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BACKGROUND: The use of indwelling closed thoracic drainage tubes in the wedge resection of the lungs is of great significance to postoperative recovery. However, there are potential risks. OBJECTIVE: To explore the design feasibility and application effect of triple-buffer-system-fixed small-diameter (18 F) thoracic closed drainage tubes following lung wedge resection. METHODS: A total of 136 patients with indwelling thoracic drainage tubes following pulmonary wedge resection were recruited, with 70 patients allocated to the control group and 66 to the experimental group. The drainage tube in the experimental group was fixed with the triple-buffer system, while that in the control group was fixed using the conventional lifting platform method. The incidence of unplanned extubation, the indwelling time of the drainage tube and the time and material costs, as well as information regarding any subcutaneous emphysema and skin tension blisters, were recorded following the operation. The pain and degree of comfort were assessed using a chi-square test and a rank sum t -test to compare the differences between the two groups. RESULTS: There were no statistically significant differences in terms of age, gender and sweating between the two groups. Compared with the control group, the unplanned extubation rate of the experimental group was lower ( χ 2 = 8.513; P = 0.004), the indwelling time of the drainage tube was shorter (t = 2.108; P = 0.037), the cumulative material cost was lower (t = 3.778; P < 0.001), the time cost was also lower (Z = 2.717; P = 0.008), the degree of comfort was higher (Z = 2.752; P = 0.006), and the degree of pain was lower (Z = 4.019; P < 0.001). The incidence of subcutaneous emphysema was significantly lower in the experimental group than in the control group ( χ 2 = 8.513; P = 0.004). CONCLUSION: The use of the triple-buffer system to fix small-diameter (18 F) thoracic closed drainage tubes can reduce the unplanned extubation rate, indwelling time of the drainage tube and the incidence of adverse reactions. [ABSTRACT FROM AUTHOR]

• A robustness index considering cascading failures caused by failed units is proposed. • Robustness improvement by structure adjustment is studied for fixed characteristics. • TS with directed and undirected edge swap operators is designed to solve the problem. • A case study of structure adjustment for improving robustness in the HST is performed. • Robustness of the optimized HST is greatly enhanced and hardly affected by failures. To enhance the ability of a high-speed train (HST) to provide services under various adverse conditions, this paper studies robustness improvement of the HST by adjusting its structure. We model the HST as an interdependent machine-electricity-communication network (IMECN) composed of a machine network (MN), an electricity network (EN) and a communication network (CN), and propose a robustness metric of the IMECN subject to node failures considering failure propagation. Then, a robustness optimization model is constructed for structure adjustment. A Tabu search algorithm with directed and undirected edge exchange operators is designed to solve this problem. A case study on a practical HST is used to verify the feasibility and effectiveness of the proposed method. The results show that robustness of the IMECN is significantly improved by structure adjustment. Furthermore, most nodes have little effect on robustness, and impact of failures on any node is minimized after optimization. In addition, in terms of topology, nodes with low and high degrees in the MN and CN reconnect with those with a similar degree, their clustering coefficients become larger, and closeness of all nodes in the subnetworks increases. Finally, adjusting the structures leads to a slight difference of loads in the EN and CN. [ABSTRACT FROM AUTHOR]

This paper analyzes the relation of arrival-departure tracks and the platforms of various types of stations, and the minimum dwelling time and adjacent train arrival interval to avoid contact of the boarding and alighting passengers. It constructs an integrated model of high-speed train rescheduling and the arrival-departure tracks utilization planning, taking the minimum dwelling time at stations, train tracking interval, minimum train running time in sections, and the arrival-departure tracks occupation as the constraints. The genetic algorithm based on the tabu search strategy is utilized to address the problem. A comparison is conducted between the computational results based on the classical genetic algorithm and the designed algorithm, revealing a 18.92% reduction in total calculation time consumption and a 1.11% improvement in accuracy. The proposed method can provide decision support for train dispatching work in epidemic situations. [ABSTRACT FROM AUTHOR]

