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Decoding HERO: Predicting psychological capital with subjective well-being
The positive psychology movement has gained momentum in recent years and organizations have ascribed great importance to employee well-being in light of the favorable outcomes associated with it. The widely researched Psychological Capital (PsyCap) has been consistently linked to well-being across a variety of contexts but a gap still exists in literature about what lies to the 'left' of psychological capital. The present study attempts to fill this gap by examining subjective well being components- positive and negative affect and life satisfaction, as potential antecedents of PsyCap. The Academic PsyCap questionnaire, the Positive and Negative Affect Schedule (PANAS) and the Satisfaction with Life Scale (SWLS) were administered to participants. Results confirmed the expected associations between affect and PsyCap-positive affect positively predicted PsyCap and its four constituents whereas negative affect emerged as a negative predictor of PsyCap and its dimensions. Life satisfaction positively predicted only individuals' total hope scores. Thus, highlighting the role of subjective well-being components as antecedents of PsyCap, these findings suggest that promoting higher positive affect and lower negative affect can do more than just make individuals feel good, rather, it can bolster their reservoirs of crucial psychological resources as well. 2021 Ecological Society of India. All rights reserved. -
DECODING INTENTIONS TO PURCHASE ORGANIC FOOD PRODUCTS IN AN EMERGING ECONOMY VIA ARTIFICIAL NEURAL NETWORKS
Purpose. This study investigates the factors influencing consumers intentions to purchase organic food products in an emerging economy. It addresses the knowledge gap regarding the slower growth of the organic food market in these regions despite the global trend toward environmental sustainability. Methodology / approach. A survey approach involving 350 participants was used. Structural equation modeling (SEM) with SmartPLS 4 and Artificial Neural Network (ANN) with IBM SPSS 28 were used to analyse the impact of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products. Results. The study found significant positive influences of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products, explaining 63.1 % of the variance. Both the analysis approaches (PLS-SEM & ANN) revealed that, health consciousness, followed by awareness of need, emerged as the most important factor related to the intention to purchase organic food products. The results highlight the importance of awareness and personal values in driving pro-environmental behaviour. Originality / scientific novelty. This research offers essential insights into the determinants of organic food purchase intentions in an emerging economy. It emphasises the significance of awareness and personal values in fostering sustainable consumption behaviour, addressing a less explored area in existing literature. Practical value / implications. The findings have important implications for policymakers and marketers. Strategies focused on consumer education about the benefits of organic food can enhance awareness and appeal. Understanding core psychological needs and beliefs that shape consumer motivations can guide the development of effective marketing strategies. The study highlights the strong environmental consciousness among consumers and their desire to protect the environment. 2024, Institute of Eastern European Research and Consulting. All rights reserved. -
Decoding Retail Realities: Traditional Retailers' Outlook on Sales Erosion to Modern Retail Economy
The traditional retail landscape in Indian metropolises has changed significantly in the last several decades, mostly due to the modern retail economy's growth, including corporate chain stores and e-commerce sites. Small merchants have been gradually displaced as a result of this paradigm shift, which has been exacerbated by changed Foreign Direct Investment (FDI) laws that have brought significant money into the Indian market, as well as a rise in consumer disposable income and the wave of digitalization. This study explores small merchants' consequences as they contend with the growing power of organized retail and e-commerce behemoths. Despite earlier research studies mostly focusing on the organized trade's exponential rise due to changing customer behavior, this paper fills this gap by illuminating the traditional retailers perspective towards the contemporary retail landscape and highlighting the threats to small businesses with a traditional focus. The study uses empirical analysis using tools like SPSS and SEM models to examine the initial troubles faced by small retailers of fast-moving consumer goods (FMCG), highlighting the difficulties they face in competing with the powerful forces of deep discounting, massive sales events, and evolving consumer tastes. This exploratory research analyzed the undermining factors like utilitarian and hedonic, purchasing patterns, menaces, hindrances, pecuniary and location as reasons for the retail paradigm from traditional to modern trade. The outcome emphasized that utilitarian factors like ambience, experience, status, variety, payment modes, single-store distribution and assortment are the drivers behind the explosion of traditional trade by the modern trade in retail economy. 2024 The Author(s). -
Decoding the alchemy of employee retention: A case of the manufacturing sector of the National Capital Region, India
