Abstract
Small enterprises (SEs) represent the majority of the businesses worldwide, playing a leading role in job creation and economic development. The success of these firms substantially depends on the financial knowledge of their owners/managers. Previous literature in the field of household finances has indicated that financial literacy declines as individual ages. However, the scarce literature on entrepreneurs' financial literacy has not addressed this issue. Using a sample of 896 SE owners/managers, drawn from the survey of small enterprises' financial literacy in Spain, the authors observe a decline in objective financial knowledge with age through multivariate analyses using probit and ordered probit models. The lack of financial knowledge may put at risk the economic feasibility of an SE. Therefore, it is essential to design financial education mechanisms that are sensitive to the needs of SE owners/managers at different stages of their working lives. © 2024 by IGI Global. All rights reserved.

Abstract
While a significant number of databases are deployed in cloud environments, pushing part or all data storage and querying planes closer to their sources (i.e., to the edge) can provide advantages in latency, connectivity, privacy, energy, and scalability. This article dissects the advantages provided by databases in edge and fog environments by surveying application domains and discussing the key drivers for pushing database systems to the edge. At the same time, it also identifies the main challenges faced by developers in this new environment and analyzes the mechanisms employed to deal with them. By providing an overview of the current state of edge and fog databases, this survey provides valuable insights into future research directions.

Abstract
Food waste is responsible for severe environmental, social, and economic issues and therefore it is imperative to prevent or at least minimize its generation. The main cause of food waste is poor demand forecasting and so it is essential to improve the accuracy of the tools tasked with these forecasts. The present work proposes four models meant to help food catering services predict food demand accurately and thus avoid overproducing or underproducing. Each model is based on a different machine learning technique. Two baseline models are also proposed to mimic how food catering services estimate future demand and to infer the added value of employing machine learning in this context. To verify the impact of the proposed models, they were tested on data from the three different canteens chosen as case studies. The results show that the models based on the random forest algorithm and the long short-term memory neural network produced the best forecasts, which would lead to a 14% to 52% reduction in the number of wasted meals. Furthermore, by basing their decisions on these forecasts, the food catering services would be able to reduce unmet demand by 3% to 16% when compared with the forecasts of the baseline models. Thus, employing machine learning to forecast future demand can be very beneficial to food catering services. These forecasts can increase the service level of food services and reduce food waste, mitigating its environmental, social, and economic consequences.

Abstract
This article seeks to present an initiative to integrate a serious game into an entrepreneurship course, attended by tourism students, which enables them to have a more reliable and comprehensive experience of the multiple dimensions of this phenomenon. The study uses a mixed-methods approach to explore several dimensions of the impact on the use of the game by measuring student performance and conducting semi-structured interviews. The findings indicate that FLIGBY has helped the tourism students to have a more complete and reliable perception of the business reality and to practice their skills in a wide range of areas such as emotional intelligence, conflict management, time management, strategic thinking, or leadership. The results also indicate the development of analytical skills in the area of business management and viniculture due to the central theme of FLIGBY.

Abstract

Abstract
Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.