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To explore the temperature field, temperature effect and mechanical behaviors of the track structure under combined load in high geo-temperature tunnel, the temperature field test platform is built to simulate the conditions under which the track bottom is heated in high geo-temperature tunnel, and the temperature distribution of the track bed slab in the transverse, longitudinal, and vertical directions is investigated. Then, the numerical model is built to examine the displacement and stress of the track structure under temperature load and combined load consisting of temperature and static vehicle load. The results show that when the temperature load on the track bottom is 30 ℃, the vertical temperature gradient inside the slab is negative, with a maximum of 20 ℃/m, and the maximum temperature differences in the vertical, transverse, and longitudinal directions are 5 ℃, 3.25 ℃ and 2 ℃, respectively. The temperature deformation of the track bed slab is manifested as sinking in the middle and warping at the edge, with the maximum tensile stress occurring in the upper-middle region and the maximum compressive stress in the lower-middle region. Under the combined load, the most unfavorable position of the vehicle load is where the front wheel is in the center of the slab, and for every 5 tons increase in axle load, the maximum tensile stress of the slab increases by about 4%, and the maximum compressive stress increases by about 2%. The findings provide reference for the optimization design of track in high geo-temperature tunnel.

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.

Due to the large daily temperature difference on the ultra-high altitude plateau of China, the actual vertical temperature gradient (TG) of ballastless track may exceed the design value in the Code. The temperature monitoring platform of the double-block ballastless track was established to obtain the temperature distribution characteristics of track. The finite element model of the temperature field of the track is developed to study the warping deformation of track, and then the influence of TG on the dynamic responses of the vehicle-track-bridge system (VTBS) are investigated. The results show that the maximum positive temperature gradient (PTG) and negative temperature gradient (NTG) of the track bed slab are 88.875 ℃/m and − 47.159 ℃/m, respectively, and the latter exceeds the design value in the Code. The dynamic responses are positively correlated with the TG, and the influence of the NTG is more significant. The dynamic response of the rail is most sensitive to the TG.

Arch deformation and interface failure of CRTS-II slab track have become crucial issues for the operation safety of high-speed railways. However, few researchers investigated the mechanics and deformation behaviour on CRTS-II slab track exposed to high-altitude plateau climatic environments. This study proposes an analysis approach for thermal arching and interface damage evolution of the slab track structure under solar radiation in alpine and plateau environments. The time-dependent solar temperature distribution is loaded on the refined finite element model (FEM) of the slab track structure’s thermal deformation in high-altitude plateau climatic environments. The analysis approach is validated by comparing the calculated and experimental data. The track slab's maximum and minimum vertical temperature gradient (VTG) are 94.49 °C/m and −45.02 °C/m, respectively. The narrow joint damage is an incentive for the inverted V-shaped thermal arching and interface separation of the slab track structure under solar radiation. The interfacial gap of slab track structure in alpine and plateau regions reaches more than 14 mm during summer. The proposed analysis approach is a valuable reference to the construction temperature, maintenance, and damage monitoring of slab track structure in alpine and plateau regions.

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.

The main challenge of continuous sign language translation (CSLT) lies in the extraction of both discriminative spatial features and temporal features. In this paper, a spatial-temporal feature extraction network (STFE-Net) is proposed for CSLT, which optimally fuses spatial and temporal features, extracted by the spatial feature extraction network (SFE-Net) and the temporal feature extraction network (TFE-Net), respectively. SFE-Net performs pose estimation for the presenters in sign-language videos. Based on COCO-WholeBody, 133 key points are abbreviated to 53 key points, according to the characteristics of the sign language. High-resolution pose estimation is performed on the hands, along with the whole-body pose estimation, to obtain finer-grained hand features. The spatial features extracted by SFE-Net and the sign language words are then fed to TFE-Net, which is based on Transformer with relative position encoding. In this paper, a dataset for Chinese continuous sign language was created and used for evaluation. STFE-Net achieves Bilingual Evaluation Understudy (BLEU-1, BLEU-2, BLEU-3, BLEU-4) scores of 77.59, 75.62, 74.25, 72.14, respectively. Furthermore, our proposed STFE-Net was also evaluated on two public datasets, RWTH-Phoenix-Weather 2014T and CLS. The BLEU-1, BLEU-2, BLEU-3 and BLEU-4 scores achieved by our method on the former dataset are 48.22, 33.59, 26.41 and 22.45, respectively, and the corresponding scores are 61.54, 58.76, 57.93 and 57.52, respectively, on the latter dataset. Experiment results show that our model can achieve promising performance. If any reader needs the code or dataset, please email lunfee@whut.edu.cn.

The surface settlement formula of large slope tunnel is derived firstly. Then, the finite element model of shield tunnel is established by using ABAQUS software. Finally, the influences of tunnel slope, grouting pressure and shield tail clearance on ground surface settlement, and mechanical properties of surrounding rock and segments are studied by the field measurement and numerical simulation method. The results show that (1) For the slope is less than 4°, the slope increases by 1°, the settlement of ground surface increases by about 2.85 %, the shear stress of surrounding rock increases by about 4.5 kPa, and the tensile and circumferential stresses of segment increase by about 5 % and 3.1 %, respectively. (2) The proper grouting pressure is beneficial to the settlement of ground surface, and the mechanical properties of surrounding rock and segment. (3) For the shield tail clearance is less than 15 cm, the clearance increases by 1 cm, the settlement of ground surface increases by about 0.65 mm, the shear stress of surrounding rock increases by about 4.77 kPa, and the circumferential stress of segment reduces by about 0.02 MPa. Therefore, the grouting pressure with 200 kPa and the shield tail clearance with 10 cm are recommended.

