The clarification and deacidification of grape juice by ion-exchange resins

Studies on the clarification (prevention of argol precipitation) and deacidification of grape juice using locally prepared ion-exchange resins are reported. The process consists of passing the juice through a cation-exchange column followed by deacidification to the desired level in an anion-exchange column. The loss of minerals attended by cation-exchange treatment could be made up by limiting the ion exchange or by blending the ion-exchange-treated juice with the fresh juice so that the potassium content of the juice is nearly 100 mg-%. The juice thus obtained compared favourably with the conventionally clarified juice with respect to clarity, organoleptic quality and chemical composition.     

On-Line Learning from Search Failures

Learning by explaining failures and avoiding similar ones thereafter is an attractive way to speed up problem solving. However, previous methods for explanation-based learning from failure can take too long to detect failures, explain them, or test the learned rules. This expense is especially critical for adaptive search, in which control knowledge acquired while solving an individual problem instance must be learned quickly enough to speed up its solution.We present an adaptive search technique that speeds up state-space search by learning heuristic censors while searching. The censors speed up search by pruning away more and more of the space until a solution is found in the pruned space. Censors are learned by explaining dead ends and other search failures. To learn quickly, the technique overgeneralizes by assuming that certain constraints are preservable, i.e., remain true along at least one solution path. A recovery mechanism detects violations of this assumption and selectively relaxes learned censors. The technique, implemented in an adaptive problem solver named FAILSAFE-2, learns useful heuristics that cannot be learned by other reported methods.We present experimental evidence that FAILSAFE-2 is effective (learns useful rules, even in recursive domains where PRODIGY and STATIC do not), adaptive (learns fast enough to pay off even within a single problem), and general (speeds up diverse problem solvers, even initially strong ones).     

Ultrasound‐induced nucleation in microcellular polymers

In this study, microcellular Acrylonitrile–Butadiene–Styrene foams with high cell density and expansion ratio has been manufactured using ultrasound‐induced nucleation technique in solid‐state batch foaming process. Influence of sonication time, sonication frequency, and ultrasound power were found very crucial in designing of cellular morphology. The initial 10 s of ultrasound exposure was found to influence the foam morphology critically. Longer periods of ultrasound exposure developed foams with lower average cell size as compared to foams developed with lesser ultrasound exposure time. Higher sonication power resulted in foams with uniform morphology and higher cell densities as compared to foams developed with lower sonication intensities. Finally, the ultrasonic frequency was also found to influence the morphology intensely. Low frequency sonication resulted in foams with uniform cell distribution, whereas high frequency sonication developed bimodal microcellular type of microstructure. The results coherently demonstrate that with the advent of ultrasonic waves, the energy barrier for cell nucleation swiftly decreases which enhances the cell density in the final foamed product. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40742.     

A Fractal Dimension Based Framework for Night Vision Fusion

In this paper, a novel fusion framework is proposed for night-vision applications such as pedestrian recognition, vehicle navigation and surveillance. The underlying concept is to combine low-light visible and infrared imagery into a single output to enhance visual perception. The proposed framework is computationally simple since it is only realized in the spatial domain. The core idea is to obtain an initial fused image by averaging all the source images. The initial fused image is then enhanced by selecting the most salient features guided from the root mean square error (RMSE) and fractal dimension of the visual and infrared images to obtain the final fused image. Extensive experiments on different scene imaginary demonstrate that it is consistently superior to the conventional image fusion methods in terms of visual and quantitative evaluations.      

Information system selection for a supply chain based on current trends: the BRIGS approach

Neural Computing and Applications - Information systems are very crucial in today’s organizations, and hence the selection of the right system has become a very critical decision. As time has...     

Electromagnetic Functionalization of Wide‐Bandgap Dielectric Oxides by Boron Interstitial Doping

A surge in interest of oxide‐based materials is testimony for their potential utility in a wide array of device applications and offers a fascinating landscape for tuning the functional properties through a variety of physical and chemical parameters. In particular, selective electronic/defect doping has been demonstrated to be vital in tailoring novel functionalities, not existing in the bulk host oxides. Here, an extraordinary interstitial doping effect is demonstrated centered around a light element, boron (B). The host matrix is a novel composite system, made from discrete bulk LaAlO₃:LaBO₃ compounds. The findings show a spontaneous ordering of the interstitial B cations within the host LaAlO₃ lattices, and subsequent spin‐polarized charge injection into the neighboring cations. This leads to a series of remarkable cation‐dominated electrical switching and high‐temperature ferromagnetism. Hence, the induced interstitial doping serves to transform a nonmagnetic insulating bulk oxide into a ferromagnetic ionic–electronic conductor. This unique interstitial B doping effect upon its control is proposed to be as a general route for extracting/modifying multifunctional properties in bulk oxides utilized in energy and spin‐based applications.     

Neuro-Fuzzy Control of Railcar Vibrations Using Semiactive Dampers

Abstract:   This article describes a new approach to reducing vertical vibrations in a 70-ton railcar using a neuro-fuzzy controller and a magnetorheological (MR) damper. A semiactive control technique is developed for a two-degree-of-freedom quarter car model of the railcar that has an installed MR damper. A fuzzy controller in real time continuously updates damping properties of the device. The controller uses feedback acceleration of the freight mass to specify a voltage signal to the MR damper. Correlations between acceleration (controller input) and voltage applied to the MR damper (controller output) are developed using Neuro Fuzzy Controller (NEFCON). To assess effectiveness of the semiactive control scheme, responses of the railcar to various haul conditions are compared with those for uncontrolled and for passive operating conditions of the MR damper. Results indicate that semiactively controlled MR dampers can reduce vibrations to acceptable levels provided that sufficient force capacity can be supplied by the damper .