Accurately diagnosing the faults of wheelset bearings in high-speed trains is critical for ensuring the safety of train operation. In recent years, deep learning methods have made significant progress in this area. However, collecting enough labeled data in high-speed operation would be highly impracticable. In addition, the harsh operating environment of wheelset bearings, coupled with fluctuations in loads due to passenger flow, weather conditions, and other factors, makes it challenging to identify the fault signatures submerged in background noise. To address these issues, this paper proposes a semi-supervised fault diagnosis method based on autocorrelation and an improved flow Gaussian mixture model. First, the autocorrelations of raw bearing vibration signals are calculated to reduce the impact of noise while highlighting the discriminative fault signatures. Second, an improved flow Gaussian mixture model is established, preserving its simplicity and interpretability, to handle the constraints of bearing fault diagnosis with limited labeled samples under variable loads. Finally, the proposed method is tested by using a bearing dataset collected from an industrial railway wheelset bearing test rig. The results demonstrate that the proposed method outperforms the five state-of-the-art methods in terms of diagnostic accuracy and robustness. • A semi-supervised method is proposed for high-speed train wheelset bearing fault diagnosis with limited labeled samples under variable loads. • Autocorrelation is used to pre-process the raw vibration signals while providing an input to the model. • An improved flow Gaussian mixture model with a simple structure and interpretability is used to diagnose the bearing faults under variable loads. • The effectiveness of the method is verified on a railway wheelset bearing dataset. [ABSTRACT FROM AUTHOR]

Spatiotemporal information on individual trajectories in urban rail transit is important for operational strategy adjustment, personalized recommendation, and emergency command decision-making. However, due to the lack of journey observations, it is difficult to accurately infer unknown information from trajectories based only on AFC and AVL data. To address the problem, this paper proposes a spatiotemporal probabilistic graphical model based on adaptive expectation maximization attention (STPGM-AEMA) to achieve the reconstruction of individual trajectories. The approach consists of three steps: first, the potential train alternative set and the egress time alternative set of individuals are obtained through data mining and combinatorial enumeration. Then, global and local potential variables are introduced to construct a spatiotemporal probabilistic graphical model, provide the inference process for unknown events, and state information about individual trajectories. Further, considering the effect of missing data, an attention mechanism-enhanced expectation-maximization algorithm is proposed to achieve maximum likelihood estimation of individual trajectories. Finally, typical datasets of origin-destination pairs and actual individual trajectory tracking data are used to validate the effectiveness of the proposed method. The results show that the STPGM-AEMA method is more than 95% accurate in recovering missing information in the observed data, which is at least 15% more accurate than the traditional methods (i.e., PTAM-MLE and MPTAM-EM). [ABSTRACT FROM AUTHOR]

• The main accident causes at the macro level are not applicable to specific scenes. • The distribution of accident causes concentrates with the refinement of the scene. • A data-driven Bayesian network construction approach is proposed. • The significant causes in various specific scenes can be accurately identified. As the freight railway system is a typical complex system, freight railway accidents have various and complex accident scenes. A Data-Driven Bayesian Network (DDBN) with random variables representing scene elements, accident causes, and accident consequences as nodes, and conditional dependencies between nodes as edges is proposed to identify the most significant accident causes in various and complex specific accident scenes. First, an unsupervised-supervised method is designed to define the states of nodes in the DDBN, considering the characteristics of the data involving both continuous and discrete states. Second, a greedy algorithm is proposed to mine the causal sequence between nodes, and the direction of edges in DDBN is established accordingly. Then, an NB-K2-MLE approach is proposed to generate the structure and parameters of the DDBN from data. Finally, a risk calculation function based on DDBN is proposed to calculate the risk of various accident causes in specific scenes. In empirical analysis based on real accident data, the inference accuracy of DDBN reached 87.92 %, with precision and recall exceeding 70 %. More importantly, the research results indicate that the distribution of accident causes will concentrate with the refinement and specific of the scene, and the main accident causes at the macro level cannot be fully applicable to accident prevention in specific scenes. The DDBN constructed in this study can provide data support for the determination of the significant accident causes and the development of targeted accident prevention strategies in various and complex specific scenes. [ABSTRACT FROM AUTHOR]

Remaining useful life prediction (RUL) is a critical procedure in the application of prognostics and health management for devices or systems. It is difficult to predict the RUL in a time‐varying external environment. Specifically, many mechanical systems typically experience various operating conditions, which have impacts on the degradation process and degradation rate. In particular, the linear degradation modeling of the Wiener process‐based RUL prediction method has attracted considerable attention recently. However, the dependency of degradation rate and operating conditions is generally ignored in the current degradation modeling, which leads to inaccurate issues in the RUL prediction. Therefore, to solve the above issues, a novel RUL prediction method based on the Wiener process considering parameter dependence is proposed in this paper. At first, a linear Wiener process degradation model considering parameter dependence is constructed to describe the dependency of the drift coefficient and operating conditions. Secondly, the probability density function of RUL is derived under the concept of first hit time. After that, the collaboration between the Bayesian update and expectation maximization algorithm is introduced to update and estimate the model parameters. Finally, the validity and applicability of the proposed method are verified by a numerical simulation and three case studies of bearings. [ABSTRACT FROM AUTHOR]

