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Applied Sciences
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Emerging Industry – Promoting Human Performance and Health
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Emerging Industry – Promoting Human Performance and Health

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 44919

Special Issue Editor

Prof. Dr. Peng-Yeng Yin

E-Mail Website
Guest Editor
Information Technology and Management Program, Ming Chuan University, Taoyuan City 333, Taiwan
Interests: artificial intelligence; evolutionary computation; wind and solar energy; metaheuristics; pattern recognition; image processing; machine learning; software engineering; computational intelligence; operations research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The high velocity of evolution in innovative technologies and business models is overwhelmingly challenging. This is partly due to the emerging of new technologies such as IoT, I4.0, deep learning, self-media, renewable energy, fintech¸ and partly due to international trade protection, which constrains the development of the global supply chain and regional tax-free agreement. The advent of the pandemic posed a tremendous setback on the economy but also gives birth to new opportunities of emerging industry and health promotion products, ranging from E-business, delivery platform, work-from-home appliances, facial mask design, disinfectant, food security, agricultural technology, and aromatherapy products. This sort of emerging industry will definitely play a major role in the future. Moreover, successful entrepreneurs in this era mandatorily require high-quality human mind and performance enhancement, which may be obtained from such training as aromatherapy, yoga, breathing, and meditation. Practical experiences have many proven benefits to human performance and health. This Special Issue aims to collect quality scientific contributions on the emerging industry of human performance and health promotion. To augment the readership of this Special Issue, we are working in collaboration with the Information Technology and Applications Conference 2022 – ITAC 2022 to be held in China University of Technology, Taipei, Taiwan on March 16, 2022. The best papers selected from this conference will be invited to submit an extended version for possible publication in this Special Issue. We also welcome contributions (research and review articles) covering a broad range of topics on emerging industry and human performance and health promotion, including (though not limited to) the following:

Applied industrial technology

Innovation and technology management

Renewable energy

Fintech

Artificial intelligence

Internet of things

Industry 4.0

Knowledge management

Facial masks, disinfectants, and health-promotion products

Sustainable development

Local featured agriculture

Agricultural technology

Food security

Aroma science and aromatherapy

Breathing and meditation for enhancing human performance and health

Medical education

Dr. Peng-Yeng Yin
Guest Editor

Manuscript Submission Information

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Keywords

  • applied industrial technology
  • artificial intelligence
  • Internet of things
  • Industry 4.0
  • renewable energy
  • breathing and meditation
  • agricultural technology
  • aroma science and aromatherapy
  • health promotion

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Published Papers (5 papers)

