Search Results (13,616)

The extraction of multiple coal seams not only increases the risk of water inrush disasters in mines but also exacerbates the long-term depletion of groundwater, posing challenges for sustainable resource management in ecologically sensitive areas. This study utilizes the plastic damage–permeability coupling model in Abaqus CAE to analyze the impact of coal seam thickness and pillar layout on the evolution of the plastic zone and groundwater loss in the Shen Dong mining area, specifically at the Buertai coal mine. The results indicate that coal seam thickness is a strong driving factor for aquifer depletion: the water inflow under a 10 m thick coal seam is 1.56 times that under a 4 m thick coal seam. In contrast, the optimized staggered pillar layout alters stress distribution and reduces the water inflow under deeper coal seams by approximately 38%, demonstrating excellent water-saving potential. To translate these findings into a sustainability framework, this study proposes three new indicators: the Groundwater Loss Index (GLI) to quantify depletion intensity, the Aquifer Protection Efficiency (APE) to assess protection benefits, and the Sustainability Trade-off Index (STI) to balance coal recovery, safety, and groundwater protection. These metrics establish a dual-objective optimization approach that ensures safe mining and the sustainability of the aquifer. This study provides practical benchmarks for environmental impact assessment and aligns with the global sustainable development agenda, particularly the United Nations Sustainable Development Goals concerning clean water (SDG 6), responsible consumption (SDG 12), and terrestrial ecosystems (SDG 15). By incorporating groundwater protection into the design of the Buertai coal mine, this study advances the transition of multi-seam mining at Buertai from disaster prevention to sustainability orientation. Full article

Buildings with electrified heat pump systems, onsite photovoltaic (PV) generation, and energy storage offer strong potential for demand flexibility. This study compares two storage configurations, thermal energy storage (TES) and battery energy storage (BESS), to evaluate their impact on cooling performance and cost savings. A Model Predictive Control (MPC) framework was developed to optimize system operations, aiming to minimize costs while maintaining occupant comfort. Results show that both configurations achieve substantial savings relative to a baseline. The TES system reduces daily operating costs by about 50%, while the BESS nearly eliminates them (over 90% reduction) and cuts grid electricity use by more than 65%. The BESS achieves superior performance because it can serve both the controllable heating, ventilation, and air conditioning (HVAC) system and the home’s broader electrical loads, thereby maximizing PV self-consumption. In contrast, the TES primarily influences the thermal load. These findings highlight that the choice between thermal and electrical storage greatly affects system outcomes. While the BESS provides a more comprehensive solution for whole-home energy management by addressing all electrical demands, further techno-economic evaluation is needed to assess the long-term feasibility and trade-offs of each configuration. Full article

As high-energy-consumption underground public space, subway stations are responsible for a particularly significant proportion of air-conditioning energy use, especially during the cooling season, making the investigation of cooling-load characteristics highly important. However, the determination of independent influencing factors in different situations has not yet reached a consensus, and the role of interaction effects is lacking, which hinders the development of energy-saving strategies. For this purpose, this study proposes a sensitivity analysis framework based on 10 typical influencing factors from thermal parameters, meteorological parameters, internal heat disturbances, and indoor environmental setpoints. An input set was generated by integrating equal-step parameter discretization and Saltelli quasi-MonteCarlo sampling. A database containing 11,264 samples was constructed through an EnergyPlus–Python co-simulation method. Based on the Sobol global sensitivity analysis, the key influencing factors of subway station cooling load were identified and quantified, and the impact of these 10 factors was systematically analyzed. Results show that occupant density (S_i^T = 0.5605) and fresh air volume (S_i^T = 0.4546) are the dominant factors, contributing more than 50% of the load variance. In contrast, the characteristics of an underground structure significantly weaken the influence of the building-envelope heat transfer coefficient (S_i^T = 0.1482) and soil temperature (S_i^T = 0.0884). Furthermore, five groups of strong interaction effects were identified in this study, including occupant density–fresh air volume (S_ij = 0.1094), revealing a nonlinear load response mechanism driven by multi-parameter coupling. This research provides a theoretical foundation and quantitative tool for the refined design and optimized dynamic coupled operation of underground transportation hubs. Full article

