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Understanding user behavior is increasingly critical for information security in the use of smartphones. There is, however, lack of empirical studies about the behavior of smartphone users for information security in China. The purpose of this paper is to present an empirical analysis of the behavior of smartphone users in China in relation to information security.

A review of the related literature is conducted, leading to the development of a questionnaire for investigating the behavior of smartphone users. An online survey of the smartphone users in China is conducted. The collected data are analyzed with the use of descriptive analysis and Pearson’s chi-square test to better understand the behavior of smartphone users on information security.

The paper shows that there are serious concerns about information security in the use of smartphones in China including the ignorance of security information in downloading and using applications, inadequate phone settings, inappropriate enabling of add-on utilities and lack of proper disaster recovery plans. The study also reveals that there is a significant difference between different groups of users on information security in smartphone use.

This paper is based on a purposeful sample of smartphone users in China. It is exploratory in nature.

The paper can lead to a better understanding of the behavior of smartphone users and information security in China and provide relevant government departments and institutions with useful information for developing appropriate strategies and policies and designing specific training programs to improve information security in the smartphone use.

This paper is the first of this kind to collect quantitative data from users in China for better understanding the behavior of smartphone users on information security. It provides insight towards the adoption of various measures for information security from the perspective of smartphone users in China.

This study aims to focus on the authority formation process of Chinese enterprise leaders, with the purpose of finding out how an ordinary newly established firm leader develops into a real top leader and achieves the status of legitimacy in a well-known enterprise.

Based on constructivist grounded theory, this paper investigates the formation mechanism of entrepreneurial authority in China by using the rich data of Liu Chuan-zhi’s leader activities.

In the “evolution” path of authority formation, leaders continually consolidate and improve their authority through two classes of exceptional management activities: “emergency rescue” and “promotion activities”. The successful realization of exceptional management activities benefits from a leader’s management experience accumulation and relationship maintenance with the government. In the “design” path of authority formation, leaders consolidate and improve their authority by exercising their position of power. Leaders’ legitimacy is reflected by making strategic decision and demonstrating discretion of position power. Additionally, passing on an inspiring leader’s thoughts and ideas to an organization’s members is accomplished through the construction of organization culture, institutionalization and convention.

First, the findings are based on only Liu Chuan-zhi’s case. The authors still need more cases to compare and develop the findings and seek theoretical saturation in a broader sense. Second, the qualitative analysis is based on secondary data and future research could consider the introduction of interviews, video and other types of research data.

Under the parallel paths which are “evolution” and “design”, the dynamic leader authority formation model in China is founded.

This paper seeks to develop a conceptual model that enables understanding of consumer purchase decisions in relation to the first customized products, including steps of the decision process, determinants, and consequences of each step.

The classic grounded theory approach was used.

The study found that the process consists of six steps called knowledge learning and preliminary information searching, problem recognition, budget setting, detailed information searching and solution creating, purchase involvement, and use and post‐purchase. The process is distinct from the process of traditional models of consumer decision mainly from three aspects: the process starting from “knowledge learning and information searching”, not from “problem recognition”, while “problem recognition” is the second step; information searched in the process mainly on components of products, with little on products; and the process includes a step “solution creating”, but without steps “alternative evaluation” and “choice”. The consequences of the first step consist of knowledge mastered, market information acquired, pre‐decisions, perceived value of customized PC, positive attitude towards customized PC, and self‐confidence, which are the main determinants of the following steps. Besides these determinants, others included are interest, curiosity, work with computers as tools, sources of knowledge and information on PC, and income.

The study provides a foundation for future quantitative research on determinants of consumers' purchasing the first customized products and provides insights into marketing: for example, target consumers of customized PC are characterized at least by having great self‐confidence in their capability to make an effective decision in purchasing customized PC, and having mastered enough PC knowledge.

Across a variety of domains, consumers are demanding increased customized products. While consumer decision making has become a prominent research topic in various fields of marketing and consumer science over the past decades, surprisingly little research has examined consumer purchase decisions in relation to the first customized personal computer (PC) and factors influencing such a process. The study may fill this gap.