Abstract LiNi0.5Co0.2Mn0.3O2 (NCM523) has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance. However, the rapid capacity fading of NCM severely hinders its development and applications. Here, the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy (STEM). Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images. The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process. The space between the transition metal columns shrinks obviously, inducing dramatic capacity decay. This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation. It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.

Abstract Fire safety issues hinder the large‐scale application of lithium‐ion batteries (LiBs). Here, a new type of fire‐response separators is prepared by loading the microcapsule fire extinguishing agent on the surface of the separator. The shell of the microcapsule will break automatically at a certain temperature and release the fire extinguishing agent when thermal runaway of LiBs occurs, which can quickly absorb heat through endothermic reaction and ensure LiBs will not burn or explode. The porosity of fire‐response separator is 53.6%, the electrolyte uptake is 132% and the ionic conductivity is 1.00 mS cm‐1. The initial specific capacity is 2643 mAh g‐1 at 4°C and the capacity retention rate is 93% after 200 cycles for NCM523 battery based fire‐response separator. The temperature of LiBs based on the fire‐response separator can automatically drop to room temperature in 20 seconds, which ensures the safety of other adjacent LiBs. This work proposes a new active protection concept to solve the safety problem of LiBs.

Deep learning-based methods have demonstrated remarkable success in object detection tasks when abundant training data are available. However, in the industrial domain, acquiring a sufficient amount of training data has been a challenge. Currently, many synthetic datasets are created using 3D modeling software, which can simulate real-world scenarios and objects but often cannot achieve complete accuracy and realism. In this paper, we propose a synthetic data generation framework for industrial object detection tasks based on image-to-image translation. To address the issue of low image quality that can arise during the image translation process, we have replaced the original feature extraction module with the Residual Dense Block (RDB) module. We employ the RDB-CycleGAN network to transform CAD models into realistic images. Additionally, we have introduced the SSIM loss function to strengthen the network constraints of the generator and conducted a quantitative analysis of the improved RDB-CycleGAN-generated synthetic data. To evaluate the effectiveness of our proposed method, the synthetic data we generate effectively enhance the performance of object detection algorithms on real images. Compared to using CAD models directly, synthetic data adapt better to real-world scenarios and improve the model’s generalization ability.

In recent years, the utilization of dual-arm robots has gained substantial prominence across various industries owing to their collaborative operational capabilities. In order to achieve collision avoidance and facilitate cooperative task completion, efficient path planning plays a pivotal role. The high dimensionality associated with collaborative task execution in dual-arm robots renders existing path planning methods ineffective for conducting efficient exploration. This paper introduces a multi-agent path planning reinforcement learning algorithm that integrates an experience replay strategy, a shortest-path constraint, and the policy gradient method. To foster collaboration and avoid competition between the robot arms, the proposed approach incorporates a mechanism known as “reward cooperation, punishment competition” during the training process. Our algorithm demonstrates strong performance in the control of dual-arm robots and exhibits the potential to mitigate the challenge of reward sparsity encountered during the training process. The effectiveness of the proposed algorithm is validated through simulations and experiments, comparing the results with existing methods and showcasing its superiority in dual-arm robot path planning.

Object detection can locate objects in an image using bounding boxes, which can facilitate classification and image understanding, resulting in a wide range of applications. Knowing how to mine useful features from images and detect objects of different scales have become the focus for object-detection research. In this paper, considering the importance of foreground features in the process of object detection, a foreground feature extraction module, based on deformable convolution, is proposed, and the attention mechanism is integrated to suppress the interference from the background. To learn effective features, considering that different layers in a convolutional neural network have different contributions, we propose methods to learn the weights for feature fusion. Experiments on the VOC datasets and COCO datasets show that the proposed algorithm can effectively improve the object detection accuracy, which is 12.1% higher than Faster R-CNN, 1.5% higher than RefineDet, and 2.3% higher than the Hierarchical Shot Detector (HSD).

Objectives The aims of the study were to assess the management of low-density lipoprotein cholesterol (LDL-C) and the goal achievement, as well as to investigate the association between baseline LDL-C level, lipid-lowering treatment (LLT), and stroke recurrence in patients with ischaemic stroke or transient ischaemic attack (TIA).Design Our study was a post hoc analysis of the Third China National Stroke Registry (CNSR-III).Setting We derived data from the CNSR-III - a nationwide clinical registry of ischaemic stroke and TIA based on 201 participating hospitals in mainland China.Participants 15,166 patients were included in this study with demographic characteristics, etiology, imaging, and biological markers from August 2015 to March 2018.Primary and secondary outcome measures The primary outcome was a new stroke, LDL-C goal (LDL-C

Traffic flow prediction is the basis of dynamic strategies and applications of intelligent transportation systems (ITS). Accurate traffic flow prediction is of great practical significance in alleviating road congestion and reducing urban road traffic safety hazards. It is challenging since the traffic flow has highly non-linear and complex patterns due to external factors such as time and space. Due to the high stochasticity and uncertainty of traffic flow, the difficulty of traffic flow prediction increases gradually with increasing time steps. The prediction performance of most existing short-term traffic flow prediction methods deteriorates rapidly for longer time steps. In addition, different methods are compared on the same time-granularity dataset, leaving the adaptability and robustness of these methods undervalidated. To address the above challenges, a new traffic forecasting method, named Attention-Based Gated Recurrent Graph Convolutional Network (AGRGCN) is presented for short-term traffic flow prediction. The method can extract spatialtemporal dependencies in traffic flow. In addition, an attention mechanism, which can adaptively capture traffic data relationships at different time steps, is introduced to alleviate the problem of faster deterioration of model prediction performance for longer time steps. Using a road network distance-based graph enables the method better to capture the topological information in traffic flow data. Experiments were conducted on two traffic datasets with different time granularity to predict traffic flow in highway and urban contexts. The experimental results show that our model has certain advantages.

