Producción Científica

Major sudden disasters, such as floods, earthquakes, and fires, often cause significant casualties. Emergency evacuation is crucial in mitigating these impacts. Different types of disaster incidents vary significantly in terms of impact scope, suddenness, and urgency. Each type of disaster possesses distinct characteristics, necessitating varying requirements for emergency evacuation. Consequently, we conducted a bibliometric analysis and visual mapping of evacuation processes in major natural disasters from 2004-2023, analyzing 7213 publications from the Web of Science database via VOSviewer and ArcGIS. Our study identified three developmental phases: an initial phase (pre-2011) with 1169 publications, a growth phase (2012-2018) with 2772 publications, and an expansion phase (post-2019) with 3335 publications. This study provides a comprehensive review and classification of emergency evacuation theories and methods in major disaster scenarios. It emphasizes the necessity of assessing the scope and intensity of different types of major emergent disasters, defining and simulating the affected behaviors of the influenced populations, and formulating differentiated emergency evacuation strategies accordingly. Keyword analysis reveals two main trends supporting these findings: an increasing focus on complex evacuation modeling and simulation techniques, manifested in the application of various simulation-optimized microscopic and macroscopic models such as cellular automata, social force models, agent-based models, pedestrian flow, and network flow models, enhancing disaster understanding and prediction capabilities; and the strategic development of tailored evacuation strategies for specific disaster contexts, thereby improving disaster response efficiency. Three key future pathways for safety evacuation research are outlined: refining evacuation behavior models for greater accuracy, improving the coordination of complex, multi-level evacuation procedures, and integrating indoor and outdoor evacuation strategies more seamlessly. It establishes a forward-looking framework for advancing safety evacuation studies in major emergencies.

The study explored rural livelihood sustainability in South Asia and Africa through a bibliometric analysis and systematic review approach. The purpose of the study was to identify the trend of scholarly discourse, the dominant themes that have attracted scholarly interest over the past decade, and to shed light on the emerging opportunities for enhancing rural livelihood sustainability. The study carefully selected 139 articles sourced from Scopus, Web of Science, Google Scholar, and other institutional websites. Our findings revealed noticeable growth in scholarly output over the past decade and identified some scholars with noteworthy contributions. Notably, it was found that the impact of climate change, drought, poverty, and food security on rural livelihoods has largely dominated the scholarly investigations over the period. However, the potentials of pyrolis, biochar, and dryland forestry for rural livelihood sustainability were found to be underexplored. In light of these insights, we recommend the scholarly community develop much interest in less explored areas that equally hold great potential for enhancing rural livelihoods. We also recommend that rural livelihood programming by policymakers must seek the integration of indigenous knowledge and be tailored to strengthen the resilience of rural households against the perils of climate change and weather variability.

Recent years have seen an increasing interest in 3D concrete printing (3DCP); however, its sustainability challenges, particularly regarding environmental concerns, require close attention. To mitigate sustainability-related problems of technology, new realm of research has emerged: sustainable 3DCP. However, this field currently lacks a classification framework for sustainability practices and approaches. To fill this gap, current study aims to classify strategies in 3DCP to enhance environmental sustainability and offer recommendations for further research. To achieve this goal, first, a bibliometric analysis, a computer-aided statistical method for reviewing the literature, is employed. This analysis will explore, measure, and map existing research to identify current trends and the state-of-the-art in the field. Subsequently, a thematic analysis of the keywords categorises prior sustainability strategies. Finally, benchmarking concrete technology offers some recommendations for mitigating sustainability concerns and assessing the benefits of applied strategies. The results show that research in the realm of sustainable 3DCP began in 2017 and has a significant growth rate of 1.5 times per year. Besides, strategies to improve the sustainability performance of 3DCP pursue four main targets: reducing carbon footprint and raw material consumption, increasing durability characteristics, and enhancing thermal performance.

This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the “black-box” nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges.