Search Results (98)

To address the limitations of traditional hardened concrete road surfaces in coal mine tunnels, which are prone to damage and entail high maintenance costs, this study proposes using modular concrete blocks composed of fly ash and coal gangue as an alternative to conventional materials. These blocks offer advantages including ease of construction and rapid, straightforward maintenance, while also facilitating the reuse of substantial quantities of solid waste, thereby mitigating resource wastage and environmental pollution. Initially, the mineral composition of the raw materials was analyzed, confirming that although the physical and chemical properties of Liangshui Well coal gangue are slightly inferior to those of natural crushed stone, they still meet the criteria for use as concrete aggregate. For concrete blocks incorporating 20% fly ash, the steam curing process was optimized with a recommended static curing period of 16–24 h, a temperature ramp-up rate of 20 °C/h, and a constant temperature of 50 °C maintained for 24 h to ensure optimal performance. Orthogonal experimental analysis revealed that fly ash content exerted the greatest influence on the compressive strength of concrete, followed by the additional water content, whereas the aggregate particle size had a comparatively minor effect. The optimal mix proportion was identified as 20% fly ash content, a maximum aggregate size of 20 mm, and an additional water content of 70%. Performance testing indicated that the fabricated blocks exhibited a compressive strength of 32.1 MPa and a tensile strength of 2.93 MPa, with strong resistance to hydrolysis and sulfate attack, rendering them suitable for deployment in weakly alkaline underground environments. Considering the site-specific conditions of the Liangshuijing coal mine, ANSYS 2020 was employed to simulate and analyze the mechanical behavior of the blocks under varying loads, thicknesses, and dynamic conditions. The findings suggest that hexagonal coal gangue blocks with a side length of 20 cm and a thickness of 16 cm meet the structural requirements of most underground mine tunnels, offering a reference model for cost-effective paving and efficient roadway maintenance in coal mines. Full article

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

Leakages in water distribution networks pose a formidable challenge, often leading to substantial water wastage and escalating operational costs. Traditional methods for leak detection often fall short, particularly when dealing with complex or subtle data patterns. To address this, a comprehensive comparison of fourteen machine learning algorithms was conducted, with evaluation based on key performance metrics such as multi-class classification metrics, micro and macro averages, accuracy, precision, recall, and F1-score. The data, collected from an experimental site under leak, major leak, and no-leak scenarios, was used to perform multi-class classification. The results highlight the superiority of models such as Random Forest, K-Nearest Neighbours, and Decision Tree in detecting leaks with high accuracy and robustness. Multiple models effectively captured the nuances in the data and accurately predicted the presence of a leak, burst, or no leak, thus automating leak detection and contributing to water conservation efforts. This research demonstrates the practical benefits of applying machine learning models in water distribution systems, offering scalable solutions for real-time leak detection. Furthermore, it emphasises the role of machine learning in modernising infrastructure management, reducing water losses, and promoting the sustainability of water resources, while laying the groundwork for future advancements in predictive maintenance and resilience of water infrastructure. Full article

Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (L_i), electrical conductivity (EC), pH, and photosynthesis rate (P_n), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in P_n and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article

Excessive bait wastage is a major issue in aquaculture, leading to higher farming costs, economic losses, and water pollution caused by bacterial growth from unremoved residual bait. To address this problem, we propose a bait residue detection and counting model named YOLOv8-BaitScan, based on an improved YOLO architecture. The key innovations are as follows: (1) By incorporating the channel prior convolutional attention (CPCA) into the final layer of the backbone, the model efficiently extracts spatial relationships and dynamically allocates weights across the channel and spatial dimensions. (2) The minimum points distance intersection over union (MPDIoU) loss function improves the model’s localization accuracy for bait bounding boxes. (3) The structure of the Neck network is optimized by adding a tiny-target detection layer, which improves the recall rate for small, distant bait targets and significantly reduces the miss rate. (4) We design the lightweight detection head named Detect-Efficient, incorporating the GhostConv and C2f-GDC module into the network to effectively reduce the overall number of parameters and computational cost of the model. The experimental results show that YOLOv8-BaitScan achieves strong performance across key metrics: The recall rate increased from 60.8% to 94.4%, mAP@50 rose from 80.1% to 97.1%, and the model’s number of parameters and computational load were reduced by 55.7% and 54.3%, respectively. The model significantly improves the accuracy and real-time detection capabilities for underwater bait and is more suitable for real-world aquaculture applications, providing technical support to achieve both economic and ecological benefits. Full article

