Search Results (138)

Problem-based learning (PBL) has been recognized for enhancing student motivation and key competencies. However, its integration with emerging topics such as ESG (Environmental, Social, and Governance) in general education remains underexplored. This study implemented a PBL-based instructional design in a general education course titled “Organizational Greenhouse Gas Inventory and Net-Zero Transition,” integrating practical tasks and ESG case studies to enhance students’ sustainability literacy and core competencies. Pre- and post-course assessments were conducted using the University Career and Competency Assessment Network (UCAN) questionnaire, analyzed through paired sample t tests and Wilcoxon signed rank tests. Results showed significant improvements in the innovation and communication aspects, with upward trends observed in other domains. Students also demonstrated strong engagement and learning motivation through tasks such as carbon footprint estimation, data integration, and field-based assessments. The findings support the feasibility of embedding ESG and PBL frameworks in general education. Future course iterations will consider differentiated instructional design and the incorporation of qualitative methods to accommodate diverse student backgrounds and enhance learning outcomes, contributing to the advancement of sustainability education in higher education. Full article

Background: Urban rail transit ensures efficient mobility in densely populated metropolitan areas. This study focuses on the Cairo Metro Network and addresses the Rolling Stock Rotation Planning Problem (RSRPP), aiming to improve operational efficiency and service quality. Methods: A Mixed-Integer Linear Programming (MILP) model is developed to integrate rolling stock rotation, deadhead routing, and maintenance scheduling. Two single-objective formulations are introduced to separately minimize denied passengers and the number of Electric Multiple Units (EMUs) used. To address scalability for larger instances, a Simulated Annealing (SA) metaheuristic is designed using a list-based solution representation and customized neighborhood operators that preserve feasibility. Results: Computational experiments based on real-world data validate the practical relevance of the model. The MILP achieves optimal solutions for small and medium-sized instances but becomes computationally infeasible for larger ones. In contrast, the SA algorithm consistently produces high-quality solutions with significantly reduced solve times. Conclusions: To the best of the authors’ knowledge, this is the first study to apply SA to the urban rail RSRPP while jointly integrating deadhead routing and maintenance scheduling. The proposed approach proves to be robust and scalable for large metro systems such as Cairo’s. Full article

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

After the disruption of intercity railways, in order to effectively enhance system resilience and improve the sustainability of the intercity transit system, this paper studies the emergency response problem of multimodal collaboration based on the intercity multimodal transit system. Considering the constraints of the disrupted network structure, multimodal emergency resources, dynamic passenger demand, and passenger participation willingness, a bi-level optimization model is established for maximizing system resilience and minimizing the deviation of passengers’ desired arrival time. This paper integrally determines the transit capacity, timetable, and passenger quantity on each line of each mode. A hybrid genetic and ant colony algorithm is designed to solve the problem. Taking the regional disruption of the Beijing–Tianjin–Hebei intercity railway network as a case study, the research results show that 59% of demand can be met with a single attempt and 70% of the arrival time is within the planned period. Based on this resilience-enhancement strategy, the imbalance between travel demand and transit capacity can be sustainably alleviated after railway disruption. Full article

The rail transit switch machine ensures the safe turning and operation of trains on the track by switching switch positions, locking switch rails, and reflecting switch status in real time. However, in the detection of complex rail transit switch machine parts such as augmented reality and automatic inspection, existing algorithms have problems such as insufficient feature extraction, large computational complexity, and high demand for hardware resources. This article proposes a complex scene rail transit switch machine parts detection network YOLO-SMPDNet (YOLO-based Switch Machine Parts Detecting Network). The YOLOv8s backbone network is improved, and the number of network parameters are reduced by introducing MobileNetV3. Then a parameter-free attention-enhanced ResAM module is designed, which forms a lightweight detection network with the improved network, improving detection efficiency. Finally, Focal IoU Loss is introduced to more accurately define the scale information of the prediction box, alleviate the problem of imbalanced positive and negative samples, and improve the relative ambiguity of CIoU Loss in YOLOv8s on the definition of aspect ratio. By validating the performance of YOLO-SMPDNet on a self-made dataset of rail transit switch machines, the results show that YOLO-SMPDNet can significantly improve detection accuracy and real-time performance and has robust comprehensive detection capabilities for rail transit switch machine parts and good practical application performance. Full article