This paper seeks to empirically identify factors influencing Chinese consumers' intention to purchase customized desk top (PC for short) and their effect levels.

Survey and structural equation modeling techniques were used.

This research finds that: attitude toward purchasing customized PC, followed by self‐confidence, and subjective norm influence behavioral intention most significantly; perceived knowledge has a very strong and positive effect on self‐confidence and attitude; and subjective norm and perceived usefulness influence attitude positively. In contrast, a direct effect of perceived knowledge on behavioral intention is rejected. Experience for males and females moderates the confirmed relationships except the relationship between perceived knowledge and attitude for females. Gender for respondents with and without experience moderates the confirmed relationships except the effects of attitude and subjective norm on behavioral intention for respondents without experience and the effect of self‐confidence on behavioral intention for respondents with experience.

It appears that customized PC providers should be aware that mass customization is applicable to markets where consumers are familiar with PC, hold a positive attitude toward purchasing a customized PC, and have confidence in their capability to make an effective decision in purchasing a customized PC.

Little attention has been paid to empirical testing factors and their effect levels on consumers' intention to purchase customized products. No research has been conducted to empirically identify factors influencing Chinese consumers' intention to purchase customized PC and their effect levels, while this research fills this gap.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.

The purpose of this chapter is to optimize multi-criteria formulation for green vehicle routing problems by mixed integer programming. This research is about the road freight transportation of a Spanish company of groceries. This company has more power in the north of Spain and hence it was founded there. The data used for the computational experiments are focused in the northern region of Spain. The data have been used to decide the best route in order to obtain a minimization of costs for the company. The problem focused on the distance traveled and the altitude difference; by studying these parameters, the best solution of route transportation has been made. The software used to solve this model is CPLEX solver with AMPL programming language. This has been helpful to obtain the results for the research and some conclusions have been obtained from them.

With the development of materials science and technology, composite workpieces are increasingly used. This paper aims to discuss a non-destructive testing (NDT) solution for semi-enclosed composite workpieces. A dual-robot system with one robot that grips an irregular-shaped ultrasonic probe (tool) is established.

According to robotics, this paper defines the orientations of the discrete points coordinate frames in trajectory and proposes an orientation constraint rule between the tool coordinate frame and the scanning trajectory. A four-posture calibration method for calibrating the transformation relationship of the irregular-shaped tool frame relative to the robot flange frame is presented in detail.

Calibration and verification experiments were performed, and good-quality C-scan images were obtained by applying the constraint rule and the calibration method. Experimental results show that the calibration method used to determine the tool centre point (TCP) position is correct, effective and efficient; the TCP orientation constraint rule can ensure the extension pole of the irregular-shaped ultrasonic probe is parallel to the axis of the semi-enclosed cylindrical workpieces; and the ultrasonic transducer axis is perpendicular to the surface of the workpiece.

This paper proposes a constraint method for the posture of an irregular-shaped tool in this scheme. Theoretical foundations for the four-posture calibration method of the irregular-shaped tool for dual-robot-assisted ultrasonic NDT are presented in detail. This strategy has been successfully applied in the NDT experiment of semi-enclosed composite workpieces.

The purpose of this chapter is to solve multi-objective formulation for traveling salesman and transportation problems. Computations are based on real data for the road freight transportation of a Spanish company. The company was selected because of its importance in Spanish economy and market. This company is important in the whole country; however, it has its higher importance in the northern part of Spain. The requirements for these models are the minimization of total distance and the CO₂ emissions. To achieve this, it is required to know and carry out the minimization of the total distance traveled by the trucks during the deliveries. The deliveries are going to be executed between the different locations, nodes, in the region, and Elorrio, where the depot is situated. The data have been used to decide the best route in order to obtain a minimization of cost for the company. As it was mentioned earlier, the problems are focused on the reduction of the amount of CO₂ emissions and minimization of total distance; by studying different parameters, the best solutions of route transportation have been obtained. The software used to solve these models is CPLEX solver with AMPL programming language.

Fire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.

This paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.

Based on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.

Aiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.