scholarly journals Development of an Automatic Solar-driven Hand Sanitizing System (AHSS) using Proximity Sensors

2021 ◽  
pp. 7-16
Author(s):  
Adegoke B. O. ◽  
Olokun M. S. ◽  
Agboola S.
Keyword(s):  
False Positive ◽  
Delay Time ◽  
Active Zone ◽  
Photovoltaic Cell ◽  
Hand Washing ◽  
The Sun ◽  
True Positive ◽  
Dc Voltage ◽  
Contagious Nature ◽  
Community Transmission

Inception of COVID ’19 has brought new normal globally. Contagious nature of various infectious diseases necessitated frequent hand washing in order to reduce rate of contamination and community transmission. The need to contain the spread of COVID-19 necessitated the development of an Automatic Hand Sanitizing System (AHSS). The AHSS employed proximity sensor (IR) to sense the hand and actuate the 5V DC submersible pumps in charge of both water and sanitizer units of the AHSS. The DC voltage that powered the system was harvested from the Sun with the help of 5v Photovoltaic cell connected to a controlled charging circuit. The system responded to presence of user object within the active zone of the IR proximity sensors. This presence sends signal to the pumps to release either the Sanitizer/water. Evaluation based on Delay Time (DT), Average DT (ADT), True Positive (TP), False Positive (FP), Unable to Detect (UTD) and Accuracy (A) was conducted. The system was tested 180 times among students of School of Engineering, Federal Polytechnic, Ile-Oluji (FEDPOLEL). Results of evaluation indicate 12s, 180, 0.00, 0.00 and 100% for ADT, TP, FP, UTD and Accuracy, respectively. Accuracy of the designed AHSS was encouraging. An AHSS that can notify user about level of water and sanitizer, also test for presence of COVID-19 infection can also be designed and constructed.

The Plausible role of Indian Traditional Medicine in combating Corona Virus (SARS-CoV 2): a mini-review

2020 ◽  
Vol 21 ◽  
Author(s):  
Adithya J ◽  
Bhagyalakshmi Nair ◽  
Aishwarya S ◽  
Lekshmi R. Nath
Keyword(s):  
Specific Treatment ◽  
Ocimum Sanctum ◽  
Drug Candidate ◽  
Asparagus Racemosus ◽  
Traditional System ◽  
Traditional Medicines ◽  
Contagious Nature ◽  
Community Transmission ◽  
Novel Virus

: SARS-CoV 2 is a novel virus strain of Coronavirus, reported in China in late December 2019. Its highly contagious nature in humans has prompted WHO to designate the ongoing pandemic as a Public Health Emergency of International Concern. At this moment, there is no specific treatment and the therapeutic strategies to deal with the infection are only supportive, and prevention aimed at reducing community transmission. A permanent solution for the pandemic, which has brought the world economy to the edge of collapse, is the need of the hour. This situation has brought intense research in traditional systems of medicine. Indian Traditional System, Ayurveda has a clear concept of the cause and treatment of pandemics. Through this review, information on the potential antiviral traditional medicines along with their immunomodulatory pathways is discussed. We have covered the seven most important Indian traditional plants with antiviral properties :Withaniasomnifera (L.) Dunal(family: Solanaceae),Tinosporacordifolia(Thunb.)Miers (family:Menispermaceae),Phyllanthusemblica L.(family:Euphorbiaceae),Asparagus racemosus L.(family:Liliaceae), Glycyrrhizaglabra L.(family:Fabaceae), Ocimum sanctum L.(family:Lamiaceae) and Azadirachta indica A.Juss(family:Meliaceae)in this review. An attempt is also made to bring into limelight the importance of dietary polyphenol, Quercetin which is a potential drug candidate in the making against the SARS-CoV2 virus.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller

2018 ◽  
Vol 10 (9) ◽  
pp. 83 ◽  
Author(s):  
Wentao Wang ◽  
Xuan Ke ◽  
Lingxia Wang
Keyword(s):  
Data Center ◽  
False Positive ◽  
Data Flow ◽  
Denial Of Service ◽  
True Positive ◽  
Ddos Attacks ◽  
Data Center Network ◽  
Data Packets ◽  
Positive Rate ◽  
Sdn Controller

A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze data packets entering the data center network and calculate the Renyi entropies base on IP of data packets, and then combine them with the hidden Markov model to get a probability model HMM-R to detect L-DDoS attacks at different rates. Compared with the four common attack detection algorithms (KNN, SVM, SOM, BP), HMM-R is superior to them in terms of the true positive rate, the false positive rate, and the adaptivity.

Evaluation of the Diagnostic Criteria for Anti-NMDA Receptor Encephalitis in Japanese Children

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000011789
Author(s):  
Hiroya NISHIDA ◽  
Kuniko KOHYAMA ◽  
Satoko KUMADA ◽  
Jun-ichi TAKANASHI ◽  
Akihisa OKUMURA ◽  
...  
Keyword(s):  
Nmda Receptor ◽  
Diagnostic Criteria ◽  
Predictive Value ◽  
False Positive ◽  
Pediatric Population ◽  
True Positive ◽  
Positive Group ◽  
Nmdar Encephalitis ◽  
Clinical Diagnostic ◽  
Clinical Diagnostic Criteria

