Search Results (242)

To address the substantial disparities in operational characteristics between social vehicles and dedicated bus lanes, as well as the sub-optimal coordination control effects, a comprehensive approach is proposed. This approach integrates social vehicle arterial coordination with bus priority control in dedicated bus lanes. Initially, an analysis of the differences in travel time distribution on both types of roads is conducted. The likelihood of buses passing through upstream and downstream intersections without stopping is also assessed. This analysis aids in determining the correlated traffic states and the corresponding signal adjustment strategies for arterial coordination. Subsequently, an incentive mechanism is established by quantitatively analyzing vehicle delay losses and bus priority benefits based on the signal adjustment strategy. Finally, a deep reinforcement learning framework is proposed to solve, in real-time, the optimal signal adjustment strategy. Simulation experiments indicate that, in comparison to the arterial coordination of social vehicles and dedicated bus arterial coordination control, this method significantly reduces the average per capita delay by 38.63% and 27.43%, respectively, under conventional traffic flow scenarios. This is in contrast to the separate arterial coordination for social vehicles and dedicated bus lanes. Furthermore, it leads to a reduction of 52.17% in the number of bus stops at intersections when compared solely with the arterial coordination of social vehicles. In saturated traffic flow scenarios, this method achieves a reduction in average per capita delay by 29.7% and 9.6%, respectively, while also decreasing the number of bus stops at intersections by 39.5% and 8.7%, respectively. Full article

Background: Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease and a major cause of morbidity and mortality among preterm infants. Traditional diagnostic methods such as abdominal radiography have limited sensitivity in early disease stages, prompting interest in bowel ultrasound (BUS) as a complementary imaging modality. Objective: This scoping review aims to synthesize existing literature on the role of ultra sound in the early detection, diagnosis, and management of NEC, with emphasis on its diagnostic performance, integration into clinical care, and technological innovations. Methods: Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Embase, Cochrane Library, and Google Scholar for studies published between January 2000 and December 2025. Inclusion criteria encompassed original research, reviews, and clinical studies evaluating the use of bowel, intestinal, or Doppler ultrasound in neonates with suspected or confirmed NEC. Data were extracted, categorized by study design, population characteristics, ultrasound features, and diagnostic outcomes, and qualitatively synthesized. Results: A total of 101 studies were included. BUS demonstrated superior sensitivity over radiography in detecting early features of NEC, including bowel wall thickening, portal venous gas, and altered peristalsis. Doppler ultrasound, both antenatal and postnatal, was effective in identifying perfusion deficits predictive of NEC onset. Neonatologist-performed ultrasound (NEOBUS) showed high interobserver agreement when standardized protocols were used. Emerging tools such as ultra-high-frequency ultrasound (UHFUS) and artificial intelligence (AI)-enhanced analysis hold potential to improve diagnostic precision. Point-of-care ultrasound (POCUS) appears feasible in resource-limited settings, though implementation barriers remain. Conclusions: Bowel ultrasound is a valuable adjunct to conventional imaging in NEC diagnosis. Standardized protocols, validation of advanced technologies, and out come-based studies are essential to guide its broader clinical adoption. Full article

Effective energy management is vital for enhancing reliability, reducing operational costs, and supporting the increasing penetration of electric vehicles (EVs) and renewable energy sources (RESs) in distribution networks. This study presents a dynamic reconfiguration strategy for distribution feeders that integrates EV charging stations (EVCSs), RESs, and capacitors. The goal is to minimize both Energy Not Supplied (ENS) and operational costs, particularly under varying demand conditions caused by EV charging in grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. To improve optimization accuracy and avoid local optima, an improved Whale Optimization Algorithm (IWOA) is employed, featuring a mutation mechanism based on Lévy flight. The model also incorporates uncertainties in electricity prices and consumer demand, as well as a demand response (DR) program, to enhance practical applicability. Simulation studies on a 95-bus test system show that the proposed approach reduces ENS by 16% and 20% in the absence and presence of distributed generation (DG) and EVCSs, respectively. Additionally, the operational cost is significantly reduced compared to existing methods. Overall, the proposed framework offers a scalable and intelligent solution for smart grid integration and distribution network modernization. Full article

Accurate bus travel time prediction is crucial for improving travel experience, especially in transfer journeys. This study introduces a novel multi-route bus travel time prediction system-based PMLNet, a partition and combination prediction framework, addressing the gap in accurate prediction models by incorporating macro and local impact factors. The system employs a pre-processing algorithm for constructing travel chains, partitions travel time into four components, utilizes LSTM along with the newly proposed MDARNN model for predicting each component, and applies four real-time traffic impact factors to calibrate the predictions of each component. Experimental validation on four bus routes demonstrates PMLNet’s superior performance, achieving mean absolute percentage errors (MAPE) as low as 2.91% and mean absolute errors (MAE) below 1.45 min, outperforming traditional models and various partitioned combination frameworks. These findings underscore PMLNet’s potential to significantly improve public transportation services by providing more accurate travel time predictions, ultimately enhancing the user experience in intelligent transportation systems. Full article

In response to the challenges associated with weld treatment during the on-site corrosion protection of hydraulic steel gates, this paper proposes a method utilizing a magnetic adsorption climbing robot to perform corrosion protection operations. Firstly, a magnetic adsorption climbing robot with a multi-wheel independent drive configuration is proposed as a mobile platform. The robot body consists of six joint modules, with the two middle joints featuring adjustable suspension. The joints are connected in series via an EtherCAT bus communication system. Secondly, the kinematic model of the climbing robot is analyzed and a PID trajectory tracking control method is designed, based on the kinematic model and trajectory deviation information collected by the vision system. Subsequently, the proposed kinematic model and trajectory tracking control method are validated through Python3 simulation and actual operation tests on a curved trajectory, demonstrating the rationality of the designed PID controller and control parameters. Finally, an intelligent software system for weld defect detection based on computer vision is developed. This system is demonstrated to conduct defect detection on images of the current weld position using a trained model. Full article

When planning distributed intelligent power distribution networks, it is necessary to take into account the interests of various distributed generation (DG) operators and power supply enterprises, thereby diversifying and complicating planning models. Additionally, the integration of a high proportion of distributed resources has triggered a transformation in the power flow pattern of active distribution networks, shifting from the traditional unidirectional flow mode to a bidirectional interactive mode. The intermittent and fluctuating operation modes of distributed photovoltaic and wind power generation have also increased the difficulty of distribution network planning. To address the aforementioned challenges, this paper proposes an active distribution network planning strategy that considers the allocation of diverse flexible resources, exploring scheduling flexibility from both the power supply side and the load side. Firstly, a bi-level optimization model integrating planning and operation is constructed, where the upper-level model determines the optimal capacity of investment and construction equipment, and the lower-level model formulates an economic dispatching scheme. Through iterative solving of the upper and lower levels, the final planning strategy is determined. Meanwhile, to reduce the complexity of problem-solving, this paper employs an improved PSO-CS hybrid algorithm for iterative optimization. Finally, the effectiveness and feasibility of the proposed algorithm are demonstrated through validation using an improved IEEE-33-bus test system. Compared with conventional algorithms, the convergence speed of the method proposed in this paper can be improved by up to 21.4%, and the total investment cost can be reduced by up to 3.26%. Full article

Frequency instability poses a significant challenge to the overall stability of islanded microgrid systems. Deep reinforcement learning (DRL)-based intelligent control strategies are drawing considerable attention for their ability to operate without the need for previous system dynamics information and the capacity for autonomous learning. This paper proposes an intelligent frequency secondary compensation solution that divides the traditional secondary frequency control into two layers. The first layer is based on a PID controller and the second layer is an intelligent controller based on DRL. To address the typically extensive training durations associated with DRL controllers, this paper integrates transfer learning, which significantly expedites the training process. This scheme improves control accuracy and reduces computational redundancy. Simulation tests are executed on an islanded microgrid with four distributed generators and an IEEE 13-bus system is utilized for further validation. Finally, the proposed method is validated on the OPAL-RT real-time test platform. The results demonstrate the superior performance of the proposed method. Full article

