Search Results (6,798)

Altered precipitation regimes, both in intensity and duration, can profoundly influence the structure and function of soil microbial communities, yet the patterns and drivers of these responses remain unclear across ecosystem types. Here, using data exclusively from 101 field experiments conducted in China (yielding 695 observations), we investigated the impacts of altered precipitation on soil microbial biomass, diversity, and enzymatic activity in forest and grassland ecosystems. Soil microbial biomass carbon (MBC) and nitrogen (MBN) increased in response to precipitation addition, particularly in grasslands, but they decreased under reduced precipitation, with the decline being more pronounced in forests. The magnitude and duration of precipitation manipulation significantly influenced these effects, with moderate and long-term changes producing divergent responses. Bacterial diversity was largely unaffected by all precipitation treatments, whereas fungal diversity decreased significantly under intense and short-term reductions in precipitation. Enzyme activities exhibited the following element-specific patterns: carbon- and phosphorus-cycling enzymes and antioxidant enzymes were suppressed by precipitation reduction, especially in grasslands, while nitrogen-cycling enzymes showed no consistent response. Moreover, microbial responses were significantly shaped by environmental factors, including mean annual temperature (MAT), mean annual precipitation (MAP), and elevation. Our region-specific analysis highlights precipitation-driven microbial dynamics across China’s diverse climatic and ecological conditions. These findings demonstrate that soil microbial communities respond asymmetrically to precipitation changes, with responses shaped by both ecosystem type and climatic context, underscoring the need to account for environmental heterogeneity when predicting belowground feedback to climate change. Full article

This study presents a mathematical model to describe the transmission dynamics of asthma, explicitly accounting for the impact of dust waves and airborne particulate matter in the environment, recognized as key triggers of asthma exacerbations. The model incorporates a single endemic equilibrium point, which is shown to be locally asymptotically stable. To mitigate the burden of asthma, we employed the Pontryagin Maximum Principle within an optimal control framework, incorporating three time-dependent intervention strategies: vaccination, treatment, and avoidance of environmental triggers such as dust exposure. The model was numerically solved using the fourth-order Runge–Kutta method in conjunction with a forward–backward sweep algorithm to investigate the effects of various control combinations on the prevalence of asthma. Additionally, a comprehensive cost-effectiveness analysis was conducted to evaluate the economic viability of each strategy. The results indicate that the combined application of vaccination and treatment is the most cost-effective approach among the strategies analyzed, significantly reducing the number of asthma cases at minimal cost. All simulations and numerical experiments were performed to validate the theoretical findings and quantify the effectiveness of the proposed interventions under realistic environmental conditions driven by dust activity. The model highlights the importance of integrated medical and environmental control policies in mitigating asthma outbreaks, particularly in regions frequently exposed to dust storms. Full article

This study analyzes the dynamics of temperature and moisture in a cylindrical silo with a conical roof and floor used for storing corn in the Bajío region of Mexico, considering conditions both with and without aeration. The model incorporates external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity through the sorption isotherm (water activity), and grain respiration to simulate real storage conditions. The model is based on continuity, momentum, energy, and moisture conservation equations in porous media. This model was solved using the finite element method (FEM) to evaluate temperature and interstitial humidity variations during January and May, representing cold and warm environmental conditions, respectively. The simulations show that, without aeration, grain temperature progressively accumulates in the center and bottom region of the silo, reaching critical values for safe storage. In January, the low ambient temperature favors the natural dissipation of heat. In contrast, in May, the combination of high ambient temperatures and solar radiation intensifies thermal accumulation, increasing the risk of grain deterioration. However, implementing aeration periods allowed for a reduction in the silo’s internal temperature, achieving more homogeneous cooling and reducing the threats of mold and insect proliferation. For January, an airflow rate of 0.15 m³/(min·ton) was optimal for maintaining the temperature within the safe storage range (≤17 °C). In contrast, in May, neither this airflow rate nor the accumulation of 120 h of aeration was sufficient to achieve optimal storage temperatures. This indicates that, under warm conditions, the aeration strategy needs to be reconsidered, assessing whether a higher airflow rate, longer periods, or a combination of both could improve heat dissipation. The results also show that interstitial relative humidity remains stable with nocturnal aeration, minimizing moisture absorption in January and preventing excessive drying in May. However, it was identified that aeration period management must be adaptive, taking environmental conditions into account to avoid issues such as re-wetting or excessive grain drying. Full article

