Search Results (568)

Endotoxins, lipopolysaccharides released from the outer membrane of Gram-negative bacteria, pose a significant risk in healthcare environments, particularly in Central Sterile Supply Departments (CSSDs), where the delivery of sterile pyrogen-free medical devices is critical for patient safety. Traditional methods for controlling endotoxins in water systems, such as ultraviolet (UV) disinfection, have proven ineffective at reducing endotoxin concentrations to comply with regulatory standards (<0.25 EU/mL). This limitation presents a significant challenge, especially in the context of reverse osmosis (RO) permeate used in CSSDs, where water typically has very low conductivity. Despite the established importance of endotoxin removal, a gap in the literature exists regarding effective chemical-free methods that can meet the stringent endotoxin limits in such low-conductivity environments. This study addresses this gap by evaluating the effectiveness of the eBooster™ electrochemical technology—featuring proprietary electrode materials and a reactor design optimized for potable water—for endotoxin removal from water, specifically under the low-conductivity conditions typical of RO permeate. Laboratory experiments using the B250 reactor achieved >90% endotoxin reduction (from 1.2 EU/mL to <0.1 EU/mL) at flow rates ≤5 L/min and current densities of 0.45–2.7 mA/cm². Additional real-world testing at three hospitals showed that the eBooster™ unit, when installed in the RO tank recirculation loop, consistently reduced endotoxin levels from 0.76 EU/mL (with UV) to <0.05 EU/mL over 24 months of operation, while heterotrophic plate counts dropped from 190 to <1 CFU/100 mL. Statistical analysis confirmed the reproducibility and flow-rate dependence of the removal efficiency. Limitations observed included reduced efficacy at higher flow rates, the need for sufficient residence time, and a temporary performance decline after two years due to a power fault, which was promptly corrected. Compared to earlier approaches, eBooster™ demonstrated superior performance in low-conductivity environments without added chemicals or significant maintenance. These findings highlight the strength and novelty of eBooster™ as a reliable, chemical-free, and maintenance-friendly alternative to traditional UV disinfection systems, offering a promising solution for critical water treatment applications in healthcare environments. Full article

LPG fuel supply systems are increasingly important for improving energy efficiency and reducing carbon emissions in the shipping industry. The primary objective of this research is to investigate the heat transfer phenomena to enhance the thermal performance of double-pipe heat exchangers (DPHEs) in LPG fuel supply systems. This study investigates the heat transfer performance of a glycol–steam double-pipe heat exchanger (DPHE) within an LPG fuel supply system under varying operating conditions. A computational model and methodology were developed and validated by comparing the numerical results with experimental data obtained from commissioning tests. Additionally, the effects of turbulence models and parametric variations were evaluated by analyzing the glycol–water mixing ratio and flow direction—both of which are critical operational parameters for DPHE systems. Numerical validation against the commissioning data showed a deviation of ±2% under parallel-flow conditions, confirming the reliability of the proposed model. With respect to the glycol–water mixing ratio and flow configuration, thermal conductance (UA) decreased by approximately 11% in parallel flow and 13% in counter flow for every 20% increase in glycol concentration. Furthermore, parallel flow exhibited approximately 0.6% higher outlet temperatures than counter flow, indicating superior heat transfer efficiency under parallel-flow conditions. Finally, the heat transfer behavior of the DPHE was further examined by considering the effects of geometric characteristics, pipe material, and fluid properties. This study offers significant contributions to the engineering design of double-pipe heat exchanger systems for LPG fuel supply applications. Full article

Background: Access to safe and reliable water and sanitation remains a critical public health and development challenge, with rural and low-income communities being disproportionately affected by inadequate services and heightened exposure to waterborne diseases. Despite global efforts and infrastructure-based progress indicators, significant disparities persist, and these often overlook users’ perceptions of water quality, reliability, and safety. This study explores the determinants of household satisfaction with drinking water in rural areas, comparing subjective user feedback with official access data to reveal gaps in current monitoring approaches and support more equitable, user-centered water governance. Methods: This study was conducted in Kazakhstan’s Atyrau Region, where 1361 residents from 86 rural villages participated in a structured survey assessing household access to drinking water and perceptions of its quality. Data were analyzed using descriptive statistics and multinomial logistic regression to identify key predictors of user satisfaction, with results compared against official records to evaluate discrepancies between reported experiences and administrative data. Results: The field survey results revealed substantial discrepancies between official statistics and residents’ reports, with only 58.1% of respondents having in-house tap water access despite claims of universal coverage. Multinomial logistic regression analysis identified key predictors of user satisfaction, showing that uninterrupted supply and the absence of complaints about turbidity, odor, or taste significantly increased the likelihood of higher satisfaction levels with drinking water quality. Conclusions: This study underscores the critical need to align official water access statistics with household-level experiences, revealing that user satisfaction—strongly influenced by supply reliability and sensory water quality—is essential for achieving equitable and effective rural water governance. Full article

