Search Results (10,778)

The transition toward low-carbon energy systems has intensified interest in sustainable hydrogen production technologies. One of the most promising methods for producing green hydrogen is water electrolysis powered by renewable energy. This work reviews recent advances in electrode materials used in four major electrolysis technologies: alkaline (ALK), proton exchange membrane (PEM), solid oxide electrolysis cells (SOEC), and anion exchange membrane (AEM). A bibliometric analysis of scientific publications from 2021 to 2025 highlights the rapid growth of research and the increasing importance of electrode materials in improving electrolysis performance. Operating environments, material requirements, and catalytic properties are compared across these systems. Recent developments in electrocatalysts—including transition-metal alloys, heterostructured catalysts, defect-engineered materials, and nanostructured systems—are evaluated in terms of catalytic activity, durability, and scalability. Particular attention is given to reducing noble metal usage while maintaining high electrochemical performance. Results indicate that transition-metal-based catalysts and engineered interfaces can achieve activity comparable to noble-metal systems while offering better cost efficiency. However, challenges related to long-term durability, large-scale synthesis, and standardized testing persist. Continued interdisciplinary research in materials design and electrochemical engineering is essential to enable efficient, durable, and cost-effective green hydrogen production. Full article

The complex fracture networks, multiphase flow behavior, and nonlinear flow mechanisms induced by hydraulic fracturing in horizontal wells of shale oil reservoirs pose significant challenges to production evaluation. In this study, a semi-empirical productivity evaluation method for multiphase shale oil systems is developed by integrating production dynamics with flow mechanisms. Three-phase productivity equations for oil, gas, and water are established, explicitly incorporating the underlying flow mechanisms. A nonlinear flow index is introduced to characterize both the stress sensitivity of fractures and the threshold pressure gradient in the matrix. Key unknown parameters, including oil saturation, water cut, stimulated reservoir volume, and nonlinear coefficients, are determined through history matching of production data. The impacts of geological properties, fracturing parameters, operating conditions, and nonlinear flow parameters on oil–gas productivity are systematically investigated using the proposed multiphase semi-empirical model. The model is validated against production data from fractured horizontal wells in a field case, demonstrating its accuracy and applicability. Furthermore, the model enables reliable production forecasting based on the derived productivity relationships. The proposed approach provides a practical and efficient tool for rapid post-fracturing productivity evaluation in shale oil reservoirs. Full article

Agriculture currently faces multiple challenges associated with climate change, the reduction in arable land, and the need to produce food more efficiently in terms of water and nutrient use. This study evaluated an Internet of Things (IoT)-based hydroponic monitoring system with threshold-controlled recirculation for lettuce (Lactuca sativa) under open-field thermal stress conditions, comparing it with a conventional closed recirculating PVC pipe-based hydroponic system operated using fixed pump timing. The architecture integrated an ESP32 microcontroller, sensors for nutrient solution temperature, pH, total dissolved solids (TDS), turbidity voltage, dissolved oxygen (DO), and electrical conductivity (EC), Wi-Fi/HTTPS connectivity, a PHP–MySQL server, and a web interface for near-real-time monitoring. During the growing period, 241,797 readings were recorded between 21 January and 13 February 2026. The threshold-based logic activated the pump mainly according to nutrient solution temperature and DO, while pH, EC, TDS, and relative turbidity voltage were monitored as operational indicators. The sensor-instrumented system operated with pump activation during approximately 28.5% of the monitoring period, while temperature exhibited high variability and peaks of 40.19 °C. Visual crop monitoring showed greater canopy uniformity in the sensor-instrumented system, supporting the technical feasibility of low-cost IoT-based monitoring and threshold-controlled recirculation for open-field hydroponic production of lettuce. Full article

