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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]

Autonomous railway inspection with unmanned aerial vehicles (UAVs) has huge advantages over traditional inspection methods. As a prerequisite for UAV‐based autonomous following of railway lines, it is quite essential to develop intelligent railway track detection algorithms. However, there are no existing algorithms currently that can efficiently adapt to the demand for the various forms and changing inclination angles of railway tracks in the UAV aerial images. To address the challenge, this paper proposes a novel anchor‐adaptive railway track detection network (ARTNet), which constructs a dual‐branch architecture based on projection length discrimination to realize full‐angle railway track detection for the UAV aerial images taken from arbitrary viewing angles. Considering the potential capacity imbalance of the two branches that can be caused by the uneven distribution of railway tracks in the dataset, a balanced transpose co‐training strategy is proposed to train the two branches coordinately. Moreover, an extra customized transposed consistency loss is designed to guide the training of the network without increasing any computational complexity. A set of experiments have been conducted to verify the feasibility and superiority of the ARTNet. It is demonstrated that our approach can effectively realize full‐angle railway track detection and outperform other popular algorithms greatly in terms of both detection accuracy and reasoning efficiency. ARTNet can achieve a mean F1 of 76.12 and run at a speed of 50 more frames per second. [ABSTRACT FROM AUTHOR]

A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and evaluating the grid structure performance have received significant attention. Complex network theory transfers the power grid to a network with nodes and links, which helps evaluate the system conveniently with a global view. In this paper, we employ the complex network method to address the cascade failure process and grid structure performance assessment simultaneously. Firstly, a grid cascade failure model based on network topology and power system characteristics is constructed. Then, a set of performance evaluation indicators, including invulnerability, reliability, and vulnerability, is proposed based on the actual functional properties of the grid by renewing the power-weighted degree, medium, and clustering coefficients according to the network cascade failure. Finally, a comprehensive network performance evaluation index, which combines the invulnerability, reliability, and vulnerability indicators with an entropy-based objective weighting method, is put forward in this study. In order to confirm the approach's efficacy, an IEEE-30 bus system is employed for a case study. Numerical results show that the weighted integrated index with a functional network could better evaluate the power grid performance than the unweighted index with a topology network, which demonstrates and validates the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Glutathione S-transferases (GSTs) are important type-II detoxification enzymes that protect DNA and proteins from damage and are often used as protein tags for the expression of fusion proteins. In the present work, octa-aminopropyl caged polyhedral oligomeric silsesquioxane (OA–POSS) was prepared via acid-catalyzed hydrolysis of 3-aminopropyltriethoxysilane and polymerized on the surface of graphene oxide (GO) through an amidation reaction. Glutathione (GSH) was then modified to GO–POSS through a Michael addition reaction to obtain a GSH-functionalized GO–POSS composite (GPG). The structure and characteristics of the as-prepared GPG composite were characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), thermogravity analysis, and surface charge analysis. The specific binding interactions between glutathione and GST gave GPG favorable adsorption selectivity towards GST, and other proteins did not affect GST adsorption. The adsorption behavior of GST on the GPG composite conformed to the Langmuir isotherm model, and the adsorption capacity of GST was high up to 364.94 mg g−1 under optimal conditions. The GPG-based solid-phase adsorption process was applied to the extraction of GST from a crude enzyme solution of pig liver, and high-purity GST was obtained via SDS-PAGE identification. [ABSTRACT FROM AUTHOR]

At present, China's academic circles have made some research results on the hidden dropout of junior middle school students, but they have not formed a relatively mature theory. Based on Durkheim's "social integration" theory, this paper studies hidden dropout from the perspective of class sociology and puts forward the influencing mechanism of "class integration" of junior middle school students' hidden dropout behavior. By class observation, this paper obtains the hidden dropout theoretical model of the interaction between individuals and class organization. [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]

Reliability assessments of mechatronic systems, considering covariates, are becoming more complex not just as a result of the integration of more diverse technologies into such systems but also because of increasing complexities in system topology; but traditional reliability assessment models with covariates tend to ignore the influence of system topology. This paper addresses such an exclusion and proposes a novel methodology dedicated to the analysis and quantification of system reliability using system topology and multiple covariates. There are essentially two new aspects contributing to the overall methodology. Firstly, using network theory, the system topology is abstracted as a network; in which network nodes represent a mechatronic system's minimum maintenance units, and the connections between them are regarded as edges. Secondly, the resultant topological network is combined with the inherent properties of the minimum maintenance units to construct the system baseline hazard function and considering the associated multi covariates from the perspective of failure propagation to provide a foundation for assessing system reliability. To validate the proposed methodology, by way of a case study, it was applied to the reliability assessment of a traction power supply system for a high‐speed train. The results, compared with three other assessment approaches, suggest that the proposed methodology provides for significantly more accurate assessments while requiring less computational resources. Furthermore, it can be flexibly extended to many other mechatronic systems. [ABSTRACT FROM AUTHOR]

The interdependent network is particularly vulnerable to attacks on high degree nodes; therefore, the improvement of its robustness under intentional attacks has become an important topic. In this paper, we put forward a new metric to quantify the robustness of interdependent networks against intentional attacks and develop an improved simulated annealing algorithm (ISAA) to maximize this metric by optimizing the allocation of intra-links in subnetworks. Based on the comparison between the ISAA and existing algorithms, it is found that the algorithm presented in this paper is more effective to enhance the robustness of an interdependent scale-free network (ISFN). By applying the ISAA to ISFNs with different coupling preferences, there is a key finding that the robustness of the optimized ISFN is significantly stronger than that of the original ISFN. In particular, for cases of disassortative and random couplings, no sudden collapse occurs in optimized ISFNs. According to the analysis of the degree and the clustering coefficient, we find that the subnetwork of the optimized ISFN exhibits an onion-like structure. In addition, the ISFN whose robustness is enhanced to resist the attacks on high degree nodes is still robust to the intentional attacks concerning the betweenness and PageRank. [ABSTRACT FROM AUTHOR]

