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The flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control performance monitoring to ensure high operation efficiency. This paper proposes a data-driven approach to carry out controller performance monitoring within batch based on linear quadratic Gaussian (LQG) method.

A linear time-varying LQG method is proposed to obtain the joint covariance benchmark for the stochastic part of batch process input/output. From historical golden operation batch, linear time-varying (LTV) system and noise models are identified based on generalized observer Markov parameters realization.

Open/closed loop input and output data are applied to identify the process model as well as the disturbance model, both in Markov parameter form. Then the optimal covariance of joint input and output can be obtained by the LQG method. The Hotelling's Tˆ2 control chart can be established to monitor the controller.

(1) An observer Markov parameter approach to identify the time-varying process and noise models from both open and closed loop data, (2) a linear time-varying LQG optimal control law to obtain the optimal benchmark covariance of joint input and output and (3) a joint input and output multivariate control chart based on Hotelling's T² statistic for controller performance monitoring.

The purpose of this study is to extend the classical noncentral F-distribution under normal settings to noncentral closed skew F-distribution for dealing with independent samples from multivariate skew normal (SN) distributions.

Based on generalized Hotelling's T² statistics, confidence regions are constructed for the difference between location parameters in two independent multivariate SN distributions. Simulation studies show that the confidence regions based on the closed SN model outperform the classical multivariate normal model if the vectors of skewness parameters are not zero. A real data analysis is given for illustrating the effectiveness of our proposed methods.

This study’s approach is the first one in literature for the inferences in difference of location parameters under multivariate SN settings. Real data analysis shows the preference of this new approach than the classical method.

For the real data applications, the authors need to remove outliers first before applying this approach.

This study’s approach may apply many multivariate skewed data using SN fittings instead of classical normal fittings.

This paper is the research paper and the authors’ new approach has many applications for analyzing the multivariate skewed data.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

Smart tourism (ST) needs the development of smart business. The purpose of this study is to evaluate the future of the smart component of tourism companies, what their perspectives are and what factors can help to accelerate it.

A survey of 133 managers of tourist companies is the basis of the empirical information. The study was a personal survey carried out during FITUR 2019 International Tourism Fair of Madrid. The main element of the study is the future development of Information and Communication Technologies (ICTs) and smart tourism (ST) in business.

The results indicate that there is little development at present of the smart business eco-system and that development will continue to be slow in the future. Moreover, this is not a critical issue in the agendas of companies. It was found that tourists pressure tourism through the extensive use of their smartphones, but only at the level of tourism resources. Furthermore, it will be the consolidation of the smart tourism destination that marks the medium and long-term design of smart business.

The limitations concern the problems of a sampling procedure. Firstly, it operates with a database of managers’ opinions; secondly, there are specificities of each company in particular.

The design of the smart tourism destination must incorporate the integration of tourism companies, both with a useful vision of ICTs towards the creation of experiences.

Research on smart business tourism is very scarce compared to smart destination and smart tourists. Also, the data are supported by managers of important tourism companies, as their companies are present at FITUR.

The novel coronavirus (COVID-19) has completely changed the landscape of the hospitality industry. The World Health Organization does not officially recommend wearing face masks in the workplace. Wearing face masks is controversial worldwide, however it has been widely adopted in Hong Kong society. Hospitality practitioners have worn face masks to work and serve customers for almost a year long, matching the duration of the COVID-19 pandemic. This paper proposes a conceptual model of face mask effects and also discusses and evaluates the effects of wearing face masks during the pandemic.

A convenience sampling method is employed to investigate hospitality operators using in-depth and focus group interviews with managers, front-line staff and customers.

The perspectives of both hospitality practitioners and customers are included in this study. The concepts of (1) invisible care, (2) sense of safety and (3) service trust have been introduced in this study. These provide valuable insights for the service industry when facing a large-scale health crisis, now and in the future.

This paper analyzes interview data collected from 35 respondents – 14 managers, 6 practitioners and 15 customers – in order to understand the critical effects of wearing face masks during the pandemic and the perspectives of both hospitality practitioners and customers.

For the hospitality industry, wearing face mask in service has already become a “new normal”, face mask effects might create an impact on service design, service delivery and service quality.

The findings show that wearing face masks turns hygiene and safety into a form of invisible care in the Asian hospitality industry. Practitioners' perspective regarding the necessity of a smile is less important to Asian customers, showing a discrepancy between the two parties. Customers do not believe that service quality has dropped due to the wearing of masks, but that the level of hygiene has risen. Unlike customers, practitioners are more concerned about not providing good quality service. However, the interview data show that respondents generally agree that mask wearing is a gesture and symbol for the hospitality industry to make tangible a new form of caring, professionalism, safety concern and communication.

During the pandemic, two types of fear were identified that occur significantly in all groups or profiles of people. The aim of this paper is to determine which of the two types of fears exist in certain psychological groups, and which of the fears strongly influence the decision to travel.

The VALS 2 method and standardized questionnaire were used for the segmentation of the tourist market or for the determination of the psychographic profiles of the consumers, and three additional questions were joined to it as they were vital for the research of the type of fear and its impact on the decision for traveling. When the reliability of the questionnaire and the validity of the sample were determined, the data were further processed using a computer program package IMB AMOS SPSS 21.00, and then, based on the theoretical suppositions and hypotheses, the SEM structural model was created.

The paper indicates the existence of established types of fears in humans, when it comes to pandemics and similar crisis situations. People are most afraid of infection during travel, and lack of funds and job loss during the critical period of the pandemic. The research conducted confirms that all groups of people, who are determined by the psychological technique VALS 2, react with a certain dose of fear and make decisions under the pressure of fears.

The research had limitations in terms of contact with people and conducting live surveys. The measures during the pandemic, which were carried out by the state, included social distance and limited movement of people.

The work can contribute to the community, along with similar research. The results of the research will be available, and it will be possible to see the behavior of people during crisis situations, and the impact of fears on making decisions, both travel decisions and decisions related to other areas of life.

The paper provides research results on a large sample of respondents, and can serve as a basis for further research in the field of tourism, psychology and similar fields. It is crucial to consider the type of fears, and the strength of the impact of these fears on the decision to travel, during crisis situations.

As the Malacca and Singapore Straits are part of the shortest route between Europe and Asia any impedance to shipping has serious commercial and strategic repercussions. What would be the consequences to tankers and container shipping if access was restricted or prevented? This issue is addressed by examining the costs of using alternative tanker routes to the Straits and the flow-on consequences of removing a mega-hub port from the container-shipping network. The analysis highlights differences between tanker shipping, where the ship itself is the prime unit of interest, and container shipping, where the door-to-door network is of paramount importance.