Search Results (218)

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article

Traditional river quality models struggle to accurately predict river water quality in watersheds dominated by non-point source pollution due to computational complexity and uncertain inputs. This study addresses this by developing a novel coupling model integrating a gradient boosting algorithm (Light GBM) and a long short-term memory network (LSTM). The method leverages Light GBM for spatial data characteristics and LSTM for temporal sequence dependencies. Model outputs are reciprocally recalculated as inputs and coupled via linear regression, specifically tackling the lag effects of rainfall runoff and upstream pollutant transport. Applied to predict the concentrations of chemical oxygen demand digested by potassium permanganate index (COD) in South China’s Jiuzhoujiang River basin (characterized by rainfall-driven non-point pollution from agriculture/livestock), the coupled model outperformed individual models, increasing prediction accuracy by 8–12% and stability by 15–40% than conventional models, which means it is a more accurate and broadly applicable method for water quality prediction. Analysis confirmed basin rainfall and upstream water quality as the primary drivers of 5-day water quality variation at the SHJ station, influenced by antecedent conditions within 10–15 days. This highly accurate and stable stack coupling method provides valuable scientific support for regional water management. Full article

Currently, the growing demand for sustainable hydrogen makes the oxygen evolution reaction (OER) increasingly important. To boost the performance of electrochemical cells for water electrolysis, both cathodic and anodic sides need to be optimized. Noble metal catalysts for the OER suffer from high costs and limited availability; therefore, developing efficient, low-cost alternatives is crucial. This work investigates manganese-based materials as potential noble-metal-free catalysts. Mn antimonates, Mn chlorates, and Mn bromates were synthesized using ultrasound-assisted techniques to enhance phase composition and homogeneity. Physicochemical characterizations were performed using X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM), together with energy-dispersive X-ray spectroscopy (EDX) and surface area analyses. All samples exhibited a low surface area and inter-particle porosity within mixed crystalline phases. Among the catalysts, Mn_7.5O₁₀Br₃, synthesized via ultrasound homogenization (30 min at 59 kHz) and calcined at 250 °C, showed the highest OER activity. Drop-casted on Fluorine-Doped Tin Oxide (FTO)-coated Ti mesh, it achieved an overpotential of 153 mV at 10 mA cm⁻², with Tafel slopes of 103 mV dec⁻¹ and 160 mV dec⁻¹ at 1, 2, and 4 mA cm⁻² and 6, 8, 10, and 11 mA cm⁻², respectively. It also demonstrated good short-term stability (1 h) in acidic media, with a strong signal-to-noise ratio. Its short-term stability is comparable to that of the benchmark IrO₂, with a potential drift of 15 mV h⁻¹ and a standard deviation of 3 mV for the best-performing electrode. The presence of multiple phases suggests room for further optimization. Overall, this study provides a practical route for designing noble metal-free Mn-based OER catalysts. Full article

Accurate water demand prediction is essential for optimizing the daily operations of water treatment plants and pumping stations. To achieve accurate prediction of water demand for university campuses, this study utilizes real hourly water consumption data collected over 380 observation days from a water treatment plant located on a university campus in Zhenjiang, Jiangsu Province. Based on periodicity analysis of the original data through Fast Fourier Transform (FFT) and autocorrelation coefficients, the data were preprocessed and aggregated into two-hour intervals. The processed water consumption data, along with temporal information (month, day of the week, date, and hour) and weather conditions (daily average wind speed, maximum and minimum temperature), were used as model inputs. The first 352 days of data were utilized to train the model, followed by 14 days serving as the validation set and the final two weeks as the test set. A hybrid forecasting model for campus water demand was developed by integrating a Back Propagation (BP) neural network with a Long Short-Term Memory (LSTM) neural network. The model’s performance was compared with standalone BP, LSTM, and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Simulation results demonstrate that, compared to other models, the proposed BP–LSTM hybrid model achieves a reduction in Mean Absolute Percentage Error (MAPE) ranging from 4.4% to 15.8%, and a decrease in Root Mean Squared Error (RMSE) between 2.5% and 16.8%. These findings indicate that the BP–LSTM model offers higher prediction accuracy and greater reliability compared to traditional single-model approaches. Full article