• The general speed-density diagram of bidirectional pedestrian flow is proposed. • The speed-density diagram of bidirectional pedestrian flow is calibrated by the experimental data. • The pedestrian counter flow is simulated by using the collection of ordinary differential equations. • A pedestrian counter flow control model is proposed to avoid blocking in bidirectional corridors. In this paper, we develop a control model for pedestrian counter flow to avoid blocking in bidirectional corridors. Considering the conflict between two streams, we formulate a general speed-density diagram of bidirectional pedestrian flow based on the previous models in the literature. To accommodate a possible inhomogeneous pedestrian density along the corridor and inflows from various directions, the corridor is divided into several sections. A pedestrian counter flow model is developed to simulate the pedestrian flow between sections by using a collection of ordinary differential equations. The control objective is proposed based on user equilibrium and a capacity-oriented principle, and a linear feedback control model is developed to achieve a controlled inflow that reduces the density of each section to an acceptable level. The level of service of the corridor is improved, and blocking is eliminated by the proposed control model. Finally, sensitivity analysis suggests that the model performance can be improved if the conflict level is reduced. [ABSTRACT FROM AUTHOR]

• The pedestrians cell transmission model is extended to simulate the pedestrian following behavior. • The leader movement is simulated by an improved social force model. • A multi-scale model is proposed to simulate the guided crowd dynamics. • A strategic guidance model including leader location plan and exit selection is proposed. • The proposed model can suggest the optimal number of leaders and the optimal exit selection. Guidance is an efficient crowd management measure used to save lives in emergencies because it can reduce the disorientation of pedestrians. This study investigates an optimal guidance strategy for large-scale crowd evacuations. To increase computational efficiency, a pedestrian cell transmission model is extended to create a rapid simulation of guided crowd dynamics. To solve the conflicts between the limited guidance capacity and the desire to improve evacuation efficiency, a strategic guidance model is proposed, which generates a leader location and exit selection plan. A simulation algorithm is proposed to integrate a pedestrian following model and strategic guidance model based on the follower-leader interaction. Finally, a hybrid multiscale approach for modeling guided crowd evacuations is established to evaluate the performance of the guidance strategy. The experimental results show that the required CPU time of the proposed model is much less than that of the microscopic models. Because the number of leaders is minimized and the exit is selected by taking both risk and congestion into account, the obtained guidance strategy can realize the full use of guidance capacity, information confusion reduction and uniform exit usage, all of which contribute to a reduction in evacuation time. [ABSTRACT FROM AUTHOR]

Many studies on cascading failures adopt the degree or the betweenness of a node to define its load. From a novel perspective, we propose an approach to obtain initial loads considering the harmonic closeness and the impact of neighboring nodes. Based on simulation results for different adjustable parameter θ, local parameter δ and proportion of attacked nodes f, it is found that in scale-free networks (SF networks), small-world networks (SW networks) and Erdos-Renyi networks (ER networks), there exists a negative correlation between optimal θ and δ. By the removal of the low load node, cascading failures are more likely to occur in some cases. In addition, we find a valuable result that our method yields better performance compared with other methods in SF networks with an arbitrary f, SW and ER networks with large f. Moreover, the method concerning the harmonic closeness makes these three model networks more robust for different average degrees. Finally, we perform the simulations on twenty real networks, whose results verify that our method is also effective to distribute the initial load in different real networks. [ABSTRACT FROM AUTHOR]

In recent years, several time-frequency representation (TFR) and convolutional neural network- (CNN-) based approaches have been proposed to provide reliable remaining useful life (RUL) estimation for bearings. However, existing methods cannot tackle the spatiotemporal continuity between adjacent TFRs since temporal proposals are considered individually and their temporal dependencies are neglected. In allusion to this problem, a novel prognostic approach based on discrete cosine transformation (DCT) and temporal adjacent convolutional neural network (TACNN) is proposed. Wavelet transform (WT) is applied to effectively map the raw signals to the time frequency domain. Considering the high load and complexity of model computation, bilinear interpolation and DCT algorithm are introduced to convert TFRs into low-dimensional DCT spectrum coding matrix with strong sparsity. Furthermore, the TACNN model is proposed which is capable of learning discriminative features for temporal adjacent DCT spectrum coding matrix. Effectiveness of the proposed method is verified on the PRONOSTIA dataset, and experiment results show that the proposed model is able to realize automatic high-precision estimation of bearings RUL with high efficiency. [ABSTRACT FROM AUTHOR]

DNA damage repair is a key factor in the maintenance of cell genome stability, plays an important role in the regulation of tumour evolution, and can affect the prognosis of cancer patients. This study aimed to detect the protein expression of the DNA damage repair protein P53 and its upstream and downstream regulators, CHK1, GADD45A, and MDM2, in oral squamous cell carcinoma (OSCC), in order to analyse the association between the expression of these proteins and overall survival, and to assess their prognostic implications for OSCC patients. The expression of the above proteins was detected by immunohistochemistry in 80 human OSCC tissue samples and in non‐cancerous tissue samples. Compared to that in the non‐cancerous tissue, the expression of CHK1, GADD45A, and MDM2 in OSCC tissue was significantly increased. The protein expression of the tumour suppressor gene P53 was also increased. Patients with high CHK1 and MDM2 expression levels had a reduced survival time and a poor prognosis, whereas patients with high GADD45A expression levels had a good prognosis. Our results indicate that high CHK1 expression is an independent risk factor for poor OSCC prognosis, and that CHK1 may be a potential target for OSCC clinical treatment. [ABSTRACT FROM AUTHOR]

