SARS-CoV-2 (COVID-19) is a new Coronavirus, with first reported human infections in late 2019. learn more COVID-19 has been officially declared as a universal pandemic by the World Health Organization (WHO). The epidemiological characteristics of COVID-2019 have not been completely understood yet. More than 200,000 persons were killed during this epidemic (till 1 May 2020). Therefore, developing forecasting models to predict the spread of that epidemic is a critical issue. In this study, statistical and artificial intelligence based approaches have been proposed to model and forecast the prevalence of this epidemic in Egypt. These approaches are autoregressive integrated moving average (ARIMA) and nonlinear autoregressive artificial neural networks (NARANN). The official data reported by The Egyptian Ministry of Health and Population of COVID-19 cases in the period between 1 March and 10 May 2020 was used to train the models. The forecasted cases showed a good agreement with officially reported cases. The obtained results of this study may help the Egyptian decision-makers to put short-term future plans to face this epidemic.Currently no specific medicinal treatment exists against the new SARS-CoV2 and chloroquine is widely used, since it can decrease the length of hospital stay and improve the evolution of the associated COVID-19 pneumonia. However, several safety concerns have been raised from chloroquine use due to the lack of essential information regarding its dosing. The aim of this study is to provide a critical appraisal of the safety information regarding chloroquine treatment and to apply simulation techniques to unveil relationships between the observed serious adverse events and overdosing, as well as to propose optimized dosage regimens. The dose related adverse events of chloroquine are unveiled and maximum tolerated doses and concentration levels are quoted. Among others, treatment with chloroquine can lead to severe adverse effects like prolongation of the QT interval and cardiomyopathy. In case of chloroquine overdosing, conditions similar to those produced by SARS-CoV2, such as pulmonary oedema with respiratory insufficiency and circulatory collapse, can be observed. Co-administration of chloroquine with other drugs for the treatment of COVID-19 patients, like azithromycin, can further increase the risk of QT prolongation and cardiomyopathy. For elder patients there is a high risk for toxicity and dose reduction should be made. This study unveils the risks of some widely used dosing regimens and binds the observed serious adverse events with dosing. Based on simulations, safer alternative dosage regimens are proposed and recommendations regarding chloroquine dosing are made.Recent advances in nucleic acid based testing using bio-optical sensor approaches have been introduced but most are based on hybridization between the optical sensor and the bio-molecule and not on an amplification mechanism. Direct nucleic acid amplification on an optical sensor has several technical limitations, such as the sensitivity of the temperature sensor, instrument complexity, and high background signal. We here describe a novel nucleic acid amplification method based on a whispering gallery mode active resonator and discuss its potential molecular diagnostic application. By implanting nanoclusters as active compounds, this active resonator operates without tapered fiber coupling and emits a strong photoluminescence signal with low background in the wavelength of low absorption in an aqueous environment that is typical of biosensors. Our method also offers an extremely low detection threshold down to a single copy within 10 min due to the strong light-matter interaction in a nano-gap structure. We envision that this active resonator provides a high refractive index contrast for tight mode confinement with simple alignment as well as the possibility of reducing the device size so that a point-of-care system with low-cost, high-sensitivity and simplicity.Prediction of individual mobility is crucial in human mobility related applications. Whereas, existing research on individual mobility prediction mainly focuses on next location prediction and short-term dependencies between traveling locations. Long-term location sequence prediction is of great importance for long-time traffic planning and location advertising, and long-term dependencies exist as individual mobility regularity typically occurs daily and weekly. This paper proposes a novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction. The proposed hierarchical attention mechanism captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential and periodical mobility patterns in an interpretable manner by incorporating the calendar cycle of individual travel regularities into location prediction. More specifically, the hierarchical attention consists of local temporal attention to identify highly related locations in each day, and global temporal attention to discern important travel regularities over a week. Experiments on individual trajectory datasets with varying degree of traveling uncertainty demonstrate that our method outperforms four baseline methods on three evaluation metrics. In addition, we explore the interpretability of the proposed model in understanding individual daily, and weekly mobility patterns by visualizing the temporal attention weights and frequent traveling patterns associated with locations.The airway/lung mechanics is usually represented with nonlinear 0-D models based on a pneumatic-electrical analogy. The aim of this work is to provide a detailed description of the human respiratory mechanics in healthy and diseased conditions. The model used for this purpose employs some known constitutive functions of the main components of the respiratory system. We give a detailed mathematical description of these functions and subsequently derive additional key ones. We are interested not only in the main output such as airflow at the mouth or alveolar pressure and volume, but also in other quantities such as resistance and pressure drop across each element of the system and even recoil and compliance of the chest wall. Pathological conditions are simulated by altering the parameters of the constitutive functions. Results show that increased upper airway resistance induces airflow reduction with concomitant narrowing of volume and pressure ranges without affecting lung compliance. Instead, increased elastic recoil leads to low volumes and decreased lung compliance.