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A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

Due to the increased pollution and global warming, the banking sector is also implementing green practices in their operations to improve business ethics. However, there are few studies that have looked at how green practices affect performance outcomes. Considering this, the study aims to examine the impact of green banking adoption practices (GRBP) on consumer-related performance outcomes (i.e. consumer green satisfaction, consumer green perceived quality, consumer green trust, environmental friendliness and continuing relations with bank). The study used resource-based view theory and triple bottom line in connecting GBRP and consumer-related performance outcomes.

The data was gathered via a Likert scale questionnaire from banking personnel and consumers using a non-probability purposive sampling technique. The data of GRBP was collected from the banking employees, whereas the data for consumer-related performance outcomes were gathered from the banking consumers, and “Partial least square-structural equation modeling” (PLS-SEM) was used to examine research hypotheses.

The results of PLS-SEM reveal that GRBP positively affects consumer green trust, green perceived quality and green satisfaction. However, GRBP does not have any impact on environmental friendliness. The results further reveal that GRBP largely affects consumer green trust followed by green perceived quality and green satisfaction, respectively. Moreover, consumer green perceived quality, green trust and environmental friendliness positively affect the continuing relationship with the bank.

To the best of the authors’ knowledge, this is the first study in the context of green banking, i.e. two-dimensional, as it examines the impact of GRBP on consumer-related performance outcomes and confirms that GRBP enhances consumer-related performance outcomes. The findings of the study provide numerous insights to bank managers, environmentalists and policymakers.

The authors believe the COVID-19 pandemic has an impact on supply and demand. The potential decline in real sector performance leads to lower expectations of securities performance. The uncertainty of future performance can change investor behaviour. This study tried to gain insight into stock investor behaviour during the COVID-19 pandemic. The results showed that the majority of the investor realized and believed the pandemic would affect the stock market performance. Hence, they did not show herding behaviour and were very confident during the COVID-19 pandemic. The survey also indicates that investors tend to avoid risk rather than take the opportunity to buy at a lower price. Moreover, investors believe that the COVID-19 vaccine will soon be found, and the economy will return to normal. Government and self-regulated organizations (SRO) are responsible for making effective policies to convince the investors about the future prospect.

According to the Indian Sign Language Research and Training Centre (ISLRTC), India has approximately 300 certified human interpreters to help people with hearing loss. This paper aims to address the issue of Indian Sign Language (ISL) sentence recognition and translation into semantically equivalent English text in a signer-independent mode.

This study presents an approach that translates ISL sentences into English text using the MobileNetV2 model and Neural Machine Translation (NMT). The authors have created an ISL corpus from the Brown corpus using ISL grammar rules to perform machine translation. The authors’ approach converts ISL videos of the newly created dataset into ISL gloss sequences using the MobileNetV2 model and the recognized ISL gloss sequence is then fed to a machine translation module that generates an English sentence for each ISL sentence.

As per the experimental results, pretrained MobileNetV2 model was proven the best-suited model for the recognition of ISL sentences and NMT provided better results than Statistical Machine Translation (SMT) to convert ISL text into English text. The automatic and human evaluation of the proposed approach yielded accuracies of 83.3 and 86.1%, respectively.

It can be seen that the neural machine translation systems produced translations with repetitions of other translated words, strange translations when the total number of words per sentence is increased and one or more unexpected terms that had no relation to the source text on occasion. The most common type of error is the mistranslation of places, numbers and dates. Although this has little effect on the overall structure of the translated sentence, it indicates that the embedding learned for these few words could be improved.

Sign language recognition and translation is a crucial step toward improving communication between the deaf and the rest of society. Because of the shortage of human interpreters, an alternative approach is desired to help people achieve smooth communication with the Deaf. To motivate research in this field, the authors generated an ISL corpus of 13,720 sentences and a video dataset of 47,880 ISL videos. As there is no public dataset available for ISl videos incorporating signs released by ISLRTC, the authors created a new video dataset and ISL corpus.

The goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).

Using Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.

According to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.

The findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.

The current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.

With the growing popularity of social media, it has become common practice for consumers to write online reviews to share their opinion and experience as well as consider others' reviews to inform purchase decision-making. This study investigated how online review sentiments towards four key aspects (food, service, ambience and price) change after a restaurant is awarded a Michelin Star to shed light on how the award of a Michelin Star affects online reviews as well as what factors contribute to positive online restaurant reviews.