The global energy crisis demands efficient electricity production solutions, especially for isolated communities where hydraulic energy can be harnessed sustainably. This paper presents a case study analyzing the efficiency of a 13,330 kW hydrogenerator, consisting of a bulb-type hydro-aggregate using the calorimetric method—a viable alternative when testing at nominal load is not feasible due to technical limitations. The method involves measuring the thermal energy absorbed by the cooling water under three operating conditions: no-load unexcited, no-load excited, and symmetric three-phase short-circuit. Measurements followed IEC standards and were conducted with high-precision instruments for temperature, flow, voltage, and current. The results quantify mechanical, ventilation, iron, and copper losses, as well as additional losses via radiation and convection. Thermal analysis revealed significant heat accumulation in the rotor and stator windings, indicating the need for improved cooling solutions. The calorimetric method enables efficiency evaluation without interrupting generator operation, offering a valuable tool for diagnostics, predictive maintenance, and informed decisions on modernization. Furthermore, integrating an intelligent operational control system could enhance efficiency and improve the quality of the supplied energy, supporting long-term sustainability in hydroelectric power generation. Full article

This study addresses the critical challenge of optimizing water resource allocation in fragmented citrus cultivation zones, particularly in Anfusi Town, a key citrus production area in China’s middle-lower Yangtze River region. To overcome the limitations of traditional deterministic models and spatially heterogeneous water supply–demand dynamics, an innovative framework integrating interval two-stage stochastic programming (ITSP) with long short-term memory (LSTM) neural networks is proposed. The LSTM component forecasts irrigation demand and supply under climate variability, while ITSP optimizes dynamic allocation strategies by quantifying uncertainties through interval analysis and balancing economic returns with hydrological risks. Key results demonstrate an 8.67% increase in system-wide benefits compared to baseline practices in the current year scenario. For the planning year (2025), the model identifies optimal water distribution thresholds: an upper limit of 3.85 × 10⁶ m³ for high-availability zone A and lower limits of 1.62 × 10⁶ m³ for moderate-to-low-availability zones B and C. These allocations minimize water scarcity penalties while maximizing net benefits, prioritizing local over external water sources to reduce costs. The study innovates by integrating stochastic-economic analysis with spatial prioritization of high-marginal-benefit zones and uncertainty robustness via interval analysis and two-stage decision making. By bridging a research gap in citrus irrigation optimization, this approach advances sustainable water management in complex agricultural systems, offering a scalable solution for regions facing fragmented landscapes and climate-driven water scarcity. Full article

Accurate forecasting of language service demand is essential for language industry planning and resource allocation, yet it remains challenging due to small sample sizes, noisy data, and nonlinear dynamics in industry-level time series. To enhance forecasting accuracy, this study proposes a novel hybrid forecasting framework, called the Sine Cosine Algorithm-optimized wavelet analysis-based new information priority nonhomogeneous discrete grey model (SCA–WA–NIPNDGM). By integrating wavelet-based denoising with the NIPNDGM, the model effectively extracts intrinsic signals and prioritizes recent observations to capture short-term trends while addressing nonlinear parameter estimation via heuristic optimization. Empirical studies are conducted across three high-demand sectors in China from 2000 to 2024, including manufacturing; water conservancy, environmental, and public facilities management; and wholesale and retail. The findings show that the proposed model displays superior performance to 11 benchmark grey models and five optimization algorithms across six evaluation metrics, achieving test Mean Absolute Percentage Error (MAPE) values as low as 1.2%, with strong generalization, stable iterations, and fast convergence. These results underscore its effectiveness in forecasting complex time series and offer valuable insights for language service market planning under emerging AI-driven disruptions. Full article

The mutagenic potential of coffee by-products, including Coffea leaves, blossoms, cherries, and silverskin, was studied using thin-layer chromatography (TLC) coupled with the recent planar Ames bioassay via pH indicator endpoint. The 2LabsToGo-Eco allowed for the separation and detection of mutagens in complex samples. Hot water was the most effective extraction solvent in terms of yield and closely simulated the typical human consumption of coffee by-products. Separation was performed on TLC plates with a mixture of ethyl acetate, n-propanol, and water, followed by bioassay detection. The positive control 4-nitroquinoline 1-oxide exhibited clear mutagenic responses, confirming the proper bioassay performance. In the Ames bioautogram, none of the tested coffee by-products showed mutagenic zones, suggesting the absence of strongly acting, acute mutagens under the applied test conditions; however, given the only 5 h short incubation and the use of TA98 strain only, a longer incubation time and testing with additional Salmonella strains is recommended. The results provide new safety data for Coffea leaves and blossoms and are consistent with some previous studies demonstrating the safety of coffee by-products. However, further improvements in the sensitivity and selectivity of the planar Ames bioassay are demanded, and further in vivo and long-term safety studies are recommended. Considering natural variability, the different uses of pesticides and treatments, and the fluctuating supply chains, coffee by-products may differ highly. The planar bioassay technology using the affordable 2LabsToGo-Eco is a powerful toxicological screening option for the coffee industry, considering the increasing interest in utilizing coffee by-products. Full article

