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With the rapid development of global trade, the cargo throughput of automated container terminals (ACTs) has increased significantly. To meet the demands of large-scale, high-intensity, and high-efficiency ACT operations, the seamless integration of various terminal facilities has become crucial, particularly the collaboration between yard cranes (YCs) and automated guided vehicles (AGVs). Therefore, an integrated scheduling problem for YCs and AGVs (YAAISP) is proposed and formulated in this paper, considering stacking containers and bidirectional transport of AGVs. As the YAAISP is an NP-hard problem, an Improved Whale Optimization Algorithm (IWOA) is proposed in which a reverse learning strategy is used for the population to enhance population diversity; a random difference variation strategy is employed to improve individual exploration capabilities; and a nonlinear convergence factor alongside an adaptive weighting mechanism to dynamically balance global exploration and local exploitation. For container tasks of size 100, the objective function value (OFV) of the IWOA was reduced by 9.25% compared to the standard Whale Optimization Algorithm. Comparisons with other algorithms, such as the Genetic Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer, showed an OFV reduction of 9.61% to 11.75%. This validates the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In urban logistics distribution, vehicle carbon emissions during the distribution process significantly contribute to environmental pollution. While developing green logistics is critical for the sustainable growth of the logistics industry, existing studies often overlook the potential benefits of depot sharing among enterprises. By enabling depots belonging to different enterprises to be shared, it would shorten the distance traveled by vehicles returning to depots and reduce carbon emissions. And it would also reduce the number of depots being built. Therefore, a green vehicle routing problem with depot sharing is presented in the paper. To solve this problem, an improved adaptive large neighborhood search algorithm is presented, in which the Split strategy and two new operators are proposed to enhance solution quality and computational efficiency. Extensive numerical experiments are conducted on instances of varying scales to evaluate this algorithm, and also demonstrate its effectiveness and efficiency. Furthermore, the experimental results demonstrate that depot sharing significantly reduces carbon emissions, achieving an average optimization rate of 10.1% across all instances compared to returning to the original depot. [ABSTRACT FROM AUTHOR]

The dislocation of the active fault zone may cause some threat to the track structures passing through it, in order to investigate the response of prefabricated-polyurethane reinforced ballasted track and CRTS III slab track to different forms of fault and to compare their adaptability in fault zone, a finite element model of the track-tunnel-surrounding rock system and a dynamic model of vehicle-track system were integrated together in this study. Based on the models, the changes of track geometry of the two track structures were studied, the mechanical behaviour of track components was revealed, and the effect of irregularity caused by fault dislocation on vehicle dynamic response was studied. Results show that the components of CRTS III slab track are damaged at a smaller dislocation displacement than those of prefabricated-polyurethane reinforced ballasted track. With the increase of the train speed and dislocation displacement, the derailment coefficient and wheel load reduction rate also increase. The safe speed limits for travelling under different types of fault and different dislocation displacements are obtained, which can provide reference for similar engineering applications. [ABSTRACT FROM AUTHOR]

Face recognition is widely used in daily life such as shopping,security check,travel,payment and work attendance.Face recognition systems need strong computing power and large storage space,so face images that need to be recognized are usually transmitted to the cloud platform through the network.Due to the problems of network coverage,congestion and delay,face re-cognition systems are difficult to meet the needs of actual application,and the user experience is poor.Aiming at the problems in face recognition,a face recognition method based on edge-cloud collaboration is proposed.This method combines the processing ability of cloud computing and the real-time performance of edge computing,so that face recognition systems are not constrained by the network status,and its application is more extensive and the user experience is better.In the cloud,the LResNet feature extraction method is proposed to improve the ResNet34 network structure,and the ArcFace face loss function is used to supervise the training process,so that the network can learn more face angle features.At the edge,due to the limited computing resources and storage resources,a SResNet feature extraction method is proposed.Deep separable convolution is used to lighten the LResNet network structure,and it has greatly reduced network parameters and computation.The face recognition experiment on edge-cloud collaboration shows that the system can recognize faces in real time with a high accuracy rate under any network status.

When high-speed trains (HSTs) rapidly enter or exit the wind barrier area of the bridge, the quick change in the operating environment can lead to sudden changes in train aerodynamic loads, resulting in the deterioration of their aerodynamic performance and adversely affecting safe and stable operation. In this paper, the effects of wind barrier porosity, crosswind speed, and train speed on the sudden change in the aerodynamic load of the HST induced by wind barriers are analyzed, and the reasons for the sudden change from a flow field perspective are given. Additionally, the influences of the buffer structure with three lengths of 45 m, 90 m, and 135 m on the sudden change in the aerodynamic load of HSTs are studied. The results show that the lower the porosity of the wind barrier, the higher the crosswind speed, and the lower the speed of trains entering and exiting the wind barrier area, resulting in a greater degree of sudden change in the aerodynamic load of the HST. The buffer structure measuring 90 m in length is considered the most suitable, as it can significantly alleviate the sudden change in the aerodynamic load and effectively enhance the safety of train operations. [ABSTRACT FROM AUTHOR]

Fault diagnosis is one of the important applications of edge computing in the Industrial Internet of Things (IIoT). To address the issue that traditional fault diagnosis methods often struggle to effectively extract fault features, this paper proposes a novel rolling bearing fault diagnosis method that integrates Gramian Angular Field (GAF), Convolutional Neural Network (CNN), and Vision Transformer (ViT). First, GAF is used to convert one-dimensional vibration signals from sensors into two-dimensional images, effectively retaining the fault features of the vibration signal. Then, the CNN branch is used to extract the local features of the image, which are combined with the global features extracted by the ViT branch to diagnose the bearing fault. The effectiveness of this method is validated with two datasets. Experimental results show that the proposed method achieves average accuracies of 99.79% and 99.63% on the CWRU and XJTU-SY rolling bearing fault datasets, respectively. Compared with several widely used fault diagnosis methods, the proposed method achieves higher accuracy for different fault classifications, providing reliable technical support for performing complex fault diagnosis on edge devices. [ABSTRACT FROM AUTHOR]