Browse Items (11809 total)
Sort by:
-
Anti-vibrio effects of the precious Tibetan pill, Rinchen Drangjor Rilnag Chenmo (RDRC)
Tibetan precious pills are an integral part of TTM (Traditional Tibetan Medicine). Among them, Rinchen Drangjor Rilnag Chenmo (RDRC) has been named King of Precious Pills due to its efficacy in treating a multitude of human disorders. RDRC has a complex formulation with about 140 ingredients, mostly from medicinal plants and a few precious stones and metals. Not many studies have been done on the experimental validation of antimicrobial properties of this important pill. The current study investigated the antimicrobial activity of the extracts of RDRC. Both aqueous and chloroform extracts were evaluated for their antibacterial potential against a total of seven different bacterial species, which are pathogenic, including three species of Vibrio, viz. V. vulnificus, V. parahaemolyticus and V. harveyi using the well-diffusion method and also by assessing MIC and MBC values. Its antifungal potential was also studied against two fungal strains Aspergillus Niger and Talaromyces islandicus. It was found that the chloroform extract of RDRC exerted a positive antibacterial effect on all the Vibrio species tested, and the least MIC of 3.33 mg/ml was observed for V. parahaemolyticus. This is the first study of its kind on the anti-Vibrio effect of the Tibetan precious pill, Rinchen Drangjor Rilnag Chenmo. Dhargyal et al (2021). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). -
Analyzing the Performance of Conformable and Non-Conformable Patch Antennas
This paper presents a performance analysis between a conventional triangular shaped patch antenna and a future reconfigurable patch antenna. There are different materials with different electronic properties for the simulation of triangular shaped patch antenna. All the materials for the triangular patch antenna are simulated using FEKO tool. Materials selected for triangular patch antenna are Copper, Single-wall Carbon Nano-tube (SCNT), Multiple-wall Carbon Nano-tube (MCNT) and Graphene. For the futuristic antennas, cotton fabric based reconfigurable patch antenna is also analyzed and compared with triangular shaped patch antenna. Graphene based triangular patch antenna has been analyzed best out of other materials. Reconfigurable cotton fabric-based patch antenna provides better bandwidth and results are validated through simulation and experimental setup. 2024 IEEE. -
Strategies for Sustainable Practices in a Post-Pandemic World
We know from experience that possessions dont better themselves with time. In order to generate income, we must work. So, what do sustainability experts in the global business-school community recommend companies do when calamity begins to subside and some kind of reality is restored? Companies worldwide have suffered greatly from the COVID-19 pandemic. As a result of the epidemic, businesses are being forced to develop long-term strategies that are both profitable and sustainable in terms of society, the economy, and the environment. There is little doubt that businesses contribute significantly to the overall gross domestic product (GDP) of any nation. Because of the pandemic, businesses had to work even harder to reduce waste, adapt to changing demands from customers in record speed, and achieve peak performance alone. This has further raised the status of sustainability to one of moderate significance. Businesses must develop novel and cutting-edge approaches to ensure economic, environmental, and social longevity. Scholars have yet to settle on a single definition due to the business model's complexity. A study found that COVID-19 had both positive and negative impacts on educational technology (edtech) companies. As a result of the pandemic, businesses in the edtech sector no longer needed to make the case for the importance of their products in classrooms. Second, there was a rise in the acceptance of online education among educators. Also, edtech companies grew rapidly and benefited greatly from user feedback, and investors showed a lot of interest in the sector. However, due to the obstacles, sales discussions were either put on hold or cancelled. Edtech companies were unable to expand into new areas as a result of travel limitations. The purpose of this research is to investigate the link between the elements of sustainable development that contribute to the occurrence of enterprises and edtech enterprise employment and interest. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
ICT as a driver of women's social and economic empowerment
The role of information and communication technologies as a tool for development has attracted the sustained attention of various agencies worldwide. If the gender dimensions of information and communication technologies-in terms of access and use, capacity-building opportunities, employment, and potential for empowerment-are explicitly identified and addressed, information and communication technologies can be a powerful catalyst for the political and social empowerment of women and the promotion of gender equality. ICT as a Driver of Women's Social and Economic Empowerment contributes to the growing body of literature and present state of knowledge by offering evidence on how new information and communication technologies impact women's economic and social empowerment and overall welfare creation leading to inclusive growth. Covering key topics such as economics, entrepreneurship, digital technologies, and inclusion, this premier reference source is ideal for industry professionals, policymakers, administrators, business owners, managers, researchers, academicians, scholars, practitioners, instructors, and students. 2023 by IGI Global. All rights reserved. -
Model between mind share branding factors and trustworthiness /
Patent Number: 202111055024, Applicant: Dr.Vikas Singla.
