Search Results (2,271)

In the review, the collection of population genetics papers from 1973 to 2025 comprises 400 publications, 81 of which were significant and consulted with representatives from water and sewage companies. Reviewed Proteobacteria (mean H_S = 0.42), Firmicutes (mean H_S = 0.43), Actinobacteria (mean H_S = 0.33), and Spirochaetes (mean H_S = 0.54) represent the 60 species under investigation through the lens of “h” coefficients related to gene diversity and expected heterozygosity. The research also included ESKAPE, emerging pathogens, bacterial indicators of wastewater treatment efficiency, environmental sanitary surveillance and public health. The restoration of the expected heterozygosity for haploids “h” was proposed in wastewater-based epidemiology as an innovative tool for public health. The unique “h” coefficient allows for the comparison of genetic variability in various organisms, regardless of their ploidy, using multiple markers and traits. The parameter represents a noble character for both the variability of phenotypes (proteins) and genotypes (nucleic acids). Leveraging the genetic diversity highlighted by the “h” coefficient can support wastewater-based epidemiology, offering the ability to predict the stages and trajectories of disease outbreaks. Full article

As critical actuating components in vehicular transmission systems, wet clutches exhibit strongly nonlinear thermal responses in their friction pairs during engagement operations. Although existing temperature prediction models achieve high-accuracy prediction performance, their practical application remains constrained by significant limitations such as high computational costs and time consumption. This study proposes an Optuna-LSTM temperature prediction model for wet clutch friction pairs, developed through the integration of long short-term memory (LSTM) deep learning theory with finite element method generated training datasets under diverse operating conditions. By synergistically combining the automated hyperparameter optimization library (Optuna) framework and early stopping mechanisms, the model enables dynamic temperature prediction of friction pairs. Experimental results indicate that the proposed model achieves prediction metrics of Root Mean Squared Error (RMSE) of 1.42 °C, Mean Absolute Error (MAE) of 1.09 °C, Coefficient of Determination (R²) of 0.9930, and Mean Absolute Percentage Error (MAPE) of 0.72% with a prediction duration of 60 ms. These findings confirm that the Optuna-LSTM model enables both accurate and rapid temperature prediction for friction pairs, providing an efficient solution for thermal management in wet clutch systems. Full article

As the world’s leading carbon emitter, China’s ability to reach its pledged carbon peak by 2030 is pivotal for its own green transition and global climate governance. This research proposes a novel integration of spatial proximity neural networks with attention-enhanced geographically weighted neural network regression. This new model integrates spatial dependencies and an attention mechanism into the traditional geographically weighted neural network regression framework. The model demonstrates good performance in forecasting carbon emissions (coefficient determination = 0.904, root mean square error = 48.927). Using this model, alongside population, GDP, total energy consumption, and other influencing factors, the research integrated scenario forecasting to project China’s total carbon emissions from 2023 to 2040. Three policy-relevant scenarios—baseline, low-carbon, and extensive development—were set to forecast and analyze various potential outcomes under uncertain conditions. Under the baseline scenario, China’s emissions peak in 2029 at 9926.26 Mt; the low-carbon scenario advances the peak to 2027 at 9688.88 Mt; whereas an extensive growth path delays the peak to 2032 at 10,347.70 Mt. These findings underscore the urgency of optimizing energy structure, curbing fossil fuel dependence, and balancing economic growth with the deep decoupling of emissions. This research offers policymakers a robust, spatially explicit tool for evaluating future trajectories under diverse development pathways. Full article

Continuous cropping (CC) poses serious challenges to the sustainable production of Dioscorea opposita Thunb. cv. Tiegun yam. The aim of this study is to illustrate the formation mechanisms of CC obstacles by analyzing rhizosphere soil from yam fields with 0 to 2 years of replanting. Metabolomic and microbiome sequences were used to assess variations in yam yield, underground tuber traits, soil properties, metabolite profiles, and microbial communities. The results show that CC significantly reduced tuber yield, shortened stalk length, and altered tuber morphology, leading to the accumulation of soil available phosphorus and potassium and a notable decrease in pH. A total of 38 differentially expressed metabolites, including organoheterocyclic compounds, lipids, and benzenoids, were identified and linked to pathways such as starch and sucrose metabolism, linoleic acid metabolism, and ABC transporters. Microbial alpha diversity increased with CC duration, and both bacterial and fungal community structures were notably reshaped. Metabolite profiles correlated more strongly with fungal than bacterial communities. Partial least squares path modeling revealed that CC years had a negative indirect impact on tuber yield and morphology (the path coefficient was −0.956), primarily through direct effects on soil properties (p < 0.01) and metabolites (p < 0.001), which, in turn, influenced microbial diversity. These findings emphasize the vital role of soil properties in reshaping the rhizosphere environment under CC and provide a theoretical basis for mitigating CC obstacles through rhizosphere regulation. Full article

