scholarly journals Coronavirus Spread Limitation Using Detective Smart System

2021 ◽  
Author(s):  
Morsy Ismail ◽  
Osama Galal ◽  
Waleed Saad
Keyword(s):  
Text Message ◽  
Data Sets ◽  
The Novel ◽  
Data Set ◽  
Smart System ◽  
Medical Centers ◽  
Image Capturing ◽  
Thermal Cameras ◽  
Novel Coronavirus ◽  
Isolation Facility

Abstract Given the circumstances the world is going through due to the novel coronavirus (Covid-19); this paper proposes a new smart system that aims to reduce the spread of the virus. The proposed Covid-19 containment system is designed to be installed outside hospitals and medical centers. Additionally, it works at night as well as at daylight. The system is based on Deep Learning applied to pedestrian temperature data sets that are collected using thermal cameras. The data set is primarily of temperature of pedestrians around medical centers. The thermal cameras are paired with conventional cameras for image capturing and cross referencing the target pedestrian with an existing central database (Big Data). If target is positive, the system sends a text message to the potentially infected person's cell phone upon recognition. The advisory sent text may contain useful information such as the nearest testing or isolation facility. This proposed system is assumed to be linked with the bigger network of the country’s Covid-19 response efforts. The simulation results reveal that the system can achieve an average precision of 90% fever detection among pedestrians.

Volatility transmission across international markets amid COVID 19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hechem Ajmi ◽  
Nadia Arfaoui ◽  
Karima Saci
Keyword(s):  
Crude Oil ◽  
International Markets ◽  
The Novel ◽  
Data Set ◽  
Health Crisis ◽  
Content Type ◽  
Volatility Transmission ◽  
Oil Markets ◽  
Novel Coronavirus ◽  
Death Cases

Purpose This paper aims to investigate the volatility transmission across stocks, gold and crude oil markets before and during the novel coronavirus (COVID-19) crisis. Design/methodology/approach A multivariate vector autoregression (VAR)-Baba, Engle, Kraft and Kroner generalized autoregressive conditional heteroskedasticity model (BEKK-GARCH) is used to assess volatility transmission across the examined markets. The sample is divided as follows. The first period ranging from 02/01/2019 to 10/03/2020 defines the pre-COVID-19 crisis. The second period is from 11/03/2020 to 05/10/2020, representing the COVID-19 crisis period. Then, a robustness test is used using exponential GARCH models after including an exogenous variable capturing the growth of COVID-19 confirmed death cases worldwide with the aim to test the accuracy of the VAR-BEKK-GARCH estimated results. Findings Results indicate that the interconnectedness among the examined market has been intensified during the COVID-19 crisis, proving the lack of hedging opportunities. It is also found that stocks and Gold markets lead the crude oil market especially during the COVID-19 crisis, which explains the freefall of the crude oil price during the health crisis. Similarly, results show that Gold is most likely to act as a diversifier rather than a hedging tool during the current health crisis. Originality/value Although the recent studies in the field focused on analyzing the relationships between different markets during the first quarter of 2020, this study considers a larger data set with the aim to assess the volatility transmission across the examined international markets Amid the COVID-19 crisis, while it shows the most significant impact on various financial markets compared to other diseases.

scholarly journals RNAseq in the mosquito maxillary palp: a little antennal RNA goes a long way

2015 ◽  
Author(s):  
David C. Rinker ◽  
Xiaofan Zhou ◽  
Ronald Jason Pitts ◽  
Patrick L. Jones ◽  
Antonis Rokas ◽  
...  
Keyword(s):  
Anopheles Gambiae ◽  
Strong Evidence ◽  
Maxillary Palp ◽  
Data Sets ◽  
The Novel ◽  
Cross Contamination ◽  
Data Set ◽  
Insect Olfaction ◽  
Data Support ◽  
Rnaseq Data

A comparative transcriptomic study of mosquito olfactory tissues recently published in BMC Genomics (Hodges et al., 2014) reported several novel findings that have broad implications for the field of insect olfaction. In this brief commentary, we outline why the conclusions of Hodges et al. are problematic under the current models of insect olfaction and then contrast their findings with those of other RNAseq based studies of mosquito olfactory tissues. We also generated a new RNAseq data set from the maxillary palp of Anopheles gambiae in an effort to replicate the novel results of Hodges et al. but were unable to reproduce their results. Instead, our new RNAseq data support the more straightforward explanation that the novel findings of Hodges et al. were a consequence of contamination by antennal RNA. In summary, we find strong evidence to suggest that the conclusions of Hodges et al were spurious, and that at least some of their RNAseq data sets were irrevocably compromised by cross-contamination between samples.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We perform the numerical analysis using the stochastic differential equation in Itô’s calculus (SDE) for the estimating of novel cases daily as well as analytical calculations solving the correspondent Fokker-Planck equation for the density probability distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô diffusion fits well to the results due to uncertain in the official data and to the number of tests realized in the populations of each country.

scholarly journals The COVID-19 Outbreak in Iran; The First Patient with a Definite Diagnosis

2020 ◽  
Vol 23 (7) ◽  
pp. 503-504 ◽  
Author(s):  
Mohammadreza Ghadir ◽  
Ali Ebrazeh ◽  
Javad Khodadadi ◽  
Masumeh Zamanlu ◽  
Saeed Shams ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
The Novel ◽  
Definite Diagnosis ◽  
Medical Centers ◽  
Clinical Presentations ◽  
The World ◽  
Novel Coronavirus ◽  
Current Report

The novel coronavirus, formerly named as 2019 novel coronavirus (2019-nCov) caused a rapidly spreading epidemic of severe acute respiratory syndrome (SARS) in Wuhan, China and thereafter, progressed globally to form a pandemic of coronavirus disease 2019 (COVID-19) in numerous countries; and now confirmed cases are reported from several provinces of Iran. Now various medical centers, clinicians and researchers around the world share their data and experiences about COVID-19 in order to participate in the global attempt of controlling the pandemic. The current report investigates the clinical presentations and paraclinical findings of the first confirmed cases and mortalities in the initiation of the outbreak of COVID-19 in Iran.

