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Synthesis and catalytic applications of metal boride ceramics
Metal borides belong to the class of high-temperature ceramics and have conventionally been used for high-temperature applications. However, in the past few decades, new variants of metal borides have emerged, with high catalytic capabilities. Owing to their tuneable structural, compositional, and morphological properties, metal borides have huge potential for industrially relevant catalytic applications. This chapter compiles the existing knowledge on the ever-expanding family of metal borides. Various synthesis strategies that are commonly adopted for the fabrication of metal borides, both in crystalline and amorphous/nanocrystalline forms, are discussed in detail. The chapter also aims to explain the origin of catalysis in metal borides. Some of the most prominent catalytic applications of metal borides are vividly discussed in this chapter. At the end of this chapter, a brief outlook is provided for future research initiatives with metal borides. 2023 Elsevier Ltd. All rights reserved. -
A review of cobalt-based catalysts for sustainable energy and environmental applications
In a bid to tackle the degrading climate conditions, the new age research in catalysis is predominantly focused on sustainable technologies associated with renewable energy conversion and environment purification. One of the primary motivations for the research in catalysis is the use of low-cost, earth-abundant materials that can fulfill the scale-up needs of respective technologies. Cobalt (Co) based catalysts have been an indispensable part of almost all areas of catalysis and they are often looked at as low-cost substitutes for precious metal-based catalysts. In the context of energy and environmental applications, Co-based catalysts are more commonly used for reactions such as hydrogen evolution reaction (HER), oxygen evolution reaction (OER), hydrolysis of chemical hydrides, CO2 reduction reaction (CO2RR) and advanced oxidation processes (AOPs). Co-based catalysts are interesting compounds as Co plays a diverse role in facilitating different reactions. This review provides a brief account of the significance of Co-based catalysts and elaborates their advancement in each of the above-mentioned applications and presents future research directions with the use of Co-based catalysts. An in-depth analysis to gain a deeper understanding of the Co-based systems is highly desired to promote breakthroughs in catalysis. 2023 The Authors -
Artificial Intelligence in Healthcare Supply Chain Management: A Bibliometric Analysis: Subtitle as needed (AI in Healthcare Supply Chain)
The presented paper discussed the review of Healthcare Supply Chain Management (HSCM) using Artificial Intelligence (AI). The implementation of artificial intelligence (AI) in HSCM has numerous benefits, including accurate demand forecasting of medical supplies, cost reduction, increased transparency, visibility, data-driven decision-making, enhanced supply chain resilience, streamlined healthcare operations, optimized transportation, and many more. Our approach to using AI in HSCM involved a thorough examination of the literature and bibliometric analysis. Research was started by exploring the Scopus database using suitable keywords. After the inclusion and exclusion criteria have been applied, the relevant papers were gone through full-text readings. Using Vos-viewer, the research papers were further analyzed for bibliometric analysis. 2024 IEEE. -
Decision making framework for foreign direct investment: Analytic hierarchy process and weighted aggregated sum product assessment integrated approach
Foreign direct investment (FDI) plays a paramount role in economic and social growth of every country. FDI acts as a source of external capital and helps in economic growth of the host country. Making decision for FDI during uncertain business environment is a challenge for all stakeholders. Therefore, in this study, we are proposing a decision making framework for FDI. Through literature review, we have identified the factors, on which FDI depends. A process-based, multi-criterion, integrated hierarchical approach for deciding about FDI, has been illustrated. In this study, five sectors are considered, that is, petroleum and natural resource, retailing and e-commerce, healthcare, information technology, and road and highways for illustrating the proposed framework. It is observed that information technology sector has got top priority for FDI followed by retailing and e-commerce and health care sector. Findings will help in taking appropriate decision by stakeholders for FDI. Ultimately it will also help in creating employment, economic growth, and welfare of society at large in the host country. 2021 John Wiley & Sons, Ltd. -
Search for brown dwarfs in IC 1396 with Subaru HSC: interpreting the impact of environmental factors on substellar population
