In this article, we analyse whether the management structure of a company plays a role in the sustainability of companies. More specifically, we study the impact of occupied outside directors, outside directors sitting on several boards of directors, on the environmental, social and governance (ESG) performance of the company. We collect information about board characteristics, information about the board and management from MSCI ESG Research and financial information from Compustat. The study collects data based on panel data, which ranges from 2014 to 2020. The final sample consists of 550 US companies over a five-year period and contains 3850 firm-year observations. The study finds a positive relationship between busy outside directors and ESG performance. Busy outside directors have a positive impact not only on the overall ESG score, but also on individual ESG components. The environmental score is most affected by busy external directors, while the governance score appears to be little affected. Contrary to the theory that busy outside directors are overly engaged and degrade the fixed value, the findings support the theory that busy outside directors improve a company's sustainability performance because of their engagement, experience and the ESG performance.

Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients’ lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia. These promising results open the door to a new era of early prognosis of Alzheimer’s Disease and Related Dementias (ADRD).

After the outbreak of COVID-19, Taiwan has implemented rigorous border control and taken specific measures such as virus detection, contact tracing, and quarantine since 2020. Its epidemic prevention performance has been quite outstanding. Even in May 2021, when the epidemic situation worsens, the people in Taiwan fully cooperate with the government’s control measures so as to successfully alleviate and control the epidemic in less than three months. Among them, the detection policy has played a pivotal role. We analyze and discuss the false positive and false negative problems from rapid antigen and PCR detection in the screening policy as well as the timing of using these two instruments. This paper provides theoretical verification of the appropriateness of screening policy in Taiwan, offering a few feasible suggestions for related policies in other countries or regions at different stages of this and other potential epidemics.

With increasing concerns and challenges to climate change in recent years, green operations management (GOM) has gained significant attention from society for achieving sustainable growth. GOM is a set of practices that can be applied in production processes to produce goods with improved productivity and significantly reduced threats of carbon emission to the environment and Mother Nature. GOM mainly includes green manufacturing, green design, green logistics, and green purchases. In the paper, fuzzy best-worst method (FBWM) is used to determine the best and worst criteria affected by GOM practices. Thus, the paper attempts to explicitly analyze and highlight the significance of GOM in preserving the environment and manage the triple bottom line for achieving overall sustainable business operations.