Journal articles

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    Effects of Mobile Phone Use on Driving Performance: An Experimental Study of Workload and Traffic Violations
    (International Journal of Environmental Research and Public Health, MDPI, 2021) Ortega Carlos Alberto Catalina;  Mariscal Miguel Angel ; Boulagouas Wafa;  Herrera Sixto;  Espinosa Juan Maria;  Garcia-Herrero Susana
    The use of communication technologies, e.g., mobile phones, has increased dramatically in recent years, and their use among drivers has become a great risk to traffic safety. The present study assessed the workload and road ordinary violations, utilizing driving data collected from 39 young participants who underwent a dual-task while driving a simulator, i.e., respond to a call, text on WhatsApp, and check Instagram. Findings confirmed that there are significant differences in the driving performance of young drivers in terms of vehicle control (i.e., lateral distance and hard shoulder line violations) between distracted and non-distracted drivers. Furthermore, the overall workload score of young drivers increases with the use of their mobile phones while driving. The obtained results contribute to a better understanding of the driving performance of distracted young drivers and thus they could be useful for further improvements to traffic safety strategies.
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    Assessment of the Influence of Technology-Based Distracted Driving on Drivers' Infractions and Their Subsequent Impact on Traffic Accidents Severity
    (International Journal of Environmental Research and Public Health, MDPI, 2021) Garcia-Herrero Susana;  Febres Juan Diego;  Boulagouas Wafa;  Gutierrez Jose Manuel; Mariscal Saldana Miguel Angel
    Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.
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    Effects of mobile phone-related distraction on driving performance at roundabouts: Eye movements tracking perspective
    (Heliyon, ELSEVIER, 2024) Boulagouas Wafa; Catalina Ortega Carlos Alberto; Mariscal Miguel Angel; Herrera Sixto; Garcia-Herrero Susana
    Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points. This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers’ characteristics (i.e., driving experience) were examined. In total, 43 participants, aged 19–56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers’ gaze direction and fixation on nine areas of interest were captured with an eye tracker. Results showed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers’ attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.
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    De La Mobilité De La Poésie Et De La Prose Quels debats? Quels criteres ?
    (El-ihyaa journal, 2022) Khadraoui, Fatima-Zohra
    Le présent article traite de la problématique de la poésie et de la prose comme deux productions artistiques unies par l’appartenance à un domaine commun, mais différenciées par des caractéristiques singulières. Dans cette optique, nous opterons pour une démarche chronologique qui atteste de la dynamique de la pensée humaine en matière de production artistique. Pour nous inscrire dans la mobilité en question et respecter le principe de la contextualisation de tout discours, nous partirons de «La Poétique» et «La Rhétorique» d’Aristote pour passer en revue les conceptions données à ces deux genres artistiques par Barthes, Genette, Jakobson, Sartre, Todorov, et tant d’autres théoriciens.
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    Federated Learning for Condition Monitoring of Industrial Processes: A Review on Fault Diagnosis Methods, Challenges, and Prospects.
    (2023) Berghout T, Benbouzid M, Bentrcia T, Lim W-H, Amirat
    Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.