This paper examines the influence of green accounting and environmental performance on stock prices, focusing on Indonesia’s mining sector. It aims to understand whether these factors, along with profitability, impact the growth of stock prices. The study is grounded in stakeholder, legitimacy, and signal theories, emphasizing the role of stakeholder support and environmental responsibility in company survival. The research explores the conflicting results of previous studies on the impact of green accounting on stock prices. It uses various indicators, such as environmental costs for green accounting and the PROPER rating system, to measure environmental performance. The study also considers profitability as a moderating variable. The population in this research is all mining companies listed on the Indonesia Stock Exchange in 2017–2021. The sample was selected based on purposive sampling with several criteria. Multiple regression analysis and hypothesis testing were used to analyze the data. Key findings suggest that green accounting positively influences stock prices, while environmental performance has a negative effect. Profitability positively affects stock prices but does not significantly moderate the impact of green accounting on stock prices. However, it does enhance the relationship between environmental performance and stock prices. The study concludes that companies should increase disclosures related to green accounting and environmental performance, which are crucial for long-term investment considerations.

In the rapidly expanding Chinese high-tech industry, high employee turnover poses a significant challenge. This study employs a mixed-methods approach to explore the association between transformational leadership and turnover intentions, utilizing both survey responses and detailed interviews. Findings from this investigation demonstrate a strong negative correlation between transformational leadership and turnover intentions. Increased job satisfaction and organizational commitment, crucial factors for employee retention, mediate this relationship. The study underscores the strategic significance for high-tech enterprises in China to nurture transformational leadership as a means to mitigate turnover, thereby fostering a more engaged and dedicated workforce, and sustaining a competitive advantage in this dynamic industry.

The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.

We present an innovative enthalpy method for determining the thermal properties of phase change materials (PCM). The enthalpy-temperature relation in the “mushy” zone is modelled by means of a fifth order Obreshkov polynomial with continuous first and second order derivatives at the zone boundaries. The partial differential equation (PDE) for the conduction of heat is rewritten so that the enthalpy variable is not explicitly present, rendering the equation nonlinear. The thermal conductivity of the PCM is assumed to be temperature dependent and is modelled by a fifth order Obreshkov polynomial as well. The method has been applied to lauric acid, a standard prototype. The latent heat and the conductivity coefficient, being the model parameters, were retrieved by fitting the measurements obtained through a simple experimental procedure. Therefore, our proposal may be profitably used for the study of materials intended for heat-storage applications.