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Rural credit policies have a strong impact on food production and food security. The attribution of credit policies to agricultural production is one of the main problems preventing the guarantee of agricultural expansion. In this work, we conduct family typology analysis applied to a set of research data to characterize different regions. Through genetic programming, a model was developed using user-defined terms to identify the importance and priority of each criterion used for each region. Access to credit results in economic growth and provides greater income for family farmers, as observed by the results obtained in the model for the Sul region. The Nordeste region indicates that the cost criterion is relevant, and according to previous studies, the Nordeste region has the highest number of family farming households and is also the region with the lowest economic growth. An important aspect discovered by this research is that the allocation of rural credit is not ideal. Another important aspect of the research is the challenge of capturing the degree of diversity across different regions, and the typology is limited in its ability to accurately represent all variations. Therefore, it was possible to characterize how credit is distributed across the country and the main factors that can influence access to credit. [ABSTRACT FROM AUTHOR]

The growing issue of demand-supply management between the prosumers and the local energy market requires an efficient and reliable energy management model. The microlayers, such as prosumers, energy districts, and macro players, namely retail dealers and wholesale dealers play a pivotal role in achieving mutual benefits. The stochastic nature of renewable energy generation in energy districts requires an effective model that can contemplate all stochastic complexities. Therefore, this paper proposes a mutual trade model between energy districts and smart grid to authorize the prosumers for mutual energy transactions under the stochastic adaptive-service level agreement. Moreover, multiple smart contacts are developed between the stakeholders to design adaptability and stochastic behavior of wind speed and solar irradiance. The real-time adaptations of the stochastic adaptive-service level agreement are based on technical beneficial feasibility and achieved through stochastic and adaptive functions. The optimized solution based on a genetic algorithm is proposed for the energy cost and energy surplus of prosumers and output parameters of the mutual trade model (grid revenue). In the context of mutual benefits associated with balanced demand and supply, the economic load dispatch and simplex method maximization are used for optimized demand-supply energy management. Moreover, the effectiveness of the proposed adaptive and stochastic mutual trade model is validated through simulation and statistical analysis. [ABSTRACT FROM AUTHOR]

Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure. [ABSTRACT FROM AUTHOR]

This paper provides an overview of multi-criteria decision analysis (MCDA) applications in managing water-related disasters (WRD). Although MCDA has been widely used in managing natural disasters, it appears that no literature review has been conducted on the applications of MCDA in the disaster management phases of mitigation, preparedness, response, and recovery. Therefore, this paper fills this gap by providing a bibliometric analysis of MCDA applications in managing flood and drought events. Out of 818 articles retrieved from scientific databases, 149 articles were shortlisted and analyzed using a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) approach. The results show a significant growth in MCDA applications in the last five years, especially in managing flood events. Most articles focused on the mitigation phase of DMP, while other phases of preparedness, response, and recovery remained understudied. The analytical hierarchy process (AHP) was the most common MCDA technique used, followed by mixed-method techniques and TOPSIS. The article concludes the discussion by identifying a number of opportunities for future research in the use of MCDA for managing water-related disasters. [ABSTRACT FROM AUTHOR]