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Catalytic steam reforming offers a groundbreaking approach for converting industrial chlorinated volatile organic compound (CVOC) waste into valuable syngas (H2and CO) and recovering HCl. However, the lack of C–Cl bond activation ability in traditional transition metal catalysts results in their insufficient reforming activity toward CVOCs. Herein, a novel molybdenum carbide (β-Mo2C) catalyst is developed and loaded onto a γ-Al2O3support synthesized through a self-assembly method. The γ-Al2O3support provides abundant unsaturated coordinated Al3+ions, which effectively anchor and disperse β-Mo2C nanoparticles. In the catalytic steam reforming reaction at 600 °C, the β-Mo2C/γ-Al2O3catalyst achieves a conversion efficiency higher than 95% and syngas yields of 82.4–92.3% for various typical industrial CVOCs. The mechanistic research reveals that the coordination between C and Mo atoms in β-Mo2C leads to a slightly electron-deficient state of the Mo sites, accompanied by a high density of unoccupied 4d orbitals. These characteristics are highly advantageous for the adsorption and dechlorination of CVOC molecules. The produced nonchlorinated intermediates can subsequently be oxidized to CO and H2by hydroxyl radicals on adjacent Mo sites.

Multi-camera vehicle tracking is a fundamental task for city traffic management to count traffic flow or monitor roads. This paper focuses on multi-camera tracking on the highway, which is more challenging compared with city streets in some problems such as fast-moving vehicles, tiny similar vehicles in appearance, longer tracking distance, and lighting intensity changes in the dark tunnels. In this paper, we propose a practical Appearance-Parsing Spatio-Temporal Trajectory Matching Network (ASTM-Net) based on the global appearance matching of local trajectory for addressing the cross-camera tracking tasks on the highway. Specifically, considering that the environmental disturbance and small vehicles have a similar appearance, we propose a multiple appearance-attribute parsing (MAP) module consisting of a Bi-propagation top-down (Bi-TD) block and appearance re-identification (ARe-ID) block to obtain salient global appearance-attribute features through given a video sequence. To address discrete tracking fragments caused by occlusion, we develop an appearance-joint-tracking (AJT) mechanism to merge the isolated tracklets with target interaction and occlusion handling. We then exploit an appearance-informed spatio-temporal matching (ASTM) module to achieve multi-camera tracklet-to-target assignment, which employs spatio-temporal consistency relation for intra-camera trajectory correction and coarse inter-camera tracklet correlation and aggregate appearance matrix of local trajectories for assigning global trajectory ID. Finally, in order to evaluate our proposed ASTM-Net, a new dataset, named HST, collected on the highway is established. We verify the ASTM-Net on the HST and the other three public datasets, i.e., CityFlow, UA-DETRAC, and Synthehicle, whose experimental results demonstrate the effectiveness and robustness of the proposed method.

Acinetobacter baumanniiposes a serious threat to human health. Pathogenic bacterial lipopolysaccharides (LPSs) are potent immunogens for the development of antibacterial vaccines. To investigate the antigenic properties of A. baumanniiLPS, five well-defined core oligosaccharide fragments from the LPS of A. baumanniiSMAL and ATCC 19606 were synthesized. A divergent synthesis strategy based on orthogonally protected α-(2 → 5)-linked Kdo dimer 6was developed. Selective exposure of different positions in this key precursor and then elongation of sugar chains via stereocontrolled formation of both 1,2-transand 1,2-cis-2-aminoglycosidic linkages permitted the efficient synthesis of the targets. The synthetic route also highlights a 4-Oand then 7-Oglycosylation sequence for assembly of the novel 4,7-branched Kdo framework. Antigenicity assay using the glycan microarray technique disclosed that tetrasaccharide 3featuring both 4,7-branch and α-(2 → 5)-Kdo-Kdo structural elements was a potential antigenic determinant.

Bicyclohexane is a hydrogen storage reagent with high hydrogen density and low boiling point. Compared with the hydrogenation of biphenyl, the alkylation of benzene and cyclohexene to cyclohexylbenzene and hydrogenation is a promising way to prepare cyclohexane on a large scale. The research and development of high-efficiency cyclohexyl benzene hydrogenation catalyst should be further developed based on mature alkylation technology. This paper used an acidified USY molecular sieve to catalyze the alkylation of benzene and cyclohexene to cyclohexylbenzene, which achieved 100% conversion and selectivity. Furthermore, Pt/TiO2/γ-Al2O3catalyst is prepared by pre-deposition TiO2film of different thicknesses on γ-Al2O3surface and then supported with platinum particles by Atomic layer deposition (ALD). The role of TiO2film in improving the cyclohexylbenzene hydrogenation performance of the catalyst is studied. TEM, CO pulse chemisorption, CO-DRIFTs, quasi-in situXPS, H-D exchange, and H2-TPR characterization show that compared with Pt/γ-Al2O3, TiO2thin films on Pt/TiO2/γ-Al2O3do not change the dispersion of Pt particles, but can form new Pt-TiO2interactions. The hydrogenation performance of cyclohexylbenzene was improved by increasing the electron density and the proportion of planar active sites on the surface of platinum and reducing the energy barrier of hydrogen spillover. The research provides theoretical support for further bicyclohexane organic liquid hydrogen storage reagent development. The relevant metal-support interaction regulation strategy can be applied to the development of efficient catalysts for other aromatic molecules hydrogenation.

This study aims to explore the dynamics of total factor productivity (TFP) in 40 advanced economies and its implications for economic stability, growth patterns, and technological integration. Understanding the concept of long memory in TFP is crucial in comprehending the diverse paths of economic development and technological advancement in different nations. The study endeavors to clarify the concept of long memory in TFP and analyze its impact on economic stability, growth patterns, and technological integration on a global scale. The research employs a sophisticated methodological framework that integrates a combination of unit root tests, non-parametric methods, fractional integration techniques, and ARFIMA models. This multifaceted approach provides a nuanced understanding of the persistence in TFP series and helps distinguish between stable and mean-reverting TFP dynamics across different nations. The analysis highlights the varying trajectories of TFP across these economies, which sheds light on the diverse paths of economic development and technological advancement. The findings of this research are pivotal in comprehending the role of TFP in shaping growth convergence, influencing business cycle fluctuations, and technological progress. The study contributes to policy discourse by proposing strategic policy measures that harness the long-term trends of TFP. These recommendations encompass key areas such as labor, capital, institutional frameworks, and innovation policies. Countries can optimize sustainable economic development and foster effective technological integration in an increasingly interconnected global economy by implementing these measures.