Conventional water mist systems require high-pressure pumps and complex piping networks to generate fine water droplets, which often results in high installation costs and maintenance difficulties. Recently, a water mist system with ultrasonic waves has been proposed as a viable alternative system to address those limitations. However, there is a lack of experimental data for evaluating the fire suppression performance of water mist systems using ultrasonic waves. Therefore, in this study, a simplified water mist system with an ultrasonic wave was suggested for evaluating the fire suppression performance. Subsequently, a reduced-scale room corner test (RCT) was conducted to investigate suppression performance under various fire sizes and suppression conditions. The experimental cases were classified according to pool size, door condition, and operation of the ultrasonic water mist system. Ultimately, fire suppression performance was quantitatively evaluated using performance indices derived from fire duration and indoor temperature variation. The results demonstrate that the ultrasonic water mist system effectively suppresses fires through combined cooling and oxygen-blocking effects, while significantly reducing indoor temperature compared to oxygen-blocking suppression. The proposed performance indices enable quantitative comparison of suppression effectiveness and confirm the feasibility of ultrasonic water mist systems as an alternative to conventional high-pressure water mist systems. Full article

Urban drought pressure is increasing the operational risk and cost of maintaining municipal green infrastructure. Irrigation is still widely managed through fixed routines and fragmented information. To address this challenge, the study develops an integrated operational analysis by combining water consumption records, maintenance data and a GIS inventory for twenty municipal green spaces. System characterisation and performance screening were carried out using hourly meter readings to distinguish typical scheduled irrigation peaks from non-standard consumption patterns. To move from monitoring to control, irrigation needs were estimated using evapotranspiration (ET₀) and a garden-coefficient logic adapted to urban planting conditions and compared with measured consumption. The comparison indicates a potential reduction of 29–61% through improved scheduling and system adjustment. Based on the diagnosis, technical intervention scenarios were defined and assessed using techno-economic metrics, including ground-cover redesign and Mediterranean-adapted planting strategies. To support implementation, options were organised into intervention priorities using a multicriteria tool that balances water savings, costs and feasibility under municipal operations. Coimbra, Portugal is used as a case study, and a pilot application in a city garden, supported by 797 user surveys, clarifies practical constraints for scaling beyond isolated pilots. Turf-free scenarios indicate a 53.4% reduction in water use and a 60.5% reduction in operational costs, with a payback period below three years. The results highlight the potential of data-driven irrigation management to support more resilient, cost-effective and water-efficient municipal green infrastructure across diverse urban contexts. Full article

Reference evapotranspiration (ET₀) is most commonly estimated using the FAO-56 Penman–Monteith (PM) equation. However, its application is often limited by the lack of required meteorological parameters. Due to their flexibility, ability to operate with limited input, and high accuracy in estimating ET₀, machine learning models have become increasingly relevant in scientific research, offering a practical alternative under limited data conditions. In this study, artificial neural networks (ANNs) were applied to estimate daily ET₀ using meteorological data from the Novi Sad station in Vojvodina (Serbia). The dataset consisted of eight meteorological variables relevant to evapotranspiration processes. Analysis showed that some variables had a stronger influence on ET₀ prediction than others. To evaluate their combined effect, a series of ANN models with different input combinations were developed and tested. The random forests, gradient boosting and k-nearest neighbors models were used as a benchmark, and model performance was evaluated using R², NSE, RMSE, and MAE. The highest accuracy was achieved when all variables were included, providing the model with maximum information. The best performance was obtained using a two-hidden-layer architecture with 32 and 16 neurons, resulting in R² = 0.97, NSE = 97.07%, RMSE = 0.23 mm/day, and MAE = 0.21 mm/day. The results showed that a limited number of input variables can be used to estimate ET₀ with high accuracy, achieving an R² value of 0.95 using only three input variables. Therefore, the findings of this study may contribute to more accurate and cost-effective irrigation scheduling and water balance estimation, providing practical benefits for agricultural water management and farmers in Serbia. Full article

