Search Results (234)

Increasing penetration of offshore renewable energy has highlighted the challenges posed by strong intermittency, output uncertainty, and insufficient utilization of marine energy resources. To address these issues, this study investigates an offshore multi-energy coupling system integrating wind, photovoltaic, tidal, and wave energy with flexible loads such as seawater desalination and hydrogen production. A coordinated two-stage optimization framework is proposed. In the planning stage, a joint operation–planning capacity configuration model is formulated to minimize the annualized system cost while determining the optimal sizes of generation units and energy storage. In the operational stage, a multi-time-scale rolling scheduling model combining day-ahead and intra-day optimization is developed to dynamically mitigate renewable output fluctuations and enhance system flexibility. Case studies verify that the proposed framework significantly improves renewable energy utilization, reducing the curtailment rate to 0.7%, while achieving stable and cost-effective operation. The results demonstrate the effectiveness of coordinated planning and rolling scheduling for future offshore integrated energy systems. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

Background: Tumor necrosis factor-α (TNFα) inhibitors have significantly improved outcomes in children with non-systemic juvenile idiopathic arthritis (JIA), achieving long-term clinical remission for many patients. However, the optimal strategy for TNF-α inhibitor withdrawal remains unknown, whether through abrupt discontinuation, gradual dose reduction, or interval extension. Objective: We aim to identify patient-, disease-, and treatment-related predictors of successful TNF-α inhibitor withdrawal in children with non-systemic JIA. Methods: In this prospective, randomized, open-label, single-center study, 76 children with non-systemic JIA in stable remission for ≥24 months on etanercept or adalimumab were enrolled. At the time of TNF-α inhibitor discontinuation, all patients underwent a comprehensive evaluation, including a clinical examination, laboratory tests (serum calprotectin [S100 proteins] and high-sensitivity C-reactive protein [hsCRP]), and advanced joint imaging (musculoskeletal ultrasound and magnetic resonance imaging [MRI]) to assess subclinical disease activity. Patients were randomized (1:1:1, sealed-envelope allocation) to one of three predefined tapering strategies: (I) abrupt discontinuation; (II) extension of dosing intervals (etanercept 0.8 mg/kg every 2 weeks; adalimumab 24 mg/m² every 4 weeks); or (III) gradual dose reduction (etanercept 0.4 mg/kg weekly; adalimumab 12 mg/m² every 2 weeks). Follow-up visits were scheduled at 3, 6, 9, 12, and 18 months to monitor for disease relapse. Results: Higher baseline Childhood Health Assessment Questionnaire (CHAQ) scores (≥2), elevated serum calprotectin [S100 proteins] and hsCRP levels at withdrawal, imaging evidence of subclinical synovitis, and a history of uveitis were all significantly associated with increased risk of flare. No significant associations were found for other clinical or demographic characteristics. Conclusions: Early significant clinical response, absence of subclinical disease activity, and concomitant low-dose methotrexate therapy were key predictors of sustained drug-free remission. These findings may inform personalized strategies for biologic tapering in pediatric JIA. Full article

Complex water networks face prominent contradictions among flood control, water supply, and ecological protection, and traditional scheduling models struggle to address multi-dimensional water security challenges. To solve this problem, this study proposes a multi-objective coordinated water resources scheduling model for complex water networks, taking the Taihu Lake Basin as a typical case. First, a multi-objective optimization indicator system covering flood control, water supply, and aquatic ecological environment was constructed, including 12 key indicators such as drainage efficiency of key outflow hubs and water supply guarantee rate. Second, a dynamic variable weighting strategy was adopted to convert the multi-objective optimization problem into a single-objective one by adjusting indicator weights according to different scheduling periods. Finally, a combined solving mode integrating a basin water quantity-quality model and a joint scheduling decision model was established, optimized using the particle swarm optimization (PSO) algorithm. Under the 1991-Type 100-Year Return Period Rainfall scenario, three scheduling schemes were designed: a basic scheduling scheme and two enhanced discharge schemes modified by lowering the drainage threshold of the Xinmeng River Project. Simulation and decision results show that the enhanced discharge scheme with the lowest drainage threshold achieves the optimal performance with an objective function value of 98.8. Compared with the basic scheme, it extends the flood season drainage days of the Jiepai Hub from 32 to 43 days, increases the average flood season discharge of the Xinmeng River to the Yangtze River by 9.5%, and reduces the maximum water levels of Wangmuguan, Fangqian, Jintan, and Changzhou (III) stations by 5 cm, 5 cm, 4 cm, and 4 cm, respectively. This model effectively overcomes technical bottlenecks such as conflicting multi-objectives and complex water system structures, providing theoretical and technical support for multi-objective coordinated scheduling of water resources in complex water networks. Full article