Under the background of intelligent manufacturing, industrial systems are developing in a more complex and intelligent direction. Equipment maintenance management is facing significant challenges in terms of maintenance workload, system reliability and stability requirements and the overall skill requirements of maintenance personnel. Equipment maintenance management is also developing in the direction of intellectualization. It is important to have a method to construct a domain knowledge graph and to organize and utilize it. As is well known, traditional equipment maintenance is mainly dependent on technicians, and they are required to be very familiar with the maintenance manuals. But it is very difficult to manage and exploit a large quantity of knowledge for technicians in a short time. Hence a method to construct a knowledge graph (KG) for equipment maintenance is proposed to extract knowledge from manuals, and an effective maintenance scheme is obtained with this knowledge graph. Firstly, a joint model based on an enhanced BERT-Bi-LSTM-CRF is put forward to extract knowledge automatically, and a Cosine and Inverse Document Frequency (IDF) based on semantic similarity a presented to eliminate redundancy in the process of the knowledge fusion. Finally, a Decision Support System (DSS) for equipment maintenance is developed and implemented, in which knowledge can be extracted automatically and provide an equipment maintenance scheme according to the requirements. The experimental results show that the joint model used in this paper performs well on Chinese text related to equipment maintenance, with an F1 score of 0.847. The quality of the knowledge graph constructed after eliminating redundancy is also significantly improved.

Smart security based on object detection is one of the important applications of edge computing in IoT. Anchors in object detection refer to points on the feature map, which can be used to generate anchor boxes and serve as training samples. Current object detection models do not consider the aspect ratio of the ground-truth boxes in anchor assignment and are not well-adapted to objects with very different shapes. Therefore, this paper proposes the Lightweight Anchor Dynamic Assignment algorithm (LADA) for object detection. LADA does not change the structure of the original detection model; first, it selects an equal proportional center region based on the aspect ratio of the ground-truth box, then calculates the combined loss of anchors, and finally divides the positive and negative samples more efficiently by dynamic loss threshold without additional models. The algorithm solves the problems of poor adaptability and difficulty in the selection of the best positive samples based on IoU assignment, and the sample assignment for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample assignment algorithms, the LADA algorithm outperforms the MS COCO dataset by 1.66% over the AP of the baseline FCOS, and 0.76% and 0.24% over the AP of the ATSS algorithm and the PAA algorithm, respectively, with the same model structure, which demonstrates the effectiveness of the LADA algorithm.

ObjectiveIn intensive care units (ICUs), carbapenem-resistant Enterobacterales (CRE) pose a significant threat. We aimed to examine the distribution, epidemiological characteristics, and risk factors for CRE positivity in ICUs.Materials and methodsThis cross-sectional study was conducted in 96 ICUs of 78 hospitals in Henan Province, China. The clinical and microbiological data were collected. A multivariable logistic regression model was used to analyze the risk factors for CRE positivity.ResultsA total of 1,009 patients were enrolled. There was a significant difference in CRE positive rate between pharyngeal and anal swabs (15.16 vs. 19.13%, P < 0.001). A total of 297 carbapenem-resistant Klebsiella pneumoniae (CR-KPN), 22 carbapenem-resistant Escherichia coli (CR-ECO), 6 carbapenem-resistant Enterobacter cloacae (CR-ECL), 19 CR-KPN/CR-ECO, and 2 CR-KPN/CR-ECL were detected. Klebsiella pneumoniae carbapenemase (KPC), New Delhi metallo-beta-lactamase (NDM), and a combination of KPC and NDM were detected in 150, 9, and 11 swab samples, respectively. Multivariable logistic regression analysis determined length of ICU stay, chronic neurological disease, transfer from other hospitals, previous infection, and history of antibiotics exposure as independent risk factors for CRE positivity. Age and cardiovascular diseases were independent risk factors for mixed infections of CRE. The occurrence of CRE in secondary and tertiary hospitals was 15.06 and 25.62%, respectively (P < 0.05). Patients from tertiary hospitals had different clinical features compared with those from secondary hospitals, including longer hospital stays, a higher rate of patients transferred from other hospitals, receiving renal replacement therapy, exposure to immunosuppressive drugs, use of antibiotics, and a higher rate of the previous infection.ConclusionIn ICUs in Henan Province, CRE positive rate was very high, mostly KPC-type CR-KPN. Patients with prolonged ICU stay, chronic neurological disease, transfer from other hospitals, previous infection, and history of antibiotic exposure are prone to CRE. Age and cardiovascular diseases are susceptibility factors for mixed infections of CRE. The CRE positive rate in tertiary hospitals was higher than that in secondary hospitals, which may be related to the source of patients, antibiotic exposure, disease severity, and previous infection.

Abstract Studying representation learning and generative modelling has been at the core of the 3D learning domain. By leveraging the generative adversarial networks and convolutional neural networks for point‐cloud representations, we propose a novel framework, which can directly generate 3D objects represented by point clouds. The novelties of the proposed method are threefold. First, the generative adversarial networks are applied to 3D object generation in the point‐cloud space, where the model learns object representation from point clouds independently. In this work, we propose a 3D spatial transformer network, and integrate it into a generation model, whose ability for extracting and reconstructing features for 3D objects can be improved. Second, a point‐wise approach is developed to reduce the computational complexity of the proposed network. Third, an evaluation system is proposed to measure the performance of our model by employing various categories and methods, and the error, considered as the difference between synthesized objects and raw objects are quantitatively compared, is less than 2.8%. Extensive experiments on benchmark dataset show that this method has a strong ability to generate 3D objects in the point‐cloud space, and the synthesized objects have slight differences with man‐made 3D objects.