This paper aims to assess the water quality of the Ismailia Canal, Egypt, in accordance with Article 49 of Law 92/2013. QUAL2K and Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) are utilized to simulate the water quality parameters of dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), nitrate nitrogen (NO₃-N), and ammonium (NH₃-N) in winter and summer 2023. The parameters of the QUAL2K and CNN-LSTM models were calibrated and validated in both winter and summer through trial and error, until the simulated results agreed well with the observed data. Additionally, the model’s performance was measured using different statistical criteria such as mean absolute error (MAE), root mean square (RMS), and relative error (RE). The results showed that the simulated values were in good agreement with the observed values. The results show that all parameter concentrations follow and did not exceed the limit of Article 49 of Law 92/2013 in winter and summer, except for dissolved oxygen concentration (8.73–4.53 mg/L) in winter and summer, respectively, which exceeds the limit of 6 mg/L, and in June, biological oxygen demand exceeds the limit of 6 mg/L due to increased organic matter. It is imperative to compare QUAL2K and CNN-LSTM models because QUAL2K provides a physics-based simulation of water quality processes, whereas CNN-LSTM employs deep learning in modeling complex temporal patterns. The two models enhance prediction accuracy and credibility towards enabling enhanced decision-making for Ismailia Canal water management. This research can be part of a decision support system regarding maximizing the benefits of the Ismailia Canal. Full article

China, facing severe saline–alkali land degradation, is grappling with the paradox of technically adequate but systemically deficient land consolidation. In response to the existing evaluation system’s over-reliance on physicochemical indicators and neglect of socioeconomic value, this study proposes the use of the Optimal Land Use Value (OLV) to construct a comprehensive benefit evaluation indicator system for saline–alkali land consolidation that encompasses ecosystem resilience, supply–demand balancing, and common prosperity. Considering a case project implemented from 2019 to 2022 in the Western Songnen Plain of China—one of the world’s most severely affected soda saline–alkali regions—this study combines the land use transition matrix with a comprehensive evaluation model to systematically assess the effectiveness and sustainability of land consolidation. The results reveal systemic deficiencies: within ecological spaces, short-term desalination succeeds but pH and organic matter improvements remain inadequate, while ecosystem vulnerability increases due to climate fluctuations and grassland conversion. In production spaces, cropland expansion and saline land reduction are effective, but water resource management proves unsustainable. Living spaces show improved infrastructure and income but face threats due to economic simplification and intergenerational unsustainability. For the investigated case, recommendations include shifting from technical restoration to systemic governance via three strategies: (1) biological–engineering synergy employing green manure to enhance soil microbial activity; (2) hydrological balancing through groundwater quotas and rainwater utilization; (3) specialty industry development for rural economic diversification. This study contributes empirical evidence on the conversion of saline–alkali land, as well as an evaluation framework of wider relevance for developing countries combating land degradation and pursuing rural revitalization. Full article

Background/Objectives: In recent years, the discharge of antibiotics into rivers and irrigation canals has increased. However, few studies have addressed the impact of these compounds on agricultural fields that use such water to meet crop demands. Methods: In this study, the transport of two types of gentamicin (pure gentamicin and gentamicin sulfate) was modeled at concentrations of 150 and 300 μL/L, respectively, in a soil with more than 60 years of agricultural use. Infiltration tests under constant head conditions and gentamicin transport experiments were conducted in acrylic columns measuring 14 cm in length and 12.7 cm in diameter. The scaling parameters for the Richards equation were obtained from experimental data, while those for the advection–dispersion equation were estimated using inverse methods through a nonlinear optimization algorithm. In addition, a fractal-based model for saturated hydraulic conductivity was employed. Results: It was found that the dispersivity of gentamicin sulfate is 3.1 times higher than that of pure gentamicin. Based on the estimated parameters, two simulation scenarios were conducted: continuous application of gentamicin and soil flushing after antibiotic discharge. The results show that the transport velocity of gentamicin sulfate in the soil may have short-term consequences for the emergence of resistant microorganisms due to the destination of wastewater containing antibiotic residues. Conclusions: Finally, further research is needed to evaluate the impact of antibiotics on soil physical properties, as well as their effects on irrigated crops, animals that consume such water, and the soil microbiota. Full article

