Search Results (16,362)

Accurate prediction of Air Quality Index (AQI) concentrations remains a critical challenge in environmental monitoring and public health management due to the complex nonlinear relationships among multiple atmospheric factors. To address this challenge, we propose a novel prediction model that integrates an adaptive-weight particle swarm optimization (AWPSO) algorithm with a back propagation neural network (BPNN). First, the random forest (RF) algorithm is used to scree the influencing factors of AQI concentration. Second, the inertia weights and learning factors of the standard PSO are improved to ensure the global search ability exhibited by the algorithm in the early stage and the ability to rapidly obtain the optimal solution in the later stage; we also introduce an adaptive variation algorithm in the particle search process to prevent the particles from being caught in local optima. Finally, the BPNN is optimized using the AWPSO algorithm, and the final values of the optimized particle iterations serve as the connection weights and thresholds of the BPNN. The experimental results show that the RFAWPSO-BP model reduces the root mean square error and mean absolute error by 9.17 μg/m³, 5.7 μg/m³, 2.66 μg/m³; and 9.12 μg/m³, 5.7 μg/m³, 2.68 μg/m³ compared with the BP, PSO-BP, and AWPSO-BP models, respectively; furthermore, the goodness of fit of the proposed model was 14.8%, 6.1%, and 2.3% higher than that of the aforementioned models, respectively, demonstrating good prediction accuracy. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Polyvinyl alcohol (PVA) hydrogels have emerged as versatile materials due to their exceptional biocompatibility and tunable mechanical properties, showing great promise for flexible sensors, smart wound dressings, and tissue engineering applications. However, rational design remains challenging due to complex structure–property relationships involving multiple formulation parameters. This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. XGBoost demonstrated superior performance after Optuna-based optimization, achieving R² values of 0.964 for training and 0.801 for testing. SHAP analysis provided unprecedented mechanistic insights, revealing that PVA molecular weight dominates mechanical performance (SHAP importance: 84.94) through chain entanglement and crystallization mechanisms, followed by degree of hydrolysis (72.46) and cross-linking parameters. The interpretability analysis identified optimal parameter ranges and critical feature interactions, elucidating complex non-linear relationships and reinforcement mechanisms. By addressing the “black box” limitation of machine learning, this approach enables rational design strategies and mechanistic understanding for next-generation multifunctional hydrogels. Full article

Background: Ambulatory blood pressure monitoring (ABPM) is increasingly recognized as a more reliable indicator of blood pressure status in children than clinic-based measurements, with superior predictive value for cardiovascular morbidity and mortality. However, evidence on the clinical utility of ABPM-derived indices, such as pulse pressure (PP), pulse pressure index (PPI), rate pressure product (RPP), ambulatory arterial stiffness index (AASI), and average real variability (ARV), remains underexplored in the pediatric population, particularly among children with chronic kidney disease (CKD). Objective: To evaluate the correlation between ABPM-derived indices in children, with a subgroup analysis comparing those with and without CKD. Secondary objectives included identifying factors associated with AASI and ARV and assessing their utility in cardiovascular risk stratification. Methods: In this bicentric cross-sectional study, 70 children (41 with CKD and 29 controls) were enrolled. ABPM indices (PP, PPI, RPP, AASI, and ARV) were calculated, and both descriptive and inferential statistical analyses, including linear regression, were performed. Results: Systolic and diastolic hypertension were significant predictors of elevated ARV (p < 0.05), while body mass index (BMI) and glomerular filtration rate (GFR) were positively associated with AASI (p < 0.05). Use of angiotensin-converting enzyme inhibitors (ACEIs) was associated with reduced arterial stiffness (p = 0.02). Significant differences were observed in weight, BMI, PP, and PPI between the CKD and non-CKD groups, with ABPM demonstrating greater sensitivity in detecting vascular health markers. Conclusions: ABPM-derived indices, particularly PP, PPI, and ARV, show promise in improving cardiovascular risk assessment in children. These findings support the broader use of ABPM metrics for refined cardiovascular evaluation, especially in pediatric CKD. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

