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Synthesis, Mechanical Properties and Thermal Stability of Polydimethylsiloxane Nanocomposites
The polydimethylsiloxane/nano-graphite (PDMS-NG) nanocomposites were prepared via a two rolled mixing mill and subjected to characterization using techniques such as Transmission Electron Microscopy (TEM), stress-strain analysis during elongation, as well as thermal properties including Thermo-Gravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). The transition temperature was observed to be below-70C for PDMS nanocomposites reinforced with Nano-Graphite (NG). The thermogram from the thermo-gravimetric analysis indicated that at 10%, 20%, 30%, and 50% weight loss, the temperatures for PDMS nanocomposites were higher compared to unfilled PDMS. These findings suggest a substantial improvement in the thermal stability of PDMS-NG nanocomposites. 2023, Books and Journals Private Ltd. All rights reserved. -
Thermal Studies of Multiwalled Carbon Nanotube Reinforced with Silicone Elastomer Nanocomposites
This article studies the enhancement in the properties of silicon elastomer (SiR) reinforced by multiwalled carbon nanotube (MWCNT). Multiwalled carbon nanotube filled silicone rubber composites were prepared. The effects of loading levels of MWCNT on the thermal properties of silicone elastomer were investigated. SEM studies reveal the smooth distribution of MWCNT in silicon matrix. At higher concentration nanoparticles collapse together to form agglomerates. The high resolution transmission electron microscopy (HR-TEM) photographs shows excellent/homogeneous distribution of MWCNT in silicon matrix and agglomeration occurs at higher concentrations. Thermal properties of nanocomposites have been characterized using differential scanning calorimetry (DSC) and thermo-gravimetric analysis (TGA). The transition temperature appears at below -25C for MWCNT reinforced SiR nanocomposites. TGA thermogram, shows that temperature at 10%, 20%, 30%, and 50% weight loss for SiR nanocomposites is higher than as compared to unfilled SiR. The results indicate that the addition of MWCNT significantly enhanced the thermal stability of silicon elastomer. 2018 Elsevier Ltd. -
Nitrogen-doped carbonized polymer dots (CPDs) and their optical and antibacterial characteristics: A short review
Substantial advancements in the field of Carbon Dots (CDs) and their derivatives in recent years can be accredited to their tunable properties. Recently Carbonized Polymer Dots (CPDs) are the emerging form in the CDs family, which possesses a typical polymer/Carbon hybrid structure and properties due to its incomplete carbonization. Alteration of various parameters during the synthesis process suggested that the properties of CPDs depend on temperature and pH. It was found that doping of CPDs using nitrogen enhanced its optical properties, thereby being used as biomarkers. CPDs generally hold a strong green and blue emission, while intense red luminescence was observed doping with nitrogen. Photoluminescence Quantum Yield (PLQY) was also found to increase with the increase in doping and temperature. Doped CPDs find several applications, including bio-imaging, LEDs, etc. In this review, we focus on analyzing the increase in efficiency of CPDs with the process of doping considering optical and antibacterial applications. 2021 by the authors. -
Rescue Operation with RF Pose Enabled Drones in Earthquake Zones
The main objective of this research is to use machine learning algorithms to locate people stranded by an earthquake or other big disasters. Disasters are often unpredictable, they can result in significant economic loss, and the survivors may struggle with despair and other mental health issues. The time, the victim's precise location, the possible condition of the victim, the resources and manpower on hand are the main challenges the rescue team must deal with. This article examines a model that gathers data and, using that data, predicts risk analysis and probability of finding the shortest distance to reach the person in need. Using a drone equipped with RF-pose technology and EHT sensors, it will be able to locate any individuals trapped inside a collapsed structure. To determine the dataset's extreme points and the shortest route to the victim's location by using the Dijkstra's algorithms. The primary aim of this article is to discuss the idea of applying these ML (Machine Learning) algorithms and creating a model that aids in rescuing those trapped beneath collapsed buildings. Devices that are part of the Internet of Things (IoT) have grown in popularity over the past few years as a result of their capacity for data collection and transmission. Particularly in disaster management, search and rescue operations, and other related disciplines, drones have shown to be useful IoT devices. These tools are perfect for emergency response circumstances because they can be utilized to access locations that are hard to get to or too dangerous for humans. Drones with cameras and other sensors can be used in disaster management to gather data in real-time on the severity of the damage caused by earthquakes and other disasters. The afflicted area may be mapped out with their help, and they can also be used to find survivors and spot dangerous places that should be avoided. The rescue operation can then be planned and the resource allocation made more efficient using this information. Drones can be used in search and rescue operations to find and follow people who are stuck or lost. Drones can be equipped with the RF-pose sensors used in the research described in the abstract to assist in locating people who are buried under debris. Thermal camera-equipped drones can also be used to locate people in low-light or night-time conditions by detecting their body heat. The capacity of drones to offer real-time data is one of the benefits particularly disaster management. 2023 IEEE. -