Railway scene understanding is key to autonomous train operation and important in active train perception. However, most railway scene understanding methods focus on track extraction and ignore other components of railway scenes. Although several semantic segmentation algorithms are used to identify railway scenes, they are computationally expensive and slow with limits applications in railways. To solve these problems, we propose efficient dual-stream fusion network (EDFNet), a lightweight semantic segmentation algorithm, for understanding railway scenes. First, a dual-stream backbone network based on mobile inverted residual blocks is proposed to extract and fuse detailed features and semantic features. Next, a bi-directional feature pyramid pooling module is proposed to obtain multi-scale features and deep semantic features. Finally, a multi-task aggregate loss is designed to learn semantic and boundary information, thus improving the accuracy without increasing the computational complexity. Extensive experimental results demonstrate that EDFNet outperforms the lightweight state-of-the-art algorithms with high accuracy and fast speed on two railway datasets.

Deep coalbed methane (DCBM), an unconventional gas reservoir, has undergone significant advancements in recent years, sparking a growing interest in assessing pore pressure dynamics within these reservoirs. While some production data analysis techniques have been adapted from conventional oil and gas wells, there remains a gap in the understanding of pore pressure generation and evolution, particularly in wells subjected to large-scale hydraulic fracturing. To address this gap, a novel technique called excess pore pressure analysis (EPPA) has been introduced to the coal seam gas industry for the first time to our knowledge, which employs dual-phase flow principles based on consolidation theory. This technique focuses on the generation and dissipation for excess pore-water pressure (EPWP) and excess pore-gas pressure (EPGP) in stimulated deep coal reservoirs. Equations have been developed respectively and numerical solutions have been provided using the finite element method (FEM). Application of this model to a representative field example reveals that excess pore pressure arises from rapid loading, with overburden weight transferred under undrained condition due to intense hydraulic fracturing, which significantly redistributes the weight-bearing role from the solid coal structure to the injected fluid and liberated gas within artificial pores over a brief timespan. Furthermore, field application indicates that the dissipation of EPWP and EPGP can be actually considered as the process of well production, where methane and water are extracted from deep coalbed methane wells, leading to consolidation for the artificial reservoirs. Moreover, history matching results demonstrate that the excess-pressure model established in this study provides a better explanation for the declining trends observed in both gas and water production curves, compared to conventional practices in coalbed methane reservoir engineering and petroleum engineering. This research not only enhances the understanding of DCBM reservoir behavior but also offers insights applicable to production analysis in other unconventional resources reliant on hydraulic fracturing.

ABSTRACTBased on train operation data, Bayesian networks (BN) are used to model the cascading effects of traffic control actions and their influences (such as changes in train delays) for two and three consecutive trains. Influence factors are first determined to describe the interactions between train delays and control actions, i.e. the delay changes due to control actions over time and space. Considering the interdependence of these factors, their causal relationships are obtained, and the BN model’s connection paradigm is determined based on these causal ties. The BN structures are then proposed by combining domain knowledge and a data-driven method. The proposed models are tested on the train operation data from the Chinese high-speed railways. The results show that the proposed method exhibits a good fit for the train operation data and outperforms other conventional train operation models in terms of various evaluation metrics. Besides, the strength of train control actions’ cascading effects is investigated. It shows that section train control actions are stronger than those in stations, and both are considerably correlated with train delays and recovery times in sections and stations. Finally, a macroscopic model for control actions between several trains (more than three) is obtained based on the learned paradigm of the presented models and the cascading effects between two and three trains, demonstrating the model’s extensibility. The proposed models are intended to support traffic controllers with the estimation of future train delay change patterns, the expected control actions, and the cascading effects of control actions, and they are imperative for aiding the decision-making of controllers to manage high-speed railway traffic.

A chemical synthesis of a unique nanosaccharide fragment from Helicobacter pylorilipopolysaccharide was achieved via a convergent glycosylation method. Challenges involved in the synthesis include the highly stereoselective construction of β-3-deoxy-d-manno-oct-2-ulosonic acid (Kdo) and two 1,2-cis-glycosidic linkages, as well as the formation of a branched 2,7-disubstituted heptose subunit. Hydrogen-bond mediated aglycone delivery strategy and benzoyl-directing remote participation effect were employed, respectively, for the efficient generation of the desired β-Kdo glycoside and 1,2-cis-α-l-fucoside/d-glucoside. Moreover, the key branched framework was successfully established through a [(7 + 1) + 1] assembly approach involving the stepwise glycosylation of the heptasaccharide alcohol with two monosaccharide donors. The synthesized 1containing a propylamine linker at the reducing end can be covalently bound to a carrier protein for further immunological studies.

To evaluate the safety and efficacy of endovascular denervation (EDN) as an adjunct to percutaneous vascular intervention (PVI) for peripheral artery disease (PAD).

A new asymmetric synthesis of the A/E-ring fragment of C18-diterpenoid alkaloids is described. The crucial contiguous stereogenic centers at C4, C5, and C11 were established through an asymmetric Michael addition/allylation sequence. The unique azabicyclo[3.3.1]nonane motif (A/E rings) was assembled by employing ring-closing metathesis and Mitsunobu reaction as key strategies.

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.

The first asymmetric total synthesis of the hexacyclic veatchine-type C20-diterpenoid alkaloid (−)-garryine is presented. Key steps include a Pd-catalyzed enantioselective Heck reaction, a radical cyclization, and a photoinduced C–H activation/oxazolidine formation sequence. Of note, a highly enantioselective Heck reaction developed in this work provides efficient access to 6/6/6 tricyclic compounds, in particular, containing a C19-functionalitiy, which is useful for diverse transformations.

