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.

Abstract
In the field of intelligent autonomous robots, integrating optimization techniques with classical control theory methods for mobile robot control is an increasingly prominent area of research. The combination enhances robots' ability to perform their tasks more efficiently, reliably, and safely. This paper addresses the development of a path and motion planning framework for omnidirectional robots, leveraging B-Splines and Trajectory Tracking with Model Predictive Control. The proposed framework is evaluated through software-in-the-loop tests using two distinct dynamical models and sets of hyperparameters. Final validation is conducted by implementing the framework within a ROS environment and performing field tests on a robotic platform. The results demonstrate that the robot can reliably track trajectories at its actuation limits, and the proposed framework enables the robot to increase its velocity up to 50% when compared to a PID path-following controller.

Abstract
Digital transformation has been taking place for several decades in different sectors of activity and is contributing significantly to mitigating the environmental impacts of those sectors. Various digital solutions are related to energy consumption and production, which is crucial to ensure continuous decarbonisation. Most of them are targeted to be used by general consumers. Therefore, it is essential to consider consumers' attitudes towards those solutions and their adoption behaviour to ensure a broad diffusion of them. This study uses the Technology Acceptance Model to understand the adoption of energy-related digital solutions in Europe. We conclude that the perceived usefulness of the solutions is more relevant in attitude formation than the perceived ease of use. Moreover, attitude highly influences adoption behaviour, as reported in the literature. Finally, these relations seem to be highly influenced by the belief that, by adopting digital solutions, consumers contribute to a better balance between energy supply and demand.