The thermo-hydrological (TH) coupling model constitutes the foundational framework for investigating the temperature distribution of surrounding rock in cold region tunnels. In this study, a fully coupled TH model is proposed that takes into account multiple physical phenomena during the freezing process of surrounding rock. Firstly, the model was established based on thermodynamics, seepage theory, and ice–water phase change theory, which accounted for unfrozen water, latent heat of phase change, ice impedance, and convective heat transfer. The model was successfully verified by comparing its results to field data. Next, the sensitivity of surrounding rock temperature to environmental, thermodynamic, seepage, and coupling parameters in the fully coupled TH model was systematically studied using a numerical analysis method. The results show that the annual temperature amplitude and thermal conductivity represent the main factors affecting the surrounding rock temperature at a radial depth of 0 m, while the initial temperature and porosity are the key factors at a radial depth of 5 m. Permeability has the least influence on the surrounding rock temperature, but the temperature field will experience sudden changes if its value exceeds its value exceeds 1 × 10⁻¹² m². Finally, using the proposed numerical model, the thickness of insulation layer was simulated, and the degree of influence of the parameters on the thickness of insulation layer was analyzed. This study reveals that the annual temperature amplitude has the greatest influence on the calculation of insulation layer thickness, with its normalized sensitivity factor being approximately 50%. These findings not only expand the methodology for exploring the laws of TH coupling but also provide a theoretical foundation for improving the parameter calibration efficiency and calculation accuracy of the fully coupled TH model, and they have significant reference value. Full article

Climate change poses unprecedented challenges to forest ecosystems. Betula alnoides, a tree species with significant ecological and economic value in southern China, has been the subject of studies on its distribution pattern and response to climate change. However, research on the distribution pattern of B. alnoides and its response to climate change remains relatively limited. In this study, we developed a MaxEnt model incorporating multiple environmental variables, including climate, topography, soil, vegetation, and human activities, to evaluate model performance, identify key factors influencing the distribution of B. alnoides, and project its potential distribution under various future climate scenarios. Species occurrence data and environmental layers were compiled for China, and model parameters were optimized using the ENMeval package. The results showed that the optimized model achieved an AUC value of 0.956, indicating extremely high predictive accuracy. The four key factors affecting the distribution of B. alnoides were standard deviation of temperature seasonality (Bio4), normalized difference vegetation index (NDVI), mean temperature of driest quarter (Bio9), and annual precipitation (Bio12). Among them, the cumulative contribution rate of climatic factors reached 68.9%, but the influence of NDVI was significantly higher than that of precipitation factors. The current suitable habitat of B. alnoides is mainly concentrated in the southwestern region, covering an area of 179.32 × 10⁴ km², which accounts for 18.68% of China’s land area. Under the SSP126 scenario, the suitable habitat area first decreases and then increases in the future, while under the SSP370 and SSP585 scenarios, the suitable habitat area continues to shrink, with significant losses in high-suitability areas. In addition, the centroid of the suitable habitat of B. alnoides shows an overall trend of shifting northwestward. This indicates that B. alnoides is highly sensitive to climate change and its distribution pattern will undergo significant changes in the future. In conclusion, the distribution pattern of B. alnoides shows a significant response to climate change, with particularly prominent losses in high-suitability areas in the future. Therefore, it is recommended to strengthen the protection of high-suitability areas in the southwestern region and consider B. alnoides as an alternative tree species for regions facing warming and drying trends to enhance its climate adaptability. Full article