As the final stage of electrical energy delivery, distribution networks play a vital role in ensuring reliable power supply to end users. In regions with limited distribution automation, reliance on operator experience for fault handling often prolongs outage durations, undermining energy sustainability through increased economic losses and carbon-intensive backup generation. Remote-controlled switches (RCSs), as fundamental components of distribution automation, enable remote operation, rapid fault isolation, and load transfer, thereby significantly enhancing system reliability. In the process of intelligent distribution network upgrading, this study targets scenarios with sufficient line capacity and constructs a reliability-oriented analytical model for optimal RCS allocation by traversing all possible faulted lines. The resulting model is essentially a mixed-integer linear programming formulation. To address bilinearities, the McCormick envelope method is applied. Multi-binary products are decomposed into bilinear terms using intermediate variables, which are then linearized in a stepwise manner. Consequently, the model is transformed into a computationally efficient mixed-integer linear programming problem. Finally, the proposed method is validated on a 53-node and a 33-bus test system, with an approximately 30 to 40 times speedup compared to an existing mixed-integer nonlinear programming formulation. By minimizing outage durations, this approach strengthens energy sustainability through reduced socioeconomic disruption, lower emissions from backup generation, and enhanced support for renewable energy integration. Full article

As the Internet of Vehicles (IoV) rapidly gains popularity, the Controller Area Network (CAN) faces increasingly severe security threats. Most of the existing research on protecting the CAN bus has been based on artificial intelligence models, which require complex feature extraction and training processes and are too resource-intensive for deployment in resource-constrained CAN environments. To address these challenges, we propose a lightweight intrusion detection system based on locality-sensitive hashing that achieves efficient security protection without relying on complex machine learning and deep learning frameworks. We employ the Nilsimsa algorithm to compute hash digests of the data, using the similarity scores of these digests as anomaly scores to identify abnormal traffic. Evaluations show that our method achieves an accuracy of 98%, and tests of the system’s overhead confirm its suitability for deployment in resource-limited CAN scenarios. Full article

Urban rail transit (URT) systems are critical for sustainable urban mobility but are increasingly vulnerable to disruptions and emergencies. While extensive research has examined the built environment’s influence on transit demand under normal conditions, the nonlinear causal mechanisms shaping URT passenger flow during emergencies remain understudied. This study proposes an artificial intelligence-based causal machine learning framework integrating causal structure learning and causal effect estimation to investigate how the built environment, network structure, and incident characteristics causally affect URT station-level ridership during emergencies. Using empirical data from Shanghai’s URT network, this study uncovers dual pathways through which built environment attributes affect passenger flow: by directly shaping baseline ridership and indirectly influencing intermodal connectivity (e.g., bus connectivity) that mitigates disruptions. The findings demonstrate significant nonlinear and heterogeneous causal effects; notably, stations with high network centrality experience disproportionately severe ridership losses during disruptions, while robust bus connectivity substantially buffers such impacts. Incident type and timing also notably modulate disruption severity, with peak-hour incidents and severe disruptions (e.g., power failures) amplifying passenger flow declines. These insights highlight critical areas for policy intervention, emphasizing the necessity of targeted management strategies, enhanced intermodal integration, and adaptive emergency response protocols to bolster URT resilience under crisis scenarios. Full article

Facial expression recognition (FER) plays a critical role in affective computing, enabling machines to interpret human emotions through facial cues. While recent deep learning models have achieved progress, many still fail under real-world conditions such as occlusion, lighting variation, and subtle expressions. In this work, we propose FERONet, a novel hyper-attentive multimodal transformer architecture tailored for robust and real-time FER. FERONet integrates a triple-attention mechanism (spatial, channel, and cross-patch), a hierarchical transformer with token merging for computational efficiency, and a temporal cross-attention decoder to model emotional dynamics in video sequences. The model fuses RGB, optical flow, and depth/landmark inputs, enhancing resilience to environmental variation. Experimental evaluations across five standard FER datasets—FER-2013, RAF-DB, CK+, BU-3DFE, and AFEW—show that FERONet achieves superior recognition accuracy (up to 97.3%) and real-time inference speeds (<16 ms per frame), outperforming prior state-of-the-art models. The results confirm the model’s suitability for deployment in applications such as intelligent tutoring, driver monitoring, and clinical emotion assessment. Full article

