Search Results (79)

Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese provinces based on multi-regional input–output tables from 2012, 2015, and 2017, using total trade decomposition, social network analysis, and exponential random graph models. The key findings are as follows: (1) The total virtual water trade volume remains stable, with Xinjiang, Jiangsu, and Guangdong as the core regions, while remote areas such as Shaanxi and Gansu have lower trade volumes. The primary industry dominates, and it is driven by simple value chains. (2) Provinces such as Xinjiang, Heilongjiang, and Jiangsu form the network’s core. Network density and symmetry increased from 2012 to 2015 but declined slightly in 2017, with efficiency peaking and then dropping, and the clustering coefficient decreased annually. Four economic sectors exhibit distinct interactions: frequent two-way flows in Sector 1, significant inflows in Sector 2, prominent net spillovers in Sector 3, and key brokers in Sector 4. (3) The network evolved from a core-periphery structure with weak ties to a stable, heterogeneous, and resilient system. (4) Influencing factors, such asper capita water resources, economic development, and population, significantly impact trade. Similarities in economic levels, population, and water endowments promote trade, while spatial distance has a limited effect, with geographic proximity showing a significant negative impact on long-distance trade. Full article

Water distribution systems (WDSs) exhibit intricate, nonlinear behaviors shaped by both internal dynamics and external influences. The incorporation of additional models, such as contamination or population models, further increases their complexity. This study investigated WDSs under various uncertainty scenarios to enhance system stability, robustness, and control. In particular, we built upon prior research by exploring an Agent-Based Modeling (ABM) framework integrated within a WDS, focusing on three types of uncertainties: (1) adjustments to existing probabilistic parameters, (2) variations in agent movement across network nodes, and (3) changes in agent distributions across different node types. We conducted our analysis using the virtual city of Micropolis as a testbed. Our findings indicate that while the system remains resilient to uncertainties in predefined probabilistic parameters, substantial and often nonlinear effects arise when uncertainties are introduced in agent mobility and distribution patterns. These results emphasize the significance of understanding how WDSs respond to external behavioral dynamics, which is essential for managing real-world challenges, such as pandemics or shifts in urban behavior. This study underscores the necessity for further research into broader uncertainty categories and emergent effects to enhance WDS modeling and inform decision-making. Full article

Modular wastewater treatment units in large residential complexes in India’s crowded cities often lack stringent monitoring due to cost constraints and limited technical manpower. Although these plants must meet effluent standards, testing often requires sending samples to external labs, causing delays and added costs. As a result, they are rarely monitored, risking improper effluent discharge. Quick, cost-effective assessments of effluent quality could significantly improve plant operation and maintenance. Addressing the special challenges faced by such wastewater treatment systems, artificial intelligence (AI)-based soft sensors and virtual instruments have been developed to forecast effluent quality with the help of a water quality parameter that is inexpensively, easily, and immediately measurable with a hand-held device. In this study, advanced artificial neural network (ANN)-based soft sensors were developed to enhance the monitoring and management of effluent quality in five modular wastewater treatment plants in Bangalore. The models serve as virtual instruments for the measurement of total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), using the wastewater turbidity as the input parameter. By using these AI models, operators can better anticipate and manage water quality, ultimately contributing to more efficient and effective wastewater treatment operations. This innovative approach represents a significant advancement in wastewater treatment technology providing a practical and efficient solution to streamline monitoring and enhance overall plant performance. Full article

The current unmanned surface vehicle (USV) intelligent obstacle avoidance algorithm based on deep reinforcement learning usually adopts the mass point model to train in an ideal environment. However, in actual navigation, due to the influence of the ship model and the water surface environment, the training set is triggered. The reward function does not match the actual situation, resulting in a poor obstacle avoidance effect. In response to the above problems, this paper proposes a long and short memory network-proximal strategy optimization (LSTM-PPO) intelligent obstacle avoidance algorithm for non-particle models in non-ideal environments, and designs a corresponding deep reinforcement learning training environment. We integrate the motion characteristics of the unmanned boat and the influencing factors of the surface environment, based on the curiosity-driven set reward function, to improve its autonomous obstacle avoidance ability, combined with the LSTM network to identify and save obstacle information to improve the adaptability to the unknown environment; virtual simulation is performed in Unity. The engine builds a USV physical model and a refined water deep reinforcement learning training environment including a variety of obstacle models. The experimental results demonstrate that the LSTM-PPO algorithm exhibits an effective and rational obstacle avoidance effect, with a success rate of 86.7%, an average path length of 198.52 m, and a convergence time of 1.5 h. A comparison with the performance of three other deep reinforcement learning algorithms reveals that the LSTM-PPO algorithm exhibits a 21.5% reduction in average convergence time, an 18.5% reduction in average path length, and an approximately 20% enhancement in the success rate of obstacle avoidance in complex environments. These results indicate that the LSTM-PPO algorithm can effectively enhance the search efficiency and optimize the path planning in obstacle avoidance for unmanned boats, rendering it more rational. Full article