Maximizing the carbon reduction in substations with minimum cost investments can be achieved by taking advantage of the potential of substations in terms of the envelope and renewable energy, which is significant in promoting carbon reduction in substations. Therefore, firstly, the relationship between building cost–energy consumption–carbon emissions is explored, and then the global optimal calculation model of substation envelope–renewable energy is established, with the lowest life-cycle carbon emission of the substation as the optimization goal. Finally, the validity of the model is verified based on a case study of a typical 110 kV outdoor substation. The model calculation results show that, without considering the cost constraint, Harbin has the highest maximum carbon reduction of 180,350 kg, which is 25.15% and 13.74% higher than the maximum carbon reduction in Shanghai and Haikou, respectively. Furthermore, based on the comparison of the cost and benefits of each carbon reduction technology, a prioritization of various carbon reduction technologies is given for each climate zone. The model established in this paper can provide the optimal configuration of substation carbon reduction technologies with different incremental cost constraints, and provide a reference for the low-carbon design of substations.

Automated guided vehicle (AGV) scheduling and routing are critical factors affecting the operation efficiency and transportation cost of the automated container terminal (ACT). Searching for the optimal AGV scheduling and routing plan are effective and efficient ways to improve its efficiency and reduce its cost. However, uncertainties in the physical environment of ACT can make it challenging to determine the optimal scheduling and routing plan. This paper presents the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT. By introducing the digital twin, uncertain factors can be detected and handled through the interaction and fusion of physical and virtual spaces. The improved artificial fish swarm algorithm Dijkstra (IAFSA-Dijkstra) is proposed for the optimal AGV scheduling and routing solution, which will be verified in the virtual space and further fed back to the real world to guide actual AGV transport. Then, a twin-data-driven conflict prediction method is proposed to predict potential conflicts by constantly comparing the differences between physical and virtual ACT. Further, a conflict resolution method based on the Yen algorithm is explored to resolve predicted conflicts and drive the evolution of the scheme. Case study examples show that the proposed method can effectively improve efficiency and reduce the cost of AGV scheduling and routing in ACT.

Research on tunnel excavation has rarely considered the effect of the tunnel excavation on a complete building. Therefore, this paper considered a building with a double basement and piles, and a three-dimensional finite-element model for shield tunnels undercrossing an existing building was established to study the effects of the excavation of double-shield tunnels on the displacement and internal forces of soil, segments, piles, and buildings. Grouting reinforcement technology was used in the model to analyze the effect of grouting reinforcement on pile displacement and building subsidence. The results showed that for every 100 kPa increase in grouting pressure, the maximum subsidence of the soil was reduced by 3.512 mm. The successive excavation of double-shield tunnels resulted in elliptical segments. The longitudinal and transverse stresses of the segments were effectively reduced by grouting pressure of 250 kPa. The excavation of tunnels had an obvious nonlinear effect on the maximum lateral displacement of the piles along the direction of excavation and the maximum subsidence of the building. When using deep-hole grouting reinforcement, the maximum lateral displacement of piles and the maximum subsidence of the building were effectively reduced by increasing the radial grouting reinforcement radius and adjusting the reinforcement range.

The key issues in designing ballastless track for high-speed railway bridges are to reduce maintenance and improve track smoothness by understanding fatigue damage characteristics. This paper is based on the principle of bridge-rail interaction and train-track-bridge coupling dynamics, the refined simulation model of bridge-CRTS I Bi-block ballastless track system is established by using the finite element method. The longitudinal force distribution law of CWR (Continuously Welded Rail) and the dynamic response characteristics of coupling systems are studied, based on the Miner rule and S-N curve. The fatigue characteristics of ballastless track system laying on long-span bridge under the dynamic train load and the effect of ballastless track system design parameters changes on fatigue characteristics are discussed. The results show that the extreme values of longitudinal force of CWR all appear in the middle of the bridge span or near the bridge bearing, and attention should be paid to the strength checking of CRW laying on long-span bridge. Under the dynamic train load, the fatigue life curve of rail on the bridge is relatively smooth and the minimum life of rail which is laying on continuous bridge decreases from 27.1 years to 17 years that which is laying on cable-stayed bridge. The life curve of track plate laying on continuous bridge is relatively smooth, and the life curve of track plate laying on cable-stayed bridge is related to the stiffness of elastic cushion, which decreases in a stepped manner, and there will be no fatigue failure on the track plate during service. The life curve of the baseplate is related to the type of bridge, the minimum life value of the baseplate appears near the bridge bearing, and there will be no fatigue failure on the baseplate during service. Increasing the stiffness of elastic cushion can effectively improve the fatigue life of track plate, and increasing the vertical stiffness of fasteners can enhance the connection between rail and track plate and improve the fatigue life of rail. The increase in train speed will increase the dynamic stress amplitude of track structure and reduce the fatigue life of the rail.

With the development of information and network technology, a new type of manufacturing paradigm, known as a service-oriented Cloud Manufacturing (CMfg), is emerged. In the CMfg paradigm, geographically distributed various manufacturing resources authorized by different companies are encapsulated as different manufacturing services (MSs) supported by the enabling technologies, such as cloud computing, Industrial Internet of Things, virtualization technologies, etc. MSs are provided according to the demands of manufacturing tasks (MTs) presented by various customers via the IoT (Internet of Things) in CMfg paradigm. Generally, one MS may complete one MT, but mostly one MT needs more MSs to complete it cooperatively. Furthermore, these MSs are perhaps controlled and managed by different enterprises. As an autonomous entity, each MS generally makes decision in light of its own interests, and their own interests generally have conflicts with the others' and the collective ones. To solve the conflicts through MSs' cooperation is an efficient and low-cost method to maximize both individual and collective interests. Hence, the influences of individual decision-making behaviors on cooperation are explored and discussed in CMfg paradigm. The evolutionary game theory and agent-based modeling and simulating method are employed to model and simulate individual behaviors of MSs during the process of forming the coalition for completing MTs. In addition, the learning mechanism of MSs is discussed in the decision-making process. The simulation results show that decision behaviors of MSs are positively influenced by the information transparency of the CMfg environment and the learning mechanism of MSs effects on the cooperative emergence.