This study aimed to determine carbonyl compound concentrations and assess their potential health risk in indoor air at three homes in different areas of the Mexico City Metropolitan Area (MCMA) during the COVID-19 lockdown. Sampling was conducted from March to April 2021, during the home office confinement period. Average concentrations of formaldehyde, acetaldehyde, acrolein, acetone, propionaldehyde, and butyraldehyde in living rooms ranged from 84.15 to 74.93 μg m⁻³, 66.49 to 50.20 μg m⁻³, 60.01 to 41.35 μg m⁻³, 74.58 to 63.02 μg m⁻³, 10.90 to 6.21 μg m⁻³, and 12.45 to 9.91 μg m⁻³, respectively. In bedrooms, concentrations ranged from 84.76 to 59.70 μg m⁻³, 50.12 to 51.73 μg m⁻³, 59.74 to 37.25 μg m⁻³, 76.62 to 59.72 μg m⁻³, 14.45 to 8.40 μg m⁻³, and 10.72 to 8.82 μg m⁻³, respectively. All measured carbonyls had significant indoor concentrations, exceeding those reported in other studies worldwide. From the statistical analysis, it was found that there were significant differences in carbonyl levels between the studied homes. This suggests diverse and prevalent sources in these environments. E-cigarette vapors clearly increased acrolein levels, and the use of personal care and household products (PCHPs) also contributed to higher carbonyl concentrations indoors. The lifetime cancer risk coefficient (LTCR) and hazard quotient (HQ) values for formaldehyde and acetaldehyde exceeded WHO and US EPA recommendations, indicating increased risks of both carcinogenic and non-carcinogenic effects. Full article

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model. Full article

Spatial heterogeneity in economic benefits of water use provides crucial evidence for the evaluation of water diversion projects and the spatial equilibrium of water resource allocation. Using city-level data from 2017 on the sectoral water use and value added in 334 Chinese cities, we estimated the economic benefits of water use in the agricultural, industrial, and service sectors using the allocation coefficient method. We then revealed the spatial heterogeneity combining an exploratory spatial data analysis (ESDA) method. For the agricultural sector, the high economic benefit of water use regions are primarily concentrated on both sides of the “Hu Huanyong Line”; regions with high economic benefit of industrial water use are mainly found in the North China Plain, the middle and lower Huanghe River basin, the Yangtze River Delta, the Pearl River Delta, Chongqing and Chengdu, and the economic benefit of service water use is higher in the north than in the south. ESDA provides significant evidence for the analysis of spatial heterogeneity with regard to the economic benefits of water use in China. Based on the fundamental distribution of water resources and the spatial heterogeneity in the economic benefits of water use, potential water diversion areas can be preliminarily identified. The Haihe River Basin in the North China Plain and some areas in the southeast coastal region are potential receiving areas, and the eastern regions of Southwest China with abundant water resources and lower elevations, along with the middle and lower reaches of the Yangtze River are potential source areas. Further research about marginal benefits and water use costs, along with dynamic updates, is required for water resource allocation of China. Full article