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Research

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19 pages, 3704 KiB  
Article
A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
by Peng-Yeng Yin
Appl. Sci. 2023, 13(9), 5808; https://doi.org/10.3390/app13095808 - 8 May 2023
Cited by 1 | Viewed by 1263
Abstract
Air pollution has been a global issue that solicits proposals for sustainable development of social economics. Though the sources emitting pollutants are thoroughly investigated, the transportation, dispersion, scattering, and diminishing of pollutants in the spatiotemporal domain are underexplored, and the relationship between these [...] Read more.
Air pollution has been a global issue that solicits proposals for sustainable development of social economics. Though the sources emitting pollutants are thoroughly investigated, the transportation, dispersion, scattering, and diminishing of pollutants in the spatiotemporal domain are underexplored, and the relationship between these activities and atmospheric and anthropogenic conditions is hardly known. This paper proposes machine learning approaches for the spatiotemporal analysis of air pollution episode associations. We deployed an internet of low-cost sensors for acquiring the hourly time series data of PM2.5 concentrations in Puli, Taiwan. The region is resolved into 10 × 10 grids, and each grid has an area size of 400 × 400 m2. We consider the monitored PM2.5 concentration at a grid as its gray intensity, such that a 10 × 10 PM2.5 image is obtained every hour or a PM2.5 video is obtained for a time span. We developed shot boundary detection methods for segmenting the time series into pollution episodes. Each episode corresponds to particular activities, such as pollution concentration, transportation, scattering, and diminishing, in different spatiotemporal ways. By accumulating the concentrations within the episode, we generate a condensed but effective representation for episode clustering. Three clustering approaches are proposed, ranging from histogram-, edge-, and deep-learning-based. The experimental results manifest that the episodes contained in the same cluster have homogeneous patterns but appear at different times in a year. This means that some particular patterns of pollution activities appear many times in this region that may have relations with local weather, terrain, and anthropogenic activities. Our clustering results are helpful in future research for causal analysis of regional pollution. Full article
(This article belongs to the Special Issue Emerging Industry – Promoting Human Performance and Health)
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16 pages, 249 KiB  
Article
Effects of Aromatherapy on the Physical and Mental Health and Pressure of the Middle-Aged and Elderly in the Community
by Mei-Hua Ke, Kun-Ta Hsieh and Wen-Ying Hsieh
Appl. Sci. 2022, 12(10), 4823; https://doi.org/10.3390/app12104823 - 10 May 2022
Cited by 7 | Viewed by 17432
Abstract
The physical and mental health of an aging society has become a major issue, and stress reduction and the improvement of physical and mental health are important physical and mental health issues for middle-aged and elderly people. This research sought to explore the [...] Read more.
The physical and mental health of an aging society has become a major issue, and stress reduction and the improvement of physical and mental health are important physical and mental health issues for middle-aged and elderly people. This research sought to explore the application of aromatherapy for the improvement of physical and mental health and stress levels, as well as other issues, that concern the elderly in the community. The research was based on intentional sampling. A pre- and post-test design with unequal groups was employed. The experimental treatments were divided into five groups: Group A (compound essential oil massage plus sniffing), Group B (compound essential oil massage), Group C (pure base oil massage), Group D (compound essential oil sniffing), and control Group E (without any aromatherapy intervention). To explore the effects of aromatherapy on physical and mental health and stress relief among the elderly in the community, the self-completed Mental and Physical Health Scale for the Elderly and the Stress Index Measurement Scale were used to collect data. The obtained data were analyzed using descriptive statistics and by paired sample t-test. It was concluded that aromatherapy can improve the physical and mental health of the elderly in the community and can significantly reduce stress. The experimental results on aromatherapy in this study can provide a basis for home application to help the elderly in the community. They also provide a foundation for the organization of health promotion courses for the elderly and other practical applications in social welfare group planning. Full article
(This article belongs to the Special Issue Emerging Industry – Promoting Human Performance and Health)
17 pages, 3336 KiB  
Article
Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
by Peng-Yeng Yin, Ray-I Chang, Rong-Fuh Day, Yen-Cheng Lin and Ching-Yuan Hu
Appl. Sci. 2022, 12(1), 71; https://doi.org/10.3390/app12010071 - 22 Dec 2021
Cited by 6 | Viewed by 3723
Abstract
The rapid development of industrialization and urbanization has had a substantial impact on the increasing air pollution in many populated cities around the globe. Intensive research has shown that ambient aerosols, especially the fine particulate matter PM2.5, are highly correlated with [...] Read more.
The rapid development of industrialization and urbanization has had a substantial impact on the increasing air pollution in many populated cities around the globe. Intensive research has shown that ambient aerosols, especially the fine particulate matter PM2.5, are highly correlated with human respiratory diseases. It is critical to analyze, forecast, and mitigate PM2.5 concentrations. One of the typical meteorological phenomena seducing PM2.5 concentrations to accumulate is temperature inversion which forms a warm-air cap to blockade the surface pollutants from dissipating. This paper analyzes the meteorological patterns which coincide with temperature inversion and proposes two machine learning classifiers for temperature inversion classification. A separate multivariate regression model is trained for the class with or without manifesting temperature inversion phenomena, in order to improve PM2.5 forecasting performance. We chose Puli township as the studied site, which is a basin city easily trapping PM2.5 concentrations. The experimental results with the dataset spanning from 1 January 2016 to 31 December 2019 show that the proposed temperature inversion classifiers exhibit satisfactory performance in F1-Score, and the regression models trained from the classified datasets can significantly improve the PM2.5 concentration forecast as compared to the model using a single dataset without considering the temperature inversion factor. Full article
(This article belongs to the Special Issue Emerging Industry – Promoting Human Performance and Health)
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15 pages, 2634 KiB  
Article
A Decision Support System with Artificial Intelligence and Natural Language Processing to Mitigate the Deduction Rate of Health Insurance Claims
by Shey-Chiang Su, Chun-Che Huang, Roger R. Gung, Li-Kai Hsiung, Zhi-Wei Gao and Cheng-En Tsai
Appl. Sci. 2021, 11(24), 11623; https://doi.org/10.3390/app112411623 - 7 Dec 2021
Cited by 2 | Viewed by 2547
Abstract
Globally, 20% to 40% of medical resources are wasted, which could be avoided through professional audit of health insurance claims. The professional audit can pinpoint excessive use of unnecessary medicines and medical examinations. Taiwan’s National Health Insurance Bureau (TNHIB) deducts the weight that [...] Read more.
Globally, 20% to 40% of medical resources are wasted, which could be avoided through professional audit of health insurance claims. The professional audit can pinpoint excessive use of unnecessary medicines and medical examinations. Taiwan’s National Health Insurance Bureau (TNHIB) deducts the weight that medical resources carry if regarded as unnecessary or abused when examining health insurance claims. The ratio of the deducted weight to the total weight claimed by a hospital is defined as the health insurance claim deduction rate (HICDR). A high HICDR increases the operating expenses of the hospital. In addition, it takes the hospital many resources to prepare and file appeals for the deduction. This study aims to: (1) minimize the weight deducted by the TNHIB for a hospital; and (2) facilitate efficient appeals to claim denials. It is expected that HICDR will be reduced through big data analytics. In this study, evidence-based medicine (EBM) is involved to clarify the debate, dilemmas, conflicts of interests in examining health insurance claims. A natural language method—latent Dirichlet allocation (LDA), was used to analyze patients’ medical records. The topics derived from the LDA are used as factors in the logistic regression model to estimate the probability of each claim to be deducted. The experimental results on various medical departments show that the proposed predictive model can produce accurate results, and lead to more than 41.7% reduction to the deduction of the health insurance claims. It is equivalent to more than a 750 thousand NT dollars saving per year. The efficiency of application is validated compared to the manual process that is time-consuming and labor intensive. Moreover, it is expected that this study will supplement the insufficiency of traditional methods and propose a new and effective solution to reduce the deduction rate. Full article
(This article belongs to the Special Issue Emerging Industry – Promoting Human Performance and Health)
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Review