High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). Specifically, a deep temporal feature extraction model based on Long Short-Term Memory Autoencoder (LSTM-AE) is integrated with K-Means clustering to generate four typical operation scenarios, effectively capturing complex source-load fluctuations. To further enhance system efficiency and environmental sustainability, a refined Power-to-Gas (P2G) model considering waste heat recovery is developed to realize energy cascading, coupled with a joint market mechanism that integrates Green Certificate Trading (GCT) and tiered carbon pricing. Building on this, a multi-objective DRO model based on Conditional Value at Risk (CVaR) is formulated to optimize the trade-off between operating costs and carbon emissions. Case studies based on California test data demonstrate that the proposed method reduces total operating costs by 9.0% and carbon emissions by 139.9 tons compared to traditional robust optimization (RO). Moreover, the results confirm that the system maintains operational safety even under extreme source-load fluctuation scenarios. Full article

Directional flying ad hoc networks (FANETs) equipped with phased array antennas are pivotal for applications demanding high-capacity, low-latency communications. While directional beamforming extends the communication range, it necessitates the intricate joint optimization of the beam direction, power, and time-slot scheduling under hardware constraints. Existing resource allocation schemes predominantly follow two paradigms: (i) conventional physical-layer multiple access (CPMA) approaches, which enforce strict orthogonality within each beam and thus limit spatial efficiency; and (ii) advanced physical-layer techniques like rate-splitting multiple access (RSMA), which have been applied to terrestrial and omnidirectional UAV networks but not systematically integrated with the beam-based scheduling constraints of directional FANETs. Consequently, jointly optimizing the beam direction, intra-beam rate-splitting-based node pairing, transmit power, and time-slot scheduling remains largely unexplored. To bridge this gap, this paper introduces an intra-beam rate-splitting-based resource allocation (IBRSRA) framework for directional FANETs. This paper formulates an optimization problem that jointly designs the beam direction, constrained rate-splitting (CRS)-based node pairing, power control, modulation and coding scheme (MCS) selection, and time-slot scheduling, aiming to minimize the total number of time slots required for data transmission. The resulting mixed-integer nonlinear programming (MINLP) problem is solved via a computationally efficient two-stage algorithm, combining greedy scheduling with successive convex approximation (SCA) for non-convex optimization. Simulation results demonstrate that the proposed IBRSRA algorithm substantially enhances spectral efficiency and reduces latency. Specifically, for a network with 16 nodes, IBRSRA reduces the required number of transmission time slots by more than 42% compared to the best-performing baseline scheme. This confirms the significant practical benefit of integrating CRS into the resource allocation design of directional FANETs. Full article

To address the challenges of improving energy balance and extending the operational lifetime of wireless sensor networks for Automated Railway Track Fastener Maintenance Robots (ARTFMR) along railways, this paper proposes an enhanced LEACH protocol incorporating Particle Swarm Optimization (PSO). Initially, network nodes are deployed, and their energy consumption is calculated to formulate a non-uniform deployment model aimed at improving energy balance, followed by network clustering. Subsequently, a routing protocol is designed, where the cluster head election mechanism integrates two critical factors—dynamic residual energy and distance to the base station—to facilitate dynamic and distributed cluster head rotation. During the communication phase, a Time Division Multiple Access (TDMA) scheduling mechanism is employed in conjunction with an inter-cluster multi-hop routing scheme. Additionally, a joint data-volume and energy optimization strategy is implemented to dynamically adjust the transmission data volume based on the residual energy of each node. Finally, simulations were conducted using MATLAB, and the results indicate that the proposed energy-balanced non-uniform deployment optimization strategy improves network energy utilization, effectively extends network lifetime, and exhibits favorable scalability. Full article

We study UAV-assisted 5G uplink connectivity for disaster response, in which a UAV (unmanned aerial vehicle) acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink non-orthogonal multiple access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic line of sight/non-line of sight (LoS/NLoS), realistic receiver noise levels and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, and edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2–10.1 percentage points compared to strong baselines (OFDMA with heuristic placement, PSO-based placement/power, and PPO without NOMA) while reducing median UE transmit power by $64.6\%$. The results are averaged over at least five random seeds, with $95\%$ confidence intervals. Ablations isolate the gains from NOMA, adaptive SIC order, and bounded-action parameterization. We discuss robustness to imperfect SIC and CSI errors and release code/configurations to support reproducibility. Full article