This article focuses on the research and development of innovative polymer-concrete composites for the manufacture of precision machine tool frames and critical mechanical engineering components. The relevance of this work stems from the need to replace traditional cast iron and cement concrete with materials with superior damping properties and thermal stability. The polymer matrix used in this study was ED-20 epoxy-diane resin, modified with (FAM) furan resin and cured with polyethylenepolyamine (PEPA), which together ensured minimal linear shrinkage (less than 0.5–1%) during polymerization. The focus was on the effect of multimodal filler distribution, including quartz sand, gabbro, and basalt, as well as reinforcing additives such as silicon carbide and fiberglass, on the final performance characteristics of the material. Experimental studies determined the key physical and mechanical parameters of the obtained samples. The results showed that the optimized composition (Smp_001) exhibited compressive strength up to 92.3 MPa, significantly exceeding that of standard high-strength concrete. It was established that the use of silicon carbide and glass fiber promotes the formation of a dense heterogeneous microstructure characterized by extremely low porosity (1.2–2.5%) and record-low water absorption (less than 0.05%). These characteristics guarantee high dimensional stability of the frames during prolonged contact with process fluids and cutting fluids. The scanning electron microscopy (SEM) and (EDS) energy dispersive X-ray spectroscopy methods confirmed the dense packing and high degree of interaction of the polymer matrix with the crystalline phases of the filler. This condition of the interfacial boundaries guarantees stable stress transfer throughout the entire volume of the material, which minimizes the risk of local damage during operation. The study confirmed that the developed material has vibration damping properties 6–10 times more effective than gray cast iron, a critical factor in improving machining accuracy on modern metal-cutting machines. The scientific novelty of the study lies in its substantiation of the synergistic effect of the combined use of basalt fillers and silicon carbide to achieve the precision properties of a structural material. Its practical significance is confirmed by the possibility of producing large-scale parts by casting without the need for complex finishing, opening up new prospects for modernizing the machine tool industry. Full article

To deliver freshwater for drip irrigation, our study presents an optimal techno-economic based on a Water Pumping Photovoltaic System (WPPVS) that integrates a Hydrogen Energy Storage System (HySS) to ensure reliable freshwater for agricultural irrigation in remote arid regions. A critical operational challenge in WPPVS is mechanical vibration at low flow rates, which degrades the pump efficiency and lifespan. Our methodology directly addresses this issue by incorporating a vibration-avoidance strategy that ensures that the pump operates only within its stable and, efficient range. To reduce the loss of water supply probability and overall annual costs of the drip irrigation system, a multi-objective optimization framework using Multi-Objective Particle Swarm Optimization (MOPSO) and Gaussian Mixture Model (GMM) clustering to simultaneously minimize the Loss of Water Supply Probability (LWSP), and the system’s total life-cycle cost. The model’s practical applicability is demonstrated through a detailed techno-economic feasibility analysis for a tomato crop drip irrigation project in Sakaka, Saudi Arabia. Sensitivity analysis is performed on dynamic head, crop prices, and interest and inflation rates, confirming the robustness of the system against variable economic indicators. In comparison to 1071 h without HySS, the results revealed that the seasonal irradiation harvest hours are 1863, which represents 21% of the seasonal hours employing the developed hybrid energy storage coordination. This integrated approach provides a holistic and economically viable solution for designing reliable solar irrigation systems with long-term mechanical integrity. Full article

This study investigates the transient thermo-hydrodynamic behaviour of millimetric water droplets impacting heated solid substrates under subcooled conditions. The effects of wall temperature, wall material, and impact velocity on droplet spreading, heat transfer, and cooling performance are examined using high-resolution CFD simulations, validated against in-house experimental measurements of transient temperature evolution. The results show that droplet spreading is highly affected by impact inertia, with higher velocities producing faster radial expansion and larger maximum spreading. In contrast, the thermal response is strongly influenced by substrate properties. Steel exhibits steeper temperature gradients and stronger localized cooling within the substrate, while aluminium, owing to its higher thermal diffusivity and effusivity, sustains higher total heat-transfer rates at the wall–liquid interface. Increasing wall temperature significantly enhances the absolute heat-transfer rate due to the larger thermal driving potential, although normalized temperature profiles indicate reduced relative cooling. The analysis highlights the distinct roles of hydrodynamic and thermal mechanisms: impact velocity governs the lateral distribution of cooling, whereas substrate properties control the depth-wise thermal response. These findings provide a comprehensive understanding of droplet-induced cooling from a substrate perspective and offer insights for optimizing material selection and operating conditions in spray cooling, surface quenching, and high-heat-flux thermal management applications. Full article