From 2016 to 2021, a field experiment was conducted in the North China Plain to study the long-term effects of drip irrigation and nitrogen coupling on the growth, biomass allocation, and irrigation water and fertilizer use efficiency of short-rotation triploid Populus tomentosa plantations. The experiment adopted a completely randomized block design, with one control (CK) and six water–nitrogen coupling treatments (IF, two irrigation levels × three nitrogen application levels). Data analysis was conducted using ANOVA, regression models, Spearman’s correlation analysis, and path analysis. The results showed that the effects of water and nitrogen treatments on the annual increment of diameter at breast height (ΔDBH), annual increment of tree height (ΔH), basal area of the stand (BA_S), stand volume (V_S), and annual forest productivity (AFP) in short-rotation forestry exhibited a significant stand age effect. The coupling of water and nitrogen significantly promoted the DBH growth of 2-year-old trees (p < 0.05), but after 3 years of age, the promoting effect of water and nitrogen coupling gradually diminished. In the 6th year, the above-ground biomass of Populus tomentosa was 5.16 to 6.62 times the under-ground biomass under different treatments. Compared to the I45 treatment (irrigation at soil water potential of −45 kPa), the irrigation water use efficiency of the I20 treatment (−20 kPa) decreased by 88.79%. PFP showed a downward trend with the increase in fertilization amount, dropping by 130.95% and 132.86% under the I20 and I45 irrigation levels. Path analysis indicated that irrigation had a significant effect on the BA_S, V_S, AFP, and TGB of 6-year-old Populus tomentosa (p < 0.05), with the universality of irrigation being higher than that of fertilization. It is recommended to implement phased water and fertilizer management for Populus tomentosa plantations in the North China Plain. During 1–3 years of tree age, adequate irrigation should be ensured and nitrogen fertilizer application increased. Between the ages of 4 and 6, irrigation and fertilization should be ceased to reduce resource wastage. This work provides scientific guidance for water and fertilizer management in short-rotation plantations. Full article

To optimize water–nitrogen management for mulched drip-irrigated peanuts in Xinjiang, a three-season field experiment was conducted to assess the impacts of drip irrigation rates and water–nitrogen coupling on peanut growth, yield, quality, and water–nitrogen use efficiency. Two irrigation accounts (30 and 37.5 mm, denoted as W1 and W2), three nitrogen application levels (half nitrogen application and conventional nitrogen application, denoted as N1 and N2), and a control treatment (CK) without nitrogen application, and two drip discharge rates (3.0 and 6.0 L h⁻¹, denoted as Q1 and Q2) were utilized for a total of five treatments per year, and the experiment was repeated three times. The results demonstrated that the irrigation and fertilization parameters of the W2N1Q2 treatment could significantly improve peanut growth, yield, quality, and water–nitrogen use efficiency, achieving optimal values for all measured indicators. Compared with the control (W2N0Q1), the main stem height increased by 9.59% and 13.13%, the aboveground biomass increased by 6.32% and 34.67%, the yield increased by 26.69% and 20.97% (p < 0.01), the water use efficiency increased by 27.08% and 16.33%, the nitrogen partial factor productivity values were 47.39 and 77.00 kg kg⁻¹, the protein content increased by 3.99% and 4.63%, and the oil content increased by 1.68% and 8.53%, respectively. A PCA was performed using five key performance indicators (yield, protein content, oil content, water use efficiency, and nitrogen partial factor productivity) to evaluate different treatment combinations. The W2N1Q2 treatment obtained the highest composite score, indicating its overall superior performance among all treatments. Therefore, under the conditions of this experiment, the irrigation and nitrogen application parameters for achieving both a high yield and quality of peanuts under mulched drip irrigation in Xinjiang were determined to be W2N1Q2 treatment (irrigation account of 37.5 mm, nitrogen application of 118 kg ha⁻¹, and drip discharge of 6.0 L h⁻¹). This optimized combination brings three key advantages to water-scarce regions: (1) maximizing yield water use efficiency through precise irrigation scheduling; (2) balanced nutrient management to prevent nitrogen wastage; and (3) providing a key technological reference for agricultural production in Xinjiang and other similar ecological zones. Full article