Defect detection for the infrastructure of high-speed rails is key to the rails’ safety. In recent years, such detection has been based on deep learning with a substantial amount of labeled samples. However, the defect samples of high-speed rail infrastructure are few, resulting in insufficient training data. Meanwhile, the features provided by supervised pretraining are not targeted at the complex backdrop of rail operation. To make up for insufficient data and un-targeted features, we propose a few-shot detector with self-supervised pretraining for high-speed rail infrastructure defects (FDSP-HRIDs). Our approach incorporates three stages. First, we undertake self-supervised pretraining with a large amount of unlabeled infrastructure data. After that, we transfer the backbone network weight onto the few-shot base detector. Then, we use a large amount of defect-free, base-class data to train each layer of the detector. We use the squeeze-and-excitation network-multiscale attention mechanism (SE-MAM) to improve the network’s recognition of small objects and the model’s sensitivity to channel features. In this case, the context semantic fusion (CSF) module effectively fuses features of different scales and learns global and local features for comprehensive feature representation. At last, we use a small amount of novel-class defect data to fine-tune the detector and run the detector on the test set. Experimental results show that our approach achieves mAP50 of 27.47% and 34.08% for one and five shots on the dataset of drone images captured ourselves, meaning that our approach achieves an obvious advantage over other approaches.

UAVs have a broad application prospect in the field of railway inspection due to their excellent mobility and flexibility. However, it still faces challenges, such as high human labor costs and low intelligence levels. Therefore, it is of great significance to develop a real-time intelligent rail recognition algorithm that can be deployed on the onboard computing device to guide the UAV’s camera to follow the target rail area and complete the inspection automatically. However, a significant issue is that rails from the perspective of UAVs may appear with changing pixel widths and various inclination angles. Concerning the issue, a general and adaptive rail representation method based on projection length discrimination (RRM-PLD) is proposed. It can always select the optimal representation direction, horizontal or vertical, to represent any kind of rails. With the RRM-PLD, a novel architecture (Real-Time Rail Recognition Network, TriRNet) is proposed. In TriRNet, a designed inter-rail attention (IRA) mechanism is presented to fuse local features of single rails and global features of other rails to accurately discriminate the geometric distribution of all rails in the image in a regressive way and thus improve the final recognition accuracy. Further, one-to-one mapping from anchor points to final feature maps is established. It greatly simplifies the model design process and improves the model’s interpretability. Besides, detailed model training strategies are also presented. Extensive experiments have verified the effectiveness and superiority of the proposed formulation in terms of both network reasoning latency and recognition accuracy.

Anomaly detection of the railway track is essential to protect the safety of railway transportation. However, the railway track is often disturbed by unknown anomalies, making it challenging to detect anomalies using only supervised learning and visual sensors. To address these problems, this article proposes a railway track anomaly detection method using semisupervised learning and vision-lidar decision fusion. The method consists of three parts: supervised pre-detection, semisupervised re-detection, and decision-level fusion. In the pre-detection stage, an enhanced instance segmentation algorithm is used to pre-detect known and unknown anomalies to improve the overall performance. In the re-detection stage, the reconstruction method, semisupervised learning, and structured light point cloud are combined to re-detect anomalies, especially unknown anomalies. In the fusion stage, the fusion of pre-detection and re-detection at the decision level further advances the accuracy and credibility of anomaly detection. Experimental results show that the proposed method achieves a mean average precision (mAP 0.50:0.95) of 92.2% and a mean average recall (mAR 0.50:0.95) of 94.5%. The proposed method is superior to the single vision-based or point cloud-based methods in anomaly detection. In addition, the proposed method provides a general solution to anomaly detection and extends the application of structured light point cloud.

Abstract: The differences of coalbed methane (CBM) desorption-diffusion from coal drilling-core under various drilling fluid medium are not considered in the present calculating methods of lost CBM quantity, which leads possibly to the inaccuracy of CBM quantity in coal seam. Here we took the desorption of CBM from coal core under drilling fluid medium as a pressure-swing process, and based on the Langmuir equation and Fick-first law, established the desorption-diffusion model and numerical modeling method of lost gas (including free CBM) calculation in coal core under various drilling fluid mediums through physical simulation test and by considering comprehensively primary factors. The results showed that the physical simulated t-Qt curves can be rightly fitted by the numerical modeling data, which indicated the ultimate desorption quantity from the numerical modeling was adjacent to that from the physical simulation as a whole. It was found that the lost CBM quantity from the modeling method was generally higher than that from the direct method when lost time was relatively long. Thus, we suggest that it is necessary to emend the active China national standard through further investigation, since the lost CBM quantity from coal drilling-core was generally underestimated using the method in the current standard.

Semantic segmentation in railway scenes constitutes a fundamental task in the context of autonomous train driving, serving as a critical source of positional information for other visual tasks. Yet, prior semantic segmentation algorithms have faced challenges in achieving real-time and precise railway panoptic segmentation. Railway scene images often have highly imbalanced content density. Hence, the distribution of different regions in the image is highly nonuniform, which leads to the issue of semantic sample imbalance. To achieve this, we propose a dense multiscale fusion network (DMFNet), a lightweight and precise semantic segmentation framework tailored for railway scenes. DMFNet employs a network framework that combines dense connections and multiscale fusion to efficiently extract and utilize features. To enhance the feature extraction capability of the neural network, we design a lightweight residual block with multiple scales, composed of large convolution kernels. Additionally, we introduce a novel method for reassigning loss function weights to balance the samples within railway scenes. Extensive experimentation demonstrates that our DMFNet achieves outstanding results on railway scene datasets. It is designed to provide a lightweight and accurate semantic segmentation solution for railway scenes.

The train operation process is characterized by strong coupling and nonlinearity, often accompanied by significant and unobservable external disturbances, which makes train operation control a challenging task. To address these problems, a new design method of the fuzzy sliding mode controller is proposed for the train operation speed tracking control. Firstly, the global fast terminal sliding mode control is introduced to enable the system to reach a steady state in finite time. Then, an adaptive disturbance estimation term is created, which is compensated by the absolute value of the derivative of the Lyapunov function. And a double-layer hierarchical and exponential reaching law global fast terminal fuzzy sliding mode controller is proposed, where the exponential reaching law is introduced to regulate the dynamic quality of the sliding mode surface, this controller can quickly converge to the stable state and effectively eliminate the chattering of the controller. Finally, the proposed method is verified through simulation experiments, demonstrating its effectiveness in improving the train operation control performance.