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Large Scale Transportation Data Analysis and Distributed Computational Pipeline for Optimal Metro Passenger Flow Prediction
Transportation has a signifcant impact on controlling traffc around a busy city. Among the transport system, metro rails became the backbone by operating above the traffc. For this reason, we have to take special consideration of the passenger and#64258;ow in the transport system and, by understanding the needs, take timely actions for smooth running. Every metro system stores information about the and#64258;ow of passengers in the form of transactions known as Automatic Fare Collection (AFC) data. For this research, AFC data is taken as the primary newlinesource of information to identify the passenger and#64258;ow within the metro rail platform. Each metro system generates massive data throughout its running period and stores data within the system and considering the size of data generated, the analytic platform has to process them in a distributed paradigm to handle quotBig Dataquot. Artifcial Intelligence (AI) algorithms can derives information, insights, and patterns from this data. The patterns in time series can be identifed from the passenger and#64258;ow data using exploratory analysis. The step is an essential step in data science for understanding the underlying properties of the raw data. The research uses a data platform with a distributed computing and storage mechanism called the JP-DAP. The research leverages the above mentioned platform to extract passenger and#64258;ow data from AFC Ticketing data. After the data engineering, the results of passenger and#64258;ow information underwent further visualization and trend analysis. Based on the facts or patterns identifed from the passenger and#64258;ow information, a decision is taken for forecasting. The initial study will reveal the characteristics of metro usage and practices within the system and fnally derive a solution with machine learning-based forecasting method. The passenger and#64258;ow newlineforecasts based on the above patterns depend on factors like seasonality, trends, cyclicity, location, events, and random effects. -
Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems
Metro rail systems are increasingly becoming relevant and inevitable in the context of rising demand for sustainable transportation methods. Metros are therefore going to have a consistently expanding user-base and hence user satisfaction will require meticulous planning. Usage forecast is clearly an integral component of metro planning as it enables forward looking and efficient allocation of resources leading to greater commuter satisfaction. An observation from studying the usage of Kochi Metro Rail Ltd. is that there is a consistently occurring temporal pattern in usage for every station. But the patterns differ from station to station. This hinders the search for a global model representing all stations. We propose a way to overcome this by using station memorizing Long Short-Term Memory (LSTM) which takes in stations in encoded form as input along with usage sequence of stations. This is observed to significantly improve the performance of the model. The proposed architecture with station parameter is compared with algorithms like SVR (support vector regression) and neural network implementation with the best architecture to testify the claim. The proposed model can predict the future flow with an error rate of 0.00127 MSE (mean squared error), which is better than the other models tested. CTU FTS 2021. -
Evaluation of Remote Sensing and Meteorological parameters for Yield Prediction of Sugarcane (Saccharum officinarum L.) Crop
In the Agriculture sector, the farmers need a reliable estimation for pre-harvest crop yield prediction to decide their import-export policies. The present work aims to assess the impact of remote sensing-based derived products with Climate data on the accuracy of a prediction model for the sugarcane yield. The regression method was used to develop an empirical model based on VCI, Historical Sugarcane Yield, and Climatic Parameters of 75 districts of six major sugar-producing states of India. The MOD13Q1 product of MODIS on Board Terra Satellite at 16-day intervals was accessed during the growing season of sugarcane crop with 36 meteorological parameters for experimentation. The accuracy of the model was evaluated using R2, Root Mean square Metric (RMSE), Mean Absolute Error (MAE), and mean square error (MSE). The preliminary results concluded that the proposed methodology achieved the highest accuracy with (R2 =0.95, MAE=5.18, MSE=34.5, RMSE=5.87). The conclusion of the study highlighted that the coefficient of determination can be improved significantly by incorporating maximum and minimum temperature parameters with Remote sensing derived vegetation indices for the sugarcane yield. 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY NC) license (https://creativecommons.org/licenses/by-nc/4.0/). -