This study investigates the spatial relationship between population distribution and tree cover loss in Nepal from 2000 to 2020, using satellite-based forest cover and population data along with statistical and geospatial analysis. Two statistical methods—linear regression (LR) and Geographically Weighted Regression (GWR)—were used to assess the influence of population on forest cover change. The correlation between total population and forest loss at the national level suggested little to no direct impact of population growth on forest loss. However, sub-national analysis revealed localized forest degradation, highlighting the importance of spatial and regional assessments to uncover land cover changes masked by national trends. While LR showed a weak national-level correlation, GWR revealed substantial spatial variation, with the coefficient of determination values increasing from 0.21 in 2000 to 0.59 in 2020. In some regions, local R² exceeded 0.75 during 2015 and 2020, highlighting emerging hotspot clusters where population pressure is strongly linked to deforestation, especially along major infrastructure corridors. Using very high-resolution spatial data enabled pixel-level analysis, capturing fine-scale deforestation patterns, and confirming hotspot accuracy. Overall, the findings emphasize the value of spatially explicit models like GWR for understanding human–environment interactions guiding targeted land use planning to balance development with environmental sustainability in Nepal. Full article

As the global demand for eco-friendly food ingredients grows, marine macroalgae emerge as a valuable resource for multiple applications using a circular bioeconomy approach. In this study, green macroalgae Ulva flexuosa, naturally accumulated in aquaculture ponds as a residual biomass (by-product) of shrimp and oyster farming, were investigated regarding their bioactivity, chemical composition, and antioxidant properties. The use of aquaculture by-products as raw materials not only reduces waste accumulation but also makes better use of natural resources and adds value to underutilized biomass, contributing to sustainable production systems. For this, a comprehensive approach including the evaluation of its composition and environmentally friendly extraction of bioactive compounds was conducted and discussed. Green macroalgae exhibited high fiber (37.63% dry weight, DW) and mineral (30.45% DW) contents. Among the identified compounds, palmitic acid and linoleic acid (ω-6) were identified in the highest concentrations. Pigment analysis revealed a high concentration of chlorophylls (73.95 mg/g) and carotenoids (17.75 mg/g). To evaluate the bioactivity of Ulva flexuosa, ultrasound-assisted solid–liquid extraction was performed using water, ethanol, and methanol. Methanolic extracts showed the highest flavonoid content (59.33 mg QE/100 g), while aqueous extracts had the highest total phenolic content (41.50 mg GAE/100 g). Ethanolic and methanolic extracts had the most potent DPPH scavenging activity, whereas aqueous and ethanolic extracts performed best at the ABTS assay. Overall, we show the upcycling of Ulva flexuosa, an underexplored aquaculture by-product, as a sustainable and sensible strategy for multiple value-added applications. Full article

The value realization of forest ecological products (VRF) is crucial for rural revitalization, while the rural digital economy (RDE) plays a central role in enhancing farmers’ income (FI). This study constructs index systems to evaluate the RDE and VRF using the entropy weight method and the input–output model. Based on panel data from 31 Chinese provinces (2011–2021), we employ a comprehensive analytical framework that includes spatiotemporal evolution analysis, benchmark regression models, mediation effect analysis, and heterogeneity analysis. The results of the benchmark regression models show that the RDE significantly boosts FI, with each unit of increase in the RDE leading to a 2579-unit rise in income. Spatiotemporal evolution analysis reveals that the positive effect of the RDE weakens from the Eastern coastal regions to the less developed Western regions. Furthermore, mediation effect analysis indicates that VRF mediates the relationship between the RDE and FI. Heterogeneity analysis demonstrates that the impact of the RDE varies across regions and income levels. These findings provide strong evidence of the role of the RDE in promoting FI and highlight VRF as a mediating mechanism, offering policy insights for integrating digital and ecological strategies to foster inclusive rural growth. Full article

Graph-based multi-view clustering is a fundamental analysis method that learns the similarity matrix of multi-view data. Despite its success, it has two main limitations: (1) complementary information is not fully utilized by directly combining graphs from different views; (2) existing multi-view clustering methods do not adequately address redundancy and noise in the data, significantly affecting performance. To address these issues, we propose the Enhanced Similarity Matrix Learning (ES-MVC) for multi-view clustering, which dynamically integrates global graphs from all views with local graphs from each view to create an improved similarity matrix. Specifically, the global graph captures cross-view consistency, while the local graph preserves view-specific geometric patterns. The balance between global and local graphs is controlled through an adaptive weighting strategy, where hyperparameters adjust the relative importance of each graph, effectively capturing complementary information. In this way, our method can learn the clustering structure that contains fully complementary information, leveraging both global and local graphs. Meanwhile, we utilize a robust similarity matrix initialization to reduce the negative effects caused by noisy data. For model optimization, we derive an effective optimization algorithm that converges quickly, typically requiring fewer than five iterations for most datasets. Extensive experimental results on diverse real-world datasets demonstrate the superiority of our method over state-of-the-art multi-view clustering methods. In our experiments on datasets such as MSRC-v1, Caltech101, and HW, our proposed method achieves superior clustering performance with average accuracy (ACC) values of 0.7643, 0.6097, and 0.9745, respectively, outperforming the most advanced multi-view clustering methods such as OMVFC-LICAG, which yield ACC values of 0.7284, 0.4512, and 0.8372 on the same datasets. Full article

