The significant climate change the planet has faced in recent decades has prompted global leaders, policymakers, business leaders, environmentalists, academics, and scientists from around the world to unite their efforts since 1987 around sustainable development. This development not only promotes economic sustainability but also environmental, social, and corporate sustainability, where clean production, responsible consumption, and sustainable infrastructures prevail. In this context, the present article aims to propose a development framework for sustainability in food sector SMEs, which includes Life Cycle Assessment (LCA) and the integration of Environmental, Social, and Governance (ESG) strategies as key elements to reduce CO₂ emissions and improve operational efficiency. The methodology includes a comparative analysis of strategies implemented between 2019 and 2023, supported by quantitative data showing a 20% reduction in operating costs, a 10% increase in market share, and a 25% increase in productivity for companies that adopted clean technologies. This study offers a significant contribution to the field of corporate sustainability, providing a model that is adaptable and applicable across different regions, enhancing innovation and business resilience in a global context that requires collective efforts to achieve the sustainable development goals.

Environmental regulation is globally recognized for its crucial role in mitigating environmental pollution and is vital for achieving the Paris Agreement and the United Nations Sustainable Development Goals. There is a current gap in the comprehensive overview of the significance of environmental regulation research, necessitating high-level insights. This paper aims to bridge this gap through an exhaustive bibliometric review of existing environmental regulation research. Employing bibliometric analysis, this study delineates publication trends, identifies leading journals, countries, institutions, and scholars. Utilizing VOSviewer software, we conducted a frequency and centrality analysis of keywords and visualized keyword co-occurrences. This research highlights current hotspots and central themes in the field, including “innovation”, “performance”, “economic growth”, and “pollution”. Further analysis of research trends underscores existing knowledge gaps and potential future research directions. Emerging topics for future investigation in environmental regulation include “financial constraints”, “green finance”, “green credit”, “ESG”, “circular economy”, “labor market”, “political uncertainty”, “digital transformation”, “exports” and “mediating effects”. Additionally, “quasi-natural experiments” and “machine learning” have emerged as cutting-edge research methodologies in this domain. The focus of research is shifting from analyzing the impact of environmental regulation on “innovation” to “green innovation” and from “emissions” to “carbon emissions”. This study provides a comprehensive and structured understanding, thereby guiding subsequent research in this field.

In developing countries, urban mobility is a significant challenge due to convergence of population growth and the economic attraction of urban centers. This convergence of factors has resulted in an increase in the demand for transport services, affecting existing infrastructure and requiring the development of sustainable mobility solutions. In order to tackle this challenge, it is necessary to create optimal services that promote sustainable urban mobility. The main objective of this research is to develop and validate a comprehensive methodology framework for assessing and selecting the most sustainable and environmentally responsible urban mobility services for decision makers in developing countries. By integrating fuzzy multi-criteria decision-making techniques, the study aims to address the inherent complexity and uncertainty of urban mobility planning and provide a robust tool for optimizing transportation solutions for rapid urbanization. The proposed methodology combines three-dimensional fuzzy methods of type-1, including AHP, TOPSIS and PROMETHEE, using the Borda method to adapt subjectivity, uncertainty, and incomplete judgments. The results show the advantages of using integrated methods in the sustainable selection of urban mobility systems. A sensitivity analysis is also performed to validate the robustness of the model and to provide insights into the reliability and stability of the evaluation model. This study contributes to inform decision-making, improves policies and urban mobility infrastructure, promotes sustainable decisions, and meets the specific needs of developing countries.

The sense of belonging in any organization is vital to generate a work motivation with the objective of a good organizational performance, because of this, companies usually take this point into account, ensuring that this leads to greater performance. For this reason, the objective of this article is to determine the relationship between the sense of belonging and the work motivation in the workers of a small Peruvian research company. For this purpose, a quantitative methodology was used, with a cross-sectional descriptive design. The instrument used was a survey consisting of 10 items, which were interpreted using the Likert scale. The survey was conducted and delivered to 24 workers, who were selected by non-probabilistic convenience sampling. After verifying the validity of the instrument and the study variables by means of Cronbach's Alpha statistic, we proceeded to determine the existence of correlation between the variables, which, using Spearman's Rho coefficient, obtained a 70.2% which demonstrates a moderate positive correlation, therefore it indicates that employees feel highly motivated as they feel an indispensable part of the company, therefore they feel job satisfaction by being part of the organization.

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.

Recently, carbon nanocomposites have garnered a lot of curiosity because of their distinctive characteristics and extensive variety of possible possibilities. Among all of these applications, the development of sensors with electrochemical properties based on carbon nanocomposites for use in biomedicine has shown as an area with potential. These sensors are suitable for an assortment of biomedical applications, such as prescribing medications, disease diagnostics, and biomarker detection. They have many benefits, including outstanding sensitivity, selectivity, and low limitations on detection. This comprehensive review aims to provide an in-depth analysis of the recent advancements in carbon nanocomposites-based electrochemical sensors for biomedical applications. The different types of carbon nanomaterials used in sensor fabrication, their synthesis methods, and the functionalization techniques employed to enhance their sensing properties have been discussed. Furthermore, we enumerate the numerous biological and biomedical uses of electrochemical sensors based on carbon nanocomposites, among them their employment in illness diagnosis, physiological parameter monitoring, and biomolecule detection. The challenges and prospects of these sensors in biomedical applications are also discussed. Overall, this review highlights the tremendous potential of carbon nanomaterial-based electrochemical sensors in revolutionizing biomedical research and clinical diagnostics.