On the influence and limitations of hyper-resolution hydrological modelling – application of the 1 km PCR-GLOBWB model over Europe

2021 ◽  
Author(s):  
Jannis Hoch ◽  
Edwin Sutanudjaja ◽  
Rens van Beek ◽  
Marc Bierkens
Keyword(s):  
Global Scale ◽  
Data Sets ◽  
The Novel ◽  
Data Set ◽  
Meteorological Forcing ◽  
Recent Developments ◽  
Terrestrial Water ◽  
Multiple Challenges ◽  
Global Data ◽  
Development Application

<p>Developing and applying hyper-resolution models over larger extents has long been a quest in hydrological sciences. With the recent developments of global-scale yet fine data sets and advances in computational power, achieving this goal becomes increasingly feasible.</p><p>We here present the development, application, and results of the novel 1 km version of PCR-GLOBWB for the period 1981 until 2020. Even though employing global data sets only, we developed, ran, and evaluated the 1 km model for the continent Europe only. In comparison to past versions of PCR-GLOBWB, input data was replaced with sufficiently fine data, for example the recent SoilGrids and MERIT-DEM data. Preliminary results indicate an improvement of model outcome when evaluating simulated discharge, evaporation, and terrestrial water storage.</p><p>Additionally, we aim to answer the question to what extent developing hyper-resolution models is actually needed of whether the run times could be saved by using hyper-resolution state-of-the-art meteorological forcing. Therefore, the relative importance of model resolution and forcing resolution was cross-compared. To that end, the ERA5-Land data set was employed at different resolutions, matching the model resolutions at 1 km, 10 km, and 50 km.</p><p>Despite multiple challenges still lying ahead before achieve true hyper-resolution, this application of a 1 km model across an entire continent can form the basis for the next steps to be taken.</p>

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
Probability Density Distribution ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supply by the public health agencies in countries as Brazil, United States and India is investigated. We perform a numerical analysis using the stochastic differential equation in Itô’s calculus for the estimating of novel cases daily, as well as analytical calculations solving the correspondent Fokker-Planck equation for the probability density distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô’s diffusion fits well to the results due to uncertainty in the official data and to the number of testsrealized in populations of each country.

Machine Learning for Clinical Data Processing

2011 ◽  
pp. 193-215
Author(s):  
Guo-Zheng Li
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Clinical Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Sets ◽  
The Novel ◽  
Real World Data ◽  
Data Set ◽  
Learning Techniques

This chapter introduces great challenges and the novel machine learning techniques employed in clinical data processing. It argues that the novel machine learning techniques including support vector machines, ensemble learning, feature selection, feature reuse by using multi-task learning, and multi-label learning provide potentially more substantive solutions for decision support and clinical data analysis. The authors demonstrate the generalization performance of the novel machine learning techniques on real world data sets including one data set of brain glioma, one data set of coronary heart disease in Chinese Medicine and some tumor data sets of microarray. More and more machine learning techniques will be developed to improve analysis precision of clinical data sets.

Machine Learning for Clinical Data Processing

2012 ◽  
pp. 875-897
Author(s):  
Guo-Zheng Li
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Clinical Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Sets ◽  
The Novel ◽  
Real World Data ◽  
Data Set ◽  
Learning Techniques

This chapter introduces great challenges and the novel machine learning techniques employed in clinical data processing. It argues that the novel machine learning techniques including support vector machines, ensemble learning, feature selection, feature reuse by using multi-task learning, and multi-label learning provide potentially more substantive solutions for decision support and clinical data analysis. The authors demonstrate the generalization performance of the novel machine learning techniques on real world data sets including one data set of brain glioma, one data set of coronary heart disease in Chinese Medicine and some tumor data sets of microarray. More and more machine learning techniques will be developed to improve analysis precision of clinical data sets.

Covid-19 Attitudes Data

2021 ◽  
Author(s):  
Jessica Fagan ◽  
Jon E. Grahe ◽  
Kate Faasse ◽  
Amber Matteson ◽  
Ricky Haneda ◽  
...  
Keyword(s):  
Research Methods ◽  
Perceived Risk ◽  
The United States ◽  
The Novel ◽  
Real World Data ◽  
Data Set ◽  
Psychological Variables ◽  
Avoidance Behaviors ◽  
Novel Coronavirus ◽  
Risk Knowledge

Dr. Jon Grahe and the students of his 2020 Fall Semester class, Advanced Statistics and Research Methods at Pacific Lutheran University, three collaborators outside of the class, and three collaborators from other universities, collected data from online participants living in the United States (Nraw = 1019, Nprocessed = 821). Participants answered questions pertaining to various subjects related to the Novel Coronavirus-2019 pandemic including how closely they had been gathering information, their information sources, level of trust in various authorities, perceived risk, knowledge of COVID-19, avoidance behaviors, demographics, and COVID-19 exposure. The data are available here: https://osf.io/e8rzm/. These data could be used to examine psychological variables during a pandemic as well as provide a novel, real-world data set for students studying statistics and research methods.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2020 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Public Health ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We performed the numerical analysis using the stochastic differential equation for estimating of the novel cases diary as well as analytical calculations solving the correspondent partial equation for the distribution of novel cases P. Our results display that the model based in the Itô diffusion fits well to the results diary due to uncertain in the official data and to the number of tests realized in the populations of each country.

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