A macroscopic passenger flow simulation model based on system dynamics is proposed in this paper. It considers the key factors influencing the dynamics of passenger flow from a holistic perspective of the stations and then models the dynamic change in the number of passengers. Firstly, the transmission of passenger flow for a general many-to-many relation between nodes are presented. When the sum of sending capacities heading for a downstream node is less than the receiving capacity of this node, the aggregation of stranded passengers will form the queuing part of the node. The results coming from a passenger flow simulation of a subway station in Beijing show that the proposed model performs well by comparing it with the real data. It can be applied to describe the dynamic change in the number of passengers and the level of service for the facilities. [ABSTRACT FROM AUTHOR]

An analytical model is proposed to simulate the passenger flow in a subway station using the ordinary differential equation with the average passenger density in the facilities as the state variable. In order to realize the well-organized inbound process, a linear programming-based feedback control model (LFCM) is proposed to compute the optimal feedback passenger inflows of various facilities and velocities to improve the level of service. In order to deal with the unsolvable LFCM, which is caused by the cyclic operation characteristic of a subway station, a network-switch mechanism is incorporated to the LFCM to improve the performance of control model. Finally, a small numerical example is used to illustrate the features of our proposed model. [ABSTRACT FROM AUTHOR]

• The coaxial binary composite MnO 2 /polyaniline was prepared. • The coaxial structure is convenient for the ion and electrons transportation. • The MnO 2 /polyaniline composite exhibit excellent capacitive behavior. • The assembled asymmetric supercapacitor displays high performance. • The composite can produce microsupercapacitor combining 3D-printed stamp. Controllable design over the structure and morphology of active material is highly desirable for high performance supercapacitors. Here, coaxial binary composite MnO 2 /polyaniline was prepared by introducing tetraphenyl porphyrin tetrasulfonic acid interlayer (TPPS), as a crosslinking agent and dopant, to fabricate the three-dimensional hollow polyaniline (PANI) nanotube gel and then synthesizing nanoflaky MnO 2 uniformly on the surface of PANI. Moreover, this coaxial structure is convenient for the ion and electrons transportation to exhibit excellent capacitive behavior with the capacitance of 575 F/g at 0.4 A/g. In addition, we assembled an asymmetric supercapacitor using MnO 2 /PANI as positive electrode, displaying high energy density (52.7 Wh/kg), remarkable durability and long cycling life (maintaining 90.2% initial capacitance after 2000 cycles). Using MnO 2 /PANI as inks for 3D-printed stamping is promising to be a novel fabricating method which can rapidly produce flexible microsupercapacitor for wearable micro devices. [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]

Wheelset bearing fault diagnosis is essential to guarantee safe rail system operation. However, real-time monitoring signals of high-speed trains are nonstationary under variable conditions. Additionally, wheelset bearings operate in a normal state most of the time, and therefore, only normal data are available for training in practice. Under such a scenario of variable operating conditions and lacking labelled data, it is still necessary to timely detect the possible abnormal states of wheelset bearings. Currently, one-class classification is a suitable approach for real-time anomaly detection, but its accuracy is affected by the linear relationship of network nodes. Concerning this issue, this paper presents a novel method named scale-independent shrinkage broad learning to detect the abnormal states of wheelset bearings in real time. First, scaled rotation regularization is presented to convert the output constraint into the node distance constraint. Then, the variance-guided mapping mechanism is constructed to solve the linear relationship between nodes. Meanwhile, the optimal network mapping parameters are generated iteratively according to a so-called high-polymerization strategy, with which new output weights are formed after the feedback calculation. This method eliminates the linear correlation of node features during the iteration and realizes one-class classification detection of abnormal states. Finally, wheelset bearing data collected from a test rig of a Chinese rolling stock company are utilized to demonstrate the feasibility and effectiveness of the proposed approach in variable conditions. [ABSTRACT FROM AUTHOR]

• A high-speed train is modeled as an interdependent network. • A robustness metric is proposed based on network theory and practical factors. • Assessment and optimization models of resilience are constructed. • The tabu search algorithm is developed to solve the optimization problem. • The optimal repair strategy of failed units in the high-speed train is analyzed. High-speed trains (HSTs) are used to transport passengers to destinations efficiently and safely. However, when HST units fail, strategies for repairing them are not available. Hence, to optimize repair strategies, an interdependent network is used to model an HST, and a metric of resilience based on network theory is introduced. Based on practical factors, an interdependent machine–electricity–communication network (IMECN) and related cascading failure models are developed. Comprehensive robustness metrics for the IMECN and nodes are proposed considering the topology of networks and functional features of nodes. Accordingly, a resilience optimization model of the IMECN subject to disturbances is formulated and solved using a tabu search (TS) algorithm. Finally, a real-world HST is considered to analyze the optimal repair strategy for different numbers of failed nodes. Analysis results show that the recovery process under the optimal repair strategy has at most three stages. When a few nodes and less than half of the nodes fail, the highest and lowest resilience levels of the IMECN are achieved, respectively. Moreover, the characteristics of the preferentially repaired node in terms of importance, vulnerability, network type, and interdependency are analyzed. Results indicate that the optimal repair strategy is not necessarily determined by topological metrics. [ABSTRACT FROM AUTHOR]