Background: Exclusive breastfeeding (EBF) is crucial for infant health, and maternal mental health significantly influences breastfeeding practices. This study investigates the relationships among postpartum depression (PPD), maternal dietary diversity, and exclusive breastfeeding in Kinshasa, Democratic Republic of Congo. Methods: A cross-sectional study was conducted involving 793 mother–child pairs. Data were collected through structured interviews using a validated questionnaire administered by trained enumerators. Statistical analyses included descriptive statistics, chi-square tests, and structural equation modeling (SEM) to evaluate the relationships between maternal and child characteristics and EBF. Results: The proportion of infants in the study sample who were exclusively breastfed was 29.1% (95% CI: 26.0–32.3%). Breastfeeding self-efficacy is positively associated by nutritional advice during pregnancy, with a coefficient of 2.17 (p = 0.003). The husband’s support in exclusive breastfeeding positively correlates with breastfeeding self-efficacy (coefficient = 0.23, p < 0.001). A significant negative relationship exists between child age and EBF (coefficient = −0.095, p < 0.001). EBF is positively associated by nutritional advice during pregnancy, with a coefficient of 0.12 (p = 0.016). Child morbidity in the last 2 weeks showed a negative association with EBF practice (coefficient = −0.09, p = 0.014). Conclusions: This study highlights the multifaceted challenges faced by mothers in Kinshasa regarding exclusive breastfeeding. By prioritizing husband involvement, nutritional counseling, and robust health-system engagement, we can create a more supportive framework for breastfeeding practices. Future research should focus on longitudinal approaches to understand the long-term impacts of these factors on breastfeeding and infant health. Additionally, exploring the potential benefits of integrated maternal health programs that address nutritional needs will be crucial in developing comprehensive support systems for new mothers. Full article

To address the limitations of the traditional Balancing Composite Motion Optimization (BCMO) algorithm—namely weak directional global exploration, insufficient local exploitation accuracy, and a tendency to fall into local optima with reduced population diversity in feature selection tasks—this paper proposes a Multi-Strategy Enhanced Balancing Composite Motion Optimization algorithm (MEBCMO). From a symmetry perspective, MEBCMO exploits the symmetric and asymmetric relationships among candidate solutions in the search space to achieve a better balance between exploration and exploitation. The performance of MEBCMO is enhanced through three complementary strategies. First, an adaptive heat-conduction search mechanism is introduced to simulate thermal transmission behavior, where a Sigmoid function adjusts the heat-conduction coefficient α_T from 0.9 to 0.2 during iterations. By utilizing the symmetric fitness–distance relationship between the current solution and the global best, this mechanism improves the directionality and efficiency of global exploration. Second, a quadratic interpolation search strategy is designed. By constructing a quadratic model based on the current individual, a randomly selected individual, and the global best, the algorithm exploits local symmetric characteristics of the fitness landscape to strengthen local exploitation and alleviate performance degradation in high-dimensional spaces. Third, an elite population genetic strategy is incorporated, in which the top three individuals generate new candidates through symmetric linear combinations with non-elite individuals and Gaussian perturbations, preserving population diversity and preventing premature convergence. To evaluate MEBCMO, extensive global optimization experiments are conducted on the CEC2017 benchmark suite with dimensions of 30, 50, and 100, and comparisons are made with eight mainstream algorithms, including PSO, DE, and GWO. Experimental results demonstrate that MEBCMO achieves superior performance across unimodal, multimodal, hybrid, and composite functions. Furthermore, MEBCMO is combined with LightGBM to form the MEBCMO-LightGBM model for feature selection on 14 public datasets, yielding lower fitness values, higher classification accuracy, and fewer selected features. Statistical tests and convergence analyses confirm the effectiveness, stability, and rapid convergence of MEBCMO in symmetric and complex optimization landscapes. Full article

Objectives: This study aimed to examine the association between the visceral adiposity index and arterial stiffness in healthy adults via original data from the EVasCu study and to contextualize these findings through a meta-analysis of previously published studies in the general population. Methods: A cross-sectional analysis was conducted in 389 healthy adults from the EVasCu study. The visceral adiposity index was calculated on the basis of waist circumference, body mass index, triglycerides, and high-density lipoprotein cholesterol, integrating the anthropometric and metabolic components of visceral adiposity. Arterial stiffness was assessed by the aortic pulse wave velocity. These original findings were complemented by a meta-analysis, including EVasCu data and data from prior studies, to obtain pooled correlation coefficients and 95% confidence intervals (CIs) for the association between visceral adiposity and arterial stiffness. Results: In the EVasCu study, the visceral adiposity index showed a statistically significant moderate correlation with the aortic pulse wave velocity (r = 0.281, p < 0.001). In the meta-analysis, the pooled correlation coefficient was 0.34 (95% CI: 0.27, 0.42), supporting a consistent association between the visceral adiposity index and both central and peripheral arterial stiffness across diverse populations. Conclusions: These findings indicate a positive association between the visceral adiposity index and arterial stiffness in both healthy individuals and populations with cardiometabolic conditions. However, given the predominantly cross-sectional nature of the evidence and the heterogeneity among the included studies, the results should be interpreted with caution. Further longitudinal, multivariable, and mechanistic studies are needed to clarify the clinical relevance of the visceral adiposity index beyond correlation and to determine its potential role as a complementary marker in cardiovascular risk assessment. Full article

