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.     

Photo-electrocatalytic methanol oxidation on Pd decorated BiOI nanoparticles in alkali

Journal of Materials Science - Palladium–bismuth oxoiodide nanocomposites of various molar ratios of Pd and BiOI have been synthesized by the facile solvothermal method following the chemical...     

EAAM: Energy-aware application management strategy for FPGA-based IoT-Cloud environments

The Journal of Supercomputing - An efficient integration of Internet of Things (IoT) and cloud computing techniques accelerates the evolution of next-generation smart environments (e.g., smart...     

A position and rotation invariant framework for sign language recognition (SLR) using Kinect

Multimedia Tools and Applications - Sign language is the only means of communication for speech and hearing impaired people. Using machine translation, Sign Language Recognition (SLR) systems...     

A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel

Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current (I), pulse-on time (T _on), pulse-off time (T _off) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.

     

Use of Spectral Clustering Combined with Normalized Cuts (N-Cuts) in an Iterative k-Means Clustering Framework (NKSC) for Superpixel Segmentation with Contour Adherence

Superpixel segmentation methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while trying to respect existing contours. In this paper, we propose a fast Superpixels segmentation algorithm with Contour Adherence using spectral clustering, combined with normalized cuts in an iterative k-means clustering framework. It produces compact and uniform superpixels with low computational costs. Normalized cut is adapted to measure the color similarity and space proximity between image pixels. We have used a kernel function to estimate the similarity metric. Kernel function maps the pixel values and coordinates into a high dimensional feature space. The objective functions of weighted K-means and normalized cuts share the same optimum point in this feature space. So it is possible to optimize the cost function of normalized cuts by iteratively applying simple K-means clustering algorithm. The proposed framework produces regular and compact superpixels that adhere to the image contours. On segmentation comparison benchmarks it proves to be equally well or better than the state-of-the-art super pixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation. In addition, our method is computationally very efficient and its computational complexity is linear.     

Genetic Algorithms for Optimal Urban Transit Network Design

This article attempts to highlight the effectiveness of genetic algorithm (GA)–based procedures in solving the urban transit network design problem (UTNDP). The article analyzes why traditional methods have problems in solving the UTNDP. The article also suggests procedures to alleviate these problems using GA–based optimization technique. The thrust of the article is three–fold: (1) to show the effectiveness of GAs in solving the UTNDP, (2) to identify features of the UTNDP that make it a difficult problem for traditional techniques, and (3) to suggest directions, through the presentation of GA–based methodologies for the UTNDP, for the development of GA–based procedures for solving other optimization problems having features similar to the UTNDP.     

Estimating weak ratiometric signals in imaging data. II. Meta-analysis with multiple, dual-channel datasets

Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest. Here, we outline a statistical optimization method that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol. This method takes advantage of correlated information across multiple datasets for objectively detecting and estimating ratiometric signals. We demonstrate our method by showing results of its application on multiple, ratiometric calcium imaging experiments.