Complex engineering systems—such as ultra-long horizontal wells in energy exploitation and distributed rail transit infrastructure—operate under harsh physical and environmental conditions, where accurate physical modeling and real-time parameter estimation are essential for ensuring safety, efficiency, and reliability. Traditional empirical and black-box data-driven approaches often fail to account for the underlying physical mechanisms, thereby limiting interpretability and generalizability. To address this, we propose a unified framework that integrates physics-informed scenario-based modeling with data-driven parameter inversion. In the first stage, critical system parameters—such as friction coefficients in drill string movement or contact forces in rail–wheel interactions—are explicitly formulated based on mechanical theory, leveraging symmetries and boundary conditions to improve model structure and reduce computational complexity. In the second stage, model parameters are identified or updated through inverse modeling using historical or real-time field data, enhancing predictive performance and engineering insight. The proposed methodology is demonstrated through two representative cases. The first involves friction estimation during tripping operations in the SU77-XX-32H5 ultra-long horizontal well of the Sulige Gas Field, where a mechanical load model is constructed and field-calibrated. The second applies the framework to rail transit systems, where wheel–rail friction is estimated from dynamic response signals to support condition monitoring and wear prediction. The results from both scenarios confirm that incorporating physical symmetry and data-driven inversion significantly enhances the accuracy, robustness, and interpretability of engineering analyses across domains. Full article

Background: Understanding the psychological drivers of the energy transition is essential for accelerating the shift to low-carbon societies. The aim of this study is to examine how green consumer values (GCV), energy-saving knowledge (KES) and consumer energy awareness (CEA) jointly shape pro-environmental energy behaviors (EEB), while accounting for citizens’ perceived cost barriers (PESC). Methods: We conducted a nationally representative Computer-Assisted Web Interviewing (CAWI) survey of 1405 Polish households and employed structural-equation modeling to test an integrated framework linking values, awareness, knowledge, perceived costs and two behavioral domains: high-commitment efficiency investments and low-cost curtailment actions. Results: The structural-equation model confirms that green consumer value significantly enhance both knowledge of energy-saving (β = 0.434) and consumer energy awareness (β = 0.185), thereby driving two distinct pro-environmental pathways: high-commitment efficiency investments (energy efficiency behavior) (β = 0.488) and curtailment behaviors (β = 0.355). Green consumer value also reduces perception of energy-saving costs (β = −0.344), yet these costs themselves exert strong inhibitory effects on both energy efficiency behavior (β = −0.213) and curtailment behaviors (β = −0.302). Conclusions: Our findings validate an integrated value–awareness–behavior framework, demonstrating that fostering green values and improving informational access are critical to enhancing energy-saving practices, while cost-reduction measures remain indispensable. Policymakers should combine value-based education, transparent feedback tools and targeted financial incentives to unlock citizens’ full potential in driving the energy transition. Full article

The construction industry increasingly integrates technological advancements to enhance efficiency and meet technical, environmental, and economic requirements. Self-compacting mortars are gaining popularity due to their superior fluidity, optimized compaction, and improved mechanical properties. This study explores the potential of statistical mix design methodology to optimize self-compacting mortars’ fresh properties and strength development by replacing up to 20% of cement with pozzolana, limestone, and marble powder. A self-compacting mortar repository was used to develop robust models predicting slump flow, compressive strength at 28 days, water absorption, and capillary absorption. Results indicate that marble powder mixtures exhibit superior slump flow, up to 9% higher than other formulations. Compressive strengths range from 50 MPa to 70 MPa. Pozzolana and marble-based mortars show 15% and 12% strength reductions compared to the limestone-based mix, respectively. Water absorption increases slightly for mortars with marble (+2%) or pozzolana (+3%). The mortar containing marble powder has the lowest sorptivity coefficient due to its high specific surface area. The statistical analysis was conducted using a mixture design approach based on a second-order polynomial regression model. ANOVA results for the studied responses indicate that the calculated F-values exceed the critical thresholds, with p-values below 0.05 and R-squared values above 0.83, confirming the robustness and predictive reliability of the developed models. Life cycle assessment reveals that cement production accounts for over 80% of the environmental impact. Partial replacement with pozzolana, limestone, and marble powder reduces up to 19% of greenhouse gas emissions and 17.22% in non-renewable energy consumption, demonstrating the environmental benefits of optimized formulations. Full article

