Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers

A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An ANN trained by back-propagation algorithms was developed to predict two performance indices based on the three input variables. The results showed good agreement between predicted results using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45%. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.     

Optimal neuro‐fuzzy control of hepatitis C virus integrated by genetic algorithm

Hepatitis C blood born virus is a major cause of liver disease that more than three per cent of people in the world is dealing with, and the spread of hepatitis C virus (HCV) infection in different populations is one of the most important issues in epidemiology. In the present study, a new intelligent controller is developed and tested to control the hepatitis C infection in the population which the authors refer to as an optimal adaptive neuro‐fuzzy controller. To design the controller, some data is required for training the employed adaptive neuro‐fuzzy inference system (ANFIS) which is selected by the genetic algorithm. Using this algorithm, the best control signal for each state condition is chosen in order to minimise an objective function. Then, the prepared data is utilised to build and train the Takagi–Sugeno fuzzy structure of the ANFIS and this structure is used as the controller. Simulation results show that there is a significant decrease in the number of acute‐infected individuals by employing the proposed control method in comparison with the case of no intervention. Moreover, the authors proposed method improves the value of the objective function by 19% compared with the ordinary optimal control methods used previously for HCV epidemic.Inspec keywords: epidemics, diseases, blood, medical computing, microorganisms, genetic algorithms, fuzzy control, neurocontrollers, adaptive control, medical control systemsOther keywords: genetic algorithm, hepatitis C blood born virus, liver disease, hepatitis C virus infection, epidemiology, intelligent controller, optimal adaptive neuro‐fuzzy controller, adaptive neuro‐fuzzy inference system, ANFIS, genetic algorithm, control signal, state condition, objective function minimisation, Takagi‐Sugeno fuzzy structure, acute‐infected individuals, ordinary optimal control methods, HCV epidemic     

New efficient and practical verifiable multi-secret sharing schemes

In 2006, Zhao et al. proposed a practical verifiable multi-secret sharing based on Yang et al.’s and Feldman’s schemes. In this paper we propose two efficient, computationally secure (t,n), and verifiable multi-secret sharing schemes based on homogeneous linear recursion. The first scheme has the advantage of better performance, a new simple construction and various techniques for the reconstruction phase. The second scheme requires fewer public values with respect to Zhao et al.’s and Shao and Cao schemes. These schemes are easy to implement and provide great capabilities for many applications.     

The power of ants in solving Distributed Constraint Satisfaction Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important area of research for artificial intelligence and multi-agent systems. Also, Ant Colony Optimization (ACO) is an important evolutionary method for solving various optimization problems. This paper demonstrates the power of ants in solving DCSPs and describes a new approach for such a solution, showing how it differs from previous ACO-based DCSP solvers. The presented algorithm is designed to provide the special requirements that are important in the distributed form of Constraint Satisfaction Problem (CSP). The paper describes the important criteria for distributed CSP and then demonstrates how the presented algorithm stands out over similar DCSP solvers considering these criteria. Finally, the proposed approach is evaluated on random binary problems. The practical results show that this method, in most of the cases, outperforms the Asynchronous Backtracking Algorithm (ABT) and Distributed Breakout Algorithm (DBA) two important algorithms in this field of research.     

Influence of solvent,Lewis acid–base complex,and nanoparticles on the morphology and gas separation properties of polysulfone membranes

A series of polysulfone (PSF) membranes were prepared using different solvents: dimethylformamide (DMF), tetrahydrofuran, dimethylacetamide, and n-methyl-2-pyrrolidone (NMP). The PSF membrane prepared by NMP showed the highest gas permeability. The influence of propionic acid as a Lewis acid on gas separation properties of the PSF was explored. The PSF membrane prepared by the casting solution containing 25 wt% PSF, 35 wt% propionic acid, and 40 wt% NMP showed a superior gas separation performance. The gas permeation measurements indicated that incorporating 30 wt% γ-alumina nanoparticles into the PSF matrix resulted in about the respective 43% and 41% increase in CO₂ and O₂ permeability together with a rise in CO₂/CH₄ and O₂/N₂ selectivities (13% and 7%, respectively). Furthermore, by rearranged modified Maxwell model, the role and nature of the interfacial layer in the PSF-based mixed matrix membranes were mathematically analyzed considering a reduced permeability factor.     

Micromorphology and Optical Bandgap Characterization of Copper Oxide Nanowires

Due to the industrial applications of nasno materials, the growth of Copper oxide (CuO) nanowires (NWs) at the same and opposite directions of the electric and gravitational fields was investigated to study the effects of fields on the NWs properties. The experiments were designed to grow NWs using thermal oxidation method at 450 ^°C for 50 h. NWs growth was evaluated to study two distinct cases; first, substrates exposed to the gravitational field and second those treated with electric field (EF) in-lined with gravitation field (GF). It was observed that the electric field developed a diameter homogeneity while compressing the NWs and decreasing the diameters. Furthermore, the GF influenced only the length of the NWs, while the EF had an impact on both length and diameter of the NWs. The direction of fields played an important role in NWs morphology and intensity of XRD pattern and optical properties. It was also observed that field direction greatly influenced the NWs length and diameter.