Background Small single subcortical infarction (SSSI) may be classified as parent artery disease-related or only branch involved according to the stenosis of parent artery. The study aimed to evaluate short-term and long-term prognoses and the effectiveness of antiplatelet therapy in SSSI.Methods We prospectively enrolled 2890 patients with SSSI from the Third China National Stroke Registry (CNSR-III) database from August 2015 to March 2018. We assessed clinical outcomes and antiplatelet treatment effects in patients with SSSI with and without parent artery stenosis (PAS) identified by magnetic resonance angiography.Results Among 2890 patients with SSSI in the perforator territory of the middle cerebral artery and the basilar artery, there were 680 (23.53%) patients with PAS and 2210 (76.47%) patients without PAS, respectively. After adjusting for potential confounders, the PAS group had a greater initial stroke severity (OR 1.262, 95% CI 1.058 to 1.505; p=0.0097) and a higher risk of ischaemic stroke recurrence at 3 months (OR 2.266, 95% CI 1.631 to 3.149; p

With the development of lean manufacturing and economic globalization, supply networks increasingly become complex and large-scale, within which thousands of firms inter-depend with each other. Due to these increasing inter-dependencies, disruption of a quite few critical suppliers, namely bottleneck suppliers, can induce high loss to a supply network and even make the whole network dysfunction. Identification of bottleneck suppliers is significantly important for supply network risk management. Thus, in this article, a method based on a memetic algorithm with local neighborhood search (MALNS) is proposed to identify bottleneck suppliers in a two-stage supply network. Firstly, a model based on multipartite network is designed to describe the product supply-demand relations between multiple manufacturers and suppliers, which considers the different roles of manufacturer and supplier and differentiates the products that suppliers supply. To assess the loss caused by supplier disruptions, two performance metrics of supply networks, average product availability rate and manufacturer functioning rate, are presented. Then, a MALNS-based method is proposed to identify bottleneck suppliers, i.e., suppliers whose disruption will decrease both performance metrics most greatly. Finally, a case study based on a real automobile supply network is presented to validate the applicability and effectiveness of the proposed method.

As a key equipment of manufacturing, the intelligence of CNC machine tools affects the development and progress of intelligent manufacturing. At the same time, the rapid development of various technologies in recent years, such as cloud computing and edge computing, has brought new methods to improve the intelligence of CNC machine tools. This article proposes a new architecture of intelligent machine tools based on edge-cloud collaboration (IMT-ECC). The hierarchical structure of IMT-ECC is introduced and consists of three layers, data acquisition, network communication and edge-cloud collaboration. Combining the real-time characteristic of edge computing and complex problem processing ability of cloud computing, the edge-cloud collaboration is designed to improve the intelligence of machine tools through data collaboration, information collaboration and knowledge collaboration. Finally, the feasibility of the new intelligent machine tool architecture based on edge-cloud collaboration is verified by experiments with gantry heavy-duty CNC machine tools.

Non-uniform temperature distributions of concrete box girders are induced by solar radiation depending on the structural shapes and shadows cast on them. Using a real-time shadow-selection algorithm and finite element method, the thermodynamic analysis of concrete box girders is carried out to simulate their time-varying temperature distributions. The theoretical results of the finite element model (FEM) are verified by measured data. Based on this, a series of FEMs of concrete box girders are established considering regional influences, and the temperature gradients of concrete box girders in different climatic zones in China are studied. The most unfavorable temperature gradient of concrete box girders in China is obtained, and it is important for design and not only for health monitoring. Moreover, comparing the temperature gradient modes in the specifications of various countries, the temperature gradient of concrete box girders in the China Highway Bridges Design Code is conservative for structural design. Furthermore, the influences on structural temperature distributions of varying environmental factors induced by regional influences are explored, including atmospheric transparency coefficient (ATC) and solar radiation absorptivity (SRA).

Compared with straight tunnels, over-excavation occurs on the inner side of the curved section during shield construction of small radius curved tunnels, and the disturbance to the ground surface and mechanical properties of surrounding rock and segment are more severe. This paper establishes the numerical models of small radius curve tunnels and straight tunnels to study the characteristics of surface deformation caused by the shield excavation of small radius curved tunnels and the influence of shield construction parameters on ground settlement, surrounding rock deformation, and segment force. The maximum error between the numerical simulation results and the measured surface settlement curve is 7.3%, which is in good agreement. The results show that: (1) The maximum value of the surface settlement of the small radius curve tunnel appears inside the curve section, and with the decrease in the curve radius, the surface settlement increases, and the distance between the peak settlement point and the tunnel center is larger. (2) When the curve radius of the tunnel is smaller, the lateral displacement of the ground surface moves farther to the inner side, and the range of soil mass with lateral displacement in the inner side is also wider. (3) Increasing the heading face pressure and grouting pressure can reduce surface settlement, but the heading face pressure should not exceed 350 kPa, and the grouting pressure should not exceed 250 kPa. (4) When the curve radius is smaller, the deformation of surrounding rock and the segment stress is larger.

Ultra-wideband (UWB) technology is used for indoor positioning, but its positioning accuracy is usually degenerated by various obstacles in the indoor environment because of non-line-of-sight (NLOS). Facing the complex and changeable indoor environment, an indoor positioning system with UWB based on a digital twin is presented in this paper. The indoor positioning accuracy is improved with a perception–prediction feedback of cyber-physics space in this indoor positioning system. In addition, an anchor layout method with virtuality–reality interaction and an error mitigation method based on neural networks is put forward in this system. Finally, a case study is presented to validate this indoor positioning system with a significant improvement in positioning accuracy.

