Browse Items (11809 total)
Sort by:
-
Growth trajectories for executive and social cognitive abilities in an Indian population sample: Impact of demographic and psychosocial determinants
Cognitive abilities are markers of brain development and psychopathology. Abilities, across executive, and social domains need better characterization over development, including factors that influence developmental change. This study is based on the cVEDA [Consortium on Vulnerability to Externalizing Disorders and Addictions] study, an Indian population based developmental cohort. Verbal working memory, visuo-spatial working memory, response inhibition, set-shifting, and social cognition (faux pas recognition and emotion recognition) were cross-sectionally assessed in > 8000 individuals over the ages 623 years. There was adequate representation across sex, urban-rural background, psychosocial risk (psychopathology, childhood adversity and wealth index, i.e. socio-economic status). Quantile regression was used to model developmental change. Age-based trajectories were generated, along with examination of the impact of determinants (sex, childhood adversity, and wealth index). Development in both executive and social cognitive abilities continued into adulthood. Maturation and stabilization occurred in increasing order of complexity, from working memory to inhibitory control to cognitive flexibility. Age related change was more pronounced for low quantiles in response inhibition (??4 versus =2 for higher quantiles), but for higher quantiles in set-shifting (? > ?1 versus ?0.25 for lower quantiles). Wealth index had the largest influence on developmental change across cognitive abilities. Sex differences were prominent in response inhibition, set-shifting and emotion recognition. Childhood adversity had a negative influence on cognitive development. These findings add to the limited literature on patterns and determinants of cognitive development. They have implications for understanding developmental vulnerabilities in young persons, and the need for providing conducive socio-economic environments. 2023 Elsevier B.V. -
Recommendation System using Clustering and Comparing Clustering and Topic Modelling Techniques
In this paper, we have used a technique called clustering to recommend the products to the customer and also tried to compare clustering and Topic modelling to find out which technique is better for our purpose. From all the papers that have been reviewed, we observed that the greater part of the proposal approaches applied content-based filtering (55%). Collaborative-based filtering was applied by just 18% of the looked into approaches, and hybrid based by 16%. Other suggestion ideas included generalizing, thing driven proposals, and crossover suggestions. The content-based filtering approaches overwhelmingly utilized papers that the clients had made, marked, examined, or downloaded [1]. To begin with, it stays muddled which suggestion ideas and approaches are the most encouraging. For instance, analysts demonstrated different results on the presentation of content based and collaborative filtering. A portion of the time content-based filtering performed better contrasted with collaborative filtering sand a portion of the time it performed all the more regrettable. 2022 IEEE. -
Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA): A developmental cohort study protocol
Background: Low and middle-income countries like India with a large youth population experience a different environment from that of high-income countries. The Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA), based in India, aims to examine environmental influences on genomic variations, neurodevelopmental trajectories and vulnerability to psychopathology, with a focus on externalizing disorders. Methods: cVEDA is a longitudinal cohort study, with planned missingness design for yearly follow-up. Participants have been recruited from multi-site tertiary care mental health settings, local communities, schools and colleges. 10,000 individuals between 6 and 23 years of age, of all genders, representing five geographically, ethnically, and socio-culturally distinct regions in India, and exposures to variations in early life adversity (psychosocial, nutritional, toxic exposures, slum-habitats, socio-political conflicts, urban/rural living, mental illness in the family) have been assessed using age-appropriate instruments to capture socio-demographic information, temperament, environmental exposures, parenting, psychiatric morbidity, and neuropsychological functioning. Blood/saliva and urine samples have been collected for genetic, epigenetic and toxicological (heavy metals, volatile organic compounds) studies. Structural (T1, T2, DTI) and functional (resting state fMRI) MRI brain scans have been performed on approximately 15% of the individuals. All data and biological samples are maintained in a databank and biobank, respectively. Discussion: The cVEDA has established the largest neurodevelopmental database in India, comparable to global datasets, with detailed environmental characterization. This should permit identification of environmental and genetic vulnerabilities to psychopathology within a developmental framework. Neuroimaging and neuropsychological data from this study are already yielding insights on brain growth and maturation patterns. 2019 The Author(s). -
Reaction of Indian Stock Market to Outbreak of COVID-19: An Empirical Analysis of Extreme Inter-day Movements