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20 pages, 7173 KiB  
Review
Essential Oils, Phytoncides, Aromachology, and Aromatherapy—A Review
by Subramanian Thangaleela, Bhagavathi Sundaram Sivamaruthi, Periyanaina Kesika, Muruganantham Bharathi, Wipada Kunaviktikul, Areewan Klunklin, Chatnithit Chanthapoon and Chaiyavat Chaiyasut
Appl. Sci. 2022, 12(9), 4495; https://doi.org/10.3390/app12094495 - 28 Apr 2022
Cited by 33 | Viewed by 18143
Abstract
Chemical compounds from plants have been used as a medicinal source for various diseases. Aromachology is a unique field that studies the olfactory effects after inhaling aromatic compounds. Aromatherapy is a complementary treatment methodology involving the use of essential oils containing phytoncides and [...] Read more.
Chemical compounds from plants have been used as a medicinal source for various diseases. Aromachology is a unique field that studies the olfactory effects after inhaling aromatic compounds. Aromatherapy is a complementary treatment methodology involving the use of essential oils containing phytoncides and other volatile organic compounds for various physical and mental illnesses. Phytoncides possess an inherent medicinal property. Their health benefits range from treating stress, immunosuppression, blood pressure, respiratory diseases, anxiety, and pain to anti-microbial, anti-larvicidal, anti-septic, anti-cancer effects, etc. Recent advancements in aromatherapy include forest bathing or forest therapy. The inhalation of phytoncide-rich forest air has been proven to reduce stress-induced immunosuppression, normalize immune function and neuroendocrine hormone levels, and, thus, restore physiological and psychological health. The intricate mechanisms related to how aroma converts into olfactory signals and how the olfactory signals relieve physical and mental illness still pose enormous questions and are the subject of ongoing research. Aromatherapy using the aroma of essential oils/phytoncides could be more innovative and attractive to patients. Moreover, with fewer side effects, this field might be recognized as a new field of complementary medicine in alleviating some forms of physical and mental distress. Essential oils are important assets in aromatherapy, cosmetics, and food preservatives. The use of essential oils as an aromatherapeutic agent is widespread. Detailed reports on the effects of EOs in aromatherapy and their pharmacological effects are required to uncover its complete biological mechanism. This review is about the evolution of research related to phytoncides containing EOs in treating various ailments and provides comprehensive details from complementary medicine. Full article
(This article belongs to the Special Issue Emerging Industry – Promoting Human Performance and Health)
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