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Decentralized periodic event‐triggered control can reduce the working time and data transmissions in the sensor nodes for feedback control loops, making it suitable for wireless networked control systems, especially those with large physical scales, such as smart water distribution systems. However, the design of this control approach requires accurate system model, which is normally difficult to obtain. In this work, we assume a digital twin of the original system can be obtained by some modeling and simulation tools, for example, Simscape in MATLAB/Simulink for water distribution systems, and thus present an event‐triggered controller design approach. We first present the design of such an event‐triggered controller for the digital twin; and then analyze the condition under which if both the original system and its digital twin can be satisfied, the same controller can work for the original system and some type of stability can be guaranteed at the same time. The proposed approach is applied in a smart water distribution system to show the feasibility. [ABSTRACT FROM AUTHOR]

Amid the escalating global climatic challenges, hydrological risks significantly influence human settlement patterns, underscoring the imperative for an in-depth comprehension of hydrological change's ramifications on human migration. However, predominant research has been circumscribed to the national level. The study delves into the nonlinear effects of hydrological risks on migration dynamics in 46,776 global subnational units. Meanwhile, leveraging remote sensing, we procured globally consistent metrics of hydrological intrusion exposure, offering a holistic risk assessment encompassing hazard, exposure, and vulnerability dimensions, thus complementing previous work. Here, we show that exposure is the primary migration driver, surpassing socioeconomic factors. Surrounding disparities further intensified exposure's impact. Vulnerable groups, especially the economically disadvantaged and elderly, tend to remain in high-risk areas, with the former predominantly migrating within proximate vicinities. The nonlinear analysis delineates an S-shaped trajectory for hydrological exposure, transitioning from resistance to migration and culminating in entrapment, revealing dependence on settlement resilience and adaptability. Hydrological risks drive migration more than socioeconomic factors. Vulnerable groups often stay in high-risk areas or migrate nearby. The study reveals an S-shaped migration pattern influenced by settlement resilience and adaptability. [ABSTRACT FROM AUTHOR]

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error. [ABSTRACT FROM AUTHOR]

Compared to large companies, the small and medium enterprises (SMEs) in China are more likely to fail in developing green supply chain management (GSCM). Capturing its failure factors could be useful for managers and policymakers to decide how green supply chains can be successfully designed and developed. Meanwhile, these factors can include complex interrelationships. Properly dealing with them can help identify the critical factors which can assist decision-makers to efficiently manage green supply chains. This study thus proposes a comprehensive framework to explore and understand failure factors of GSCM in China's SMEs to improve the opportunities of GSCM in China. The fuzzy synthetic evaluation (FSE) and decision-making trial and evaluation laboratory (DEMATEL) are combined and applied to identify critical failure factors through translating interrelationships in a hierarchical cause–effect model. The results indicate that collaboration and support are the most critical perspectives for GSCM, and these two perspectives also interrelated with each other and affect the perspectives of knowledge, technology and economy to varying degrees. Moreover, the critical failure factors include lack of top management support, poor guidance from authorities, difficulty in supplier selection and inadequate supplier commitment. Sensitivity analysis is conducted to check the robustness. Conceptual and managerial implications are discussed. [ABSTRACT FROM AUTHOR]