The contagious COVID-19 pandemic has been considered a massive global crisis since World War II and has disturbed business and economic activities across the globe. The current study examined the reaction of the stock markets to the outbreak of COVID-19, considering the extreme inter-day movements in the Indian stock market. The extreme inter-day movements in S&P CNX Nifty-50 have been identified during the study period from January 2020 to December 2021 and further classified into decline and gain events based on positive and negative announcements related to COVID-19. The study utilized an event study approach and panel regression for empirical investigation. The results of the event study analysis illustrate that the significant abnormal loss ranges from 12.86% to 2.47% for the major decline events and significant abnormal return from 8.43% to 3.23% for the gain events. The regression analysis results showed that real return and Central Bank Policy rate have a considerable impact on the abnormal returns during COVID-19. The studys findings are helpful to policy implications that identified the need to focus on financial education and strengthen the health and finance-related policies to deal with such pandemics in the future. 2023 MDI. -
Co- Integration and Causality between Macroeconomics Variables and Bitcoin
The fintech sector has been booming for the past decade, especially with the unprecedented expansion in cryptocurrency innovation. Many countries and their central banks are working to accommodate cryptocurrency in a regulated format into their financial system anywise. This research paper investigates the long-run and short-run relationship between Bitcoin (INR) and the macroeconomic variables of the Indian economy, such as two major stock indices (NSE and BSE), money supply M1, foreign exchange rate (INR/US dollar), and indicators of inflation rate (CPI and WPI). For this purpose, monthly data of the variables from October 2014 to December 2020 are considered. The Johansen co-integration approach depicts the long-run association between Bitcoin and the economic variables, whilst VECM and the Wald coefficient reveal no short-run causality between the variables. The Granger Causality test shows a one-way causal relationship of NSE, BSE and WPI to Bitcoin. Hence, it concluded that stock indices and inflation have a cogent effect and exert on bitcoin prices. The findings will be helpful for policy-makers and investors alike, for an outlook to strategize and explore this everchanging digital instrument. 2024 CRC Press. -
MIST-based Tuning of Cyber-Physical Systems Towards Holistic Healthcare Informatics
The entire world seems shaken and disrupted since the strike of Covid-19 ever since its outbreak towards the end of 2019 and its continued perils. During this unprecedented event of the century, people's health emerged as the most vulnerable and affected area either directly or indirectly by the coronavirus and its new variants. Disrupting almost all spheres of life, patients' health and care systems required timely support from healthcare professionals to provide the needed medical advice on one hand and a prescriptive mechanism to avoid another impending casualty. Similarly, a proactive approach became desirable from the health ministry, pharmaceutical firms, medical insurance companies, and other stakeholders in fine-tuning their offerings to the patients as per the recommender systems. The devices to measure the vitals of a person, became more efficient and ergonomically sound with the advent of wearable gadgets. These devices monitored the physical activities of the user and transferred the vital signals wirelessly to any base computing device and cloud-based repositories. This mechanism, however, was reported to fail in addressing the issues with non-communicating or stand-alone devices that were used by the masses in developing countries including India. If the real-time data could be used from these devices, the healthcare diagnosis and analysis of a patient's medical condition could have taken a progressive dimension with the addition of missing data points. This research thus aims to fill the information gap and proposes a transforming approach towards existing non-communicating devices used to measure the vitals like blood pressure, oxygen level, blood sugar, etc. The proposed MIST-based Cyber-Physical System shall create extensive scalability towards the retrieval of the vital details from the devices which were otherwise captured offline previously and were unused at multiple critical points of healthcare processes. 2022 IEEE. -
Threat Intelligence Model to Secure IoT Based Body Area Network and Prosthetic Sensors
This research work proposes a threat intelligence model for Internet-of-Things (IoT) sensors-based Body Area Network (BAN). It is focused primarily to be used in healthcare monitoring of vital parameters of critically ill patients and on the contrary performance measurement system for healthy sportspersons. The end-point control based applications are growing enormously with the advent of IoT based sensors and actuators being used in intelligent real-time systems. At the same time, it is expected to keep the ecosystem safe for the user while delivering the constant updates. However, the process for the monitoring health and wellness parameters of a patient, or measuring endurance and performance of a sportsperson, it remains vulnerable without a secure environment. Using the proposed model, the entire healthcare ecosystem may be designed for the personalized medication of a patient who are using sophisticated life-saving device like prosthetic heart valve or an elderly person dependent on medical-aided ambulatory devices or a sportsperson on performance measurement system. The Electrochemical Society -
Humanizing technology: The impact of emotional intelligence on healthcare user experience