Continual learning is promising in intelligent fault diagnosis of three-phase motors, which allows diagnosis networks to increase diagnosable fault classes without tedious retraining during various mechanical and electrical fault sample accumulations. Existing studies, however, have the following limitations. 1) The representation learning ability of diagnosis networks is nonaugmentable because of predefined and fixed network structures. 2) The impacts of different working conditions are not explicitly considered during continual learning. To overcome the abovementioned limitations, an elastic expandable continual learning framework is proposed for class-added motor fault diagnosis. First, an elastic expansion mechanism is developed to improve the representation learning ability during continual learning by progressively introducing new network branches. In addition, the new branches are pruned concurrently with training to elastically adjust the network sizes, thereby avoiding bringing excessive computational complexity. Then, an adaptive multiscale convolution mechanism is designed to learn robust fault features by fusing working condition information, increasing the diagnostic accuracy of continual learning under complex working conditions. Finally, a new training strategy with a multiobjective loss is formulated to guide continual learning. The effectiveness of the proposed method is demonstrated through motor fault simulation experiments.

Fasteners are a critical part of the rail and are used to fix the rail, which are important for train operation. Rail vibrations during train operation can cause anomalies in fasteners. At present, the 3-D structured light camera is used to detect anomalies on railway sites, but there is a lack of sufficient mining of 3-D data and effective fusion of multisensor data. To address this issue, this article proposes a novel approach for railway fastener anomaly detection via multisensor fusion and self-driven loss reweighting. First, a pixel-level attention mechanism multisensor fusion method is applied, where the depth map is used as an attention factor to highlight edge contours and enhance abrupt changes in the gray level. Second, a feature fusion-decoupled module is proposed to obtain the dense feature maps and then decouple the detection task to output class, location, and confidence. Finally, given the characteristics of the sample imbalance in the fastener dataset, a dynamic self-driven loss reweighting method is used to improve the detection accuracy of difficult samples. The experimental results show that the proposed method can achieve 86.6% precision and 61.72-FPS detection speed, better than other state-of-the-art algorithms.

Railway intrusion seriously threatens railway safety and can cause enormous loss of life and property. Therefore, railway intrusion detection is crucial for the safety of railway operation. Among the current methods of intrusion detection, machine vision-based methods have been widely used in railways, and have attracted close attention because of their great benefits. This paper proposes a comprehensive review of railway intrusion detection based on machine vision, covering ground monitoring, on-board inspection, and unmanned aerial vehicle (UAV) inspection. First, this paper systematically reviews most of the studies over the past two decades and presents the survey in three parts. Second, by analyzing these studies and the requirements for railway monitoring, we summarize the major challenges that hinder railway intrusion detection based on machine vision. Finally, we propose several promising perspectives for railway intrusion detection based on machine vision by comprehensively considering the development of machine vision, sensors, and pattern recognition together with the needs of railway scenes.

This study aims to quantitatively assess the total organic carbon (TOC) and total nitrogen (TN) content of reservoir sediments in southwest China using Fourier transform infrared spectroscopy (FTIRS). FTIRS measurements were performed on 187 sediment samples from four reservoirs to develop calibration models that relate FTIR spectral information with conventional property concentrations using partial least squares regression (PLSR). Robust calibration models were established for TOC and TN content. The external validation of these models yielded a significant correlation between FTIR-inferred and conventionally measured concentrations of R2= 0.88 for TOC, R2= 0.90 for TN. This method can be performed with a small sample size and is nondestructive throughout the simple measurement process. The TOC and TN content in the sediment can be determined with high effectiveness without being overly expensive, making it an advantageous method when measuring a large number of samples.

AbstractAs two powerful and flexible tools for decision-makers (DMs) to model the complex cognition, the hesitant fuzzy set (HFS) and hesitant fuzzy linguistic term set (HFLTS) allow DMs to express their opinions with several possible membership values or linguistic terms on the objects over each criterion. The aim of this article is to develop a novel score function of the HFS and HFLTS including hesitant degree and fuzzy degree information. For this purpose, the notion of fuzzy degree of the hesitant fuzzy element (HFE) and hesitant fuzzy linguistic element (HFLE) is introduced first. Then, considering both the hesitant degree and fuzzy degree information in expressions, the new score function, namely the Score-H&FD, is designed. Based on which, we extend the classical SMART (simple multi-attribute rating technique) method to the hesitant fuzzy environment. As a result, the hesitant fuzzy SMART (HF-SMART) method is developed in this article. Afterwards, we apply our proposed approach to assess and rank several teachers concerning information literacy. Finally, sensitive analysis and comparative analysis are carried out. The results show that the proposed method in this article has substantial advantages and applicability.

Point cloud semantic segmentation (PCSS) is crucial for digital twins of high-speed railways. By now, the concerned subjects are confined within the interior infrastructures of railways. However, the surrounding environments are also important for the safe operation. Concerning this issue, a full-range high-speed railway scanning scheme based on unmanned-aerial-vehicle-borne LiDAR is utilized. However, the massive data volume and data distribution imbalance pose great challenges for PCSS. To address these issues, a novel PCSS framework called 3DGraphSeg is proposed in this article. To cope with the massive data volume, a structural representation algorithm named local embedding super-point graph is proposed to represent the vast point cloud into a concise graph while retain the data's inherent topology structure by local spatial embedding. Then, the gated integration graph convolutional network (GIGCN) is proposed to contextual segment the graph. In the GIGCN, to prevent the gradients from vanishing or exploding, the hidden states of gated recurrent units in every layer are integrated using a new layer named gated hidden states integration (GHSI). GHSI strengthens the back propagation by giving the loss function direct access to each layer and absorbs the features of different layers comprehensively, which enables the network to produce a smoother decision boundary and prevents the overfitting problem. Besides, to enhance its robustness to data imbalance, we propose a loss function: adaptive weighted cross entropy. Finally, five experiments are designed for verification. The proposed framework has excelled in different datasets and outperforms state-of-the-art approaches on the SemanticRail dataset.

