Aida Haghighi

Research organizations and academics often seek to map the development of scientific fields, identify research gaps, and guide the direction of future research. In cobot-related research, the scientific literature consulted does not propose any comprehensive research agenda. Moreover, cobots, industrial robots inherently designed to collaborate with humans, bring with them emerging issues. To solve them, interdisciplinary research is often essential (e.g., combination of engineering, ergonomics and biomechanics expertise to handle safety challenges). This paper proposes an exhaustive study that employs a scoping review and bibliometric analysis to provide a structured macro perspective on the developments, key topics, and trends in cobot research for industry. A total of 2,195 scientific publications were gained from the Web of Science database, and a thorough selection process narrowed them down to 532 papers for comprehensive analysis. Descriptive statistics were employed to analyze bibliometric measures, highlighting publication trends, leading journals, the most productive institutions, engaged countries, influential authors, and prominent research topics. Co-authorship and bibliographic couplings were also examined. Through a co-occurrence analysis of terms, the content and research objectives of the papers were systematically reviewed and lead to a univocal categorization framework. That categorization can support organizations or researchers in different cobotics (collaborative robotics) fields by understanding research developments and trends, identifying collaboration opportunities, selecting suitable publication venues, advancing the theoretical and experimental understanding of automatic collaborative systems, and identifying research directions and predicting the evolution of publication quantity in cobotics.

Price prediction tools play a significant role in small investors’ behavior. As such, this study aims to propose a method to more effectively predict stock prices in North America. Chiefly, the study addresses crucial questions related to the relevance of news and tweets in stock-price prediction and highlights the potential value of considering such parameters in algorithmic trading strategies—particularly during times of market panic. To this end, we develop innovative multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to investigate the influence of Twitter count (TC), and news count (NC) variables on stock-price prediction under both normal and market-panic conditions. To capture the impact of these variables, we integrate technical variables with TC and NC and evaluate the prediction accuracy across different model types. We use Bloomberg Twitter count and news publication count variables in North American stock-price prediction and integrate them into MLP and LSTM neural networks to evaluate their impact during the market pandemic. The results showcase improved prediction accuracy, promising significant benefits for traders and investors. This strategic integration reflects a nuanced understanding of the market sentiment derived from public opinion on platforms like Twitter.

Women play integral roles across various sectors, including mining. Moreover, they often form a majority in certain sectors, such as healthcare and education. Biological (sex) and social (gender) differences can influence how hazards are assessed and controlled for women at work. Therefore, because of the importance of women’s occupational health and safety (OHS), this study reviews and analyzes OHS-related research studies to explore (i) the attention given to women’s OHS; (ii) the specific occupations studied; and (iii) the primary OHS issues and challenges faced by women. Following PRISMA guidelines, the study examined articles from 2010–2021, selecting 62 that utilized primary data, with all or part of their participants being female. The results indicate that the included studies examined women’s OHS in specific occupations. These include healthcare workers, farm and forestry workers, office staff, teachers, firefighters, police officers, nail technicians, workers in the clothing industry, and general industrial workers. The trend of publishing articles on women’s OHS has been growing, with most studies focusing on healthcare and agriculture. The USA and South Korea are leading in publications in the field of women’s OHS, while the USA, Australia, and the Netherlands have the highest collaboration rates. Key findings reveal that the most common OHS issues faced by women in various occupations include stress, fatigue, musculoskeletal disorders and pain, sleep disorders, long working hours, depression and anxiety, workplace violence, and allergies and skin problems. Many of these issues are related to mental health. Specific issues based on the nature of the work vary; for example, teachers experience voice disorders, while farmers face digestive problems. This study contributes theoretically by enhancing understanding of women’s OHS, serving as a foundation for further research, and providing practical guidance for employers and policymakers seeking to implement effective strategies for guaranteeing women’s OHS across sectors.