Background: Antimicrobial resistance (AMR) remains a critical major public health challenge, largely driven by the inappropriate use of antibiotics in the community. In Spain, the National Action Plan on AMR (PRAN) emphasizes the need for robust surveillance systems based on standardized indicators and high-quality data sources. Objective: This study aimed to evaluate the feasibility of calculating PRAN prescribing indicators using the National Electronic Database for Pharmacoepidemiological Research in Primary Care (BIFAP) and to validate BIFAP as a data source for national antimicrobial prescribing surveillance. Methods: A population-based cross-sectional study was conducted using 2018 data from 9.4 million individuals. Results: Overall, 23.3% received at least one antibiotic prescription during the year, with an average of 1.8 treatments per patient. First-line recommended antibiotics represented 26.5% of total dispensed defined daily doses. Notable age-related variability in prescribing patterns was observed: children predominantly received first-line narrow-spectrum antibiotics, whereas older adults were more frequently prescribed broad-spectrum agents. Discusion: BIFAP-based indicators closely aligned with PRAN data while allowing for the calculation of additional metrics, such as prevalence of use, treatments per patient-year, and variations by age and sex. The findings underscore the importance of patient-level monitoring to identify demographic-age-specific priorities for targeted interventions aimed at optimizing antibiotic use in Primary Care. Conclusions: This study confirms the feasibility of using BIFAP to strengthen antibiotic consumption monitoring and policy evaluation efforts in Spain. Full article

The centrifuge method serves as an efficient and rapid approach for determining the soil–water characteristic curve (SWCC). However, soil shrinkage during centrifugation remains overlooked and prior modified methods may suffer from complex operations, high costs, time consumption, and limited applicability. To address these issues, this study introduces a simple correction scheme (G3) for determining drying SWCCs using the centrifuge method based on high matric suction calibration points. The performance of the proposed G3 method was systematically evaluated against a modified method considering soil shrinkage (G1) and the conventional uncorrected method (G2). Results revealed significant soil linear shrinkage post-centrifugation, accompanied by a reduction in total soil porosity and an increase in soil bulk density. SWCCs from all methods exhibited strong consistency at low matric suction ranges but diverged markedly at high matric suction segments. High matric suction data dominated the SWCC fitting. The G1 method achieved the highest fitting accuracy, while the G3 method performed the worst yet maintained acceptable reliability. The G2 method yielded optimal SWCC for simulating saturated soil water content, field capacity, and permanent wilting point. Conversely, Hydrus-1D simulations revealed superior performance of the G3 method in simulating farmland soil moisture dynamics during the dehumidification process. Values of R² across methods followed G3 > G1 > G2, while mean absolute error, mean absolute percentage error, and root mean square error exhibited the opposite trend. These findings highlight that the previous modified approaches are more suitable for low and medium matric suction ranges. The proposed correction method enhances drying SWCC performance across the full matric suction range, offering a practical refinement for the centrifuge method. This advancement could enhance the reliability in soil hydraulic characterization and contribute to a better understanding of the hydraulic–mechanical–chemical behavior in soils. Full article

The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation, particularly in adverse weather. This study addresses this gap by analyzing the fast-charging behaviour, net battery capacity and highway energy consumption of 62 EVs from different market segments. Charging power curves were obtained experimentally at high-power DC stations, with data recorded through both the charging infrastructure and the vehicles’ battery management systems. Tests were conducted, under optimal conditions, between 10% and 90% state of charge (SoC), with additional sessions performed under both cold and preconditioned battery conditions to show thermal effects on the batteries’ fast-charging capabilities. Real-world highway consumption values were applied to simulate 1000 km journeys at 120 km/h under cold (−10 °C, cabin heating) and mild (23 °C, no AC) weather scenarios. An optimization model was developed to minimize total trip time by adjusting the number and duration of charging stops, including a 5 min detour for each charging session. Results show that the optimal charging cutoff point consistently emerges around 59% SoC, with a typical deviation of 10, regardless of ambient temperature. Charging beyond 70% SoC is generally inefficient unless dictated by charging station availability. The optimal strategy involves increasing the number of shorter stops—typically every 2–3 h of driving—thereby reducing total trip. Full article

Global electric vehicle sales are growing exponentially, with the European Union actively promoting the adoption of electric vehicles to significantly reduce mobility-related emissions. Concurrently, research efforts are increasingly directed toward optimizing vehicle charging strategies for the effective integration of renewable energy sources. Nevertheless, despite extensive theoretical studies, few practical implementations have been carried out. In response, this paper presents a digital twin of a microgrid designed specifically for optimizing the charging schedules of an electric vehicle fleet, with the goal of maximizing photovoltaic self-consumption. Machine learning algorithms are utilized to forecast vehicle energy consumption, and various heuristic optimization methods are applied to determine optimal charging schedules. The system incorporates an interactive dashboard, enabling users to input specific preferences or delegate charging decisions to a real-time optimizer. Additionally, a user-centric decision support system was developed to provide recommendations on optimal vehicle connection timings and heat pump setpoints. Certain algorithms failed to converge on a feasible optimal solution, even after 340 s and over 500 generations, particularly within high-production scenarios. Conversely, using the GWO-WOA algorithm, optimal charging schedules are computed in less than 25 s, balancing photovoltaic power exports under varying weather conditions. Furthermore, K-Means was identified as the most effective clustering technique, achieving a Silhouette Score of up to 0.57 with four clusters. This configuration resulted in four distinct velocity ranges, within which energy consumption varied by up to 5.8 kWh/100 km, depending on the vehicle's velocity. Finally, the facility managers positively assessed the usability of the DT dashboard and the effectiveness of the decision support system. Full article

Thorium has emerged as a promising alternative to uranium in nuclear energy systems due to its higher natural abundance, favorable conversion to fissile ²³³U, and reduced generation of long-lived transuranic waste. This review provides a comprehensive overview of advanced techniques for thorium recovery from primary ores and secondary resources. The main mineralogical carriers—including monazite, thorianite, thorite, and cheralite as well as industrial by-products such as rare-earth processing tailings—are critically examined with respect to their occurrence and processing potential. Physical enrichment methods (gravity, magnetic, and electrostatic separation) and hydrometallurgical approaches (acidic and alkaline leaching) are analyzed in detail, highlighting their efficiencies, limitations, and environmental implications. Particular emphasis is placed on modern separation strategies such as solvent extraction with organophosphorus reagents, diglycolamides, and ionic liquids, as well as extraction chromatography, nanocomposite sorbents, ion-imprinted polymers, and electrosorption on carbon-based electrodes. These techniques demonstrate significant progress in enhancing selectivity, reducing reagent consumption, and enabling recovery from low-grade and secondary feedstocks. Environmental and radiological aspects, including waste minimization, immobilization, and regulatory frameworks, are discussed as integral components of sustainable thorium management. Finally, perspectives on hybrid technologies, digital process optimization, and economic feasibility are outlined, underscoring the need for interdisciplinary approaches that combine chemistry, materials science, and environmental engineering. Collectively, the analysis highlights the transition from conventional practices to integrated, scalable, and environmentally responsible technologies for thorium recovery. Full article

Passive immunity, the acquisition of specific immune protection through external antibodies or immune components, is critically important for neonatal survival. In ruminants, however, neonatal hypogammaglobulinemia, a consequence of their epitheliochorial placental structure preventing prenatal antibody transfer, often leads to high morbidity and mortality. Consequently, neonatal ruminants are entirely dependent on the timely consumption of colostrum to acquire sufficient immunoglobulin G (IgG) for protection. Establishing robust passive immunity is therefore a cornerstone for their survival, healthy development, and future production efficiency. This review synthesizes current knowledge on the establishment of passive immunity in neonatal ruminants. We first outline the fundamental principles of passive immunity transfer, then delve into the specific pathways and molecular mechanisms in ruminants. Key factors influencing this process are subsequently discussed. Furthermore, we highlight the long-term impact of passive immunity on adult production performance. This review aims to provide a scientific foundation for optimizing colostrum management strategies and to stimulate future research into the intricate mechanisms of IgG absorption. Full article

