Impact of viscous dissipation and Dufour on MHD natural convective flow past an accelerated vertical plate with Hall current

The present research work concentrates on viscous dissipation, Dufour, and heat source on an unsteady magnetohydrodynamics natural convective flow of a viscous, incompressible, and electrically conducting fluid past an exponentially accelerated infinite vertical plate in the existence of a strong magnetic field. The presence of the Hall current induces a secondary flow in the problem. The distinguishing features of viscous dissipation and heat flux produced due to gradient of concentration included in the model along with heat source as they are known to arise in thermal-magnetic polymeric processing. The flow equations are discretized implicitly using the finite difference method and solved using MATLAB fsolve routine. Numerical values of the primary and secondary velocities, temperature, concentration, skin friction, Nusselt number, and Sherwood number are illustrated and presented via graphs and tables for various pertinent parametric values. The Dufour effect was observed to strengthen the velocity and temperature profile in the flow domain. In contrast, due to the impact of viscous dissipation, the local Nusselt number reduces. The study also reveals that the inclusion of the chemical reaction term augments the mass transfer rate and diminishes the heat transfer rate at the plate.     

A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs

The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum.     

A Comprehensive Review on Tunnel Field-Effect Transistor (TFET) Based Biosensors: Recent Advances and Future Prospects on Device Structure and Sensitivity

Silicon - In this fast-growing technological world biosensors become more substantial in human life and the extensive use of biosensors creates enormous research interest among researchers to...     

EGGS: Sparsity-Specific Code Generation

Sparse matrix computations are among the most important computational patterns, commonly used in geometry processing, physical simulation, graph algorithms, and other situations where sparse data arises. In many cases, the structure of a sparse matrix is known a priori, but the values may change or depend on inputs to the algorithm. We propose a new methodology for compile-time specialization of algorithms relying on mixing sparse and dense linear algebra operations, using an extension to the widely-used open source Eigen package. In contrast to library approaches optimizing individual building blocks of a computation (such as sparse matrix product), we generate reusable sparsity-specific implementations for a given algorithm, utilizing vector intrinsics and reducing unnecessary scanning through matrix structures. We demonstrate the effectiveness of our technique on a benchmark of artificial expressions to quantitatively evaluate the benefit of our approach over the state-of-the-art library Intel MKL. To further demonstrate the practical applicability of our technique we show that our technique can improve performance, with minimal code changes, for mesh smoothing, mesh parametrization, volumetric deformation, optical flow, and computation of the Laplace operator.     

Doping-Dependent Nonlinear Electron Mobility in GaAs|InxGa1 –xAs Coupled Quantum-Well Pseudo-Morphic MODFET Structure

Semiconductors - We analyze the asymmetric delta-doping dependence of nonlinear electron mobility μ of GaAs|InxGa1 –xAs double quantum-well pseudo-morphic modulation doped field-effect...     

High Performance Lithium‐Ion Batteries Using Layered 2H‐MoTe2 as Anode

The major challenges faced by candidate electrode materials in lithium‐ion batteries (LIBs) include their low electronic and ionic conductivities. 2D van der Waals materials with good electronic conductivity and weak interlayer interaction have been intensively studied in the electrochemical processes involving ion migrations. In particular, molybdenum ditelluride (MoTe₂) has emerged as a new material for energy storage applications. Though 2H‐MoTe₂ with hexagonal semiconducting phase is expected to facilitate more efficient ion insertion/deinsertion than the monoclinic semi‐metallic phase, its application as an anode in LIB has been elusive. Here, 2H‐MoTe₂, prepared by a solid‐state synthesis route, has been employed as an efficient anode with remarkable Li⁺ storage capacity. The as‐prepared 2H‐MoTe₂ electrodes exhibit an initial specific capacity of 432 mAh g^?1 and retain a high reversible specific capacity of 291 mAh g^?1 after 260 cycles at 1.0 A g^?1. Further, a full‐cell prototype is demonstrated by using 2H‐MoTe₂ anode with lithium cobalt oxide cathode, showing a high energy density of 454 Wh kg^?1 (based on the MoTe₂ mass) and capacity retention of 80% over 100 cycles. Synchrotron‐based in situ X‐ray absorption near‐edge structures have revealed the unique lithium reaction pathway and storage mechanism, which is supported by density functional theory based calculations.     

