a wetland park in Zhengzhou, China. Electroencephalography (EEG) and perceived restoration scores were employed to evaluate recovery outcomes across four environmental types: waterfront, wetland, forest, and plaza. Key perceptual factors—including landscape design, spatial configuration, biodiversity, and facility quality—were validated and analyzed for their roles in shaping restorative experiences. Results reveal significant variation in recovery effectiveness across environments. Waterfront areas elicited the strongest physiological responses, while plazas demonstrated lower restorative benefits. Two recovery pathways were identified: a direct, sensory-driven process and a cognitively mediated route. Biodiversity promoted physiological restoration only when mediated by perceived restorative qualities, whereas landscape and spatial attributes produced more immediate effects. Facilities supported psychological recovery mainly through cognitive appraisal. The study proposes a smart park framework that incorporates environmental sensors, adaptive lighting, real-time biofeedback systems, and interactive interfaces to enhance user engagement and monitor well-being. These technologies enable urban parks to function as intelligent, health-supportive infrastructures within the broader built environment. The findings offer evidence-based guidance for designing responsive green spaces that contribute to mental resilience, aligning with the goals of smart city development and healthy life-building environments. Full article

Urban green spaces are important nature-based solutions to mitigate climate change. While the distribution of green spaces within cities is well documented, few studies assess whether inequities in green space quantity (i.e., percent cover) are mirrored by inequities in green space quality (i.e., vegetation health or sensitivity to stressors). Green space quality is important to measure alongside green space quantity because vegetation that is healthier and less sensitive to stressors such as climatic fluctuations sustain critical ecosystem services through stressful environmental conditions, especially as the climate changes. We use a 40-year remote sensing dataset to examine the spatial patterns and underlying drivers of vegetation sensitivity to short-term (monthly) climate fluctuations in Chicago. Our results show that although vegetation cover was not equitably distributed between racially and ethnically segregated census tracts, socio-demographic composition was not a key driver of spatial variation in short-term vegetation sensitivity to climate. Instead, we found that vegetation growth form was a strong predictor of differences in vegetation sensitivity among communities. At the census tract level, higher herbaceous/shrub cover was associated with increased sensitivity to climate, while higher tree cover was associated with decreased sensitivity. These results suggest that urban green spaces comprising trees will be less sensitive (i.e., more resistant) to short-term climate fluctuations than those comprising predominately herbaceous or shrub cover. Our findings highlight that urban green space quality can vary spatially within cities; however, more work is needed to understand how the drivers of vegetation sensitivity vary among cities, especially those experiencing different climatic regimes. This work is key to planning and planting high-quality, climate change-resilient and equitable urban green spaces. Full article