Abstract Traction energy is the main component of railway operation energy, and a timetable that predefines the running time of train operation can be used to determine the traction energy consumption. This study proposes a bi-level model that optimizes timetables to achieve the energy-saving control of railway systems. The upper level of the model ensures the relative stability of the timetable while maintaining railway safety constraints, which makes train operations more convenient for the railway sector as well as passengers; while the lower level of the model optimizes the arrival and departure time among intermediate stations to minimize the energy consumption of each train. Then, a unified iterative optimization algorithm combining particle swarm is developed to solve the model, and a timetable that ensures energy consumption optimizations is thus obtained. A case study using actual operation data from the Beijing-Shanghai high-speed railway is developed to illustrate the proposed method. Results show that the total energy consumption is reduced by more than 7.6%, and the average adjustment time for each distance interval is approximately 1 min, which maintains the stability of the original timetable. Highlights • A bi-level model is developed to optimize railway energy and passenger convenience. • Energy saving, timetable stability, and train tracking constraints are investigated. • Spare time of timetable are optimally distributed by an iterative-loop algorithm. • Train optimal energy-saving operation strategies are proposed and verified by a case. • The model is applied in a real case saving 7.6% energy consumption. [ABSTRACT FROM AUTHOR]

Highlights • Ultrafine porous carbon/MnO 2 nanowires were expected to shorten the ion diffusion path. • The carbon aerogel derived from methyl cellulose provides a cost-consuming system. • The hierarchical structure serves as stable framework to reduce the agglomeration. • The composite can be stamped into arbitrary patterns to be applied in micro-devices. Abstract A green and scaled-up strategy has been successfully employed to fabricate asymmetric supercapacitors (ASC) in conjunction with high energy density as well as highly efficient electrode materials. In this study, a B, N-dual doped porous carbon aerogel negative and a novel ultrafine nanowire positive composite, composed of manganese oxide (MnO 2) core and carbon aerogel shell, were proposed. Methyl cellulose (MC) is spaciously available and the affluent hydroxyl groups on the molecular chains are conductive to trigger supramolecular assemble process to form self-crosslinking hydrogel. And then the hydrogel was dried and carbonized to obtain the B, N-dual doped porous carbon aerogel (BN-MC), with the specific area of 989.8 m2 g−1. Subsequently, a facile and efficient hydrothermal system, introduced to synthesize BN-MC/α-MnO 2 composite, is prone to achieve hybrid nanowires with an average width of ∼15 nm. The prepared 0.1BN-MC/MnO 2 -8 sample (0.1 M NH 4 HB 4 O 7 doping agent and 8 mg BN-MC addition) exhibits highest electrochemical behavior, whose capacitance is up to 338 F g−1 at 1 A g−1. That is ascribed to the well-dispersed α-MnO 2 nanowires and uniformly cladding BN-MC shell. The ASC, assembled with BN-MC/MnO 2 and BN-MC, significantly enhances energy density reaching to 27.75 Wh kg−1 at a power density of 1.35 kW kg−1 operating under 1.8 V voltage window. This ASC also shows superior cycling stability for maintaining 95.6% initial capacitance after 2000 cycles. What's more, the BN-MC/MnO 2 can be stamped into arbitrary patterns in seconds. This provides a new rapid prototyping technology for fabricating flexible and portable micro-supercapacitor devices. [ABSTRACT FROM AUTHOR]

• A high-speed train is modeled based on network theory. • A multi-objective optimization model for identifying critical elements is proposed. • A novel importance metric of critical elements is designed. • The proposed model is verified by a practical high-speed train. • Critical nodes, intra-links and inter-links for a high-speed train are identified. This paper proposes a multi-objective optimization-based approach to identify critical elements, including units and interactions within and between systems, in a high-speed train (HST). In the framework, network theory is used to 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). Cascading failure models for the subnetworks and IMECN, and topological and functional metrics for robustness are developed. We then formulate a multi-objective optimization model for maximizing the impact of the failure of critical elements on the topological and functional robustness of the IMECN and minimizing their number. We use NSGA-II to solve the optimization problem. Considering a practical HST as a case study, we apply the multi-objective optimization framework to search the groups of critical nodes, intra-links and inter-links. The results show that critical nodes, intra-links and inter-links of the IMECN are within the MN and CN. In particular, end nodes of the critical intra-links and inter-links may also be critical, and the critical elements of subnetworks tend to also be critical for the IMECN. In addition, we find that the critical nodes, intra-links and inter-links are not related to their topological importance. [ABSTRACT FROM AUTHOR]

Rotating machineries often work under severe and variable operation conditions, which brings challenges to fault diagnosis. To deal with this challenge, this paper discusses the concept of adaptive diagnosis, which means to diagnose faults under variable operation conditions with self-adaptively and little prior knowledge or human intervention. To this end, a novel algorithm is proposed, information geometrical extreme learning machine with kernel (IG-KELM). From the perspective of information geometry, the structure and Riemannian metric of Kernel-ELM is specified. Based on the geometrical structure, an IG-based conformal transformation is created to improve the generalization ability and self-adaptability of KELM. The proposed IG-KELM, in conjunction with variation mode decomposition (VMD) and singular value decomposition (SVD) is utilized for adaptive diagnosis: (1) VMD, as a new self-adaptive signal processing algorithm is used to decompose the raw signals into several intrinsic mode functions (IMFs). (2) SVD is used to extract the intrinsic characteristics from the matrix constructed with IMFs. (3) IG-KELM is used to diagnose faults under variable conditions self-adaptively with no requirement of prior knowledge or human intervention. Finally, the proposed method was applied on fault diagnosis of a bearing and hydraulic pump. The results show that the proposed method outperforms the conventional method by up to 7.25% and 7.78% respectively, in percentages of accuracy. [ABSTRACT FROM AUTHOR]

Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree. [ABSTRACT FROM AUTHOR]

[Display omitted] • A multi-objective model is proposed to design guidance information system. • The model is designer-friendly due to simultaneous optimization of signage location and information allocation plans. • The model produces cost-effective GIS plans for underground spaces. • The model improves wayfinding efficiency by satisfying potential information demand in underground spaces. Guidance information systems (GISs) are widely used for pedestrian navigation in underground spaces. We investigate GIS design methods in this paper. First, the information demand and potential information locations are abstracted based on the network discretization of underground spaces. Second, a multiobjective GIS design (MOGISD) model that focuses on economic costs and wayfinding is developed, and a dynamic solution evaluation-based heuristic algorithm is proposed to determine the optimal GIS design plan. To obtain a GIS plan that can achieve a perfect match between demand and information, a three-dimensional microscopic pedestrian-information interaction model that considers both human and information factors is formulated. Finally, to evaluate the effectiveness of the MOGISD model, we use the model to design a GIS plan for the underground floor of a multimodal transportation hub. The results show that the proposed method produces cost-effective GIS design plans contributing to high-efficiency wayfinding. The MOGISD model is designer-friendly due to the simultaneous optimization of signage location and information allocation plans. [ABSTRACT FROM AUTHOR]

Objective: There is a paucity of research on patients presenting with uninfected diabetic foot ulcers (DFU) that go on to develop infection. We aimed to investigate the incidence and risk factors for developing infection in a large regional cohort of patients presenting with uninfected DFUs. Methods: We performed a secondary analysis of data collected from a validated prospective state-wide clinical diabetic foot database in Queensland (Australia). Patients presenting for their first visit with an uninfected DFU to a Diabetic Foot Service in one of thirteen Queensland regions between January 2012 and December 2013 were included. Socio-demographic, medical history, foot disease history, DFU characteristics and treatment variables were captured at the first visit. Patients were followed until their DFU healed, or if their DFU did not heal for 12-months, to determine if they developed a foot infection in that period. Results: Overall, 853 patients were included; mean(standard deviation) age 62.9(12.8) years, 68.0% male, 90.9% type 2 diabetes, 13.6% indigenous Australians. Foot infection developed in 342 patients for an overall incidence of 40.1%; 32.4% incidence in DFUs healed <3 months, 55.9% in DFUs healed between 3–12 months (p<0.05). Independent risk factors (Odds Ratio (95% confidence interval)) for developing infection were: DFUs healed between 3–12 months (2.3 (1.6–3.3)), deep DFUs (2.2 (1.2–3.9)), peripheral neuropathy (1.8 (1.1–2.9)), previous DFU history (1.7 (1.2–2.4)), foot deformity (1.4 (1.0–2.0)), female gender (1.5 (1.1–2.1)) and years of age (0.98 (0.97–0.99)) (all p<0.05). Conclusions: A considerable proportion of patients presenting with an uninfected DFU will develop an infection prior to healing. To prevent infection clinicians treating patients with uninfected DFUs should be particularly vigilant with those presenting with deep DFUs, previous DFU history, peripheral neuropathy, foot deformity, younger age, female gender and DFUs that have not healed by 3 months after presentation. [ABSTRACT FROM AUTHOR]

Rolling bearing as one of the key mechanical components plays a significant role in the field of industrial. To improve the ability of rolling bearing fault diagnosis under multi-rotating situation, this paper proposes a novel rolling bearing fault characteristic product function correntropy (PFC), and employs Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification of rolling bearings under multi-stationary working situations. Firstly, rolling bearing vibration acceleration signal is decomposed by Local Mean Decomposition (LMD) to extract product functions (PF). Secondly, PFC needs to be obtained. PFC is the solution of the correntropy mathematical model of primary signal and PF component that is modified by Correlation Coefficient Entropy (CCE) as amplitude modulation operator. Finally, drawing support from LSSVM, the fault identification is achieved. Through the bearing identification experiments in different rotating situations, it is verified that PFC generates higher diagnosis accuracy than traditional fault features. Meanwhile, it is proved that PFC has more robustness than traditional fault features under cross-mixed roller bearing rolling status. Above all, the higher efficiency and availability of LMD-PFC-LSSVM are confirmed from the experiment consequence. It can be concluded that LMD-PFC-LSSVM is a reliable technology for rolling bearing fault diagnosis online under complicated rolling conditions and possesses the broad application prospect. [ABSTRACT FROM AUTHOR]

The smooth and fast evacuation of a pedestrian group is dependent on guidance services provided by signage systems. This paper investigates the location method of an evacuation signage system in a public space. We proposed a calculation method to determine the guidance efficiency of signage and further present a piece-wise probability function to explain interactions between pedestrians and signage. The interaction between one pedestrian and one signage was extended to the interaction between a pedestrian crowd and a signage system. A location model of the signage system was proposed to determine the minimum number of signs necessary to meet guidance demands. The location model was based on the Cooperative Location Set Cover Problem (CLSCP) and was correspondingly solved by a proposed exponential binary heuristic search algorithm (EBHS), i.e., a combined exponential binary search method and a heuristic search algorithm for solving the Cooperative Maximum Cover Location Problem (CMCLP). Finally, the proposed model was applied to determine the location of an evacuation signage system in a hall. The parameters used in the location model were calibrated based on experimental data. The model results showed that the proposed model can suggest the optimal number and best locations of signs. A sensitivity analysis showed that the guidance capacity of the signage system can be increased by improving the attractiveness of signage and pedestrian trust and familiarity with environment. The same number of signs is suggested for evacuation scenarios wherein crowd following behavior is present. [ABSTRACT FROM AUTHOR]

