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With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

This study draws on the affective events theory (AET) to understand how telework may influence workers' well-being. Hence this study aimed to (1) analyze the indirect relationship between telework and well-being via daily micro-events (DME), and (2) test whether procrastination would moderate this indirect effect.

To test the goals, data were gathered from a sample of teleworkers in the IT sector (N = 232). To analyze the data, a moderated mediation analysis was performed in SPSS with PROCESS macro.

The results showed that micro-daily events mediated the positive relationship between telework and well-being; however, this relation was conditional upon the levels of workers' levels of procrastination, that is, this link became weaker for those who were procrastinators.

By highlighting the importance of telework, DME and procrastination, this study offers managers distinct strategies for enhancing their employees' well-being.

Despite the existing research investigating the effect of telework on well-being, studies investigating the intervening mechanisms between these two constructs are scarce. Moreover, there is a lack of research investigating the moderating effect of procrastination in these relations. Hence, this study fills these gaps and advances knowledge on the process that explains how (via DME) and when (when procrastination is low) teleworking influences workers' well-being.

While the literature has highlighted the impacts of COVID-19, there is limited evidence on the gendered determinants of the impact of COVID-19 among small-scale rural traders in developing and emerging economies.

Cross-border fish traders who had operated before and during the COVID-19 pandemic were interviewed in a survey conducted in Zambia and Malawi. Logistic regressions among male and female traders were employed to assess the gendered predictors.

Heterogeneous effects in geographical location, skills, and knowledge were reported among male cross-border traders. Effects of household structure and composition significantly influenced the impact of COVID-19 among female traders. Surprisingly, membership in trade associations was associated with the high impact of COVID-19.

Due to the COVID-19 pandemic and the migratory nature of cross-border fish traders, the population of cross-border fish traders at the time of the study was unknown and difficult to establish, cross-border fish traders (CBFT) at the landing sites and market areas were targeted for the survey without bias.

This paper addresses a gap in the literature on understanding gendered predictors of the impacts of COVID-19 among small-scale cross-border traders.