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In an integrated circuit (IC) substrate, more fillers, including talcum powder and aluminium hydroxide, are added, which leads to much higher rigidity and hardness compared with a traditional printed circuit board. However, the micro drilling of IC substrates is harder. This paper aims to test the drilling process of IC substrates to improve the drilling process and the micro hole quality.

Substrate drilling by a micro drill with 0.11-mm diameter was used under several drilling conditions. The influence of drilling conditions on the drilling process was observed. Drilling forces, drill wear and micro hole quality were also studied.

The deformation circle around holes, hole location accuracy, bugle hole and burrs were the major defects of micro holes that were observed during the drilling of the substrate. Reducing the drilling force and drill wear was the effective way to improve hole quality.

The technology and manufacturing of IC substrates has been little investigated. Research data on drilling IC substrates is lacking. The micro hole quality directly affects the reliability of IC substrates. Thus, improving the drilling technology of IC substrates is very important.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

The purpose of this paper is to establish the damage alarming indexes for ancient wood structures and study the damage sensitivity and noise robustness of these indexes under random excitation.

Xi’an Bell Tower is taken as a case in this paper to simulate the damage of ancient wood structures through finite element (FE) simulation and determine the satisfactory damage alarming indexes with wavelet packet energy spectrum.

The results of this paper show that: 1) the damage alarming indexes can effectively identify the damage of ancient wood structures, each index with a different damage sensitivity; 2) the energy ratio deviation is greater than the energy ratio variance and is close to the maximum variation of energy ratio; 3) the energy ratio deviation has a better alarming effect than the energy ratio variance during the initial period of the damage. With the accumulation of the damage, the energy ratio variance outperforms the energy ratio deviation; 4) the sensitivity of the energy ratio deviation and variance varies from positions, changing from the highest to lowest at the mortise-and-tenon joints, the beam mid-span and the plinth; 5) if signal to noise ratio (SNR) is 40db or larger, the indexes can accurately identify the damage of ancient wood structures. As SNR increases, the indexes will have an increasingly higher sensitivity and certain ability to resist noise.

The FE model is simpiy, it does not completely reflect Xi’an Bell Tower.

It will provide a theoretical basis for the damage alarming of Xi’an Bell Tower.

It makes structural health monitoring through structural vibration response under ambient excitation a new research field in damage detection as well as a positive way of ancient architecture protection.

This paper studies the damage alarming effect on ancient wood structures from different wavelet functions and wavelet packet decomposition levels. To study the effect under white noise environment, this paper adds Gaussian white noise with a SNR of 10, 20, 30, 40 and 50 db to the acceleration response signal of intact structure and damaged structure.

Textbook content and curricula are artifacts that can serve as indicators of social contexts and societal values. In this chapter, we use qualitative and quantitative content analysis to examine the content of Chinese language arts textbooks for basic education during a period of curriculum reform in China at the start of the 21st century. Given the important role of the Chinese language arts in the socialization of students into official societal values, this study seeks to provide insight into the nature of the official world view in China and addresses the societal ambivalence between global vs. national/local and traditional knowledge vs. Western/contemporary knowledge. We find that there is a slight increase in themes that reflect contemporary global concerns such as creativity and social justice. We also find that, in the face of the globalizing cultural influences of the new millennium, there is a sustained emphasis on the role of the Chinese language curriculum in the transmission of traditional Chinese cultural values and on the cultivation in Chinese students of an appreciation of their rich cultural traditions.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

Unlike most empirical investigations that have tested the relationship between abusive supervision and subordinate’s workplace deviance in a large and formal organizational setup, this study investigates the effect of abusive behavior of owner-manager of small entrepreneurial establishments on subordinate’s workplace deviance. The purpose of this paper is twofold: first, it explores the moderating effect of intention to quit on the relationship between abusive supervision and organizational as well as interpersonal deviance; and second, it investigates whether the moderating effect between abusive supervision and intention to quit will be stronger for organizational deviance (supervisor directed) than for interpersonal deviance (others directed).

The participants of this study were 240 restaurant and hotel employees working in three small entrepreneurial organizations in the eastern state of India. The authors have collected data on the predictor and criterion variables at two time points with a separation of three to four weeks for reducing common method bias (Podsakoff et al., 2012). At Time 1, participants completed measures of the perception of their owner-manager’s abusiveness and their intention to quit. At Time 2, participants responded to organizational deviance and interpersonal deviance.

The findings of the study is in line with previous research studies (Tepper et al., 2007; Thau et al., 2009) that reported intention to quit will moderate the positive relationship between abusive supervision and organizational deviance and interpersonal deviance such that the relationship will be stronger when intention to quit is high rather than low. The finding of the study also corroborates the prediction that the interactive effect between abusive supervision and intention to quit will be stronger for organizational deviance (supervisor directed) than for interpersonal deviance (aimed at other members of the organization) when intention to quit is higher.

This study is among the very few empirical research studies that have investigated the effect of abusiveness of owner-manager on subordinate’s workplace deviance in small organizations. Another unique aspect of the study is that it is one of few to propose and test, how (whether organizational deviance or/and interpersonal deviance) and to what extent (more organizational or supervisor directed than interpersonal or others directed deviance) subordinates of abusive supervisor retaliate by engaging in workplace deviant behaviors.