Studying normative values for muscle activation in the vastus lateralis (VL), vastus medialis (VM), and biceps femoris (BF), as well as the hamstrings/quadriceps (H:Q) ratio during the Countermovement Jump (CMJ). Determine whether there were differences between the CMJ and the trial of 5 consecutive CMJs (5 CMJ) and between the take-off and landing phases. A cross-sectional descriptive study. Thirty-one participants (20 females and 11 males, 22.52 ± 3.295 years, BMI 24.32, weight 58.23 ± 4.32 Surface electromyography has been used to determine muscle activation during the CMJ and 5 CMJ. Muscle activation in the VL, VM, and BF, as well as the hamstrings/quadriceps ratio in take-off and landing phases of the CMJ and 5 CMJ. The results show normative values in the VL, VM, and BF during both the CMJ and 5 CMJ, with the exception of the BF during the landing phase of the 5 CMJ. In conclusion, the activation in the take-off phase of the VM and VL is greater than during the landing phase. The BF shows similar activation in both the take-off and landing phases. The 5 CMJ does not induce greater muscular fatigue than the CMJ. Full article

Amid the increasing complexity of urban risks, urban parks not only serve ecological and recreational functions but are increasingly becoming a critical spatial foundation supporting public psychological resilience and social recovery. This study aims to systematically evaluate the daily adaptability of urban parks in the context of micro-risks. The research integrates the theories of “restorative environments,” environmental safety perception, urban resilience, and social ecology to construct a five-dimensional framework for perceived resilience, encompassing resilience, safety, sociability, controllability, and adaptability. Additionally, a dynamic feedback mechanism of perception–behavior–reperception is introduced. Methodologically, the study utilizes the Fuzzy Delphi Method (FDM) to identify 17 core indicators, constructs a causal structure and weighting system using DEMATEL-based ANP (DANP), and further employs the VIKOR model to simulate public preferences in a multi-criteria decision-making process. Taking three representative urban parks in Guangzhou as empirical case studies, the research identifies resilience and adaptability as key driving dimensions of the system. Factors such as environmental psychological resilience, functional diversity, and visual permeability show a significant path influence and priority intervention value. The empirical results further reveal significant spatial heterogeneity and group differences in the perceived resilience across ecological, neighborhood, and central park types, highlighting the importance of context-specific and user-adaptive strategies. The study finally proposes four optimization pathways, emphasizing the role of feedback mechanisms in enhancing urban park resilience and shaping “cognitive-friendly” spaces, providing a systematic modeling foundation and strategic reference for perception-driven urban public space optimization. Full article

Meat quality is a critical determinant of consumer preference and economic value in the livestock industry. However, the relationship between carcass performance and meat quality remains poorly understood. In our study, we conducted an integrative analysis of transcriptomics and metabolomics to investigate the molecular mechanisms underlying meat quality differences in Hu sheep with high (HHS, n = 10) and low (LHS, n = 10) carcass performance. Phenotypic analysis revealed that the HHS group exhibited superior meat quality traits, including higher intramuscular fat (IMF) content (reflected in elevated marbling scores), along with lower shear force, drip loss, and cooking loss, compared to the LHS group. Transcriptomic analysis identified 376 differentially expressed genes (DEGs) enriched in pathways linked to lipid metabolism, such as the PPAR signaling pathway and long-chain fatty acid metabolic process. Weighted gene co-expression network analysis (WGCNA) revealed important modules and key genes (e.g., ELOVL6, PLIN1, and ARHGEF2) associated with meat quality traits. Metabolomic profiling identified 132 differentially accumulated metabolites (DAMs), with significant enrichment in amino acid metabolism pathways, including D-amino acid metabolism, arginine biosynthesis, and glycine, serine, and threonine metabolism. Integrative analysis of transcriptomic and metabolomic data highlighted six co-enriched pathways, such as the mTOR signaling pathway and amino acid metabolism, underscoring their role in regulating meat quality. These findings provide valuable insights into the genetic and metabolic networks driving meat quality variation and offer potential biomarkers for genetic selection and nutritional strategies to enhance both carcass yield and eating quality in Hu sheep. This research enhances knowledge of the molecular basis of meat quality and supports precision breeding in livestock production. Full article

