Abstract
A significant number of dependable systems rely on scheduling algorithms to achieve temporal correctness. Despite their relevance in real-world applications, only a narrow subset of the works in the literature of real-time systems are readily available to be reproduced in real-world hardware platforms. This lack of support not only hinders the reproducibility of research results, but also reduces the opportunity for new platform-specific research directions to emerge. In this work we discuss the use and development of an open-source tool named MARS capable of porting various scheduling tests and algorithms to hardware platforms used in distributed real-time dependable systems.

Abstract
Due to the exponential increase in energy consumption and CO2 emissions, new sustainable energy sources have emerged, and hydrogen (H2) is one of them. Despite all the advantages, H2 has high flammability, so constant monitoring is essential. Two optical techniques were numerically studied and compared with the goal of H2 sensing: surface plasmon polaritons (SPP) and Tamm plasmon polaritons (TPP). The H2-sensitive material used was palladium (Pd) in both techniques. The SPP structure was found to have more sensitivity to H2 than TPP, 23 and 5nm/4vol% H2, respectively. However, the latter has lower FWHM, with the minimum of the band showing reflectivity near 0%. In addition, TPP also uses more cost-effective materials and can be interrogated at normal incidence with depolarized light. The potential of using each of these optical techniques for H2 sensing was demonstrated. © The Authors.

Abstract
This paper introduces the Large Events Model (LEM) for soccer, a novel deep learning framework for generating and analyzing soccer matches. The framework can simulate games from a given game state, with its primary output being the ensuing probabilities and events from multiple simulations. These can provide insights into match dynamics and underlying mechanisms. We discuss the framework's design, features, and methodologies, including model optimization, data processing, and evaluation techniques. The models within this framework are developed to predict specific aspects of soccer events, such as event type, success likelihood, and further details. In an applied context, we showcase the estimation of xP+, a metric estimating a player's contribution to the team's points earned. This work ultimately enhances the field of sports event prediction and practical applications and emphasizes the potential for this kind of method.

Abstract
Following the lack of existing studies about the applications development and use in the event industry, this research aims to analyze the use of applications as a tool to improve event communication. The methodology used includes an exploratory method. This research methodology is based on a review of the literature, a semi-structured interview with the CEO of the company and a case study with participant observation of the French start-up Invent App which provides event applications. The study demonstrate that the event app use allows to ease the event organization, to improve the event communication but also the interactivity and the participant engagement. All of this, increasing the customer experience but also having a part in the customer digital experience improvement. Event applications could be thus considered as amarketing and event communication tool. The research has the limitation to its application within a single company in France. Furthermore, the point of viewadopted is the one of the event organizers and not the event participant one. This research helped to better understand the role that event applications can have in the event industry and particularly in the communication around an event. In addition, this made it possible to understand the perception of market requirements and the characteristics expected of an event application.

Abstract
Diabetes, a chronic condition affecting millions of people, requires ongoing medical care and treatment, which can place a significant financial burden on society, directly and indirectly. In this paper we propose a vision-robotics system for the automatic assessment of the diabetic foot, one the exams used for the disease management. We present and discuss various computer vision techniques that can support the core operation of the system. U-Net and Segnet, two popular convolutional network architectures for image segmentation are applied in the current case. Hardcoded and machine learning pipelines are explained and compared using different metrics and scenarios. The obtained results show the advantages of the machine learning approach but also point to the importance of hard coded rules, especially when well know areas, such as the human foot, are the systems' target. Overall, the system achieved very good results, paving the way to a fully automated clinical system.

Abstract