scholarly journals Identification of the relative timing of infectiousness and symptom onset for outbreak control

2019 ◽  
Author(s):  
Robert C. Cope ◽  
Joshua V. Ross
Keyword(s):  
Symptom Onset ◽  
Bayesian Model ◽  
High Accuracy ◽  
Model Discrimination ◽  
Relative Timing ◽  
Computationally Efficient ◽  
Outbreak Response ◽  
Novel Approach ◽  
Epidemiological Characteristics ◽  
Surveillance Scheme

In an outbreak of an emerging disease the epidemiological characteristics of the pathogen may be largely unknown. A key determinant of ability to control the outbreak is the relative timing of infectiousness and symptom onset. We provide a method for identifying this relationship with high accuracy based on data from household-stratified symptom-onset data. Further, this can be achieved with observations taken on only a few specific days, chosen optimally, within each household. This constitutes an important tool for outbreak response. An accurate and computationally-efficient heuristic for determining the optimal surveillance scheme is introduced. This heuristic provides a novel approach to optimal design for Bayesian model discrimination.

Fuzzified time-frequency method for identification and localization of power system faults

2021 ◽  
pp. 1-13
Author(s):  
Pullabhatla Srikanth ◽  
Chiranjib Koley
Keyword(s):  
Power System ◽  
High Accuracy ◽  
Fuzzy Decision ◽  
Frequency Method ◽  
Time Frequency ◽  
Novel Approach ◽  
S Transform ◽  
Different Types ◽  
Power System Faults ◽  
Frequency Magnitude

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM.

scholarly journals Propagation Analysis of the Coronavirus Pandemic on the Light of the Percolation Theory

2021 ◽  
Author(s):  
Moez Guettari ◽  
Ahmed El Aferni
Keyword(s):  
Electrical Conductivity ◽  
Percolation Theory ◽  
Scaling Laws ◽  
High Accuracy ◽  
The Novel ◽  
Infected People ◽  
Boltzman Equation ◽  
Novel Approach ◽  
Propagation Analysis ◽  
Speed Of Propagation

Efforts to combat the Covid-19 pandemic have not been limited to the processes of vaccine production, but they first began to analyze the dynamics of the epidemic’s spread so that they could adopt barrier measures to bypass the spread. To do this, the works of modeling, predicting and analyzing the spread of the virus continue to increase day after day. In this context, the aim of this chapter is to analyze the propagation of the Coronavirus pandemic by using the percolation theory. In fact, an analogy was established between the electrical conductivity of reverse micelles under temperature variation and the spread of the Coronavirus pandemic. So, the percolation theory was used to describe the cumulate infected people versus time by using a modified Sigmoid Boltzman equation (MSBE) and several quantities are introduced such as: the pandemic percolation time, the maximum infected people, the time constant and the characteristic contamination frequency deduced from Arrhenius equation. Scaling laws and critical exponents are introduced to describe the spread nature near the percolation time. The speed of propagation is also proposed and expressed. The novel approach based on the percolation theory was used to study the Coronavirus (Covid-19) spread in five countries: France, Italy, Germany, China and Tunisia, during 6 months of the pandemic spread (the first wave). So, an explicit expression connecting the number of people infected versus time is proposed to analyze the pandemic percolation. The reported MSBE fit results for the studied countries showed high accuracy.

scholarly journals The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis

2022 ◽  
Vol 8 ◽  
Author(s):  
Orapun Arjkumpa ◽  
Minta Suwannaboon ◽  
Manoch Boonrod ◽  
Issara Punyawan ◽  
Supawadee Liangchaisiri ◽  
...  
Keyword(s):  
Skin Disease ◽  
Temporal Analysis ◽  
Space Time ◽  
Effective Control ◽  
Outbreak Response ◽  
Lumpy Skin Disease ◽  
Epidemiological Features ◽  
Spatio Temporal ◽  
Epidemiological Characteristics ◽  
Cattle Farms

The first outbreak of lumpy skin disease (LSD) in Thailand was reported in March 2021, but information on the epidemiological characteristics of the outbreak is very limited. The objectives of this study were to describe the epidemiological features of LSD outbreaks and to identify the outbreak spatio-temporal clusters. The LSD-affected farms located in Roi Et province were investigated by veterinary authorities under the outbreak response program. A designed questionnaire was used to obtain the data. Space-time permutation (STP) and Poisson space-time (Poisson ST) models were used to detect areas of high LSD incidence. The authorities identified 293 LSD outbreak farms located in four different districts during the period of March and the first week of April 2021. The overall morbidity and mortality of the affected cattle were 40.5 and 1.2%, respectively. The STP defined seven statistically significant clusters whereas only one cluster was identified by the Poisson ST model. Most of the clusters (n = 6) from the STP had a radius <7 km, and the number of LSD cases in those clusters varied in range of 3–51. On the other hand, the most likely cluster from the Poisson ST included LSD cases (n = 361) from 198 cattle farms with a radius of 17.07 km. This is the first report to provide an epidemiological overview and determine spatio-temporal clusters of the first LSD outbreak in cattle farms in Thailand. The findings from this study may serve as a baseline information for future epidemiological studies and support authorities to establish effective control programs for LSD in Thailand.

scholarly journals Efficient Detection of Phishing Websites Using Multilayer Perceptron

2020 ◽  
Vol 14 (11) ◽  
pp. 22
Author(s):  
Ammar Jamil Odeh ◽  
Ismail Keshta ◽  
Eman Abdelfattah
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Detection System ◽  
High Accuracy ◽  
Internet Fraud ◽  
Efficient Detection ◽  
Novel Approach

Phishing is a type of Internet fraud that aims to acquire the credential of users via scamming websites. In this paper, a novel approach is utilized that uses a Neural Network with a multilayer perceptron to detect the scam URL. The proposed system improves the accuracy of the scam detection system as it achieves a high accuracy percentage of 98.5%.

