Search Results (15,412)

With increasing traffic loads and increasingly complex climate conditions, accurate prediction of the International Roughness Index (IRI) of asphalt pavements is crucial for developing effective maintenance plans. However, traditional regression models have limitations in capturing the coupled effects of traffic, structure, and environmental factors. To overcome this limitation, this study constructed a dataset containing 10,836 samples based on the Long-Term Pavement Performance (LTPP) database, integrating traffic load, pavement structure parameters, and climate variables. The variance inflation factor (VIF) and correlation analysis were used to validate the effectiveness of feature selection. We trained nine machine learning models and optimized the hyperparameters using a Bayesian optimization method with five-fold cross-validation to ensure good generalization ability. Results show that the TabPFN model, based on prior information, achieved the best overall performance with a coefficient of determination $R^2$ = 0.9474 and a low prediction error (RMSE = 0.138) on the test set. Paired t-tests based on cross-validation further confirmed that TabPFN’s predictive performance is statistically superior to the baseline model. SHAP and generalized additive model (GAM) analyses indicate that traffic load is the main driver of IRI growth, while structural layer thickness, within a certain range, can mitigate pavement roughness. Climatic factors have indirect long-term effects through cumulative environmental exposure. Although the main drivers differ slightly among different pavement structures, traffic load consistently plays a dominant role. To enhance the model’s practical applicability, we also developed a user-friendly graphical interface (GUI) for fast and accurate IRI prediction. Full article

Cave microbiota comprise metabolically diverse organisms, including microalgae, among which Bacillariophyta (diatoms) represent one of the most prominent groups, inhabiting a wide range of substrates within cave ecosystems. In contrast to aerophytic cave habitats, aquatic cave environments remain poorly studied. Therefore, the main aims of this study were to determine the diversity, spatial distribution, and seasonal dynamics of diatom assemblages in the Ponorac Stream flowing through Ravništarka Cave, and to assess the influence of environmental variables on diatom diversity and distribution. Samples were collected from six sites along the Ponorac stream in May and November 2023. Physical and chemical water parameters showed only minor variation among sampling sites. In total, 148 diatom taxa belonging to 54 genera were recorded, including several rare diatom taxa. Diatom assemblages in the Ponorac stream were characterized by high taxonomic richness, high α-diversity, and pronounced community heterogeneity. Many taxa occurred in both seasons and across multiple sites, whereas several were restricted to a single season or exhibited clear site specificity. Most diatom index values indicated generally high ecological status. This study highlights the importance of aquatic cave habitats as reservoirs of diatom diversity and their value in studying temporal and spatial variation of their communities. Full article

Age estimation is an important component of forensic investigation, with applications in criminal casework, immigration assessments, and disaster victim identification. Determining whether an individual is a minor or an adult, or estimating the age at death of unidentified remains, can have significant legal and humanitarian implications. Traditional forensic age estimation methods rely primarily on anthropological and radiological assessment of skeletal development and degeneration; however, these approaches may be limited by subjectivity, population-specific reference standards, and reduced precision in adult age estimation. In recent years, molecular biomarkers have emerged as promising complementary tools for age prediction. Molecular approaches, including DNA methylation profiling, Y-chromosome-associated markers, RNA-based biomarkers, mitochondrial DNA alterations, proteomic signatures, and telomere length analysis, reflect biological processes associated with aging and may provide objective indicators that can be measured from biological samples. Among these methods, DNA methylation-based models currently demonstrate the strongest predictive performance and represent the most extensively studied molecular strategy for forensic age estimation. Nevertheless, several challenges remain before widespread forensic implementation can be achieved, including tissue specificity, environmental influences on biomarker stability, population variability, and the need for robust validation across laboratories and forensic sample types. This review summarises the current molecular approaches investigated for forensic age estimation, evaluates their biological basis and methodological limitations, and discusses their potential integration into forensic workflows. While molecular techniques offer promising avenues for improving age estimation, further standardisation, validation, and careful interpretation are required before they can be routinely applied in forensic practice. Full article

