scholarly journals A discrete epidemic model and a zigzag strategy for curbing the Covid-19 outbreak and for lifting the lockdown

2020 ◽  
Vol 15 ◽  
pp. 75
Author(s):  
Tahar Z. Boulmezaoud
Keyword(s):  
Discrete Model ◽  
Reproduction Number ◽  
Herd Immunity ◽  
Real Data ◽  
Dual Purpose ◽  
New Developments ◽  
Course Of Illness ◽  
Discrete Epidemic Model ◽  
Delay Times

This study looks at the dynamics of a Covid-19 type epidemic with a dual purpose. The first objective is to propose a reliable temporal mathematical model, based on real data and integrating the course of illness. It is a daily discrete model with different delay times, and whose parameters are calibrated from the main indicators of the epidemic. The model can be broken down in two decoupled versions: a mortality-mortality version, which can be used with the data on the number of deaths, and an infection-infection version to be used when reliable estimates of infection rate are available. The model allows to describe realistically the evolution of the main markers of the epidemic. In addition, in terms of deaths and occupied ICU beds, the model is not very sensitive to the current uncertainties about IFR. The second objective is to study several original scenarios for the epidemic’s evolution, especially after the period of strict lockdown. A coherent strategy is therefore proposed to contain the outbreak and exit lockdown, without going into the risky herd immunity approach. This strategy, called zigzag strategy, is based on a classification of the interventions into four lanes, distinguished by a marker called the daily reproduction number. The model and strategy in question are flexible and easily adaptable to new developments such as mass screenings or infection surveys. They can also be used at different geographical scales (local, regional or national).

scholarly journals Basic reproduction number and effective reproduction number for North Cyprus for fighting Covid-19

2020 ◽  
Vol 99 (3) ◽  
pp. 86-95
Author(s):  
Evren Hincal ◽  
◽  
Bilgen Kaymakamzade ◽  
Nezihal Gokbulut
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Herd Immunity ◽  
Real Data ◽  
Seir Model ◽  
North Cyprus ◽  
The Government

The aim of this paper is to show how North Cyprus fought with Covid-19 by using R0 and Rt, as herd immunity. For that purpose, we used a SEIR model for basic reproduction number, R0, and calculated Rt values by using R0 values. North Cyprus is the first country in Europe to free from Covid-19 epidemic. One of the most important reasons for this is that the government decided to tackle Covid-19 pandemic by using R0 and Rt daily. For R0, we constructed a new SEIR model by using real data for North Cyprus. From March 11, 2020 to May 15, 2020, R0 varies from 0.65 to 2.38.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Index of suicide risk in Mexico using Twitter

2019 ◽  
pp. 1-13
Author(s):  
Luz Judith Rodríguez-Esparza ◽  
Diana Barraza-Barraza ◽  
Jesús Salazar-Ibarra ◽  
Rafael Gerardo Vargas-Pasaye
Keyword(s):  
Social Networks ◽  
Analytic Hierarchy Process ◽  
Suicide Risk ◽  
Opinion Mining ◽  
Real Data ◽  
Analytic Hierarchy ◽  
Specialized Care ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test

2021 ◽  
pp. 190-200
Author(s):  
Lesia Mochurad ◽  
Yaroslav Hladun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Real Data ◽  
Finger Tapping ◽  
Similar Distribution ◽  
Model Learning ◽  
Machine Learning Model ◽  
Finger Tapping Test

The paper considers the method for analysis of a psychophysical state of a person on psychomotor indicators – finger tapping test. The app for mobile phone that generalizes the classic tapping test is developed for experiments. Developed tool allows collecting samples and analyzing them like individual experiments and like dataset as a whole. The data based on statistical methods and optimization of hyperparameters is investigated for anomalies, and an algorithm for reducing their number is developed. The machine learning model is used to predict different features of the dataset. These experiments demonstrate the data structure obtained using finger tapping test. As a result, we gained knowledge of how to conduct experiments for better generalization of the model in future. A method for removing anomalies is developed and it can be used in further research to increase an accuracy of the model. Developed model is a multilayer recurrent neural network that works well with the classification of time series. Error of model learning on a synthetic dataset is 1.5% and on a real data from similar distribution is 5%.

