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Visible light communication is seen as a crucial technology within optical wireless communication systems. The technology of vehicular visible light communication holds significant importance in the context of connected vehicles. This technology can serve as a supplementary solution to vehicular systems that are based on radio frequency. In this paper, the authors conduct an analysis of the performance of both line-of-sight and non-line-ofsight vehicle-to-vehicle visible light communication systems under the effect of artificial light source and weather conditions, including clear, hazy, and foggy weather. A practical vehicular laser diode, a street lamp, and an avalanche photodiode are used to design the proposed system model. Performance enhancement for the proposed system is achieved using an optical amplifier at the receiving end. An artificial light source of light-emitting diode Corn-type is used to represent an ambient artificial light source. Different metrics such as quality factor and bit error rate are used to assess the system performance of the non-lineof-sight-vehicular communication system. The proposed line-of-sight model achieves a data rate of 25 Gbps, supporting a distance of 80 m under clear sky and hazy atmospheric conditions. For foggy weather, an attainable link distance of 70 m is achieved. The achieved results emphasize the suitability of the suggested models for vehicular applications in real world environment. [ABSTRACT FROM AUTHOR]

Background: Among healthcare professions, critical care healthcare workers (HCWs) have one of the most stressful jobs. This study was conducted to determine the relationship between job stress and burnout syndrome (BOS) among nurses and healthcare technicians at the surgical emergency department and intensive care unit of Critical Care department at the Alexandria University Hospital. Methods: A cross-sectional approach was conducted from October 2014 to March 2015. Eighty-two nurses and healthcare technicians participated in the research (response rate = 80.39%). Data was collected by an interview questionnaire using selected subscales of NIOSH Generic job stress Questionnaire and Maslach Burnout Inventory of Health and human service Questionnaire. The relationship between BOS and job stress was examined using bivariate and multivariate analyses. Results: Although majority of participants reported variation of workload (84.15%), quantitative overload (76.8%), responsibility for peoples’ life (69.5%) and lack of perceived control (63.41%), yet, 85.4% were satisfied with their job. Moreover, high levels of emotional exhaustion was reported by the majority of participants (80%), while less than one third reported either high levels of depersonalization or low levels of personal accomplishment domains of BOS. In multiple regression analysis, skill underutilization, variation in workload, and intragroup conflicts were negatively associated with BOS domains. While, job satisfaction and responsibility for peoples’ life were positively associated with personal accomplishment domain of BOS. Conclusion: Critical care HCWs had high BOS. The study concluded that reducing intragroup conflict, improving skills utilization, and raising job satisfaction are crucial to reduce BOS among critical care HCWs. More attention and psychological support is recommended to critical care HCWs. Keywords: Burnout syndrome, Critical care, Healthcare workers, Job stress, Nurses

Shadow extraction and elimination is essential for intelligent transportation systems (ITS) in vehicle tracking application. The shadow is the source of error for vehicle detection, which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting, vehicle detection, vehicle tracking, and classification. Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets, but the process of extracting shadows from moving vehicles in low light of real scenes is difficult. The real scenes of vehicles dataset are generated by self on the Vadodara--Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem. This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking. The method is distributed into two phases: In the first phase, we extract foreground regions using a mixture of Gaussian model, and then in the second phase, with the help of the Gamma correction, intensity ratio, negative transformation, and a combination of Gaussian filters, we locate and remove the shadow region from the foreground areas. Compared to the outcomes proposed method with outcomes of an existing method, the suggested method achieves an average true negative rate of above 90%, a shadow detection rate SDR (η%), and a shadow discrimination rate SDR (ξ%) of 80%. Hence, the suggested method is more appropriate for moving shadow detection in real scenes. [ABSTRACT FROM AUTHOR]

COVID-19 has significantly impacted the growth prediction of a pandemic, and it is critical in determining how to battle and track the disease progression. In this case, COVID-19 data is a time-series dataset that can be projected using different methodologies. Thus, this work aims to gauge the spread of the outbreak severity over time. Furthermore, data analytics and Machine Learning (ML) techniques are employed to gain a broader understanding of virus infections. We have simulated, adjusted, and fitted several statistical time-series forecasting models, linearML models, and nonlinear ML models. Examples of these models are Logistic Regression, Lasso, Ridge, ElasticNet, Huber Regressor, Lasso Lars, Passive Aggressive Regressor, K-Neighbors Regressor, Decision Tree Regressor, Extra Trees Regressor, Support Vector Regressions (SVR), AdaBoost Regressor, Random Forest Regressor, Bagging Regressor, AuoRegression, MovingAverage, Gradient Boosting Regressor, Autoregressive Moving Average (ARMA), Auto-Regressive Integrated Moving Averages (ARIMA), SimpleExpSmoothing, Exponential Smoothing, Holt-Winters, Simple Moving Average, Weighted Moving Average, Croston, and naive Bayes. Furthermore, our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods. A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance. Then, this best method is used for future forecasts, and consequently, we can collect the most accurate and dependable predictions. [ABSTRACT FROM AUTHOR]