The normal operation of a integrated hub station is of great significance for the safe operation of the entire city's transportation network. Accurately monitoring the passenger flow operation status of the station is the fundamental basis for achieving scientific management and control of passenger flow. In response to the urgent need for accurate and real-time detection of passenger flow in station passageways, a Yolov7-based improved Deep-Sort algorithm is proposed to detect and track bi-directional passenger flow in the passageways of integrated hub stations. Based on the Yolov7 detection algorithm, the SimAM attention mechanism was introduced to improve the accuracy of detecting passenger flow in the passageways. On the basis of the Deep-Sort tracking algorithm, the Kalman Filter (KF) method was optimized to make the tracking box of the target more accurate. Meanwhile, the Fast-ReID method was used to improve the long-term tracking of targets, thereby improving the value of IDF1. This algorithm can help to achieve real-time and accurate detection and tracking of bi-directional passenger flow in station passageways. In the event of an abnormal situation, the station staff can react rapidly to improve the station's operational safety. [ABSTRACT FROM AUTHOR]

At present, the interest in non-Pd hydrogen permeation membranes mainly focuses on group 5 B metals (Nb, V, Ta) and their alloys. To solve the problem of easy oxidation and weak dissociation of hydrogen molecules, a "Pd catalytic layer" needs to be deposited on the membrane surface. Due to its limited stability towards sulfur, issues of irreversible poisoning by sulfur compounds must be addressed. To this end, Pd-, Pd 65 Ag 35 -, Pd 90 Cu 10 -, Pd 80 Au 20 -, and Pd 55 Ag 35 Au 10 -coated Nb 30 Hf 35 Co 35 membranes were firstly synthesised by magnetron sputtering, and their hydrogen permeation behaviour in pure H 2 and/or in different H 2 /H 2 S mixtures for up to 1000 ppm at 673 K were evaluated. At these conditions, the permeabilities of the Pd-coated membranes decrease rapidly with increasing the H 2 S concentration and cannot recover after removal of H 2 S from the feed mixtures. In comparison, the Pd–Ag-containing membrane greatly increases the pure H 2 permeability, whereas the membrane doped with Au can effectively improve the response to H 2 S. Specifically, the Pd 65 Ag 35 -coated membrane shows the highest pure H 2 permeability, but it loses maximum permeability value on exposure to H 2 /H 2 S feed mixtures. In contrast, Pd 80 Au 20 -containing membranes exhibit good tolerance of H 2 S poisoning and displayed a high fractional recovery (∼83%) of initial H 2 permeability. The degradation of permeability for Pd- or Pd 65 Ag 35 -coated membranes after the H 2 S exposure can be ascribed to the deterioration of the structure caused by the possible formation of bulk Pd 4 S, which has been corroborated with EDS analysis, XRD, and XPS after testing. Finally, to improve both permeability and chemical tolerance, a Pd 55 Ag 35 Au 10 -coated membrane was successfully fabricated, and it possesses excellent comprehensive performance with respect to the permeability and H 2 S response. At 120 ppm H 2 S in the feed, the H 2 flux equals ca. 43% of its original value at 673 K and can be stable for at least 166 h. Bulk sulfides did not form on the membrane surface after the testing. This paper not only developed a new type of group 5 B alloy membrane with improved resistance to corrosion by H 2 S, but also further confirmed the feasibility of improving H 2 S corrosion resistance by adjusting the catalytic layer type or composition of the outermost coating. Some novel Nb 30 Hf 35 Co 35 self-supported membranes were successfully developed by regulating the type or composition of the outermost catalytic film. Correspondingly, the compositional region suitable for hydrogen permeation with improved resistance to corrosion by H 2 S has been identified. Especially, Pd 55 Ag 35 Au 10 -coated membrane possesses excellent comprehensive performance with respect to the permeability and H 2 S response. At 120 ppm H 2 S in the feed, the H 2 flux equals ca. 43% of its original value at 673 K and can be stable for at least 166 h. Bulk sulfides did not form on the membrane surface after the testing. The first-principles calculations showed that doping alloying elements, especially gold, inhibit the formation of palladium sulphide (e.g., Pd 4 S), thus providing more activating active sites for hydrogen adsorption and dissociation. This could be the main reason for the improvement of hydrogen permeability of PdAuAg-coated composite membranes in an H 2 S atmosphere. [Display omitted] • A new way to design hydrogen permeable alloys with improved H 2 S corrosion resistance is proposed. • A novel Pd 55 Ag 35 Au 10 -coated Nb 30 Hf 35 Co 35 self-supported membrane were successfully developed. • The PdAgAu catalytic layer showed promising resistance to sulfur poisoning. • Adjusting the type or composition of the catalytic layer can alleviate H 2 S poisoning. [ABSTRACT FROM AUTHOR]

