scholarly journals Modeling the Conductivity Response to NO2 Gas of Films Based on MWCNT Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4723
Author(s):  
Ada Fort ◽  
Marco Mugnaini ◽  
Enza Panzardi ◽  
Anna Lo Grasso ◽  
Ammar Al Hamry ◽  
...  
Keyword(s):  
Dynamic Behavior ◽  
Electronic Transport ◽  
Au Nanoparticles ◽  
Model Fitting ◽  
Time Variable ◽  
Fitting Procedure ◽  
Proposed Model ◽  
Disordered Networks ◽  
Physical Quantities

This work proposes a model describing the dynamic behavior of sensing films based on functionalized MWCNT networks in terms of conductivity when exposed to time-variable concentrations of NO2 and operating with variable working temperatures. To test the proposed model, disordered networks of MWCNTs functionalized with COOH and Au nanoparticles were exploited. The model is derived from theoretical descriptions of the electronic transport in the nanotube network, of the NO2 chemisorption reaction and of the interaction of these two phenomena. The model is numerically implemented and then identified by estimating all the chemical/physical quantities involved and acting as parameters, through a model fitting procedure. Satisfactory results were obtained in the fitting process, and the identified model was used to further the analysis of the MWCNT sensing in dynamical conditions.

scholarly journals Double Two-State Opsin Model With Autonomous Parameter Inference

2021 ◽  
Vol 15 ◽  
Author(s):  
Ruben Schoeters ◽  
Thomas Tarnaud ◽  
Luc Martens ◽  
Wout Joseph ◽  
Robrecht Raedt ◽  
...  
Keyword(s):  
Differential Equations ◽  
Markov Models ◽  
Neuronal Response ◽  
Computational Cost ◽  
Model Fitting ◽  
Time Frame ◽  
Great Accuracy ◽  
Computationally Efficient ◽  
Fitting Procedure ◽  
Proposed Model

Optogenetics has a lot of potential to become an effective neuromodulative therapy for clinical applications. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin's non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a double two-state opsin model as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for autonomous model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins (ChR2(H134R) and MerMAID). Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013). Finally, a computational speed gain was observed with the proposed model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.

scholarly journals A Kalman Filter for Nonlinear Attitude Estimation Using Time Variable Matrices and Quaternions

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6731
Author(s):  
Álvaro Deibe ◽  
José Augusto Antón Nacimiento ◽  
Jesús Cardenal ◽  
Fernando López Peña
Keyword(s):  
Kalman Filter ◽  
Dynamic Behavior ◽  
State Vector ◽  
Time Variable ◽  
Explicit Calculation ◽  
Time Varying ◽  
Input And Output ◽  
Filter Approach ◽  
Augmented State ◽  
Physical Quantities

The nonlinear problem of sensing the attitude of a solid body is solved by a novel implementation of the Kalman Filter. This implementation combines the use of quaternions to represent attitudes, time-varying matrices to model the dynamic behavior of the process and a particular state vector. This vector was explicitly created from measurable physical quantities, which can be estimated from the filter input and output. The specifically designed arrangement of these three elements and the way they are combined allow the proposed attitude estimator to be formulated following a classical Kalman Filter approach. The result is a novel estimator that preserves the simplicity of the original Kalman formulation and avoids the explicit calculation of Jacobian matrices in each iteration or the evaluation of augmented state vectors.

scholarly journals Hodgkin and Huxley Opsin Model for Computationally Efficient Optogenetic Neurostimulation in Cells and Networks

2020 ◽  
Author(s):  
Ruben Schoeters ◽  
Thomas Tarnaud ◽  
Luc Martens ◽  
Wout Joseph ◽  
Robrecht Raedt ◽  
...  
Keyword(s):  
Differential Equations ◽  
Markov Models ◽  
Neuronal Response ◽  
Computational Cost ◽  
Model Fitting ◽  
Time Frame ◽  
Great Accuracy ◽  
Computationally Efficient ◽  
Fitting Procedure ◽  
Proposed Model

AbstractOptogenetics has a lot of potential to become an effective neuromodulative therapy for clinical application. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin’s non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a Hodgkin-and-Huxley based model (22HH) as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for nearly automatic model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins ChR2(H134R) and MerMAID. Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013) [1]. Finally, a computational speed gain was observed with the 22HH model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.

Diffusion of 60Co, 137Cs and 152Eu in Opalinus Clay

2008 ◽  
Vol 1124 ◽  
Author(s):  
Miguel García-Gutiérrez ◽  
José Luis Cormenzana ◽  
Tiziana Missana ◽  
Manuel Mingarro ◽  
Ursula Alonso
Keyword(s):  
Analytical Solution ◽  
Diffusion Coefficients ◽  
Model Fitting ◽  
Bedding Plane ◽  
Opalinus Clay ◽  
Apparent Diffusion Coefficients ◽  
Fitting Procedure ◽  
Large Dispersion ◽  
Apparent Diffusion ◽  
Paper Filter

AbstractThis study addresses the diffusion of representative sorbing elements, cobalt, cesium and europium in the Opalinus Clay (OPA). The methodology used here to determine diffusion coefficients is the ‘instantaneous planar source’ method. In this setup, a paper filter impregnated with tracer is introduced between two clay samples, avoiding contact between the tracer and the experimental vessels. The apparent diffusion coefficients (Da) perpendicular to the bedding plane, obtained with this experimental method and fitting the experimental results with an analytical solution, were Da(Co) = (2.4-3.5)·10-14 m2/s, Da(Cs) = (5.9-8.0)·10-14 m2/s, and Da(Eu) = (1.0-2.1)·10-15 m2/s. With cobalt and cesium, classical in-diffusion experiments were also performed for comparison, and similar Da values were obtained but with a large dispersion. To analyze the possible effects of the paper filter impregnated with the tracer on the determinations of Da with the analytical solution, one experiment was also analyzed using a detailed stochastic model of the setup. The good agreement between the two modeling approaches confirms the validity of this experimental setup and the analytical model fitting procedure.