Objective Osteoarthritis (OA) is a common disease with complex and unclear pathogenesis. Ferroptosis is a new cell death mode, which is proved to be involved in different kinds of disease. We hypothesized that ferroptosis contributes to the progress of human OA.Design Chondrocytes were extracted from waste cartilage of total knee arthroplasty, and stimulated with interleukin-1β (IL-1β). Then, we detected the morphology, proliferation, and viability, and levels of Fe3+, glutathione (GSH), reactive oxygen species (ROS), malondialdehyde (MDA), and 5 proteins related to ferroptosis with or without intervention of ferrostatin-1 (Fer-1). In addition, we compared the effect of Fer-1 and liproxstatin-1 (Lip-1) on ferroptosis and the protection of chondrocytes by detecting several markers of both ferroptosis and OA.Results After stimulation of IL-1β, there were significant changes on the shape of chondrocyte, with lower viability and proliferation. There was accumulation of intracellular Fe3+, GSH, ROS, and MDA, with the changes of expression of 5 ferroptosis-related proteins. With the contribution of Fer-1, results above were reversed. Moreover, there was no significant difference in GPX4 and ACSL4 between Fer-1 and Lip-1 group. However, the expression of COLX, ADAMTS5, and MMP-13 are lower after the treatment of Fer-1 compared with Lip-1.Conclusions Ferroptosis plays an important role in human OA chondrocytes, which can be reversed by Fer-1, illustrating that inhibitor of ferroptosis may be a potential treatment of OA. Moreover, Lip-1 and Fer-1 can both alleviate the level of ferroptosis in OA chondrocytes, but Fer-1 had a more protective effect.

Cu−Y2O3and Cu−Y2O3−Zr composites were prepared via mechanical alloying and spark plasma sintering, and their microstructure and properties were systematically studied by using optical metalloscope, scanning electron microscope, transmission electron microscope, conductivity and tensile tests. It is found that the microstructure of the composites greatly affects mechanical behavior and electrical conductivity. The improvement of electrical property can be attributed to the formation of coherent Y4Zr3O12particles and the preferential nucleation of Cu4Zr phase, which improves the interface between Y2O3and Cu matrix, and reduces the dislocation density, respectively. In addition, the Cu−Y2O3−Zr composites can achieve 265.6 MPa of yield strength, 301.0 MPa of ultimate tensile strength, 23.6% of elongation, and 92.0%(IACS) of electrical conductivity.

Noise barriers play a critical role in reducing noise and preventing foreign object from invading railway. Noise barrier structural defects such as rusted column, deteriorated mortar layer and other damages make its structure unstable, thereby threatening seriously railway operation safety. Unfortunately, existing noise barrier inspection methods still rely heavily on manual inspection, which are low-efficiency, subjective and difficult to detect the external structure of noise barriers. To solve these problems, this study proposes an automatic inspection manner for noise barrier using UAV images, and develops a fully convolutional network (FCN)-based noise barrier defect detection approach named skip connection YOLO detection network (SCYNet), which focuses on three aspects: network structure, loss function and data augmentation. First, a skip-connected feature structure Simi-BiFPN is incorporated into the network to fully fuse the features extracted from various scale layers without adding much computational overhead. Second, a NoiseIoU loss for bounding box regression is designed to improve existing IoU-based losses and get better performance on small dataset. Thirdly, a mixed sample data augmentation method named AutoFMix is proposed to eliminate the over-fitting issue caused by excessive similarity between samples, and further improve the detection accuracy. Finally, experiments conducted on the UAV railway noise barrier dataset show that the proposed SCYNet model achieves 92.2 mAP and 78.7 FPS, respectively, which outperform other models in terms of accuracy and processing speed. The fast-processing speed and high detection accuracy can quickly turn UAV images into useful information to assist railway maintenance, thereby improving the safety of train operation.

Graphical Abstract:

Point cloud semantic segmentation for railway infrastructures is an essential step towards establishing railway digital twins. Deep learning-based methods have shown great potential in this field compared to traditional methods that rely on hand-crafted features. However, deep learning-based methods for railway point clouds still face typical challenges that need to be addressed. In this regard, we propose a novel learning framework named SALAProNet, which consists of a set of effective and concise modular solutions. The first challenge addressed is the massive data scale of railway point clouds, which makes it difficult to directly process large-scale point clouds due to memory limitations. To solve this problem, we adapt efficient random sampling in the network and propose the Self-Attentive Aggregation (SAA) module based on an attention mechanism to greatly expand the receptive field, which covers the unsampled points and successfully retains information in a high-dimensional feature space. The second challenge is fine-grained segmentation, where we propose the Local Geometry Embedding (LGE) module to embed local geometry. With the help of context information provided by SAA, the network can perform fine-grained segmentation for railway infrastructures. The third challenge is the insufficient generalization ability of the network, where we propose a Prototype Guided Regularization (PGR) method to guide the network to segment the point cloud among railways with different construction standards. This method enhances the network’s interpretability and improves its generalization ability. We have validated our proposed framework through experiments on different datasets, and it outperforms state-of-the-art approaches.

Newly deployed trains have massive normal data and scarce faulty data for training, which limits the diagnosis accuracy with class imbalance problem of small samples. Considering that there are a lot unutilized information hidden in the abundant unlabeled monitoring data, this paper proposes a novel method named manifold-contrastive broad learning system, which utilizes the online updating approach for dealing with the class imbalance problem of small samples. This method constructs a novel one-class broad-learning classifier based on an inherency-guided comparison mechanism, which can classify and annotate unlabeled data online. This classifier employs contrastive manifold matrices to maintain the inherent structures, which is not affected to the overfitting caused by imbalanced samples. Secondly, inspired by the active learning, this classifier proposes the minimum-error strategy to annotate the samples by classifying the modes, which solves the problem of insufficient training data. Thirdly, this method applies an incremental learning strategy that continuously absorbs the newly annotated data to update the model online, which improves the model accuracy under the data imbalanced condition. Finally, the feasibility and effectiveness of the proposed method are verified by wheelset bearing data collected from a test rig of a Chinese rolling stock company.