This study develops a predictive model to assess the impacts of urban water blue visual index (BVI) and flow velocity on human brain states using EEG and HRV data in virtual reality simulations. By integrating Gaussian process regression (GPR) and artificial neural networks (ANN), the model accurately captures the relationships between BVI, flow velocities, and brain states, reflecting experimental observations with high precision. Applied across 31 provinces in China, the model effectively predicted regional brain state levels, aligning closely with the birthplace distribution of high-level talents, such as academicians and Changjiang scholars. These results highlight the model’s practical application in optimizing urban water features to enhance mental health, cognitive performance, and societal development. Full article

With the deepening of international trade and the increasing strain on water resources, the importance of the virtual water trade of grain products on an international level has become increasingly prominent. Based on FAOSTAT and water value research reports, this study constructed the virtual water trade networks of wheat, rice, maize, and soybeans for 29 major grain trading countries in 2012 and 2022 and measured their network indicators and virtual water flow patterns. In addition, a QAP regression analysis was used to study the influencing factors of the virtual water trade network for grain products from the perspective of four dimensions: economic scale, geographical characteristics, resource endowment, and policy agreements. The results were as follows: Firstly, from 2012 to 2022, the virtual water trade of wheat and rice shifted from a state of net virtual water outflow to net virtual water inflow, and the overall net virtual water flows of maize and soybeans both showed a net virtual water inflow. Secondly, wheat’s virtual water trade network participants had reduced obvious “small-world” features, and KOR, the USA, TUR, and IND have long been at the center of that network. When the core nodes of the virtual water trade network of rice were reduced, the network tended to be decentralized. In that network, IND, NPL, the USA, and ZAF always occupied dominant positions. The overall connectivity of the maize virtual water trade network increased, with both the USA and JPN as the trade core. The number of core countries in the soybean virtual water trade network increased; significantly, CHN, the USA, and THA were in dominant positions. Lastly, the GDP at the economic scale was the biggest core driving factor of all virtual water trade networks of various grain products, followed by per capita arable land area in terms of resource endowment. In addition, the geographic characteristics and trade agreements of the virtual water trade networks of grain products also had a more significant negative impact. This paper argues that countries should make trade adjustments for their own developing disadvantaged grain products, vigorously develop their national economies, optimize the structure of the grain trade, and promote benign cooperation in international virtual water trade for grain products. Full article

Regular on-site inspection is crucial for promptly detecting faults in water supply networks (WSNs) and auxiliary facilities, significantly reducing leakage risks. However, the fragmentation of information and the separation between virtual and physical networks pose challenges, increasing the cognitive load on inspectors. Furthermore, due to the lack of real-time computation in current research, the effectiveness in detecting anomalies, such as leaks, is limited, hindering its ability to provide immediate and direct-decision support for inspectors. To address these issues, this research proposes a mixed reality (MR) inspection method that integrates multi-source information, combining building information modeling (BIM), Internet of Things (IoT), monitoring data, and numerical simulation technologies. This approach aims to achieve in situ visualization and real-time computational capabilities. The effectiveness of the proposed method is demonstrated through case studies, with user feedback confirming its feasibility. The results indicate improvements in inspection task performance, work efficiency, and standardization compared to traditional mobile terminal-based methods. Full article

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human body is virtually impossible due to the many internal mechanisms that ensure the kinetics of metals and the wide variety of organs, tissues, cellular structures, and secretions that ensure their functional redistribution, transport, and cumulation. We have developed an intelligent multi-neural network model capable of calculating the content of metals in the human body based on data on their environmental content. The model is two interconnected neural networks trained on actual measurement data. Since metals enter the body from the environment, the predictors of the model are metal content in drinking water and soil. In this case, water characterizes the short-term impact on the organism, and drinking water, combined with metal contents in soil, is a depository medium that accumulates metals from anthropogenic sources—the long-term impact. In addition, human physiological characteristics are taken into account in the calculations. Each period of exposure is taken into account by its neural network. Two variants of the model are proposed: open loop, where the calculation is performed by each neural network separately, and closed loop, where neural networks work together. The model built in this way was trained and tested on the data of real laboratory studies of 242 people living in different districts of Kazan. As a result, the accuracy of the neural network block for calculating long-term impact was 90% and higher, and the accuracy of the block for calculating short-term impact was 92% and higher. The closed double-loop model showed an accuracy of at least 96%. Conclusions: Our proposed method of assessing and quantifying metal accumulation in the body has high accuracy and reliability. It does not require expensive laboratory tests and allows quantifying the body’s metal accumulation content based on readily available information. The calculation results can be used as a tool for clinical diagnostics and operational and planned management to reduce the levels of polymetallic contamination in urban areas. Full article