Predicting of Open Source Software Component Reusability Level Using Object-Oriented Metrics by Taguchi Approach
Component-based software development (CBSD) is an efficient approach used by software developers to develop new software. The commercial off the shelf (COTS) and open-source software (OSS) are two styles to implement CBSD. The COTS provides the interface and depicts the black-box behavior, but does not support several software quality characteristics. On the other hard, OSS is a more efficient approach compared to COTS due to its source code availability. This research aims to identify the reusability level of OSS components from an online repository of OSS. The OSS components are classified based on Chidamber and Kemerer reusability metrics (CK-metrics). This paper proposed a mathematical model to establish the relationship between the reusability of CK-metrics. Reusability level of OSS component has been measured and most effective CK-metrics obtained by applying the Taguchi design and analysis of variance (ANOVA). The input parameters for the experimental design are evaluated based on the OSS repository. Performance analysis has been carried out based upon the interaction effect between the reusability of CK-metrics. Main effect plots are created to identify the most reusable component of the OSS. The genetic algorithm (GA) is used to predict the optimized value of the different control parameters. The results indicate that the OSS component reusability level is 0.698194. The reusability of software has a significant effect on the quality of software. The quality of software can be improved by increasing the reusability of software components. 2021 World Scientific Publishing Company. -
Structural equation based model to investigate the moderating effect of fear of COVID using partial least square method
This study assesses the magnitude of work life integration among health care workers with the help of positive psychology constructs in COVID-19 crisis. The effect of optimistic approach and sense of belongingness is studied on the performance-oriented healthcare workers and how it influenced their withdrawal cognition. The moderating effect of fear of corona disease is also analysed on performance orientor and withdrawal cognition. Empirical data derived through face-to-face interactions of 357 health care professionals using partial least squares-structural equation modelling PLS-SEM 3.3.3 provides the detailed analysis of the model (measurement and structural). The results indicate that optimistic approach and sense of belongingness contribute towards performance-oriented health care work with R2 value of 79% (? =.533; t = 7.042; p< 0.00) and (? =.0.391; t = 5.43; p< 0.00) respectively. Performance orientor show negative relation with withdrawal cognition (? = -0.122.; t = 2.11; p< 0.00) and R2-value of 74.8%. The moderation effect of fear of corona disease shows negative affect on performance orientor (? = -0.044.; t = 26.10; p< 0.01); R2-value of 79.3% and positive interaction on the withdrawal cognition (? = 0.844; t = 38.42; p< 0.00) and R2-value of 76.4%. 2022 Taru Publications. -
Assessing the role of materialism and gratitude in life satisfaction through IPMA: the mediating role of meaningfulness in life
Purpose: This study aims to create a more humane and responsible workplace, individuals gratitude and meaningfulness seem of utmost importance. This study is an effort to understand the role of gratitude intent of potential managers. Design/methodology/approach: This study examines the psychological characteristic of business students in India. The researchers surveyed 333 Indian students as future managers. The collected data has been analysed with the Smart PLS 3 version to assess the formative-reflective scale by comparing model fit, measurement model and structural modelling. Findings: The results establish that gratitude significantly affects the life satisfaction of future managers. Findings also show that materialism is negatively related to life satisfaction and meaningfulness. The importanceperformance map analysis finding suggests that meaningfulness in life is a potential indicator of life satisfaction for the population studied. Originality/value: Due to the limited research available on the psychological underpinnings in the Indian context, there is a massive value in examining how materialism and gratitude concurrently and distinctively predict meaning in life and the life satisfaction of future managers. This paper gives a formative explanation of the model consisted gratitude, materialism and meaningfulness in life on the life satisfaction of future managers. This study establishes the importance of meaningfulness of life in attaining life satisfaction for young managers. 2023, Emerald Publishing Limited. -