Promoting demand-responsive transit (DRT) is crucial for developing sustainable and green transportation systems in urban areas, especially in light of decreasing transit ridership and increasingly varying demand. However, the effectiveness of such services hinges on their ability to efficiently match varying travel demand. This paper presents a data-driven framework for the joint optimization of customized bus routes and timetables, to enhance both service quality and operational sustainability. Our approach leverages large-scale taxi trip data to identify latent travel demand, applying a spatial–temporal clustering method to group trip requests and identify DRT stops by trip origin, destination, and direction. An adaptive large neighborhood search (ALNS) algorithm is improved to co-optimize passenger waiting times and bus operation costs, where an unbalanced penalty for early or late schedule deviations is developed to better reflect passengers’ discomfort. The framework’s performance is validated through a real-world case study, demonstrating its ability to generate efficient routes and schedules. The model manages to improve passenger experience and reduce operation costs. By creating a more appealing and efficient service, this model contributes directly to the goals of green transport in terms of reducing the total vehicle kilometers that are traveled, and demonstrating a viable, high-quality alternative to private car usage. This study offers a practical and robust tool for transit planners to design a next-generation DRT system that is both economically viable and environmentally sustainable. Full article

The problem of low water level and uneven distribution of flow in the multi-branch channels at the tail of the Ganjiang River (GJRT) during the dry season has been affecting the local water supply, navigation, and aquatic ecological environment. In recent years, water conservancy projects have been built in each branch of the multi-branch channels at the GJRT. Finding a way to utilize the water conservancy project group to carry out joint regulation and meet the water level and discharge requirements of each branch is an important issue that urgently needs to be solved. This paper analyzes the hydrodynamic process and its impact on water supply, navigation, and ecology in multi-branch channels without water conservation projects through hydrological data analysis and numerical simulation. By conducting numerical experiments on joint regulation of water conservation project group, a multi-objective regulation strategy is proposed to meet the water level and discharge of each branch. The results indicate that the discharge at the GJRT has been continuously decreasing from 1 September. Due to the jacking effect of Poyang Lake, the water level plunges at the GJRT from 1 October, which occurred later than the decrease in water level. The disruption of water levels and discharge makes it difficult to meet the regional water demand. The optimal time to initiate regulation is 1 October, and the target water level of Waizhou Station is 15.5 m, located upstream of the Ganjiang River tail. When the water level before each branch project gate is uniform and exceeds 15.5 m, the water level of Waizhou Station satisfies the requirement. However, the discharge of each branch does not meet the demand. In contrast to a scheduling regulation strategy that maintains the same water level in front of each gate, adopting a strategy with different water levels before each gate can effectively adjust the diversion ratio and fulfill the discharge demand of each branch at the tail of the Ganjiang River. Full article

As data centers become essential large-scale infrastructures for data processing and intelligent computing, the efficiency of their internal scheduling systems is critical for both service quality and energy consumption. The performance of these scheduling systems significantly impacts the quality of computing services and overall energy usage. However, the rapid increase in task volume, coupled with the diversity of computing resources, poses substantial challenges to traditional scheduling approaches. Conventional container scheduling approaches typically focus on either minimizing task execution time or reducing energy consumption independently, often neglecting the importance of balancing these two objectives simultaneously. In this study, a container scheduling algorithm based on the Soft Actor–Critic framework, called SAC-CS, is proposed. This algorithm aims to enhance container execution efficiency while concurrently reducing energy consumption in data centers. It employs a maximum entropy reinforcement learning approach, enabling a flexible trade-off between energy use and task completion times. Experimental evaluations on both synthetic workloads and Alibaba cluster datasets demonstrate that the SAC-CS algorithm effectively achieves joint optimization of efficiency and energy consumption, outperforming heuristic methods and alternative reinforcement learning techniques. Full article