An important challenge of crop improvement strategies is assigning function to paralogs in polyploid crops. Here we describe the circadian transcriptome in the polyploid crop Brassica rapa. Strikingly, almost three-quarters of the expressed genes exhibited circadian rhythmicity. Genetic redundancy resulting from whole genome duplication is thought to facilitate evolutionary change through sub- and neo-functionalization among paralogous gene pairs. We observed genome-wide expansion of the circadian expression phase among retained paralogous pairs. Using gene regulatory network models, we compared transcription factor targets between B. rapa and Arabidopsis circadian networks to reveal evidence for divergence between B. rapa paralogs that may be driven in part by variation in conserved non-coding sequences (CNS). Additionally, differential drought response among retained paralogous pairs suggests further functional diversification. These findings support the rapid expansion and divergence of the transcriptional network in a polyploid crop and offer a new approach for assessing paralog activity at the transcript level.

Robustness of a supply network highly depends on its structure. Although structural design methods have been proposed to create supply networks with optimal robustness, a real-life supply network can be quite different from these optimal structural designs. Meanwhile, real cases such as Thailand floods and Tohoku earthquake demonstrate the vulnerability of supply networks in real life. Obviously, it is urgent to enhance the robustness of existing real-life supply networks. Thus, in this paper, a supply network reconfiguration method based on adaptive variable neighborhood search (AVNS) is proposed to enhance the structural robustness of supply networks facing both random and target disruptions. Firstly, a supply network model considering the heterogeneous roles of entities is introduced. Based on the model, two robustness metrics, Rr and Rt, are proposed to describe the tolerance of supply networks facing random and target disruptions, respectively. Then, the problem of reconfiguration-based supply network robustness enhancement is described. To solve the problem effectively and efficiently, a new heuristic based on general variable neighborhood search, namely, AVNS, is proposed. Finally, a case study based on three real-life supply networks is presented to verify the applicability and effectiveness of the proposed robustness enhancing method.

Wind barriers can effectively reduce the risk of overturning and derailment of high-speed trains running on a bridge under crosswind. However, it can adversely affect the wind resistance of the bridge. There are few studies on the aerodynamic performance of curved wind barriers. In this paper, the effects of curved wind barriers with four curvatures (0, 0.2, 0.35, and 0.50) and different train-bridge combinations on the crosswind aerodynamic characteristics of a train-bridge system are investigated. The results show that the curved wind barrier can significantly reduce the wind speed below a certain height on the bridge deck. The curved wind barrier with small curvature can better reduce the aerodynamic force of the train; however, it greatly increases the aerodynamic force of the bridge. A wind barrier with a curvature of 0.35 is recommended because it takes into account the aerodynamic characteristics of the train and bridge at the same time. The porosity of a wind barrier greatly influences the aerodynamic performance of the train on the track of the windward side of the bridge, while the wind barrier has little effects on the train on the track of the leeward side of the bridge. The aerodynamic performance of the train on the track of the windward side of the bridge is less affected by whether or not a train on the track of the leeward side of the bridge is present.

This paper establishes a three-dimensional symmetrical shield model to investigate the influence of a double-line shield tunnel undercrossing an existing foundation pit and of changed grouting pressure on the deformation and mechanical characteristics of both the foundation pit and the tunnel itself, and it proposes a method of symmetrical segmented pressure, in which different grouting pressure is applied in different sections of the tunnel. The monitoring data are used to verify the reliability of the model, and the maximum relative error is 5.44%. The numerical results show that the maximum subsidence of the retaining pile and anchor are 3.76 mm and 10.33 mm, respectively, and the maximum tensile stress of the anchor is increased by 32.4%. The subsidence shape of the foundation pit raft is an arch with four corners warping upward and the maximum subsidence difference is 3.17 mm. Uneven subsidence of the tunnel occurs along the longitudinal direction, and large and small subsidences are located at the outside and underpart of the foundation pit, respectively, and the maximum and minimum values are 11.15 mm and 2.13 mm, respectively, and the maximum subsidence difference is 9.02 mm. The deformation and mechanical characteristics of both the foundation pit and the tunnel are significantly decreased by appropriately increasing the grouting pressure, and it is recommended that the grouting pressure should not exceed 300 kPa. The proposed method of segmented pressure can reduce the differential subsidence by 47.2% and the maximum tensile stress by 27.2%, so it can significantly reduce the uneven subsidence of the tunnel and improve the tunnel stress condition. The research results can provide a theoretical basis for the safe construction of shield tunnels under the existing foundation pit.

Cloud manufacturing (CMfg) is an innovative manufacturing paradigm in which geographically distributed various manufacturing resources are encapsulated into manufacturing services (MSs), and also these MSs can provide users with various services. Users can find and choose qualified MSs to form cliques to help them complete their manufacturing tasks via Internet. Trust makes cooperative endeavors happen. Hence, it is an important problem to evaluate the trustworthiness of MSs in CMfg. In this paper, we present a comprehensive assessment approach to evaluate the trustworthiness of a MS in CMfg. The approach evaluates a MS from both objective and subjective perspectives, named as credit assessment and reliability assessment, respectively. Multinomial logistic regression analysis is presented to assess the credit of a MS, while fuzzy analytic hierarchy process is used to assess its reliability. Finally, a case study is presented and the results show the comprehensive assessment approach is efficacious at the trustworthiness evaluation of a MS.