Pedestrian flow control is usually used to manage the crowd motion in public facilities to avoid congestion. We propose a network-wide pedestrian flow model based on the modified cell transmission model which describes the link flow as ordinary differential equations. The network flow control model (NFCM) is proposed to limit the number of pedestrians in a network according to the level-of-service requirements; however, the NFCM cannot ensure the uniform link density which is a premise of using NFCM. As a solution, the link flow control model (LFCM) is proposed to adjust the walking speed of pedestrians to realize the uniform link density. The NFCM provides the inputs for the LFCM and the LFCM compensates the deficiency of NFCM. Both NFCM and LFCM control the pedestrian flow in a cooperative way, and thus they form the hierarchical feedback control model (HFCM) of network-wide pedestrian flow. At last, the proposed HFCM is applied to control the crowd of a hall and the comparison of the simulation results in the controlled and uncontrolled scenarios shows that the proposed HFCM has the capability to suggest the optimal link inflows and walking speeds in real time to meet the LOS requirement. [ABSTRACT FROM AUTHOR]

• The M-LSCSFN for an HSEMU system describes system structure and functions. • The layer reliability is calculated based on MPs using specific operators. • The M-LSCSFN reliability is estimated with the layer reliability. • The HSEMU system reliability is evaluated in terms of the M-LSCSFN reliability. • A practical case is shown to demonstrate the proposed method. This paper proposes a new reliability assessment method for a high-speed electric multiple unit (HSEMU) system based on the expanded multi-state flow network. An HSEMU system is abstracted as a multi-layer multi-source multi-commodity multi-state flow network (M-LSCSFN), where minimum maintenance units (MMUs) and three types of connections form nodes and edges in different layers, and functional characteristics are regarded as network parameters. In the M-LSCSFN, multi layers describe different types of connections and their relationships; multi-source and multi-commodity reflect system features; whereas multi-state flow indicates the dynamic realization of system functions. Considering that an HSEMU system can be divided into multiple independent holistic functions, each holistic function has a corresponding sub-M-LSCSFN, and each layer of this sub-M-LSCSFN is a multi-source multi-commodity multi-state flow network (M-SCSFN). In order to assess system reliability, layer reliability (i.e. , the M-SCSFN reliability) is first defined as the probability that the amounts of each commodity transmitted successfully from source nodes to sink nodes will not be less than the required demands. A corresponding algorithm is proposed to calculate layer reliability in terms of minimal paths (MPs) using the quick inclusion-exclusion technique (QIE). The sub-M-LSCSFN reliability is subsequently calculated based on the layer reliability and the conditional relationships among different layers. Based on these, the HSEMU system reliability is defined as the ability of a system to complete specified multiple functions under stated conditions for a specified period of time, and can be evaluated using the reliability of sub-M-LSCSFNs via the proposed operator. Finally, a case study of the "Fuxing" HSEMU system is analyzed to demonstrate the applicability of the proposed method and algorithm the effectiveness of the proposed method is demonstrated in an HSEMU system reliability assessment case study. [ABSTRACT FROM AUTHOR]

The sharp increase in residents and vehicles causes heavy traffic pressure in many cities. To ease traffic congestion, it has been the common sense that we should develop public transit system. The priority of the bus appears particularly necessary with the rapid development of the public transport system. The bus lane is an important embodiment of the bus priority. Focusing on the problem of the unreasonable dedicated bus lane (DBL) under the lower ratio of buses, this paper proposed a new bus lane with limited physical length. And this bus lane can reduce the lane-changing conflict caused by the buses and cars running on roads without bus lanes. Based on the cellular automata (CA) traffic flow model and the lane-changing behavior of the vehicle including the optional lane-changing and the mandatory lane-changing, a three-lane traffic model with an isolated no-bay bus stop is proposed. The ordinary three-lane traffic without a bus lane and the cases of traffic with a DBL or the proposed bus lane are simulated, and the comparisons in the form of the fundamental diagrams are made among them. It is shown that the no-bay bus stop can act as a bottleneck on the traffic flow because of the mandatory lane-changing behavior. Under a certain ratio of the bus number to the total vehicles number, (1) the traffic with the proposed bus lane has less lane-changing conflict and can provide higher traffic capacity than the ordinary traffic without a bus lane, (2) compared with the DBL, the proposed bus lane is advantageous in easing congestion on the ordinary lanes when the traffic flow is high and can avoid unreasonable allocation of the road resources. [ABSTRACT FROM AUTHOR]

High-speed railways (HSRs) have been widely deployed all over the world in recent years and China has entered an era with both high investments and rapid expansion of HSR transport infrastructure. One of the most challenging issues is how to keep the security and safety of millions of HSR infrastructures. Meanwhile, the emerging sensing and wireless sensor network (WSN) technologies for infrastructure health monitoring (IHM) are being substituted for traditional tethered monitoring systems. This paper presents a two-layer architecture of WSN which will be appropriate for infrastructure health monitoring of HSR. The upper layer is named as tree access network and the lower layer is called star detection network. By adapting to the special characteristics of IHM network, we design a short network address and an optimized communication frame structure, which can satisfy the actual requirements and special characteristics of the IHM network. In order to implement a better transmission performance, we propose a novel transmission power based method which adopts the knowledge update mechanism to detect the optimization result. In the end, the details of address assignment and network construction are discussed, and the effectiveness of the proposed method is validated by a practical instance. [ABSTRACT FROM AUTHOR]

