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The engineering education accreditation (EEA) is a principal quality assurance mechanism. However, at many education institutions, the most labor-intensive work of EEA process is accomplished manually. Without the support of computer and information technology, the EEA process leads to high labor intensity, low work efficiency and poor management level. The purpose of this paper is to build a complex network model and realize an information management system of talent training program for supporting the EEA process.

Based on polychromatic graph (PG), this paper builds a network model of talent training program for engineering specialty. The related information and data are organized and processed in this network model. From the bidirections of top-down and bottom-up, the user requirements are retrieved automatically in logic layer. Together with the specialty of mechanical engineering, the proposed PG-based network modeling method is applied and the corresponding information management system is realized.

The study results show that the PG-based network modeling method takes full advantages of the strong simulation ability of PG to model the complex network system and has some unique merits in formal expression of problem, efficient processing of information and lightweight realization of system. Further, the information management system of talent training program can reduce the tedious human labor and improve the management level of EEA process dramatically.

This paper proposes a PG-based network modeling method, in which the nodes and the edges can be painted by some unified colors to describe the different kinds of activities and the various types of interactions. Theoretically, this modeling method does not distinguish the activities, the interactions and their properties in graphic symbol and the problem size is diminished about a half. Furthermore, this paper provides an effective experience and idea to the education institutions for implementing the engineering education accreditation, increasing the education management efficiency and promoting the talent training quality.

The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail. The operating environment of the high-speed rail is complex, and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters, perimeter intrusion and external environmental hazards. The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.

In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments, the research status is elaborated, and the latest research progress and achievements of the team are introduced. This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological, perimeter and external environmental situation perception methods for high-speed rail operation.

Based on the technical route of “situational awareness evaluation warning active control,” a technical system for monitoring the safety of high-speed train operation environments has been formed. Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters, perimeter intrusion and the external environment on high-speed rail safety. These works strongly support the improvement of China’s railway environmental safety guarantee technology.

With the operation of CR450 high-speed trains with a speed of 400 km per hour and the application of high-speed train autonomous driving technology in the future, new and higher requirements have been put forward for the safety of high-speed rail operation environments. The following five aspects of work are urgently needed: (1) Research the single factor disaster mechanism of wind, rain, snow, lightning, etc. for high-speed railways with a speed of 400 kms per hour, and based on this, study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment, revealing the coupling disaster mechanism of multiple influencing factors; (2) Research covers multi-source data fusion methods and associated features such as disaster monitoring data, meteorological information, route characteristics and terrain and landforms, studying the spatio-temporal evolution laws of meteorological disasters, perimeter intrusions and external environmental hazards; (3) In terms of meteorological disaster situation awareness, research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines; (4) In terms of perimeter intrusion, research a multi-modal fusion perception method for typical scenarios of high-speed rail operation in all time, all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and (5) In terms of external environment, based on the existing general network framework for change detection, we will carry out research on change detection and algorithms in the surrounding environment of high-speed rail.

Ultra-high molecular weight polyethylene (UHMWPE) has an excellent performance and application value; however, as a tribological material, its main drawback is its poor performance under dry friction, impacting its ability to work in high-speed dry friction conditions. Modification of UHMWPE can be carried out to overcome these issues. A significant number of inorganic materials have been used to modify UHMWPE and provide it with good tribological performance. However, thus far, there has been no systematic investigation into the methodology of modifying UHMWPE. The authors take a quantitative approach to determine the structure tribo-ability relationship and basic principles of screening of inorganic compounds suited to modify UHMWPE.

The tribological properties of modified UHMWPE using a series of inorganic additives have been qualitatively studied by the authors’ research group previously. In this study, basic quantitative structure tribo-ability relationships (QSTRs) of inorganic additives for modifying UHMWPE were studied to predict tribological properties. A set of 15 inorganic compounds and their tribological data were used to study the predictive capability of QSTR towards inorganic additives properties.

The results show that the anti-wear and friction-reducing properties of these inorganic compounds correlate with the calculated parameters of entropy and dipole moment. Increased entropy and smaller dipole moment can effectively improve the anti-wear and friction-reducing ability of inorganic compounds as UHMWPE additives. Additives with larger molecular weight, lower hardness and lower melting and boiling points provide good tribological properties for UHMWPE. For inorganic compounds to act as UHMWPE additives, the chemical bond should be less covalent and have more ionic character.

Only 15 inorganic compounds and their tribological data were used to study the predictive capability of QSTR towards inorganic additives properties. If the samples number is more than 30, the other QSTR methodology can be used to study the modified UHMWPE, and the models finding can be more precise.

A QSTR model for modified UHMWPE has been studied systematically. While the results are not more precise and detailed, the model provides a new way to explore the modified UHMWPE characteristics and to reveal new insight into the friction and wear process.

Because the method of studying tribological materials is entirely different from others, the authors want to present the works and discuss it with colleagues.

The paper presents a new method to study the modified UHMWPE. A QSTR is used to study the tribology capability of compounds from calculated structure descriptors. This study uses the Hartree–Fock ab initio method to establish a QSTR prediction model to estimate the ability of 15 inorganic compounds to act as anti-wear and friction-reducing additives for UHMWPE.

The purpose of this paper is to focus on the emerging phenomenon of medical tourism in the context of Iran from a customer experience management perspective and benchmark of their judgment including positive or negative, of the experience they have achieved of the Iranian health (medical) experience (CE) and suggest scenarios for the improvement of the Iranian customer experience management (CEM).

The research methodologies and research methods that are used in this descriptive-analytical research are based on an inspection of the remarkable literature related to medical tourism and customer experience management. The data gathering instrument is a researcher-made questionnaire based on the variables in the conceptual model extracted from the research literature. The study was conducted from May to August 2019. The population cohort of this study was the foreign patients calling selected Iranian hospitals and the sampling method was a purposive and snowball sample of prospective medical tourists. As the study was conducted throughout Iran, some important hospitals in Iran were selected by stratified sampling Yang et al. (2020b). The sample size and data saturation were 500 participants Lv and Song (2019). The collected data using the questionnaire were analyzed by SPSS software and statistical tests.

According to the results, the customer experience management statistical significance in the task aspect is (p = 0.0523), in the mechanical aspect is (p = 0.0563), in the human aspect is (p = 0.0544). The study showed positive customer experience among the patients who had been treated in the Iranian hospitals.

There is a lack of study that focuses on medical tourism and customer experience management in Iran. Therefore, based on the results of this study, the experience of medical tourists in Iran proved to be positive and satisfying. As little research has been conducted in the area of customer experience management (CEM) in Iranian medical tourism, future researchers can use these valuable results precisely and in more detail to benchmark more accurately the customer experience in all areas of medical and health tourism and other research areas in different aspects of CEM in Iran.

There are many investigations in design methodologies, but there are also divergences and convergences as there are so many points of view. This study aims to evaluate to corroborate and deepen other researchers’ findings, dissipate divergences and provide directing to future work on the subject from a methodological and convergent perspective.

This study analyzes the previous reviews (about 15 reviews) and based on the consensus and the classifications provided by these authors, a significant sample of research is analyzed in the design for additive manufacturing (DFAM) theme (approximately 80 articles until June of 2017 and approximately 280–300 articles until February of 2019) through descriptive statistics, to corroborate and deepen the findings of other researchers.

Throughout this work, this paper found statistics indicating that the main areas studied are: multiple objective optimizations, execution of the design, general DFAM and DFAM for functional performance. Among the main conclusions: there is a lack of innovation in the products developed with the methodologies, there is a lack of exhaustivity in the methodologies, there are few efforts to include environmental aspects in the methodologies, many of the methods include economic and cost evaluation, but are not very explicit and broad (sustainability evaluation), it is necessary to consider a greater variety of functions, among other conclusions

The novelty in this study is the methodology. It is very objective, comprehensive and quantitative. The starting point is not the case studies nor the qualitative criteria, but the figures and quantities of methodologies. The main contribution of this review article is to guide future work on the subject from a methodological and convergent perspective and this article provides a broad database with articles containing information on many issues to make decisions: design methodology; optimization; processes, selection of parts and materials; cost and product management; mechanical, electrical and thermal properties; health and environmental impact, etc.

Bike sharing becomes suddenly hot up in China since the end of 2016. However, few studies have focused on shared bicycle users’ behavior. To fill this research gap, this paper aims to develop an integrated model to investigate factors that affect shared bicycle users’ subjective well-being.

A field survey with 220 shared bicycle users is conducted to test the research model and hypotheses.

The highlights are that perceived value has positive effect on users’ life satisfaction. Specially, it is interesting to find that hedonic value has the greatest impact on users’ life satisfaction, followed by social value and utilitarian value. Perceived risks which including privacy risks, financial risks and functional risks act as a moderating role between perceived value and users’ life satisfaction.