The fusion modeling of intra-session item information representation and inter-session item transition pattern for session recommendation has shown performance advantages. However, existing research still suffers from the following challenges: (1) the time-varying effects of complex relationships between item transitions within sessions need to be deeply explored; and (2) the lack of effective representation for inter-session item transition patterns. To address these challenges, we propose a new session recommendation, named EGDLTP-CCL. Specifically, we first design a patch-enhanced gated neural network representation of session item transition patterns, which accurately captures the time-dynamically varying impacts of the complex relationships within sessions of item transitions through a designed transformer patching strategy. Then, we develop an energy-constraint-based graph diffusion model to capture the inter-session item transition patterns, which mitigates the problem of poor simulation of real inter-session item transition patterns by the introduction of an energy-constraint strategy for the graph diffusion model. In addition, patch-enhanced gated neural networks and energy-constrained graph diffusion models are treated as two different views in the contrastive learning framework. By introducing a curriculum learning strategy that explores how to effectively select and train negative samples in a contrastive learning framework, thereby deeply improving performance in contrastive learning task. Finally, we combine and jointly train the recommendation task and the curriculum learning contrastive learning task for optimization based on a multi-task learning strategy to further improve the recommendation performance. Experiments on real-world datasets show that EGDLTP-CCL significantly outperforms state-of-the-art methods. Full article

This work proposes a power-efficient framework for adaptive video streaming in UAV-assisted wireless networks specially designed for disaster-hit areas where existing base stations are nonfunctional. Delivering high-quality videos requires higher video rates and more resources, which leads to increased power consumption. With the increasing demand of mobile video, efficient bandwidth allocation becomes essential. In shared networks, users with lower bitrates experience poor video quality when high-bitrate users occupy most of the bandwidth, leading to a degraded and unfair user experience. Additionally, frequent video rate switching can significantly impact user experience, making the video rates’ smooth transition essential. The aim of this research is to maximize the overall users’ quality of experience in terms of power-efficient adaptive video streaming by fair distribution and smooth transition of video rates. The joint optimization includes power minimization, efficient resource allocation, i.e., transmit power and bandwidth, and efficient two-dimensional positioning of the UAV while meeting system constraints. The formulated problem is non-convex and difficult to solve with conventional methods. Therefore, to avoid the curse of complexity, the block coordinate descent method, successive convex approximation technique, and efficient iterative algorithm are applied. Extensive simulations are performed to verify the effectiveness of the proposed solution method. The simulation results reveal that the proposed method outperforms 95–97% over equal allocation, 77–89% over random allocation, and 17–40% over joint allocation schemes. Full article

This paper studies the composite anti-disturbance static output feedback control problem of networked nonlinear Markov jump systems with general transition probabilities subject to multiple disturbances and deception attacks. The transition probabilities cover the known, uncertain with known bounds, and unknown cases. The unmatched disturbance and deception attacks are attenuated by the static output controller, while the matched disturbance is observed and compensated by the disturbance observer. Then, a composite anti-disturbance static output controller, including a linear part and a nonlinear part, is constructed to satisfy the stochastic $H_{\infty }$ stability. By using the Finsler lemma, sufficient conditions formed as symmetric linear matrix inequalities are derived to design the gains of disturbance observer and the output feedback controller. Finally, some simulations are given to illustrate the feasibility of the developed strategy. Full article

