Airborne droplets and contaminated surfaces are the main routes for the epidemic virus outbreak that causes the novel coronavirus. To reduce virus spread, people wear face masks and gloves daily, which massively increases the amount of waste generated in the environment. Also, the inappropriate disposal of used masks and gloves in communities may boost the spread of the novel coronavirus. However, no studies have been conducted to evaluate a public perception regarding the management of generated masks and gloves during the global health threat. Therefore, this paper proposes a study of public awareness, attitudes, and practices towards gloves and masks generated during the novel coronavirus pandemic in Saudi Arabia. The study showed that most of the participants (74%) have adequate awareness regarding the proper way to dispose of used masks and gloves, with 76% showing positive attitudes toward waste management. Gender shows a significant correlation with the regulations of medical waste collection and disposal (r = 0.169, p = 0.0001), and the consideration of the effective disposal of COVID-19 waste management as a collective responsibility of the community (r = 0.158, p = 0.0001). Also, the study shows that the majority of participants were aware on the availability of regulations for the collection and disposal of biomedical waste. Moreover, it demonstrates that public awareness regarding the attitude and measures taken by regional municipalities to reduce the spread of coronavirus has a significant positive correlation (r = 0.279, p = 0.0001). The study further recognized that the effective management of contagious wastes significantly protects the public against the improper practices of wastes disposal generated during the novel coronavirus pandemic.

A new post‐processing approach which aims at reducing the dispersion effect of the wave travelling inside a waveguide is presented. The main objective is to improve the time domain reflectometry measurements performed over long‐distance cables, relative to the wavelength λ . A real‐case scenario is presented so that the gain of this method can be appreciated.

Cardiovascular disease is the main cause of death worldwide. An electrocardiogram (ECG) signals is typically used as the first diagnosis tool to detect abnormality in the heart signal. Reliable detection of R-peak in the ECG signal indicates various heart malfunctions (e.g., arrhythmia) and allows for proactive prevention of death due to cardiovascular disease. Though several R-peak detection methods have been proposed, the existence of noise in ECG signals and changes in QRS morphology compromise the robustness and reliability of these methods. Therefore, the need for a reliable detection of R-peak is crucial for diagnosis of heart abnormalities. This paper introduces a time-efficient and novel continuous wavelet transform (CWT) based method for R-peak detection. The proposed method first transforms the ECG signal in to time-frequency spectrum. The contributions of different frequencies at every time point are then calculated from the time-frequency spectrum to efficiently reduce the impact of noise and generate a summed frequency signal. A threshold technique is also proposed to detect the R-peak from the newly generated signal allows. The MIT-BIH arrhythmia database is used as a reference for validation and comparison of the proposed method with the results of other existing R-peak detection methods. The experimental results prove the efficiency and robustness of the developed method on noisy ECG signals with changes in QRS morphology with 99.87% sensitivity, 99.76% positive prediction value and a detection error rate of only 0.37%. In addition to the high accuracy in detecting R-peaks, the ease-to-use and fast-processing make the proposed method an efficient and reliable tool for real-time abnormality detection in ECG signal.

Driver drowsiness detection has become an important application of sensor-based monitoring systems aimed at improving road safety. This review focuses on sensing technologies and physiological parameters used for real-time drowsiness detection in drivers. The surveyed approaches are categorized into physiological sensing methods, including electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and photoplethysmography (PPG), and mechanical sensing methods, including respiration rate, eye blinking, head movement, yawning, and steering wheel gripping force. Each method is analyzed from a sensor system perspective, considering signal acquisition principles, measurement location, and practical deployment constraints. In addition, the reviewed techniques are evaluated based on real-time capability, level of sensor attachment, cost, restriction of user movement, and suitability for standalone operation. The comparison highlights that mechanical sensing approaches provide non-invasive and cost-effective solutions; however, they are sensitive to environmental noise and behavioral variability. In contrast, physiological sensing methods offer more direct and earlier indicators of fatigue-related changes in biosignals, although they typically require wearable or contact-based sensors and more complex acquisition systems. The review further indicates that multimodal sensor fusion is increasingly being adopted to improve robustness and reliability in real-world driving conditions. Overall, this work provides a structured overview of sensing modalities and highlights key considerations for designing efficient, real-time driver monitoring systems.

L’utilisation de câbles électriques et leurs longueurs dans certains systèmes électriques ont fortement augmenté au cours des dernières années. Or, la fiabilité de ces systèmes repose en partie sur la fiabilité des réseaux électriques. On constate en pratique qu’une part non négligeable des pannes et des dysfonctionnements de ces systèmes proviennent des défauts dans les liaisons filaires et non des équipements électriques. La connaissance de ces réseaux filaires et en particulier la détection de leurs défauts est donc importante. De nombreuses méthodes ont été développées pour tester l’état des câbles. Parmi ces méthodes on peut distinguer les méthodes de réflectométrie largement utilisées et facilement embarquables. Généralement ces méthodes sont très bien adaptées pour détecter et localiser les défauts francs mais les défauts non francs sont pratiquement transparents à ces méthodes car ils ont des conséquences électriques très faibles. Pour s’affranchir de ces limitations des améliorations en termes de mesure et traitement sont nécessaires. Dans cette thèse, trois nouvelles méthodes de diagnostic filaire ont été développées pour améliorer et faciliter la détection et la localisation de tous types de défauts filaires. Chacune des ces méthodes répond à un obstacle que nous avons rencontré pendant les trois années de recherche. Un premier obstacle concerne le phénomène de dispersion du signal dans les câbles qui rend la détection des défauts et du vieillissement des câbles très difficile. Un autre obstacle lié à la détection des défauts non-francs présente un enjeu actuel majeur du diagnostic filaire car leurs signatures sont très faibles et parfois noyées dans le bruit ou masquées par la proximité d’une autre impulsion d’amplitude plus importante. Les trois méthodes sont les suivantes : – La première méthode proposée, baptisée « corrélation adaptative » fournit un nouvel algorithme pour compenser la dispersion du signal. Elle permet de mieux localiser et mieux détecter les singularités sur des câbles de n’importe quelle longueur. – La deuxième méthode proposée, baptisée TRR (en anglais Time reversal Reflectometry) est basée sur le principe de la réflectométrie et du retournement temporel. Elle permet de caractériser le vieillissement des câbles électriques. – La troisième méthode proposée, baptisée RART (Réflectométrie associée à un processus de retournement temporel) est basée sur les principes de la réflectométrie et du retournement temporel et permet d’améliorer la détection des défauts électriques liés à une dégradation de l’isolant. Ces travaux de thèse ont montré les performances et la facilité de ces méthodes visant à assurer la sureté de fonctionnement des systèmes électriques que ce soit dans des moyens de transport, un bâtiment ou même des réseaux de communication.