Unpredictable climate patterns and mounting groundwater depletion are major challenges to sustainable agriculture. The purpose of this research is to improve predictive accuracy in IoT-based smart irrigation systems using machine learning models for soil fertility estimation and water optimization. In contrast to existing research, this paper compares state-of-the-art ensemble learning models (LRBoost, LR+RF) with conventional methods to ascertain their real-time effectiveness in water usage prediction. Training and testing data were derived from open access agricultural data repositories, including soil moisture, temperature, humidity, and rainfall. Feature selection was performed through correlation analysis and model performance was evaluated using R² score, mean squared error (MSE), and root mean squared error (RMSE). Our results indicate that the hybrid ensemble model LR+RF performed better than others with an R² measure of 96.34%, an MSE of 0.0016, and an RMSE of 0.040. The findings confirm the capability of the system in minimizing water wastage and maximizing crop production. Full article

Background: Intensive poultry production requires highly efficient drinking systems to ensure both animal welfare and production performance; however, conventional drinkers for meat ducks often suffer from design deficiencies that compromise drinking efficiency and result in significant water wastage. Objectives: To address the drinking water demands in intensive waterfowl farming systems, a specialized drinking device tailored for meat ducks was developed. Methods: The drinking habits of meat ducks were analyzed and the performance of the existing drinkers was evaluated. The deficiencies of the current drinkers were observed and identified by high-speed video, and the parameters of the head of the meat duck were obtained by reverse-engineering technology. Based on this analysis, a specialized drinker for meat ducks was designed, and its performance was confirmed through farming trials. Results: The static and dynamic flow rate tests showed that the output of the new drinker was consistent with the nipple drinker. When the valve rod was pushed upward, the new drinker did not output, which met the design requirements. The results indicated that, under a water pressure of 2.5 kPa, the water loss rate for the designed drinker was 27.4%, which was 15.3% lower than the loss rate of 42.7% observed with the traditional nipple drinker. Conclusion: This study develops a specialized drinker for meat ducks in intensive farming, by utilizing the biting drinking method and incorporating the three-dimensional characteristics of the heads of meat ducks, significantly increasing the effective drinking rate and reducing leakage during the drinking process. Full article

The increasing pollution and wastage of food and byproducts from agro-industrial production is an increasingly worrying issue. Grape is one of the most diffused fruit crops cultivated, and grape pomace is the main solid byproduct obtained in the winemaking process; interestingly, it is rich in health-beneficial bioactive molecules. In order to recover these molecules, in this work, a green method has been developed, considering two grape pomaces from different cultivars, namely, Petit Verdot and Cabernet Sauvignon. The extraction procedure, as the first step of this process, was carried out with seven selected deep eutectic solvents (DESs). Then, analysis using HPLC-DAD allowed the detection and quantification of eight out of fifteen different phenolic compounds under examination in the extracts produced, including three quercetin glucosides. The evaluation of antioxidant activity, through the DPPH photometric assay, led to the selection of choline chloride/urea 1:2 + 40% water DES extracts as the extracts with the most promising results. Moreover, significant antibacterial activity was also achieved, in particular, for the betaine/lactic acid 1:4 + 40% water DES extract. Further studies will employ this method for numerous cultivars of grape pomaces with the ambitious aim of the production of polyphenol-enriched food and feed supplements. Full article

The water supply pipeline system is responsible for providing clean drinking water to residents, but pipeline leaks can lead to water resource wastage, increased operational costs, and safety hazards. To effectively detect the leakage level in the water supply pipelines and address the difficulty of accurately distinguishing fine-grained leakage levels using traditional methods, this paper proposes a fine-grained leakage identification method based on Convolutional Neural Networks (CNN) and the Selective State Space Model (Mamba). An experimental platform was built to simulate different leakage conditions, and multi-axis sensors were used to collect data, resulting in the creation of a high-quality dataset. The signals were converted into frequency-domain images using Short-Time Fourier Transform (STFT), and CNN was employed to extract image features. Mamba was integrated to capture the one-dimensional time dynamic characteristics of the leakage signal, and the CosFace loss function was introduced to increase the inter-class distance, thereby improving the fine-grained classification ability. Experimental results show that the proposed method achieves optimal performance across various evaluation metrics. Compared to SVM, BP neural networks, and CNN methods, the accuracy was improved by 17.9%, 15.9%, and 3.0%, respectively. Compared to Support Vector Machine (SVM), Backpropagation neural network (BP), attention mechanism with the LSTM network (LSTM-AM), CNN, and inverted transformers network (iTransformer) methods, the accuracy improved by 17.9%, 15.9%, 7.8%, 3.0%, and 2.3%, respectively. Additionally, the method enhanced intra-class consistency and increased inter-class differences, showing outstanding performance at different leakage levels, which could contribute to improved intelligent management for water pipeline leakage detection. Full article

