The 19th century proved to be one of the most complicated periods in Spanish history for the Spanish Crown, as it faced both internal conflicts—the French War of Independence—and external conflict—the independence of what were its territories in most of America. France did not remain indifferent to this and always had a clear idea of where to draw the boundaries of what “belonged” to it. Thus, amid the wave of independence movements in the Spanish colonies, the French continued to produce rich cartography to establish these boundaries and settle their power over the new nations that were arising after the period of revolutions. The cartography of Rigobert Bonne, the last cartographer of the French king and the Revolution Era, and one of its disciples, Eustache Hérisson, represent the perfect witness to the changes over the borders of the Spanish colonies during the change of the century. This study aims to analyze such cartography, examine the rich toponyms it offers, and examine the changes in the boundaries created over time between both empires. The main cartography we will rely on will be that of Bonne, one of the most important cartographers of the 18th century, and his disciple Hérisson, a geographer engineer, who lived through the onset of the conflicts and always prioritized the French perspective and the interests of their nation.

Plant growth-promoting rhizobacteria (PGPR) offer eco-friendly alternatives to chemical fertilizers, promoting sustainable agriculture by enhancing soil fertility, reducing pathogens, and aiding in stress resistance. In agriculture, they play a crucial role in plant growth promotion through the production of agroactive compounds and extracellular enzymes to promote plant health and protection against phytopathogens. In the rhizosphere, diverse microbial interactions, including those with bacteria and fungi, influence plant health by production of antimicrobial compounds. The antagonism displayed by rhizobacteria plays a crucial role in shaping microbial communities and has potential applications in developing a natural and environmentally friendly approach to pest control. The rhizospheric microbes showcase their ecological importance and potential for biotechnological applications in the context of plant-microbe interactions. The extracellular enzymes produced by rhizospheric microbes like amylases, chitinases, glucanases, cellulases, proteases, and ACC deaminase contribute to plant processes and stress response emphasizing their importance in sustainable agriculture. Moreover, this review highlights the new paradigm including artificial intelligence (AI) in sustainable horticulture and agriculture as a harmonious interaction between ecological networks for promoting soil health and microbial diversity that leads to a more robust and self-regulating agricultural system for protecting the environment in the future. Overall, this review emphasizes microbial interactions and the role of rhizospheric microbial extracellular enzymes which is crucial for developing eco-friendly approaches to enhance crop production and soil health.

Through Qualitative Comparative Analysis (QCA) on destination attractiveness characteristics at the country level, this study identifies attribute configurations in the pre- and post-pandemic period to analyze the changes and differences generated by an exogenous event (COVID-19). The results suggest that the destination attractiveness attributes work together, in multidimensional configurations, to increase leisure travel volume. We found an important change in pat-terns/configurations of attractiveness between the pre- and post-pandemic scenarios. Our findings suggest that the destination attributes may change in importance and valuation or disappear for some configurations. The conclusion has implications for the stakeholders related to the destination attractiveness development, showing possible patterns of tourism attributes to guide the action to improve the resilience in the tourism sector and recover these activities in a disaster scenario.

The potential of entrepreneurship to reduce poverty is closely tied to critical factors such as access to finance, training and education, networks and social capital, and supportive regulatory environments. Understanding and addressing these underlying issues through the lens of the Social Capital theory can help foster an entrepreneurial spirit in cities and mitigate poverty through business and community development. This paper explores the insights and standpoints of key stakeholders about poverty in Saint John and its impact on entrepreneurship. The study uses a quantitative method and analyzes data from surveys with stakeholders. The results show that social isolation, system inflexibility, individual issues, housing, and financial support programs are significant poverty challenges in Saint John, and these issues have implications for entrepreneurship. By integrating Social Capital Theory into policy initiatives, policymakers can enhance community resilience and empower vulnerable individuals. This application of social capital principles provides a holistic framework for designing effective poverty-reduction measures, offering transformative insights applicable not only to Saint John but also to diverse small cities. The study contributes a nuanced understanding of poverty’s impact on entrepreneurship, advocating for inclusive strategies that resonate with the social fabric of communities.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.