Groundwater flow problems involve complex nonlinear and spatiotemporal characteristics, where traditional numerical methods (e.g., finite element, finite difference) often encounter challenges such as low computational efficiency and insufficient accuracy when dealing with complex boundary conditions and heterogeneous media. To address these issues, this study proposes a novel physics-informed Kolmogorov–Arnold network (PKAN) framework that combines the unique variable decomposition mechanism of KAN networks with physical constraints. The framework introduces three key innovations: (1) implementing KAN network’s univariate function decomposition to enhance the network’s ability to express nonlinear features; (2) designing a pre-training network mechanism to effectively handle complex boundary conditions; and (3) innovatively incorporating a distance function to achieve natural transition from boundary to interior solutions. The results demonstrate that in one-dimensional heterogeneous medium transient simulation, PKAN achieves superior prediction accuracy (R² = 0.9966, RMSE = 0.0313) compared to traditional PINN (R² = −0.7194, RMSE = 0.7001). In two-dimensional multi-well pumping system simulations, PKAN (R² = 0.917, RMSE = 0.077) similarly exhibits exceptional performance (PINN: R² = −0.3043, RMSE = 0.3067). Notably, in handling local strong gradient problems, PKAN accurately captures cone of depression characteristics and precisely reproduces inter-well interference effects, with maximum error only one-fourth that of traditional PINN. Sensitivity analysis reveals that a configuration of 50 × 50 uniform sampling points combined with four hidden layers and 64 neurons per layer achieves optimal balance between computational efficiency and simulation accuracy. These findings demonstrate PKAN’s breakthrough in groundwater numerical simulation, offering a novel approach for the efficient solution of complex hydrogeological problems. Full article

To reduce airport operational costs and minimize environmental pollution, an increasing number of airports are transitioning from fuel-powered to electric ground-handling vehicles. However, the limited battery capacity of electric vehicles and the need for charging make the scheduling of these vehicles more complex. To address this scheduling problem, this paper proposes an electric ground-handling vehicle scheduling algorithm that combines the MAMBA model with an attention-based neural network. The MAMBA model is designed to process multi-dimensional features such as flight information, vehicle locations, service demands, and time window constraints. Subsequently, an attention mechanism-based neural network is developed to dynamically integrate vehicle states, service records, and operational and charging constraints, in order to select the most suitable flights for electric ground-handling vehicles to service. The experiments use flight data from Xiamen Gaoqi International Airport and compare the proposed method with CPLEX solvers, existing heuristic algorithms, and custom heuristic algorithms. The results demonstrate that the proposed method not only effectively solves the electric ground-handling vehicle scheduling problem and provides high-quality solutions, but also exhibits good scalability in different parameter settings and real-time scheduling scenarios. Full article

Optimizing railway station operations necessitates the identification of critical track sections that constrain design throughput capacity under fixed infrastructure conditions. This paper proposes a novel multi-layer complex network-based approach for modeling and analyzing railway station yard diagrams, reframing the identification of key track sections affecting station throughput capacity as a node importance evaluation problem. In this model, nodes represent track sections included in routes specified by the station interlocking tables, while edges denote sequential connections between nodes. The structural relationships among nodes are captured using adjacency matrix (AM), structural matrix (SM), connection count matrix (CCM), and transition probability matrix (TPM). To evaluate node importance, five key indicators are introduced: connectivity strength (CS), destination node count (DNC), source node count (SNC), node efficiency (NE), and an extended PageRank (EPR). Additionally, a layered network node importance analysis method based on a single indicator, along with a comprehensive evaluation approach for the importance of the multi-layer network node, is presented. A case study conducted on a conventional railway station demonstrates that the proposed method effectively identifies key track sections through both hierarchical single-indicator evaluation and comprehensive assessment approaches. Furthermore, this paper investigates key node evaluation indicators and explores an alternative method based on Principal Component Analysis and Rank Sum Ratio (PCA-RSR), which also proves effective in identifying critical track sections. Full article

With the increasing importance of renewable energy in the global energy transition, the microgrid has attracted wide attention as an efficient and flexible power solution. However, there are some problems in current networked microgrid systems, such as complex structure, numerous parameters, and significant fluctuations in generation capacity. Aiming at the parameter optimization problem of networked microgrids integrating multiple energy generation and energy storage forms, this paper constructs a multi-objective microgrid structure decision-making model. The model comprehensively considers operation and maintenance costs, fuel costs, power abandonment and lack-of-power punishment costs, power transaction costs, and pollution treatment costs, aiming to realize the joint optimization of economic benefits and environmental sustainability. Furthermore, an improved multi-objective particle swarm optimization (IMOPSO) algorithm is designed to solve the model. In order to verify the effectiveness of the model in the scenarios of distributed energy and energy load fluctuation, this paper uses the scenario analysis method to realize the data analysis of 2000 scenarios, and obtains four typical deterministic scenarios for simulation experiments. The experimental results show that, compared with the traditional microgrid, when the capacity configuration is determined by the number of wind driven generators, photovoltaic panels, diesel generators, and batteries being 5, 189, 2, and 107, respectively, the proposed net-connected economic dispatch optimization method based on hybrid renewable energy in this paper reduces the generation cost and environmental cost of the system by 96.76 ¥ to 428.19 ¥, and keeps the load loss rate stable between 0.34% and 4.56%. The utilization rate of renewable energy has been raised to about 95%. Full article

