Applications of XAI for Decision Making in the Manufacturing Domain

This chapter discusses an important topic in factory management, that of improving the understandability of AI applications for group multi-criteria decision making in manufacturing systems. Due to its long-term and cross-functional impact, decision making may be more critical to the competitiveness and sustainability of manufacturing systems than production planning and control. This chapter uses the example of choosing the right smart and automation technologies for factories during the COVID-19 pandemic. This topic is of particular importance as many factories are forced to close or operate on a smaller scale (using a smaller workforce), thus pursuing further automation. Artificial intelligence and Industry 4.0 technologies have many applications in this area, most of which can also be applied for other decision-making purposes in manufacturing systems. First, a systematic procedure was established to guide the group multi-criteria decision-making process. Applications of AI and XAI to identify targets are first reviewed. Subsequently, the application of AI and XAI to selection factors and development of criteria is presented. Artificial intelligence techniques are widely used to derive criteria priorities. Therefore, it is particularly important to explain XAI techniques and tools for such AI applications. Aggregating the judgments of multiple decision makers is the next focus, followed by the introduction of AI and XAI applications to evaluate the overall performance of each alternative. Taking fuzzy ranking preference based on similarity to ideal solution (FTOPSIS) as an example, the application of XAI techniques and tools in explaining comparison results using FTOPSIS is illustrated. Another AI technology used for the same purpose is fuzzy VIKOR. XAI techniques and tools for interpreting fuzzy VIKOR are also presented. Finally, several metrics are proposed to evaluate the effectiveness of XAI techniques or tools for decision making in the manufacturing domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

National Health and Nutrition Examination Survey, 2017–March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines

Background The National Health and Nutrition Examination Survey (NHANES) produces national estimates that are representative of the total noninstitutionalized civilian U.S. population. The NHANES sample is selected using a complex, four-stage sample design. NHANES sample weights are used by analysts to produce estimates of the health statistics that would have been obtained if the entire sampling frame (the noninstitutionalized civilian U.S. population) had been surveyed. Sampling errors should be calculated for all survey estimates to assess their statistical reliability. Variance approximation procedures are required to provide reasonable, approximately unbiased, and design-consistent variance estimates for complex sample surveys like NHANES. The 2017–March 2020 files represent a unique public-use data release from NHANES. The coronavirus disease 2019 (COVID-19) pandemic required suspension of data collection in March 2020. As a result, the partially completed NHANES 2019–2020 cycle was not nationally representative. Therefore, the 2019–March 2020 data were combined with the data from the 2017–2018 cycle to create the nationally representative 2017–March 2020 prepandemic data files. Objective This report describes the creation of the NHANES 2017–March 2020 prepandemic data files, including the selection of the appropriate NHANES sample design (2015–2018) to create sample weights and variance units for public-use data files. Additionally, the development of a factor applied to the primary sampling units to adjust the 2017–March 2020 data to fit the NHANES 2015–2018 sample design is described. Analyses to assess representativeness of the target population were performed, and a simulation to replicate the impact of interrupted data collection using earlier NHANES cycles was undertaken. Analytic guidance specific to use for prepandemic data files is also included. © 2022, National Center for Health Statistics. All rights reserved.

An intelligent system for assisting personalized COVID-19 vaccination location selection: Taiwan as an example

In many regions of the world, with the gradual increase in the supply of COVID-19 vaccines, COVID-19 vaccination has changed from centralized government control to personalized selection. When choosing a location for COVID-19 vaccination, in addition to subjective preferences, objective information (such as the expected waiting time at a COVID-19 vaccination location and the crowdedness and reliability of the vaccination location) also need to be considered. However, it is not convenient for an individual to collect and compare such information. To address this issue, this research applies web content mining to extract the conditions of COVID-19 vaccination locations. Then, a novel asymmetric calibrated fuzzy inverse of column sum and fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje recommendation mechanism is proposed. Finally, an intelligent system is developed to assist a user in selecting a personalized COVID-19 vaccination location. In a regional experiment conducted in Taichung City, Taiwan, the developed intelligent system was applied to assist 20 users in choosing personalized COVID-19 vaccination locations. The successful recommendation rate was 95%.

