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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/). -
More Than Skills: How Digital Competence and Partner Attitudes Shape Financial Resilience in Couples
In the rapidly digitizing financial landscape, the ability to effectively use digital tools has become essential for financial well-being. This study examines the impact of digital competence on financial resilience within dual-income married couples, adopting a dyadic perspective. Drawing on the Actor-Partner Interdependence Moderation Model(APIMoM), it studies both actor and partner effects of digital competence. As well as the moderating role of each partners attitude toward FinTech. Data was collected from 107 (214 individuals) working couples in Gurgaon, India. Covariance-Based Structural Equation Modeling using SmartPLS revealed that digital competence significantly influences both individuals and their partners financial resilience. Moreover, attitudes toward FinTech were found to moderate these relationships, strengthening the positive effects of digital competence. Notably, the husbands attitude had a stronger moderating impact on the wifes resilience than vice versa, indicating potential gender-based dynamics. The study marks the importance of addressing both digital skills and relational attributes in aiding household financial resilience. Practical implications suggest that digital literacy programs should consider couple-based interventions that target both digital competence and attitude change. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Human-Centered Intelligence in Financial Decision-Making
In the rapidly evolving landscape of twentyfirst-century finance, digitized financial services have become ubiquitous, with Artificial Intelligence (AI) emerging as a transformative force. This chapter investigates consumers contemporary dilemma in personal finance, navigating the intersection between traditional reliance on social networks for financial wisdom and the allure of AI-driven FinTech solutions. The study, encompassing 163 individuals, explores their financial decision-making preferences to discern whether they gravitate toward human connections or AI. The literature review categorizes findings into Digital Financial Literacy, FinTech, and Financial Socialization, offering insights into aspects such as autonomy, confidence, subjective well-being, and the digital divide. The chapter employs regression analysis, revealing the substantial impact of human connection and artificial intelligence on financial well-being. The significant relationships are the predictive power of these variables in shaping financial outcomes. This comprehensive exploration contributes valuable insights into the dynamic interplay between human connections and AI in personal finance. It provides a structured overview of key themes and emphasizes the need for a balanced approach that leverages the strengths of both human connections and AI to enhance overall financial well-being. The findings are helpful for individuals navigating the contemporary financial landscape, emphasizing the importance of informed decision-making that integrates the strengths of interpersonal relationships and cutting-edge AI technologies. The nexus of personal relationships, social networks, and AI has wide-ranging socioeconomic implications for various domains, individuals, households, and financial institutions. Financial well-being and artificial intelligence are emerging subjects of paramount importance in the current phase of the economy. Closer collaboration between these will likely generate new and useful insights into the financial decision-making of individuals and households. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploring Self-Objectification Among Queer Community in IndiaA Qualitative Study
The objectification theory proposes that membership in sexually objectifying societies gradually socializes queer individuals to adopt an observers perspective on their physical self, which in turn leads to negative emotional and behavioral consequences. The queer community in India has existed for centuries, but their rights have only received attention recently. They still continue to be deprived of societal and legal status. With an aim to understand self-objectification among the queer community in India, this qualitative study attempts to explore the interplay of external factors and their relationships in shaping self-image and self-acceptance. This phenomenological study used an interpretive paradigm on the self-objectification of members of the queer community in India. The study was conducted on 10 individuals from the queer community using semi-structured interviews, and the obtained data was analyzed using thematic analysis. Findings indicate the profound impact of societal beauty norms, family dynamics, peer interactions, and media influence on their self-esteem, body image, and identity. Implications of these findings call for increased awareness, education, and support to create a more inclusive and accepting environment for the queer community. Future recommendations include the development of interventions, educational programs, and mental health support services tailored to the needs of this community. 2025 The Author(s). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
A novel multigrade classification in FL using brain MRI images based on FHAT_EfficientNet
This paper establishes the fractional harmony artificial tree (FHAT)_EfficientNet for multi-grade classification in federated learning (FL). Here, the established FHAT is attained by the integration of the fractional calculus (FC) and harmony search-based feedback artificial tree (HSFAT) algorithm, and the HSFAT is developed by the combination of harmony search (HS) and feedback artificial tree (FAT). Initially, the input MRI image is taken from a particular dataset and subjected to pre-processing. Thereafter, tumour segmentation is accomplished based on fuzzy local information c-means (FLICM). Later, image augmentation and feature extraction are performed. Finally, the multi-grade classification is carried out using EfficientNet fine-tuned based on the proposed FHAT. Moreover, the established FHAT_EfficientNet attained better accuracy, specificity, sensitivity, mean squared error (MSE), root mean square error (RMSE), and loss function of 0.917, 0.936, 0.966, 0.058, 0.241, and 0.083. Copyright 2025 Inderscience Enterprises Ltd. -
Understanding artificial intelligence and its major role in branding
This chapter explores the transformative role of Artificial Intelligence (AI) in branding, focusing on how AI-driven tools like machine learning, predictive analytics, and chatbots are reshaping brand communication, personalization, and consumer engagement. It examines the shift from traditional branding to AI-enhanced strategies leveraging real-time data to anticipate customer needs and delivering highly personalized experiences, fostering brand loyalty and deeper consumer connections. Key benefits, such as improved customer retention, enhanced engagement, and smarter decision-making, are discussed alongside ethical considerations, including data privacy, transparency, and bias in AI algorithms. The chapter also highlights practical applications of AI in monitoring customer sentiment, optimizing interactions, and adapting brand messaging dynamically. Concluding with a look at future trends like AI-powered voice assistants and AR, offering insights for businesses to leverage AI responsibly, ensuring personalization, ethical practices, consumer trust, and long-term success. 2025, IGI Global Scientific Publishing. All rights reserved. -
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. -
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. -
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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. -
At the frontlines of the invisible: PVDF/MWCNT nanocomposite shields
Polyvinylidene fluoride (PVDF)/multi-walled carbon nanotube (MWCNT) nanocomposites prepared by the solution casting method were examined for their electromagnetic interference shielding effectiveness (EMI-SE) in the frequency range of the X band (8.212.4 GHz). The study revealed that increasing MWCNT concentration significantly enhances the electrical conductivity, and EMI shielding performance of the PVDF matrix. Scanning electron microscopy (SEM) confirmed uniform dispersion of MWCNTs within the polymer matrix, contributing to the formation of an effective conductive network. As the MWCNT concentration increased, a transition from insulating to conductive behaviour was observed, with 6 wt% MWCNT composites achieving a conductivity of 0.058 S?cm?1 and an EMI-SE of 22.5 dB. These results demonstrate that PVDF/MWCNT nanocomposites are promising materials for lightweight and efficient EMI shielding applications. 2025 Informa UK Limited, trading as Taylor & Francis Group.