scholarly journals Simulations of the spread of COVID-19 and control policies in Tunisia

2021 ◽  
Author(s):  
Slimane Ben Miled ◽  
Amira Kebir
Keyword(s):  
Mass Screening ◽  
Tunisian Population ◽  
Model Parameters ◽  
Control Policies ◽  
Model Based ◽  
Asymptomatic Patients ◽  
And Control

Background: On March 11, 2020, the WHO announced that the COVID-19 outbreak had become pandemic, indicating that it was au- tonomous on several continents. Tunisia’s targeted containment and screen- ing strategy aligns with the WHO’s initial guidelines. This method is now showing its limitations. Mass screening in some countries shows that asymptomatic patients play an important role in spreading the virus through the population.Objective: Our goals are first to assess Tunisia’s COVID-19 control policies, and then understand the effect of various detection, quarantine and confinement strategies and the rule of asymptomatic patients on the spread of the virus in the Tunisian population. Methods: We develop and analyze a mathematical and epidemiologi- cal models for COVID- 19 in Tunisia. The data come from the Tunisian Health Commission dataset. Results: We calibrate different parameters of the model based on the Tunisian data, we calculate the expression of the basic reproduction num- ber R0 as a function of the model parameters and, finally, we carry out simulations of interventions and compare different strategies for suppress- ing and controlling the epidemic. Conclusions: We show that Tunisia’s control policies are effective in screening infected and asymptomatic persons.

scholarly journals Simulations of the spread of COVID-19 and control policies in Tunisia

2020 ◽  
Author(s):  
Slimane BenMiled ◽  
Amira Kebir
Keyword(s):  
Epidemiological Model ◽  
Tunisian Population ◽  
Control Policies ◽  
Asymptomatic Patients ◽  
Asymptomatic Individuals ◽  
And Control

AbstractWe develop and analyze in this work an epidemiological model for COVID-19 using Tunisian data. Our aims are first to evaluate Tunisian control policies for COVID-19 and secondly to understand the effect of different screening, quarantine and containment strategies and the rule of the asymptomatic patients on the spread of the virus in the Tunisian population. With this work, we show that Tunisian control policies are efficient in screening infected and asymptomatic individuals and that if containment and curfew are maintained the epidemic will be quickly contained.

scholarly journals Model-Guided Data-Driven Optimization for Automotive Compression Ignition Engine Systems

2019 ◽  
Vol 141 (03) ◽  
pp. S16-S23 ◽  
Author(s):  
Qingyuan Tan ◽  
Xiang Chen ◽  
Ying Tan ◽  
Ming Zheng
Keyword(s):  
Optimization Problems ◽  
Model Parameters ◽  
Compression Ignition Engine ◽  
Automotive Systems ◽  
Model Based ◽  
Expected Performance ◽  
And Control ◽  
Parameter Values ◽  
Engine Systems ◽  
Operating Points

Essentially, the performance improvement of automotive systems is a multi-objective optimization problem [1–4] due to the challenges in both operation management and control. The interconnected dynamics inside the automotive system normally requires precise tuning and coordination of accessible system inputs. In the past, such optimization problems have been approximately solved through expensive calibration procedures or an off-line local model-based approaches where either a regressive model or a first-principle model is used. The model-based optimization provides the advantage of finding the optimal model parameters to allow the model to be used to predict the real system behavior reasonably [5]. However, other than the model complexities, there are practically two issues facing the integrity of these models: modeling uncertainty due to inaccurate parameter values and/or unmodeled dynamics, and locally effective range around operating points. As a result, the optimum solutions extracted from the model-based approach could be subject to failure of expected performance [6].

scholarly journals Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties

2021 ◽  
Vol 8 ◽  
Author(s):  
S. M. Nahid Mahmud ◽  
Scott A. Nivison ◽  
Zachary I. Bell ◽  
Rushikesh Kamalapurkar
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Model Parameters ◽  
Parametric Uncertainties ◽  
Learning Approaches ◽  
Learning Method ◽  
Critical Systems ◽  
Control Policies ◽  
Safety Critical ◽  
Model Based

Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases. In general, safety guarantees are critical in reinforcement learning when the system is safety-critical and/or task restarts are not practically feasible. In optimal control theory, safety requirements are often expressed in terms of state and/or control constraints. In recent years, reinforcement learning approaches that rely on persistent excitation have been combined with a barrier transformation to learn the optimal control policies under state constraints. To soften the excitation requirements, model-based reinforcement learning methods that rely on exact model knowledge have also been integrated with the barrier transformation framework. The objective of this paper is to develop safe reinforcement learning method for deterministic nonlinear systems, with parametric uncertainties in the model, to learn approximate constrained optimal policies without relying on stringent excitation conditions. To that end, a model-based reinforcement learning technique that utilizes a novel filtered concurrent learning method, along with a barrier transformation, is developed in this paper to realize simultaneous learning of unknown model parameters and approximate optimal state-constrained control policies for safety-critical systems.

Model-based programming tools for integrated monitoring, simulation, diagnosis and control

1993 ◽  
Author(s):  
Gabor Karsai ◽  
Samir Padalkar ◽  
Hubertus Franke ◽  
Janos Sztipanovits
Keyword(s):  
Integrated Monitoring ◽  
Model Based ◽  
Programming Tools ◽  
And Control

Model-based Management and Control of the Bioreactions in a Collection System

2018 ◽  
Vol 2018 (13) ◽  
pp. 2700-2708 ◽  
Author(s):  
Lisha Guo ◽  
John Walton ◽  
Sovanna Tik ◽  
Zachary Scott ◽  
Keshab Raj Sharma ◽  
...  
Keyword(s):  
Collection System ◽  
Model Based ◽  
And Control

scholarly journals Creating and Maintaining Structural Hindrances to Criminal Justice Control – A Policy Analysis on the Normalisation of Parental Violence as a Crime in Finland

2021 ◽  
pp. 096466392110208
Author(s):  
Riikka Kotanen
Keyword(s):  
Criminal Justice ◽  
Criminal Law ◽  
Interpersonal Violence ◽  
Legal Regulation ◽  
Division Of Labour ◽  
Legal Form ◽  
Parental Violence ◽  
Control Policies ◽  
And Control

In the context of home, violence remains more accepted when committed against children than adults. Normalisation of parental violence has been documented in attitudinal surveys, professional practices, and legal regulation. For example, in many countries violent disciplining of children is the only legal form of interpersonal violence. This study explores the societal invisibility and normalisation of parental violence as a crime by analysing legislation and control policies regulating the division of labour and involvement between social welfare and criminal justice authorities. An empirical case study from Finland, where all forms of parental violence were legally prohibited in 1983, is used to elucidate the divergence between (criminal) law and control policies. The analysis demonstrates how normalisation operates at the policy-level where, within the same system of control that criminalised these acts, structural hindrances are built to prevent criminal justice interventions.

scholarly journals A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Frauke Kachholz ◽  
Jens Tränckner
Keyword(s):  
Land Use ◽  
River Flow ◽  
Land Use Changes ◽  
Small Rivers ◽  
Small Scale ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Model Based ◽  
Current State ◽  
The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

scholarly journals Model-Based Slippage Estimation to Enhance Planetary Rover Localization with Wheel Odometry

2021 ◽  
Vol 11 (12) ◽  
pp. 5490
Author(s):  
Anna Maria Gargiulo ◽  
Ivan di Stefano ◽  
Antonio Genova
Keyword(s):  
Inertial Measurement Unit ◽  
Correction Method ◽  
Harsh Environments ◽  
Measurement Unit ◽  
Planetary Rovers ◽  
Model Based ◽  
Accurate Knowledge ◽  
And Control ◽  
Wheeled Vehicles ◽  
Rover Localization

The exploration of planetary surfaces with unmanned wheeled vehicles will require sophisticated software for guidance, navigation and control. Future missions will be designed to study harsh environments that are characterized by rough terrains and extreme conditions. An accurate knowledge of the trajectory of planetary rovers is fundamental to accomplish the scientific goals of these missions. This paper presents a method to improve rover localization through the processing of wheel odometry (WO) and inertial measurement unit (IMU) data only. By accurately defining the dynamic model of both a rover’s wheels and the terrain, we provide a model-based estimate of the wheel slippage to correct the WO measurements. Numerical simulations are carried out to better understand the evolution of the rover’s trajectory across different terrain types and to determine the benefits of the proposed WO correction method.

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