As one kind of significant manufacturing equipment, computer numerical control (CNC) machine tools have to be endowed with new functions to meet the requirements of processing devices in the era of “Industry 4.0.”Inter-connection and intelligence are the fundamental characteristics of CNC machine tools in this era. To make CNC machine tools be more accessible and promote them to a higher level of intelligence, this paper presents a new architecture of CNC machine tools based on a cyberphysical system and fog computing, named as a fog computing-based cyber-physical machine tool system (FC-CPMTS). The definition, functions, and hierarchical structure of the FC-CPMTS are described respectively. CNC machine tools, cyber space, and human beings are connected closely through sensing, computing, communicating, and controlling in the FC-CPMTS. The application of fog computing enhances autonomy and collaboration of CNC machine tools. It also reduces network traffic and calculation workload of the cloud platform in the FC-CPMTS. To demonstrate the rationality and feasibility of the FC-CPMTS, an FC-CPMTS for a heavy-duty CNC machine tool is taken as a case study. The result shows that autonomy, intelligence, interconnection, and interoperability of the CNC machine tool are improved.

Having great impacts on machining precision, thermal error is one of the main error sources for heavy-duty computer numerical control machine tools. Thermal error compensation using prediction models with temperature field is an effective way to improve machining precision of computer numerical control machine tools. The accuracy and robustness of thermal error prediction models depend considerably on the selection of temperature measurement points. Too many temperature measurement points will increase the complexity of thermal error prediction models and incur over-fitting problems. To improve the complexity and performances of prediction models, a selection method of key temperature measurement points based on density peaks clustering is presented in this article. This method is able to cluster massive temperature measurement points quickly and select the key temperature measurement point which characterizes the common feature of each cluster automatically. It is verified on the ZK5540A heavy-duty computer numerical control gantry drilling machine tool. Six key temperature measurement points are selected from the total 222 temperature measurement points with this method. Then, the back propagation neural network optimized by genetic algorithm thermal error model with the six key temperature measurement points is built and the accuracy and robustness of the model are analyzed. The results show that the model has high prediction accuracy and strong robustness.

The dynamic model for the vehicle-CRTS III slab track system is established subjected to uniform seismic excitation, and the calculation program with MATLAB is compiled and verified. The influences of track parameters, seismic intensity, and running speed of the vehicle on the dynamic responses of the system and the vehicle running safety are analyzed. The results show that (1) the track parameters have certain influence on the dynamic responses of the system, and the seismic intensity and the running speed of the vehicle have important influence on the vehicle running safety; (2) the derailment coefficient is highly sensitive to seismic intensity, and the wheel load reduction rate is also highly sensitive to the running speed of the vehicle.

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

In order to study the axial compression performance of the T-shaped multi-cavity concrete-filled steel tube shear wall, first, three specimens were designed to perform the axial compression test. Then three-dimensional finite element analysis by the ABAQUS software was used to obtain the axial bearing capacity of the shear wall with different parameters. According to the results of the finite element model, the computational diagram in the limit state was obtained. The diagram was simplified into the core concrete in the non-enhanced area that was not constrained by the steel tube and the core concrete in the enhanced area that was uniformly constrained by the steel tube. Finally, a new practical equation for calculating the axial bearing capacity of a multi-cavity concrete-filled steel tubular shear wall was deduced and proposed based on the theory of ultimate equilibrium. The calculation results of the proposed equation were in good agreement with the finite element results, and the proposed equation can be used in practical engineering design.

Due to the temperature change of bridges, there is a great additional force in continuously welded rails on continuous bridges. Laying rail expansion regulators is an effective measure to reduce the additional force. The nonlinear finite element model is presented for a continuously welded rail track with a rail expansion regulator resting on the embankment and simple and continuous beams, considering the temperature change of the bridge. Then, a method is proposed to determine the locations of the rail expansion regulator and the fixed bearing of the continuous beam, corresponding to the maximum additional forces of rail reaching minimum values. Their appropriate matching locations are recommended based on the obtained influence laws of any locations of the rail expansion regulator and the fixed bearing of the continuous beam on the maximum additional forces of rail. The results can provide the theoretical basis for the design of the rail expansion regulator and the fixed bearing of long-span continuous bridges.

The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.

Thermal errors account for more than 40% of the machining errors in CNC machine tools, and even reach 70% in precision and ultra-precision machine tools. One approach to reducing thermal errors is to build thermal error prediction models by monitoring the temperature field of machine tools with the data-driven modeling approaches. Usually the data-driven approaches have over-fitting and under-fitting problems. The prediction performances of the data-driven models are greatly dependent on the input of the models, namely the number of temperature measuring points (TMPs) and their locations. In this paper, a selection method of key TMPs is presented to improve the accuracy and generalization of predictive models. In this selection method, correlation analysis is used to eliminate the uncorrelated or weakly correlated TMPs to thermal errors at first; then the Minimum Redundancy Maximum Relevance (MRMR) is presented to narrow the searching scope of TMPs; finally Wrapper method is used to test the candidate set of TMPs with cross-validation accuracy to find the key TMPs. To validate this method, a test platform is built on the CNC gantry drilling machine ZK5540A. 56 Fiber Bragg Grating (FBG) temperature sensors are amounted on the body, column, spindle, and base of this machine. 7 key TMPs was selected from the 56 ones with this method. And Multiple linear regression (MLR) approach is used to build the thermal error prediction model with the 7 key TMPs, 56 TMPs and key TMPs selected by Miao's method(Miao et al., 2014) respectively. The result shows that the prediction model built with MLR using the 7 key TMPs is much more accurate and has stronger generalization.

Plant development is exquisitely sensitive to the environment. Light quantity, quality, and duration (photoperiod) have profound effects on vegetative morphology and flowering time. Recent studies have demonstrated that ambient temperature is a similarly potent stimulus influencing morphology and flowering. In Arabidopsis, ambient temperatures that are high, but not so high as to induce a heat stress response, confer morphological changes that resemble the shade avoidance syndrome. Similarly, these high but not stressful temperatures can accelerate flowering under short day conditions as effectively as exposure to long days. Photoperiodic flowering entails a series of external coincidences, in which environmental cycles of light and dark must coincide with an internal cycle in gene expression established by the endogenous circadian clock. It is evident that a similar model of external coincidence applies to the effects of elevated ambient temperature on both vegetative morphology and the vegetative to reproductive transition. Further study is imperative, because global warming is predicted to have major effects on the performance and distribution of wild species and strong adverse effects on crop yields. It is critical to understand temperature perception and response at a mechanistic level and to integrate this knowledge with our understanding of other environmental responses, including biotic and abiotic stresses, in order to improve crop production sufficiently to sustainably feed an expanding world population.