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

Elasmobranch species are considered a global conservation priority due to their susceptibility to fishing pressure. In the Madeira Archipelago, Northeastern Atlantic, most elasmobranch species are caught as bycatch in artisanal drifting longline fishery targeting scabbardfishes. All commercial elasmobranch landings carried out in this archipelago over three decades (1990–2020) were analysed, aiming to provide a reliable overview of Madeira’s elasmobranch fisheries and their evolution. A total of 2316 tonnes of elasmobranchs were landed during the study period, corresponding to approximately EUR 2.1 million in first-sale value. The most representative period occurred from 2003 to 2013, corresponding to 75.21% of the total elasmobranch landings. A general pattern of supply and demand was evident, with mean price values typically showing an inverse trend to landed tonnage. At the species level, Centrophorus squamosus appears as the dominant species, representing about 89% of the total elasmobranch species landed, followed by Prionace glauca, with approximately 3%. The high dominance of C. squamosus in the scabbardfish fishery raises significant ecological and management concerns, as this deep-water shark species is known for its vulnerability to overexploitation. Management measures currently in place need to be updated and ought to be based on studies on the type and size of hooks for each fishery, to ultimately infer about species-specific survival rates, as well as the fishing gears’ soak time. Moreover, studies on the enhancement of food supply through fisheries discards are still missing, even though it is highly likely that this input may alter the dynamics of marine food webs. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article

Safe and adequate water supply, sanitation, and hygiene (WASH) in schools are prerequisites within the right to basic education. WASH facilities across schools in developing nations, particularly in Africa, are unsatisfactory and expose children to risks of disease and infection. This study aims to gather insights into the WASH status of secondary schools in Ibadan, Nigeria, to develop a sustainable water management framework for schools. A concurrent mixed-method design (questionnaires and interviews) was adopted to benchmark water management in schools and inform the design of a framework. Results reveal a wealth of issues and concerns that include infrastructure challenges accessing reliable and safe water supplies, rundown and unhygienic toilet/urinal facilities, and dilapidated sinks/taps, plus resource challenges, such as an absence of tissue paper and soap. These issues are exposing schoolchildren to unnecessary health risks, further supported by reported illnesses and reduced school attendance. Based on these findings, and guided by the UN SDG#6 targets, a water improvement framework has been created and validated by school officials. The framework identifies both short-term and long-term guidance/actions to improve water management in schools across Sub-Saharan Africa. These form crucial steps toward better WASH, building healthier communities and enhancing educational environments and outcomes for schoolchildren. Full article

Under the influence of global climate change, water conservancy projects located in the high-latitude cold regions of the world are facing severe challenges. This study addresses the contradiction between water supply stability and ecological flow during the dry season in cold regions. Taking Linhai Reservoir as the core, it integrates the HEC-RAS hydrodynamic model with multi-source data such as basin topography, hydro-meteorological data, and water conservancy project parameters to construct a multi-scenario water supply scheduling model during the dry season. The aim is to provide scientific recommendations for different reservoir operation strategies in response to varying frequencies of upstream inflow, based on simulations conducted after the reservoir’s completion. Taking into account winter runoff reduction characteristics and engineering parameters, we simulated the relationships between water level and flow, ecological flow requirements, and urban water shortages. The results indicate that in both flood and normal years, dynamic coordination of storage and discharge can achieve a daily water supply of 120,000 cubic meters, with 100% compliance for the ecological flow rate. For mild and moderate drought years, additional water diversion becomes necessary to achieve 93.5% and 89% supply reliability, respectively. During severe and extreme droughts, significantly reduced reservoir inflows lower ecological compliance rates, necessitating emergency measures, such as utilizing dead storage capacity and exploring alternative water sources. The study proposes operational strategies tailored to different drought intensities: initiating storage adjustments in September for mild droughts and implementing peak-shifting measures by mid-October for extreme droughts. These approaches enhance storage efficiency and mitigate ice blockage risks. This research supports the water supply security and river ecological health of urban and rural areas in Mudanjiang City and Hailin City and provides a certain scientific reference basis for the multi-objective coordinated operation of reservoirs in the same type of high-latitude cold regions. Full article