The importance of Mindshare branding (MB) strategy in building long-term and sustainable psychological links with consumers had been sufficiently highlighted in literature. However, very few research attempted to provide a structured tool for its measurement. This study proposed a 13-point four-factor multidimensional scale which could be used to measure MB formally. Dimensions measuring MB were derived from literature and then examined on different brands in order to achieve a reliable and valid scale. -
Brand review scale for brand management /
Patent Number: 202211039578, Applicant: Dr. Mahesh Chandra Joshi.
Big firms have enough resources for various activities such as branding, market research, innovation, product development etc. which are very important for survival and growth of an organization. Small firms also wish to execute these activities but many times resource constraints refrain them form activities like market research which either requires inhouse research team or hiring of external agency for the task. -
Body image issues and self-concept dilemmas in adolescents living with thalassemia
Thalassemia, a genetic blood disorder, involves an inability to produce sufficient hemoglobin and comprises two types: alpha thalassemia and beta thalassemia. Beta thalassemias immediate treatment measures include frequent blood transmissions, stem cell and bone marrow transplants; all capable of altering an individuals idea of body image, self-concept, growth, and socialization, resulting in several emotional, psychological, and behavioral concerns. This study aimed at comprehending the dilemmas of body image and self-concept encountered by adolescents with thalassemia, particularly the resulting influence on physical development and socialization. Using the phenomenological interpretivism approach of qualitative research, data was collected using purposive-convenient sampling from 11 adolescents, both boys and girls ranging from ages 12 to 18, living with thalassemia and undergoing treatment. The research highlights adolescent concerns with body image, specifically with complexion, facial features, being either underweight or overweight, all amalgamating into a self-concept dilemma. Moreover, results point to the significant influence of experiences with family, peers, educational institutions, and hospital staff. Therapeutic attention, through regular screening and counselling, should be provided to adolescent thalassemia patients to address the psychological aspects of the chronic illness. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Friction stir welding of aluminum alloy 1100 and titanium-al alloy
A intercalating joint between Al and Ti alloy is friction stir welded using a high speed steel tool. The material mixing occurs mainly in the shoulder region while the pin region shows nominal mixing. Microscopy and hardness experiments indicate sporadic formation of intermetallic compounds. The joint region near the shoulder and to some extent below it shows increase in hardness compared to the base Ti alloy. Copyright 2016 by ASME. -
Skin lesion classification using decision trees and random forest algorithms
Any superficial skin growth that does not resemble the surrounding area is referred to as skin lesion. It can occur in the form of mole, bump, cyst, rash or other changes that can be classified either as primary or secondary lesion. While primary skin lesions correspond to those changes in color or texture, secondary lesions occur as a primary lesion progression. Skin lesion image segmentation and classification at the early stages can help the patients recover through proper medication and treatment. Many algorithms for segmentation and classification are available in the literature but they all fail to extract lesion boundaries perfectly and classify them with more accuracy. To improve the reliability of the skin image segmentation and classification, we propose to use decision trees and random forest algorithms in this works and compare them with different data sets. The proposed method can generate high-resolution feature maps that can help to preserve the spatial details of the image. While tested against the ISIC 2017 and HAM10000 dataset, we found that the proposed method is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Handwritten tibetan character recognition using hidden markov model
The Tibetan language which is one of the four oldest and most original languages of Asia is elemental to Tibetan identity, culture and religion and it convey very specific social and cultural behaviors, and ways of thinking. The annihilation of the Tibetan language will have tremendous consequences for the Tibetan culture and hence it is important to preserve it. Tibetan language is mainly used in Tibet, Bhutan, and also in parts of Nepal and India. Tibetan script is devised based on the Devanagari model and Sanskrit based grammars. In this paper, a method for Tibetan handwritten character recognition based on density and distance feature detection is presents. To get a better classification result, images are converted into binary and noise removal is done by using Otzsos method. Features are extracted by normalizing the image based on distance and density of the pixel in the image. Finally, Hidden Markov Model is used for character classification. BEIESP. -
