Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay

Mobile Networks and Applications - 5G/6G communication are first generation high speed wireless communication network which integrates the aerial data, terrestrial data and maritime data via...     

Platinum nanoparticles decorated on graphitic carbon nitride-ZIF-67 composite support: An electrocatalyst for the oxidation of butanol in fuel cell applications

In the present study, we report an eco-friendly and simple route to design and synthesize novel nanocomposite catalyst based on platinum nanoparticles anchored on binary support of graphitic carbon nitride (g-C₃N₄) and cobalt-metal-organic framework (ZIF-67). For this purpose, ZIF-67 was prepared by precipitation method and g-C₃N₄ was prepared through thermal polymerization method. Later, ZIF-67 and g-C₃N₄ were hybridized through sonication to get homogeneous g–C₃N₄–ZIF-67 nanocomposite support material. Platinum nanoparticles (PtNPs) were uniformly deposited on g–C₃N₄–ZIF-67 by an electrochemical method. The as-developed nanocatalyst was characterized by morphological, structural and electrochemical techniques. The electrocatalytic activity of PtNPs@g–C₃N₄–ZIF-67 nanocatalyst towards butanol oxidation was evaluated via CV, CA, LSV and EIS in an alkaline medium. Results revealed that the proposed catalyst showed greatly enhanced electrooxidation of butanol in terms of high magnificent current density, lower oxidation potential, excellent long-term stability, large surface area, low charge transfer resistance and less toxic ability. Enhanced catalytic performance of the proposed catalyst could be ascribed to the synergistic effect of g–C₃N₄–ZIF-67 nanocomposite and PtNPs. The PtNPs@g–C₃N₄–ZIF-67 catalyst holds promising potential applications to be used as an anodic electrocatalyst for the development of high-performance alkaline fuel cells.     

Cooperative Diversity in Secondary Systems: Efficient Relaying Protocol for Cognitive Radio

Wireless Personal Communications - This paper considers half-duplex cooperative spectrum sharing scheme where both primary and secondary systems mutually cooperate with each other to exploit...     

Decay Rate Kinetics of Ozone Gas in Rice Grains

The present study was carried out to evaluate the effects of ozone on rice grains for the following three conditions: saturation time, decay rate, and half-life of ozone. Experiments were performed in different bed thicknesses (5 and 10 cm) and moisture content (11.4 and 14.2% wb) at atmospheric conditions. The lowest saturation time of ozone was 119 min, with the concentration of 516 ppm for rice grains ozonated at 5-cm bed thickness with 11.4% (wb) moisture content. The decay rate kinetics of ozone obtained were consistent with a first-order model. Regarding the half-life of ozone, the lowest value obtained was 6.78 min for rice grains ozonated at 10-cm bed thickness with 14.2% (wb) moisture content.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

Development of porous defects in plasma membranes of adenosine triphosphate-depleted Madin-Darby canine kidney cells and its inhibition by glycine

Studies during the past decade have led to the recognition of a fundamental, widely expressed mechanism of structural damage in energy-deprived cells, which is suppressed by physiologic levels of glycine and is independent of Ca2+ availability or alterations of cytosolic free Ca2+. To gain insight into this process, Madin-Darby canine kidney (MDCK) cells were depleted of adenosine triphosphate (ATP) by a mitochondrial uncoupler in glucose-free medium, and intracellular free Ca2+ was clamped at 100 nM to avoid calcium cytotoxicity. Although the ATP-depleted cells swelled and blebbed and their plasma membranes appeared to be under tension, they nevertheless became permeable to macromolecules. The plasma membranes of these cells retained structural continuity, as determined by morphologic observations, and confocal microscopy of a plasma membrane protein label (Biotin: Ultra Avidin-Texas Red) and a lipid label (NBD-sphingomyelin). Using fluoresceinated dextrans of graded molecular size, membrane permselectivity was examined noninvasively by confocal microscopy. Measured as inside/outside ratios of fluorescence intensity, the permeability indices showed progressively greater restriction to diffusion of increasingly larger dextran molecules across plasma membranes, with sharp break-points between 70,000 and 145,000 daltons (d). The results indicated that the membranes behaved as if they were perforated by water-filled channels or "pores," with size-exclusion limits of molecular dimensions. The membrane defects evolved from small pores permeable only to propidium iodide (668 d) and the smallest dextran (4,000 d), before enlarging with time to become permeable to larger dextrans. Inclusion of glycine during ATP depletion did not affect cell swelling or blebbing but completely prevented the development of permeability defects. Treatment of cells before ATP depletion with a membrane-impermeant homobifunctional "nearest neighbor" cross-linking agent, 3,3' dithiobis(sulfosuccinimidylpropionate), suppressed the development of permeability defects, even in the absence of glycine. These observations suggest that the cellular abnormality that is suppressed by glycine involves rearrangement of plasma membrane proteins to form water-filled pores large enough to leak macromolecules.     

Electrospun MgO/Nylon 6 Hybrid Nanofibers for Protective Clothing

Nano-Micro Letters - Magnesia (MgO) nanoparticles were produced from magnesite ore (MgCO3) using ball mill. The crystalline size, morphology and specific SSA were characterized by X-ray diffraction...     

Polyaniline–13X zeolite composite‐supported platinum electrocatalysts for direct methanol fuel cell applications

We successfully synthesized 13X zeolite using a hydrothermal method. Then, composites of polyaniline (PANI) with 13X zeolite and PANI–13X with platinum were prepared by chemical oxidative polymerization and chemical reduction, respectively. Field emission scanning electron microscopy, X‐ray diffraction, Raman spectroscopy and Brunauer–Emmett–Teller techniques were used to characterize the PANI–Pt and PANI–Pt–13X composites. Further, the electrocatalytic activity towards methanol oxidation of the synthesized catalysts was explored using cyclic voltammetry in 1 mol L^?1 CH₃OH + 0.5 mol L^?1 H₂SO₄ solution. From the obtained results, PANI–Pt–13X shows superior performance compared to PANI–Pt towards methanol oxidation and electrical conductivity. Hence, the 13X zeolite‐incorporated PANI–Pt composite could be an efficient catalyst for direct methanol fuel cell applications. © 2019 Society of Chemical Industry