Frequent cloud cover severely limits the use of optical remote sensing for continuous ecological monitoring. Synthetic aperture radar (SAR) offers an all-weather alternative, but translating SAR data to optical equivalents is challenging, particularly in cloudy regions where paired training data are scarce. To address this, we developed an enhanced CycleGAN (denoted by SA-CycleGAN) to derive a high-fidelity, temporally continuous normalized difference vegetation index (NDVI) from SAR imagery. The SA-CycleGAN introduces a novel spatiotemporal attention generator that dynamically computes global and local feature relationships to capture long-range spatial dependencies across diverse landscapes. Furthermore, a structural similarity (SSIM) loss function is integrated into the SA-CycleGAN to preserve the structural and textural integrity of the synthesized images. The performance of the SA-CycleGAN and three unsupervised models (DualGAN, GP-UNIT, and DCLGAN) was evaluated by deriving NDVI time series from Sentinel-1 SAR images across four sites with different vegetation types. Ablation experiments were conducted to verify the contributions of the key components in the SA-CycleGAN model. The results demonstrate that the SA-CycleGAN significantly outperformed the comparison models across all four sites. Quantitatively, the proposed method achieved the lowest Root Mean Square Error (RMSE) of 0.0502 and the highest Coefficient of Determination (R²) of 0.88 at the Zhangbei and Xishuangbanna sites, respectively. The ablation experiments confirmed that the attention mechanism and SSIM loss function were crucial for capturing long-range features and maintaining spatial structure. The SA-CycleGAN proves to be a robust and effective solution for overcoming data gaps in optical time series. Full article

The Xinjiang Brown cattle (XJBC) is one of China’s five major dual-purpose dairy and beef breeds. Analyzing the genetic diversity of the Xinjiang Brown cattle population lays the theoretical groundwork for identifying and conserving its genetic resources. This study employed the Illumina Bovine SNP 150K chip to analyze genetic diversity, inbreeding coefficient, kinship, and genetic distance in a population of 750 Xinjiang Brown cattle from three breeding farms in Xinjiang. Genetic diversity was assessed by calculating minimum allele frequency (MAF), observed heterozygosity (Ho), expected heterozygosity (He), polymorphic information content (PIC), and linkage disequilibrium (LD). Population structure was analyzed using PCA. ROH was calculated to derive ROH-based inbreeding coefficients, pedigree-based inbreeding coefficients (F_PED) were estimated using CFC software for comparison, and candidate genes within high-frequency ROH regions in Xinjiang Brown cattle were identified. A G matrix was constructed to analyze population kinship. Results revealed 94,173 high-quality SNP loci in Xinjiang Brown cattle, with an average MAF of 0.276, PIC of 0.376, Ho of 0.345, and He of 0.376. Breeding farm 3 exhibited the fastest LD decay, indicating relatively high genetic diversity across Xinjiang Brown cattle populations, with farm 3 demonstrating greater diversity. The IBS genetic distance was 0.313. The G matrix results aligned with the IBS distance matrix, both indicating close kinship among some individuals within the Xinjiang Brown cattle population. The ranges for average F_PED and average F_ROH across farms were 0.0017–0.0189 and 0.0609–0.0878, respectively. Short ROH segments (0.5–2 Mb) constituted the largest proportion (51.31%) of all ROHs. Within high-frequency ROH enrichment regions, 61 genes, including LCORL, FAM110B, NR4A1, and PER2, were identified as potentially associated with economic traits in Xinjiang Brown cattle. These findings provide relevant marker sites for genomic selection in Xinjiang Brown cattle and lay a theoretical foundation for subsequent research. Full article

