Freeze-drying modeling and monitoring using a new neuro-evolutive technique

This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too

CONTRIBUTIONS REGARDING THE DEFINITION OF THE TERM NEURAL NETWORK VS. NEURONAL NETWORK APPLICABLE IN THE ORGANIZATIONAL MANAGEMENT

Since there is no clear definition of the terms "neural network" and "neuronal network", this paper is aimed primarily to establish the difference between them by a rangeof comparative research.As a result, the paper  presents some argumentation regarding the differentiation and the point of views of its author, on the use of neural networks in studies and research regarding organizational management

CONTRIBUTIONS REGARDING THE DESIGN OF SOME NEURO-FUZZY NEURAL NETWORKS APPLICABLE IN THE STRATEGIC MANAGEMENT FOR ORGANIZATIONS SPECIALISED IN NONCONVENTIONAL TECHNOLOGIES

This paper presents, in particular, some results obtained from modeling neural networks of neuro-fuzzy type and the similarity with the results obtained using neural feed-forward type networks. The application is developed to the level of organizational management regarding the establishing of a future strategy of a commercial organization. It was designed a neuro-fuzzy network with five input variables of type technical and economical indicators and an exit strategy type. Solving the problem was made using Matlab system

Modeling of brick obtaining proces with artificial neural networks

In this study, neural network models were developed to predict the amount of NO discharged into the furnace chimney of a brick factory. The best performances were obtained with the MLP (6:18:6:1) model. Thus, in the training stage, the correlation coefficient was 0.9626 and the standard deviation was ± 5.61 mg/m3 and in the validation phase, a standard deviation of ± 15.24 mg/m3 is obtained. The advantages of this study derive from the important savings of time, materials and energy obtained by reducing the number of test loads in the analyzed industrial process

Experimental analysis and mathematical prediction of Cd(II) removal by biosorption using support vector machines and genetic algorithms

We investigated the bioremoval of Cd(II) in batch mode, using dead and living biomass of Trichoderma viride. Kinetic studies revealed three distinct stages of the biosorption process. The pseudo-second order model and the Langmuir model described well the kinetics and equilibrium of the biosorption process, with a determination coefficient, R2 > 0.99. The value of the mean free energy of adsorption, E, is less than 16 kJ/mol at 25°C, suggesting that, at low temperature, the dominant process involved in Cd(II) biosorption by dead T. viride is the chemical ion-exchange. With the temperature increasing to 4050°C, E values are above 16 kJ/mol, showing that the particle diffusion mechanism could play an important role in Cd(II) biosorption. The studies on T. viride growth in Cd(II) solutions and its bioaccumulation performance showed that the living biomass was able to bioaccumulate 100% Cd(II) from a 50 mg/L solution at pH 6.0. The influence of pH, biomass dosage, metal concentration, contact time and temperature on the bioremoval efficiency was evaluated to further assess the biosorption capability of the dead biosorbent. These complex influences were correlated by means of a modeling procedure consisting in data driven approach in which the principles of artificial intelligence were applied with the help of support vector machines (SVM), combined with genetic algorithms (GA). According to our data, the optimal working conditions for the removal of 98.91% Cd(II) by T. viride were found for an aqueous solution containing 26.11 mg/L Cd(II) as follows: pH 6.0, contact time of 3833 min, 8 g/L biosorbent, temperature 46.5°C. The complete characterization of bioremoval parameters indicates that T. viride is an excellent material to treat wastewater containing low concentrations of metal

Aplicațiile inteligenței artificiale în oftalmologie

Department of Ophtalmology, “Grigore T. Popa” University of Medicine and Pharmacy, Iasi,Romania, Gheorghe Asachi” Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection 11 “Cristofor- Simionescu”, Department of Chemical EngineeringRezumat. Instrumentele inteligenței artificiale și, îndeosebi, rețelele neuronale artificiale, sunt tot mai des implicate în diagnosticul și managementul personalizat al bolilor oftalmologice. Imaginile OCT sunt utilizate pentru diagnosticul precoce, monitorizarea și managementul bolilor retinei, cum ar fi edem macular diabetic (EMD) și degenerescență maculară legată de vârstă (DMLV). Citirea automată a OCT a avut rezultate promițătoare în EMD și în identificarea formelor exudative ale DMLV. Cea mai frecventă utilizare a rețelelor neuronale în oftalmologie a fost în stabilirea precoce a diagnosticului de glaucom, atunci când sunt dubii de diagnostic. Rețelele neuronale au avut un rol important în stabilirea necesităţii iniţierii terapiei precoce antiglaucomatoase pentru a preveni progresia bolii. Numeroase studii din literatura de specialitate demonstrează folosirea cu succes a acestor instrumente ale inteligenței artificiale în oftalmologie, pe direcții cum ar fi: evaluarea câmpului vizual, a nervului optic, a stratului fibrelor nervoase retiniene, oferind astfel o mai bună precizie în identificarea progresiei în glaucom şi a modificărilor retiniene în diabet. În oftalmologie, Inteligenta artificiala are potențialul de a crește accesul pacientului la screening / diagnostic clinic și la scăderea costurilor enorme solicitate de asistența medicală, mai ales atunci când riscul apariției bolii este ridicat sau comunitățile se confruntă cu resurse financiare reduse. Reţelele neuronale artificiale sunt utile în stabilirea diagnosticului diferitelor boli, însă informaţiile obţinute au rolul de a ajuta decizia finală care va fi luată de clinician, dar nu va înlocui rolul acestuia.Artificial intelligence tools, and especially artificial neural networks, are increasingly involved in the diagnosis and personalized management of ophthalmic diseases. OCT images are used for early diagnosis, monitoring and management of retinal diseases such as diabetic macular edema (EMD) and age-related macular degeneration (DMLV). Automatic reading of OCT has had promising results in EMD and in identifying exudative forms of DMLV. The most common use of neural networks in ophthalmology has been in early diagnosis of glaucoma, when there is doubt about the diagnosis. Neural networks have played an important role in determining the need for early antiglaucoma therapy to prevent disease progression. Numerous studies in the literature demonstrate the successful use of these tools of artificial intelligence in ophthalmology, in directions such as: assessment of visual field, optic nerve, retinal nerve fiber layer, thus providing better accuracy in identifying progression in glaucoma. and retinal changes in diabetes. Conclusions. In ophthalmology, Artificial Intelligence has the potential to increase patient access to screening / clinical diagnosis and to reduce the enormous costs required by healthcare, especially when the risk of disease is high or communities face limited financial resources. Artificial neural networks are useful in diagnosing various diseases, but the information obtained is intended to help the final decision that will be made by the clinician, but will not replace his role