Drought stress has a significant impact on cotton growth, development, and productivity. This study conducted drought stress treatment and normal water treatment (control group) on 502 cotton accessions and analyzed data on eight phenotypic traits closely related to drought stress tolerance. The results showed that all indicators changed significantly under drought stress conditions compared to the control group, with varying degrees of response among different indicators. To comprehensively evaluate the drought resistance of cotton during the germination period, the values of drought resistance comprehensive evaluation (D-value), weight drought resistance coefficient (WDC-value), and comprehensive drought resistance coefficient (CDC-value) were calculated based on membership function analysis and principal component analysis. Cluster analysis based on the D-value divided the germplasm into five drought-resistant grades, followed by the selection of one extreme material, each from the strongly drought-resistant and strongly drought-sensitive groups. An evaluation model was established using stepwise regression analysis, including the following effective indicators: Relative Fresh Weight (RFW), Relative Hypocotyl Length (RHL), Relative Seeds Water Absorption Rate (RAR), Relative Germination Rate (RGR), Relative Germination Potential (RGP), and Relative Drought Tolerance Index (RDT). The validation of the D-value prediction model based on the Best Linear Unbiased Prediction (BLUP) showed that the results obtained from two independent biological replicates were highly consistent. The comprehensive evaluation system and screening indicators established in this study provide a reliable method for identifying drought tolerance during the germination period. Full article

Necropsies are commonly used to diagnose the causes of death in feedyard cattle, but the documentation of multiple organ system involvement and concurrent lesions is limited. This observational study aimed to determine the frequency of such findings and their associations with animal demographics. Systemic necropsies were conducted for 889 cattle mortalities with minimal autolysis across six feedyards in the Central High Plains during the summers of 2022 and 2023. Lesions and abnormalities were recorded along with arrival weight, sex, days on feed (DOFs), and number of treatments. The results showed that 72% of mortalities had more than one gross lesion, averaging 2.3 lesions per animal. The most common organ systems affected together were digestive and pulmonary (19%), followed by cardiovascular, digestive, and pulmonary (6%), and cardiovascular and pulmonary (5%). Common concurrent lesions included bronchopneumonia with an interstitial pattern (BIP) and gastrointestinal lesions (GI) (8%), bronchopneumonia and GI (7%), and acute interstitial pneumonia (AIP) and GI (3%). A generalized linear mixed effects model revealed that the likelihood of multiple lesions increased with DOFs (p = 0.02). These findings highlight the value of thorough necropsy documentation to enhance our understanding of disease and guide improved feedyard management and treatment practices. Full article

Background/Objectives: Accurate classification of brain tumors is critical for treatment planning and prognosis. While deep convolutional neural networks (CNNs) have shown promise in medical imaging, few studies have systematically compared multiple architectures or integrated ensemble strategies to improve diagnostic performance. This study aimed to evaluate various CNN models and optimize classification performance using a majority voting ensemble approach on T1-weighted MRI brain images. Methods: Seven pretrained CNN architectures were fine-tuned to classify four categories: glioblastoma, meningioma, pituitary adenoma, and no tumor. Each model was trained using two optimizers (SGDM and ADAM) and evaluated on a public dataset split into training (70%), validation (10%), and testing (20%) subsets, and further validated on an independent external dataset to assess generalizability. A majority voting ensemble was constructed by aggregating predictions from all 14 trained models. Performance was assessed using accuracy, Kappa coefficient, true positive rate, precision, confusion matrix, and ROC curves. Results: Among individual models, GoogLeNet and Inception-v3 with ADAM achieved the highest classification accuracy (0.987). However, the ensemble approach outperformed all standalone models, achieving an accuracy of 0.998, a Kappa coefficient of 0.997, and AUC values above 0.997 for all tumor classes. The ensemble demonstrated improved sensitivity, precision, and overall robustness. Conclusions: The majority voting ensemble of diverse CNN architectures significantly enhanced the performance of MRI-based brain tumor classification, surpassing that of any single model. These findings underscore the value of model diversity and ensemble learning in building reliable AI-driven diagnostic tools for neuro-oncology. Full article