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The purpose of this paper is to study the influence of braking speed at –20 °C on the wear property of high-speed railway braking materials and the temperature also stress analyses of brake disc friction surface.

Friction brake tester was used to simulate the wear test of high-speed railway braking materials at diverse braking speeds (2,100, 2,400, 2,700 and 3,000 rad/min) at –20 °C and the stress and temperature analyses of brake disc friction surface were carried out by COMSOL.

Compared with 20°C, there is initial stress of brake disc friction surface before brake starting; also, the maximum wear depth is larger at –20°C. Besides, at –20 °C, with the rising of braking speed, the graphite particles on the friction surface of brake pad significantly reduce. And scratches and cracks are formed on brake pad friction surface. Besides, the abrasive wear, adhesive wear and thermal cracks of brake disc friction surface are aggravated. Moreover, the maximal worn depth also increase. Meanwhile, the highest temperature and the maximum thermal stress of brake disc friction surface both raise. Furthermore, the temperature and thermal stress gradients at radial direction of brake disc friction surface aggrandize, which makes the thermal cracks on brake disc friction surface further exacerbated.

In this paper, the wear property of the high-speed railway braking materials is studied by combining experiment and simulation. However, due to the low-speed traveling of high-speed railway was mainly studied in this paper, there may be no comprehensive simulation of the real running condition of high-speed railway. At the same time, the working condition of low-temperature environment cannot be completely simulated and controlled.

The research results of this paper provide a basic instruction for other researchers and also provide an important reference for relevant personnel to choose the braking speed of high-speed railway at –20 °C.

The research of this paper provides a brick for the study of high-speed railway braking materials and also provides some references for the safe service of trains in low-temperature environment.

This paper studied the wear property and carried out the simulation analysis of braking materials at –20 °C at diverse braking speed. The research findings provide an important reference for the selection of braking speed of high-speed railway at –20 °C.

The purpose of this paper is to study the wear of railway brake disc/pad in low-temperature environment and to explore the damage form of brake disc/pad materials and the law of temperature rise in braking process and its influence on friction pair material damage.

The influence of ambient temperature on tribological properties of brake materials was studied by using low-temperature environment simulation device and MM-1000 high-speed brake testing machine. The law of temperature rise in the braking process was simulated by temperature field module of COMSOL.

The damage of disc sample increases with the decrease of ambient temperature, and the main damage form is furrow. With the decrease of ambient temperature, pitting corrosion, wear, spalling and cracks appear successively. The maximum temperature of brake disc decreases linearly with the decrease of ambient temperature. However, when the ambient temperature is 0 in the experiment, the surface temperature of the disc will increase abnormally because of the increase of abrasive particles caused by the toughening and brittleness transformation of the material.

In this paper, through the study of train braking in low-temperature environment, the damage mechanism and law of train braking pair in low-temperature environment are found, which provide some basis for the development of high-speed railway in low-temperature environment.

The continuous impact of the pandemic and the downturn of the global economy have brought new challenges to the tourism industry. In this context, effectively attracting consumers and improving user stickiness are the top priorities of tourism platform companies. This study explores the impact of ethical concerns raised by new issues under the multi-governance environment on user stickiness. Based on the trust theory, the authors provide solutions for tourism platforms.

This study adopted a quantitative approach, gathering survey data via an online platform. A total of 400 participants were investigated, and 356 valid questionnaires were returned, with a recovery rate of 89%. Questionnaires that did not meet the inclusion criteria were excluded, leaving 298 valid responses.

Studies have found that consumers' ethical concerns about platform companies are key factors affecting user stickiness, and among these, consumer trust plays a mediating role. They have found that corporate social responsibility (CSR) behaviours help alleviate ethical concerns and improve trust in enterprises. At the same time, enterprises should properly control the number of platform collaborators, and excessive platform cooperation negatively moderates the impact of consumer ethical concerns on competence-based trust.

This study complements the deficiency of previous research with regard to ethical concerns in a multi-governance environment. These findings indicate that subject diversity exacerbates the negative impact of ethical concerns on consumer trust; however, CSR alleviates the impact of ethical concerns on consumer trust.

To avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.

The proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.

Results reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.

The proposed system was applied only to the forging process but could be extended to other manufacturing processes.

This paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.

The purpose of this paper is to introduce a motion control method for WFF-AmphiRobot, which can effectively realize the flexible motion of the robot on land, underwater and in the transition zone between land and water.

Based on the dynamics model, the authors selected the appropriate state variables to construct the state space model of the robot and estimated the feedback state of the robot through the maximum a posteriori probability estimation. The nonlinear predictive model controller of the robot is constructed by local linearization of the model to perform closed-loop control on the overall motion of the robot. For the control problem of the terminal trajectory, using the neural rhythmic movement theory in bionics to construct a robot central pattern generator (CPG) for real-time generation of terminal trajectory.

In this paper, the motion state of WFF-AmphiRobot is estimated, and a model-based overall motion controller for the robot and an end-effector controller based on neural rhythm control are constructed. The effectiveness of the controller and motion control algorithm is verified by simulation and physical prototype motion experiments on land and underwater, and the robot can ideally complete the desired behavior.

The paper designed a controller for WFF-AmphiRobot. First, when constructing the robot state estimator in this paper, the robot dynamics model is introduced as the a priori estimation model, and the error compensation of the a priori model is performed by the method of maximum a posteriori probability estimation, which improves the accuracy of the state estimator. Second, for the underwater oscillation motion characteristics of the flipper, the Hopf oscillator is used as the basis, and the flipper fluctuation equation is modified and improved by the CPG signal is adapted to the flipper oscillation demand. The controller effectively controls the position error and heading angle error within the desired range during the movement of the WFF-AmphiRobot.

The cities, for the most part, appeared up until the middle of the 1990s to be islands within the larger Chinese political economy in which job-secure workers could be certain that their livelihood, health, education, and living abodes would evermore undergird their and their children's sustenance. At least until the late 1980s, urbanites who stuck with the state sector even considered good treatment on the job a kind of birthright, an entitlement that was sure to be enforced. In the cities, true, there had always been the disadvantaged after 1949 – those without offspring or spouses, the disabled, and people unable to support themselves. But this relatively tiny batch of individuals generally survived in the shadows and out of sight, subsisting – but just barely – as members of the “three withouts” on a mere pittance, in the form of meager “social relief” disbursed by civil affairs departments.⁸