Search Results (769)

This paper presents a theoretical engineering feasibility analysis of the RK-X photocatalytic process for In Situ Resource Utilisation (ISRU) on Mars. Experimental validation under simulated Martian conditions is the essential next step before any mission deployment claim can be made. The RK-X process converts the two most abundant Martian resources, atmospheric carbon dioxide (CO₂) and subsurface water ice (H₂O), into formic acid (HCOOH) and oxygen (O₂) through a fulvic acid-based photocatalytic cycle validated at the industrial scale in Hungary. A reference module processing 10 tonnes of CO₂ per Earth year yields 10.459 tonnes of formic acid and 3.636 tonnes of oxygen, sufficient to sustain a six-person crew for approximately two Earth years with a 198% safety margin over nominal respiratory demand. The economic analysis indicates that importing equivalent oxygen from Earth costs $1.82–$3.64 million per year; equivalent energy storage (Li-ion) costs $30.5–$61 million for one-time use. Formic acid stores 15.25 MWh of energy in ambient-stable liquid form at a round-trip efficiency of 68.64% without cryogenic infrastructure. A photovoltaic array of 55.37 m² provides the primary energy source; a kilowatt-class nuclear fission reactor constitutes the strategic opportunity for continuous, dust-storm-immune operation with free thermal co-generation. Three critical research gaps have been identified requiring laboratory validation before Mars deployment: (i) catalyst performance at the Martian CO₂ partial pressure (p(CO₂) < 10 mbar, T = 15 °C); (ii) water ice and dry ice extraction at an operational scale; and (iii) integrated closed-loop system demonstration. Built on Earth-proven chemistry with identified, addressable development pathways, the RK-X process theoretically resolves the problems of oxygen supply, seasonal energy storage, water management, and cryogenic infrastructure within a single closed-loop chemical cycle. Full article

The problem of industrial waste disposal is becoming increasingly pressing. For a long time, one of the primary methods of managing hazardous industrial waste was to dispose of it for long periods (decades) in engineered landfills. However, over time, due to various climatic, geological, and other changes, landfills begin to cause significant harm to the environment and human health. Old landfills, many built in the mid-20th century, pollute the air, soil, and groundwater. Therefore, the issue of decommissioning “old” landfills is becoming increasingly pressing. This study aimed to develop technological solutions for the safe extraction and processing of hazardous liquid waste from an aged industrial landfill. An integrated treatment chain was designed, comprising extraction, multi-barrier water treatment, vacuum evaporation, and lithification. Optimal lithification compositions were identified: for the salt concentrate–sludge–spent media mixture, a ratio of 68.2% sorbent D, 28.0% salt concentrate, and 3.8% dewatered sludge/spent media yielded a loose granular geocomposite; for oil-containing waste, the optimal ratio using lime and opoka was 1:0.9:0.5 (bottom sediments/CaO/opoka). Biotesting confirmed that the lithified waste is Hazard Class V (non-hazardous), whereas the untreated waste is Class III (moderately hazardous). The resulting geocomposite is suitable for on-site technical reclamation, closing the material cycle. Full article

Regular inspection of defects like sprayed concrete cracking and water seepage is crucial for the long-term safety of reservoir slopes in hydraulic engineering. Traditional manual inspections suffer from low efficiency and high cost. This paper presents a weighted Traveling Salesman Problem (TSP) model established by a Genetic Algorithm (GA) to optimize Unmanned Aerial Vehicle (UAV) inspection paths for these slopes. The model integrates UAV acceleration and deceleration physics. It weights the flight distance, converting it into flight time, and uses 3D-coordinate data to form the objective function. We calibrated key parameters, including acceleration and speed thresholds, by fitting displacement-time quadratic functions to field data from a DJI Matrice 350 RTK UAV. Tests on multiple slope models show the weighted GA optimizes the planned path by 46.2%, improves average inspection efficiency by 7.90% over an algorithm simulating human decision-making, and by 7.66% over a standard (non-weighted) GA. This work provides a reference for intelligent path planning on reservoir slopes and is applicable to similar scenarios like highway and railway slopes. Full article

