Artificial neural networks for bilateral prediction of formulation parameters and drug release profiles from cochlear implant coatings fabricated as porous monolithic devices based on silicone rubber

2013 ◽  
Vol 66 (5) ◽  
pp. 624-638 ◽  
Author(s):  
Pedram Nemati ◽  
Mohammad Imani ◽  
Farhid Farahmandghavi ◽  
Hamid Mirzadeh ◽  
Ehsan Marzban-Rad ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Drug Release ◽  
Cochlear Implant ◽  
Silicone Rubber ◽  
Artificial Neural ◽  
Formulation Parameters ◽  
Implant Coatings ◽  
Release Profiles

scholarly journals STUDY OF INFLUENCE OF STABILIZERS ON BURNING OF SILICONE RUBBER WITH HELP ARTIFICIAL NEURAL NETWORKS

2017 ◽  
Vol 61 (1) ◽  
pp. 84
Author(s):  
Viktor S. Abrukov ◽  
Konstantin V. Efimov ◽  
Nikolai A. Tarasov ◽  
Nikolay I. Kol'tsov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Silicone Rubber ◽  
Combustion Process ◽  
Carbon Chain ◽  
Relative Height ◽  
Ann Model ◽  
Multifactor Model ◽  
Artificial Neural ◽  
Silicone Rubbers

Silicone rubbers are used for manufacturing fireproof insulating sheaths for cables and high voltage insulators. Highly oil-resistant silicone rubber of brand IRP 1338 of JSC “Kazan plant of synthetic caoutchouc” production should be singled out among silicone rubbers. This rubber is made from synthetic caoutchouc SKTV and contains Aerosil A-300, titanium oxide, fumed silica U-333 curing agent Perkadox BC-FF and anti-structuring agent ND-8 - α,ω-polydimethyl-siloxanediol. Previously we investigated the kinetics of combustion process of this rubber containing anti-structuring agent ND-8 as well as Ca/Zn-stabilizer Kompanoks (2,6-bis((3,5-di-tert-butyl-4-hydroxyphenyl)methyl)cyclohexane-1-one) and their combination which are used to enhance the thermal stability of the rubbers on the basis of carbon-chain caoutchoucs. In given paper using artificial neural networks (ANN) the computational multifactor model of combustion of rubber IRP 1338 has been obtained. The influence of selected stabilizers on the combustion rate of silicone rubber IRP 1338 has been studied. The combustion process was investigated by measuring at different points in time the relative height of the unburned portion of the rubber samples in the form of standard rectangular bars with a size of 10x2x2 mm. Combustion occurred under the action of the infrared beam (wavelength 10.6 μm) of laser LG-25  at three temperatures radiation (800, 900, 1000°C). As the main factors influencing the combustion of rubber, the laser radiation temperature was chosen, under which the forced combustion of rubber occurred. The nature of the stabilizers introduced into the rubber and the combustion temperature of the rubber was measured by a thermocouple placed on the surface of combustion rubber. The objective function of ANN-model was the relative height of the unburned portion of the rubber samples. Moreover, it was believed, the greater the relative height of the unburned portion of the rubber samples, the lower the burning rate and the higher the efficiency of the stabilizer. The received ANN-model has allowed to reveal three stages of combustion of rubber and to investigate features of influence of stabilizers on process of combustion. It was established that from the three stabilizers studied, the Ca/Zn stabilizer most effectively slows down the combustion of rubber due to the interaction of calcium oleates and zinc of this stabilizer with caoutchouc molecules. Thus, the possibility of increasing the fire resistance of rubber based on the silicone rubber SKTV by replacing the anti-structuring agent ND-8 on the Ca/Zn stabilizer has been established.Forcitation:Abrukov V.S., Efimov K.V., Tarasov N.A., Koltsov N.I. Study of influence of stabilizers on burning of silicone rubber with help artificial neural networks. Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol. 2018. V. 61. N 1. P. 84-88

scholarly journals Effect of Processing/Formulation Parameters on Particle Size of Nanoemulsions Containing Ibuprofen - An Artificial Neural Networks Study

2020 ◽  
Vol 27 (2) ◽  
pp. 230-237
Author(s):  
Ali Hanafi ◽  
Amir Amani
Keyword(s):  
Neural Networks ◽  
Particle Size ◽  
Artificial Neural Networks ◽  
Droplet Size ◽  
Tween 80 ◽  
Homogenization Time ◽  
Minimum Particle Size ◽  
Artificial Neural ◽  
Formulation Parameters ◽  
Positive Effect

Background: Nanoemulsions are colloidal transparent systems for the delivery of hydrophobic drugs. This study aimed to determine the effect of parameters affecting particle size of a nanoemulsion containing ibuprofen using artificial neural networks (ANNs). Methods: Nanoemulsion samples with different values of independent variables, namely, concentration of ethanol, ibuprofen and Tween 80 as well as exposure (homogenization) time were prepared and their particle size was measured using dynamic light scattering (DLS). The data were then modelled by ANNs. Results: From the results, increasing the exposure time had a positive effect on reducing droplet size. The effect of concentration of ethanol and Tween 80 on droplet size depended on the amount of ibuprofen. Our results demonstrate that ibuprofen concentration also had a reverse relation with the size of the nanoemulsions. Conclusion: It was concluded that to obtain minimum particle size, exposure (homogenization)time should be maximized.

Application of artificial neural networks (ANNs) and genetic programming (GP) for prediction of drug release from solid lipid matrices

2012 ◽  
Vol 436 (1-2) ◽  
pp. 877-879 ◽  
Author(s):  
Sinan Güres ◽  
Aleksander Mendyk ◽  
Renata Jachowicz ◽  
Przemysław Dorożyński ◽  
Peter Kleinebudde
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Drug Release ◽  
Genetic Programming ◽  
Solid Lipid ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document