In this very personal hunt for the meaning of the bacterial cell cycle, the snark, I briefly revisit and update some of the mechanisms we and many others have proposed to regulate the bacterial cell cycle. These mechanisms, which include the dynamics of calcium, membranes, hyperstructures, and networks, are based on physical and physico-chemical concepts such as ion condensation, phase transition, crowding, liquid crystal immiscibility, collective vibrational modes, reptation, and water availability. I draw on ideas from subjects such as the ‘prebiotic ecology’ and phenotypic diversity to help with the hunt. Given the fundamental nature of the snark, I would expect that its capture would make sense of other parts of biology. The route, therefore, followed by the hunt has involved trying to answer questions like “why do cells replicate their DNA?”, “why is DNA replication semi-conservative?”, “why is DNA a double helix?”, “why do cells divide?”, “is cell division a spandrel?”, and “how are catabolism and anabolism balanced?”. Here, I propose some relatively unexplored, experimental approaches to testing snark-related hypotheses and, finally, I propose some possibly original ideas about DNA packing, about phase separations, and about computing with populations of virtual bacteria. Full article

The paper demonstrates the effectiveness of the SNOWED dataset, specifically designed for identifying water bodies in Sentinel-2 images, in developing a remote sensing system based on deep neural networks. For this purpose, a system is implemented for monitoring the Po River, Italy’s most important watercourse. By leveraging the SNOWED dataset, a simple U-Net neural model is trained to segment satellite images and distinguish, in general, water and land regions. After verifying its performance in segmenting the SNOWED validation set, the trained neural network is employed to measure the area of water regions along the Po River, a task that involves segmenting a large number of images that are quite different from those in SNOWED. It is clearly shown that SNOWED-based water area measurements describe the river status, in terms of flood or drought periods, with a surprisingly good accordance with water level measurements provided by 23 in situ gauge stations (official measurements managed by the Interregional Agency for the Po). Consequently, the sensing system is used to take measurements at 100 “virtual” gauge stations along the Po River, over the 10-year period (2015–2024) covered by the Sentinel-2 satellites of the Copernicus Programme. In this way, an overall space-time monitoring of the Po River is obtained, with a spatial resolution unattainable, in a cost-effective way, by local physical sensors. Altogether, the obtained results demonstrate not only the usefulness of the SNOWED dataset for deep learning-based satellite sensing, but also the ability of such sensing systems to effectively complement traditional in situ sensing stations, providing precious tools for environmental monitoring, especially of locations difficult to reach, and permitting the reconstruction of historical data related to floods and draughts. Although physical monitoring stations are designed for rapid monitoring and prevention of flood or other disasters, the developed tool for remote sensing of water bodies could help decision makers to define long-term policies to reduce specific risks in areas not covered by physical monitoring or to define medium- to long-term strategies such as dam construction or infrastructure design. Full article

Drinking water disinfection by water utilities aims to ensure the safety and high quality of the provided water; however, it can pose a threat to human health due to the formation of disinfection byproducts (DBPs). The prediction and modeling of DBPs are challenging tasks due to the complex reactions within water distribution networks (WDN). To address this challenge, we introduce a virtual testbed based on a realistic WDN in Cyprus that utilizes the EPANET and EPANET-MSX engines to model multi-species reactions for the execution of simulation experiments under various conditions regarding the formation and fate of two families of DBPs within WDNs. Full article

Essential for decision-making, measurement is a cornerstone of various fields including energy management. While direct methods exist for some quantities like length, most physico-chemical properties require indirect assessment based on observable effects. Historically, pressure was measured by the water column height, and temperature by mercury expansion. Recent advancements in artificial intelligence (AI) offer a transformative approach by combining vast datasets with traditional measurements. This holds immense potential for applications facing extreme conditions and involving complex fluids where measurement is extremely challenging (over 1500 K and 5 MPa). In this study, an AI model is evaluated to replace online rheometers (293–1173 K, 0.15–3.5 MPa). A machine learning model utilizes a neural network with up to 8000 neurons, eight hidden layers, and over 448 million parameters. Trained, tested, and validated on three experimental databases with over 600 test conditions, the New Generation Predicted Viscosity Sensor (NGPV sensor) achieves exceptional accuracy (less than 4.8 × 10⁻⁷ Pa·s). This virtualized sensor proves highly relevant for hypersonic airbreathing applications involving fuel degradation and energy conversion. It maintains excellent predictability (accuracy below 6 × 10⁻⁶ Pa·s) even at flow rates 10 times higher than calibration, surpassing traditional rheometers limited by calibration needs and a lower viscosity measurement threshold (10⁻⁴ Pa·s). Full article

