No abstract

This study investigates the processing of voice signals for detecting Parkinson's disease. This disease is one of the neurological disorders that affect people in the world most. The approach evaluates the use of eighteen feature extraction techniques and four machine learning methods to classify data obtained from sustained phonation and speech tasks. Phonation relates to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. The audio tasks were recorded using two microphone channels from acoustic cardioid (AC) and a smartphone (SP), thus allowing to evaluate the performance for different types of microphones. Five metrics were employed to analyze the classification performance: Equal Error Rate (EER) and Area Under Curve (AUC) measures from Detection Error Tradeoff (DET) and Receiver Operating Characteristic curves, Accuracy, Specificity, and Sensitivity. We compare this approach with other approaches that use the same data set. We show that the task of phonation was more efficient than speech tasks in the detection of disease. The best performance for the AC channel achieved an accuracy of 94.55%, AUC 0.87, and EER 19.01%. When using the SP channel, we have achieved an accuracy of 92.94%, AUC 0.92, and EER 14.15%.

Mathematical models suggest that seasonal transmission and temporary cross-immunity between serotypes can determine the characteristic multi-year dynamics of dengue fever. Seasonal transmission is attributed to the effect of climate on mosquito abundance and within host virus dynamics. In this study, we validate a set of temperature and density dependent entomological models that are built-in components of most dengue models by fitting them to time series of ovitrap data from three distinct neighborhoods in Rio de Janeiro, Brazil. The results indicate that neighborhoods differ in the strength of the seasonal component and that commonly used models tend to assume more seasonal structure than found in data. Future dengue models should investigate the impact of heterogeneous levels of seasonality on dengue dynamics as it may affect virus maintenance from year to year, as well as the risk of disease outbreaks.

With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.