Safety is essential when building a strong transportation system. As a key development direction in the global railway system, the intelligent railway has safety at its core, making safety a top priority while pursuing the goals of efficiency, convenience, economy, and environmental friendliness. This paper describes the state of the art and proposes a system architecture for intelligent railway systems. It also focuses on the development of railway safety technology at home and abroad, and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems (CPS), data-driven models, and intelligent computing. Finally, several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.

Knee segmentation and landmark localization from 3D MRI are two significant tasks for diagnosis and treatment of knee diseases. With the development of deep learning, Convolutional Neural Network (CNN) based methods have become the mainstream. However, the existing CNN methods are mostly single-task methods. Due to the complex structure of bone, cartilage and ligament in the knee, it is challenging to complete the segmentation or landmark localization alone. And establishing independent models for all tasks will bring difficulties for surgeon’s clinical using. In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. We use a shared encoder for feature extraction, then SDMT utilizes the spatial dependence of segmentation results and landmark position to mutually promote the two tasks. Specifically, SDMT adds spatial encoding to the features, and a task hybrided multi-head attention mechanism is designed, in which the attention heads are divided into the inter-task attention head and the intra-task attention head. The two attention head deal with the spatial dependence between two tasks and correlation within the single task, respectively. Finally, we design a dynamic weight multi-task loss function to balance the training process of two task. The proposed method is validated on our 3D knee MRI multi-task datasets. Dice can reach 83.91% in the segmentation task, and MRE can reach 2.12 mm in the landmark localization task, it is competitive and superior over other state-of-the-art single-task methods.

An innovative suspension roasting−leaching technology in the atmosphere was proposed to improve the vanadium extraction from refractory stone coal. The leaching rate of vanadium was enhanced from 20% previously to 47.14% by present technology at a roasting temperature of 800 °C, a roasting time of 20 min, and a gas flow rate of 400 mL/min. Throughout the suspension roasting process, the surface of the stone coal gradually became rough and irregular. The specific surface area of the particles increased and the lamellar structure of the silicate minerals was destroyed, contributing to the release of vanadium. Meanwhile, the vanadium in the stone coal was oxidized to V(V) or V(IV). The results indicated that the leaching rate was improved owing to the release and transformation of vanadium in the suspension roasting.

Bridges are an important part of railway infrastructure and need regular inspection and maintenance. Using unmanned aerial vehicle (UAV) technology to inspect railway infrastructure is an active research issue. However, due to the large size of UAV images, flight distance, and height changes, the object scale changes dramatically. At the same time, the elements of interest in railway bridges, such as bolts and corrosion, are small and dense objects, and the sample data set is seriously unbalanced, posing great challenges to the accurate detection of defects. In this paper, an adaptive cropping shallow attention network (ACSANet) is proposed, which includes an adaptive cropping strategy for large UAV images and a shallow attention network for small object detection in limited samples. To enhance the accuracy and generalization of the model, the shallow attention network model integrates a coordinate attention (CA) mechanism module and an alpha intersection over union (α-IOU) loss function, and then carries out defect detection on the bolts, steel surfaces, and railings of railway bridges. The test results show that the ACSANet model outperforms the YOLOv5s model using adaptive cropping strategy in terms of the total mAP (an evaluation index) and missing bolt mAP by 5% and 30%, respectively. Also, compared with the YOLOv5s model that adopts the common cropping strategy, the total mAP and missing bolt mAP are improved by 10% and 60%, respectively. Compared with the YOLOv5s model without any cropping strategy, the total mAP and missing bolt mAP are improved by 40% and 67%, respectively.

Vilmoraconitine belongs to one of the most complex skeleton types in the C19-diterpenoid alkaloids, which architecturally features an unprecedented heptacyclic core possessing a rigid cyclopropane unit. Here, we report the first total synthesis of vilmoraconitine relying on strategic use of efficient ring-forming reactions. Key steps include an oxidative dearomatization-induced Diels–Alder cycloaddition, a hydrodealkenylative fragmentation/Mannich sequence, and an intramolecular Diels–Alder cycloaddition.

The health monitoring of traction motors is crucial for the prognostics and health management of high-speed trains. The temperature signal is an outstanding health indicator. Due to the representing of the traction motor's health conditions and low cost, accurate prediction for the motor temperature is conducive to early detection of abnormalities. However, the traditional prediction models are trained offline with high dependency on training data and cannot adapt to varying distributions of real data timely. Therefore, over time, the accuracies of these models always decrease noticeably. Concerning this issue, we propose an online health monitoring framework for traction motors using temperature signals. First, in the offline phase, multisensor signals are utilized to develop a generalized prediction model to absorb extensive information from temperature and relevant signals. Second, during the online phase, the training parameters are dynamically estimated to fulfill individualized learning by adopting a combination of the sample complexity and real-time prediction errors so as to fulfill individualized training according to the monitored data samples. Furthermore, a low-regret strategy is also presented in the online phase to determine the optimization target of the model to make the online update adaptive enough to the online prediction task. Consequently, the model can obtain new knowledge and greater understanding about the real data by online-learning continuously. Finally, the proposed framework is verified by actual data collected from Chinese high-speed trains. Compared with the conventional multilayer perceptron, gated recurrent unit, and long short-term memory, new patterns of stream data can be captured and adapted by using our framework, and the average root mean square errors of prediction results are reduced by 5%, 12%, and 11%, the average mean absolute percentage errors are reduced by 10%, 12%, and 11%, respectively. It is proven that our framework has high prediction accuracy and well-performed adaptability on real datasets.

Intelligent fault diagnosis of train bogie bearings based on edge computing is a promising technology to ensure the safety and reliability of train operation, which can give fault diagnosis systems better real-time performance and lower communication costs. Lightweight diagnosis networks are the core of this technology. However, existing lightweight diagnosis networks have the following limitations: 1) they lack a lightweight learning mechanism for edge computing scenarios to deal with time-scale changes of bearing fault features when trains operate under variational conditions and 2) an adaptive neural network pruning technique synchronized with training is expected to avoid the repetitive and cumbersome processes of traditional pruning techniques. To overcome the above limitations, this article proposes a new multiscale lightweight network with adaptive pruning for the intelligent diagnosis fault of train bogie bearings in edge computing scenarios. First, weight-sharing multiscale convolutions (WSMSCs) are developed to capture multi-time scale features from vibration signals with extremely low computing costs. Then, inverse separable convolution (ISC) blocks are built to further capture high-level features, and an adaptive pruning technique that does not require artificial jobs is proposed to eliminate useless network structures concurrently with training. Finally, diagnostic results are obtained through a dense layer activated by Softmax. Experimental results demonstrate that the accuracy and complexity of the proposed network are superior to other state-of-the-art lightweight bearing fault diagnosis networks under varying operating conditions. Furthermore, the proposed networks can be deployed on more general edge devices.