Aircraft flow angles calibration via observed-based wind estimation

2019 ◽  
Vol 91 (7) ◽  
pp. 1033-1038
Author(s):  
Francesco Schettini ◽  
Gianpietro Di Rito ◽  
Eugenio Denti
Keyword(s):  
Kalman Filter ◽  
Reference Data ◽  
Angle Of Attack ◽  
High Accuracy ◽  
Data Systems ◽  
Content Type ◽  
Novel Approach ◽  
Reference Measurements ◽  
Long Endurance ◽  
Practical Implications

Purpose This paper aims to propose a novel approach, in which the reference data for the flow angles calibration are obtained by using measurements coming from an inertial navigation system and an air data sensor. Design/methodology/approach This is obtained by using the Kalman filter theory for the evaluation of the reference angle-of-attack and angle-of-sideslip. Findings The designed Kalman filter has been implemented in Matlab/Simulink and validated using flight data coming from two very different aircraft, the Piaggio Aerospace P1HH medium altitude long endurance unmanned aerial system and the Alenia-Aermacchi M346 Master™ transonic trainer. This paper illustrates some results where the filter satisfactory behaviour is verified by comparing the filter outputs with the data coming from high-accuracy nose-boom vanes. Practical implications The methodology aims to lower the calibration costs of the air data systems of an advanced aircraft. Originality/value The calibration of air-data systems for the evaluation of the flow angles is based on the availability of high-accuracy reference measurements of angle-of-attack and angle-of-sideslip. Typically, these are obtained by auxiliary sensors directly providing the reference angles (e.g. nose-boom vanes). The proposed methodology evaluates the reference angle-of-attack and angle-of-sideslip by analytically reconstructing them using calibrated airspeed measurements and inertial data.

Damage Identification in Collocated Structural Systems Using Structural Markov Parameters

2013 ◽  
Author(s):  
Ramin Bighamian ◽  
Hamid Reza Mirdamadi ◽  
Jin-Oh Hahn
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Mode Shapes ◽  
Structural Systems ◽  
Structural System ◽  
Computationally Efficient ◽  
Markov Parameters ◽  
Novel Approach ◽  
Stiffness Matrices ◽  
Stiffness Characteristics

This paper presents a novel approach to damage identification in a class of collocated multi-input multi-output structural systems. In the proposed approach, damage is identified via the structural Markov parameters obtained from a system identification procedure, which is in turn exploited to localize and quantify damage by evaluating relative changes occurring in the mass and stiffness matrices associated with the structural system. To this aim, an explicit relationship between structural Markov parameters versus mass and stiffness matrices is developed. The main strengths of the proposed approach are that it is capable of quantitatively identifying the occurrence of multiple damages associated with both mass and stiffness characteristics in the structural system, and it is computationally efficient in that it is solely based on the structural Markov parameters but does not necessitate costly calculations related to natural frequencies and mode shapes, making it highly attractive for structural damage detection and health monitoring applications. Numerical examples are provided to demonstrate the validity and effectiveness of the proposed approach.

Efficient Spatial Dynamics for Continuum Arms

2015 ◽  
Author(s):  
Isuru S. Godage ◽  
Raul Wirz ◽  
Ian D. Walker ◽  
Robert J. Webster
Keyword(s):  
Dynamic Models ◽  
Spatial Dynamics ◽  
High Accuracy ◽  
Computationally Efficient ◽  
Pneumatic Muscle ◽  
Computational Overhead ◽  
Proposed Model ◽  
Continuum Robot ◽  
Transient And Steady State ◽  
The Continuum

Continuum robot dynamic models have previously involved a choice between high accuracy, numerically intensive models, and low accuracy, computationally efficient models. The objective of this paper is to provide an accurate dynamic model with low computational overhead. Our approach is to place point masses at the center of gravity of the continuum section, rather than along the robot’s backbone or centerline. This enables the model to match the robot’s energetic characteristics with many fewer point masses. We experimentally validate the model using a pneumatic muscle actuated continuum arm. We find that the proposed model successfully captures both the transient and steady state dynamics of the arm.

A Fuzzy-Pod Based Estimation of Unsteady Mixed Convection in a Partition Located Cavity with Inlet and Outlet Ports

2015 ◽  
Vol 12 (01) ◽  
pp. 1350107 ◽  
Author(s):  
Fatih Selimefendigil ◽  
Hakan F. Öztop
Keyword(s):  
Mixed Convection ◽  
Strouhal Number ◽  
Computationally Efficient ◽  
Validation Data ◽  
Data Set ◽  
Square Enclosure ◽  
Unsteady Mixed Convection ◽  
Novel Approach ◽  
Fuzzy Clustering Method ◽  
Proper Orthogonal

In the present study, a novel approach based on Proper Orthogonal Decomposition (POD) and fuzzy clustering method is utilized to predict the flow field and heat transfer for the unsteady mixed convection in a square enclosure with two ventilation ports. An adiabatic thin fin is placed on the bottom wall of the cavity and all walls of the enclosure are kept at constant temperature. An oscillating velocity is imposed at the inlet port for a range of Strouhal numbers between 0.1 and 1. Reduced order models of the system are obtained with fuzzy-POD approach for Richardson number of 1 and 100. The estimation data set is obtained for Strouhal numbers 0.1 and 0.5, and the validation data set is obtained for Strouhal number of 0.25. A comparison of the modal coefficients obtained from the proposed approach compares well with the modal coefficients obtained by projecting the CFD data at Strouhal number of 0.25 onto the POD modes. The proposed approach is computationally efficient and the problem of numerical instability in the computation with the conventional Galerkin-POD approach can be circumvented.

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