Young stellar clusters are predominantly the hub of star formation and hence, ideal to perform comprehensive studies over the least explored substellar regime. Various unanswered questions like the mass distribution in brown dwarf regime and the effect of diverse cluster environment on brown dwarf formation efficiency still plague the scientific community. The nearby young cluster, IC 1396 with its feedback-driven environment, is ideal to conduct such study. In this paper, we adopt a multiwavelength approach, using deep Subaru HSC along with other data sets and machine learning techniques to identify the cluster members complete down to ? 0.03 M? in the central 22 arcmin area of IC 1396. We identify 458 cluster members including 62 brown dwarfs which are used to determine mass distribution in the region. We obtain a star-to-brown dwarf ratio of ? 6 for a stellar mass range 0.03-1 M? in the studied cluster. The brown dwarf fraction is observed to increase across the cluster as radial distance from the central OB-stars increases. This study also compiles 15 young stellar clusters to check the variation of star-to-brown dwarf ratio relative to stellar density and ultraviolet (UV) flux ranging within 4-2500 stars pc?2 and 0.7-7.3 G0, respectively. The brown dwarf fraction is observed to increase with stellar density but the results about the influence of incident UV flux are inconclusive within this range. This is the deepest study of IC 1396 as of yet and it will pave the way to understand various aspects of brown dwarfs using spectroscopic observations in future. 2024 The Author(s). -
Subaru Hyper Suprime-Cam Survey of Cygnus OB2 Complex - I. Introduction, photometry, and source catalogue
Low-mass star formation inside massive clusters is crucial to understand the effect of cluster environment on processes like circumstellar disc evolution, planet, and brown dwarf formation. The young massive association of Cygnus OB2, with a strong feedback from massive stars, is an ideal target to study the effect of extreme environmental conditions on its extensive low-mass population. We aim to perform deep multiwavelength studies to understand the role of stellar feedback on the IMF, brown dwarf fraction and circumstellar disc properties in the region. We introduce here, the deepest and widest optical photometry of 1. diameter region centred at Cygnus OB2 in r2, i2, z, and Y-filters, using Subaru Hyper Suprime-Cam (HSC). This work presents the data reduction, source catalogue generation, data quality checks, and preliminary results about the pre-main sequence sources. We obtain 713 529 sources in total, with detection down to ?28, 27, 25.5, and 24.5 mag in r2, i2, z, and Y-band, respectively, which is ?3 - 5 mag deeper than the existing Pan-STARRS and GTC/OSIRIS photometry. We confirm the presence of a distinct pre-main sequence branch by statistical field subtraction of the central 18 arcmin region. We find the median age of the region as ?5 2 Myr with an average disc fraction of ?9 per cent. At this age, combined with A $ 6 - 8 mag, we detect sources down to a mass range of ?0.01-0.17 M. The deep HSC catalogue will serve as the groundwork for further studies on this prominent active young cluster. 2021 The Author(s). -
Psychological health among armed forces doctors during COVID-19 pandemic in India
Background: A pandemic poses a significant challenge to the healthcare staff and infrastructure. We studied the prevalence of anxiety and depressive symptoms among armed forces doctors in India during the COVID-19 pandemic and the factors that contribute to these symptoms. Methods: The study was conducted from March 30, 2020, to April 2, 2020, using a self-administered questionnaire questionnaire using the hospital anxiety and depression scale (HADS), which was sent through Google Forms. Responses were received from 769 respondents. Data were analyzed for demographic details and HADS scores using the chi-square test and backward logistic regression. Results: Anxiety and depressive symptoms were seen in 35.2% and 28.2% of the doctors, respectively. In doctors with anxiety symptoms, significant associations were observed with age (2035 years, 39.4%, P = 0.01), gender (females, 44.6%, P < 0.001), duration of service (010 years, 38%, P = 0.03), and clinical versus non-clinical specialties (non-clinical, 41.3%, P < 0.001) as opposed to marital status, education level, and current department of work. In doctors with depressive symptoms, significant associations were observed with age (P = 0.04), clinical versus non-clinical specialties (P < 0.001), duration of service (010 years, 30.1%, P = 0.03), and doctoral degree (P = 0.04) as opposed to gender, marital status, education level, and current working department. Conclusion: The study revealed a high prevalence of anxiety and depressive symptoms among armed forces doctors. The main contributing factors are female gender, young age group, non-clinical specialties, and having a doctoral degree. Copyright 2020 Indian Psychiatric Society - South Zonal Branch. -
The mediating role of positive perceptions on coping strategies and psychological well-being among mothers of children with intellectual disabilities