In urban mass transit network, when passengers’ trip demands exceed capacity of transport, the numbers of passengers accumulating in the original or transfer stations always exceed the safety limitation of those stations. It is necessary to control passenger inflow of stations to assure the safety of stations and the efficiency of passengers. We define time of delay (TD) to evaluate inflow control solutions, which is the sum of waiting time outside of stations caused by inflow control and extra waiting time on platform waiting for next coming train because of insufficient capacity of first coming train. We build a model about cooperative passenger inflow control in the whole network (CPICN) with constraint on capacity of station. The objective of CPICN is to minimize the average time of delay (ATD) and maximum time of delay (MTD). Particle swarm optimization for constrained optimization problem is used to find the optimal solution. The numeral experiments are carried out to prove the feasibility and efficiency of the model proposed in this paper. [ABSTRACT FROM AUTHOR]

• The high-speed railway network is modeled as a three-layer network. • Network-specific characteristics are considered in performance evaluation instead of pure complex network analysis. • Railway stations are prioritized and compared within different layers of the network. • The current physical rail network in China is generally sufficient to satisfy the requirements of its transportation accessibility. • Management implications are provided to facilitate the rail network planning, transportation organization and operations. High-speed railways, as dominant transportation patterns, constitute a large-scale complex network. Network performance is critical for high-level transportation services in transportation management. However, existing studies generally adopt pure topological indicators to evaluate performance in a single network and without considering the specific characteristics of rail transportation. This paper modeled the high-speed railway as a three-layer network including topological, functional, and service layers and assessed the integrated network performance from the view of transportation accessibility. In addition, the rail stations were prioritized and their performances in different layers were compared to analyze whether the total integrated performance was restricted by its connection abilities, functional capacities, and transportation service level. Finally, a case study of the high-speed rail network in China and comparisons with other traditional evaluation methods were conducted to verify the feasibility and superiority of the model. The findings of this study enable to provide management implications for rail transportation planning, organization, and operations. [ABSTRACT FROM AUTHOR]

With excessive passenger demand and uneven distribution of passenger flow, long-distance subway lines face oversaturated conditions during peak hours, leading to potential safety problems for subway systems. To improve travel efficiency and reduce safety problems, this study proposes a method of constructing a passenger flow control jointly optimized train timetable for an oversaturated subway line under a short-turning operation pattern. Considering the time-dependent and dynamic characteristics of passenger demand, an integer nonlinear programming model is formulated to minimize the total passenger waiting time. To solve the problem, the tabu search algorithm and the IBM ILOG CPLEX solver are combined to seek the approximate optimal solution. Finally, a series of numerical experiments are conducted on the Beijing Fangshan line to demonstrate the effectiveness of the proposed model and approach, which shows that the method of jointly optimizing the train timetable and passenger flow control can drastically decrease the total passenger waiting time and reduce congestion. • A congested subway line under a short-turning operation pattern is considered. • Two types of passenger flow control and boarding strategies are investigated. • Passenger flow control jointly optimized train timetable model is formulated. • Tabu search algorithm with CPLEX solver is designed to search the solutions. • Real-world case study to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In accordance with the uncertainty of decision rules extraction process in boundary region of the rough set, this study proposes a rough‐set belief rule model using multinomial subjective logic. The model builds the belief rule expectation for classification and recognition. It not only considered the effects of single condition attributes on decision, but also the multi‐attributes. Finally, the example analysis and comparison with the R0.5(X) model indicates the validity and advantage of this model. [ABSTRACT FROM AUTHOR]

Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%. [ABSTRACT FROM AUTHOR]

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. It is very desirable to monitor the health condition and the performance degradation of the suspension system online, which offers the important information of the suspension system and is critically important for the condition-based maintenance rather than scheduled maintenance in the future. Advanced fault diagnosis method is one of the most effective means for the health monitoring of the suspension system. In this paper, taking the lateral suspension system as an examcple, the fault isolation issue for different component faults occurring in the suspension system is concerned. The sensor configuration for obtaining the vehicle state information and the mathematical model for the lateral suspension system are presented. Four fault features in the time domain and three fault features in the frequency domain are used for each sensor signal. Three different methods, Dempster–Shafer (D–S) evidence theory, Fisher discrimination analysis (FDA) and support vector machine (SVM) techniques are applied to the fault isolation problem. Simulation study is carried out by means of the professional multi-body simulation tool, SIMPACK. The simulation results show that these methods can isolate the considered component faults effectively with a high accuracy. The D–S evidence-based fault isolation approach outperforms the other two methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper improved the Jøsang's subjective logic through the establishment of rectangular coordinate system, study on subjective logic properties in the two-dimensional image and using fitting line algorithm to depict dynamic trajectory of the opinion. At the same time, the Consensus operator was improved and made it more realistic. On this basis, this paper proposed a network survivability assessment model. It established a set of indicator system that could reflect the survivability and the formal description was given. Using the structures of the network environment for testing, the simulation results show that the indicator system is normative and complete. This model has higher sensitivity and effectiveness, which can better adapt to dynamic changes in the assessment of environment. [Copyright &y& Elsevier]