Liner shipping companies commonly pursue strategies such as forming strategic alliances and attracting new customers to strengthen competitiveness and improve operational performance. However, in the shipping of perishable goods, inadequate ship scheduling and bunker management can result in substantial customer loss and increased operational costs. This paper examines a scenario in which a large volume of perishable goods is shipped by liner ships. The specific demand characteristics of perishable goods—requiring rapid port handling and expedited shipping—are analyzed. To address these challenges, we propose a mixed-integer nonlinear programming (MINLP) model to optimize ship scheduling and refueling decisions for liner cold chain services under cooperative agreements. The model minimizes total liner shipping service costs while explicitly accounting for the decay of perishable goods. Nonlinear elements are linearized using a piecewise linear secant approximation, enabling efficient solution of the model with commercial solvers. Numerical experiments based on the AEU6 route operated by China COSCO Shipping Group validate the model and provide practical managerial insights. The results indicate that: (1) incorporating collaborative agreements can reduce total route service costs by 4.5% and total port handling costs by 7.5%, while also lowering late arrival penalties and losses from perishable goods decay; (2) joint consideration of refueling strategies and collaborative agreements improves both decision flexibility and solution accuracy; (3) the shipping of perishable goods has differentiated effects across voyage legs, highlighting the need for liner shipping companies to enhance cooperation with ports and refine bunker fuel procurement planning; and (4) it is essential to improve ship performance and appropriately design bunker fuel tank capacity to respond to dynamic changes in the shipping market. Full article

This study addresses the joint scheduling problem of flexible job shop scheduling and automated guided vehicles with the objective of minimizing the makespan. We propose an efficient optimization approach based on a critical-path-driven variable neighborhood descent. The core contribution lies in the development of a critical path detection mechanism that incorporates transportation processes, along with the design of tailored neighborhood structures. Building on this foundation, a problem-specific variable neighborhood descent search strategy is implemented. Unlike traditional variable neighborhood descent approaches, the proposed critical path analysis accurately identifies bottleneck operations in both processing and transportation stages. The designed neighborhood structures effectively coordinate machine scheduling and automated guided vehicles transportation, enabling synergistic optimization. To enhance overall performance, auxiliary strategies such as an external memory archive and population diversity maintenance are integrated. Experimental results on multiple benchmark datasets demonstrate that the proposed method achieves significant improvements in solution quality compared to existing algorithms. Ablation experiments further confirm the critical role of the critical-path-driven variable neighborhood descent mechanism in enhancing algorithmic performance. Full article

With the rapid advancement of battery technology, new energy hybrid tugboats have been progressively adopted. In order to align with the trend of electrifying tugboat fleets, a mixed-integer linear programming (MILP) model for the joint scheduling of new energy hybrid tugboats and berths has been established. The model incorporates the constraint imposed by the limited number of tugboat charging connectors. The objective is to minimize the total cost over the scheduling horizon, including ship waiting, delayed-departure costs, and the operating costs of both conventional diesel and hybrid tugboats. In light of the characteristics inherent to the problem, a hybrid solution approach combining CPLEX with a heuristic-enhanced whale optimization algorithm (WOA) is employed to solve the model. A case study was conducted using data on the energy consumption of tugboats at Xiamen Port. The effectiveness of the model and algorithm was then verified through a series of small-scale instance experiments. Finally, a comprehensive sensitivity analysis of key parameters is finally conducted, including the number of tugboat charging connectors, battery capacity, and charging rate. This analysis provides valuable guidance for port tugboat operations. Full article

Driven by the proliferation of the Internet of Things (IoT), Mobile Edge Computing (MEC) is a key technology for meeting the low-latency and high-computational demands of future wireless networks. However, ground-based MEC servers suffer from limited coverage and inflexible deployment. Unmanned Aerial Vehicles (UAVs), with their high mobility, can serve as aerial edge servers to extend this coverage. This paper addresses the multi-user serial task offloading problem in cache-assisted UAV-MEC systems by proposing a joint optimization algorithm for service caching, UAV positioning, task offloading, and serial processing order. Under the constraints of physical resources such as UAV cache capacity, heterogeneous computing capabilities, and wireless channel bandwidth, an optimization problem is formulated to minimize the weighted sum of task completion time and user cost. The method first performs service caching based on task popularity and then utilizes the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to optimize the UAV’s position, task offloading decisions, and serial processing order. The MADDPG algorithm consists of two collaborative agents: a UAV position agent responsible for selecting the optimal UAV position, and a task scheduling agent that determines the serial processing order and offloading decisions for all tasks. Simulation results demonstrate that the proposed algorithm can converge quickly to a stable solution, significantly reducing both task completion time and user cost. Full article