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81.
Seed processing technologies are essential for seed safety and functionality through protection of physicochemical quality, pathogen inactivation, aflatoxin detoxification and alleviation of mutagenicity. Design of a pilot-scale unit of pulsed electric fields (PEF) to treat sesame seeds with respect to quality parameters, Aspergillus parasiticus inactivation and aflatoxin reduction as well as alleviation of aflatoxin mutagenicity were prompted in this study. PEF energy ranged from 0.97 to 17.28 J achieved maximum reductions of peroxide value and acidity number of 67.4 and 85.7%, respectively, and did not change color L*, a*, b* and hue values. A 60% reduction of A. parasiticus counts occurred at the maximum PEF energy. Aflatoxins G1, G2, B1, and B2 contents decreased by 94.7, 92.7, 86.9, and 98.7%, respectively. Except for the samples treated by 2.16 J with 100 μg/plate and by 6.80 J with 10 μg/plate, PEF treatment provided elimination of aflatoxin mutagenity. It is concluded that PEF treatment can be used to treat sesame seeds with preservation of physicochemical properties, inactivation of A. parasiticus and decomposition of aflatoxins with reduced mutagenicity.  相似文献   
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Theoretical Foundations of Chemical Engineering - The extraction of nitric and oxalic acids by neutral compounds TBP, DBBP, and TBPO have been studied. The optimal conditions of their separation...  相似文献   
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Journal of Communications Technology and Electronics - Abstract—The article presents a rigorous numerical solution, using the Wiener–Hopf method, to the problem of plane wave...  相似文献   
84.
The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.  相似文献   
85.

Surface integrity characterization of manufactured component is very important as it significantly affects the in-service performance of the component. Till now, surface integrity was evaluated using conventional measurement technique like microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester. But, this technique being laboratory based cannot be used for in-service monitoring of the surface integrity. The present study focuses on the characterization of surface integrity upon electric discharge machined sample using non-destructive magnetic Barkhausen noise technique. Electric discharge machining was performed in die-sinking mode on die steel using copper–tungsten electrode (negative polarity). Experiment was performed by selecting different levels of peak current, gap voltage and pulse on time. Surface integrity characteristics like microhardness change, residual stress, microstructural alteration and surface roughness were analysed using microhardness tester, X-ray diffraction, optical microscopy and surface roughness tester, respectively, and were then correlated with magnetic parameter like root mean square value and peak value obtained from Barkhausen noise signal. The results show a good correlation between magnetic parameter (RMS and Peak value) of Barkhausen noise with the microhardness and surface roughness of the machined sample.

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