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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. -
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). -
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. -
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. -
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. -
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. -
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. -
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. -
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/). -
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. -
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. -
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. -
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. -
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. -
Investigation of fluorescence enhancement and antibacterial properties of nitrogen-doped carbonized polymer nanomaterials (N-CPNs)
Carbonized Polymer Nanomaterials (CPNs) have acquired substantial research interest in recent years due to their budding applications in various optical and electrochemical studies like electrocatalysis, solar cells, biosensing, etc. Due to their stability and toxicity, the enhancement of CPNs' properties was the primary cause of concern. Herein, we synthesized Nitrogen-doped (N-doped) N-CPNs using the one-step hydrothermal approach of PVA and PVDF polymers with Nitric acid (HNO3) as the nitrogen source. The luminescence intensity was observed to be enhanced by increasing nitrogen doping concentration. The synthesized fluorescent samples exhibited significant antibacterial properties, making them useful in biomarkers, sensing strategies, drug delivery, etc. Doped PVA samples exhibited negligible antibacterial activity, but nitrogen-doped PVDF samples displayed considerable biocidal activity against gram-positive bacteria, according to antibacterial research. Each sample's growth inhibition was distinct and species-specific. 2022 Taylor & Francis Group, LLC. -
Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction
Artificial Intelligence plays a vital role in developing machines or software that can create intelligence. Artificial Neural Networks is a field of neuroscience which contributes tremendous developments in Artificial Intelligence. This paper focuses on the study of performance of various training algorithms of Multilayer Perceptrons in Diabetes Prediction. In this study, we have used Pima Indian Diabetes data set from UCI Machine Learning Repository as input dataset. The system is implemented in MatlabR2013. The Pima Indian Diabetes dataset consists of about 768 instances. The input data is the patient history and the target output is the prediction result as tested positive or tested negative. From the performance analysis, it was observed that out of all the training algorithms, Levenberg-Marquardt Algorithm has given optimal training results. 2015 IEEE. -
Improved Security of the Data Communication in VANET Environment Using ASCII-ECC Algorithm
Now-a-days, with the augmenting accident statistics, the Vehicular Ad-hoc Networks (VANET) are turning out to be more popular, helping in prevention of accidents in addition to damage to the vehicles together with populace. In VANET, message can well be transmitted within a pre-stated region to attain systems safety and also improveits efficacy. Ensuring authenticity of messages is a challenge in such dynamic environment. Though few researchers worked on this, security level is very less. Hence enhanced communicationsecurity on the VANET environment utilizing the American Standard Code for Information Interchange centred Elliptic Curve Cryptography (ASCII-ECC) is proposedin this paper. The network design is definedinitially. Subsequently, the entire vehicles get registered to the Trusted Authority (TA); similarly, all vehicle users areregistered with their On-Board Unit (OBU). This is followed byMedian-centred K-Means (MKM) performs the cluster formation together with Cluster Head Selection (CHS). Next, TA takes care of the verification procedure. Modified Cockroach Swarm Optimization (MCSO) calculates the shortest path and the ASCII-ECC carries out the secure data communication if the vehicle is an authorized one. If not, TA sends the alert message for discarding the request. The system renders better performance when it was weighed against the top-notch methods. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A Comprehensive Review of Small Building Detection in Collapsed Images: Advancements and Applications of Machine Learning Algorithms
Accurately identifying small buildings in images of collapses is essential for disaster assessment and urban planning. In the context of collapsed images, this study provides an extensive overview of the methods and approaches used for small building detection. The investigation covers developments in machine learning algorithms, their uses, and the consequences for urban development and disaster management. This work attempts to give a brief grasp of the difficulties, approaches, and potential paths in the field of small building detection from collapsed imaging through a thorough investigation of the body of existing literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Review on Image Processing-Based Building Damage Assessment Techniques
Quick damage assessment is essential for starting efficient emergency response operations following natural calamities or any other kind of disasters. After a disaster, it is crucial for rescue departments to produce judgments and distribute the resources based on a fast retrieval of precise building damage status. A ground survey is used to implement traditional building assessment, and this is labor-intensive, dangerous, and time-consuming. Studies on building damage extraction over the past few decades have generally concentrated on localizing and evaluating the destructed structures, analyzing the ratio of damaged constructions, and determining the sort of destruction each construction has sustained. Recent research trends are mainly concentrated on the utilization of data collected from multiple sensors for the damage assessments of buildings. Each stage of digital image processing can be carried out in multiple ways and several novel ideas are emerging every single day. This paper reviews the various damage assessment techniques in the different steps of digital image processing. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Controlling the Accuracy and Efficiency of Collision Detection in 2d Games using Hitboxes
Collision detection is a process in game development that involves checking if two or more objects have intersected or collided with each other. It is a fundamental aspect of game mechanics that cannot be overlooked. Games invloves assets/sprites, which tend to be drawn digitally with the help of a computer program. This paper discusses controlling and detecting collisions in games that make use of PNG images as game assets. The conventional way to detect collision in a game is to check if the object is within the bounding box of another object or asset. However, such a method lacks realism and doesn't work well with much complex shapes as the game might register a hit when another object collides with the transparent part of the object being checked for collision. In order to overcome these limitations, the proposed algorithm divides the entire image into smaller rectangles and stores its coordinates in an array. The array is then pruned by removing coordinates with no translucent or opaque elements. Collision is detected by checking if any of the points of the collision object is inside the image array. 2023 IEEE.