α-Glucosidase, α-amylase inhibition kinetics,in vitro gastro-intestinal digestion,and apoptosis inducing abilities of Ficus microcarpa L. f. and Ficus racemosa L. fruit polysaccharides

Food Science and Biotechnology - A rich source of nutrients, figs have a number of clinically validated benefits. This study aimed to evaluate the in vitro simulated gastrointestinal digestion, and...     

Point Contact Spectroscopy and Temperature Dependence of Resistivity of Metallic Sodium Tungsten Bronzes - Role of Optical Phonons

In this paper we report the results of electrical resistivity (1.5≤ T ≤ 300K) and point contact spectroscopy (PCS) measurements on single crystals of metallic sodium tungsten bronze with varying sodium content. We have shown that the electron-phonon coupling function as measured through PCS can explain quantitatively the large temperature dependence of resistivity ρ seen in these materials over the entire temperature range. The electron-phonon coupling function shows predominately large peaks for phonon frequency range of 30 meV ≤ ω ≤ 100 meV which match well with the calculated optical phonons for WO₆ octahedron. The integrated electron-phonon coupling constant λ from this data is ≈ 0.25-0.45, depending on the Na content. PACS numbers: 72.15.-v, 72.80.Ga     

Simple substitution distance and metamorphic detection

To evade signature-based detection, metamorphic viruses transform their code before each new infection. Software similarity measures are a potentially useful means of detecting such malware. We can compare a given file to a known sample of metamorphic malware and compute their similarity—if they are sufficiently similar, we classify the file as malware of the same family. In this paper, we analyze an opcode-based software similarity measure inspired by simple substitution cipher cryptanalysis. We show that the technique provides a useful means of classifying metamorphic malware.     

An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power reduction for a few decades, but the design of low power approximate multiplier is not so easy. This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy. To overcome these issues, the digital circuits are applied to the Deep Learning (DL) approaches for higher accuracy. In recent times, DL is the method that is used for higher learning and prediction accuracy in several fields. Therefore, the Long Short-Term Memory (LSTM) is a popular time series DL method is used in this work for approximate computing. To provide an optimal solution, the LSTM is combined with a meta-heuristics Jellyfish search optimisation technique to design an input aware deep learning-based approximate multiplier (DLAM). In this work, the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier. The optimal hyperparameters of the LSTM model are identified by jelly search optimisation. This fine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy. The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a function of area, delay, power and error metrics. The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approximate computing multiplier achieved a superior area and power reduction with very good results on error rates.     

Efficient skycube computation using point and domain-based filtering

Skyline queries have been increasingly used in multi-criteria decision making and data mining applications. They retrieve a set of interesting points from a potentially large set of data points. A point is said to be interesting if it is not dominated by any other point. Skyline cube (skycube) consists of skylines of all possible non-empty subsets of a given set of dimensions. In this paper, we propose two algorithms for computing skycube using bitmaps that are derivable from indexes. The Point-based skycube algorithm is an improvement over the existing Bitmap algorithm, extended to compute skycube. The Point-based algorithm processes one point at a time to check for skylines in all subspaces. The Domain-based skycube algorithm views points as value combinations and probes entire search space for potential skyline points. It significantly reduces bitmap access for low cardinality dimensions. Our experimental study shows that the two algorithms strictly dominate, or at least comparable to, the current skycube algorithm in most of the cases, suggesting that such an approach could be a useful addition to the set of skyline query processing techniques.     

Performance Evaluation of Compressed Air Energy Storage Using TRNSYS

The appreciable economic growth in some of the developing countries like India in the recent years, towards providing energy security causes large environmental impact. Renewable Energy (RE) is being seen as one of the important means to meet the growing power needs of the economy while enhancing energy security and providing opportunities for mitigating greenhouse gas emissions. However, RE sources are highly intermittent in nature. The variability of these sources has led to concerns regarding the reliability of an electric grid that derives a large fraction of its energy from these sources as well as the cost of reliably integrating large amounts of variable generation into the electric grid. Hence at this juncture, it is necessary to explore the benefits of suitable Energy storage technologies. Compressed air energy storage (CAES) is a commercial, utility-scale technology that provides long-duration energy storage with fast ramp rates and good part-load operation. It is a promising storage technology for balancing the large-scale penetration of renewable energies, such as wind and solar power, into electric grids. Considering the potential of CAES storage, the present work, a thermodynamic model is developed with suitable assumptions and the simulation analysis is performed using transient system simulation (TRNSYS) v17 software. The system performance is compared by considering the recovery during the heat of compression using a thermal storage system and without considering the heat recovery. The overall turnaround efficiency of the system without considering the thermal energy storage (TES) system is 57% and with TES system the efficiency is increased to 70%.     

Ultrasound-assisted Acid red dye removal using leather fibre wastes as matrix: ‘intra wastes interaction’ approach

It is desirable to make effective use of solid wastes, as well as waste liquors, of the dye house generated from leather processing. The present study deals with the adsorption of Acid red 26 (Xylidine ponceau) dye using leather fibre waste (buffing dust) as substrate matrix by the means of ultrasound, magnetic stirring and conventional heating. The research examined the effect of various process parameters: ultrasonic power, concentration of dye, temperature and time. The dye uptake data have been fitted with Freundlich and Langmuir isotherms, indicating a useful dye adsorption process. A mechanism for dye adsorption in leather fibres has also been proposed. This study shows that it is possible to remove dyes from dye house effluent streams using leather waste fibres of buffing dust by means of ultrasound. Two different toxic wastes produced by the same industry can be effectively contained. This ‘Intra wastes interaction’ approach could reduce the burden of discarding wastes of other materials in the leather industry.     

Synthesis and magnetic properties of nano-sized Cu0.5Ni0.5Fe2O4 via citrate and aloe vera: A comparative study

In this report, Cu_0.5Ni_0.5Fe₂O₄ nanoparticles were synthesized by solgel using eco-friendly aloe vera extract and citric acid separately and their properties were discussed. Structural, Functional, morphological, magnetic properties of the samples were characterized using X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Transmission electron microscopy (TEM), Vibrating sample magnetometer (VSM). XRD exhibited the samples have single phase cubic spinel with average size of 46.4?nm and 35.45?nm. Two characteristics bands of ferrite were confirmed by FTIR. TEM indicated different morphology of the samples with some regularity. VSM data showed that higher coercivity for the sample prepared by aloe vera extract.     

Hydrogen storage in coiled carbon nanotubes

We report a density functional calculation of the adsorption of molecular hydrogen on the external surface of coiled carbon nanotube (CCNT). Binding energies of single molecule have been studied as a function of three different orientations and at three different sites like hexagon, pentagon and heptagon. The binding energy values are larger than linear (5,5) armchair nanotube, which has approximately same diameter as that of coiled carbon nanotube. The curvature and topology of CCNT are responsible for this considerable enhancement. The system with full coverage is also studied. When the nanotube surface is fully covered with one molecule per graphitic hexagon, pentagon and heptagon gives the 6.8 wt% storage capacity. The binding energy per molecule decreases due to repulsive interactions between neighbor molecules. It gives good storage medium for hydrogen. Almost it meets the DOE target.