scholarly journals FRACS: modelling of the dust disc of the B[e] CPD-57° 2874 from VLTI/MIDI data

2010 ◽  
Vol 6 (S272) ◽  
pp. 382-383
Author(s):  
Philippe Bendjoya ◽  
Armando Domiciano de Souza ◽  
Gilles Niccolini
Keyword(s):  
Computing Time ◽  
Mid Infrared ◽  
Fitting Procedure ◽  
Dust Temperature ◽  
Tracing Algorithm ◽  
Short Computing Time ◽  
Time Required ◽  
Best Fit ◽  
Physical Quantities ◽  
Automatic Fitting

AbstractThe physical interpretation of spectro-interferometric data is strongly model dependent. On one hand, models involving elaborate radiative transfer solvers are in general too time consuming to perform an automatic fitting procedure and derive astrophysical quantities and their related errors. On the other hand, using simple geometrical models does not give sufficient insights into the physics of the object. We developed a numerical tool optimised for mid-infrared (mid-IR) interferometry, the Fast Ray-tracing Algorithm for Circumstellar Structures (FRACS). Thanks to the short computing time required by FRACS, best-fit parameters and uncertainties for several physical quantities were obtained, such as inner dust radius, relative flux contribution of the central source and of the dusty CSE, dust temperature profile, disc inclination.

Familial aggregation ofV˙o 2 max response to exercise training: results from the HERITAGE Family Study

1999 ◽  
Vol 87 (3) ◽  
pp. 1003-1008 ◽  
Author(s):  
Claude Bouchard ◽  
Ping An ◽  
Treva Rice ◽  
James S. Skinner ◽  
Jack H. Wilmore ◽  
...  
Keyword(s):  
Training Program ◽  
Familial Aggregation ◽  
Model Fitting ◽  
Heritability Estimate ◽  
Cycle Ergometer ◽  
Fitting Procedure ◽  
Response Variance ◽  
Parsimonious Models ◽  
The Mean ◽  
Response To Exercise

The aim of this study was to test the hypothesis that individual differences in the response of maximal O2 uptake (V˙o 2 max) to a standardized training program are characterized by familial aggregation. A total of 481 sedentary adult Caucasians from 98 two-generation families was exercise trained for 20 wk and was tested for V˙o 2 max on a cycle ergometer twice before and twice after the training program. The mean increase inV˙o 2 max reached ∼400 ml/min, but there was considerable heterogeneity in responsiveness, with some individuals experiencing little or no gain, whereas others gained >1.0 l/min. An ANOVA revealed that there was 2.5 times more variance between families than within families in theV˙o 2 max response variance. With the use of a model-fitting procedure, the most parsimonious models yielded a maximal heritability estimate of 47% for the V˙o 2 max response, which was adjusted for age and sex with a maternal transmission of 28% in one of the models. We conclude that the trainability ofV˙o 2 max is highly familial and includes a significant genetic component.

scholarly journals A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
G. Kothai ◽  
E. Poovammal ◽  
Gaurav Dhiman ◽  
Kadiyala Ramana ◽  
Ashutosh Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dynamic Behavior ◽  
Traffic Congestion ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Experimental Result ◽  
Learning Models ◽  
Essential Information ◽  
Proposed Model

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

scholarly journals A Robust Adiabatic Model for a Quasi-Steady Prediction of Far-Off Non-Measured Performance in Vaneless Twin-Entry or Dual-Volute Radial Turbines

2020 ◽  
Vol 10 (6) ◽  
pp. 1955
Author(s):  
José Ramón Serrano ◽  
Francisco J. Arnau ◽  
Luis Miguel García-Cuevas ◽  
Vishnu Samala
Keyword(s):  
Mass Flow ◽  
Performance Parameters ◽  
Flow Conditions ◽  
Fitting Procedure ◽  
Radial Turbine ◽  
Novel Approach ◽  
Proposed Model ◽  
Double Entry ◽  
Radial Turbines ◽  
Fitting Parameters

The current investigation describes in detail a mass flow oriented model for extrapolation of reduced mass flow and adiabatic efficiency of double entry radial inflow turbines under any unequal and partial flow admission conditions. The model is based on a novel approach, which proposes assimilating double entry turbines to two variable geometry turbines (VGTs) using the mass flow ratio ( MFR ) between the two entries as the discriminating parameter. With such an innovative approach, the model can extrapolate performance parameters to non-measured MFR s, blade-to-jet speed ratios, and reduced speeds. Therefore, the model can be used in a quasi-steady method for predicting double entry turbines performance instantaneously. The model was validated against a dataset from two different double entry turbine types: a twin-entry symmetrical turbine and a dual-volute asymmetrical turbine. Both were tested under steady flow conditions. The proposed model showed accurate results and a coherent set of fitting parameters with physical meaning, as discussed in this paper. The obtained parameters showed very similar figures for the aforementioned turbine types, which allows concluding that they are an adequate set of values for initializing the fitting procedure of any type of double entry radial turbine.

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