Sampling global roundtables and Chinese cinephilic communities in the pandemic era

In the era of pandemic cinephilia, when social distancing, lockdown, isolation, quarantine, and online platforms have become the new normal, cinephiles' collective longing for community, communication, connection, and contact through the love of cinema has become both dangerous and precious. Three parts constitute the introduction, which is situated in a wide, transnational and translocal context. The first part samples global roundtables that have arised from pandemic cinephilia. The second part maps and samples global Chinese cinephilic communities-including Film 101 Workshop, Rear Window, Deep Focus, O Cinephiles, and DIRECTUBE-since the digital turn in the 1990s. The aim is to problematize the Deep Focus collective by examining its complex relationship with both capital and censorship. The third part gives a roadmap for this special issue on global Chinese cinephilia.

National health and nutrition examination survey 2017–march 2020 prepandemic data files-development of files and prevalence estimates for selected health outcomes

Background and objectives-In March 2020, the coronavirus disease 2019 (COVID-19) pandemic halted National Health and Nutrition Examination Survey (NHANES) field operations. As data collected in the partial 2019–2020 cycle (herein referred to as 2019–March 2020) are not nationally representative, they were combined with previously released 2017–2018 data to produce nationally representative estimates. This report explains the creation of the 2017–March 2020 prepandemic data files, provides recommendations for and limitations of the files’ use, and presents prevalence estimates for selected health outcomes based on the files. Methods-The 2019–2020 primary sampling units (PSUs) were reassigned to the 2015–2018 sample design strata and combined with the 2017–2018 data to create a data set that could be used to calculate nationally representative estimates. A PSUlevel adjustment factor was created to equalize the contribution of each stratum to the total survey sample and applied to participant base weights. Interview and examination weights were calculated from the adjusted base weights. The performance of final interview weights was assessed by comparing the demographic characteristics of the weighted NHANES 2017–March 2020 prepandemic sample with nationally representative estimates from the 2018 5-year American Community Survey. Prevalence estimates and 95% confidence intervals were calculated for selected health outcomes. Results-Among children and adolescents aged 2–19 years, the prevalence of obesity was 19.7% and the prevalence of untreated or restored dental caries in one or more primary or permanent teeth was 46.0%. Among adults aged 20 and over, the age-adjusted prevalence of obesity was 41.9%, severe obesity was 9.2%, and diabetes was 14.8%. Among adults aged 18 and over, the age-adjusted prevalence of hypertension was 45.1%. Among adults aged 65 and over, the age-adjusted prevalence of complete tooth loss was 13.8%. Conclusion-A PSU-level adjustment factor and additional weighting adjustments made nationally representative estimates from the 2017–March 2020 prepandemic data files possible;this was the last NHANES data collected before widespread transmission of COVID-19. © 2021, National Center for Health Statistics. All rights reserved.

Impact of web-based teaching on the learning performance of education and training in the service industry during COVID-19

In response to the impact of the COVID-19 pandemic in 2020, companies around the world have suspended on-site jobs and adopted remote operations. Education and training in some companies are also being carried out through web-based teaching. In addition to bringing new challenges to traditional education and training, web-based teaching platforms also provide a wealth of information sources and application channels for corporate education and training. This study targeted service staff in the service industry as subjects, and incorporated three types of teaching methods into the research design, namely video tutorial, computer-aided teaching and web-based teaching. ANOVA and stepwise regression are then used to analyze the learning motivation, learning attitude and learning performance in an integrated comparison. The results showed that in the service industry, using web-based teaching to conduct employee training for service staff had a substantial impact on improving their learning performance. © 2020 by the authors;licensee CEDTECH by Bastas.