Time gap effect on bond strength of 3D-printed concrete

An advancing technology that combines the concrete extrusion with a motion control to create structures with complex geometrical shapes without the need for formwork is known as 3D concrete printing. Since this technique prints layer by layer, the time taken to reach the same position in the subsequent layer is important as it will create an anisotropic property that has a weaker tensile strength at the bond interface of the two printed filaments. Through rheological measurement, which reveals the material deformation and flow behaviour, it is possible to examine the material structural build-up due to time-gap effect by measuring at different time delay. This paper focuses on investigating the time-gap effect on the printed filament with rheological and observation at macroscopic-scale to understand the material behaviour of the initial and subsequent printed layer during its fresh phase. Rheological experiment findings reveal that the tensile strength of the printed specimen is correlated to the material modulus at the initial layer.     

Study of the influence of the structural grain size on the mechanical properties of technical materials

The mechanical properties of technical materials depend on their structure. They are influenced not only by their chemical composition, but particularly by the structural grain size. Significant changes in the mechanical behaviour of materials are related both to surface and volume properties, and not only in the field of mechanical parameters. A wide range of physical and chemical parameters changes as well. Nano‐materials are the materials, the structural grain size of which is in the dimensional area from 10^?9 to 10^?7 m. Nano‐particles and nanostructures are thus so small that their behaviour is affected by atomic forces, properties of chemical bonding, and quantum phenomena. The wave nature of the very small particles begins to manifest itself. The aim of the authors is to contribute by their paper to the solution of the problems in the field of material engineering. This means to investigate the specifics in the behaviour of technical materials depending on the change in the structural grain size towards the nano‐areas, as well as the design and use of new techniques of mathematical and physical modelling including the operative measurement method.     

Drying Urban lakes: A consequence of climate change,urbanization or other anthropogenic causes? An insight from northern India

Urban lakes in many places around the world are rapidly becoming vulnerable because of such factors as urbanization, climate change, anthropogenic pollutant inputs, etc. The influence of such forcing factors on lakes hydrology must be correctly recognized and addressed in order to protect them over the long term. Facing similar challenges, Sukhna Lake, an urban lake in northern India, has apparently dried up frequently in the recent past. Numerous hypotheses were subsequently proposed to isolate the possible factors affecting the lake and its water budget, including the potential impacts of land use changes, climate change, anthropogenic activities and other natural processes. Using meteorological data, lake‐catchment information and a hydrologic model, these hypotheses were comprehensively analysed. Relevant data on rainfall, wind, temperature, lake inflows, groundwater, lake physical characteristics, catchment land uses, soil texture, etc., were gathered for the analysis. A temporal trend analysis of factors relevant to these hypotheses was undertaken to identify critical drivers of hydrological changes. A sensitivity analysis also was performed, using the lake water budget, to determine and prioritize the predominant factors affecting the lake, leading to the creation of an annual lake water budget for the period from 1971 to 2013, highlighting the lake inflows and outflows. The lake annual inflow (catchment run‐off) was computed by adopting a rainfall–run‐off model based on the SCS‐curve number. Lacking any anthropogenic water withdrawals, the outflow was quantified by estimating the evaporation loss (using the FAO‐based Penman–Monteith Equation). The results of the present study  indicate that the process of siltation and the construction of check dams in the catchment, rather than urbanization and climate change, were the dominating reasons contributing to changes in the lake hydrology, and affecting the lake most in recent years.     

Normalization-Based Task Scheduling Algorithms for Heterogeneous Multi-Cloud Environment

Cloud computing is one of the most successful technologies that offer on-demand services through the Internet. However, datacenters of the clouds may not have unlimited capacity which can fulfill the demanded services in peak hours. Therefore, scheduling workloads across multiple clouds in a federated manner has gained a significant attention in the recent years. In this paper, we present four task scheduling algorithms, called CZSN, CDSN, CDN and CNRSN for heterogeneous multi-cloud environment. The first two algorithms are based on traditional normalization techniques, namely z-score and decimal scaling respectively which are hired from data mining. The next two algorithms are based on two newly proposed normalization techniques, called distribution scaling and nearest radix scaling respectively. All the proposed algorithms are shown to work on-line. We perform rigorous experiments on the proposed algorithms using various synthetic as well as benchmark datasets. Their performances are evaluated through simulation run by measuring two performance metrics, namely makespan and average cloud utilization. The experimental results are compared with that of existing algorithms to show the efficacy of the proposed algorithms.