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This paper aims to overview the current status of development and application of digital image processing technology used for the yarn hairiness evaluation.

Digital image processing technology is one of the new methods used for the yarn detection, which can be used for the digital characterization and objective evaluation of yarn appearance. The comparison between the traditional detection methods and this new developed method was made and analyzed.

Compared with the traditional methods, image-based methods have the advantages of being objective, fast and accurate. Therefore, it was proved that digital image processing techniques should be a new trend in terms of the yarn appearance evaluation.

Research has shown that creative self-efficacy is an important antecedent of workplace creativity, but recent research indicates that this relationship may be moderated by contextual factors. The current study investigates whether leader dialectical thinking and leader member exchange moderate the relationship between employee creative self-efficacy and employee creativity.

A survey sample of 222 employees in 43 teams from Chinese high-tech companies was collected and HLM was used to test our research model.

The positive association between employee creative self-efficacy and employee creativity was strengthened when a leader displayed a dialectical thinking style. Additional analyses failed to find support for the moderating role of leader-member exchange (LMX).

These findings establish leadership cognitive style as a potential boundary condition of the relationship between creative self-efficacy and employee creativity.

Companies can make an active effort in recruiting and training leaders who have a dialectical mindset as they can play significant roles in facilitating employee creativity.

Technological advancement and innovation is important for social welfare. This paper helps to improve the efficiency of creativity processes and finally benefits the whole society.

This is the first introduction of the leader's dialectical thinking as a moderator of the relationship between creative self-efficacy and creativity.

Although there have been considerable amounts of research documenting the effects of narcissism on workplace outcomes, studies of the impact of narcissism on job performance have produced inconclusive results. This study aims to provide insight into this issue by using a new model of narcissism, the Narcissistic Admiration and Rivalry Concept model to explore the processes by which narcissism can impact job performance.

Two studies (Study 1 with 1,176 employees and 217 managers cross-sectional data; Study 2 with 209 employees and 39 managers time-lagged data) were conducted and multilevel technique was used to test the research model.

Narcissistic rivalry is associated with higher levels of family–work conflict (FWC) and that these effects are magnified when narcissists also have competing demands in the form of expectations to conform to traditional values. Furthermore, this study documents that higher levels of FWC are associated with greater emotional exhaustion and lower job performance. However, narcissistic admiration only has direct effect on job performance.

This paper not only suggests that narcissism is a previously untested dispositional antecedent for FWC, but it also uses a facet-based approach to examine when and how narcissism impacts job performance.

The differences and relations between grey sets and rough sets are discussed. Based on the concept of a rough grey set, its entropy is studied. Moreover, the grey inference resulting from a rough grey set is pointed out. The relative propositions and theorems are obtained.

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.

In this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.

With the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.

The threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.

It can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.

The classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

The purpose of this paper is to focus on the risk decision making of entrepreneurial team, deconstruct the intermediate process mechanism of cognitive adaptability in promoting risk decision making and reveal the role of opportunity identification and entrepreneurial efficacy in the decision-making process, which clarifies how cognitive adaptability affects decision-making speed and effect.

This study establishes a relationship model among entrepreneurial team’s cognitive adaptability, opportunity identification, entrepreneurial efficacy and risk decision making, and selects 316 entrepreneurial teams to empirically study the relationship among core variables using Bootstrap analysis and Johnson–Neyman technology.

Cognitive adaptability though has no direct impact on risk decision-making speed, whereas it directly affects risk decision-making effect; opportunity identification has a full mediating effect between cognitive adaptability and decision-making speed, and a partial mediating effect between cognitive adaptability and decision-making effect; entrepreneurial efficacy plays a moderating role between opportunity identification and decision-making speed, and a same role between opportunity identification and decision-making effect.

First, in setting the research model, the study does not take other moderators into consideration, which might be improved. Second, the study ignores the origin and formation of entrepreneurial team’s cognitive adaptability, the predisposing factors of which might be discussed in the future research.

The practical implication of this paper is to guide the entrepreneurial team to turn their focus on the impact of highly implicit cognitive adaptability on decision making, which might be divided into two aspects: the first is to enhance the cognitive adaptability of the entrepreneurial team, cultivate team members’ self-examination awareness and self-monitoring habits. The second is to strengthen team’s psychological capital and value the cultivation of entrepreneurial efficacy.

This paper breaks through the team process and structure perspectives, explores the driving mechanism of entrepreneurial team risk decision making from team cognition perspective, and deconstructs the logical framework of cognitive adaptability’s influence on risk decision making. This paper applies Johnson–Neyman technology to quantify the mediating effect entrepreneurial efficacy exerts on cognitive adaptability and decision-making speed, as well as on cognitive adaptability and decision-making effect.

In this paper, microbial transglutaminase (MTG) was applied to process silk fabric for improving its crease resistance under the prerequisite of maintaining other performances. Not only was the effect of MTG on silk fabric investigated through the Fourier transform infrared spectroscopy (FR), but analysis was also undertaken in the microcosmic structure of fibroin through the scanning electron microscope (SEM). Solo MTG treatment as well as compound treatments of MTG followed by hydrogen peroxide, protease and ultrasonic, all showed that MTG can improve the crease resistance of silk fabric. It also enhanced its tensile breaking strength or amended damage in the tensile breaking strength caused by pretreatments.

Simultaneously, comparison with other treatments showed that compound treatment of MTG followed by ultrasonic exerted a better coordinated effect and conferred better performances, which made the wrinkle recovery angle (WRA) increase by 17.4% and tensile breaking strength improve by 11.2% respectively. At the same time, other performances were still maintained well.

The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges.

By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot.

The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot.

The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot.

A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.