Split pins (SPs), as essential fasteners in the pantograph-catenary system, play a critical role in maintaining the structural stability of the catenary support device (CSD) in high-speed railroad. Unfortunately, impact and vibration generated by the interaction between catenary system and high-speed running vehicles will progressively damage SPs, including but not limited to the loose, breakage, and missing of SPs. Current image processing technologies have limited accuracy. Traditional convolutional neural network (CNN)-based systems that directly learn deep features are inefficient in detecting subtle defects (such as fractured and loose SPs). This study presents a novel SP inspection system based on an innovative destruction and reconstruction learning (DRL). In the DRL, a novel cross stage partial network (CSPNet)-based classifier is firstly developed to alleviate the impact of redundant semantic information and optimize gradient regression process. Then, "destruction" in the DRL stream means these partitioned sub-regions in an input image are shuffled by a region shuffling mechanism (RSM); this guides the classifier to pay more attention to the distinguishable regions in the destroyed and original images for a purpose of correct recognition. For "reconstruction", a newly developed position reconstruction network (PRN) is utilized to restore the original spatial distribution of the sub-regions by modeling the semantic correlation among the sub-regions. The proposed DRL can injects more discriminative details to the classifier by jointly training the classification network and PRN. Finally, comprehensive experiments are conducted on CSD datasets captured from real railroad lines, and the results demonstrate that the proposed approach outperforms existing state-of-the-art models yet keeps a reasonable inference speed. [ABSTRACT FROM AUTHOR]

• Accident semantic network framework and a conceptual model of accident risk control chain can provide great help for the study of accidents. • A method for acquiring and constructing key contents based on semantic analysis is elaborated. • A management matrix is established to serve safety decision. • It provides suggestions for the actual operation management of urban rail transit. Although the occurrence of urban rail transit operation accidents threatens the safety of people's lives and property, there is little research on how to systematically mine the knowledge behind accident data in China, and there is also lack of methods to support the safety management strategy of urban rail transit operation. To address these shortcomings, this study proposes a comprehensive analysis method that combines qualitative and quantitative analysis of urban rail operation accident factors and safety management strategies based on accident data, which can provide reference for systematic analysis of such accidents in the future and will help urban rail safety researchers and decision makers to take effective safety management measures. The new contributions of the method are as follows: (1) a semantic network framework for accidents and a conceptual model of accident risk control chain are proposed; (2) a method for acquiring and constructing key contents based on semantic analysis is elaborated, and indicators for evaluating the influence degree of key item and the influence possibility of entries are proposed; (3) a management matrix is established to serve safety decision making based on indicators for the degree of attention that key item of accidents should receive. In order to explain the application of the comprehensive analysis method, 910 urban rail transit operation safety accident cases are analyzed in detail, and suggestions for the actual operation management of urban rail transit are proposed in the light of the actual situation of urban rail transit operation in China. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis and continual learning techniques for train transmission systems are becoming more appealing to ensure the operation safety and reliability of trains. However, existing related methods have the following limitations. 1) They build separate fault diagnosis networks for each key component in train transmission systems, which not only brings burdensome work for training and managing diagnosis networks but also ignores the influence of fault propagation and interaction between adjacent components. 2) They rely on enough class-incremental samples for continual learning to obtain the growth diagnosis ability, but the accumulations of incremental fault samples are long-period processes, leading to belated network evolution and hysteretic performance enhancement. To overcome the above-mentioned limitations, an evolvable system-level fault diagnosis framework is proposed for train transmission systems. In the proposed framework, a novel graph neural network is first constructed based on component spatial relationships, which is able to effectively learn and capture the interaction between components and integrate fault diagnosis tasks into a unified framework. Then, an anhysteretic evolution learning mechanism is developed to avoid lengthy waiting for new fault sample accumulations, in which over-fitting due to insufficient incremental fault sample is addressed by employing prototype-based classifiers and diagnostic knowledge is transferred and captured by maintaining and generating prototypes. The effectiveness of the proposed diagnosis framework is verified by taking a case study of fault diagnosis for subway train transmission systems, and its superiority is demonstrated by comparison with some stage-of-the-art diagnosis methods. [ABSTRACT FROM AUTHOR]