Analysis of the multiple factors affecting human identification ability based on pyroelectric infrared technology is a complex problem. First, we examine various sensed pyroelectric waveforms of the human body thermal infrared signal and reveal a mechanism for affecting human identification. Then, we find that the mechanism is decided by the distance, human target, pyroelectric infrared (PIR) sensor, the body type, human moving velocity, signal modulation mask, and Fresnel lens. The mapping relationship between the sensed waveform and multiple influencing factors is established, and a group of mathematical models are deduced which fuse the macro factors and micro factors. Finally, the experimental results show the macro-factors indirectly affect the recognition ability of human based on the pyroelectric technology. At the same time, the correctness and effectiveness of the mathematical models is also verified, which make it easier to obtain more pyroelectric infrared information about the human body for discriminating human targets.

In the finite element method, there are shortcomings using the conventional formulae to calculate the sectional forces, i.e., the bending moment and the shear force, at any cross-section of Bernoulli-Euler beam under dynamic loads. This paper presents some new finite-element formulae overcoming the shortcomings of the conventional ones to calculate the sectional forces at any cross-section of a Bernoulli-Euler beam on continuously viscoelastic foundation subjected to concentrated moving loads. The proposed formulae can easily degenerate into the formulae for calculating the sectional forces of a simply supported or a continuous Bernoulli-Euler beam subjected to concentrated moving loads, and into the formulae for calculating the sectional forces of a Bernoulli-Euler beam on Winkler foundation under static loads. Five numerical examples including static and dynamic analyses are chosen to illustrate the application of the proposed formulae. Numerical results show: (1) compared with the conventional formulae, the proposed formulae can improve the calculation accuracy of the sectional forces of beam; (2) one should use the proposed formulae, not the conventional formulae, to calculate the sectional forces at any cross-section in Bernoulli-Euler beam.

This paper presents finite element formulations for an elastic Timoshenko beam subjected to moving concentrated forces. The results obtained by the present method are compared with those obtained by the assumed mode method published in the existing literature to verify the correctness of the present method. The present method can analyze the dynamic response for a Timoshenko or Bernoulli-Euler beam with various boundary conditions subjected to moving concentrated forces. Numerical results show that the present method is more effective than the discrete element technique published in the existing literature for investigating the dynamic problem of Timoshenko beam.

Based on energy approach, the equations of motion in matrix form for the railway freight vehicle-bridge interaction system are derived, in which the dynamic contact forces between vehicle and bridge are considered as internal forces. The freight vehicle is modelled as a multi-rigid-body system, which comprises one car body, two bogie frames and four wheelsets. The bogie frame is linked with the car body through spring-dashpot suspension systems, and the bogie frame is rigidly linked with wheelsets. The bridge deck, together with railway track resting on bridge, is modelled as a simply supported Bernoulli-Euler beam and its deflection is described by superimposing modes. The direct time integration method is applied to obtain the dynamic response of the vehicle-bridge interaction system at each time step. A computer program has been developed for analyzing this system. The correctness of the proposed procedure is confirmed by one numerical example. The effect of different beam mode numbers and various surface irregularities of beam on the dynamic responses of the vehicle-bridge interaction system are investigated.

This paper investigates the random vibration and the dynamic reliability of operation stability of train moving over slab track on bridge under track irregularities and earthquakes by the pseudoexcitation method (PEM). Each vehicle is modeled by multibody dynamics. The track and bridge is simulated by a rail-slab-girder-pier interaction finite element model. The coupling equations of motion are established based on the wheel-rail interaction relationship. The random excitations of the track irregularities and seismic accelerations are transformed into a series of deterministic pseudoexcitations by PEM. The time-dependent power spectral densities (PSDs) of the random vibration of the system are obtained by step-by-step integration method, and the corresponding dynamic reliability is estimated based on the first-passage failure criterion. A case study is then presented in which a high-speed train moves over a slab track resting on a simply supported girder bridge. The PSD characteristics of the random vibration of the bridge and train are analyzed, the influence of the wheel-rail-bridge interaction models on the random vibration of the bridge and train is discussed, and furthermore the influence of train speed, earthquake intensity, and pier height on the dynamic reliability of train operation stability is studied.

The paper describes the numerical simulation of the vertical random vibration of train-slab track-bridge interaction system by means of finite element method and pseudoexcitation method. Each vehicle is modeled as four-wheelset mass-spring-damper system with two-layer suspension systems. The rail, slab, and bridge girder are modeled by three-layer elastic Bernoulli-Euler beams connected with each other by spring and damper elements. The equations of motion for the entire system are derived according to energy principle. By regarding rail irregularity as a series of multipoint, different-phase random excitations, the random load vectors of the equations of motion are obtained by pseudoexcitation method. Taking a nine-span simply supported beam bridge traveled by a train consisting of 8 vehicles as an example, the vertical random vibration responses of the system are investigated. Firstly, the suitable number of discrete frequencies of rail irregularity is obtained by numerical experimentations. Secondly, the reliability and efficiency of pseudoexcitation method are verified through comparison with Monte Carlo method. Thirdly, the random vibration characteristics of train-slab track-bridge interaction system are analyzed by pseudoexcitation method. Finally, applying the 3σ rule for Gaussian stochastic process, the maximum responses of train-slab track-bridge interaction system with respect to various train speeds are studied.