Water quality plays a central role in determining the environmental performance of pond-based tropical aquaculture systems. This study aimed to evaluate the relative environmental performance of different tropical pond-based aquaculture systems by identifying multivariate water quality patterns that allow their discrimination and comparison under commercial production conditions. Four pond-based production systems were evaluated: an aquaponic system (APS), a recirculating aquaculture system (RAS), a conventional earthen pond system (CEP), and an integrated rice–chame system (RCS). Fourteen physicochemical water quality variables were monitored throughout the production cycle under real commercial conditions using a comparative observational design. Multivariate discriminant analysis was applied to identify the variables with the highest discriminatory power and evaluate the ability of water quality patterns to correctly classify observations among production systems. The results revealed a clear multivariate separation between technologically intensive systems (APS and RAS) and less intensive and integrated systems (CEP and RCS), reflecting distinct water quality structures and environmental functioning. Variables associated with mineralization and nutrient dynamics, including electrical conductivity, dissolved solids, turbidity, phosphates, chlorides, dissolved oxygen, nitrites, and temperature, contributed most strongly to system discrimination. The discriminant functions achieved a high overall correct classification rate, demonstrating the robustness of the multivariate approach. These findings support the use of water quality variables as consistent environmental signatures for distinguishing tropical pond-based aquaculture systems, providing an operational framework for assessing their relative environmental performance. Discriminant analysis emerges as a valuable tool for system characterization and comparative evaluation, supporting environmentally informed management and optimization of chame aquaculture under tropical conditions. Although water quality represents a robust integrative indicator, it captures only one dimension of environmental performance, and additional factors such as production efficiency, energy use, and effluent characterization should be incorporated in future studies to achieve a comprehensive sustainability assessment. Full article

Hot-air drying is widely adopted for herbs because it is robust and easy to control, yet it is often energy-intensive and may operate far from optimal conditions when industrial dryers rely on fixed airflow paths and large air recirculation rates. This work investigates a conventional basket-type, adiabatic hot-air dryer through an instrumented 30 h drying campaign and a psychrometric energy analysis. The hot-air drier is designed to reduce the relative humidity of herbs from the environmental value (highly variable as a function of the species, the weather conditions, and, mostly, the seasonality) to 20%. Temperature and relative humidity were measured at four positions to characterize the shelf-by-shelf drying sequence and to identify process phases. A mass balance indicated that approximately 3.8 t of water was removed during the trial. Based on the measured thermodynamic states of the moist air and estimated airflow rates (35,000–53,000 m³/h), the baseline configuration was analyzed and an upgrade strategy was proposed to improve dehumidification and overall efficiency while preserving the conventional hot-air-drying concept. The alternative solution integrates a refrigeration-based dehumidification loop (heat pump) to decouple moisture removal from sensible heating; three plant layouts and seasonal boundary conditions (summer/winter) were simulated. For the most favorable configurations, the specific final–primary energy demand and the associated CO₂-equivalent emissions were reduced by about 70–85% compared with the baseline, depending on the airflow rate and recirculation strategy. The results highlight practical retrofit options for existing herb dryers and provide a transparent framework for translating measured psychrometric states into energy and emission indicators. The results, achieved and discussed in this study, were used to optimize the utilization of an already existing and operative hot-air dryer. Based on the proposed working configuration, the dryer now allows achieving the fixed target for herb mixtures of the previous configuration and, at the same time, reducing the energy consumption and associated equivalent CO₂ emitted, as well as achieving process completion in less time. Full article