This investigation underscores the importance of humanizing technology within the healthcare sector, with a specific focus on the significant role of emotional intelligence in shaping the interactions between patients and healthcare providers, particularly in the context of advancing healthcare technology. By integrating empathy into medical interfaces and devices, the user experience is fundamentally grounded in human aspects. The study delves into firsthand experiences of patients using emotionally intelligent healthcare solutions that not only meet their medical needs but also address the emotional complexities of illness and recovery. The integration of emotional sensitivity in medical technology strives to enhance patient comfort and foster more open and communicative relationships between healthcare providers and recipients. Moreover, the research presents a framework for emotional intelligence in healthcare technology, encompassing elements such as emotional recognition, response, and management. This framework is designed to promote a culture of patient understanding and support, enabling healthcare technology to adapt to the emotional requirements of patients. In the ever-evolving healthcare landscape,it is essential to recognize the profound impact of embedding empathy in medical technology, ultimately shaping a more empathetic future for healthcare interactions. 2024 by IGI Global. -
Parallelizing keyframe extraction for video summarization
In current era, most of the information is captured using multimedia techniques. Most used methods for information capturing is through images and videos. In processing a video, large information needs to be processed and a number of frames could contain similar information which could cause unnecessary delay in gathering the required information. Video summarization can speed up video processing. There are different techniques for video summarization. In this paper key frames are used for summarization. Key frames are extracted using discrete wavelet transforms. Two HD videos having 356 frames and 7293 frames were used as test videos and the runtime was 17 seconds and 98 seconds respectively in CPU and 11 seconds and 53 seconds respectively in GPU. 2015 IEEE. -
Radon transform processed neural network for lung X-ray image based diagnosis
A novel method for image diagnosis with artificial learning is presented-ray images tuberculosis patients is subjected to neural network learning for prediction of diagnosis. The X-ray images of lungs are normally difficult for diagnosis, since its similarity to lung cancer. Under and over diagnosis of lung X-ray images is a difficult medical problem to resolve. In the present work radon transform of the x-ray images is fed to back propagation neural network trained with Levenberg algorithm. The present methodology gives sharp results, distincting the normal and abnormal images. 2014 IEEE. -
Comparing keyframe extraction for video summarization in CPU and GPU
Most of the information is captured through multimedia techniques. Videos contain many frames which might be redundant. Since processing of many frames is involved, these redundant frames must be removed for better and efficient results. Summarizing these frames by removing similar frames can speed up processing. In this paper video summarization is achieved by generating key frames. Key frames are generated using discrete wavelet transforms (DWT) technique and we subtract background from the keyframes to get region of interest. A video of 920&Times;720 resolution and length 120 second was used as test video and the run-time was 111 second in CPU and 60 second in GPU. The speed up is nearly 100%. A HD video which took 23 minutes in serial implementation to extract foreground object from key frames generated was reduced to 7 minutes using GPU acceleration. 2015 IEEE. -
Is Industry-Specific Value Premium Declining? Evidence from India
This article examines whether the literature promised value effect exists and the changing nature of value premium at the industry level. It also determines the value premiums strength by controlling the January effect within and across the regulated industry groups. This is done by utilizing the two most prominent pricing models: FamaFrench three- and five-factor, considering all listed firms trading at BSE India between 1999 and 2020. The results show that a significant value effect exists in 15 of the 17 regulated industry groups over 21.5 years, while sub-period analysis revealed variation in the value effect at industry-based portfolio returns. We developed quintile and multivariate portfolios within and across the industries. Results show that the industry-specific value premium has been relatively low in the current decade due to decreasing industry portfolio returns and increasing P/B ratios within industry groups. The study also used the GRS test to explore the explanatory power of models. Results indicated that the explanatory power of models has declined in post-crisis periods. While controlling the January effect, the value premium has slightly diminished within and across the industry groups in the recent decade. We also observed that investors who seek to allocate assets within and across industries are likely to have potentially predictable and pretty stable returns. While other countries have found industry-specific value premiums, no such study has been conducted in India. As a first attempt, these findings are relevant for investors and academia. 2022 Management Development Institute. -
Size, Value Effects and the Explanatory Power of Pricing Models: Evidence from BSE listed Indian Industries