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset. Full article

In response to the challenges posed by climate change, including extreme weather events, such as heavy rainfall and droughts, the agricultural sector is increasingly seeking solutions for the efficient use of resources, particularly water. Pivotal aspects of smart agriculture include the establishment of weather-independent systems and the implementation of precise monitoring and control of plant growth and environmental conditions. Hydroponic cultivation techniques have emerged as transformative solutions with the potential to reduce water consumption for cultivation and offer a sheltered environment for crops, protecting them from the unpredictable impacts of climate change. However, a significant challenge lies in the frequent need for human intervention to ensure the efficiency and effectiveness of these systems. This paper introduces a novel system with a modular architecture, offering the ability to incorporate new functionalities without necessitating a complete system redesign. The autonomous hydroponic greenhouse, designed and implemented in this study, maintains stable environmental parameters to create an ideal environment for cultivating tomato plants. Actuators, receiving commands from a cloud application situated at the network’s edge, automatically regulate environmental conditions. Decision-making within this application is facilitated by a PID control algorithm, ensuring precision in control commands transmitted through the MQTT protocol and the NGSI-LD message format. The system transitioned from a single virtual machine in the public cloud to edge computing, specifically on a Raspberry Pi 3, to address latency concerns. In this study, we analyzed various delay aspects and network latency to better understand their significance in delays. This transition resulted in a significant reduction in communication latency and a reduction in total service delay, enhancing the system’s real-time responsiveness. The utilization of LoRa communication technology connects IoT devices to a gateway, typically located at the main farm building, addressing the challenge of limited Internet connectivity in remote greenhouse locations. Monitoring data are made accessible to end-users through a smartphone app, offering real-time insights into the greenhouse environment. Furthermore, end-users have the capability to modify system parameters manually and remotely when necessary. This approach not only provides a robust solution to climate-induced challenges but also enhances the efficiency and intelligence of agricultural practices. The transition to digitization poses a significant challenge for farmers. Our proposed system not only represents a step forward toward sustainable and precise agriculture but also serves as a practical demonstrator, providing farmers with a key tool during this crucial digital transition. The demonstrator enables farmers to optimize crop growth and resource management, concretely showcasing the benefits of smart and precise agriculture. Full article

The flow routing process plays a crucial role in underpinning the execution of real-time operations within interbasin water transfer projects (IWTPs). However, the water transfer process within the supplying area is significantly affected by the time lag of water flow over extended distances, which results in a misalignment with the water demand process in the receiving area. Hence, there is an imperative need to investigate the flow routing patterns in long-distance water transfer processes. While MIKE11(2014 version) software and the Muskingum method are proficient in simulating flow routing within a water transfer network, they fall short in addressing issues arising from mixed free-surface-pressure flows in water transfer pipelines. This study enhanced the capabilities of the MIKE11(2014 version) software and the Muskingum method by introducing the Preissmann virtual narrow gap method to tackle the challenge of simulating mixed free-surface-pressure flows, a task unattainable by the model independently. This approach provides a clear elucidation of hydraulic characteristics within the water transfer network, encompassing flow rates and routing times. Furthermore, this is integrated with the Muskingum inverse method to compute the actual water demand process within the supplying area. This methodology is implemented in the context of the Han River to Wei River Diversion Project (HTWDP). The research findings reveal that the routing time for the Qinling water conveyance tunnel, under maximum design flow rate conditions, is 12.78 h, while for the south and north main lines, it stands at 15.85 and 20.15 h, respectively. These results underscore the significance of the time lag effect in long-distance water conveyance. It is noteworthy that the average errors between simulated and calculated values for the south and north main lines in the flow routing process are 0.45 m³/s and 0.51 m³/s, respectively. Compared to not using the Preissmann virtual narrow gap method, these errors are reduced by 59.82% and 70.35%, indicating a significant decrease in the discrepancy between simulated and calculated values through the adoption of the Preissmann virtual narrow gap method. This substantially improves the model’s fitting accuracy. Furthermore, the KGE indices for the flow routing model are all above 0.5, and the overall trend of the reverse flow routing process closely aligns with the simulated process. The relative errors for most time periods are constrained within a 5% range, demonstrating the reasonability and precision of the model. Full article