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The recovery of a built heritage and specifically of singular buildings is a key aspect of local development. The aim of this study was to understand the influence of these regenerations on their environment by transforming adjacent businesses and initiating parallel processes of gentrification and local development. The renewed attraction of these new businesses to the area can result in increased employment and production. The methodology used was based on self-organizing maps of neural networks with matrix architecture and competitive learning. Through the analysis of neural networks, we were able to identify common relationships and behaviors in commercial properties which are adjacent to singular buildings and that share common patterns and characteristics or attributes. The singular buildings analyzed are located along the Spanish Mediterranean coast in the cities of Almería, Barcelona, and Valencia. The results obtained were based on the following hypotheses: occupancy model and the classification based on total occupancy, total variation in occupancy, and the most common types of usage of a given ground floor commercial property. Among the conclusions, we highlight the existence of commercial premises that display anti-cyclical economic behavior and the presence of commercial premises considered to be "unfortunate" or with low potential. [ABSTRACT FROM AUTHOR]

Technification in agriculture has resulted in the inclusion of more efficient companies that have evolved into a more complex sector focused on production and quality. Artificial intelligence, one of the relevant areas of technology, is transforming the agriculture sector by reducing the consumption and use of resources. This research uses a bibliometric methodology and a fractional counting method of clustering to analyze the scientific literature on the topic, reviewing 2629 related documents recorded on the Web of Science and Scopus databases. The study found significant results regarding the most relevant and prolific authors (Hoogenboom), supporting research organizations (National Natural Science Foundation of China) and countries (U.S., China, India, or Iran). The identification of leaders in this field gives researchers new possibilities for new lines of research based on previous studies. An in-depth examination of authors' keywords identified different clusters and trends linking Artificial Intelligence and green economy, sustainable development, climate change, and the environment. [ABSTRACT FROM AUTHOR]

This paper has reviewed the international research on the interactions between the Economy, Energy, and Environment (3E) in the 21st century. For this purpose, a bibliometric and cluster analysis by fractional accounting has been carried out based on the two most important databases: Web of Science (WoS) and Scopus. The research found and studied 2230 documents from the WoS Core Collection and 3,149 from Scopus. The results show a continuous increase in the number of articles that were published and citations during the whole period. They also showed that China and the United States (U.S.) were the most productive countries and there was a predominance of Asian organizations supporting and fostering researches. The main contribution of this article is the analysis of keywords from 2001 to 2018. The trends show that the main common elements are sustainable development and sustainability and they also include CO2 emissions and consumption. Future research in this field should address the energy transition issue in the area of sustainable development by adapting it to the restrictions of this economic model. [ABSTRACT FROM AUTHOR]