Cyclic pollutants such as dyes, antibiotics, phenols and VOCs in water and atmosphere feature stable structures and are difficult to mineralize, which constitutes the core problem in current environmental governance. Semiconductor photocatalysis provides a green strategy for the advanced treatment of such pollutants. Electrospun black TiO₂/Ag-loaded SiO₂ nanofiber membranes have become a research hotspot owing to their multi-component synergistic advantages. This paper systematically reviews the preparation processes and structure regulation methods of electrospun SiO₂ nanofiber membranes; expounds the loading strategies of black TiO₂ and Ag nanoparticles, the interface regulation mechanisms and the synergistic photocatalytic mechanism of the ternary composite system; summarizes the application progress in the degradation of cyclic pollutants in water and atmospheric VOCs; and emphatically analyzes the performance characteristics and key issues in the ring-opening degradation of cyclic pollutants. Studies show that the high specific surface area and porous structure of SiO₂ nanofiber membranes offer excellent support for catalytic reactions. In addition, black TiO₂ achieves a full-spectrum response through defect engineering; the SPR effect and Schottky barrier of Ag significantly improve carrier separation efficiency; and the synergistic effect of the three components enhances the adsorption–catalytic degradation capacity. Current challenges remain in ring-opening efficiency and stability, requiring multi-method breakthroughs to overcome bottlenecks, clarify mechanisms and promote engineering applications. This paper provides theoretical references for the development of high-performance fiber-based photocatalytic materials and lays a foundation for the practical application of electrospun inorganic nanofiber membranes in the field of environmental catalysis. Full article

Chronic wounds, diabetic foot ulcers, venous leg ulcers, and pressure injuries affect millions of patients worldwide and cost healthcare systems in the order of $150 billion annually, yet treatment options have changed less than the scale of the problem would suggest. Biofilm formation, documented in up to 78% of chronic wounds, is a central cause: bacteria embedded in extracellular polymeric matrices tolerate antimicrobial concentrations up to 1000-fold higher than planktonic cells and sustain a chronic inflammatory state that actively prevents tissue repair. Hydrogels, crosslinked polymer networks with high water content and tunable physicochemical properties, have been widely studied as platforms for addressing these challenges, though the distance between laboratory results and clinical practice remains considerable. While recent reviews have summarized hydrogel materials or antimicrobial strategies in isolation, this review takes a different approach: we treat infection, biofilm persistence, and impaired regeneration as interconnected processes that must be addressed simultaneously, and we examine biofilm management as a distinct therapeutic target rather than merely a subset of antimicrobial delivery. We analyze hydrogel-based wound care across three integrated domains: design principles (natural, synthetic, and hybrid polymer systems; crosslinking strategies; and stimuli-responsive architectures), antimicrobial delivery (silver, antibiotics, antimicrobial peptides, natural agents, and controlled-release systems), and biofilm management (nanoparticle-mediated disruption, enzymatic EPS degradation, photodynamic approaches, quorum-sensing inhibition, and anti-adhesive surface engineering). For each area, we critically evaluate what the preclinical evidence supports, where it falls short, and what would be needed to bridge the gap to clinical application. Translation remains uneven. Among the many FDA- and EMA-cleared hydrogel dressings currently in clinical use, most are simple moisture-retaining or silver-containing formulations, while the multifunctional systems that dominate the research literature are at earlier stages of development. We discuss the main translational priorities, including more predictive preclinical models, long-term nanomaterial safety, harmonized outcome reporting, manufacturing scalability, and health economic evidence, as areas where further work can meaningfully accelerate clinical adoption. Full article

Accurate prediction of concrete compressive strength remains a challenging problem due to the complex and nonlinear interactions among mixture components and curing conditions. While machine learning approaches have shown promising results, existing studies are typically limited by static model integration strategies and insufficient consideration of structural relationships among input variables. To address these limitations, this study proposes a novel Adaptive Symmetry-Aware Physics-Informed Hybrid (ASAPH) learning framework. The proposed approach integrates three key components: (i) symmetry-consistent feature representation that preserves invariant relationships among mixture parameters, (ii) a stability-driven feature selection mechanism with a relevance–redundancy trade-off, and (iii) an adaptive input-dependent ensemble strategy that dynamically combines multiple learners. In contrast to conventional stacking methods, the proposed framework employs a learnable weighting function to adjust model contributions based on input characteristics, enabling more flexible, robust, and input-adaptive predictions. The framework combines an attention-based tabular model (TabNet) for representation learning and a tree-based ensemble model (XGBoost) for predictive robustness within a unified adaptive fusion architecture. Experimental results on a benchmark dataset using 10-fold cross-validation demonstrate that the proposed model achieves strong predictive performance, with R² = 0.9162, RMSE = 4.8271, and MAE = 3.4118, outperforming strong baseline models including XGBoost and TabNet. Furthermore, explainability analysis based on SHAP reveals that curing age, cement content, and water-related parameters are the most influential factors governing compressive strength, consistent with established engineering knowledge. Among these, curing age emerges as the most dominant factor, followed by water-related ratios and cement content, indicating strong alignment with established domain knowledge. These findings confirm that incorporating symmetry-aware and physics-informed representations enhances both interpretability and predictive reliability. Overall, the proposed framework provides a principled and generalizable approach for modeling complex engineering systems, bridging the gap between data-driven learning and physically consistent modeling. Full article