In order to advocate for green and environmentally friendly travel modes, enhance the attractiveness of rail transit, and promote the sustainable development of rail transport, we focus on the transportation organization problem under limited-resource conditions. This paper studies the formulation of a train plan under the condition of through operation between intercity and high-speed railway, constructing a multi-objective nonlinear optimization model with train frequency, a stop plan, and turn-back station locations as decision variables. Given the high dimensionality of model variables and complex constraints, an improved multi-population genetic algorithm (IMGA) is designed. Through an actual case study of the through operation between the Chengdu–Mianyang–Leshan Intercity Railway and the Chengdu–Chongqing High-Speed Railway, a staged solution method is adopted for analysis. The results indicate that the through-operation mode can save operational costs for enterprises and travel costs for passengers, while also better adapting to changes in passenger flow. Additionally, the IMGA demonstrates better solution quality and higher efficiency compared to the classical genetic algorithm. The main contribution of this paper is to propose a novel approach to solve the train plan problem. It also contributes to creating a high-quality, high-efficiency, and high-comfort integrated transportation service network, promoting the sustainable development of rail transit. Full article

With the rapid development of in-vehicle communication technology, the Internet of Vehicles (IoV) is gradually becoming a core component of next-generation transportation networks. However, ensuring the activity and reliability of IoV nodes remains a critical challenge. The emergence of blockchain technology offers new solutions to the problem of node selection in IoV. Nevertheless, traditional blockchain networks may suffer from malicious nodes, which pose security threats and disrupt normal blockchain operations. To address the issues of low participation and security risks among IoV nodes, this paper proposes a federated learning (FL) scheme based on blockchain and reputation value changes. This scheme encourages active involvement in blockchain consensus and facilitates the selection of trustworthy and reliable IoV nodes. First, we avoid conflicts between computing power for training and consensus by constructing state-channel transitions to move training tasks off-chain. Task rewards are then distributed to participating miner nodes based on their contributions to the FL model. Second, a reputation mechanism is designed to measure the reliability of participating nodes in FL, and a Proof of Contribution Consensus (PoCC) algorithm is proposed to allocate node incentives and package blockchain transactions. Finally, experimental results demonstrate that the proposed incentive mechanism enhances node participation in training and successfully identifies trustworthy nodes. Full article

In the context of the global response to climate change and the promotion of an energy transition, the Internet of Things (IoT), sensor technologies, and big data analytics have been increasingly used in power systems, contributing to the rapid development of distributed energy resources. The integration of a large number of distributed energy resources has led to issues, such as increased volatility and uncertainty in distribution networks, large-scale data, and the complexity and challenges of optimizing security and economic dispatch strategies. To address these problems, this paper proposes a day-ahead scheduling method for distribution networks based on an improved multi-agent proximal policy optimization (MAPPO) reinforcement learning algorithm. This method achieves the coordinated scheduling of multiple types of distributed resources within the distribution network environment, promoting effective interactions between the distributed resources and the grid and coordination among the resources. Firstly, the operational framework and principles of the proposed algorithm are described. To avoid blind trial-and-error and instability in the convergence process during learning, a generalized advantage estimation (GAE) function is introduced to improve the multi-agent proximal policy optimization algorithm, enhancing the stability of policy updates and the speed of convergence during training. Secondly, a day-ahead scheduling model for the power distribution grid containing multiple types of distributed resources is constructed, and based on this model, the environment, actions, states, and reward function are designed. Finally, the effectiveness of the proposed method in solving the day-ahead economic dispatch problem for distribution grids is verified using an improved IEEE 30-bus system example. Full article