The ability of a company to retain its staff is referred to as employee retention. It may also be referred to as a decrease in employee attrition or employee turnover rate. Employee retention is one such mechanism which ensures that the human capital stays with the organisation for a longer duration. The study focusses on identifying the drivers of employee retention in the manufacturing industry with respect to certain factors such as mentoring, career development, work environment, job autonomy, and compensation. This research has used the descriptive research design with some elements of exploratory research design. The sample size for the study was 122. Primary data has been collected with the help of a prevalidated questionnaire with multiple-choice closed-ended questions on a five-point Likert scale. The collected data was analysed using Excel and SPSS with statistical tools like T-test, ANOVA, multiple linear regression, etc. A direct positive relation has been found between mentoring, work environment and compensation, and the employees' intention to stay. 2024, IGI Global. -
Decoding the X-Ray Flare from MAXI J0709-159 Using Optical Spectroscopy and Multiepoch Photometry
We present a follow-up study on the recent detection of two X-ray flaring events by MAXI/Gas Slit Camera observations in soft and hard X-rays from MAXI J0709-159 in the direction of HD 54786 (LY CMa), on 2022 January 25. The X-ray luminosity during the flare was around 1037 erg s-1 (MAXI), which got reduced to 1032 erg s-1 (NuSTAR) after the flare. We took low-resolution spectra of HD 54786 from the 2.01 m Himalayan Chandra Telescope and the 2.34 m Vainu Bappu Telescope (VBT) facilities in India, on 2022 February 1 and 2. In addition to H? emission, we found emission lines of He i in the optical spectrum of this star. By comparing our spectrum of the object with those from the literature we found that He i lines show variability. Using photometric studies we estimate that the star has an effective temperature of 20,000 K. Although HD 54786 is reported as a supergiant in previous studies, our analysis favors it to be evolving off the main sequence in the color-magnitude diagram. We could not detect any infrared excess, ruling out the possibility of IR emission from a dusty circumstellar disk. Our present study suggests that HD 54786 is a Be/X-ray binary system with a compact object companion, possibly a neutron star. 2022. The Author(s). Published by the American Astronomical Society. -
Decolonising Caste in the Indian Context: The Psyche of the Oppressor
Caste is a social construct as well as a psychological phenomenon. So far, it has been predominantly viewed, understood and researched through the lens of anthropology, sociology, economics and political science. However, very little understanding has been gained in the domain of psychological science with respect to caste in the Indian context. The population of the Global South (includes the regions of Asia, Africa, Latin America and Oceania) cannot be understood with the frameworks and research undertaken by the Global North (Europe and North America, known as the West, the industrialised world) because the knowledge production centres of psychology have predominantly been Euro-American centric, as many critics have pointed out. Although research has been scarce in relation to caste and psychology, it has mostly revolved around the oppressed. Therefore, this article aims to shift the focus from the oppressed to the oppressor. To understand Indian human behaviour and thought, it is essential to view it through the lens of the colonial past, the caste system and religion, which are intertwined with each other. This article aims to look at the psychology of the oppressor in the Indian context through the psychological frameworks of purity and pollution. It also stems from the premise that casteism is inculcated through modelling and intergenerational learning. Hence, the above-mentioned factors help to understand unequal power relations and discrimination, which facilitate the decolonisation of the Indian psyche. It also highlights the influence of colonisation on the mind and behaviour with respect to caste. 2023 Department of Psychology, University of Allahabad. -
Decolonising the Gateway of India
This article interrogates how a colonial monument, the Gateway of India in Mumbai, former Bombay, continues to carry and be endowed with a title that is a misplaced embodiment of Indian social histories. Built in the 1920s, this monument, definitely a work of architectural grandeur, continues to carry its erroneous rendition and confines Indias vast social histories to the colonial moment, with an anglo-centric focus. As the monument symbolises the memory of the colonial regime, it also signifies its oppression as well as its exit from the subcontinent, rather than witnessing anyone coming to India, except King George in 1911, as the monuments title seems to suggest. A mnemonic device of colonialism, this misleading label needs to be seriously revisited, for it not only romanticises the colonial past but also fails to lead our memories back to certain crucial episodes in earlier social histories, from which the monument and its specific place, Mumbai, are more or less fully absent. 2023 The Author(s). -
Decolonizing Open Science: Southern Interventions