AI infrastructure is entering a constraint-dominated regime in which power access, cooling, water conditions, reliability, and financing jointly shape cost, sustainability, and operational risk. Yet the metrics used to evaluate these systems remain fragmented across facility engineering, compute/workload performance, and economic or risk analysis, with definitions that often sit at different layers and under different boundaries. This fragmentation weakens cross-layer reasoning and makes decision-traceable trade-off analysis difficult. This paper proposes a structured, decision-oriented measurement architecture for AI infrastructure metrics. The framework combines a 6 × 3 taxonomy, which organizes metrics across six layers and three semantic domains, with a procedural workflow built around a problem card, variable registry, minimality gate record, activated-cell map, boundary log, metric ledger, and a results sheet with case-pack manifest. Within this protocol, the Metric Propagation Graph is used as a case-specific dependency representation for tracing decision-facing metrics back to minimal boundary-consistent inputs. It is introduced as a traceability layer within the framework rather than as a stand-alone graph-theoretic method. The paper is illustrated through one fully worked case and one scoped portability illustration. The first is a fully worked large-load planning case for the Northern Virginia data-center corridor within PJM’s Dominion zone, showing that a boundary-consistent integrated metric can reverse the ranking obtained under a simpler screening view. The second is a scoped portability illustration for hourly matching under dual Scope 2 boundaries. Its purpose is not to provide a second full empirical validation, but to show how the same dossier logic, boundary discipline, and traceable metric construction transfer to a distinct decision setting. Full article

To solve the problems of easy fracture of reaming cutter arms and mechanical jamming leading to equipment damage when mechanical reaming equipment is used for anchor hole reaming in soft rock roadways, this study proposes the development of a high-efficiency reaming device with a simple structure. This study combines theoretical analysis, numerical simulation, and laboratory experiments to systematically investigate the key parameters of high-pressure water jet reaming equipment. The results show that under the same conditions, the maximum velocity of the high-pressure water jet decreases with an increase in the number of nozzles and the nozzle spacing. Although the correlation between the maximum jet velocity and nozzle angle is weak, the jet velocity acting on the anchor hole wall reaches its peak at a nozzle angle of 60°. Based on the simulation results, a 1:1 scale nozzle model was manufactured using 3D printing technology, and high-pressure water jet reaming experiments and bolt pull-out tests were carried out at a pressure of 20 MPa. The experimental results demonstrate that the optimal reaming effect is achieved with a nozzle configuration of 3 nozzles, 10 mm spacing, and a nozzle angle range of 45–60°. Specifically, after reaming with the nozzle at a 60° angle and 10 mm spacing, the bolt anchoring force reaches 51.99 kN, representing a 41.16% increase in anchoring strength compared with conventional anchoring. This research provides technical support for the engineering application of anchor hole reaming technology in soft rock roadways and is of great significance for improving the support effect of soft rock roadways. Full article