A thorough understanding of the dispersion characteristics of graphene oxide (GO), its micro-pore enhancement mechanisms, and correlations with mechanical properties are crucial for advancing high-strength, durable green concrete. Introducing recycled concrete powder (RCP) can weaken the interfacial transition zone (ITZ) and inhibit hydration reactions, degrading the pore structure and affecting mechanical strength and durability. However, traditional methods struggle to accurately characterize and quantitatively analyze GO-modified pore structures due to their nanoscale size, microstructural diversity, and characterization technique limitations. To address these challenges, this study integrates deep learning-based backscattered electron image analysis with deep Taylor decomposition feature extraction. This innovative method systematically analyzes pore characteristic evolution and the correlation between porosity and mechanical strength. The results indicate that GO promotes Calcium Silicate Hydrate gel growth, refines pores, and reduces pore connectivity, decreasing the maximum pore size by 33.4–45.2%. Using a Convolutional Neural Network architecture, BSE images are efficiently processed and analyzed, achieving an average recognition accuracy of 94.3–96.9%. The optimized degree of GO coating on enhanced regions reaches 30.2%. Fitting porosity with mechanical strength and chloride ion permeability coefficients reveals that enhanced regions exhibit the highest correlation with mechanical strength and durability in regenerated cementitious materials, with R² values ranging from 0.79 to 0.99. The deep learning-assisted pore structure characterization method demonstrates high accuracy and efficiency, providing a critical theoretical basis and data support for performance optimization and engineering applications of recycled cementitious materials. This research expands the application of deep learning in building materials and offers new insights into the relationship between the microstructural and macroscopic properties of recycled cementitious materials. Full article

Climate-induced flooding is a major issue throughout the globe, resulting in damage to infrastructure, loss of life, and the economy. Therefore, there is an urgent need for sustainable flood risk management. This paper assesses the effectiveness of the hybrid defense system using advanced artificial intelligence (AI) techniques. A data series of energy dissipation (ΔE), flow conditions, roughness, and vegetation density was collected from literature and laboratory experiments. Out of the selected 136 data points, 80 points were collected from literature and 56 from a laboratory experiment. Advanced AI models like Random Forest (RF), Extreme Boosting Gradient (XGBoost) with Particle Swarm Optimization (PSO), Support Vector Regression (SVR) with PSO, and artificial neural network (ANN) with PSO were trained on the collected data series for predicting floodwater energy dissipation. The predictive capability of each model was evaluated through performance indicators, including the coefficient of determination (R²) and root mean square error (RMSE). Further, the relationship between input and output parameters was evaluated using a correlation heatmap, scatter pair plot, and HEC-contour maps. The results demonstrated the superior performance of the Random Forest (RF) model, with a high coefficient of determination (R² = 0.96) and a low RMSE of 3.03 during training. This superiority was further supported by statistical analyses, where ANOVA and t-tests confirmed the significant performance differences among the models, and Taylor’s diagram showed closer agreement between RF predictions and observed energy dissipation. Further, scatter pair plot and HEC-contour maps also supported the result of SHAP analysis, demonstrating greater impact of the roughness condition followed by vegetation density in reducing floodwater energy dissipation under diverse flow conditions. The findings of this study concluded that RF has the capability of modeling flood risk management, indicating the role of AI models in combination with a hybrid defense system for enhanced flood risk management. Full article

Constructed-response items offer rich evidence of writing proficiency, but the linguistic signals they contain vary with grade level. This study presents a cross-sectional analysis of 5638 English Language Arts essays from Grades 6–12 to identify which linguistic features predict proficiency and to characterize how their importance shifts across grade levels. We extracted a suite of lexical, syntactic, and semantic-cohesion features, and evaluated their predictive power using an interpretive dual-model framework combining LASSO and XGBoost algorithms. Feature importance was assessed through LASSO coefficients, XGBoost Gain scores, and SHAP values, and interpreted by isolating both consensus and divergences of the three metrics. Results show moderate, generalizable predictive signals in Grades 6–8, but no generalizable predictive power was found in the Grades 9–12 cohort. Across the middle grades, three findings achieved strong consensus. Essay length, syntactic density, and global semantic organization served as strong predictors of writing proficiency. Lexical diversity emerged as a key divergent feature, it was a top predictor for XGBoost but ignored by LASSO, suggesting its contribution depends on interactions with other features. These findings inform actionable, grade-sensitive feedback, highlighting stable, diagnostic targets for middle school while cautioning that discourse-level features are necessary to model high-school writing. Full article