Purpose: Research on caregiving has been considering the positive effects experienced by the mothers of children with disabilities. This paper aims to examine whether positive perceptions mediate the relationships between coping strategies used and psychological well-being among mothers of children with intellectual disabilities. Design/methodology/approach: The study opted for a quantitative approach that includes a correlation research design to examine the relationships between the variables of coping, positive perceptions and well-being among mothers of children with intellectual disabilities attending special schools in the metropolitan city Bengaluru, India. The four-factor structure of Brief COPE examined were active avoidance coping, problem-focussed coping, positive coping and religious-denial coping. Positive perceptions refer to the positive contributions for the mother from the experiences of raising a child with intellectual disability. Mediation analysis explored the relationship between the variables. Findings: Problem-focussed coping was the most commonly reported coping factor and was associated with higher levels of well-being. Active-avoidance coping was the least commonly reported coping strategy. Positive perceptions partially mediated the relationship between the four coping factors and maternal well-being. These findings indicate that positive maternal perceptions have important implications for the employment of effective coping strategies that are associated with enhancement of psychological well-being. Originality/value: The focus on positive perceptions would help in understanding the use of coping strategies and planning of support services or interventions. The positive mental health of mothers paves the way for positive developments in the childs physical and psychological health. 2020, Emerald Publishing Limited. -
Limaco?n Inspired Particle Swarm Optimization forLarge-Scale Optimization Problem
Large-scale optimization problems are a complex problem in the class of NP-Hard. These problems are not solvable by traditional methods in a reasonable time. Single machine total weighted tardiness scheduling problem (SMTWTSP) is a complex problem in this category. It has a set of different events with varying criteria that need to be scheduled on one machine. The main aim of this problem is to find the minimum possible total weighted tardiness. Particle swarm optimization (PSO) algorithm has performed admirably in the field of optimization. To solve complex optimization problems, several new variants of this algorithm are being developed since its inception. This work proposed an influential local search (LS) technique inspired by limaco?n curve. The new local search is hybridized with PSO and named Limaco?n inspired PSO (LimPSO) algorithm. The efficiency and accuracy of the designed LimPSO strategy are tested over the large-scale SMTWTS problem, which shows that LimPSO can be considered an effective method for solving the combinatorial optimization problems. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Empirical analysis of borrowers' motivation to use online peer-to-peer lending platforms in India
Established on the technology acceptance model, this paper puts forward a model to understand the borrowers' motivation to use (MU) peer-to-peer (P2P) lending platforms. Data from 362 Indian users were employed to test the research model by applying structural equation modelling. The results show that perceived intention, ease of use, and usefulness have significant relation in motivating borrowers to use P2P lending platforms. However, borrowers' perceptions of trust had an insignificant impact on MU the P2P lending platform. When compared to the individual technology acceptance model, the integrated model provides further explanation regarding the motivation of borrowers to use P2P lending platforms. The study contributes to the theoretical area by identifying the factors that motivate borrowers to use P2P lending platforms for their short-term financial requirements, from a unified perspective. In addition, this research provides insights about borrowers' MU P2P lending platforms in India. Copyright 2023 Inderscience Enterprises Ltd. -
Sentiment Analysis of Lenders Motivation to Use a Peer-To-Peer (P2P) Lending Platform: LenDenClub.Com
Peer-To-Peer lending platforms are becoming a good investment avenue for lenders to invest their money in borrowers, who need money for a different purpose. As lending and borrowing of money is facilitated by the P2P lending platform, it becomes necessary for the platform to understand the users and accordingly fine tune the 'User Interface' (UI) and 'User Experience' (UX) of the platform. For lending and borrowing to take place through a platform it is necessary to have an 'n' number of lenders who are ready to lend money to an 'x' number of borrowers. This study is specifically done to understand lenders' motivation to use P2P lending platforms. This is a unique research work as sentiment analysis of lenders' motivation to use these platforms has not been explored earlier. The sentiment analysis technique was used to examine lenders' sentiments towards the use of P2P lending platforms. The research results show that, ~ 70 percent of lenders showed motivation to use P2P lending platforms as an investment avenue in the future. As the P2P lending platforms are relatively new more research can be carried out in future. 2024 IEEE. -