This study examines the relationships between spirituality, resilience, and happiness among higher education students, exploring the moderating roles of religious belief and years of study based on developmental and religious coping theoretical frameworks. Developmental theory suggests that university students’ psychological resources evolve across academic years, while religious coping theory posits that individual differences in religious commitment may buffer spirituality’s protective effects on well-being outcomes. Using a quantitative cross-sectional approach, data were collected from 459 university students from environmental science programs across public and private universities in northern Peru. Participants were predominantly female (59.04%) and aged 18–24 years (73%). Three validated instruments were administered: the Personal Spirituality Scale, Connor–Davidson Brief Resilience Scale, and Subjective Happiness Scale. Religious beliefs were measured on a 5-point scale, while years of study was categorized by academic year. Results from partial least squares structural equation modeling revealed significant direct effects of spirituality on both happiness (β = 0.256, p < 0.001) and resilience (β = 0.274, p < 0.001), with resilience also significantly influencing happiness (β = 0.162, p < 0.05). The structural model demonstrated exceptional explanatory power, with spirituality explaining 97.1% of variance in resilience, while spirituality and resilience together accounted for 86.2% of variance in happiness. Contrary to theoretical expectations, neither religious beliefs (β = 0.032, p = 0.489) nor years of study (β = −0.047, p = 0.443) showed significant moderating effects. These results suggest that spirituality and resilience serve as universal contributors to student well-being, operating independently of specific religious orientations and academic progression. The findings support integrating spiritual development and resilience-building components into inclusive university student support programs. Full article

Improving yield stability under water-limited conditions is a key objective of wheat breeding programmes. One trait of particular interest is carbohydrate accumulation and remobilisation. This study assessed the genetic basis of aspects of yield and flag leaf sugar contents under drought and well-watered conditions using QTL mapping in a population of 90 doubled haploid lines derived from the cross Chinese Spring × SQ1. As well as soluble sugar content, glucose, fructose, sucrose, and maltose, the traits grain yield (Yld), biomass (Bio), and thousand grain weight (TGW) were also analysed. Analysis of variance showed that genotype, environment and their interactions significantly influenced all the traits studied, with environmental effects explaining up to 74.4% of the total variation. QTL analysis identified 40 QTLs for Yld, TGW, and Bio as well as 53 QTLs for soluble carbohydrates, accounting for up to 40% of phenotypic variation. QTLs coincident for more than one trait were identified on 21 chromosome regions, associated with carbohydrate metabolism and yield performance under drought, particularly on chromosomes 2D, 4A, 4B, 5B, 5D, 6B, and 7A. Candidate genes for several yield-related QTLs were identified. These results provide useful genetic markers for the development of more drought-resistant wheat cultivars. Full article

Chile, which accounts for 27% of global cherry exports (USD 2.26 billion annually), faces a critical industry challenge in crop health monitoring. While automated sensors monitor environmental variables, phytosanitary diagnosis still relies on manual visual inspection, leading to detection errors and delays. Given this reality and the growing use of AI models in agriculture, our study quantifies the theory–practice gap through comparative evaluation of three transfer learning architectures (namely, VGG16, ResNet50, and EfficientNetB0) for automated disease identification in cherry leaves under both controlled and real-world orchard conditions. Our analysis reveals that excellent laboratory performance does not guarantee operational effectiveness: while two of the three models exceeded 97% controlled validation accuracy, their field performance degraded significantly, reaching only 52% in the best-case scenario (ResNet50). These findings identify a major risk in agricultural transfer learning applications: strong laboratory performance does not ensure real-world effectiveness, creating unwarranted confidence in model performance under real conditions that may compromise crop health management. Full article

Amid rising load volatility and uncertainty, demand-side resources with regulation capabilities are increasingly engaged at scale in ancillary service markets, facilitating sustainable peak load mitigation and alleviating grid stress while reducing reliance on carbon-intensive peaking plants. This study examines the integration of electric vehicles (EVs) in peak regulation, proposing a multi-stage operational strategy framework grounded in the analysis of EV power and energy response constraints to promote both economic efficiency and environmental sustainability. The model holistically accounts for temporal charging and discharging behaviors under diverse incentive mechanisms, incorporating user response heterogeneity alongside multi-period market peak regulation demands while supporting clean transportation adoption. An optimization model is formulated to maximize aggregator revenue while enhancing grid sustainability and is solved via MATLAB(2021b) and CPLEX(20.1.0). The simulation outcomes reveal that the discharge-based demand response (DBDR) strategy elevates aggregator revenue by 42.6% and enhances peak regulation margins by 19.2% relative to the conventional charge-based demand response (CBDR). The hybridization of CBDR and DBDR yields a threefold revenue increase and a 28.7% improvement in peak regulation capacity, underscoring the efficacy of a joint-response approach in augmenting economic returns, grid flexibility, and sustainable energy management. Full article

