Assessing Mobile and Smart Technology Applications for Active and Healthy Aging using a Fuzzy Collaborative Intelligence Approach

Improving people's health by burning low-pollution coal to improve air quality for thermal power generation

Eliminating the NOx emission after coal combustion is a critical task for thermal power plants to reduce threats to the human body, such as respiratory diseases, heart disease, lung disease and even lung cancer. To this end, various treatments have been taken to optimize, monitor and control the combustion process. However, optimizing the coal composition prior to combustion can further reduce possible NOx emissions. This topic was rarely discussed in the past. To fill this gap, this study proposes a fuzzy big data analytics approach. The proposed methodology combines recursive feature elimination, fuzzy c-means, XG Boost, support vector regression, random forests, decision trees and deep neural networks to predict post-combustion NOx emission based on coal composition and specification. Subsequently, additional treatments can be implemented to optimize boiler configuration and combustion conditions with pollution prevention equipment. In other words, the method proposed in this study is a kind of pretreatment. The proposed methodology has been applied to the real case of a thermal power plant in Taiwan. Experimental results showed that the prediction accuracy using the proposed methodology was significantly better than several existing methods. The forecasting error, measured in terms of root mean square error and mean absolute percentage error, was only 14.55 ppm and 8.9%, respectively.

A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a Case Study of Post-COVID-19 Restaurant Transformation

In a fuzzy group decision-making task, when decision makers lack consensus, existing methods either ignore this fact or force a decision maker to modify his/her judgment. However, these actions may be unreasonable. In this study, a fuzzy collaborative intelligence approach that seeks the consensus among experts in a novel way is proposed. Fuzzy collaborative intelligence is the application of biologically inspired fuzzy logic to a group task. The proposed methodology is based on the fact that a decision maker must make a choice even if he/she is uncertain. As a result, the decision maker’s fuzzy judgment matrix may not be able to represent his/her judgment. To solve such a problem, the fuzzy judgment matrix of each decision maker is decomposed into several fuzzy judgment submatrices. From the fuzzy judgment submatrices of all decision makers, a consensus can be easily identified. The proposed fuzzy collaborative intelligence approach and several existing methods have been applied to the case of the post-COVID-19 transformation of a Japanese restaurant in Taiwan. Because such transformation was beyond the expectation of the Japanese restaurant, the employees lacked consensus if existing methods were applied to identify their consensus. The proposed methodology solved this problem. The optimal transformation plan involved increasing the distance between tables, erecting screens between tables, and improving air circulation. In a fuzzy group decision-making task, an acceptable decision cannot be made without the consensus among decision makers. Ignoring this or forcing decision makers to modify their preferences is unreasonable. Identifying the consensus among experts from another point of view is a viable treatment.

The Practice of Vigorous Physical Activity Is Related to a Higher Educational Level and Income in Older Women

Few studies have shown evidence about the factors that can determine physical practice in women over 60 years of age due to educational, economic, social, or health inequalities. Its knowledge could help to understand the determinants that encourage the practice of physical activity and the improvement of health in women over 60. Therefore, the aim of this research was to evaluate the level of studies, income, and the usefulness of social and health services in physically active older women according to the level of activity they practice. The IPAQ (International Physical Activity Questionnaire) and CUBRECAVI (subjective health scale) scales have been applied to a sample of 257 women between 61 and 93 years old (M = 69.44, SD = 4.61). The results have shown that those with vigorous physical activity are related to higher levels of education (p < 0.001) and income (p = 0.004). Furthermore, being dissatisfied with social and health services is associated with low levels of physical activity (p = 0.005). Older women who perform physical activity regularly are associated with high levels in some of the socio-environmental aspects of quality of life. High physical activity is related to a higher educational level and income. Socio-environmental factors generate social inequalities and modulate the lifestyles of older women.

A diversified AHP-tree approach for multiple-criteria supplier selection

A decision maker usually holds various viewpoints regarding the priorities of criteria, which complicates the decision making process. To overcome this concern, in this study, a diversified AHP-tree approach was proposed. In the proposed diversified AHP-tree approach, the judgement matrix of a decision maker is decomposed into several subjudgement matrices, which are more consistent than the original judgement matrix and represent diverse viewpoints on the relative priorities of criteria. Thus, a nonlinear programming model was established and optimized, for which a genetic algorithm is designed. To assess the effectiveness of the proposed diversified AHP-tree approach, it was applied to a supplier selection problem. The experimental results showed that the application of the diversified AHP-tree approach enabled the selection of multiple diversified suppliers from a single judgement matrix. Furthermore, all suppliers selected using the diversified AHP-tree approach were somewhat ideal.