Measuring Consumer Perception for P2P Platform: NLP Approach
The pandemic has forced lenders and borrowers to switch to alternative borrowing., investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further., Plutchik's wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments., whereas., only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower's., emotion of joy was seen in all 4 platforms., followed by emotions of sadness., surprise., anger., disgust., and fear. 2022 IEEE. -
Promoting photocatalytic hydrogen evolution rates in layered graphitic carbon nitride through integrated non-noble CoB co-catalyst
Despite being one of the most widely studied metal-free semiconductors, graphitic carbon-nitride (gC3N4) shows meaningful photocatalytic activities only when loaded with noble-metal co-catalysts. The present work reports an alternative to noble metals in the form of cobalt boride (CoB) co-catalyst that can be easily integrated within the gC3N4 framework with facile fabrication strategies. The optimized CoB-gC3N4 composite showed ?60 times higher hydrogen generation rate compared to bare gC3N4 nanosheets, with good stability. Detailed morphological, structural, chemical, electrochemical and spectroscopic investigations revealed the key aspects of CoB-gC3N4 composite that unanimously led to higher photocatalytic activity. Computational investigations not only corroborated the experimental results but also established that the surface Co and B sites in CoB provided the most energetically favoured sites for hydrogen evolution reaction. Based on the experimental and computational investigations, a generic reaction mechanism was formulated that will prove as a guiding light for future studies on similar photocatalytic systems. 2024 The Authors -
Survey of prevalence of anxiety and depressive symptoms among 1124 healthcare workers during the coronavirus disease 2019 pandemic across India
Background: A prospective study was conducted during the second phase of the coronavirus disease 2019 (COVID-19) pandemic in India to assess the prevalence of anxiety and depressive symptoms among healthcare workers (HCWs) and factors that influence the outcome. Methods: A self-administered questionnaire was completed by 1124 HCWs during the COVID-19 pandemic (March 30, 2020, to April 2, 2020). Demographic data, questions on COVID-19 and scores of the Hospital Anxiety and Depression Scale were analysed using the chi-square test (Bonferroni correction) and binary logistic regression. Results: The study consists of 1124 HCWs, including 749 doctors, 207 nurses, 135 paramedics, 23 administrators and ten supporting staff members. The prevalence of anxiety and depressive symptoms were reported as 37.2% and 31.4%, respectively. The risk factors for anxiety were female gender (30.6% vs 45.5%), age group (2035 years) (50.4% vs 61.2%), unmarried (21.2% vs 30.6%) and job profile (nurse) (14.7% vs 26.4%). The protective factor was having service of more than 20 years (23.4% vs 14.8%). The risk factors for depression were age group (20-35 years) (51.3% vs 61.3%) and employed at a primary care hospital (16.2% vs 23.4%). The protective factors were job profile (doctor) (69.9% vs 59.6%) and having service of more than 20 years (22.3% vs 15.5%). Conclusion: Approximately one-third of the HCWs reported anxiety and depressive symptoms. The risk factors for anxiety symptoms were female gender, younger age and job profile (nurse) and for depressive symptoms were younger age and working at a primary care hospital. Future research studies should identify strategies for providing a safer and supportive work environment for HCWs to face epidemics/pandemics. 2020 -
Positive People and Confident Competitors: Resilient Youth Development Through Sport and Physical Activity
In the altering world scenario, there is a necessity to plan, prepare and progress with youth development. Research has associated positive youth development with the 5Cs model (competence, confidence, connection, character and caring) (Lerner et al., The Journal of Early Adolescence 25:17-71, 2005) to build resilience in youth. Over the past 35 years, sport psychology has established that sport helps in developing necessary psychological skills and attributes among youth. Youth sport is an extracurricular activity that provides young people with unique negative and positive experiences. Within these experiences, the individual goes beyond the self and has to work with a diverse group of others for self-development and achievement of shared goals. In this chapter, our primary objective is to review the foundations of literature concerning confidence, resilience and identity as corner-stones