Erosion-corrosion behavior of SiC particle-reinforced Al-Si alloy in NaOH slurry

The erosion-corrsosion behavior of SiC particle-reinforced Al-Si alloy has been studied in NaOH slurry simulating the mining atmosphere. The study was performed at two different sand concentrations, namely, 20 and 30 wt pct, and at a speed of 900 rpm. It is depicted that the wear rates decreased with increasing sand content, indicating that corrosion is the dominating mode of material removal. Further composite exhibited lower wear resistance than the laloys irrespective of the sand concentration. Scanning electron microscope (SEM) observations indicated the dissolution of dendrites of Al due to severe corrosion, leaving behind the network of Si. This ultimately results in the falling of Si particles from the matrix, leaving behind voids. This also results in the formation of voids around the SiC particles and leads to pullout of SiC particles from the matrix during the wear process.     

Amino acid interaction preferences in helical membrane proteins

Membrane proteins are involved in a number of important biological functions. Yet, they are poorly understood from the structure and folding point of view. The external environment being drastically different from that of globular proteins, the intra-protein interactions in membrane proteins are also expected to be different. Hence, statistical potentials representing the features of inter-residue interactions based exclusively on the structures of membrane proteins are much needed. Currently, a reasonable number of structures are available, making it possible to undertake such an analysis on membrane proteins. In this study we have examined the inter-residue interaction propensities of amino acids in the membrane spanning regions of the alpha-helical membrane (HM) proteins. Recently we have shown that valuable information can be obtained on globular proteins by the evaluation of the pair-wise interactions of amino acids by classifying them into different structural environments, based on factors such as the secondary structure or the number of contacts that a residue can make. Here we have explored the possible ways of classifying the intra-protein environment of HM proteins and have developed scoring functions based on different classification schemes. On evaluation of different schemes, we find that the scheme which classifies amino acids to different intra-contact environment is the most promising one. Based on this classification scheme, we also redefine the hydrophobicity scale of amino acids in HM proteins.     

A novel zeolite-multiwalled carbon nanotube composite for the electroanalysis of copper(II) ion

The application of zeolite Y-multiwalled carbon nanotube (MWCNT) nanocomposite modified glassy carbon electrode (zeolite Y-MWCNT/GCE) in the electroanalysis of Cu²⁺ ion is presented. In order to bring out the unique advantage of the zeolite Y-MWCNT/GCE, experiments were carried out also at graphite/GCE, MWCNT/GCE and zeolite Y-graphite/GCE. For the same surface area, the performance of zeolite Y-MWCNT/GCE was superior to the other modified electrodes in terms of current sensitivity for Cu²⁺ ion. The combination of zeolite Y and MWCNT as a nanocomposite resulted in a good synergetic effect. The Cu⁺² ion sensor exhibited a linear calibration range between 5 × 10⁻⁸ and 1 × 10⁻⁵ mol L⁻¹ with a detection limit of 1.12 × 10⁻⁸ mol L⁻¹ (0.72 ppb).     

Lipid and Phospholipid Profiling of Biological Samples Using MALDI Fourier Transform Mass Spectrometry

Here we describe a study of the feasibility of lipid and phospholipid (PL) profiling using matrix assisted laser desorption/ionization (MALDI) Fourier transform mass spectrometry (FTMS) for two different applications. In this work PL profiles of different mammalian tissues as well as those of whole cell organisms were examined. In particular, comparative analysis of lipid and PL profiles of tissues from mice fed different diets was done and, in another application, MALDI FTMS was used to analyze PL profiles of genetically modified Saccharomyces cerevisiae. Computational sorting of the observed ions was done in order to group the lipid and PL ions from complex MALDI spectra. The PL profiles of liver tissues from mice fed different diets showed a cross correlation coefficient of 0.2580, indicating significant dissimilarity, and revealed more than 30 significantly different peaks at the 99.9% confidence level. Histogram plots derived from the spectra of wild type and genetically modified yeast resulted in a cross correlation coefficient 0.8941 showing greater similarity, but still revealing a number of significantly different peaks. Based on these results, it appears possible to use MALDI FTMS to identify PLs as potential biomarkers for metabolic processes in whole cells and tissues.     