Background/Objectives: Depression is a common and consequential comorbidity among adults with diagnosed diabetes. Prior research has largely emphasized individual health behaviors, with less attention to emotional burden, social context, or theory-driven interpretation. The Multi-Theory Model (MTM) of Health Behavior Change offers an integrative framework for examining behavioral, emotional, and environmental correlates of health outcomes. This study applied MTM to examine correlates of lifetime diagnosed depression among U.S. adults with diagnosed diabetes. Methods: This cross-sectional study analyzed 2023 Behavioral Risk Factor Surveillance System (BRFSS) data from 19,967 adults with diagnosed diabetes, representing approximately 30 million U.S. adults after survey weighting. Lifetime diagnosed depression was assessed based on respondents reporting that a health professional had told them they had a depressive disorder, representing a lifetime history of depression rather than current depressive symptoms. Independent variables were organized into behavioral, emotional, and environmental domains consistent with MTM. Survey-weighted descriptive analyses, Rao–Scott χ² tests, and nested survey-weighted logistic regression models were conducted. Results: The weighted prevalence of lifetime diagnosed depression among adults with diagnosed diabetes was 24.3%. In the fully adjusted MTM-guided model, emotional and environmental domains showed the strongest associations with lifetime diagnosed depression. Frequent mental distress was associated with substantially higher odds of depression (adjusted odds ratio ≈ 10.4, p < 0.001). High social or economic stress and fair or poor self-rated health remained independently associated (p < 0.001). Behavioral factors, including physical activity, smoking, and body mass index, were attenuated after adjustment. Conclusions: Lifetime diagnosed depression among adults with diagnosed diabetes was more strongly associated with emotional burden and adverse social conditions than with health behavior alone, supporting the integration of distress screening and context-responsive interventions into diabetes care. Full article

With the continuous renewal of urban greening, pollen released by allergenic tree species has become a prominent environmental issue affecting residents’ health. However, existing research still lacks city-wide, rapidly replicable methods for identifying allergenic tree species and assessing exposure risks. Taking Beijing’s central urban districts as a case study, this research establishes a method for the automated identification of allergenic tree species and the assessment of pollen exposure risks based on high-resolution satellite imagery. This study coupled tree species distribution results derived from model inference with population density per unit area to delineate three tiers of exposure risk zones. Subsequently, these risk zones were overlaid with the road network within the study area to determine the distribution of roads with low, medium, and high exposure risk. Public transport stop locations were then introduced as a proxy variable for areas of high population mobility. Lorenz curves and Gini coefficients were calculated to quantify the spatial equity of pollen exposure risk. The results indicate that the model reliably identifies target tree species, with approximately 117,000 valid targets. Exposure risks exhibit significant clustering characteristics and can form continuous expansions along road networks. Incorporating population factors shows minimal change in risk concentration, suggesting pollen exposure risk is primarily driven by the spatial clustering of allergenic tree species and their accessibility within road networks. This risk is highly correlated with the spatial distribution patterns and accessibility characteristics of allergenic tree species, rather than being solely determined by population size. This study provides foundational data and methodological support for urban tree species identification, pollen exposure risk management, and optimised greening configurations. Full article

Rice (Oryza sativa L.) grain quality is a critical determinant of market value, consumer acceptance, and nutritional security. This multifaceted trait is governed by the dynamic interaction of genotype (G), environment (E), and management practices (M). In this review, we synthesize recent advances in understanding these multifaceted determinants. We first delineate the genetic architecture, emphasizing key genes and quantitative trait loci (QTLs) such as Wx, ALK, Chalk5, and the GS3/GW families, which control starch composition, gelatinization temperature, chalkiness, and grain dimensions, forming the foundational blueprint for quality potential. We examine how this genetic potential is influenced by environmental factors, focusing on the detrimental impacts of abiotic stresses, particularly high temperatures during grain filling and drought, which impair milling yield, increase chalkiness, and modify starch and protein profiles. Furthermore, we discuss how optimized agronomic strategies—including precision water management (e.g., alternate wetting and drying), balanced nitrogen fertilization, and targeted micronutrient (e.g., silicon) application—can mitigate these adverse effects and potentially improve specific quality parameters. Post-harvest handling is identified as the final determinant of product quality. We conclude that achieving high and stable rice quality under climate variability requires an integrated G × E × M approach. Prospects include next-generation breeding for climate-resilient quality, precision agronomy guided by real-time sensing, synergistic soil health management, and the integration of systems biology with digital agriculture to design sustainable, high-quality rice production systems. Full article