Accurate simulation of karst spring discharge is critical for sustainable water resource management, yet it remains a significant challenge due to the inherent complexity, heterogeneity, and non-linearity of karst systems. While machine learning models have been increasingly applied to this problem, standalone algorithms often struggle to simultaneously capture complex temporal dependencies and maintain robust generalization. This study provides a comprehensive comparative assessment of five state-of-the-art machine learning (ML) models for forecasting the daily discharge of the Jósva Spring, located in the World Heritage Aggtelek karst area. The main goal of the study is to determine which modern machine learning approach can most accurately forecast the daily discharge of the Jósva Spring using meteorological data and the discharge of a hydraulically connected upstream spring. This is motivated by the need for a reliable operational prediction tool for complex karst aquifers, the improved water-resource management in a climate-sensitive region, and a lack of comparative studies evaluating multiple ML paradigms on the same karst system. The study also aimed at comparing the predictive performance of five state-of-the-art ML models to identify the most accurate and robust model and to understand the predictability of the karst system by analyzing feature importance, lag effects, and temporal dependencies. Three tree-based ensemble models (Random Forest, XGBoost, and Extra Trees) and two deep learning architectures (a Bidirectional Long Short-Term Memory network, BiLSTM, and a novel Hybrid XGBoost–BiLSTM model) were trained using a five-year (2015–2019) daily dataset comprising rainfall, temperature, and upstream discharge. The modeling framework was designed for synchronous simulation (lead time = 0 days), estimating concurrent downstream discharge using upstream and meteorological measurements from the same time step. A rigorous feature-engineering workflow was implemented based on statistical characterization, correlation analysis, and time-series diagnostics. Models were trained on 80% of the dataset and evaluated on an independent 20% test set. The results demonstrate that the proposed Hybrid XGBoost-BiLSTM model achieved the highest predictive accuracy on the unseen test data (R² = 0.74, NSE = 0.74, RMSE = 716.35 L/min). While the standalone tree-based models, particularly XGBoost (R² = 0.66), also exhibited strong and competitive performance, the hybrid architecture provided a consistent and measurable improvement across all evaluation metrics. The hybrid model’s success is attributed to its synergistic design, which leverages the powerful feature extraction and refinement capabilities of XGBoost to provide a more informative input space for the BiLSTM, thereby enhancing its ability to capture complex temporal dependencies while mitigating overfitting. Feature importance analysis confirmed that upstream discharge at a 3-day lag was the most critical predictor, highlighting the system’s hydraulic connectivity. This research provides clear, evidence-based guidance showing that hybrid machine learning architectures, which integrate the strengths of different modeling paradigms, represent the most effective approach for developing robust and reliable operational prediction tools for complex karst aquifers. Full article

Soil salinity poses a major challenge to agricultural productivity, especially threatening food security in arid and semi-arid areas. Traditional soil reclamation methods, such as leaching, chemical amendments, and drainage engineering, usually need large amounts of water, involve high costs, and can lead to environmental problems. This review compiles existing knowledge on innovative strategies for managing saline soils, focusing on buried interlayer systems that use materials like straw, sand, gravel–sand mixtures, and biochar. These interlayers improve soil hydraulic properties by preventing capillary rise, encouraging salt leaching, and reducing surface salt buildup. Biochar stands out as a particularly useful material because of its stability, large surface area, porosity, and high cation exchange capacity. These features help improve soil structure, increase water retention, and effectively retain sodium. Evidence from lab and field tests shows that buried biochar layers can stop salt from moving upward, aid in desalinating the root zone, and boost crop yields. While straw and sand interlayers show potential in reducing salinity, biochar is noted for its multifunctionality and long-term effectiveness in addressing salinity problems. The success of buried biochar systems depends on several factors, including the properties of the biochar, how much is used, how deep it is buried, and the specific soil and climate conditions. This review highlights how these systems work, compares their performance, and points out research gaps, advocating for their potential as a sustainable, resource-efficient way to manage salinity and improve soil health over the long term. A substantial proportion of the existing evidence is derived from controlled laboratory studies, and the buried biochar layer approach remains an emerging technique that requires further validation under field conditions. Still, significant knowledge gaps persist regarding long-term performance and water-salt dynamics, while site-specific soil variability and scalability challenges may limit the effective implementation of biochar interlayer systems under field conditions. Full article

The large-scale accumulation of steel slag from steelmaking and the over-exploitation of natural aggregates pose significant environmental and resource challenges. Focusing on the arid-cold region of Xinjiang, China, this study proposes the use of steel slag as a substitute for natural aggregates in pavement engineering. Through experimental performance evaluation and regionalized life cycle assessment (LCA), the technical feasibility and carbon reduction potential of this application were comprehensively evaluated. Results indicate that steel slag asphalt mixtures meet or exceed specification requirements in terms of high-temperature stability, water stability, and low-temperature crack resistance. However, volume stability decreases slightly with higher steel slag content and finer particle size, necessitating pretreatment for long-term durability. A local life cycle assessment model considering regional transportation factors was applied to the G30 Luhuo Expressway project. During the materialization stage, steel slag was used to replace 30% of the natural aggregates, reducing approximately 6718 kg of carbon dioxide equivalent emissions (31.4%). This, to some extent, reduced the extraction of natural resources, saved land resources, and alleviated the problems of resource shortage and price fluctuations. Sensitivity analysis reveals a positive correlation between carbon reduction and steel slag content, while transport distance strongly influences overall benefits, with a critical threshold of about 78 km defining the effective utilization range. Furthermore, a multi-objective optimization model balancing service life, cost, and carbon reduction was developed to identify an optimal steel slag content scheme, maximizing comprehensive benefits under constrained conditions. This work confirms the technical viability of steel slag pavement in extreme climates and provides a systematic framework integrating environmental benefits and logistical constraints, supporting regional industrial synergy and promoting circular economy practices in low-carbon infrastructure. Full article