Regular inspections on track components, such as the clip, spike, and rail, are essential to maintain track quality and ensure railroad operating safety. Unfortunately, traditional image processing (IP)-based systems have limited accuracy. Existing convolutional neural network (CNN)-based approaches are designed for either detection or saliency segmentation of a specific track component (e.g., fastener or rail only). The overall track condition could not be evaluated because not all the track components are inspected simultaneously. This article presents an all-in-one YOLO (AOYOLO) framework for multitask track component inspection. First, a newly developed ConvNeXt-based backbone is constructed in AOYOLO to produce suitable hyperfeatures for both detection and segmentation tasks. Second, a novel U-shaped salient object segmentation branch is incorporated into AOYOLO to supplement the object detection branch, improving both the rail surface defect (RSD) segmentation and the detection of other components. Advanced data augmentations are integrated to further enhance the accuracy and scalability of the network. Extensive experiments conducted on a track dataset established with images taken by drone indicate that the proposed system is able to: 1) achieve 95.6% mean average precision (mAP) for track components inspection at a real-time speed of 147 frames/s and 2) reach 93.6% accuracy on RSDs detection, which surpass the current state-of-the-art (SOTA) models. The exceptional inference speed and superior detection accuracy have great potential for field applications.

In recent years, UAV(Unmanned Aerial Vehicle) has shown great vitality and potential in automated railway inspection operations. The catenary support device is an important infrastructure on the high-speed railway, which guarantees for the electric power system when the high-speed railway is running. However, the current inspection by UAV cannot automatically obtain multi-scale and standardized image data of the catenary support devices. It presents a serious challenge to the faulty diagnosis of the catenary support devices. Concerning this issue, an automatic multi-scale image capture strategy of catenary support device based on improved YOLOv7 is proposed. Due to the characteristics of less foreground and more background in the image detection of catenary support device, the Focal Loss is introduced. Experimental results show that the convergence speed of the model is improved with high detection accuracy. Furthermore, multi-scale and standardized capture strategies for catenary support device data are proposed. In the deployment experiment, improved YOLOv7 achieves 81.3% mAP, the multi-scale, standardized and normalized image data of catenary support device is captured.

Unmanned aerial vehicle (UAV) is expected to have the potential to complete the collection of defect information in track areas with lower labor costs and higher efficiency. In this field, autonomous railway track detection and real-time tracking to guide UAVs are quite essential. However, the limited computation resources of the UAV onboard computer make it difficult to maintain high accuracy in realtime detection and tracking using the deep learning model with a complex structure. Concerning this issue, for the daily detection scene of the track, this paper proposes a novel autonomous railway track detection and real-time tracking algorithm named Largest Connected-ERFNet, which combines ERFNet and the largest connected component labeling to ensure the accuracy of the track area detection and tracking. A comprehensive set of experiments on UAV onboard computer are conducted for verification. Experiments demonstrate the superior performance of the algorithm proposed in this paper. Under the condition of limited training data and computation resources, the detection precision of the algorithm reaches 89.2%, the detection speed reaches 5.5 fps, and the smoothness reaches 99.4%. It is proven that the proposed method can meet the practical needs of using UAVs for railway track inspection.

High-speed train bogies are the critical components of high-speed trains, which can play the role of traction, braking, and buffering. In long-term train service, bogies are prone to wear and aging. Currently, most studies on fault diagnosis methods are aimed at single equipment identification. It is challenging to accurately diagnose the faults of such coupled multi-equipment combination systems as bogies. Multiple devices on the bogie have implied correlations in space, and fully exploiting their spatial features enhances the fault diagnosis accuracy. This paper proposes a new bogie fault diagnosis method based on the directional graph of train bogie: RS-GAT model. The model uses the Residual-Squeeze Network (RS-Net) to construct the framework of the model and use the Graph Attention Network (GAT) for spatial information fusion and feature extraction to identify bogie faults. Using six datasets collected under the operation of high-speed trains, experimental results demonstrate that the proposed approach has better effectiveness than the RS-Net class model and single-layer graph class model, with diagnosis accuracy near 96%. Ablation experiments and comparisons between RS-GAT and RS-GCN verify the effectiveness of RS-Net framework and GAT model in fault classification. In addition, the RS-GAT model is found to have strong robustness when different models are analyzed using small-scale data sets.

Turnouts are crucial to the safety of high-speed railways. Due to the intensive use and complex environment, breakdowns caused by different faults occur frequently in practice. Considering that the operation of turnouts is a multi-stage process during which each stage has its specific health characteristics, this paper proposes segmentalized maximal-relevancy and minimal-redundancy (mRMR) for feature extraction from each stage separately. Based on mathematical analysis of the turnout mechanism, the electric power curve is segmented into four stages, from which time-domain analysis and mRMR are combined to extract valid features corresponding to different movements respectively. Then, a novel classifier named cost-sensitive Extreme Learning Machine with fixed inputs (cf-ELM) is proposed for fault classification. We modify the inputs of ELM and define a new formula to limit the input weights and biases for the sake of stability of the network structure. Besides, a cost-sensitive optimization method is also presented in this classifier to embed the failure degree and data proportion into cost calculation rules to deal with data imbalance. To verify our proposed method, real data collected from a turnout of Beijing-Shanghai high-speed railway is used. It is proven by comparisons that the accuracy of our method has achieved 100% with fast running speed and also outperforms traditional methods in terms of stability and generalization remarkably.