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. On-line health condition monitoring for the suspension system of rail vehicles offers a number of benefits such as preventing further deterioration of vehicle performance, enhancing vehicle safety, increasing operational reliability and availability, and reducing maintenance costs. It is desirable to timely detect the fault and monitor the performance degradation of vehicle suspension systems. In this paper, a comparative study on fault detection methods of urban rail vehicle suspension systems is considered. A novel sensor configuration is proposed where the underlying vehicle system is equipped with only acceleration sensors in the four corners of the carbody, the leading and trailing bogie, respectively. A mathematical model is developed for the considered vehicle suspension system. Both model-based and data-driven approaches are studied for the suspension fault detection problem. The robust observer, the Kalman filter combined with the generalised likelihood ratio test method, the dynamical principle components analysis and the canonical variate analysis approaches are applied to the fault detection problem. The simulation is carried out by means of the professional multi-body simulation tool, SIMPACK. In addition, the advantages and disadvantages of these methods are compared. The simulation results show that the data-driven methods outperform the model-based methods. [ABSTRACT FROM AUTHOR]

Illegal transshipment of maritime ships is usually closely related to illegal activities such as smuggling, human trafficking, piracy plunder, and illegal fishing. Intelligent identification of illegal transshipment has become an important technical means to ensure the safety of maritime transport. However, due to different geographical environments, legal policies and regulatory requirements in each sea area, there are differences in the movement characteristics and geographical distribution of illegal transshipment behavior in different time and space. Moreover, in areas with dense traffic flow, normal navigation behavior can easily be identified as illegal transshipment, resulting in a high rate of misidentification. This paper proposes a hybrid rule-based and data-driven approach to solve the problem of missing identification in fixed threshold methods and introduces a traffic density feature to reduce the misidentification rate in dense traffic areas. The method is both interpretable and adaptable through unsupervised clustering to get suitable threshold distribution combination for regulatory sea areas. The evaluation results in two different sea areas show that the proposed method is applicable. Compared with other widely used identification methods, this method identifies more illegal transshipment events, which are highly suspicious, and gives warning much earlier. The proposed method can even filter out misidentification events from compared methods' results, which account for more than half of the total number. [ABSTRACT FROM AUTHOR]

Abstract: Distributed logic programming languages, which allow both facts and programs to be distributed among different nodes in a network, have been recently proposed and used to declaratively program a wide-range of distributed systems, such as network protocols and multi-agent systems. However, the distributed nature of the underlying systems poses serious challenges to developing efficient and correct algorithms for evaluating these programs. This paper proposes an efficient asynchronous algorithm to compute incrementally the changes to the states in response to insertions and deletions of base facts. Our algorithm is formally proven to be correct in the presence of message reordering in the system. To our knowledge, this is the first formal proof of correctness for such an algorithm. [Copyright &y& Elsevier]

The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, towards the problems of object occlusion and category imbalance in the field images, Soft-NMS and Focal Loss are applied in RTLCap to promote defect description performance. After that, to improve the image processing speed of RTLCap and reduce the complexity of the model, a reconstructed RTLCap model named Faster RTLCap is presented with the help of YOLOv3. In the encoder part, a multi-level regional feature localization, mapping, and fusion module (MFLMF) are proposed to extract regional features, and an SPP (Spatial Pyramid Pooling) layer is employed after MFLMF to reduce model parameters. As for the decoder part, a stacked LSTM is adopted as the language model for better language representation learning. Both quantitative and qualitative experimental results demonstrate the effectiveness of the proposed methods. [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]

• A new method based on time-frequency domain combined with phase space reconstruction and singular value decomposition is proposed. • The proposed method integrated with wavelet transform, phase space reconstruction and singular value decomposition can lead to fast and efficient results. • Validity of the proposed method proposed is verified by pratical fault diagnosis application of wind turbine. Operation of wind turbines under fault state will directly affect the power output efficiency of wind farms. This paper proposes a new automatic fault diagnosis method for wind turbines. A fault diagnosis system framework is constructed and data of vibration status of wind turbines collected is processed and used for fault diagnosis. Firstly, wavelet coefficients are obtained using a discrete wavelet transform (DWT) for vibration acceleration signals collected from wind turbines. Then, the wavelet coefficients are sequentially subjected to phase space reconstruction (PSR) and singular value decomposition (SVD) to extract the fault features. Finally, an extreme learning machine (ELM) is used to classify the faults. Experimental results show that the proposed method is more effective and accurate than other fault diagnosis methods for wind turbines, such as support vector machine (SVM) and multiscale convolutional neural network (MSCNN). Image, graphical abstract [ABSTRACT FROM AUTHOR]

Hydrogen has been advocated as a promising energy carrier for railway systems, but its limited energy density may introduce mileage anxiety into the transportation system. Motivated by nonstop power exchange design, this article proposes a novel facility planning model under energy transfer conditions and natural endowments, such as photovoltaic resources in geographical locations, to optimize the overall system benefit. The supply, consumption, and replenishment mechanisms of multienergy forms (hydrogen energy, photovoltaic, electric energy) are designed on both vehicle and ground sides of the power exchange system. The optimal facility location and allocation of photovoltaic hydrogen plants, movable tank shifting devices, and train cars are collaboratively decided. A Lhasa–Xining railway case study is applied by conducting sensitivity analyses on significant parameters. Various insights hold the promise to promote policies and strategies for integrating transportation and power systems in a real-world application. It is demonstrated that energy cost reduction, solar power generation improvement, and energy-carrier capacity expansion help solve the mileage anxiety problem in nonstop power exchange systems. [ABSTRACT FROM AUTHOR]