Despite many investigations concerning antecedents of organizational commitment in the workplace, very few studies so far have analyzed the direct or indirect impact of HR change leadership role on organizational commitment via HR attribution. Therefore, given the reciprocal principle of social exchange theory, attribution theory and signal theory, this study formulated hypotheses and a model to test the relationships between included variables by employing the mixed-method approach. In-depth interviews were initially conducted to develop questionnaires to collect quantitative data. Employing PLS-SEM to analyze the data collected from 1058 employees working in 24 sustainable enterprises in Vietnam, the findings show that the degree of adopting HR change leadership role was positive, directly affecting organizational commitment. Also, both well-being and performance HR attribution play partially mediated roles in the relationship. The findings suggest that the organizational commitment depends on not only how the degree of adopting HR change leadership role is executed, but also how employees perceive and interpret the underlying management intent of these practices. In a sustainable context, adopting HR change leadership role plays a critical role in shaping employees’ interpretations of sustainable HR practices and their subsequent attributions. Besides, employees’ belief on why are sustainable HRM practices implemented has an influence on the organizational commitment that in turn contributes to the overall sustainable performance.

Based on the resource-based view and institutional theory, this study investigates the impact of their environmental management capabilities and environmental, social, and governance (ESG) pressure on the non-financial performance of small and medium-sized enterprises (SMEs). In particular, it examines the interaction effect of ESG pressures on the relationship between SMEs’ environmental management capabilities and non-financial performance. For this study, a total of 1865 SME lists were obtained through Jeonnam Techno Park and Jeonnam Small Business Job and Economy Promotion Agency. Based on this, a total of 127 questionnaires were returned as a result of a telephone, e-mail, and online survey, and finally, an empirical analysis was conducted based on 120 questionnaires. We conducted an empirical analysis of Korean SMEs and obtained the following results: First, environmental management capabilities have a significant, positive effect on SMEs’ non-financial performance. Second, ESG pressure has a significant, negative effect on the non-financial performance of SMEs. Next, we analyzed the moderating effect of ESG pressures and observed that ESG pressures strengthen the positive effect of environmental management capabilities on non-financial performance. Based on the resource-based perspective and institutional theory, this study provides meaningful academic implications by examining environmental management capabilities and ESG pressures, which have not been identified in previous studies, as factors of non-financial performance that are becoming important under the new management paradigm, such as climate change and ESG. Furthermore, while ESG pressure has a significant negative effect on non-financial performance, we find that it is a moderating variable that strengthens the relationship between SMEs’ environmental management capabilities and non-financial performance, which has useful academic and practical implications for ESG and strategic management.

The purpose of this study is to examine how financial slack and board gender diversity affect carbon emission disclosure and how that disclosure affects firm value in energy sector companies that are listed on the Indonesian stock exchange between 2017 and 2021. Annual reports and sustainability sources provide secondary data for this quantitative study. Purposive sampling was employed in this investigation, including nine companies and a five-year observation period. Thus, 45 samples altogether were employed in the present study. The partial least squares approach is the data analysis strategy used in this investigation. The study’s findings indicate that the Gender Diversity Board does not significantly affect carbon emission disclosure and significantly influences firm value. Financial slack significantly affects carbon emission disclosure but does not directly affect firm value. Financial slack and board gender diversity through carbon emission disclosure have no significant effect on firm value.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

Electrospinning nanofiber membrane has the advantages of wide raw materials, large specific surface area, and high porosity. It is an ideal separator material for lithium-ion batteries. This paper first introduces two common electrospinning nanofiber diaphragms: polymer, polymer, and inorganic composite, and then focuses on the modification methods of composite modification, blending modification, and inorganic modification, as well as the methods of electrospinning nano modified polyolefin diaphragm. Finally, the development direction of the electrospinning lithium-ion battery separator has prospected.

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.