Fine particulate matter (PM_2.5) poses a significant global environmental health threat and is closely associated with diseases across multiple organ systems. This review systematically summarizes the toxic effects and underlying mechanisms of PM_2.5 in the respiratory, cardiovascular, nervous, immune, endocrine, digestive, and genitourinary systems. Key pathogenic processes involve shared pathways such as oxidative stress, inflammatory responses, endoplasmic reticulum stress, autophagy, and apoptosis, along with the activation of system-specific signaling networks. The complex composition and notable spatiotemporal variability of PM_2.5 present challenges for assessing its health risks and clarifying its mechanisms. Moving forward, integrating multi-omics and molecular epidemiology approaches will be essential to unravel its multi-system pathogenic networks and support the development of effective intervention strategies. Full article

The combined effects of organic pollutants and vertical soil gradients on microbial community assembly in long-term contaminated sites remain insufficiently understood. In this study, high-throughput sequencing was employed to characterize prokaryotic communities across depth-resolved soil profiles at a contaminated site in Tianjin, China. Microbial diversity, taxonomic composition, and predicted functional traits varied significantly with soil depth and pollutant distribution. Surface soils exhibited higher richness and diversity, with Shannon, Sobs, and PD indices decreasing with depth (p = 0.020, p = 0.002, and p < 0.001, respectively). Redundancy analysis showed that the first two axes explained 89.91% of the total variance, indicating strong associations between microbial community structure and environmental variables. Community differentiation was related to pollutant type, with aromatic hydrocarbons more strongly linked to surface assemblages and chlorinated compounds associated with deeper horizons. Although the overall abundance of predicted metabolic genes decreased with depth, the distribution of major functional categories, including pathways related to organic matter degradation, remained comparatively stable. Co-occurrence network analysis revealed a progressive decline in network connectivity and complexity along the vertical gradient, with the number of edges decreasing from 853 (L1) to 447 (L3) and average degree decreasing from 16.404 to 9.122. These findings highlight depth-related environmental filtering as a key mechanism structuring microbial communities under long-term organic contamination and provide a scientific basis for optimizing depth-specific in situ bioremediation strategies, such as targeting aromatic hydrocarbon degradation in surface soils and chlorinated compound remediation in deeper layers. Full article

Traditional one size fits all pharmacotherapy often yields suboptimal clinical outcomes, preventable adverse drug reactions (ADRs), and significant drug waste, imposing substantial economic and ecological burdens on healthcare systems. This review evaluates the transformative potential of pharmacogenomics (PGx) testing, particularly cytochrome P450 (CYP) gene variants, as a foundation for an ecosystem-centric accountability framework for green pharmacy and links human metabolic variability to specific environmental outcomes. Personalized CYP profiling is shown to minimize the environmental release of unused drugs and potentially ecotoxic metabolites into aquatic ecosystems, in contrast to standard uniform drug use approaches. The limitations of ethnicity-based dosing models, which rely on population genetic variation, are examined in the context of increasing global genetic admixture. It is argued that individual genetic profiling, conceptualized as a PGx-Green Passport, provides a reliable safety standard that accounts for individual differences, thereby enhancing efficiency and well-being in a globalized society. By integrating clinical data, including real-world evidence on hospital utilization, with sustainability frameworks, this review demonstrates that PGx-guided therapy is not only a tool for clinical efficiency but also a fundamental requirement for systematically achieving environmentally sustainable healthcare. Full article

Greenhouse cultivation enables year-round vegetable production and high yields through precise environmental regulation. Yet, the same stable microclimate that promotes crop growth also favors the proliferation of pests and diseases. This review synthesizes current knowledge on how greenhouse climate variables govern pest and disease epidemiology in tomato, cucumber, and sweet pepper. Only greenhouse-based studies were included to ensure direct relevance to protected horticulture. Microclimatic stability determines infection probability, vector behavior, and host susceptibility. Warm, humid conditions promote fungal and bacterial pathogens, whereas dry, high vapor pressure deficit (VPD) environments favor mites and thrips and enhance virus transmission. Species-specific traits further modulate vulnerability. Tomato is dominated by virus–bacterium complexes and foliar/stem fungal diseases, cucumber by phytopathogenic fungi favored by high relative humidity (RH) and soilborne pathogens, and sweet pepper by virus–vector systems and long-cycle fungal infections. Temperature exerts the strongest influence, while RH and VPD jointly regulate surface moisture and vector activity. Light intensity and spectral composition also affect pest orientation and fungal sporulation. Integrating environmental sensing, biological control, and adaptive climate regulation offers a pathway toward preventive, climate-smart Integrated Pest Management (IPM). The review highlights the emerging role of climate-informed decision-support systems (DSSs) and the need for greenhouse-specific datasets to improve pest and disease forecasting. Full article

