Effect of 100 KeV Ar+ ion beam irradiation on ZnO thin films – Influence of morphology vis-a-vis electrode/electrolyte interface and its impact on photoelectrochemical water splitting

The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar⁺ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 10¹⁶ ions/cm² is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm² and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm^?2.     

A Deep Learning Approach to Detection and Mitigation of Distributed Denial of Service Attacks in High Availability Intelligent Transport Systems

Mobile Networks and Applications - In the era of Internet of Things (IoT) powered by 5G technologies, Automobile Industry is headed towards a revolution. In Intelligent Transport Systems (ITS),...     

A constrained non-linear optimization model for fuzzy pairwise comparison matrices using teaching learning based optimization

Non-linear optimization models have been recently proposed to derive crisp weights from fuzzy pairwise comparison matrices. In this paper, a TLBO (Teaching Learning Based Optimization) based solution is presented for solving an optimization model as a system of non-linear equations to derive crisp weights from fuzzy pairwise comparison matrices in AHP (Analytic Hierarchy Process). This fuzzy-AHP method is named as TLBO-1. It has been found that TLBO-1 can lead to inconsistent or less consistent weights. To solve the problem of inconsistent weights, a new constrained non-linear optimization model is proposed in this paper. This model is based on the min-max approach for fuzzy pairwise comparison ratios of weights. TLBO is again used to solve this optimization model, and crisp weights are derived. This fuzzy AHP method is named as TLBO-2. The effectiveness of the proposed model is illustrated by three examples. For each example, the consistency of the derived crisp weights is compared with other optimization models. The results show that the TLBO-2 method can derive more consistent weights for the fuzzy AHP based Multi-Criteria Decision Making (MCDM) systems as compared to the other optimization models.     

A Microfluidic 3D Endothelium-on-a-Chip Model to Study Transendothelial Migration of T Cells in Health and Disease

The recruitment of T cells is a crucial component in the inflammatory cascade of the body. The process involves the transport of T cells through the vascular system and their stable arrest to vessel walls at the site of inflammation, followed by extravasation and subsequent infiltration into tissue. Here, we describe an assay to study 3D T cell dynamics under flow in real time using a high-throughput, artificial membrane-free microfluidic platform that allows unimpeded extravasation of T cells. We show that primary human T cells adhere to endothelial vessel walls upon perfusion of microvessels and can be stimulated to undergo transendothelial migration (TEM) by TNFα-mediated vascular inflammation and the presence of CXCL12 gradients or ECM-embedded melanoma cells. Notably, migratory behavior was found to differ depending on T cell activation states. The assay is unique in its comprehensiveness for modelling T cell trafficking, arrest, extravasation and migration, all in one system, combined with its throughput, quality of imaging and ease of use. We envision routine use of this assay to study immunological processes and expect it to spur research in the fields of immunological disorders, immuno-oncology and the development of novel immunotherapeutics.     

Effect of functionalized silicon carbide nano-particles as additive in cross-linked PVA based composites for vibration damping application

In this research study, a comprehensive effort has been made to functionalize silicon carbide particles using the acidic oxidation with nitric acid to obtain homogeneous stabilized distribution of activated SiC particles within a polymer matrix, and develop functionalized silicon carbide (f-SiC) particle reinforced polyvinyl alcohol (PVA) based cross-linked composite. After fabrication of functionalized silicon carbide (f-SiC) particle reinforced polyvinyl alcohol based cross-linked composite with varying f-SiC weight percentages of PVA (0%, 1%, 2%, 3%, and 4%) were placed to various investigations. Processed samples are initially examined based by the physical tests (water absorption tests), followed by mechanical test (tensile test) and then micro-structural tests (scanning electron microscopy). Lastly, thermal tests were also concluded which involved the dynamic mechanical, differential thermal and thermo gravimetric analysis. The cross-linked polyvinyl alcohol-based composite with 2 weight % of f-SiC content is observed to be the superlative of all the compositions under this research study that was confirmed by the mechanical and micro-structural tests. This composite material shows high storage modulus with visco-elastic behavior, therefore, the material can be utilized to diminish the transmission of noise, as a shock absorber and vibration isolator.     