The quality of water in households can be affected by plumbing design and materials, water usage patterns, and source water quality characteristics. These factors influence stagnation duration, disinfection residuals, metal release, and microbial activity. In particular, stagnation can degrade water quality and increase lead release from lead service lines. This study employs numerical modeling to assess how combined corrosion control and flushing strategies affect lead levels in household taps with lead service lines under reduced water use. To estimate potential health risks, the U.S. EPA model is used to predict the percentage of children likely to exceed safe blood lead levels. Lead exceedances are assessed based on various regulatory requirements. Results show that exceedances at the kitchen tap range from 3 to 74% of usage time for the 5 µg/L standard, and from 0 to 49% for the 10 µg/L threshold, across different scenarios. Implementing corrosion control treatment in combination with periodic flushing proves effective in lowering lead levels under the studied low-consumption scenarios. Under these conditions, the combined strategy limits lead exceedances above 5 µg/L to only 3% of usage time, with none above 10 µg/L. This demonstrates its value as a practical short-term strategy for households awaiting full pipe replacement. Targeted flushing before peak water use reduces the median time that water remains stagnant in household pipes from 8 to 3 h at the kitchen tap under low-demand conditions. Finally, the risk model indicates that the combined approach can reduce the predicted percentage of children with blood lead levels exceeding 5 μg/dL from 61 to 6% under low water demand. Full article

Effective water infrastructure planning and management is key to sustainable water supply globally. This research assesses water infrastructure planning and management in Gauteng, South Africa, amid growing challenges from rapid urbanisation, high water demand, climate change, and resource scarcity. These challenges threaten the achievement of Sustainable Development Goals 6 and 11; hence, an integrated approach is required for water sustainability. The study responds to a gap in the literature, which often treats planning and management separately, by adopting an integrated, multi-institutional approach across the water value chain. A mixed-methods triangulation strategy was employed for data collection whereby surveys provided quantitative data, while two sets of structured interviews were conducted: the first round to determine causal relationships among the critical success factors and the second round to validate the proposed framework. The findings reveal a misalignment between infrastructure planning and implementation, contributing to infrastructure backlogs and a short- to medium-term focus. Infrastructure management is further constrained by inadequate system redundancy, leading to ineffective maintenance. External factors such as delayed adoption of 4IR technologies, lack of climate resilient strategies, and fragmented institutional coordination exacerbate these issues. Using Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis, the study identified Strategic Alignment and a Value-Driven Approach as the most influential critical success factors in water asset management. The research concludes by proposing an integrated water infrastructure and planning framework that supports sustainable water supply. Full article

Water scarcity challenges floriculture, which depends on quality irrigation for ornamental value. This study assessed short-term salinity tolerance in eight Asteraceae species by measuring physiological (proline levels, antioxidant enzyme activity) and morphological (plant height, flower number, and size) responses. Plants were irrigated with 0, 50, 100, or 300 mM NaCl for 10 days. Salinity significantly enhanced proline content and the activity of key antioxidant enzymes (catalase, peroxidase, and ascorbate peroxidase), reflecting the activation of stress defense mechanisms. However, these defenses failed to fully protect reproductive organs. Flower number and size were consistently more sensitive to salinity than vegetative traits, with significant reductions observed even at 50 mM NaCl. Responses varied between species, with Zinnia elegans and Calendula officinalis exhibiting pronounced sensitivity to salinity, whereas Tagetes patula showed relative tolerance, particularly under moderate stress conditions. The results show that flower structures are more vulnerable to ionic and osmotic disturbances than vegetative tissues, likely due to their higher metabolic demands and developmental sensitivity. Their heightened vulnerability underscores the need to prioritize reproductive performance when evaluating stress tolerance. Incorporating these traits into breeding programs is essential for developing salt-tolerant floriculture species that maintain aesthetic quality under limited water availability. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article