The firm size and value anomalies are the global-level counterpart for explaining the cross-sectional variations of equity returns. This paper aims to examine the size, value effects and explanatory power of three well-known pricing models - CAPM, three-and five-factor- across and within 15 Indian industries. The study considers all firms listed on Indian largest stock exchange, BSE (Bombay stock exchange), between 1999-2021 by developing portfolios using firm size/value, size/investment and size/profitability risk characteristics. The study employs both univariate and multivariate methods, including time series, GRS statistics, and cross-sectional models within and across industries portfolios. Results indicated that size and value effects exist in almost all industries, presenting that size and value anomalies are the most prominent determinants for industry-level equity returns. In addition, the profitability and investment effects were also investigated; however, the results are mixed from industry to industry. In the case of the explanatory power of pricing models, the five-factor performs much better within and across industry portfolios than other pricing models; however, the models' effectiveness varies by industry. We also reported that investors who seek to allocate funds within and across industries tend to be expected reasonably stable returns and conceivably predictable; the findings of this study contribute to the existing literature on asset pricing and portfolio management in emerging markets. The Author(s) 2022. -
A closer look at industry-associated value premium: evidence from India
This paper examines whether the academic literature-promised value premium has any industry association in the Indian equity market and the relationship between stock returns, value, and size within and across industries. We examine all listed firms trading at BSE India between 1999-2020, using CAPM and Fama-French three-factor models on each firm-levels and industry-level portfolio. The positive and significant value effect was found to exist in 17 out of 21 industry groups. Both industry and firm-level value effects are identified; however, the firm-level effect seems more prominent. Furthermore, the value effect is most substantial in small-cap value stocks of value- and growth-oriented industries, large-cap value stocks of value-oriented industry groups, then small-cap growth stocks of value- and growth-oriented industries and large-cap growth stocks of value- and growth-oriented industries. We also show evidence confirming the claim that value premium results from investors challenging higher returns from firms and industries operating in higher risk and distressing constraints. Copyright 2022 Inderscience Enterprises Ltd. -
Financial Distress and Value Premium using Altman Revised Z-score Model
In the stock market, investors and value managers desire to be safe. Estimating equity returns and evaluating potential financial distress risk are essential for investment and trading decisions. The link between distress risk and stock return is controversial, and current literature yields contradicting results. A variety of models may be used to evaluate distress risk-return trade-offs. This paper employs a revised Altman Z-score to examine financial distress and value premiums. Using univariate and multivariate techniques, we examine firm- and industry-level portfolio returns, encompassing all Indian companies listed on the Bombay Stock Exchange (BSE). Results confirm the existence of the distress factor effect found in industry and firm-level portfolios. It shows that the distress risk factor significantly determines stock returns as an independent systematic risk factor. This result is consistently found in most industries. The study demonstrates the existence of a value premium in both distressed and safe zones. The study also used a multivariate GRS test and the Fama-Macbeth procedure to validate the reliability of the distress factor and pricing models. Results confirm that Altman model-based distress factor augmented models improve the performance of existing pricing models with higher reliability and accuracy. 2023 MDI. -
Research & development premium in the Indian equity market: An empirical study
This article aims to investigate the research and development (R&D) premium and explore the three most prominent asset pricing models: capital asset pricing and the three-and five-factor models (Fama & French, 1993; 2015). The results show that India's annualized average R&D premium is significantly higher than the existing value, market, profitability, size and investment premiums, implying that the R&D premium is a more significant concern for Indian investors, particularly for high R&D firms. It was also observed that by applying the GRS test and the Fama and MacBeth (1973) two-pass procedure, the R&D risk factor augmented the CAPM, FF3F and FF5F models outperforming the existing CAPM, FF3F and FF5F models, respectively. We can also report that R&D is, unquestionably, a priced ingredient and a critical factor in developing pricing models for developing markets such as India. The paper's conclusions add to the current literature in R&D and asset pricing and assist investment professionals in developing better investment and trading strategies. 2021 AESS Publications. All Rights Reserved. -
EVALUATION OF THE ENVIRONMENTAL EFFECTS OF MEDICAL WASTE AND ITS INCREASE AFTER COVID-19 PANDEMIC
Medical waste is a special course of harmful contaminants. Improper treatment would cause tributary environmental pollution, expressly when countering to communal health tragedies. However, there are quite few explores on the peer group of medical waste, and there is a deficiency of basic considerate of its spatial-temporal heterogeneity. The purpose of this study is to conduct a systematic estimation of the effectiveness of these incongruous discarding procedures in expressions of water eminence and wellbeing. The research is centred on municipal areas characterised by vital medical waste production, which has the probable to taint groundwater and water sources. A complex approach is exploited in the procedure, which comprises of water sample collection, laboratory analysis, field surveys, and GIS-based spatial mapping. Medical waste disposal hotspots, such as healthcare facilities, waste collection points, and disposal sites, will be acknowledged through field surveys. Inspects will be showed on water samples poised from a variability of sources, including lakes, rivers, and groundwater wells, to find pathogens, medical residues, heavy metals, and organic pollutants, which are all gauges of medical waste contamination. The test centre analysis will utilise chic policies to portion the deliberation of pollutants in water samples, thereby gauging the likely hazards they pose to marine ecosystems and human health. Longitudinal visualisation of uncleanness distribution through GIS-based mapping facilitates the credentials of vulnerable areas and potential pathways for pollutant transport. The findings of this research will offer significant helps to our understanding of the extent of environmental deterioration resulting from the inadequate disposal of medical refuse into urban water sources. The results of this study will provide valuable insights for the creation of alertness campaigns, regulatory frameworks, and mitigation strategies that are operative in talking this urgent environmental concern and shielding the truthfulness of water in municipal regions. 