Exploring the dynamics of financial behaviors in romantic partnerships
Financial well-being is a multifaceted concept that is influenced by various factors, including individual and relational dynamics. While households are indeed vital to understanding economic dynamics, they often do not provide sufficient insight into the complex decision-making processes of romantic partners. In light of this, the chapter places its focus squarely on romantic partners as the primary unit of analysis within households, considering romantic partners to be the building blocks of the households. This chapter seeks to address a critical aspect of financial well-being by focusing on the complex dynamics of financial behaviors within romantic partnerships. By delving into the unique challenges and opportunities faced by couples in managing their finances, this chapter strives to bridge the gap by summing the current state of our knowledge proposes a comprehensive conceptual model to elucidate the factors affecting financial behaviors and, consequently, financial well-being among romantic partners. 2024, IGI Global. All rights reserved. -
Dynamic Connectedness and Volatility Spillover Effects of Indian Stock Market with International Stock Markets: An Empirical Investigation Using DCC GARCH
This study employs the DCC-GARCH model to investigate the dynamic connectedness between the Indian and significant global stock markets. Specifically, we examine daily log returns data of the National Stock Exchange (NSE) index and several international indices, including the United States, Australia, China, Germany, England, Japan, and Taiwan. Our analysis indicates a significant level of volatility spillover between the Indian stock market and the international stock market. Notably, we observe a significant positive spillover effect from the S&P 500 and FTSE 100 to the Indian stock market, suggesting contagion effects. Additionally, we find bidirectional spillover between the Indian stock market and the Nikkei 225 and Hang Seng, indicating a high level of interdependence between these markets. Our research contributes to the growing literature on the dynamic connectedness of stock markets and has important implications for policymakers and investors in emerging economies such as India. Overall, this study provides valuable insights into the nature and extent of spillover effects between the Indian and international stock markets. 2023 University of Pardubice. All rights reserved. -
Enablers of Successful Fiscal Decentralisation: A Case Study of Three Gram Panchayats in Kerala
Kerala is among the few states that have a successful record in fi scal decentralisation. This study qualitatively analyses primary data from three gram panchayats in Kerala to identify the factors that enable successful decentralised fi scal governance through panchayati raj. Based on the fi ndings of the study, we have constructed a framework to assess the readiness of gram panchayats to carry out successful decentralised fiscal governance. 2022 Economic and Political Weekly. All rights reserved. -
Lesion detection in women breasts dynamic contrast-enhanced magnetic resonance imaging using deep learning
Breast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breasts Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in womens breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F 1 -score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies. 2023, The Author(s). -
Examining the Prevalence and Impact of Physical Violence from a Psychological Perspective
Physical violence within marriages are pervasive issues that affect individuals globally, including in India. This essay examines the prevalence and impact of physical and sensual violence in India from a psychological perspective. The underreporting and stigmatization of these forms of violence pose significant challenges in understanding their true extent. Factors such as power imbalances, gender inequality, and cultural norms contribute to their perpetuation. The psychological consequences experienced by survivors include trauma, post-traumatic stress disorder (PTSD), depression, anxiety, emotional distress, sexual dysfunction, substance abuse, and self-harm. Additionally, the cycle of violence and revictimization further compounds the psychological impact. Addressing this issue requires legal reforms, raising awareness, promoting education, challenging cultural norms, providing support services, and ensuring accessible mental health support. By addressing the cultural, legal, and psychological dimensions, it is possible to create a society that is free from physical violence and supports survivors in their journey towards healing and empowerment. The objective of this study is to enhance comprehension of the issue of physical violence in India by examining its prevalence and impact. The findings of this research endeavour to facilitate the development of efficacious strategies and interventions to combat this widespread problem in the nation. 2023, Journal for ReAttach Therapy and Developmental Diversities. All Rights Reserved. -