THE CLASSIFICATION OF PIPE-LAY VESSELS AND HEAVY-LIFT CRANE VESSELS

2021 ◽  
Vol 161 (A2) ◽  
Author(s):  
M Dickin
Keyword(s):  
Critical Systems ◽  
Station Keeping ◽  
Dual Purpose ◽  
Structural Assessment ◽  
Safety Critical ◽  
Safety Assurance ◽  
General Arrangement ◽  
Heavy Lift ◽  
Safety Critical Systems

Pipe-lay vessels, heavy-lift crane vessels and dual purpose heavy-lift and pipe-lay vessels are distinct in many ways from other types of ships or offshore units. The unique functions that these vessels carry out can impact directly on the overall safety of the vessel, the personnel on-board and the potential to pollute the environment. This paper outlines some of the hull and machinery safety assurance considerations for classification and design pertinent to pipe-lay and heavy-lift operations. The considerations that are discussed in this paper include the implications of classing the vessel as a ship or an offshore unit; the interaction between classification and marine warranty; general arrangement; station-keeping; structural assessment and the interaction between safety critical systems. Specific hazards for pipe-lay vessels and their use of chemicals on-board are also discussed.

scholarly journals IMAGE BASED RECOGNITION OF DYNAMIC TRAFFIC SITUATIONS BY EVALUATING THE EXTERIOR SURROUNDING AND INTERIOR SPACE OF VEHICLES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 161-168
Author(s):  
A. Hanel ◽  
H. Klöden ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Machine Learning ◽  
Real Data ◽  
Traffic Situation ◽  
Dynamic Traffic ◽  
Interior Space ◽  
Data Set ◽  
Road Users ◽  
Vehicle Fleet ◽  
New Strategies

Today, cameras mounted in vehicles are used to observe the driver as well as the objects around a vehicle. In this article, an outline of a concept for image based recognition of dynamic traffic situations is shown. A dynamic traffic situation will be described by road users and their intentions. Images will be taken by a vehicle fleet and aggregated on a server. On these images, new strategies for machine learning will be applied iteratively when new data has arrived on the server. The results of the learning process will be models describing the traffic situation and will be transmitted back to the recording vehicles. The recognition will be performed as a standalone function in the vehicles and will use the received models. It can be expected, that this method can make the detection and classification of objects around the vehicles more reliable. In addition, the prediction of their actions for the next seconds should be possible. As one example how this concept is used, a method to recognize the illumination situation of a traffic scene is described. This allows to handle different appearances of objects depending on the illumination of the scene. Different illumination classes will be defined to distinguish different illumination situations. Intensity based features are extracted from the images and used by a classifier to assign an image to an illumination class. This method is being tested for a real data set of daytime and nighttime images. It can be shown, that the illumination class can be classified correctly for more than 80% of the images.

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

Development of ABS Rules for Construction of Offshore Drilling Rigs

1969 ◽  
Vol 6 (01) ◽  
pp. 58-59
Author(s):  
David B. Bannerman
Keyword(s):  
Classification Rules ◽  
Offshore Drilling ◽  
New Developments ◽  
Wave Heights ◽  
Drilling Rigs ◽  
Classification Procedures

The need for criteria for the construction of offshore mobile drilling units was felt rather early in their development. The applicability of classification procedures to fill this need was recognized, by which the knowledge and experience within the industry could be codified into classification rules and administered for the benefit of the industry. The special mission of the units made it necessary to include some requirements not associated with classification of conventional vessels, such as stability and anticipated wave heights. The new rules are subject to revision as experience and new developments may indicate.

Data capture for injury prevention

2020 ◽  
Vol 26 (4) ◽  
pp. 391-394 ◽  
Author(s):  
Yvette Holder
Keyword(s):  
Missing Data ◽  
Data Collection ◽  
Data Capture ◽  
Data Sources ◽  
Surveillance Systems ◽  
New Developments ◽  
Coding Systems ◽  
Wide Range ◽  
Data Elements

More than a half-century of developments have expanded the demand for data for the prevention of injuries. This article follows the progress as data collection becomes more comprehensive, encompassing all types of injuries, in a wide range of economic and cultural environments. It describes the challenges of new developments and the responses to deal with them, challenges of poor coordination of data sources, sector ownership, non-uniformity and missing data elements that are critical for prevention. The tools and approaches that may be employed are outlined, from observatories to surveillance systems, from standardised injury coding systems such as the International Classification of External Cause of Injuries to manuals and guidelines for collecting injury data through surveillance and surveys. More and better data encourages greater utilisation which in turn identifies new issues to be addressed, a most exciting situation for any injury practitioner.

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