Accurate information on waste composition is essential for strategic planning in waste management and developing environmental technologies. However, detailed analyses of individual waste containers are both time- and cost-intensive, resulting in a limited number of available samples. Therefore, it is crucial to apply statistical methods that enable reliable estimation of average waste composition and its variability, while accounting for territorial differences. This study presents a statistical approach based on territorial stratification, aggregating data from individual waste container analyses to higher geographic units. The methodology was applied in a case study conducted in the Czech Republic, where 19.4 tons of mixed municipal waste (MMW) were manually analyzed in selected representative municipalities. The method considers regional heterogeneity, monitors the precision of partial estimates, and supports reliable aggregation across stratified regions. Three alternative approaches for constructing interval estimates of individual waste components are presented. Each interval estimate addresses variability from the random selection of waste containers and the selection of strata representatives at multiple levels. The proposed statistical framework is particularly suited to situations where the number of samples is small, a common scenario in waste composition analysis. The approach provides a practical tool for generating statistically sound insights under limited data conditions. The main fractions of MMW identified in the Czech Republic were as follows: paper 6.7%, plastic 7.3%, glass 3.6%, bio-waste 28.4%, metal 2.1%, and textile 3.0%. The methodology is transferable to other regions with similar waste management systems. Full article

In this study, non-isothermal pyrolysis of a mixture of disposable surgical face masks (FMs) and nitrile gloves (NGs) was conducted, using a heating rate of 100 °C/min, N₂ flowrate of 100 mL/min, and temperatures between 500 and 800 °C. Condensable product yield peaked at 600 °C (76.9 wt.%), with gas yields rising to 31.0 wt.%, at 800 °C. GC-MS of the condensable product confirmed the presence of aliphatic compounds (>90%), while hydrogen, methane, and ethylene dominated the gas composition. At 600 °C, gasoline (C₄ to C₁₂)-, diesel (C₁₃ to C₂₀)-, motor oil (C₂₁ to C₃₅)-, and heavy hydrocarbon (C₃₅₊)-range compounds accounted for 23.7, 46.7, 12.5, and 17.1%, of the condensable product, respectively. Using model-free methods, the average activation energy and pre-exponential factor were found to be 309.7 ± 2.4 kJ/mol and 2.5 ± 3.4 × 10²⁵ s⁻¹, respectively, while a 2-dimensional diffusion mechanism was determined. Scale-up runs confirmed high yields of condensable product (60–70%), with comparable composition to that obtained from lab-scale tests. The pyrolysis oil exceeds acceptable oxygen, nitrogen, chlorine, and fluorine levels for industrial steam crackers—needing pre-treatment—while other contaminants like sulphur and metals could be managed through mild blending. In summary, this work offers a sustainable approach to address the environmental concerns surrounding disposable FMs and NGs. Full article

Understanding concrete creep aging is essential for ensuring structural safety and long-term durability, while the lack of robust numerical models limits the ability to thoroughly investigate and accurately predict time-dependent deformation and cracking behaviors. This study proposes a numerical framework integrating a discrete model and the microprestress solidification (MPS) theory to describe the aging creep and quasi-static performance of concrete at early-age and beyond. Hydration kinetics were formulated into constitutive equations to consider the time-dependent evolution of elastic modulus, strength, and fracture properties. Derived from the MPS theory, a unified creep model is developed within the equivalent rheological framework based on strain additivity. This formulation accounts for both visco-elastic and purely viscous creep phases while coupling environmental humidity effects with aging through the hydration degree. The proposed model is validated against experimental datasets encompassing diverse curing conditions, loading histories, and environmental exposures. The simulation results demonstrate that extended curing age enhances concrete strength (compression and fracture), while increased curing temperature has minimal impact due to the competing effects of microstructural refinement and thermal microcracking; both drying-induced transient creep and thermally induced microcracking contribute to increased creep deformation, driven by changes in microprestress resulting from variations in the chemical potential of nanopore water. The proposed numerical model can provide an effective tool to design and predict the long-term performance of concrete under various environmental conditions. Full article