AI Based Non-invasive Glucose Detection Using Urine
This proposed device uses urine to predict the glucose level present in the patient using non-invasive technique with a high level of accuracy for detection of diabetes. The paper presents a urine glucose level diagnosing and prediction using a computer-based polarimeter held in a portable device, to provide a fast and accurate on-field result. The instrument consists of an LCD screen, optical sensor, Benedicts reagent, a detachable tank, and an embedded system-on-chip (SoC). Springer Nature Singapore Pte Ltd 2020. -
Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint
Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO2 emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance. 2024 The authors. -
A Fog-Based Retrieval of Real-Time Data for Health Applications
Fog computing is an emerging technology that offers high-quality cloud services by providing high bandwidth, low latency, and efficient computational power and storage capacity. Although cloud computing is an efficient solution so far to store and retrieve the huge data of IoT devices, it is expected to limit its performance due to low latency and storage capacity. Fog computing addresses these limitations by extending its services to the cloud at the edge of the network. In this paper, we use a fog computing network approach for efficiently retrieving the real-time patient data. The performance of our proposed approach has been compared with the cloud computing approach in terms of retrieval time of real-time data. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Testing the Diversifying Asset Hypothesis between Clean Energy Stock Indices and Oil Price
In theory, geopolitical risk and political uncertainty can directly affect energy markets. Fluctuations lead to the cost of clean energy sources as they compete with traditional energy. The purpose of this study is to analyse financial integration and test the diversifying asset hypothesis between clean energy indices, specifically the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO) and West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. Analysing the results reveals a scenario where most of the clean energy indices show cointegration with each other, indicating long-term relationships that reflect common trends in the clean energy sector. However, the relative independence of the WTI suggests that Oil still acts as an important and potentially diversifying external factor for investors focused on sustainable energy. Structural breaks in 2021 and 2022 in several indices point to significant events that have altered market dynamics, possibly including changes in environmental policies, technological innovations and the impacts of the COVID-19 pandemic. The cointegration evidence and structural breaks provide valuable information for building investment portfolios. Investors can consider the WTI to diversify portfolios dominated by clean energy assets, taking advantage of Oils relative independence. On the other hand, the high correlation between clean energy indices suggests that, within this sector, diversification options are more limited, requiring careful analysis of the specific characteristics of each index and the macroeconomic forces affecting them. 2024, Econjournals. All rights reserved. -
Exploring the Relationship between Clean Energy Indices and Oil Prices: a Ten-Day Window approach
This paper aims to assess the comovements between clean energy indices, namely the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO), West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. We used 10-day windows to analyse the duration and nature of the shocks. Granger causality tests revealed that 20 of the 30 possible pairs showed significant movements, with the WTI influencing all the clean energy indices, highlighting its global importance. CELS also showed a robust influence on all pairs, while SPGTCLEN had a significant but less far-reaching influence. The CLNE and ECO indices showed limited influences, suggesting the potential for diversification, the TCEC.CN proved to be independent and a determining factor for portfolio diversification. The Impulse Response Functions (IRF) confirmed significant movements between CELS, SPGTCLEN and WTI, reflecting the market's response to policies and adjustments in expectations. Fluctuations in oil prices substantially affect clean energy indices, highlighting the interconnectedness and volatility of these markets. In conclusion, these results indicate that despite the growth of clean energy, the sector is still influenced by fluctuations in the fossil fuel market. 2024, Creative Publishing House. All rights reserved. -