Hegemonic Open Science, emergent from the circuits of knowledge production in the Global North and serving the economic interests of platform capitalism, systematically erase the voices of the subaltern margins from the Global South and the Southern margins inhabiting the North. Framed within an overarching emancipatory narrative of creating access for and empowering the margins through data exchanged on the global free market, hegemonic Open Science processes co-opt and erase Southern epistemologies, working to create and reproduce new enclosures of extraction that serve data colonialism-capitalism. In this essay, drawing on our ongoing negotiations of community-led culture-centered advocacy and activist strategies that resist the racist, gendered, and classed structures of neocolonial knowledge production in the metropole in the North, we attend to Southern practices of Openness that radically disrupt the whiteness of hegemonic Open Science. These decolonizing practices foreground data sovereignty, community ownership, and public ownership of knowledge resources as the bases of resistance to the colonial-capitalist interests of hegemonic Open Science. The Author(s) 2021. -
Decolonizing social psychology in India: Exploring its role as emancipatory social science /
Psychology & Society, Vol.8, Issue 1, pp.57-74, ISSN: 2041-5893. -
Decolonizing the Home at Home in the Pandemic: Articulating Women's Experience
Feminism bears the promise of liberation of and equality for women. Reading and teaching feminist texts, within the academia and in activist spaces, has provided the opportunity to explore what it means to become and be a woman. This article explores the experience of teaching a course on women's writing at the undergraduate level during the COVID-19 pandemic. Normally, a course on feminist writings is an occasion for self-reflection, thereby providing an opportunity to establish a dialogue between the domestic and the public. Such dialogues took place in secure institutional spaces such as classrooms or conference halls, without the intrusion of the domestic. However, as the teacher-student interaction shifted to an online mode during the pandemic, all the participants in this dialogue, including the instructor and the students, found themselves in domestic spaces, with family members listening. The article chronicles the anxieties of a woman instructor, as she teaches feminist texts from home to learners who are sitting behind computer screen in their homes and the possible impact of feminist ideas on the domestic spaces of all participants. 2022 The Author(s). Published by Oxford University Press on behalf of the English Association. All rights reserved. -
Decolonizing the Mind: Invoking the Vernacular Experience in a Postcolonial Language Classroom
This chapter attempts to understand the teaching-learning practices, programmes, courses, and pedagogies of an English department that recently co-opted cultural studies as a means of decolonisation in a private university in India to understand how cultural diversity, learner diversity, teacher experiences, and learner interests became considered factors in language learning pedagogies and selection of learning content. The research will employ mixed methods of qualitative and quantitative techniques of course content analysis, student interviews to gauge the impact of the learning on the decolonisation process, teacher interviews to understand approaches to task design, and the intended outcome and the strategies and perception changes in material production and task development when the learning shifted to the online mode as a result of the pandemic disruption. 2023 by IGI Global. All rights reserved. -
Decomposition of graphs into induced paths and cycles
A decomposition of a graph G is a collection ? of subgraphs H1,H2,..., Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path or an induced cycle in G, then ? is called an induced path decomposition of G. The minimum cardinality of an induced path decomposition of G is called the induced path decomposition number of G and is denoted by ?i(G). In this paper we initiate a study of this parameter. -
Decomposition of graphs into induced paths and cycles
A decomposition of a graph G is a collection ? of subgraphs H1,H2,...,Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path or an induced cycle in G, then ? is called an induced path decomposition of G. The minimum cardinality of an induced path decomposition of G is called the induced path decomposition number of G and is denoted by ?i(G). In this paper we initiate a study of this parameter. -
Decomposition of Graphs into Paths and Cycles
Journal of Discrete Mathematics Vol.2013 Article ID 721051 ISSN No. 2090-9845 -
Decoupling Identification Method of Continuous Working Conditions of Diesel Engines Based on a Graph Self-Attention Network
For diesel engine malfunction detection, machine learning-based intelligent detection approaches have made great strides, but some performance deterioration is also observed due to the significant ambient noise and the change in operating circumstances in the actual application situations. Diesel engine fault diagnostic models can be negatively impacted by complex and erratic working circumstances. Identifying the working condition can provide as a baseline for the current unit operating state, which is crucial information when trying to pinpoint the source of an issue. Many existing techniques for identifying operational states use power as an identifier, segmenting it into discrete intervals from which the current state's power may be derived using a classification model. However, the working condition characteristics should be constant, and defining it exclusively in terms of power would lead to the connection of speed and load elements. In this study, we offer a regular working situation model that is independent of speed and load characteristics, and we use a graph self-attention network to construct a model for identifying the working condition. On a diesel engine research bench, a vast amount of experimental data is acquired for training and testing models, including 32 different operating situations under normal and typical fault scenarios. The R2 adj values of 99.70% and 99.27% for normal and typical defect experimental data, correspondingly, demonstrate the efficacy of the suggested technique under the circumstance of uninformed nnerating situations. 2023 IEEE. -