The authors conducted a sentiment analysis of online restaurant reviews on TripAdvisor. A total of 8,871 English-written reviews from 87 restaurants located in Europe were extracted using a web crawler developed by Beautiful Soup, and data were then processed using Semantria.

The study findings revealed that overall sentiments decreased after restaurants were awarded a Michelin Star, in which service sentiment was the most affected aspect, followed by food and ambience. Yet, price sentiment showed a prominent increase. This provides valuable insights for Michelin-starred restaurant operators and owners to create a unique and compelling gastronomic experience that triggers positive online reviews.

The results of this study argue that consumers tend to hold higher expectations for this type of upscale restaurants given its recognition and quality assurance, so they are more likely to have negative feelings when their expectations are disconfirmed. Therefore, restaurants should continuously improve their food and service while paying attention to small details such as ambience, through creativity and innovation. Also, high-end restaurants, especially Michelin-starred restaurants, usually have the edge in premium pricing, yet competitive pricing may backfire considering its perceived luxurious values.

This study analyzed changes in customer sentiments when a restaurant is awarded a Michelin Star through text analytics. Through the lens of online restaurant reviews, the study findings contribute to identifying aspects that are most or least affected by the award of a Michelin Star as well as highlight the role of ambience in customer satisfaction which might have been overlooked in previous studies.

隨著社交媒體日趨普及，消費者出現一種常見的做法，就是在網上書寫評論，分享他們的意見和體驗，他們也會參考其他消費者的評論，以在購物時能作出知情決定。本研究擬探討當餐館獲得米其林星級時，消費者對它們在四個主要方面 (即食物、服務、情調和價格) 的網上評價會如何改變。我們藉此能更容易了解、餐館獲得米其林星級會如何影響其網上評論，以及是哪些因素、會為這些餐館帶來正面的網上評價。

我們對貓途鷹平台上的網上餐館評論進行情感分析。透過BeautifulSoup 研發的網絡爬蟲，我們取出位於歐洲87間餐館、共8,871個以英文書寫的評論，並把這些數據以Semantria加以處理。

研究結果顯示、當餐館獲得米其林星級時，顧客的整體情緒會下降，而其中最受影響的是服務情懷，其次是食物和情調; 但價格情緒卻有明顯的上升。這研究結果給獲得米其林星級餐館的經營者及其東主提供寶貴的啟示，讓他們了解如何為顧客創造一個可帶來正面網上評價的獨特而難忘的美食體驗。

本研究透過文本挖掘、去分析當餐館獲得米其林星級時，顧客情緒會如何改變; 透過網上餐館評論這面透視鏡子，本研究得到的結果、幫助我們確定米其林星級的聲譽所影響最大和最小的是哪些方面，以及讓我們更深入了解餐館的情調在顧客滿意程度上所扮演的角色，而這個角色在過去的研究中似被忽視。

本研究的結果提供了論據、證明由於消費者對擁有相關的認可和品質保證的這類高檔餐館一般予以較高的期望，故當他們發現期望與現實不符時，他們更容易產生負面的情緒; 因此，餐館在關注如情調方面的細節的同時，也應透過創造力和新觀念、去不斷改善他們提供的食物素質和服務水平; 而且，高檔餐館，尤其是獲得米其林星級的餐館，通常在溢價定價方面享有優勢，但當考慮到感知的奢華價值時，具競爭力的價格或會為餐館帶來反效果。

The purpose of this study is to understand the public value of the government of Kuwait using blockchain technology to develop the capabilities of smart cities.

Research was conducted in Kuwait, where the increased use of blockchain technology has been evidenced in both the private and public sectors. A total of seven IT managers were interviewed to gauge their responses to blockchain and its use in Kuwait ministries.

Blockchain technology offers many benefits for the development of smart cities in Kuwait. This is a statement that received almost mutual agreement amongst all the IT managers interviewed. However, as regards wider acceptance, the majority mentioned that a framework is necessary to better articulate the public value of using blockchain in smart cities in Kuwait.

This paper develops research hypotheses and a framework for articulating the public value of blockchain technology for smart cities in Kuwait.