Distinctive Microbial Signatures and Gut-Brain Crosstalk in Pediatric Patients with Coeliac Disease and Type 1 Diabetes Mellitus

Coeliac disease (CD) and Type 1 diabetes mellitus (T1DM) are immune-mediated diseases. Emerging evidence suggests that dysbiosis in the gut microbiome plays a role in the pathogenesis of both diseases and may also be associated with the development of neuropathy. The primary goal in this cross-sectional pilot study was to identify whether there are distinct gut microbiota alterations in children with CD (n = 19), T1DM (n = 18) and both CD and T1DM (n = 9) compared to healthy controls (n = 12). Our second goal was to explore the relationship between neuropathy (corneal nerve fiber damage) and the gut microbiome composition. Microbiota composition was determined by 16S rRNA gene sequencing. Corneal confocal microscopy was used to determine nerve fiber damage. There was a significant difference in the overall microbial diversity between the four groups with healthy controls having a greater microbial diversity as compared to the patients. The abundance of pathogenic proteobacteria Shigella and E. coli were significantly higher in CD patients. Differential abundance analysis showed that several bacterial amplicon sequence variants (ASVs) distinguished CD from T1DM. The tissue transglutaminase antibody correlated significantly with a decrease in gut microbial diversity. Furthermore, the Bacteroidetes phylum, specifically the genus Parabacteroides was significantly correlated with corneal nerve fiber loss in the subjects with neuropathic damage belonging to the diseased groups. We conclude that disease-specific gut microbial features traceable down to the ASV level distinguish children with CD from T1DM and specific gut microbial signatures may be associated with small fiber neuropathy. Further research on the mechanisms linking altered microbial diversity with neuropathy are warranted.     

Time scale modification and vocal tract length normalization for improving the performance of Tamil speech recognition system implemented using language independent segmentation algorithm

This paper describes the work done in improving the performance of Tamil speech recognition system by using Time Scale Modification (TSM) and Vocal Tract Length Normalization (VTLN) techniques. The speech recognition system for Tamil language was developed using a new approach of text independent speech segmentation, with a phoneme based language model for recognition. There is degradation in the performance of speech recognition due to variations in the speaking rate and vocal tract shape among different speakers. In order to improve the performance of speech recognition system, both TSM and VTLN normalization techniques were used in this work. The TSM was implemented using the Phase vocoder approach and the VTLN was implemented using speaker specific bark/mel scale in bark/mel domain. The performance of Tamil speech recognition system was improved by performing both TSM and VTLN normalization techniques.     

False alarm detection using dynamic threshold in medical wireless sensor networks

Sensor networks suffer from various sensor faults and false measurements in healthcare application and this vulnerability of the delay should handle efficiently and timely response in various application of WSN. For instance, in healthcare application, the false measurements generate false alarms which require to take unnecessary action from the healthcare department. The quality of the health care service can improve in remote healthcare monitoring system by introducing a new approach to identify the true medical condition and differentiate true and false alarms. In this paper, we proposed a novel approach to analysis past historical data collected from various medical sensors to identify the sensor anomaly. The main goal of this approach is to differentiate between true and false alarms effectively. The proposed system analysis the historical data to predicts the sensor value which compares with real sensed values at a time incident. The dynamically adjust the threshold value by comparing the difference between predicted value and historic value to determine the anomaly of sensor value. This system has been worked on huge real-time healthcare dataset and result shows that the new approach has been applied on real healthcare datasets and result of this system shows the detection rate is high and false positive rate is low which conclude that this approach is very useful in WSN application such as health monitoring system and it will be competitive with others.

     

A Reliable Lightweight Two Factor Mutual Authenticated Session Key Agreement Protocol for Multi-Server Environment

Wireless Personal Communications - Today's hyper-connected digital environment makes two-way authentication and secured key agreement a fundamental requirement for a secure connection. The...     

Deep Learning Enabled Social Media Recommendation Based on User Comments

Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect the data based on the transactional reviews of social media. The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset. Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.). In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features. Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system. The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.