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     

Enhanced anti-biofilm activity of facile synthesized silver oxide nanoparticles against K. pneumoniae

Journal of Inorganic and Organometallic Polymers and Materials - The silver oxide nanoparticle was successfully synthesized using floral waste by simple one pot, cost effective method. The complete...     

Synthesis and characterization of liquid crystalline polymers containing aromatic ester mesogen and a nonmesogenic ferrocene unit in the spacer

A new series of liquid crystalline polymers containing aromatic triad ester mesogen and 1,1′‐disubstituted ferrocene as a nonmesogenic unit along with polymethylene spacer was synthesized. The polymer was synthesized by a room temperature polycondensation reaction between bis(4‐chloroformyl phenyloxy alkyl ferrocene dicarboxylate) and quinol. The alkyl groups have been varied by an even number of methylene groups with a range from two to ten groups. All the polymers were found to possess liquid crystalline properties. The identification of the mesophase is more transparent with an increase in the spacer. The thermal characteristics were studied using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The results reveal that the thermal stability of the polymers was decreased with increasing spacer length. The T_g, T_m, and T_i of the polymers decreased with increasing methylene groups. The incorporation of the ferrocene moiety also has a considerable effect on the glass transition temperature. The char yield of the polymer decreases with an increasing methylene chain length. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 3494–3501, 2002     

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.