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments. Full article

In vitro fertilization (IVF) exposes embryos to environmental stressors that can disrupt early development and confer long-term health risks, though the mechanisms remain poorly understood. Here, we tested the hypothesis that reducing incubation temperature during the first zygotic cleavage would promote long-term developmental stability in IVF-conceived offspring. Using a mouse model, we compared the long-term effects of standard (37 °C) versus reduced (35 °C) IVF culture temperature on energy balance, circadian rhythms, sleep architecture, and brain histone modifications. Although offspring from both IVF groups exhibited increased body mass without notable effects on glucose metabolism, significant disruptions in circadian rhythms and sleep–wake patterns were detected. The 37 °C group exhibited altered amplitudes in oxygen consumption rhythms and respiratory exchange ratios, as well as pronounced alterations in sleep–wake patterns, including reduced sleep duration and increased nighttime activity. The 35 °C group displayed intermediate phenotypes, substantiating the importance of optimizing embryo incubation parameters. These metabolic and behavioral changes were paralleled by altered histone modifications in the cerebral cortex of IVF offspring, suggesting an epigenetic basis for circadian misalignment. Our results identify disrupted circadian rhythm and sleep architecture as a novel mechanism contributing to metabolic dysfunction in IVF-conceived offspring. The partial mitigation of these effects through reduced culture temperature underscores the importance of optimizing IVF protocols to minimize long-term epigenetic and metabolic risks. Full article

This systematic review applies the PRISMA methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to evaluate the use of recycled sand, obtained from construction and demolition waste (CDW), in mortars for civil construction. A total of 24 studies published between 2020 and 2025 were analyzed, retrieved from the Scopus and Web of Science databases. The main objective is to assess the technical feasibility and environmental benefits of recycled sand in mortars, while addressing research gaps such as the lack of standardized methodologies and the limited understanding of durability at higher replacement levels. Given the significant resource consumption and waste generation in the construction sector, the study highlights emerging trends in adopting recycled sand as a sustainable alternative to natural aggregates. Findings indicate that optimal replacement levels range between 30 and 50% in ordinary Portland cement (OPC) mortars, and up to 100% in geopolymer mixtures when appropriate processing and activation methods are applied, without compromising mechanical performance. Reported benefits include cost reduction, lower carbon footprint, and enhanced compactness. However, challenges such as higher porosity and the need for optimized mix designs, and high heterogeneity of CDW sources and processing methods remain. Overall, the review confirms that recycled sand is a technically viable and environmentally beneficial material for mortar production, though future research must focus on harmonizing test protocols and long-term performance evaluation. In addition, a bibliometric analysis was conducted to map scientific output on this topic, identifying key countries, journals, and publication trends. Full article

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of $44 °\mathrm{C}$, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

In the context of rapid development within the logistics sector and widespread advocacy for sustainable development, this paper proposes enhancements to the task scheduling and path planning components of four-way shuttle systems. The focus lies on refining and innovating modeling approaches and algorithms to address issues in complex environments such as uneven task distribution, poor adaptability to dynamic conditions, and high rates of idle vehicle operation. These improvements aim to enhance system performance, reduce energy consumption, and achieve sustainable development. Therefore, this paper presents an energy-saving and efficiency-enhancing optimization study for a four-way shuttle-based high-density automated warehouse system, utilizing deep reinforcement learning. In terms of task scheduling, a collaborative scheduling algorithm based on an Improved Genetic Algorithm (IGA) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) has been designed. In terms of path planning, this paper provides the A*-DQN method, which integrates the A* algorithm(A*) with Deep Q-Networks (DQN). Through combining multiple layout scenarios and adjusting various parameters, simulation experiments verified that the system error is within 5% or less. Compared to existing methods, the total task duration, path planning length, and energy consumption per order decreased by approximately 12.84%, 9.05%, and 16.68%, respectively. The four-way shuttle vehicle can complete order tasks with virtually no conflicts. The conclusions of this paper have been validated through simulation experiments. Full article

Processing complex ore remains a challenge due to energy-intensive grinding and complex beneficiation and pyrometallurgical treatments that consume large amounts of water whilst generating significant waste and polluting the environment. Sensor-based ore sorting, which separates ore particles based on their physical or chemical properties before downstream processing, is emerging as a transformative technology in mineral processing. However, its application to complex and heterogeneous ores remain limited by the constraints of single-sensor systems. In addition, existing hybrid sensor strategies are fragmented and a consolidated framework for implementation is lacking. This review explores these challenges and underscores the potential of multimodal sensor integration for complex ore pre-concentration. A multi-sensor framework integrating machine learning and computer vision is proposed to overcome limitations in handling complex ores and enhance sorting efficiency. This approach can improve recovery rates, reduce energy and water consumption, and optimize process performance, thereby supporting more sustainable mining practices that contribute to the United Nations Sustainable Development Goals (UNSDGs). This work provides a roadmap for advancing efficient, resilient, and next-generation mineral processing operations. Full article