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This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

This study aims to investigate the effect of time-varying passenger flow on the wheel wear of metro vehicles to provide a more accurate model for predicting wheel wear and a new idea for reducing wheel wear.

Sectional passage flow data were collected from an operational metro line. A wheel wear simulation based on time-varying passenger flow was performed via the SIMPACK software to obtain the worn wheel profile and wear distribution. The simulation involves the following models: vehicle system dynamics model, wheel-track rolling contact model, wheel wear model and variable load application model. Later, the simulation results were compared with those obtained under the traditional constant load condition and the measured wear data.

For different distances traveled by the metro vehicle, the simulated wheel profile and wear distribution under the variable load remained closer to the measurements than those obtained under the constant load. As the distance traveled increased, the depth and position of maximum wear and wear growth rate under the variable load tended to approach the corresponding measured values. In contrast, the simulation results under the constant load differed greatly from the measured values. This suggests that the model accuracy under the variable load was significantly improved and the simulation results can offer a more accurate basis for wear prediction.

These results will help to predict wheel wear more accurately and provide a new idea for simulating wheel wear of metro vehicles. At the same time, measures for reducing wheel wear were discussed from the perspective of passenger flow changes.

Existing research on the wheel wear of metro vehicles is mainly based on the constant load condition, which is quite different from the variable load condition where the passenger flow in real vehicles varies over time. A method of simulating wheel wear based on time-varying load is proposed in this paper. The proposed method shows a great improvement in simulation accuracy compared to traditional methods and can provide a more accurate basis for wear prediction and wheel repair.

The purpose of this paper is to study the wear evolution of metro wheels under the conditions of different track sequences, track composition and vehicle load and then to predict wheel wear and to guide its maintenance.

By using the SIMPACK and MATLAB software, numerical simulation analysis of metro wheel wear is carried out based on Hertz theory, the FASTSIM algorithm and the Archard model. First of all, the vehicle dynamics model is established to calculate the motion relationship and external forces of wheel-rail in the SIMPACK software. Then, the normal force of wheel-rail is solved based on Hertz theory, and the tangential force of wheel-rail is calculated based on the FASTSIM algorithm through the MATLAB software. Next, in the MATLAB software, the wheel wear is calculated based on the Archard model, and a new wheel profile is obtained. Finally, the new wheel profile is re-input into the vehicle system dynamics model in the SIMPACK software to carry out cyclic calculation of wear.

The results show that the setting order of different curves has an obvious influence on wear when the proportion of the straight track and the curve is fixed. With the increase in running mileage, the severe wear zone is shifted from tread to flange root under the condition of the sequence-type track, but the wheel wear distribution is basically stable for the unit-type track, and their wear growth rates become closer. In the tracks with different straight-curved ratio, the more proportion the curved tracks occupy, the closer the severe wear zone is shifted to flange root. At the same time, an increase in weight of the vehicle load will aggravate the wheel wear, but it will not change the distribution of wheel wear. Compared with the measured data of one city B type metro in China, the numerical simulation results of wheel wear are nearly the same with the measured data.

These results will be helpful for metro tracks planning and can predict the trend of wheel wear, which has significant importance for the vehicle to do the repair operation. At the same time, the security risks of the vehicle are decreased economically and effectively.

At present, many scholars have studied the influence of metro tracks on wheel wear, but mainly focused on a straight line or a certain radius curve and neglected the influence of track sequence and track composition. This study is the first to examine the influence of track sequence on metro wheel wear by comparing the sequence-type track and unit-type track. The results show that the track sequence has a great influence on the wear distribution. At the same time, the influence of track composition on wheel wear is studied by comparing different straight-curve ratio tracks; therefore, wheel wear can be predicted integrally under different track conditions.

Based on the conservation of resources view, the objective of this paper is to examine the relationship between abusive supervision and workplace thriving. Further, this study investigates the underlying mechanisms role of agentic work behaviors (i.e. task focus, heedful relating) and moderating role of employee's core self-evaluations.

Using a time-lag approach, data are collected from 360 full-time employees enrolled in an executive development program in a large university of China. To test the proposed model, data analysis is carried out through Statistical Product and Service Solutions (SPSS) and Analysis of Moment Structures (AMOS).

The results show that abusive supervision negatively influences workplace thriving. Further, the findings also confirm the mediating role of agentic work behaviors and the moderating role of core self-evaluations between the relationship of abusive supervision and thriving.

Based on study findings, this study draws the attention of managers toward the new deleterious outcomes of abusive supervision. Hence, to nurture a thriving workforce, organizations should keep abusive behaviors under keen observations to minimize their frequent occurrences. Further, it is proposed that hiring employees with higher core self-evaluations can mitigate the injurious effect of abusive supervision.

This is the first attempt to our knowledge to untapped the abusive supervision-thriving relationship via the underlying mechanisms of two agentic work behavior's and core self-evaluations as a moderator enriches the extant body of knowledge and provide valuable insight into the abusive supervision and workplace thriving literature.