As an important unconventional oil and gas resource, tight oil exploration and development is of great significance to ensure energy supply under the background of continuous growth of global energy demand. Low-porosity and low-permeability reservoirs are characterized by tight rock properties, poor physical properties, and complex pore structure, and as a result the fine calculation of logging reservoir parameters faces great challenges. In addition, the crude oil in this area has high viscosity, the formation water salinity is low, and the oil reservoir resistivity shows significant spatial variability in the horizontal direction, which further increases the difficulty of oil and water reservoir identification and affects the accuracy of oil saturation calculation. Targeting the above problems, the Nutcracker Optimization Algorithm (NOA) was used to optimize the hyperparameters of the random forest classification model, and then the optimal hyperparameters were input into the random forest model, and the conventional logging curve and oil test data were combined to identify and classify the reservoir fluids, with the final accuracy reaching 94.92%. Compared with the traditional Hingle map intersection method, the accuracy of this method is improved by 14.92%, which verifies the reliability of the model for fluid identification of low-porosity and low-permeability reservoirs in the research block and provides reference significance for the next oil test and production test layer in this block. Full article

Groundwater serves as a vital resource for sustainable water supply, particularly in semi-arid regions where surface water availability is limited. This study explores groundwater potential zones in the East Desert, Qift–Qena, Egypt, using a multidisciplinary approach that integrates remote sensing (RS), geographic information systems (GIS), geostatistics, and field validation with water wells to develop a comprehensive groundwater potential mapping framework. Sentinel-2 imagery, ALOS PALSAR DEM, and SMAP datasets were utilized to derive critical thematic layers, including land use/land cover, vegetation indices, soil moisture, drainage density, slope, and elevation. The results of the groundwater potentiality map of the study area from RS reveal four distinct zones: low, moderate, high, and very high. The analysis indicates a notable spatial variability in groundwater potential, with “high” (34.1%) and “low” (33.8%) potential zones dominating the landscape, while “very high” potential areas (4.8%) are relatively scarce. The limited extent of “very high” potential zones, predominantly concentrated along the Nile River valley, underscores the river’s critical role as the primary source of groundwater recharge. Moderate potential zones include places where infiltration is possible but limited, such as gently sloping terrain or regions with slightly broken rock structures, and they account for 27.3%. These layers were combined with geostatistical analysis of data from 310 groundwater wells, which provided information on static water level (SWL) and total dissolved solids (TDS). GIS was employed to assign weights to the thematic layers based on their influence on groundwater recharge and facilitated the spatial integration and visualization of the results. Geostatistical interpolation methods ensured the reliable mapping of subsurface parameters. The assessment utilizing pre-existing well data revealed a significant concordance between the delineated potential zones and the actual availability of groundwater resources. The findings of this study could significantly improve groundwater management in semi-arid/arid zones, offering a strategic response to water scarcity challenges. Full article

Urban water supply pipelines play a critical role in ensuring the continuous delivery of water, and their failure during earthquakes can result in significant societal disruptions. This study proposes a seismic damage prediction method for urban buried water supply pipelines affected by seismic wave propagation, grounded in empirical data from past earthquake events. The method integrates key influencing factors, including pipeline material, diameter, joint type, age, and soil corrosivity. To enhance its practical applicability and address the challenge of quantifying soil corrosivity, a simplified classification approach is introduced. The proposed model is validated using observed pipeline damage data from the 2008 Wenchuan earthquake, with predicted results showing relatively good agreement with actual failure patterns, thereby demonstrating the model’s reliability for seismic risk assessment. Furthermore, the model is applied to assess potential earthquake-induced damage to buried pipelines in the city center of Ganzhou, and the corresponding results are presented. The findings support earthquake risk mitigation and the protection of urban infrastructure, while also providing valuable guidance for the replacement of aging pipelines and the enhancement of urban disaster resilience. Full article

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. Full article

The failure of a porcelain insulator on a transmission line is a crucial cause of power supply interruptions, leading to poor reliability and revenue loss. The insulator’s performance is adversely affected by environmental contaminants, and wettability intensifies this adverse effect by developing a conductive path along the insulator’s surface, leading to premature flashover and insulator failure. This work aims to analyze the response of the electric field distribution and current density using the finite element method (FEM) under different wettability conditions. Discrete water droplets were placed along the surface, and the contact angle was varied to represent different levels of surface hydrophobicity. Abrupt rises and spikes were observed on the plots for the electric field and current density distribution, indicating distortion; however, the distortion kept on decreasing with the increase in the contact angle. Overall, the average stress followed a declining pattern, where the values of the electric field were reduced from 2.588 to 2.412 kV/cm, and current the density was reduced from 0.187 to 0.068 nA/cm² for an increase in the contact angle from 60° to 140°. Simulation results advocate for hydrophobic insulator surfaces. Therefore, a proper coating is necessary to enrich hydrophobicity and mitigate the adversity of wettability. Polyurethane, due to its excellent hydrophobic and insulating properties, offers a potential coating. Flashover voltage tests have been performed for the coated insulator under dry and wet conditions, where the flashover voltage improved from 79.14 kV to 82.04 kV and 48.4 kV to 53.8 kV, respectively, which supports the simulations’ outcomes. Full article