Synergistic effect of high-intensity ultrasound and β-cyclodextrin treatments on browning control in apple juice

This work evaluated the synergistic effects of combined high-intensity ultrasound (HIU) with β-cyclodextrin (β-CD) treatments on inhibiting browning of apple juice and explored the mechanism through simulation system. The combined treatment of 300 W HIU with 0.006 g mL⁻¹ β-CD had a synergistic impact on maintaining juice colour, resulting in a 39.06% reduction in browning degree, only a 36.64% decrease in total phenolic content, and a 17.82% reduction in PPO activity. The inhibition of enzymatic browning in simulated system revealed that HIU suppressed the enzyme (Polyphenol oxidase, PPO) and β-CD inhibited enzyme (PPO) and embedded substrate (polyphenol). The results of spectroscopic analysis showed that the particle-size distribution of PPO narrowed, the content of α-helix in the secondary structure increased, the fluorescence intensity increased, and the maximum wavelength was red-shifted after HIU and β-CD treatment. Changes in structure could further result in PPO activity loss. Hence, the combined treatment could synthetically alleviate the browning of apple juice.     

Soret and Dufour effects between two rectangular plane walls with heat source/sink

This study addresses the thermo‐diffusion and the diffusion‐thermo phenomena in a semi‐infinite absorbent channel whose walls are contracting/expanding, with heat source/sink effects. The governing partial differential equations with suitable boundary conditions are transformed to a system of dimensionless ordinary differential equations. An analytic solution of the problem has been found using a technique called homotopy analysis method (HAM). HAM gives consistently valid answers to the problem over an extensive variety of parameters and also provides better accuracy. To validate the analytical results, a comparison has been presented with a numerical solution calculated by using the parallel shooting method. The effects of dimensionless parameters, that is, deformation parameter, Reynolds number, Soret and Dufour numbers, and heat source/sink parameter on the expressions of velocity, temperature, and concentration profiles are analyzed graphically to understand the physics of the deformable channel. It has been noted that the velocity across the channel is higher for the expanding channel, as compared to that for the contracting channel. Also the Soret and Dufour number increases the temperature of the fluid, and decreases the concentration. The temperature profile has an increasing behavior in the case of heat source, and a decreasing behavior in the case of heat sink.     

Characterization of the Ion Beam Current Density of the RF Ion Source with Flat and Convex Extraction Systems

Silicon - The characterization of ion beam current density distribution and beam uniformity is crucial for improving broad-beam ion source technologies. The design of the broad ion beam extraction...     

Prediction of the maximum spoutable height in spout-fluid beds

The development of the semi-theoretical correlation in Hadzisdmajlovic et al. (1983) for predicting the maximum spoutable height in a spout-fluid bed, is observed to involve certain discrepancies. Based on their experimental data, a new correlation is proposed.     

A fuzzy logic rule-based automatic target recognizer

Methods for modeling and managing uncertainty in computer vision systems have received increased attention in recent years. Automatic target recognition is one application area of computer vision where the demands are particularly acute. In this article, fuzzy logic is proposed as a means of handling uncertainty in an expert system structure for automatic target recognition. A new technique for logical inference is described which is well-suited for this type of application. A prototype system has been developed and tested on multisensor and temporal images. the results are compared to a similar expert system which used a numeric uncertainty calculus.     

Computations of Explosive Boiling in Microgravity

Dynamics of the explosive growth of a vapor bubble in microgravity is investigated by direct numerical simulation. A front tracking/finite difference technique is used to solve for the velocity and the temperature field in both phases and to account for inertia, viscosity, and surface deformation. The method is validated by comparison of the numerical results with the available analytical formulations such as the evaporation of a one-dimensional liquid/vapor interface, frequency of oscillations of capillary waves, and other numerical results. Evolution of a three-dimensional vapor nucleus during explosive boiling is followed and a fine scale structure similar to experimental results is observed. Two-dimensional simulations yield a similar qualitative instability growth.     

Statistically correlated multi-task learning for autonomous driving

Neural Computing and Applications - Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning...     

Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.     

Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization

A mobile ad hoc network (MANET) is dynamic in nature and is composed of wirelessly connected nodes that perform hop-by-hop routing without the help of any fixed infrastructure. One of the important requirements of a MANET is the efficiency of energy, which increases the lifetime of the network. Several techniques have been proposed by researchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-efficient solution. Clustering involves the selection of cluster-heads (CHs) for each cluster and fewer CHs result in greater energy efficiency as these nodes drain more power than noncluster-heads. In the literature, several techniques are available for clustering by using optimization and evolutionary techniques that provide a single solution at a time. In this paper, we propose a multi-objective solution by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad hoc network as well as energy dissipation in nodes in order to provide an energy-efficient solution and reduce the network traffic. In the proposed solution, inter-cluster and intra-cluster traffic is managed by the cluster-heads. The proposed algorithm takes into consideration the degree of nodes, transmission power, and battery power consumption of the mobile nodes. The main advantage of this method is that it provides a set of solutions at a time. These solutions are achieved through optimal Pareto front. We compare the results of the proposed approach with two other well-known clustering techniques; WCA and CLPSO-based clustering by using different performance metrics. We perform extensive simulations to show that the proposed approach is an effective approach for clustering in mobile ad hoc networks environment and performs better than the other two approaches.     

Using genetic algorithm for the optimization of seismic behavior of steel planar frames with semi-rigid connections

Beam-column connections have a significant role in the results of the analysis and the design of steel frames. In this paper, a genetic algorithm has been used for the non-linear analysis and design of steel frames. For minimizing the weight of frames, while satisfying the applied constraints and restraints such as the limits of normal and combined stresses, criteria such as target displacement(s) and the number and locations of plastic hinges were used. To analyze and design the frame elements, I and box-shaped standard sections were used for beams and columns, respectively. Finally, some clues for finding optimizing semi-rigid connection stiffness values for beam-to-column connections have been obtained. The degrees of these rigidities are obtained by a genetic algorithm during the procedure of optimization in order to reach a frame with the minimum weight. SAP2000 structural analysis program was used to perform modal analysis and linear and non-linear static solutions as well as the design of the elements. A MATLAB program was written for the process of optimization. The procedure of optimization was based on a weight minimization carried out for 9 steel frames. Thus, the optimum connection stiffness could be obtained for minimizing the weight of the structure. The results show that the non-linear analysis gives less weight for short period frames with semi-rigid connections compared to those of linear ones. However, by increasing the periods of frames, much less weights are obtained in the case of non-linear analysis with semi-rigid connections.