The CL60 steel wheels of subway vehicles operating on specific lines require frequent refurbishment due to rapid wear and tear. Considering this issue, MoS₂-based and graphite-based solid lubricants are used to reduce the wear rate of subway wheels and extend their service life.

Under laboratory conditions, the effect of MoS₂-based and graphite-based solid lubricants on the friction and wear performance of subway wheels and rails was evaluated using a modified GPM-60 wear testing machine.

Under laboratory conditions, MoS₂-based solid lubricants have the best effect in reducing wheel/rail wear, compared to the control group without lubrication, at 2 × 105 revolutions, the total wheel-rail wear decreased by 95.07%. However, when three types of solid lubricants are used separately, the hardness evolution of the wheel-rail contact surface exhibits different characteristics.

The research results provide important support for improving the lifespan of wheel and rail, extending the service cycle of wheel and rail, reducing the operating costs of subway systems, improving the safety of subway systems and providing wear reduction maintenance for other high wear mechanical components.

The experiment was conducted through the design and modification of a GPM-60 testing machine for wear testing. The experiment simulated the wheel-rail contact situation under actual subway operation and evaluated the effects of three different solid lubricants, MoS₂-based and graphite-based, on the wear performance and surface hardening evolution of subway wheel-rail.

The CL60 steel wheels of metro vehicles running on a specific line need frequent reprofiling due to rapid wear. Considering this problem, a new material for metro wheels was designed. The friction and wear properties of the new material were studied, to reduce the wear rate and extend the service life of metro wheels.

Wheel specimens made of the two steel materials were tested using a GPM-60 wear tester under laboratory conditions. A field test was conducted on a specific metro line to track the wear in wheels made of the new material and CL60 steel wheels.

Under the laboratory conditions, the wear loss in the new material was 24.44% lower than that in CL60 steel. The field test revealed that compared to CL60 steel wheels, the new CL60 steel wheels showed a 19.42% decrease in tread wear on average. The field measurements for the wheels made of the new material are consistent with the results of laboratory simulation, suggesting relatively high wear resistance of the new material.

The results of the study can provide guidance on how to properly select steel material for metro wheels to avoid rapid wear and frequent reprofiling and reduce operating costs.

A new material for metro wheels was designed and developed by optimizing the content of Cr, Si, Mn, V and other elements. This material proved to have better wear resistance in both laboratory and field testing.

The purpose of this paper is to explore the issues on how to achieve crowdsourced real-time captioning of sign language by deaf and hard-of-hearing (DHH) people, such that how a system structure should be designed, how a continuous task of sign language captioning should be divided into microtasks and how many DHH people are required to maintain a high-quality real-time captioning.

The authors first propose a system structure, including the new design of worker roles, task division and task assignment. Then, based on an implemented prototype, the authors analyze the necessary setting for achieving a crowdsourced real-time captioning of sign language, test the feasibility of the proposed system and explore its robustness and improvability through four experiments.

The results of Experiment 1 have revealed the optimal method for task division, the necessary minimum number of groups and the necessary minimum number of workers in a group. The results of Experiment 2 have verified the feasibility of the crowdsourced real-time captioning of sign language by DHH people. The results of Experiment 3 and Experiment 4 have shown the robustness and improvability of the captioning system.

Although some crowdsourcing-based systems have been developed for the captioning of voice to text, the authors intend to resolve the issues on the captioning of sign language to text, for which the existing approaches do not work well due to the unique properties of sign language. Moreover, DHH people are generally considered as the ones who receive support from others, but our proposal helps them become the ones who offer support to others.

The purpose of this paper is to propose a novel strategy of optimal parameters configuration and placement for sensitive equipment.

In this study, clamped thin plate is considered as the foundation form, and a novel composite system is proposed based on the two-stage isolation system. By means of the theory of mechanical four-pole connection, the displacement amplitude transmissibility from the thin plate to precision equipment is derived. For the purpose of performing optimal design of the composite system, a novel multi-objective idea is presented. Multi-objective particle swarm optimization (MOPSO) algorithm is adopted as an optimization technique, which can achieve a global optimal solution (gbest), and selecting the desired solution from an equivalent Pareto set can be avoided. Maximum and variance of the four transmitted peak displacements are considered as the fitness functions simultaneously; the purpose is aimed at reducing the amplitude of the multi-peak isolation system, meanwhile pursuing a uniform vibration as far as possible. The optimization is mainly organized as a combination of parameter configuration and placement design, and the traversal search of discrete plate is performed in each iteration for the purpose of achieving the global optimum.

An important transmissibility based on the mechanical four-pole connection is derived, and a composite vibration isolation system is proposed, and a novel optimization problem is also defined here. This study reports a novel optimization strategy combined with artificial intelligence for parameters and placement design of precision equipment, which can promote the traditional view of two-stage vibration isolation.

Two-stage vibration isolation systems are widely applied to the vibration attenuation of precision equipment, but in these traditional designs, vibration participation of foundation is often ignored. In this paper, participation of foundation of equipment is considered, and a coherent new strategy for equipment isolation and foundation vibration is presented. This study shows a new vision of interdisciplinary including civil engineering, mechanical dynamics and computational science.