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The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment.

According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state.

These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use.

This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.

The purpose of this study is to prolong the service life of the Al–Si alloy cylinder and achieve the objective of energy saving and emission reduction by the composite treatments.

Chemical etching + laser texturing + filled MoS₂ composite treatment was applied to the friction surface of aluminum–silicon (Al–Si) alloy cylinder. The friction coefficient and wear loss were measured to characterize the tribology property of cylinders.

The composite-treated Al–Si alloy cylinder had the lowest friction coefficient and weight loss. The friction coefficient and weight loss of the composite treatment were approximately 27.08% and 54.17% lower than those of the untreated sample, respectively. The laser micro-textures control the release of solid lubricant to the interface of friction pairs slowly, which prolongs the service life of cylinders.

The synergistic effect of the chemical etching + laser texturing + filled MoS2 modified the tribology properties of Al–Si alloy cylinder. The chemical etching raised the silicon particles to bear the load, and laser micro-textures control the release of solid lubricant to improve the lubrication property.

This paper aims to analyze high-quality papers on the research of electronic word-of-mouth (eWOM) for product and service quality improvement from 2009 to 2022, in order to fully understand their historical progress, current situation and future development trend.

This paper adopts the bibliometrics method to analyze the relevant literature, including publishing trend and citation status, regional and discipline area distribution, and influential publications. Secondly, the VOSviewer is used for literature co-citation analysis and keyword co-occurrence analysis to obtain the basic literature and research hotspots in this research field.

Firstly, the study finds that the number of publications basically shows an increasing trend, and those publications are mainly published in tourism journals. In addition, among these papers, China has the largest number of publications, followed by the USA and South Korea. Through co-citation analysis of literature and keyword co-occurrence analysis, 22 foundational papers and six main research topics are obtained in this paper. Finally, this paper elaborates on the development trend of the research topic and future research directions in detail.

This is the first paper that uses bibliometrics to analyze and review relevant researches on eWOM for product and service quality improvement, which is helpful for researchers to quickly understand its development status and trend. This review also provides some future research directions and provides a reference for further research.

The purpose of this paper is to investigate the relationship between inventory leanness and venture survival, and demonstrate the role of organizational environments in moderating this relationship from three dimensions: environmental complexity, dynamism and munificence.

Using a large panel data of more than 150,000 new Chinese small- and medium-sized enterprises between 2000 and 2007 in the manufacturing sector, the authors employ the method of survival analysis via an accelerated failure time model to explore the non-linear relationship between inventory leanness and the likelihood of survival. Moreover, the moderation model is applied to examine the moderating role of organizational environments.

At its core, this paper demonstrates the inverted U-shaped relationship between inventory leanness and the likelihood of survival. Furthermore, the authors find that environmental complexity and dynamism can negatively moderate this relationship, whereas environmental munificence acts the exact opposite.

Managers need to realize the trade-off between inventory leanness and venture survival. Collectively, more than 90 percent of new Chinese ventures have great potential to improve the likelihood of survival by implementing inventory leanness management. In addition, firms ought to be fully aware of the internal management and the external environments.

This is the first study to confirm the inverted U-shaped relationship between inventory leanness and the likelihood of survival, and empirically verify the moderating role of environmental complexity, dynamism and munificence on this relationship.

Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach.

In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations.

One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost.

The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty.

The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.

The prevalence of artificial intelligence (AI) has considerably affected management and society. This paper aims to explore its potential impact on hospitality industry employees, bringing enlightenment to both employees and managers.

Data were collected from a survey of 432 employees who worked in full-service hotels in China. Structural equation modeling (SEM) was used to analyze the data.

Results presented a positive relationship between AI awareness and job burnout. No significant direct relationship was found between AI awareness and career competencies. Organizational commitment mediated the relationship between AI awareness and career competencies, as well as the relationship between AI awareness and job burnout.

This study contributes to human resource management in the hospitality industry to theoretical and practical aspects. Theoretically, it enriched both career theory and fit theory. Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.

Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.

The study aims to analyze the impact of AI from a career perspective. It provided theoretical support and evidence for hotel managers for the effects of AI awareness on hotel employees. The study conveys a potential topic of concern that the hospitality industry may face in the future.

This study aims to understand the factors affecting the continuance usage intention (CUI) of mHealth among the rural elderly.

An integrated model was proposed with the constructs derived from multiple models such as the unified theory of acceptance and use of technology, information system success model and expectation confirmation model. Data were collected from 400 participants who had prior experiences with mHealth services in Bangladesh. The research model was tested using the partial least squares method based upon structural equation modelling.

The findings indicated that system quality, performance expectancy, facilitating conditions and social influence were significant to the degree of confirmation and ultimately affect satisfaction and CUI. Surprisingly, service quality and information quality were insignificant.

This study has added in the field of knowledge by contributing some new thoughts and interpretations of continuance usage modelling for mHealth services. The findings may become beneficial for the government agencies, policymakers, mHealth systems developers and service providers.

As limited research was found on CUI of mHealth in the integrated view of rural elderly’s value, this research contributes to the extant literature by categorizing key factors that might support to proliferate the continuance usage of this service. Moreover, the contextualization of the related variables and integration of the existing model is theoretically original. Furthermore, because of a generic approach, the findings could be easily modified to assist other developing countries in the planning and up-take of mHealth.

With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.

This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.

The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.

The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.

Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

Kriging model has been widely adopted to reduce the high computational costs of simulations in Reliability-based design optimization (RBDO). To construct the Kriging model accurately and efficiently in the region of significance, a local sampling method with variable radius (LSVR) is proposed. The paper aims to discuss these issues.

In LSVR, the sequential sampling points are mainly selected within the local region around the current design point. The size of the local region is adaptively defined according to the target reliability and the nonlinearity of the probabilistic constraint. Every probabilistic constraint has its own local region instead of all constraints sharing one local region. In the local sampling region, the points located on the constraint boundary and the points with high uncertainty are considered simultaneously.

The computational capability of the proposed method is demonstrated using two mathematical problems, a reducer design and a box girder design of a super heavy machine tool. The comparison results show that the proposed method is very efficient and accurate.

The main contribution of this paper lies in: a new local sampling region computational criterion is proposed for Kriging. The originality of this paper is using expected feasible function (EFF) criterion and the shortest distance to the existing sample points instead of the other types of sequential sampling criterion to deal with the low efficiency problem.