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.

     

Highly enhanced energy storage properties of H2O2-hydroxylated rare earth ferrites (LaFeO3 and GdFeO3) nanofillers in poly(vinylidene fluoride)-based nanocomposite films

Journal of Materials Science: Materials in Electronics - Pure PVDF has higher breakdown strength but low dielectric and ferroelectric properties. Thus, we synthesized the LaFeO3 and GdFeO3...     

High capacity reversible and secured data hiding in images using interpolation and difference expansion technique

Multimedia Tools and Applications - Data hiding is a noteworthy research topic in digital technology for years. Reversible data hiding (RDH) technique plays a vital role for confirming security of...     

Effect of gauge length on loop strength of sewing threads

Seam strength prediction using loop strength has been found to be more accurate and has been consistently used by researchers over last few decades. But the testing gauge length for loop strength has not been standardized as in the case of thread tensile strength. In this paper, effect of gauge length on thread tensile strength and elongation in loop form and straight form has been studied. 40 tex and 60 tex mercerized cotton and spun polyester threads are used for the study. It is observed that there is no significant change in the loop strength of the threads as the gauge length increases, whereas the tensile strength of all threads decreases with the increase in gauge length. Elongation % is found to change significantly in both loop and straight form during tensile testing. Therefore, it is concluded that loop strength tests can be carried out at any gauge length unlike tensile testing of straight threads.     

Coefficient of friction and oil flow in plain finite partial journal bearings during turbulent operation

Bearings which have been properly designed with respect to the provision of a hydrodynamic oil film can sometimes fail by overheating. Hence the complete analysis of journal bearings requires the evaluation of friction and oil flow. Plain partial journal bearings of finite length with a turbulent hydrodynamic film of a newtonian lubricant were analysed to obtain the coefficient of friction and the total oil flow. The coefficient of friction, which is a performance characteristic, is presented analytically in the closed form. The total oil flow through superlaminar bearings is also considered as a bearing performance characteristic. All the results of interest are dimensionless and available in a closed form which is simple yet exact over the wide range of parameters involved.     

RAT selection for a low battery mobile device for future 5G networks

The growth of wireless communication toward fifth generation will lead to the existence of number of access technologies to provide seamless connectivity and form heterogeneous network environment. Earlier, there was single access technology to run applications, but 5G will have heterogeneous network environment and provide separate network for each application. As compared with 4G, 5G will provide increase in data rate, decrease in delay, increase in quality of service, and so on because of its various enabling technologies. Therefore, for each application, selection of best access network via its enabling technology is an important task. This selection is done either at user terminal side or at network operator side by combining preferences for network attributes and network parameters. In this paper, to enjoy 5G, selection is done in a heterogeneous networks environment for enabling technologies like device‐to‐device communication, spectrum sharing, enhancing quality of experience, energy efficiency, and so on. This selection is done via optimization techniques for a fixed duration video clip that is to be transmitted from a device running low in battery. The selection environment composed of UMTS, WLAN1, and WLAN2 as available networks. The simulation results show that the network selected for each enabling technology supports various features of 5G. Also, error analysis of selection results is done using confidence interval estimate at 90%, 92%, and 95% confidence level. From results obtained, it is seen that different optimization techniques used to access network for different enabling technologies (providing 5G features) prove to be useful for future 5G network.     

Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases

In recent years, a trend of electronic health record (EHR) system can be seen increasingly in the hospitals, which has generated huge amount of electronically stored data of patients. Association rule mining technique is very helpful in the numerous applications of healthcare (e.g., correlation between disease and symptoms, disease and offering effective treatment and predicting risks of disease based on the historical data, etc.). The data collected by an EHR system are very important for the medical research. Currently, a patient health report is derived on the basis of a physician’s own experience and on the association rule mining results of a local EHR system maintained by a particular hospital. Association rule mining results will be more accurate if the data of all local EHR systems are integrated and association rule mining is performed. Integration of local EHR systems requires the sharing of local EHR data. Sharing of patient records violates the privacy of patients. Hence, medical research is focused on the problem of mining association rules without sharing of local private EHR data. Privacy-preserving distributed association rule mining (PPDARM) solves this issue by mining the association rules while preserving the privacy of patients. In this paper, an approach for the PPDARM is proposed for collaboratively performing association rule mining by all local EHR systems while preserving the privacy. The proposed approach is also analysed with the heart disease dataset.     

Uniplanar Y and L shaped ACS fed multiband and wideband compact printed antenna for advanced wireless communication systems

Microsystem Technologies - In this paper, a very small size (14 × 20 mm2), tunable printed antenna consisting of Asymmetric Coplanar Strip (ACS) feedline with three...