Characterization of CaO-CaF2-TiO2-SiO2 Based Welding Slags for Physicochemical and Thermophysical Properties

Silicon - The work is aimed at characterizing the properties of slag obtained from CaO-CaF2- TiO2-SiO2 based coatings developed for welding the offshore structures. Extreme vertices methodology was...     

A high speed roller dung beetles clustering algorithm and its architecture for real-time image segmentation

Applied Intelligence - Several practical applications like disaster detection, remote surveillance, object recognition using remote sensing satellite images, object monitoring and tracking using...     

Characterization of head modulation during touchdown process using magnetic spacing sensitivity analysis

Microsystem Technologies - Recent growth in the cloud storage industry has created a massive demand for higher capacity hard disk drives (HDD). A sub-nanometer head media spacing (HMS) remains the...     

Electronic Noise Analysis of Source-Engineered Phosphorene/Si Heterojunction Dopingless Tunnel-FET

Silicon - The implementation of two-dimensional layered material in the source-region of a Si-based tunnel field-effect transistors (T-FETs) offers some remarkable properties viz. ultralow power...     

Non-linear steady-state data reconciliation: Theoretical perspective and practical scenarios

Data reconciliation (DR) is one of the primary error handling methods to reduce measurement errors in industries that may otherwise cause misleading information about the plant. In this article, the mathematical aspects of measurement errors and their treatment by DR are discussed in detail. The flaws in the existing DR methods have been identified and re-investigated. More importantly, the feasibility and health check-up of the DR problem have been discussed. The primary objective of the work is to develop a DR code based on the observations made in the present study, which involves DR solutions by both successive linearization (SL) and sequential quadratic programming (SQP) schemes. Benchmarking of the code with standard cases showed its wider suitability in solving DR problems. The algebraic SL method was found suitable for proper data health check-ups and reliable solutions, whereas SQP was robust. The developed code was tested successfully for a chemical plant as well.     

Wind energy potential evaluation for power generation in selected districts of Jharkhand

This study evaluated the availability of wind energy for electricity production at different locations, i.e., Ranchi, Jamshedpur, Devghar, Lohardaga, and Chaibasa, in Jharkhand, India. Due to the rapidly rising demand for power in Jharkhand, there is a requirement of an alternative renewable source of energy to lower the dependence on its limited fossil fuel resources. The studied locations were found to be unsuitable for wind to electricity generation on a large scale at 10 m height above the ground. However, small-scale wind turbines can be used to extract energy from low-speed wind, preferably at a height above 10 m from the ground.     

Biomass feedstock supply chain design – a taxonomic review and a decomposition-based methodology

In this paper, we present a taxonomic review of the literature devoted to the use of operations research approaches for the design and operation of biomass feedstock supply chains (BFSCs). A total of 185 publications that have appeared from 1989 to 2017 are classified based on (1) the embedded optimisation subproblems; (2) the modelling methodologies used for their formulations; and (3) the methods employed for their solution. Our objective of using this classification scheme is to highlight the presence of some useful substructures in a BFSC problem that can, then, be exploited in developing its solution procedure. We illustrate this idea on some generic BFSC problems and present a Dantzig–Wolfe decomposition-based methodology for developing customised approaches to effectively tackle these difficult and large-sized BFSC problems. Finally, we suggest several promising future research directions.     

Cold plasma for the preservation of aquatic food products: An overview

Cold plasma (CP) is an upcoming technology implemented for the preservation of highly perishable foods, especially aquatic food products (AFPs). The high moisture content, high-quality protein with all essential amino acids and unsaturated fatty acids makes AFP more susceptible to microbial spoilage and oxidation of lipids and proteins. Spoilage lowers the nutritive value and could generate toxic components, making it unsafe for consumption. In recent times, the rising demand for food products of aquatic origin with preserved quality and extended shelf-life has been recorded. In addition, minimally or nonthermally processed and preserved foods are gaining great attention. CP technology has demonstrated an excellent ability to inactivate microorganisms without promoting their resistance and triggering some deteriorative enzymes, which are typical factors responsible for the spoilage of AFP. Consequently, CP could be recommended as a minimal processing intervention for preserving the quality of AFP. This review focuses on different mechanisms of fish spoilage, that is, by microorganisms and oxidation, their inhibition via the application of CP, and the retention of quality and shelf-life extension of AFP.     

A deep learning based latency aware predictive routing model for network-on-chip architectures

Network on Chip (NoC) is an evolving platform for communications related applications, which are executed on a single silicon chip. There are several routing models in NoC architectures, but the accuracy of these models is limited, and the existing models are degraded because of over and under fitting issues. This research introduces the new deep learning-based latency aware predictive routing model for on-chip networks to route packets with better performance and power efficiency. The deep learning model used in this research is a new convolutional residual gated recurrent unit (CRGRU) with queuing theory. Moreover, the source and channel queuing delay is comprised of features to learn spatial and sequential information that improves the overall prediction accuracy. This router is modified by the intrusion of the Router States Monitor unit and the CRGRU hardware engine. The work is executed using the Xilinx platform, and the performance measures like latency and throughput are obtained by varying the network size as $4\times 4$, $8\times 8$, and $12\times 12$ and also varying the buffer space and length as $L=4,B=9$, $L=9,B=4$, and $L=14,B=3$, respectively. In addition, the squared correlation coefficient (SCC) and normalized root mean square error (NRMSE) are evaluated and compared with existing learning models to validate the proposed model.