AbstractThe relationship between foreign direct investment and economic growth (FDI-EG) has been subject to extensive research. The first document related to FDI-EG research in Web of Science was published in 1992 so that the research time is close to 30 years. This article aims to make a bibliometric study to measure the FDI-EG research from different points of view. 1,075 documents with respect to FDI-EG research were collected, and a strong bibliometric analysis was carried out by Bibliometrix software and review of approaches. The Bibliometrix software has revealed the leading researchers, conceptual structure, and thematic evolution of the FDI-EG research. At the same time, reviewing the literature helps to make content analysis, the most influential document analysis, co-authorship analysis, and citation and co-citation analysis. Amongst the findings, C. Chen, Journal of International Economics,and the USA are the most influential researcher, journal, and country, respectively. The FDI-EG research are focussed on the technology and firm performance, research modelling, and theoretical inquiry. More discussions are made in terms of current research status, avenues for future research, advantages and drawbacks of the methods. This article helps in understanding the evolution of the FDI-EG research from the perspectives of the bibliometric and review.

The amine submetabolome, including amino acids (AAs) and biogenic amines (BAs), is a class of small molecular compounds exhibiting important physiological activities. Here, a new pyrylium salt named 6,7-dimethoxy-3-methyl isochromenylium tetrafluoroborate ([d0]-DMMIC) with stable isotope-labeled reagents ([d3]-/[d6]-DMMIC) was designed and synthesized for amino compounds. [d0]-/[d3]-/[d6]-DMMIC-derivatized had a charged tag and formed a set of molecular ions with an increase of 3.02 m/zand the characteristic fragment ions of m/z204.1:207.1:210.1. When DMMIC coupled with liquid chromatography-mass spectrometry (LC-MS), a systematic methodology evaluation for quantitation proved to have good linearity (R2between 0.9904 and 0.9998), precision (interday: 2.2–21.9%; intraday: 1.0–19.7%), and accuracy (recovery: 71.8–108.8%) through the test AAs. Finally, the methods based on DMMIC and LC-MS demonstrated the advantaged application by the nontargeted screening of BAs in a common medicinal herb Senecio scandensand an analysis of metabolic differences among the amine submetabolomes between the carcinoma and paracarcinoma tissues of esophageal squamous cell carcinoma (ESCC). A total of 20 BA candidates were discovered in S. scandensas well as the finding of 13 amine metabolites might be the highest-potential differential metabolites in ESCC. The results showed the ability of DMMIC coupled with LC-MS to analyze the amine submetabolome in herbs and clinical tissues.

Identifying the active site of oxide-supported metal catalysts and revealing the intrinsic synergistic mechanism between metal and oxide support remain a large challenge. Herein, we report the identification and separation of the Pt-O-Ti interface and TiO2surface in Pt-TiO2-based catalysts by depositing different thickness of TiO2shell with ∼0.4-nm micropores onto the surface of Pt/TiO2catalyst through atomic layer deposition (ALD). In the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) by hydrogen peroxide (H2O2) process, the TiO2microporous shells can prevent the contact between TMB and embedded Pt clusters, but not delay the diffusion of H2O2. The heterolysis of H2O2to •OH occurs on the Pt-O-Ti interface, and the generated •OH migrates to the TiO2surface to supplement the surface lattice oxygen, which sequentially oxidizes TMB to oxTMB. And the synergistic effect between Pt-O-Ti interface active sties and TiO2surface active sites can significantly improve the catalytic performance. Our study provides a guide for the understanding of the intrinsic synergistic mechanism between the metal and oxide support in the metal-oxide catalysts.

Maintaining the structural integrity of silicon and phosphorus anodes during the Li+insertion and extraction cycles poses a significant challenge. Inspired by the natural process of plant roots anchoring into soil, we present a simple yet effective method for integrating a SiP anode into the copper current collector. This approach involves subjecting readily available and cost-effective micrometre-sized silicon particles (50–150 μm) and red phosphorus to mechanochemical treatment. Subsequently, the resulting Si/P composite is coated onto copper foil and subjected to annealing treatment, resulting in the formation of an integrated structure, incorporating Cu3Si, Cu3P, and P-doped Si, firmly established within the copper current collector. The electrode obtained (BM-Si@P/Cu-350), promoted by a crosslinked (polyimide) PI binder, outperforms not only the ball-milled Si and red P but also the same electrode in which the annealing process is absent (BM-Si@P/Cu-100). During the mechanochemical treatment, red P acts as both the doping agent and a lubricant, facilitating the grinding of the Si particles into P-doped Si particles in the range of predominantly 200–300 nm; while during the thermal treatment, Si plays a key role in preventing excessive interaction between the P and the Cu current collector. Instead, a tailored rooting of the active material into the current collector is achieved through the formation of Cu3P and Cu3Si phases, not only increasing the mechanical stability of the electrode to endure volume changes during cycling and limit developing cracks, but also enhancing the electrical conductivity of the system. This article suggests a simple and potentially low-cost strategy for the utilization of highly available micrometre-sized silicon and red-P for Li-ion storage applications.

Dielectric barrier discharge (DBD) plasma is a promising technology for treating odor gases. This study developed an innovative multineedle-to-cylinder (MC) configuration DBD plasma reactor and investigated its synergistic effect with bimetallic catalysts. The results demonstrated that MC-DBD plasma achieved complete elimination of ammonia and organic sulfides, while the removal efficiencies of inorganic H2S and CS2reached 44.0 % and 53.0 % respectively. Compared with conventional plasma, the specific energy input (SEI) of MC-DBD was significantly reduced to 4.44 J/L. Furthermore, with the incorporation of Mn-Ce/ZSM-5 catalyst, optimal removal efficiencies of H2S and CS2were significantly improved to 82.6 % and 90.7 %, and the SEI was reduced by 18 %. The electrical performance revealed that the low energy consumption was mainly attributed to stable fast-pulse micro-discharge channels and significantly enhanced inhomogeneous electric fields achieved by the filament discharge mode. Catalyst characterization (X-ray photoelectron spectroscopy and O2-temperature programming desorption) confirmed that surface catalytic reaction mainly follows the Mars-van Krevelen mechanism, where increased lattice oxygen content and the electron transfer between Mn and Ce play crucial roles in enhancing malodor degradation performance. This work proposes an efficient solution for degrading mixed malodors and provides an experimental and theoretical basis for optimizing plasma structure as well as researching treatment of mixed pollutants.