Analytic Comparison of Some Epidemic Models with Vaccination

AbstractIn this paper, we discuss the elementary properties of some simple SI, SR, SIR and SEIR epidemic models whose parameterizing functions (such as per-capita death rate, disease transmission, removal rate etc.) might be eventually time-varying but nonnecessarily time-integrable. Vaccination rules based of feedback, measuring the numbers of some of the partial populations defining the disease progress, are also discussed

Paying for parking : improving stated-preference surveys

This article describes an experiment which introduced random ranges into the variables used for the design of a stated preference survey and its effects on willingness to pay for parking. User behaviour at the time of parking was modelled to determine their willingness to pay in order to get to their final destination more quickly. Calculating willingness to pay is fundamental during the social and economic assessment of projects. It is important to correctly model how car parks and their users interact in order to get values which represent reality as closely as possible. Willingness to pay is calculated using a stated preference survey and by calibrating multinomial logit models, taking variable tastes into account. It is shown that a value with a low variability can be obtained for willingness to pay by correctly establishing the context of the choice and randomly changing the variables around an average value

On the Existence of Equilibrium Points, Boundedness, Oscillating Behavior and Positivity of a SVEIRS Epidemic Model under Constant and Impulsive Vaccination

This paper discusses the disease-free and endemic equilibrium points of a SVEIRS propagation disease model which potentially involves a regular constant vaccination. The positivity of such a model is also discussed as well as the boundedness of the total and partial populations. The model takes also into consideration the natural population growing and the mortality associated to the disease as well as the lost of immunity of newborns. It is assumed that there are two finite delays affecting the susceptible, recovered, exposed, and infected population dynamics. Some extensions are given for the case when impulsive nonconstant vaccination is incorporated at, in general, an aperiodic sequence of time instants. Such an impulsive vaccination consists of a culling or a partial removal action on the susceptible population which is transferred to the vaccinated one. The oscillatory behavior under impulsive vaccination, performed in general, at nonperiodic time intervals, is also discussed

Artificial neural network based adaptive control of single phase Dual Active Bridge with finite time disturbance compensation

Single phase Dual Active Bridge (DAB) has found numerous applications in modern energy architectures such as direct current (DC) microgrid, electrical vehicle charging and high voltage direct current (HVDC) system. Due to the model complexities of DAB, this work proposes a model free adaptive control method based on artificial neural network (AANN) which is capable of adjusting the weights online in finite time. The finite time learning property of the proposed controller makes it perfectly robust for the compensation of the disturbances due to source and load side variations. A proportional integral (PI) controller is used to stabilize the nominal dynamics of the system along with the AANN controller. The structure of the proposed controller is as simple as PID controller and as robust as any nonlinear control method. The AANN-PI controller is implemented on TI Launchpad (TMS320F28379D) with a 50 Watts laboratory scale DAB test bench. Finally, the performance of the AANN-PI method is compared experimentally with classical PI and sliding mode controllers

Estimation of annual average daily traffic with optimal adjustment factors

This study aimed to estimate the annual average daily traffic in inter-urban networks determining the best correlation (affinity) between the short period traffic counts and permanent traffic counters. A bi-level optimisation problem is proposed in which an agent in an upper level prefixes the affinities between short period traffic counts and permanent traffic counters stations and looks to minimise the annual average daily traffic calculation error while, in a lower level, an origin–destination (O–D) trip matrix estimation problem from traffic counts is solved. The proposed model is tested over the well-known Sioux-Falls network and applied to a real case of Cantabria (Spain) regional road network. The importance of determining appropriate affinity and the effect of localisation of permanent traffic counters stations are discussed