for positive youth development through sport. To achieve this objective, we adopt a global approach blending field experience from participatory sport, developmental sport and elite sport to provide an intervention framework grounded in applied sport psychology. Intervention framework provided is aligned to the COM-B behaviour change model (Michie et al. 2011) for sustainable change. The focus is on a balance between developing stable protective factors for mental health and positive youth development to ensure appropriate cognitive, social, emotional and behaviour skills to thrive in an evolving world. Implications for transferring this learning cross-culturally and in non-sport contexts such as schools and grass-root programs are discussed with recommendation for good practice. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
An effective Approach for Pneumonia Detection using Convolution Vision Transformer
Early detection of pneumonia in patients through effective medical imaging may enable timely remedial measures and reduce the severity of the infection. There is an increase in cases among new-borns, teenagers and also people with health issues in recent years. The COVID-19 pandemic also revealed the major impact pneumonia had on the lungs and the consequences of delayed detection. The presence of the infection in the lungs is examined through images of Chest X-ray, however, for an early diagnosis of the infection, this paper proposes an automated model as a more effective alternative. Convolutional Vision Transformer (CVT) which gives an accuracy of 97.13%, and is a robust combination of Convolution and Vision Transformer (ViT), is suggested in this paper as a potential model to detect pneumonia early in patients. 2022 IEEE. -
EmploChain: A Blueprint for Blockchain-Driven Transformation in Employee Life Cycle Management
Integrating blockchain technology into human resource management presents both transformative opportunities and implementation challenges that need to be addressed. This paper proposes a blockchain-based EmploChain Framework, a decentralized ledger approach specifically designed to enable Employee Life Cycle Management by harnessing the potential of blockchain technology. The study looks at the potential benefits of the proposed framework, including increased security, transparency, and automation. The paper also looks at potential limitations like scalability concerns and implementation costs and explores the possible solutions to overcome them. The aim of this research is to provide a thorough understanding of the framework's implications, thereby facilitating informed decisions to implement EmploChain Framework for managing the Employee Life Cycle of an organization.. 2024 IEEE. -
Blockchain-Enabled Resume Verification: Architectural Innovations for Secure Credential Authentication in the Digital Era
In the contemporary digital landscape, the verification of resume credentials poses a significant challenge, with the integrity of such information being crucial for job seekers and employers alike. This paper presents an avant-garde architectural framework that utilizes blockchain technology to revolutionize the storage, verification, and sharing of resume information, thus ensuring an unparalleled level of security and reliability. Through the implementation of a decentralized ledger that is both immutable and tamper-evident, this innovative architecture facilitates the permanent recording of academic credentials, employment history, and professional accomplishments, thereby enabling immediate and verifiable access for potential employers and educational institutions 2024 IEEE. -
Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model
In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models. 2024 River Publishers. -
Moving Towards Responsible Consumption: The Road Ahead for Sustainable Marketing
The fundamental tenet of consumerism revolves around the belief that the burgeoning consumption of goods is favourable for the economy. Since the dawn of the Industrial Revolution, humanity has witnessed an exponential upsurge in consumerism. It has been related both to the increase in the population size as well as an increase in our demands due to constant changes in lifestyle. Multiple sources have corroborated the fact that if this consumption behaviour continues unabated, we will soon face an acute shortage of resources of all kinds. Both consumer behaviour patterns such as addictive consumption and conspicuous consumption can be attributed to this. Amongst the solutions available, 'Demarketing' is one. It is a type of marketing when a brand wants to discourage you from buying its product. The paper is descriptive in nature and is based on secondary data which has been collected from journals, blogs, websites, magazines, books, etc. The paper intends to explore the theme of demarketing vis-vis the materialistic purchase behaviour of a modern-day consumer and green demarketing strategies that companies are adopting by way of sustainable marketing. The Electrochemical Society