A systematic approach to preliminary polymer process development: modeling and design

A general and complete methodology is presented to facilitate systematic modeling and design of polymer processes during the early development period. To capture and handle the subjective type of uncertainty, embedded in the preliminary process development, fuzzy theories are used as a basis to model and design the process in the presence of ambiguity and vagueness. Physical membership functions are developed for mapping the relation between process variables and the associated fuzzy uncertainties. Based on the qualitative results generated using our previously proposed “linguistic based preliminary design method,” the process modeling can be followed even in the absence of any process governing equations. The modeling is carried out by establishing an appropriate fuzzy reasoning system which provides a specific functional mapping that relates input process variables to one (or more than one) output performance parameter(s). A reduced yet feasible domain is generated by our qualitative design scheme to constrain the process variables. Now, any optimization routine can then be employed to search for a proper process design. We demonstrate the effectiveness of the proposed methodology by its application to a typical compression molding process.     

Enhanced Photocatalytic and Antibacterial Activities of ZnSe Nanoparticles

Water and microbial contamination is a serious issues to aquatic system and human health. The metal selenide has a tremendous technique to degrade the dyes and bacteria.The present work reports the synthesis of ZnSe nanoparticles in a simple co-precipitation method. The synthesized samples were analyzed by structural, optical, morphological, catalytic and biological activity. The size and bandgap by annealing temperature tuning which are confirm by X-ray Diffraction and UV–Visible spectrometer. The quasi-spherical shapes were confirmed by Scanning Electron Microscope and Transmission Electron Microscope.The photo excited electrons have trapped the metals and promoting the degradation system. The release of ions to the surface was acknowledged by Photo Luminescence spectroscopy. The photocatalytic dye degradation of the Methyl Orange showed that the enhanced activity in high temperature. The photocatalytic dye degradation activity suggested that the temperature change the production of free radicals and ROS formation. The hydroxyl radicals were slewing the dye molecules and bacteria. The obtained results giving the information of ZnSe nanoparticles are one of the fascinating research areas in the current research world. Because of its large application in different field it acts as a promoting catalytic and biological application.

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Compression based clustering technique for enhancing accuracy in web scale videos

Multimedia Tools and Applications - Detection and clustering of commercial advertisements plays an important role in multimedia indexing also in the creation of personalized user content. In...     

Equivalence of classicality and separability based on P phase–space representation of symmetric multiqubit states

Classical and quantum world views differ in peculiar ways. Understanding decisive quantum features—for which no classical explanation exist—and their interrelations is of foundational interest. Moreover, recognizing non-classical features carries practical significance in information processing tasks as it offers insights as to why quantum protocols work better than their classical counterparts. We focus here on two celebrated notions of non-classicality viz., negativity of P phase–space representation and entanglement in symmetric multiqubit systems. We prove that they imply each other.     

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating the pathways using multiple criteria and analytics. We present a Group Adaptive Hybrid Routing Algorithm (GAHRA) for gathering portability, which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations. Based on this aspect, the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach, with the objectives of enhancing the output of MANET routing computation in each hub. Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.     

Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography

The Journal of Supercomputing - Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure...     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Field equations for incompressible non-viscous fluids using artificial intelligence

In this article, we introduce new field equations for incompressible non-viscous fluids, which can be treated similarly to Maxwell’s electromagnetic equations based on artificial intelligence algorithms. Lagrangian and Hamiltonian formulations are used to arrive at field equations that are solved using convolutional neural networks. Four linear differential equations, which describe the two fields, namely, the dynamic pressure and the vortex fields, are derived, and these can be used in place of Euler’s equation. The only assumption while deriving this equation is that the dynamic pressure and vortex fields obey the superposition principle. The important finding to be noted is that Euler’s fluid equations can be converted into field equations analogous to Maxwell’s electromagnetic equations. We solve the flow problem for laminar flow past a cylinder, sphere, and cone in two dimensions similar to the conduction in a uniform electric field and arrive at closed-form expressions. These closed-form expressions, which are obtained for the potentials of fluid flow, are similar to the streamline potential functions in the case of fluid dynamics.

     

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.     

Mixed-Mode Stress Intensity Factor Determination of Riveted Lap Joints Used in Aircraft Fuselage Structures

Journal of Failure Analysis and Prevention - In the present work, mixed-mode stress intensity factor (SIF) of multiple cracks in a riveted lap joint has been determined, with and without the...