Karst water systems are highly vulnerable to land use pressures, requiring integrated assessments to support conservation and management. This study evaluated the physicochemical, microbiological, and pesticide-related water quality in the Environmental Protection Area Nascentes do Rio Vermelho (APANRV), a karst conservation unit in the Brazilian Cerrado. Sixteen sampling sites (rivers, springs, and cave waters) were monitored during the dry (May 2024) and rainy (October 2024) seasons. Analyses included nutrients, major ions, Escherichia coli, and a broad spectrum of pesticides. The results showed marked spatial and seasonal variability, with elevated hardness and conductivity in karst areas due to carbonate dissolution. Nitrate and total phosphorus reached peak values of 13.59 and 0.132 mg L⁻¹, respectively, indicating localized nutrient enrichment. E. coli concentrations reached ≥2419.6 MPN 100 mL⁻¹, exceeding regulatory limits, particularly during the rainy season at recreational cave sites. Pesticides were detected in both seasons, with 11 compounds in the dry season and 8 in the rainy season, including atrazine degradation products, and maximum quantified concentrations up to 1.8 µg L⁻¹ (acephate). These findings highlight the combined influence of geology, seasonality, and land use on karst water quality and reinforce the need for continuous monitoring and targeted management strategies. Full article

To address global climate challenges, China’s transition toward a green, low-carbon economy underscores the critical role of green finance (GF) as a key policy instrument. Against this backdrop, clarifying how GF influences green technology innovation (GTI) has become an urgent research priority. Using panel data from 283 Chinese cities (2012–2023), this study estimates a panel threshold model to examine the non-linear relationship between GF and GTI, with environmental regulation (ER) as the threshold variable. The results, validated by robustness and endogeneity tests, reveal the following: (1) GF exerts a double-threshold effect on GTI, with its promoting effect strengthening between thresholds but weakening beyond the second threshold. (2) ER exhibits a significant single-threshold effect; beyond it, GF’s contribution to GTI is substantially enhanced. (3) Three types of heterogeneity analysis are performed based on geographical regions, historical endowments, and whether a city is classified as an innovation-driven city. Overall, the results indicate that the threshold effects are more pronounced in eastern regions, cities with stronger historical endowments, and innovation-driven cities. These findings not only deepen the theoretical understanding of the GF–ER–GTI nexus but also provide empirically grounded insights for designing differentiated GF policies and region-specific environmental regulation strategies, thereby supporting both China’s low-carbon transition and global climate governance efforts. Full article

Nutrient inputs from human activities, such as agriculture and sewage discharge, influence algal blooms in water bodies. In Ecuador, the Daule River receives wastewater discharges. In addition, poor agricultural practices, including the unsuitable use of fertilisers in combination with soil erosion and surface runoff processes, increase the nutrient load to the river. Considering this, the objective of this study was to evaluate environmental and biological variables using statistical analysis to identify the parameters that influence algal blooms in the main stem of the Daule River. The methodology consisted of two phases: (i) data collection, including water sampling and laboratory work for the analysis of nutrients and phytoplankton, and (ii) statistical analysis, which includes univariate, bivariate, inferential and multivariate analysis (STATICO technique). The results showed that pH and dissolved oxygen were the main drivers of diatoms (Polymyxus coronalis and Aulacoseira granulate) and the charophyte Mougeotia sp. Similarly, ammonium-N was the main driver of the diatom Ulnaria ulna and the cyanobacteria Planktothrix cf. agardhii. The outcomes of this study identified the main environmental variables driving blooms of the five most abundant species, providing a basis for the development of ecological models in the context of land use and climate change. Full article