Grain Boundary Sliding and Strain Rate Sensitivity of Coarse and Fine/Ultrafine Grained 5083 Aluminum Alloys

Metallurgical and Materials Transactions A - The viscoplastic behavior of coarse and bimodal fine/ultrafine grained (F/UFG) Al5083 alloy was investigated between 20 °C and...     

Multi-Response Optimization of Process Parameters for Low-Pressure Cold Spray Coating Process Using Taguchi and Utility Concept

Most of the existing multi-response optimization approaches focus on the subjective and practical know-how of the process. As a result, some confusion and uncertainty are introduced in the overall decision-making process. In this work, an approach based on a Utility theory and Taguchi quality loss function has been applied to the process parameters for low-pressure cold spray process deposition of copper coatings, for simultaneous optimization of more than one response characteristics. In the present paper, two potential response parameters, i.e., coating thickness and coating density, have been selected. Utility values based on these response parameters have been analyzed for optimization using the Taguchi approach. The selected input parameters of powder feeding arrangement, substrate material, air stagnation pressure, air stagnation temperature, and stand-off distance significantly improve the Utility function (raw data) comprising quality characteristics (coating thickness and coating density). The percentage contribution of the parameters to achieve a higher value of Utility function is substrate material (50.03%), stand-off distance (28.87%), air stagnation pressure (6.41%), powder feeding arrangement (4.68%), and air stagnation temperature (2.64%).     

Remote sensing and GIS techniques for the study of springs in a watershed in Garhwal in the Himalayas,India

Water has been described as the elixir of life, the source of energy that sustains life on Earth and the factor that governs the evolution and the functioning of the universe. Increased use of water in the face of the impairment of the natural environment and ecology and the drying up of springs and reduction in their discharge and those of streams in the Lesser and Outer Himalayas are the most serious problems calling for study and exploration of groundwater resources in the Himalayan region. The hilly regions of India are facing a serious water availability crisis, particularly during summer months. Viable sources of water, such as springs in the Himalayas, which are plentiful in the hills, are drying up due to rapid and unplanned developments. The present study deals with the delineation of springs in the Chandrabhaga watershed using remote sensing and GIS technologies. The study demonstrates that the coincidence of lineaments, derived from merged satellite data, and the drainage density show good correlation with the present spring locations in the Chandrabhaga watershed. The study shows also that the locations of various springs have changed since 1981, perhaps due to rapid changes in the landuse pattern in the watershed between 1981 and 1997. Besides landuse, topography, geology and geological structures are among the most influential factors affecting spring location and discharge. An integrated approach of remote sensing and GIS is proved to be an efficacious technique for the study of springs in a mountainous watershed.     

Nickel Nanoparticle-Filled High-Performance Polymeric Nanocomposites for EMI Shielding Applications

Journal of Electronic Materials - In this paper, the electromagnetic interference shielding effectiveness (EMI-SE) of nickel (Ni) nanoparticle-filled high-performance polyetherketone (PEK)...     

Superconductivity at 4.4 K in PdTe<Subscript>2</Subscript> Chains of a Ta-Based Compound

Superconductivity in PdTe₂ heavy transition metal-based compounds is a rapidly developing field in condensed matter physics community. Here, we report superconductivity in a nominal ternary telluride Ta₂Pd_0.97Te₆ compound, which is synthesized in sealed evacuated quartz ampoule. The resultant compound is crystallized in layered monoclinic Ta₄Pd₃Te₁₆ phase with space group C2/m, having lattice parameters a = 21.304(6)Å, b = 3.7365(7)Å and c = 17.7330Å with β = 120.65(1)^°. Both transport and magnetic measurements demonstrated bulk superconductivity at 4.4 K in studied polycrystalline sample. The metallic normal state conductivity can be well ascribed to Fermi liquid behaviour. The superconducting upper critical field of the compound is determined to be 5.5 T based on magnetoresistivity measurements.     

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.     

Review of background subtraction methods using Gaussian mixture model for video surveillance systems

Foreground detection or moving object detection is a fundamental and critical task in video surveillance systems. Background subtraction using Gaussian Mixture Model (GMM) is a widely used approach for foreground detection. Many improvements have been proposed over the original GMM developed by Stauffer and Grimson (IEEE Computer Society conference on computer vision and pattern recognition, vol 2, Los Alamitos, pp 246–252, 1999. doi: 10.1109/CVPR.1999.784637) to accommodate various challenges experienced in video surveillance systems. This paper presents a review of various background subtraction algorithms based on GMM and compares them on the basis of quantitative evaluation metrics. Their performance analysis is also presented to determine the most appropriate background subtraction algorithm for the specific application or scenario of video surveillance systems.