Decrypting Free Expression: AMMA-WCC Conflict and Comment Culture Rattling the Malayalam Film Industry
The chapter examines the gender-power dynamics in the Malayalam film industry through an analysis of a skit, a YouTube video and trolls related to a recent controversy involving the Association of Malayalam Movies Artistes (AMMA) and the Women in Cinema Collective (WCC). This analysis is supported by an exploration of the historical roots of sexism in the industry and a discussion about how it continues to perpetuate sexism in the industry. The study also investigates the emergence of WCC as a response to the actresss molestation case and the subsequent division within the industry. The research focuses on the Sthree Shaktheekaranam skit performed at AMMAs cultural show, a YouTube video, Oru Feminichi Kadha and a sample of trolls which targeted the WCC and women who refuse to comply with AMMAs patriarchal bias. The chapter analyses the content of these representations, highlighting the power play structuring them. The study sheds light on the contradictions and hypocrisy within the industry and its portrayal of progressive values while perpetuating regressive gender norms. 2024 selection and editorial matter, Francis Philip Barclay and Kaifia Ancer Laskar; individual chapters, the contributors. -
Deducing Water Quality Index (WQI) by Comparative Supervised Machine Learning Regression Techniques for India Region
Water quality is of paramount importance for the wellbeing of the society at large. It plays avery important role in maintaining the health of the living being. Several attributes like biological oxygen demand (BOD), power of hydrogen (pH), dissolved oxygen (DO) content, nitrate content (NC) and so on help to identify the appropriateness of the water to be used for different purposes. In this research study, the focus is to deduce the Water Quality Index (WQI) by means of artificial intelligence (AI)-based machine learning (ML) models. Six parameters, namely BOD, DO, pH, NC, total coliform (CO) and electrical conductivity (EC) are used to measure, analyze and predict WQI using nine supervised regression machine learning techniques. Bayesian Ridge regression (BRR) and automatic relevance determination regression (ARD regression) yielded a low mean squared error (MSE) value when compared to other regression techniques. ARD regression model parameters as independent a priori so that non-zero coefficients do not exploit vectors that are not just sparse, but they are dependent. In the estimation process, BRR contains regularization parameters; regularization parameters are not set hard but are adjusted to the relevant data. Due to these reasons, ARD regression and BRR models performed better. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot. 2024 World Scientific Publishing Company. -
Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT
The diabetes is a critical disease from the small children to old age people. Due to improper diet and physical activities of the living population, obesity becomes prevalent in young generation. If we analyze self care of individual life, no man or women ready to spend their time for health care. It leads to problem like diabetes, blood pressure etc. Today is a busy world were robots and artificial machines ready to take care of human personal needs. Automatic systems help humans to manage their busy schedule. It motivates us to develop a diabetes motoring system for patients using IoT device in their body which monitors their blood sugar level, blood pressure, sport activities, diet plan, oxygen level, ECG data. The data are processed using feature selection algorithm called as particle swarm optimization and transmitted to nearest edge node for processing in 5G networks. Secondly, data are processed using DBN Layer. Thirdly, we share the diagnosed data output through the wireless communication such as LTE/5G to the patients connected through the edge nodes for further medical assistance. The patient wearable devices are connected to the social network. The Result of our proposed system is evaluated with some existing system. Time and Performance outperform than other techniques. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Deep CNN Based Interpolation Filter for High Efficiency Video Coding
Video coding is a current focus in research area as the world focus more on multimedia transfer. High Efficiency Video Coding (HECV) is prominent among existing one. The interpolation in HEVC with fixed half-pel interpolation filter uses fixed interpolation filter derived from traditional signal processing methods. Some research came up with CNN based interpolation filter too, here we are proposing a deep learning-based interpolation filter to perform interpolation in inter prediction in HEVC. The network extracts the low-resolution image and extract the patch and feature in that to predict a high-resolution image. The network is trained to predict the HR image for the given patch, it can be repeated to generate the full frame in the HEVC. The system uses cleave approach to reduce the computational complexity. The trained network is validated and tested for different inputs. The results show an improvement of 2.38% in BD-bitrate saving for low delay configuration. 2024 IEEE.