Predicting short-term urban water demand is essential for water resource management and directly impacts urban water resource planning and supply–demand balance. As numerous factors impact the prediction of short-term urban water demand and present complex nonlinear dynamic characteristics, the current water demand prediction methods mainly focus on the time dimension characteristics of the variables, while ignoring the potential influence of spatial characteristics on the temporal characteristics of the variables. This leads to low prediction accuracy. To address this problem, a short-term urban water demand prediction model which integrates both spatial and temporal characteristics is proposed in this paper. Firstly, anomaly detection and correction are conducted using the Prophet model. Secondly, the maximum information coefficient (MIC) is used to construct an adjacency matrix among variables, which is combined with a graph convolutional neural network (GCN) to extract spatial characteristics among variables, while a multi-head attention mechanism is applied to enhance key features related to water use data, reducing the influence of unnecessary factors. Finally, the prediction of short-term urban water demand is made through a three-layer long short-term memory (LSTM) network. Compared with existing prediction models, the hybrid model proposed in this study reduces the average absolute percentage error by 1.868–2.718%, showing better prediction accuracy and prediction effectiveness. This study can assist cities in rationally allocating water resources and lay a foundation for future research.

With the explosion of geo-distributed data, the huge treasures hidden in them are waiting to be explored to obtain valuable insights. This results in the need for an effective geo- distributed data analysis method. The traditional approach to geo-distributed data analytics is to gather all the required data into a single edge datacenter (edge DC) through one transmission and aggregation (centralized data aggregation). However, as the volume of data grows exponentially, the centralized data aggregation scheme becomes inefficient or infeasible due to the limitations of the computing and network resources. In this paper, we propose the geo-distributed data aggregation scheme in edge compute first networking (CFN) with joint consideration of computation and communication resources. The proposed scheme optimizes two objectives: the first is to minimize the job completion time (JCT) by selecting cluster centers, dividing clusters and provisioning lightpaths; the second objective is to reduce bandwidth consumption by reallocating routing and frequency slots based on JCT. To achieve the two objectives, we first formulate the optimization problem of multi-stage geo- distributed data aggregation as a linear programming (LP) model. To tackle the computational complexity issue of the LP model, a multi-stage geo-distributed data aggregation algorithm jointly with computation and communication resources (MGDD-CC) is proposed. Simulation results show that the proposed scheme can reduce JCT, alleviate the competition for bandwidth resources and is more suitable for scenarios with better data aggregation effects and larger quantities of geo-distributed data.

The imbalance between water supply and demand (WSD) has been growing noticeable as a result of the economy’s fast expansion which can be effectively alleviated using optimal allocation of water resources. An urban water resources allocation (WRA) model based on the innovative Multi-Objective Nutcracker Optimization Algorithm (MONOA) is proposed in this study. Taking into account economic, social and ecological benefits, a comprehensive multi-objective optimization (MOO) model is established. By introducing the opposite learning strategy, non-dominated sorting approach and crowding distance mechanism to a recently reported intelligent optimization algorithm called the Nutcracker Optimization Algorithm (NOA), the novel nature-inspired metaheuristic algorithm MONOA is proposed to solve the multi-objective optimization model. The MONOA is evaluated on ten benchmark test functions, and it exhibits superior distribution and convergence by comparing with some highly cited algorithms. The proposed model is applied to Handan, China, in order to obtain a reasonable water allocation scheme in the planning year. The simulation results reveal that the economic benefit is in the range CNY [1.36, 1.44] × 10¹¹, water shortage is in the range [0.66, 0.98] × 10⁸ m³ and COD emission is in the range [3.70, 3.91] × 10⁴ t in all the obtained Pareto solutions. The water resources management departments might create customized water allocation plans by balancing different goals and taking preferences into account. Moreover, the proposed method is a general approach that can be applied to many other cities. Hence, it is of great significance to the sustainable development and utilization of urban water resources.

Urban water usage spans diverse sectors, requiring effective management strategies to address increasing demand, limited supplies, and sector-specific needs. In this study, a multi-objective urban water resource allocation model is proposed to balance economic, ecological, and social benefits, focusing on social fairness. The model considers water availability, demand diversity, and environmental factors for optimized resource allocation. An improved zebra optimization algorithm-based multi-objective evolutionary algorithm (ZOA-MOEA/D) is developed, integrating zebra optimization with a decomposition-based approach to overcome the traditional methods’ limitations, improving solution diversity and convergence. ZOA-MOEA/D consistently outperforms the NSGA-II, MOPSO, and MOEA/D algorithms in solution distribution, convergence, quality, and diversity across multiple test scenarios. By applying the model to Ningbo, China, key trade-offs between economic growth, social fairness, living standards, and ecological protection are revealed. These findings provide useful insights into urban water resource management, offering a flexible framework for balancing multiple objectives and supporting sustainable development. Despite some limitations, the approach can contribute to the ongoing development of urban water resources.

In order to reduce the offshore wind power operation and maintenance vessel motion induced by severe sea states, a suitable stabilizer with the ship based on linear quadratic regulator strategy is proposed in this paper. First of all, the dynamics of the ship motion model are established to study the longitudinal control system. The six degrees of freedom nonlinear motion model and nonlinear coupled longitudinal motion (heave and pitch) model are described in detail in this paper. Secondly, this work presents matching suitability between the T-foil and the operation and maintenance vessel. Therefore, the most suitable installation position and the optimum strut’s height of T-foil are determined by meshing the ship hull model, setting the water channel, and a series of corresponding computer fluid dynamic simulation. Following that, the linear quadratic regulator controller is studied with active longitudinal control system based on the suitable T-foil. Furthermore, a longitudinal control system is built, including free vessel module and the suitable T-foil stabilizer-based proposed controller module. Finally, the simulation results indicate that the designed T-foil and the longitudinal control system are feasible and effective to ensure the heave and pitch motion reduction based on the proposed controller.