OBJECTIVE:To evaluate the validity of the 2016 clinical diagnostic criteria proposed for probable anti-NMDA receptor (NMDAR) encephalitis in children, we tested the criteria in a Japanese pediatric cohort.METHODS:We retrospectively reviewed clinical information of patients with neurological symptoms whose CSF were analyzed for NMDAR antibodies (Abs) in our laboratory from January 1, 2015, to March 31, 2019.RESULTS:Overall, 137 cases were included. Of the 41 cases diagnosed as probable anti-NMDAR encephalitis (“criteria-positive”) according to the 2016 criteria, 13 were positive and 28 were negative for anti-NMDAR Abs. Of the 96 criteria-negative cases, three were positive and 93 were negative for anti-NMDAR Abs. The sensitivity of the criteria was 81.2%, specificity was 76.9%, positive predictive value (PPV) was 31.7%, and negative predictive value was 96.9%. Compared with the true-positive group, the false-positive group contained more male than female patients (male:female, 4:9 in the true-positive vs. 19:9 in the false-positive group, p = 0.0425). The majority of the cases with false-positive diagnoses were associated with neurological autoimmunity.CONCLUSION:The clinical diagnostic criteria are reliable for deciding to start immunomodulatory therapy in the criteria-positive cases. Low PPV may be caused by a lower prevalence of NMDAR encephalitis and/or lower level of suspicion for encephalitis in the pediatric population. Physicians should therefore continue differential diagnosis, focusing especially on other forms of encephalitis.Classification of Evidence:This study provides Class IV evidence that the proposed diagnostic criteria for anti-NMDAR encephalitis in children has a sensitivity of 81.2% and a specificity of 76.9%.

The performance of delta check methods.

1979 ◽  
Vol 25 (12) ◽  
pp. 2034-2037 ◽  
Author(s):  
L B Sheiner ◽  
L A Wheeler ◽  
J K Moore
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
True Positive Rate ◽  
True Positive ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Operating Rate ◽  
Positive Rate ◽  
The Relationship

Abstract The percentage of mislabeled specimens detected (true-positive rate) and the percentage of correctly labeled specimens misidentified (false-positive rate) were computed for three previously proposed delta check methods and two linear discriminant functions. The true-positive rate was computed from a set of pairs of specimens, each having one member replaced by a member from another pair chosen at random. The relationship between true-positive and false-positive rates was similar among the delta check methods tested, indicating equal performance for all of them over the range of false-positive rate of interest. At a practical false-positive operating rate of about 5%, delta check methods detect only about 50% of mislabeled specimens; even if the actual mislabeling rate is moderate (e.g., 1%), only abot 10% of specimens flagged a by a delta check will actually have been mislabeled.

scholarly journals True and false positive rates for different criteria of evaluating statistical evidence from clinical trials

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Don van Ravenzwaaij ◽  
John P. A. Ioannidis
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
False Positive ◽  
Evaluation Criteria ◽  
Small Sample ◽  
Statistical Evidence ◽  
Bayes Factors ◽  
True Positive ◽  
Small Sample Sizes ◽  
Meta Analyses

Abstract Background Until recently a typical rule that has often been used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive rates (endorsement of an ineffective treatment). Methods In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, thresholds for what constitutes a clinically meaningful treatment effect, and number of trials conducted. Results Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when the number of trials conducted is high and when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large. Conclusions Thinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.

Prediction of Adnexal Torsion by Ultrasound in Women with Acute Abdominal Pain

2019 ◽  
Vol 41 (06) ◽  
pp. 688-694
Author(s):  
Ron Bardin ◽  
Noga Perl ◽  
Reuven Mashiach ◽  
Eitan Ram ◽  
Sharon Orbach-Zinger ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
False Positive ◽  
Acute Abdominal Pain ◽  
Cohort Analysis ◽  
False Negative ◽  
True Positive ◽  
Adnexal Torsion ◽  
Positive Group

Abstract Purpose To investigate the accuracy of ultrasound in the diagnosis of adnexal torsion. Materials and Methods Retrospective cohort analysis of 322 women, presenting to a tertiary medical center with acute abdominal pain, who underwent gynecological examination, sonographic evaluation and laparoscopic surgery, between 2010 and 2016. Findings for adnexal torsion were compared among three groups: positive sonographic findings consistent with surgically confirmed adnexal torsion (true positive, n = 228); negative sonographic findings inconsistent with surgically confirmed adnexal torsion (false negative, n = 42); and positive sonographic findings inconsistent with a surgical diagnosis other than adnexal torsion (false positive, n = 52). Outcome measures were sensitivity and positive predictive value of ultrasound, and its specific features, for the diagnosis of adnexal torsion. Results The sensitivity of ultrasound for adnexal torsion diagnosis was 84.4 %, and the positive predictive value was 81.4 %. Edematous ovary and/or tube, as well as positive whirlpool sign had the highest sensitivity and positive predictive value. The false-negative group had the highest frequency of ovarian cysts (p = 0.0086) and the lowest frequency of ovarian edema (p < 0.0001). The false-positive group had the lowest proportion of pregnant women (p = 0.0022). Significantly more women in the true-positive group had a prior event of adnexal torsion (p = 0.026). Conclusion Ultrasound examination is highly accurate in the diagnosis of adnexal torsion. Clinicians should be aware of the presence of demographic and clinical characteristics that may positively or negatively affect sonographic diagnostic accuracy.

scholarly journals Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4590
Author(s):  
Jiali Lv ◽  
Jian Wei ◽  
Zhenyu Wang ◽  
Jin Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spectral Data ◽  
Test Data ◽  
False Positive ◽  
Tandem Mass ◽  
True Positive ◽  
Mass Spectral Data ◽  
True Negative ◽  
Mass Spectral

Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402; the number of false positive test data for four models are 318, 284, 198, and 168; the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.

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