Critical infrastructure restoration (CIR) is a disaster-management and sustainability challenge because prolonged disruption of energy, water, transport, communications, healthcare, and public-administration services can amplify social, economic, and environmental losses. This PRISMA 2020-reported systematic review synthesizes post-2016 scientific literature and official policy, legal, standards, and technical documents on CIR and AI decision support. The review identified 55 records, removed 1 duplicate, excluded 1 ineligible record, and retained 53 core sources for qualitative synthesis, including 31 scholarly publications and 22 official documents. Manual screening was used; no automated screening or AI-assisted exclusion tools were applied. The results are organized around four research questions covering regulatory frameworks, recovery practices, supporting systems, and AI model families. The synthesis shows that CIR is shaped by layered governance through NIS2, the CER Directive, the AI Act, and national measures; by operational recovery practices such as continuity planning, cyber crisis coordination, interdependency mapping, and model-supported restoration; by digital platforms including SCADA/ICS, IoT sensing, GIS/common operating pictures, decision-support systems, simulation environments, and digital twins; and by AI methods ranging from classical machine learning and computer vision to reinforcement learning and generative assistants. However, evidence maturity remains uneven, with many AI applications still simulation-based, sector-specific, or weakly validated in real restoration settings. The review contributes an integrated CIR-oriented framework showing that AI creates practical value when embedded in interoperable, human-supervised, regulation-aware, and empirically validated restoration architectures that support sustainable service continuity rather than isolated automation. Full article

This study proposes a time-domain feature-based anomaly detection method for vibration data of bridge piers collected by underwater seismometers operating under alternating submerged and exposed conditions. The method aims to accurately identify anomalies under both normal and extreme events. Taking the Fuzhou Pushang Bridge as a case study, the acceleration root mean square (a_RMS) is adopted as the representative vibration feature to investigate the effects of vehicular loads, water level variations, and tidal fluctuations. The results show that pier vibrations are primarily dominated by vehicular loads, exhibiting pronounced daily periodicity, intraday non-stationarity, and non-normality, while the influences of water level and tidal variations are relatively minor. Based on these characteristics, an anomaly detection framework integrating STL decomposition (Seasonal-trend decomposition using Loess), Yeo–Johnson transformation, and control charts is developed. Historical data are used to establish control limits and conduct self-validation, yielding an anomaly rate of 0.14%, which is consistent with the theoretical probability of ±3σ control limits. When applied to the subsequent monitoring period, the anomaly rate under normal conditions is 0.18%, demonstrating the stability of the proposed method. Further analysis reveals that anomalies are primarily caused by direct hydrodynamic impacts on the instrument. Under flood conditions, continuous anomalies occur during nighttime, with the anomaly rate increasing to 4.44%. Under seismic conditions, the control chart statistic reaches 5.03, significantly exceeding the control limits. Comparative analysis shows that the percentile-based method yields a higher anomaly rate (0.65%), indicating a higher false alarm rate. Overall, the proposed method demonstrates strong generalization capability and reliability, providing effective support for long-term structural health monitoring of bridge substructures in complex environments. Full article

Environmental biofilms are persistent structural components of livestock production systems and represent under-recognized drivers of pathogen persistence and antimicrobial resistance (AMR). This review examines the engineering, ecological, and operational factors that promote biofilm formation in dairy, poultry, and swine environments, with emphasis on drinking water distribution systems, feeding infrastructure, housing surfaces, and waste channels. Biofilms develop preferentially in low-shear zones, dead ends, and aging materials, where they enhance microbial tolerance to sanitation and facilitate horizontal gene transfer. Conventional monitoring approaches, largely based on planktonic sampling and single-time-point testing, underestimate attached biomass and fail to capture spatial heterogeneity. Although molecular and sensor-based technologies provide improved resolution, their farm-level implementation remains limited by cost, standardization challenges, and the absence of validated operational thresholds. Current EU surveillance frameworks focus primarily on antimicrobial use and resistance prevalence in animal isolates, while environmental compartments are rarely incorporated as monitored system elements. This review proposes a proportionate, risk-based approach that integrates existing farm data streams such as antimicrobial use metrics and biosecurity scoring systems with targeted environmental assessment of high-risk infrastructure. Mitigation strategies emphasize mechanical disruption, combined chemical sanitation, hydraulic optimization, material selection, and infrastructure lifecycle management. Embedding environmental biofilm control within existing engineering and stewardship frameworks supports more resilient, systems-based management of infectious and AMR risks in livestock production. Full article