2024, Scibulcom Ltd.. All rights reserved. -
Responding to the pandemic: A case of the indian hotel industry
The chapter presents a case study on how Indian hotel industry was affected by COVID-19. Three hotels-Lemon Tree, Oyo Rooms, and Taj Hotels-were selected to elaborate. The study found that the hotel industry developed various policies to keep running their hotels during the pandemic. Lemon Tree joined various hospitals to provide rooms to COVID patients, provided free food and face masks to individuals. Oyo Rooms gave employee stock ownership plans of Rs 130 crore to its COVID-hit employees. Taj Hotels did not cut down the salaries of their employees and reduced its seating capacity by 50%. The study concluded that as the hospitality sector battled hard to continue during the pandemic, modernization would become an imperative tool in the post-COVID period to beat obstructions and spotlight advancement. So, the companies should minimize fixed costs and maximize variable costs. They should preferably have liquid cash available that could enable them to mitigate the risk. 2022, IGI Global. -
Powering Ahead: Navigating Opportunities and Challenges in the Electric Vehicle Revolution
The technology is clearing ways for buzz in the market brimming with innovative items and new prospects. The government has planned to shift to electric vehicles by 2030, whether it is for personal or commercial use. As innovative improvements are developing quickly, it is blasting the market with the EVs industry which expected to transform the future (Rajkumar S, in Indian electric vehicle conundrum: a tale of opportunities amid uncertainties, 2020). Volvo company has also announced that it will be fully electric by 2030 (https://gadgets.ndtv.com, in Volvo to go all electric by 2030, sell exclusively online, 2021). It is expected that EVs will generate more demand for electricity and help in settling the focus on resources problem. It will also help in improving the financial feasibility of power sector projects. In India, there is more dependency on renewable energy so this is a chance to be independent and provide cheap power to the people. The EVs are more economical than petrol or diesel vehicles. The government is also giving incentives to the makers of electric vehicles. GST on electric vehicles is 12% as compared to petrol and diesel vehicles with 28% GST. As per the Electricity Act, 2003, a distribution license is needed to supply power from respective state electricity regulatory commissions. Another challenge is that charging the EVs will lead to a rise in the demand of electricity which is risky for the electricity distribution companies (www.livemint.com, in Indias electric vehicle drive: challenges and opportunities, 2017). Indians are very price conscious. A recent study revealed that Indians are ready to compromise on more charging time, but they are not ready to pay higher price for EVs (Gupta NS, in Electric vehicle adoption in India: study reveals three tipping points, 2020). From Fig.1, it can be seen that in 2014 investment in EVs was $2.2 billion which has increased to $406 billion in 2019 (Shanti S, in The road to green: what makes electric vehicle adoption a challenge for India. 2020). This shows that people are shifting toward EVs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A ratiometric luminescence thermometer based on lanthanide encapsulated complexes
Lanthanide-containing complexes have been widely developed as ratiometric luminescence thermometers, which are non-invasive, contactless and accurate. The synthesis of these Ln complexes generally requires high temperatures, multiple steps and other harsh conditions. Moreover, bimetallic lanthanide complexes, which have been reported to be better thermometers, are even more challenging to synthesize. This complexity can be simplified by preparing a host-guest complex of lanthanides. In this work, Tb or both Tb and Eu are encapsulated in an MOF host, making them emissive. The ratio of Tb/Eu was also easily tuned by simply changing their ratio in the solution, resulting in a tunable emission. Accordingly, we were able to synthesise both the emissive Tb complex and Tb/Eu complexes at different ratios using a single host. The complexes were found to be suitable as ratiometric luminescent thermometers in the temperature range of 160-380 K, with reasonably good sensitivity and uncertainty. The thermometer's sensitivity and uncertainty were significantly improved using bimetallic Tb and Eu host-guest complexes. Calculations using the host and Eu emission ratio were found to provide better thermometer parameters than the commonly reported Tb and Eu emission ratio. Thus, using a single host, we were able to synthesise different lanthanide complexes that can sense temperature, and we improved the thermometer parameters by incorporating multiple lanthanides in a single host. This research will enable the scientific community to reexamine the applicability of unexplored host-guest lanthanide complexes. 2025 The Royal Society of Chemistry.