Machine learning models for mathematical symbol recognition: A stem to stern literature analysis

Given the ubiquity of handwriting and mathematical content in human transactions, machine recognition of handwritten mathematical text and symbols has become a domain of great practical scope and significance. Recognition of mathematical expression (ME) has remained a challenging and emerging research domain, with mathematical symbol recognition (MSR) as a requisite step in the entire recognition process. Many variations in writing styles and existing dissimilarities among the wide range of symbols and recurring characters make the recognition tasks strenuous even for Optical Character Recognition. The past decade has witnessed the emergence of recognition techniques and the peaking interest of several researchers in this evolving domain. In light of the current research status associated with recognizing handwritten math symbols, a systematic review of the literature seems timely. This article seeks to provide a complete systematic analysis of recognition techniques, models, datasets, sub-stages, accuracy metrics, and accuracy details in an extracted form as described in the literature. A systematic literature review conducted in this study includes pragmatic studies until the year 2021, and the analysis reveals Support Vector Machine (SVM) to be the most dominating recognition technique and symbol recognition rate to be most frequently deployed accuracy measure and other interesting results in terms of segmentation, feature extraction and datasets involved are vividly represented. The statistics of mathematical symbols-related papers are shown, and open problems are identified for more advanced research. Our study focused on the key points of earlier research, present work, and the future direction of MSR.

     

Hybrid neural network model for reconstruction of occluded regions in multi-gait scenario

In real-time situations such as airports, railway stations, and shopping complexes, etc. people walk in a group, and such a group of walking persons termed as multi-gait (MG). In these situations, occlusion is a serious issue that affects gait recognition performance. This issue of occlusion of body regions affects the extraction of gait features for the correct recognition of an object. The objective of this article is to reconstruct occluded regions at the preprocessing stage, which can be used for human recognition in the MG scenario. The article is divided into two folds. Firstly, we segment five regions of interest such as ankle, knee, wrist, elbow, and shoulder. We propose a particle swarm optimization (PSO) based neural network (NN) called hybrid NN to solve this problem. The performance of the proposed model is validated on our constructed dataset (SMVDU-MG), considering two view directions i.e. lateral (left to right) and oblique (left to right diagonal). Experimental results show that the proposed model gives better performance compared to an artificial neural network and alternating least square (ALS) method based on mean square error (MSE) and mean absolute percentage error (MAPE) as a performance measure function.

     

Sleep Pattern Study with Respect to Binaural Beats Using Sensors and Mobile Application

Wireless Personal Communications - Sleep disorders are common among people in the present lifestyle and this may occur due to irregular sleep patterns. The disordered sleep pattens arise due to...     

Green IoT: A Short Survey on Technical Evolution & Techniques

The Internet of Things (IoT) embodies the confluence of the virtual & physical world. IoT will play an important role in managing the managing depleting resource such as water, fuel, food, etc. However, to realize these applications enormous IoT devices will communicate with each other. This massive connectivity will directly or indirectly aid in Green House Gas emissions. Hence, to admissibly reduce this environmental impact of IoT, it must be greened in terms of energy consumption. Green IoT will reduce environmental exploitation by slashing carbon emission effectively and thus will help in achieving sustainability of the planet. This paper describes the journey of IoT to Green IoT. Along with this, the survey on recent Green-IoT techniques that will effectively help in reducing required energy consumption is presented. Along with this ability of unmanned aerial vehicle (UAV) technology to provide Green IoT and survey on recent energy-efficient UAV assisted communication is presented. In addition to this, a dual battery enabled Unmanned Aerial vehicle base station, an energy-efficient clustering algorithm, has also been proposed to prolong the battery life.

     

The case analysis on sentiment based ranking of nodes in social media space

Now-a-days, social network sites have become quite popular for communication in the society. People have entangled their day-to-day activities around social media platforms. Social Networks have allowed the users to share their opinions on different topics. In social media, sentiment analysis is an important character to determine opinions of users. Moreover, user’s can be ranked to determine their relative influence. This paper proposes a methodology to rank the users involving sentiment related parameters such as likes, comments and corresponding likescount. Analysis of users’ comments is carried-out. Weights are assigned to these parameters and scores are calculated for each user. Users are ranked on the basis of scores obtained and compared with existing technique. In order to verify the effectiveness of proposed methodology, data is extracted from a verified Facebook page ‘Panjab University, Chandigarh’. Mean, standard deviation and variance are computed to capture the usefulness of ranks obtained by the proposed method. Results depict that the proposed methodology is better than existing technique since it incorporates several features indicating positive and negative behavior of users. This technique can be used to determine the highly trusted and the most distrusted users in a social media user’s profile. Users with negative scores can be considered for outlier analysis. The proposed methodology can also be extended to work on other social media platforms.