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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.

To solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy.

Firstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment.

The simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles.

The method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.

Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.

The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.

The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.

Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.

The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.

The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

The purpose of this paper is to link subjective data obtained from a questionnaire survey with blood donation behavioral data, constructs a conceptual model of the factors that influence repeated blood donation behavior, and explores the mechanisms and degrees of influence of the value and cost elements of blood donors on repeated blood donation behavior.

First, this study constructs a conceptual model of the factors that affect repeated blood donation based on delivered value theory. Second, this paper is driven by subjective data obtained from a questionnaire and big data on blood donation behavior; the use of multisource data can help us understand repeated blood donation behavior from a broader perspective. Through data association and systematic research, it is possible to accurately explore the mechanisms through which various factors affect repeated blood donation behavior.

The results show that among the value elements, personnel value (PV), image value and blood donation value affect blood donation behavior in decreasing order. The change in PV per unit directly caused a 0.471-unit change in satisfaction, which indirectly caused a 0.098-unit change in donation behavior. Among the cost elements of blood donors, only the impact of time cost (TC) on repeated blood donation behavior was significant, and a change of one unit in TC caused a change in repeated blood donation behavior of −0.035 units. In addition, this paper groups subjects according to gender, education and age and explores the differences in the value and cost factors of different groups. Finally, based on the research results, the authors propose corresponding policy recommendations.

First, the authors expand the application field of the delivered value theory, and provide a new perspective for studying repeated blood donation. Second, through questionnaire data and blood donation behavior data, the authors comprehensively explore the factors that influence repeated blood donation behavior.

High-silicon chromium iron (HSCI) has been used in ground grids in southern China, while there was a lack of study on its corrosion behavior in this soil environment. The purpose of this paper is to discover the corrosion of HSCI in acidic and alkaline soil solutions.

The original defects on the HSCI surface were observed using optical microscopy, and the corrosion behavior of the HSCI in the acidic and alkaline soil solutions were jointly detected using electrochemical measurements and scanning electron microscopy/energy dispersive spectrometer.

The results showed the corrosion rates of the HSCI in the acidic and alkaline soil solutions were limited, and the high contents of Cr and Si in matrix was responsible for its high corrosion resistance. The HSCI showed a similar corrosion tendency in the two solutions, while its corrosion rate in the acid soil solution was higher than that in the alkaline soil solution. The corrosion pits on the specimen surface were originated from the original defects in matrix, and the edges of the corrosion pits were more rounded than the original defects after 720 h immersion in the two solutions. The original defects in the HSCI matrix played a significant role in the corrosion process.

The paper discovered the corrosion evolution of HSCI in the acidic and alkaline soil solutions. What is more, the acceleration role of the original defects on the corrosion of the HSCI in the acidic and alkaline soil solutions was discovered in the paper. The results are beneficial for the material selection of ground grid equipment in engineering.

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.

The purpose of this paper is to propose a DC-port voltage balance strategy realizing it by logic combination modulation (LCM). This voltage balance strategy is brief and high efficient, which can be used in many power electronic devices adopting the cascaded H-bridge rectifier (CHBR) such as power electronic transformer (PET).

The CHBR is typically as a core component in the power electronic devices to implement the voltage or current conversion. The modulation method presented here is aiming to solve the voltage imbalance problem occurred in the CHBR with more stable work station and higher reliability in ordinary operating conditions. In particular, by changing the switch states smoothly and quickly, the DC-port voltage can be controlled as the ideal value even one of the modules in CHBR is facing the load-removed problem.

By using the voltage balance strategy of LCM, the problem of voltage imbalance occurring in three-phase cascaded rectifiers has been solved properly. With the lower modulation depth, the efficiency of the strategy is shown to be better and stronger. The strategy can work reliably and quickly no matter facing the problem as load-removed change or the ordinary operating conditions.

The limitation of the proposed DC-port voltage balance strategy is calculated and proved, in a three-module CHBR, the LCM could balance the DC-port voltage while one module facing the load-removed situation under 0.83 modulation depth.

This paper provides a useful and particular voltage balance strategy which can be used in the topology of three-phase cascaded rectifier. The value of the strategy is that a brief and reliable voltage balance method in the power electronic devices can be achieved. What is more, facing the problem, such as load-removed, in outport, the strategy can response quickly with no switch jump and switch frequency rising.

Drawing on organizational justice theory, this study aims to investigate how perceived organizational exploitation induces frontline hospitality employees’ organizational and interpersonal deviance. Specifically, this study explored the mediating effect of distributive and procedural justice, as well as the moderating effect of justice sensitivity.

The focal research analyzed multiphase survey data from 267 frontline service employees with structural equation modeling.

The results revealed that perceived organizational exploitation induced frontline hospitality employees’ organizational and interpersonal deviance through their perceptions of distributive and procedural justice. Moreover, employees’ justice sensitivity amplified perceived organizational exploitation’s harmful impact on justice perceptions and its conditional influence on organizational and interpersonal deviance.

Organizations should take actions to reduce the occurrence of exploitation to prevent employees’ workplace deviance behaviors. Moreover, organizations can foster employees’ justice perceptions and take care of employees with strong justice sensitivity to reduce the destructive behaviors triggered by organizational exploitation.

By investigating frontline employees’ workplace deviant behaviors, this research identifies new outcomes of exploitation by hospitality organizations. Moreover, the research contributes by offering a justice-based perspective to understand the effects of perceived organizational exploitation. Furthermore, this research helps identify a new boundary condition of being exploited by organizations.

From the intra- and inter-regional dimensions, this paper investigates the linkage between industrial agglomeration and inventory performance, and further demonstrates the moderating role of firm size and enterprise status in the supply chain on this linkage.

Using a large panel dataset of Chinese manufacturers in the Yangtze River Delta for the period from 2008 to 2013, this study employs the method of spatial econometric analysis via a spatial Durbin model (SDM) to examine the effects of industrial agglomeration on inventory performance. Meanwhile, the moderation model is applied to examine the moderating role of two firm-level heterogeneity factors.

At its core, this research demonstrates that industrial agglomeration is associated with the positive change of inventory performance in the adjacent regions, whereas that in the host region as well as in general does not significantly increase. Additionally, both firm size and enterprise status in the supply chain can positively moderate these effects, except for the moderating role of firm size on the positive spillovers.

In view of firm heterogeneity, managers should take special care when matching their abilities of inventory management with the agglomeration effects. Firms with a high level of inventory management are suited to stay in an industrial cluster, while others would be better in the adjacent regions to enhance inventory performance.

This paper is the first to systematically analyze the effects of industrial agglomeration on inventory performance within and across clusters, and confirm that these effects are contingent upon firm size and enterprise status in the supply chain. It adds to the existing literature by highlighting the spatial spillovers from industrial clusters and enriching the antecedents of inventory leanness.