With the continuous implementation of the national dual carbon target and the refined control of operating costs in civil buildings, the issue of cleaning and regenerating high-consumption air filter materials in civil buildings has become a hot research topic. This study took rGO air filter material as the research object from the perspective of commercial cost optimization and, using water as the cleaning medium, compared and analyzed the changes in filtration efficiency, airflow resistance, comprehensive performance, and full dimension economy during five cycles of regeneration using water cleaning and ultrasonic cleaning methods. The results showed that ultrasonic cleaning can better maintain the microscopic morphology and structural integrity of the rGO filter, exhibiting more stable filtration performance and slower performance attenuation during repeated regeneration. After the first cleaning, the filtration effectiveness following water cleaning was higher than that following ultrasonic cleaning, with filtration efficiencies 1.21%, 0.18%, and 1.11% higher for PM₁₀, PM_2.5, and PM_1.0, respectively. After the 2nd to 5th cleaning cycles, the filtration efficiency following ultrasonic cleaning was higher than that following water cleaning, with increases of 3.79%, 2.18%, 2.20%, and 6.49% for PM₁₀; 3.20%, 1.22%, 2.96%, and 3.25% for PM_2.5; and 1.90%, 2.02%, 2.02%, and 6.21% for PM_1.0, respectively. The counting filtration efficiency of the ultrasonic cleaning method is relatively high for particle sizes roughly between 0.35 and 2.5 μm, while the difference between large particles is small. The filtration resistance value of the water cleaning method is higher than that of the ultrasonic cleaning method. The QF of the ultrasonic cleaning is always higher than that of the water cleaning method. After five washes, the QF values of PM₁₀, PM_2.5, and PM_1.0 under the ultrasonic cleaning method were 2.26, 2.04, and 2.37 times higher, respectively, than those under the water washing cleaning method. When the replacement frequency is the same, the cost of using ultrasonic cleaning is lower than that of water cleaning. It can effectively reduce the operating costs and asset replacement costs of the fresh air system and is more suitable for the landing and long-term cost control needs of large-scale civil construction projects. Therefore, it is recommended that ultrasonic cleaning be used to recycle rGO air filter materials. These findings provide reference value for the large-scale use of rGO air filter materials and the creation of low-carbon indoor environments. Full article

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation. Full article

Continuous streamflow monitoring is fundamental for water management in high-mountain Andean basins. Traditionally, this process relies on empirical regressions, although artificial intelligence (AI) has recently emerged as a robust alternative. However, extreme geomorphological dynamics compromise classical hydraulic methods, while AI models frequently lack physical validation. In this context, this study compares the performance of Artificial Neural Networks against traditional methods to reduce uncertainty in stage–discharge rating curves. The methodology, applied to a nested basin scheme in Loja, Ecuador, contrasted traditional exponential fits with a Multilayer Perceptron optimized using the Levenberg–Marquardt algorithm. The analysis included the evaluation of uncertainty bands and a sub-hourly spatial validation based on the principle of mass conservation. Results evidence that AI refines statistical accuracy (NSE > 0.95) and effectively adapts to bed non-linearity; nevertheless, cross-validation revealed a high susceptibility to algorithmic overfitting. It is concluded that while AI offers superior analytical flexibility for interpolating non-linear dynamics, traditional methods remain more robust for extreme flood extrapolation. Furthermore, while AI reduces computational complexity, it entails a higher “data cost” requiring denser field gauging campaigns. Operational viability requires rigorous dynamic uncertainty controls and spatial water balance validation. Full article