First, the authors clear factors that affect users’ subjective well-being from the perspective of perceived value. The results deepen our understanding about the effect of perceived value on users’ subjective well-being. Second, the current researches have ignored the effect of perceived risks on users’ subjective well-being. The authors filled that gap and found that perceived risks which including privacy risks, financial risks and functional risks act as a moderating role between perceived value and users’ subjective well-being.

This study aims to review the recent advancements in high entropy alloys (HEAs) called high entropy materials, including high entropy superalloys which are current potential alternatives to nickel superalloys for gas turbine applications. Understandings of the laser surface modification techniques of the HEA are discussed whilst future recommendations and remedies to manufacturing challenges via laser are outlined.

Materials used for high-pressure gas turbine engine applications must be able to withstand severe environmentally induced degradation, mechanical, thermal loads and general extreme conditions caused by hot corrosive gases, high-temperature oxidation and stress. Over the years, Nickel-based superalloys with elevated temperature rupture and creep resistance, excellent lifetime expectancy and solution strengthening L12 and γ´ precipitate used for turbine engine applications. However, the superalloy’s density, low creep strength, poor thermal conductivity, difficulty in machining and low fatigue resistance demands the innovation of new advanced materials.

HEAs is one of the most frequently investigated advanced materials, attributed to their configurational complexity and properties reported to exceed conventional materials. Thus, owing to their characteristic feature of the high entropy effect, several other materials have emerged to become potential solutions for several functional and structural applications in the aerospace industry. In a previous study, research contributions show that defects are associated with conventional manufacturing processes of HEAs; therefore, this study investigates new advances in the laser-based manufacturing and surface modification techniques of HEA.

The AlxCoCrCuFeNi HEA system, particularly the Al0.5CoCrCuFeNi HEA has been extensively studied, attributed to its mechanical and physical properties exceeding that of pure metals for aerospace turbine engine applications and the advances in the fabrication and surface modification processes of the alloy was outlined to show the latest developments focusing only on laser-based manufacturing processing due to its many advantages.

It is evident that high entropy materials are a potential innovative alternative to conventional superalloys for turbine engine applications via laser additive manufacturing.

The purpose of this paper is to develop sliding mode control with linear and nonlinear manifolds in discrete‐time domain for robot manipulators.

First, a discrete linear sliding mode controller is designed to an n‐link robot based on Gao's reaching law. In the second step, a discrete terminal sliding mode controller is developed to design a finite time and high precision controller. The stability analysis of both controllers is presented in the presence of model uncertainties and external disturbances. Finally, sampling time effects on the continuous‐time system outputs and sliding surfaces are discussed.

Computer simulations on a three‐link SCARA robot show that the proposed controllers are robust against model uncertainties and external disturbance. It was also shown that the sampling time has important effects on the closed loop system stability and convergence.

The proposed controllers are low cost and easily implemented in practice in comparison with continuous‐time ones.

The novelty associated with this paper is the development of an approach to finite time and robust control of n‐link robot manipulators in discrete‐time domain. Also, obtaining an upper bound for the sampling time is another contribution of this work.

As the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.

The external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.

Compared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.

Owing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

Since the introduction of VIX to measure the spot volatility in the stock market, VIX and its futures have been widely considered to be the standard of underlying investor sentiment. This study aims to examine how the magnitude of contango or backwardation (MCB volatility risk factor) derived from VIX and VIX3M may affect the pricing of assets.

This paper focuses on the statistical inference of three defined MCB risk factors when cross-examined with Fama–French’s five factors: the market factor R_m–R_f, the size factor SMB (small minus big), the value factor HML (high minus low B/M), the profitability factor RMW (robust minus weak) and the investing factor CMA (conservative minus aggressive). Robustness checks are performed with the revised HML-Dev factor, as well as with daily data sets.

The inclusions of the MCB volatility risk factor, either defined as a spread of monthly VIX3M/VIX and its monthly MA(20), or as a monthly net return of VIX3M/VIX, generally enhance the explanatory power of all factors in the Fama and French’s model, in particular the market factor R_m–R_f and the value factor HML, and the investing factor CMA also displays a significant and positive correlation with the MCB risk factor. When the more in-time adjusted HML-Dev factor, suggested by Asness (2014), replaces the original HML factor, results are generally better and more intuitive, with a higher R² for the market factor and more explanatory power with HML-Dev.

This paper introduces the term structure of VIX to Fama–French’s asset pricing model. The MCB risk factor identifies underlying configurations of investor sentiment. The sensitivities to this timing indicator will significantly relate to returns across individual stocks or portfolios.