Gender gap in travel behaviour and public opinion on proposed policy measures: Evidence from India
Employing primary survey data collected from Jaipur city in India, this work attempts to evaluate inconsistencies in travel behaviour based on gender. It also intends to discuss the public opinion on a few proposed policy changes which can aid in bridging the established gender gap. Stratified random sampling approach is used to gather data on travel pattern measures and socioeconomic attributes. Descriptive statistics complemented with bivariate probit model and seemingly unrelated bivariate probit model is applied on the data acquired. The obtained results confirm the existence of a gender gap in all observed measures of travel behaviour. Compared to men, women travel shorter distances, use more of non-motorised modes of transport, have lower frequency of travelling, and travel majorly for purposes other than work. Results of the study also highlight how a majority of the respondents are in favour of policy changes aimed at narrowing the observed gender disparities. The analysis demands infrastructural development of non-motorised transportation and public transportation in the city in such a way which is both efficient and secured, so as to neither obstruct the objective of empowerment nor of sustainability. 2023 John Wiley & Sons Ltd. -
Understanding environmentally sustainable Indian travel behaviour: an analysis of 2011 census data
Using census data of non-agricultural workers for 2011, this study aims to examine trends and determinants of travel behaviour in India. Descriptive statistics accompanied by a beta regression model of proportional outcomes are implemented on the obtained data. The study finds that men are the dominant users of motorized transport in the country. Most workers travel a short distance of less than 5km, irrespective of area or gender. Population density, the share of married population and the share of rural population in a district significantly influence the share of environmentally sustainable travel behaviour displayed by that region. To the best of our comprehension, this is one of the primary studies elucidating the comparison of travel behaviour in ruralurban areas of Indian states. Not many studies in India have addressed the issue of influence of socio-demographic factors on environmentally sustainable travel choices. With this analysis, policymakers in the transportation sector can get a clearer idea of the behaviour and demands of different divisions of society. The findings of this study demand the evolution of infrastructure of public transportation and non-motorized transportation in the country in such a way that is both efficient and secure to neither impede the goals of empowerment or sustainability. The Author(s), under exclusive licence to Institute for Social and Economic Change 2023. -
Intricate Plane of Adversarial Attacks in Sustainable Territory and the Perils faced Machine Intelligent Models
The issue of model security and reliability in Artificial Intelligence (AI) is a concern due to adversarial attacks. In order to tackle this issue, researchers have developed sustainable defense strategies, but certain challenges remain. These challenges involve transferability, higher computing costs, and adaptability. Striking a balance between accuracy and robustness is difficult, as defense mechanisms often come with trade-offs between the two. Real-world situations demonstrate the practical implications of sustainable adversarial AI. For example, it improves the security of self-driving vehicles, enhances the accuracy of medical imaging diagnoses, and incorporates AI-driven defenses into network intrusion detection and phishing detection systems. It is crucial to consider ethical aspects throughout this process. Future trends in adversarial AI research for cybersecurity will involve ensemble defense mechanisms, adversarial learning from limited data, and hybrid attacks. By embracing the evolving landscape, researchers and practitioners can develop sustainable AI systems that are more secure and resilient, effectively countering adversarial threats. 2023 IEEE. -
Classification of Skin Diseases Using Convolutional Neural Networks (VGG) with Histogram Equalization Preprocessing
Skin diseases are a major global health concern for which prompt and precise diagnosis is necessary for effective treatment. Convolutional neural networks (CNN), one of the deep learning techniques, have shown potential in automating the diagnostic procedure. The goal of this research is to enhance the effectiveness of skin disease categorization by fusing the capabilities of CNNs - particularly the VGG architecture - with the histogram equalization preprocessing method. In image processing, histogram equalization is a commonly used approach to enhance the contrast and general quality of medical photographs, which include photos of skin conditions. In order to improve the characteristics and details of dermatological pictures for this study, we employed histogram equalization as a preprocessing step. This allowed CNN to extract pertinent features more quickly. 2024 IEEE.