Fault recovery in distribution networks is a complex, high-dimensional decision-making task characterized by partial observability, dynamic topology, and strong interdependencies among components. To address these challenges, this paper proposes a graph-based multi-agent deep reinforcement learning (DRL) framework for intelligent fault restoration in power distribution networks. The restoration problem is modeled as a partially observable Markov decision process (POMDP), where each agent employs graph neural networks to extract topological features and enhance environmental perception. To address the high-dimensionality of the action space, an action decomposition strategy is introduced, treating each switch operation as an independent binary classification task, which improves convergence and decision efficiency. Furthermore, a collaborative reward mechanism is designed to promote coordination among agents and optimize global restoration performance. Experiments on the PG&E 69-bus system demonstrate that the proposed method significantly outperforms existing DRL baselines. Specifically, it achieves up to 2.6% higher load recovery, up to 0.0 p.u. lower recovery cost, and full restoration in the midday scenario, with statistically significant improvements ($p<0.05$ or $p<0.01$). These results highlight the effectiveness of graph-based learning and cooperative rewards in improving the resilience, efficiency, and adaptability of distribution network operations under varying conditions. Full article

The collapse of Baltimore’s Francis Scott Key Bridge in March 2024 has stressed the need for urban traffic network optimization within smart city initiatives. This paper utilizes the ARIMA model to forecast what traffic would have been like if the bridge had not collapsed, giving us a benchmark to assess the impact. It then identifies the roads most affected by comparing these forecasts with the actual post-collapse traffic data. To address the increased demand for efficient public transport, we propose an intelligent bus network model. This model uses principal component analysis and grid segmentation to inform decisions on increasing bus stations and adjusting bus frequencies on key routes. It aims to satisfy stakeholders by enhancing service coverage and reliability. The research also presents a comprehensive traffic model that leverages principal component analysis, genetic algorithms, and KD-tree to evaluate overall and directional traffic flow, providing strategic insights into congestion mitigation. Furthermore, it examines traffic safety issues, including accident-prone areas and traffic signal intersections, to offer recommendations. Finally, the study evaluates the effectiveness, stability, and benefits of the proposed intelligent traffic network model, aiming to improve the city’s traffic infrastructure and safety. Full article

Multi-source systems are gaining attention as an effective approach to seamlessly incorporate renewable energies within electrical networks. These systems offer greater flexibility and better energy management possibilities. The considered multi-source system is based on a 50 MW wind farm connected to battery energy storage and electrolyzers through modular multi-level DC/DC converters. Wind energy systems interface with the DC-bus via rectifier power electronics that regulate the DC-bus voltage and implement optimal power extraction algorithms for efficient wind turbine operation. However, integrating intermittent renewable energy sources with optimal microgrid management poses significant challenges. It is essential to mention that the studied multi-source system is connected to the DC loads (modular electrolyzers and local load). This work proposes a new regulation method designed specifically to improve the performance of the system. In this strategy, the excess wind farm energy is converted into hydrogen gas and may be stored in the batteries. On the other hand, when the wind speed is low or there is no excess of energy, electrolyzer operations are stopped. The battery energy management depends on the power balance between the DC load (modular electrolyzers and local load) requirements and the energy produced from the wind farm. This control should lead to eliminating the fluctuations in energy production and should have a high dynamic performance. This work presents a nonlinear control method using a backstepping concept to improve the performances of the system operations and to achieve the mentioned goals. To evaluate the developed control strategy, some simulations based on real meteorological wind speed data using Matlab are conducted. The simulation results show that the proposed backstepping control strategy is satisfactory. Indeed, by integrating this control strategy into the multi-source system, we offer a flexible solution for battery and electrolyzer applications, contributing to the transition to a cleaner, more resilient energy system. This methodology offers intelligent and efficient energy management. Full article

The introduction of Bus Rapid Transit (BRT) systems has the potential to alleviate urban traffic congestion. However, in certain cities in China, the increasing prevalence of privately owned vehicles, combined with the underutilization of bus lanes due to infrequent bus departures, has contributed to heightened congestion in general lanes. The advent of Internet of Things (IoT) technology offers a promising opportunity to develop intelligent public transportation systems, facilitating efficient management through seamless information transmission to end devices. This paper presents an IoT-based shared bus lane (IoT-SBL) that integrates intersection information, real-time traffic queuing conditions, and bus location data to encourage passenger vehicles to utilize the bus lane. This encouragement can be communicated through traditional signaling methods or future Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication technologies. To evaluate the effectiveness of the IoT-SBL strategy, we proposed a stochastic model that incorporates queuing effects and derived a series of performance metrics through model analysis. The experimental findings indicated that the IoT-SBL strategy significantly reduces vehicle queuing, decreases vehicle delays, enhances intersection throughput efficiency, and lowers fuel consumption compared to the traditional bus lane strategy. Full article