North Eastern Regional Institute of Science and Technology

About: North Eastern Regional Institute of Science and Technology is a education organization based out in Itanagar, India. It is known for research contribution in the topics: Population & Raman spectroscopy. The organization has 813 authors who have published 1429 publications receiving 16122 citations.

Microstructural Investigation and Integrated Optimization of Weld Bead Characteristics in Electron Beam Welding of Inconel 825

Abstract: This work presents welding investigation of a popular Ni–Fe–Cr-based alloy (i.e. Inconel 825) using electron beam welding process, considering beam current (I), accelerating voltage (V), welding speed (S), and beam oscillation (O) as weld parameters. Microstructural investigations of the different zones of the welded specimen have been studied. Predictive models for depth of penetration (P) and width of the weld (W) are developed using response surface methodology (RSM). The novelty of this work is the metallurgical investigation and the development of RSM integrated with the JAYA algorithm to optimize weld parameters so as to minimize the width-to-depth ratio (W/P ratio) by maximizing P and minimizing W. Accelerating voltage (V) is found to be the predominant weld parameter on P and W followed by beam current (I). Microstructural study reveals the presence of beneficial twin boundary and strengthening precipitates like TiN and secondary phase precipitate Al4C3 in SEM/EDX analysis. Detrimental structures like migrated grain boundaries and unmixed zone are not witnessed. The RSM-JAYA optimization approach results in an optimum W/P ratio of 1.2405 mm at parameter setting V = 54.52 kV, I = 46 mA, S = 900 mm/min, O = 200 Hz. The validation result shows an improvement of 4.71% in the optimum fitness value. The proposed integrated optimization approach is found robust in locating the optimal solution with minimum numbers of iterations.

Performance of Data-Rate Analysis of Massive MIMO System Using User Grouping

Abstract: The major challenges in massive MIMO networks are to increase the system throughput and capacity with low complexity and reliability of the wireless communication system. The computational complexity and system capacity of massive MIMO (M-MIMO) systems depend on adequate user grouping, user selection, and antenna scheduling methods. Additionally, the interference of the M-MIMO system is reduced when used suitable signal processing schemes. In this paper, we proposed a novel circular user grouping technique to reduce system complexity. Later, semi-orthogonal user selection with three different combinations of user selection schemes and norm-based antennas scheduling method is used to achieve higher system capacity in M-MIMO systems. Moreover, simple linear zero-forcing and minimum mean square error signal processing schemes are used to minimize the user’s interference. The simulation results show that our proposed algorithm provides very low computational complexity, reduce the interference and higher the system capacity compare to the traditional M-MIMO system.

Investigation of rare phase transitions by density measurements

Abstract: We report here the study of nature of few rare phase transitions viz., Isotropic ‒ Sm C, Sm C ‒ Sm F and Srn A ‒ Sm F transition in two homologues of α, ω-bis(4-n-alkylanilinebenzylid- ine-4′-oxy)alkane series using density measurements. Compound 10.040.10 exhibits Iso- tropic ‒ Sm C, Sm C ‒ Sm F and Sm F ‒ Crystal phase transitions. Whereas 10.0100.10 exhibits Isotropic ‒ Sm A, Sm A ‒ Sm F and Sm F ‒ Crystal transitions. Variation of density with temperature across the phase transitions Isotropic ‒ Sm A, Isotropic ‒ Sm C, Sm C ‒ Sm F and Sm A ‒ Sm F show a jump in density confirming that all these transitions are first order in nature. However, the magnitude of the density jump is smaller than expected at some of these transitions. The calculated pressure dependence of transition temperature is also reported.

Significance of Laccases in Food Chemistry and Related Bioremediation

Abstract: Due to wide applications in synthetic and other biotechnological chemistry domains, laccase has become a hot enzyme in area of research related to biological chemistry. In last few years, a number of tedious applications were performed through laccase catalysis. Paper/pulp industries, food industries, bioremediation, synthetic chemistry, medicinal chemistry, hormonal chemistry, carbohydrate chemistry, dye chemistry, biosensor developments, and polymer chemistry are some important fields of chemistry in which laccase has proved their strength. Laccase has an important property to convert the molecular oxygen into the water molecule, which is a green step for environmental studies. This property is significant in food chemistry and bioremediation, making laccase a valuable enzyme. In food chemistry, laccase has unique roles in stabilization of beverages, enhancement in taste of food materials, stopping the unwanted change in foodstuffs, checking the unwanted color change in juices, enhancing the food quality by eliminating the dissolved oxygen, and so on. In a similar way, it has also unique and proven broad significances in bioremediation chemistry. It has significant values in wastewater treatments, treatment of toxic pesticides, food waste materials, phenolics, polyaromatic and carcinogenic hydrocarbons, and so on. In this chapter only food-based bioremediation studies have been discussed in brief. This chapter mainly appraises the applications of laccases in titled fields with a brief laccase-based introductory discussion.

Retinal blood vessels extraction and detection of exudates using wavelet transform and pnn approach for the assessment of diabetic retinopathy

Abstract: In ophthalmology, human eye is an important diagnostic factor for studying vascular network. Segmentation of fundus image is important due to varying size of blood vessels, contrast and presence of different pathologies such as microaneurysms and hemorrhages. This work proposes a new method to find the exudates (patches) from the blood vessels of an eye during diabetic retinopathy treatment. For analysis, input retinal image is separated into three planes i.e., red, green and blue, from which green channels are selected, which are further processed using Daubechies wavelet transform to estimate the grey level co-occurrence matrix (GLCM) features. These features are processed using probabilistic neural network (PNN) and input retinal image is compared with the database image to classify it as normal or abnormal. Morphological operations are applied to the abnormal image to extract the blood vessels and then fuzzy c-means clustering is applied in the extracted blood vessels to detect the exudates. The accuracy and sensitivity of the result obtained by the proposed method is comparatively better and are 0.9776 and 0.9677 respectively. The method can be applied successfully and the blood vessels and exudates can be effectively detected.