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article

This article reports on a mixed-methods evaluation of a hydroponics-based learning curriculum for fourth and fifth grade students that incorporated 3D design and 3D printing technologies. This study provides a better understanding of the extent to which experiential indoor gardening applications can be used in the formal curriculum as an effective teaching tool to sensitize participants to the prudent use of water, avoiding its wastage. The primary objective was to introduce students to the processes of 3D printing and hydroponics, while also assessing the enhancement of their 21st century skills. The participating students presented significant improvement in environmental knowledge scores about hydroponics, as well as high overall scores on collaboration, creativity, communication, and critical thinking (the 4Cs). The teachers noted the modern, innovative character of the program, as well as the ease of use of the included, offered, educational material. Full article

Background: The Yellow River irrigation area in Ningxia faces spring drought, resalting, severe water resource shortage, and significant water wastage in saline–alkali soils. Objective: To explore the effects of two different improvement measures on maize fresh biomass and the basic physical and chemical properties of saline soil under four irrigation gradients, aiming to provide a theoretical basis for water-saving irrigation in the Yellow River irrigation area of Ningxia while ensuring maize yield. Methods: The experiment designed four irrigation gradients, W1: local conventional water volume (240 mm), W2: 10% water-saving (216 mm), W3: 20% water-saving (192 mm), W4: 30% water-saving (168 mm), and two different soil improvement treatments, a combination treatment of desulfurization gypsum, ETS microbial agent, and biochar (JC), and a combination treatment of desulfurization gypsum, humic acid, and mulching (FS), with a blank control (CK), resulting in 12 treatments in total. Results: The results showed that compared with CK, both JC and FS treatments reduced soil pH, with JC treatment showing a more significant reduction in soil alkalinity than FS treatment. Both JC and FS treatments inhibited the rise in soil electrical conductivity (EC), with JC showing a significantly higher ability to suppress the rise in EC than FS treatment. Both FS and JC treatments improved soil water retention, but in May 2023 during the maize seedling stage, FS treatment had a stronger water retention ability than JC treatment; however, in July at the maize big jointing stage and in September at the maize maturity stage, JC treatment exhibited better water retention ability than FS treatment. Both JC and FS treatments increased maize fresh biomass under four water conditions, but under WI and W2 conditions, there was no significant difference in the ability of JC and FS treatments to increase maize fresh biomass. Under any irrigation condition, the ability of JC treatment to improve WUE is higher than that of FS treatment. Under W3 and W4 conditions, JC treatment significantly outperformed FS treatment in increasing maize fresh biomass yield. Additionally, under W3 irrigation conditions, using JC treatment not only achieved greater water-saving goals but also prevented crop yield reduction due to water-saving measures. This article can provide a theoretical basis for agricultural irrigation management, especially in the Ningxia Yellow River irrigation area of China. It can help ensure crop yields while protecting the ecological environment and promoting sustainable agricultural development. Full article

In Burkina Faso, small-scale, community-managed irrigation systems play a crucial role in stabilizing agricultural production and improving food security. Over the past three decades, the state has transferred the management of these irrigation systems to local farmer organizations in the hope of improving efficiency and sustainability. This study assesses the long-term performance of six irrigation perimeters Dakiri, Gorgo, Itenga, Mogtedo, Savili, and Wedbila through an in-depth analysis of governance models, infrastructure conditions, and financial sustainability. Performance indicators such as relative water supply (RWS), gross production per unit of irrigation water (PbIr), and water charge recovery rates were used to assess the effectiveness of farmer-led irrigation management. The results reveal persistent governance and financial challenges as well as issues such as water wastage and low yield persisting, despite decades of implementation of farmer-led management. The degradation of irrigation infrastructure, coupled with declining water fee collection rates, threatens the sustainability of these systems. A comparative analysis of international cases suggests that a hybrid governance model, in which the state provides technical and financial support while strengthening accountability mechanisms, could improve the performance of these irrigation systems. This study recommends a shift towards greater state intervention, improved financial mechanisms, and the adoption of digital monitoring tools to ensure a more efficient and sustainable management framework. Full article