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.     

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.     

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.     

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...     

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.     

Downlink Radio Resource Management Through CoMP and Carrier Aggregation for LTE-Advanced Network

Long Term Evolution-Advanced (LTE-A) offers several new technologies to improve the performance of the user. However, poor received signal and interference from adjacent cells in the cell-edge area can reduce the efficiency of using individual technology. Therefore, the cell-edge users have lower throughput compared to the other users in the cell and LTE-A standard. An efficient downlink radio resource management scheme is proposed in this paper by combining the coordinated multipoint transmission and reception technique along with carrier aggregation technique to achieve higher throughput for the cell-edge user and better overall performance. The proposed method jointly transmits multiple component carriers to the cell-edge user from different cells to increase the bandwidth, strengthen the received signal, and reduce the interference while it satisfies several constraints. Modified largest weighted delay first packet scheduling algorithm is deployed for resource allocation, which takes into account the delay parameters, the probability of packet loss, and data rates of the user. The obtained system-level simulation results show that the proposed method significantly enhances the throughput performances, spectral efficiency, and fairness index, compared with the existing conventional methods.

     

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.