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International audience; A smart grid is an electrical network enabling a two-way flow of both data and electricity. During electrical power generation in the Smart Grid, a vast amount of electricity is lost. Mainly in transmission and distribution lines, where 17% of electricity is lost in transmission lines, and 50% is lost in distribution lines. This is called Transmission and Distribution Losses (T&D), and it describes when the amount of electricity generated is greater than the amount of electricity delivered to end-users. There are two types of T&D losses. First are Technical Losses (TL), which are ordinarily 22.5%, and directly depend on the network characteristics and mode of operation. Second is Non-technical Losses (NTL) at 16.6% and related to meter reading, defective meter and error in meter reading, billing of customer energy consumption, lack of administration, financial constraints, and estimating the unmetered supply of energy as well as energy thefts. Previously proposed solutions for detecting TL and NTL depend on monitoring user behavior, analysis, and feature extraction. In contrast, new proposed solutions for detecting TL and NTL made use of Machine Learning (ML) Algorithms and Synthesis data, differing from each other in applying various ML approaches (Supervised, Unsupervised, Semi-Supervised) and diverse Data processing techniques. As the rising need to detect TL and NTL in the Smart Grid, the industry is not equipped with an applicable solution to overcome the problem. The overall deliverables of this study were: 1. multi-layers system model, 2. Machine learning model, 3. new methodology. In principle, the model uses a new method to detect TL and NTL and Real-time data from Smart Grid.

International audience; Energy losses has increased over the years and are expected to continue to rise. Hence, energy optimization became an important aspect in today's economy. Residential buildings' energy consumption account for almost half of the electricity consumption. Consequently, studying the behaviors of consumers can lead to minimize such losses. One of the main reasons of the over-consuming of energy in homes is the inconsiderate behavior of consumers. Such behaviors include forgetting to switch off lights, turn off air conditioners, TVs, and other electronics when not in use. Therefore, in this research, we propose a system model approach that studies the surrounding environment and analyze household behavior. This model studies users' preferences and actions in order to predict and generate the installed preferences with the aim of optimizing energy consumption.

Usability is vital to every functioning system, especially when the functioning system is intertwined with people's safety and needs. Radiological diagnosis is a critical tool for identifying diseases and tumors. Therefore, its usefulness is beyond questionable despite the risk of radiation. In general, Medical devices follow a trend, just like consumer electronic products, where they are becoming more complicated and performance is doubling every two years. Consequently, medical devices require a multidisciplinary team for design and development. Therefore, with so many commands and systems to deal with, users' cognitive loads are challenged to effectively execute commands. In fact, Current radiology practices face extreme pressure on available resources with demands of multi-dimensional requirements. Technicians are at the center of a constant drive for optimal productivity and optimization with the minimal possible resources. In medical devices, the cognitive workload will play more of a role because it uses advanced technology, is extremely expensive and takes a great deal of specialization to operate. Thus, effective and economical methods are required to evaluate user experience. Heuristic evaluation is an effective method to identify major usability problems and highlight issues faced by users. Heuristic evaluation was adapted to identify the usability of a CT scan on Saudi Arabian users and to identify if operating the CT scan can lead to physical and mental load effort. Subsequently, an in-depth investigation was conducted to identify major contributing factors impacting operational usability of the CT scan, La convivialité est vitale pour chaque système fonctionnel, en particulier lorsque le système fonctionnel est étroitement lié à la sécurité et aux besoins des personnes. Le diagnostic radiologique est un outil essentiel pour identifier les maladies et les tumeurs. Par conséquent, son utilité est incontestable malgré le risque de rayonnement. En général, les appareils médicaux suivent une tendance, tout comme les produits électroniques grand public, où ils deviennent plus compliqués et les performances doublent tous les deux ans. Par conséquent, les dispositifs médicaux nécessitent une équipe multidisciplinaire pour la conception et le développement. Par conséquent, avec autant de commandes et de systèmes à gérer, les charges cognitives des utilisateurs sont mises au défi d'exécuter efficacement les commandes. En fait, les pratiques actuelles de radiologie sont confrontées à une pression extrême sur les ressources disponibles et aux exigences multidimensionnelles. Les techniciens sont au centre d'un effort constant pour une productivité et une optimisation optimales avec un minimum de ressources possibles. Dans les dispositifs médicaux, la charge de travail cognitive jouera davantage un rôle car elle utilise une technologie de pointe, est extrêmement coûteuse et nécessite une grande spécialisation pour fonctionner. Ainsi, des méthodes efficaces et économiques sont nécessaires pour évaluer l'expérience utilisateur. L'évaluation heuristique est une méthode efficace pour identifier les principaux problèmes de convivialité et mettre en évidence les problèmes rencontrés par les utilisateurs. L'évaluation heuristique a été adaptée pour identifier l'utilité d'une tomodensitométrie sur les utilisateurs saoudiens et pour déterminer si l'utilisation de la tomodensitométrie peut entraîner un effort physique et mental. Par la suite, une enquête approfondie a été menée pour identifier les principaux facteurs contribuant à l'utilisation opérationnelle du scanner

International audience; Current radiology practices face extreme pressure on available resources with demands of multi-dimensional requirements. Technicians are at the center of a constant drive for optimal productivity and optimization with the minimal possible resources. This paper evaluates the invisible physical and mental exertions resulting from operating computed tomography (CT) scans by fifty-seven technicians surveyed following current radiology practices. Demographic characteristics were reviewed to evaluate differences across the study variables based on gender, level of education, years of experience, and working sector.

International audience; Product usability has been subjected to many evaluation techniques. Many evaluation techniques use heuristics approach to evaluate the overall usability of the product. Considering heuristics works in some particular examples, In the healthcare sector, heuristics approach is hard to implement due to fundamental constraints (stakeholders, safety, and time requirements). This paper examines a scheduling algorithm that correlates to usability problem. The scheduling algorithm has a definite impact on the product Return of investment and customer satisfaction.

International audience; Advancement in science and technology has increased work exertion tremendously. Many researchers and professionals have noted the importance of work exertion in correlation with the work environment. However, there is no mention of the principle of invisible exertion correlated with the current advancement of science and technology. This paper evaluates the invisible physical and mental exertion for CT scan technicians. Fifty-seven CT scan technicians were surveyed to determine the extent of their invisible physical and mental exertion. The overwhelming majority of technicians reported significant exertion while operating the CT scan. The invisible physical and mental exertion occurred differently in accordance with demographic characteristics. Gender is a significant factor, where male technicians reported more physical invisible exertion than female technicians. The invisible physical exertion occurred more than invisible mental exertion.