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The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

This chapter is a narrative account of a Ford Foundation sponsored project from mid-2005 through 2008 for investigating into the causes and engineering a solution to the migrant workers education access problems in manufacturing areas in Shanghai, China. The project team was comprised of faculty members and students from East China Normal University, consulted by two professors from Columbia University. This chapter describes how the team arrived at a solution to the problem by investigating the problems, socializing with the migrant workers, and doing experiments that helped us to make adjustments on the proposed solution from time to time. Unlike the popular understanding of the educational needs of the migrant workers that the workers just need some short-term training for a job, our finding is that the workers need degree and certificate programs too. They need further education for personal and career development, not just a job that can feed them. The workers are Internet fans too, most of those who want further education would prefer a blended learning. We ran quite a few courses and a learning center to observe the learning behavior of the workers, which allowed us to actually interact with the workers and see how they respond to our experimental stimuli. While most of the migrant workers show an interest in learning, we did not observe much active learning involvement of the migrant workers. We identified seven factors that limit access to learning engagement: (1) inadequate transportation means; (2) very long work days and weeks and irregular shifts; (3) difficult living conditions; (4) restricted computer and Internet access; (5) inaccessibility of information; (6) unsupportive social environment; (7) lack of educational infrastructure. As a solution to the identified problem, we propose a learning center based community college network in all the manufacturing areas. A learning center serves as a social learning incubator to nurture the learning practices of the academically less prepared learner, the migrant workers.

Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the failure to fulfill debt payments of a specific company or the death of a patient in a medical study or the inability to pass some educational tests.

This paper introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining procedures. By employing regularization, basis expansion, boosting, bagging, Markov chain Monte Carlo (MCMC) and many other tools, we effectively calibrate a large and flexible class of proportional hazard models.

The proposed methods have important applications in the setting of credit risk. For example, the model for the default correlation through regularization can be used to price credit basket products, and the frailty factor models can explain the contagion effects in the defaults of multiple firms in the credit market.

Purpose – The chapter studies gender occupational segregation of rural-urban migrant workers in China based on 2006 survey data from five Chinese cities.

Methodology – The multinomial logit (MNL) model is used to analyze migrant workers' occupational attainment by gender. The Oaxaca–Blinder decomposition method is employed to analyze factors affecting gender occupational segregation, which can be classified into observed factors and unobserved factors, including gender discrimination.

Findings – The index of dissimilarity based on the data shows that gender occupational segregation for migrant workers exists. The result of Oaxaca–Blinder decomposition shows that the unobserved effects account for more than three-fourths of the total gender occupational segregation.

Research limitations – The “index problem” and the assumption of the same occupational preference between men and women of the Oaxaca–Blinder decomposition need to be addressed further.

Social implications – The existing gender equality policies and social protection confined to urban workers should be extended to migrant workers. Increasing training investment in migrant workers is also recommended.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The growth of luxury tourism has been brought to a grinding halt due to the COVID-19 pandemic affecting most parts of the world since early 2020. Although the time for recovery of hospitality and tourism industries is still unclear, experience from the SARS outbreak in 2003 showed that the bounce back from consumers could be fast. The group of most affluent consumers, mostly known as HNWIs (high net worth individuals), will resume their original consumption behaviour much sooner than the rest of the market; these affluent consumers are the main target market of luxury hospitality and tourism industries.

This chapter presents different types of luxury tourism, luxury lifestyle, luxury tourists' decision-making and luxury hospitality products. The luxury food and beverage business in Singapore is presented to illustrate the commercial environment during the pandemic.