Urbanization fundamentally fractures the natural water cycle, leading to a cascade of interconnected problems including increased flood risk, degraded water quality, stressed groundwater resources, and inefficient distribution networks. Traditional, fragmented management approaches that address these issues in isolation have proven inadequate. This research argues for a paradigm shift towards an Integrated Urban Water Management (IUWM) framework anchored in the concept of the “river-aquifer-pipe network continuum”, treating these components as a single, dynamic hydrological and infrastructural entity. Drawing upon a series of detailed case studies from Eastern Romania, this paper synthesizes the systemic impacts of development across the entire urban water system. Evidence from the Prut, Olt, and Bahlui river basins demonstrate how channelization exacerbates flood peaks and leads to severe biochemical degradation. Hydrogeological modeling of the Gherăești-Bacău wellfield reveals the vulnerabilities of over-extraction, while analysis of the Iași water network highlights the challenge of water losses in the aging infrastructure. In response, a modern, multi-tool approach is consolidated into a practical, three-stage framework for action: Diagnose, Prescribe, and Optimize. This framework advocates for (1) a comprehensive diagnosis using a suite of predictive numerical models (a “digital twin”); (2) the prescription of foundational, nature-based solutions, such as floodplain restoration, to heal core ecological functions; and (3) the continuous optimization of engineered infrastructure using smart, real-time control technologies. The synthesis concludes that an integrated, data-driven, and collaborative approach is the only sustainable path forward. Future research should focus on formally coupling these diagnostic models to create true Digital Twins of urban water systems—an essential step towards building resilient, water-secure cities for the 21st century. Full article

To address the problem of temperature cracking caused by the concentrated release of hydration heat in mass concrete, this study developed a low-heat composite cementitious material (CWCM) by partially replacing conventional mineral admixtures with construction spoil. A multi-factor synergistic optimization design based on response surface methodology (RSM) was conducted. The water–binder ratio, spoil replacement ratio, curing temperature, and ball-milling time were selected as influencing factors, while the 28-day flexural strength, 28-day compressive strength, and 72 h cumulative hydration heat were used as response variables. A four-factor, three-level Box–Behnken model was established. The results show that the regression model exhibits good fitting performance, and the prediction errors between the predicted and experimental values of all response variables are within a reasonable range. Under the optimized mixture proportion (15% spoil replacement), the system achieves a 28-day compressive strength of 61.03 MPa, while the 72 h cumulative hydration heat is reduced by approximately 15%, meeting the requirements for low-heat cement. Microstructural analyses using XRD, SEM, and TG/DTG indicate that a decrease in the Ca/Si ratio and an increase in the Al/Si ratio promote the formation of a denser C-(A)-S-H gel structure, enhancing the pozzolanic reaction. This mechanism plays a key role in achieving the synergistic regulation of strength enhancement and hydration heat reduction. Compared with conventional fly ash or slag systems, this study innovatively utilizes construction spoil as a partial substitute for traditional mineral admixtures. While maintaining satisfactory mechanical performance, the proposed system effectively reduces hydration heat release, providing a new pathway for temperature control design in mass concrete engineering and high-value resource utilization of construction waste. Full article

The foundry industry produces over 66 million tons of mixed casting waste sand, containing toxic and harmful substances such as phenols and aldehydes, every year, which has caused serious soil pollution, water source pollution, and large amounts of CO₂ emissions. Green resource recycling and utilization are urgently needed. The hot method circulating fluidized bed furnace is currently the mainstream technology for the regeneration of casting waste sand. However, traditional equipment has a series of key technical bottlenecks, such as VOC (volatile organic compound) emissions, low yield of fine sand, poor stability of phase change sand, and uneven fluidization, which directly limit the effectiveness, large-scale promotion, and application of waste sand regeneration. This study, based on a self-designed experimental prototype, constructed models with different hood densities and inlet air velocity parameters. A CFD-DEM coupled model, combined with two turbulence models, was used for numerical simulations and experimental validation, and the optimal combination of fluidization parameters was determined. The study confirmed that the k–ω SST model is more suitable for precise simulation of such gas–solid two-phase flows. The research revealed quantitative relationships between key parameters and sand particle fluidization states, addressing the core problem of uneven fluidization in conventional bubbling furnaces and providing important guidance for the optimized design of new thermal cycle bubbling furnaces. It has significant engineering value for promoting the efficient resource utilization of foundry waste sand and the green and sustainable development of the industry. Full article