Delving into the Exchange-Traded Funds (ETFs) Market: Understanding Market Efficiency
Exchange-traded funds (ETFs) are the most popular products in the financial sector today. There is extensive literature on the multifractal analysis of some stock markets, but not about the multifractal behaviour of the ETF market. This study examines the efficiency of stock index ETFs worldwide from an Efficient Market Hypothesis (EMH) perspective, using the ETFs: Ishares Msci World ETF (URTH), Ishares Russell 1000 ETF (IWB), SPDR S&P 500 ETF TRUST (SPY), Ishares Global Clean En. ETF (ICLN), Ishares USD Green Bond ETF (BGRN), from 1 January 2021 to 24 May 2024. It analyses a pre-conflict and a geopolitical conflict to uncover distinct patterns of behaviour reflecting significant changes in market conditions. Before the conflict, the Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500 and Ishares USD Green Bond ETFs showed signs of anti-persistence in returns, indicating a lack of strong relationship or predictability between short-term price movements. The Ishares Global Clean Energy ETF did not reject the random walk hypothesis, suggesting that returns follow a pattern closer to random, where market prices already efficiently reflect all available information. During the conflict, there was a transition in the ETFs' behaviour patterns, as evidenced by the increases in slope values for Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500, Ishares Global Clean Energy and Ishares USD Green Bond. Thus, the possible transition from anti-persistence to long-term memories in ETF returns during the conflict. For portfolio managers, these findings highlight the need to continually adapt investment strategies to manage risks better and take advantage of opportunities in a dynamic and complex investment environment. 2024, Creative Publishing House. All rights reserved. -
Antecedents of Adoption of Peer to Peer (P2P) Lending-A Fintech Innovation in India
This study examines the association between adoption variables and behavioural intention (BI) to adopt Peer to Peer (P2P) lending technology platform in India. A critical review of literature on technological and personal adoption factors led to development of the theoretical framework using multiple technology adoption models. Results support the generalizability of technology adoption readiness (AR), a parsimonious higher-order construct for the use and acceptance of technology context In addition, a personal antecedent, personal innovativeness (PIIT) was shown to positively affect behavioural intentions and technology adoption readiness. 2022 IEEE. -
Innovation Characteristics, Personality traits and their impact on Fintech Adoption-P2P Lending
This paper investigates moderating influence of innovation attributes on the perceptions of Peer-to-Peer or P2P lending users and the influence of innovativeness traits on instrumental beliefs regarding the adoption of P2P lending. Two technology adoption theories were combined to develop the conceptual map denoting antecedent factors. Using 464 responses, structural equation modeling analysis was used to test the hypotheses. Performance expectancy, effort expectancy, social influence, and perceived compatibility were salient antecedents of P2P lending adoption. Perceived compatibility moderates the relationship between performance expectancy, facilitating conditions, and buying intentions. Innovativeness trait predicts performance expectancy and effort expectancy of P2P lending users. 2024 IEEE. -
Antecedents of Behavioural Intention : Study of Indian Consumer Perceptions Towards P2P Lending Using Technology Adoption Model
Fintech is a rapidly developing area of the financial services business where tech-focused startups and other new players are upending how the sector has historically operated. One of the emerging fintech areas under digital lending is Peer to Peer lending or (P2P) lending; Consumers and authorities are both showing interest in this alternative lending innovation. Results of a literature review show that India is still in the early stages of P2P lending research. The study examines the association between behavioural intention to use P2P lending in India and technological and personal adoption factors. The study model was developed with the help of a literature review and tested using data from 536 respondents who completed an online survey and was tested using covariance-based structural equation modelling (SEM). The results confirm that personal innovativeness, performance expectancy, hedonic motivation, effort expectancy, social influence, and perceived risk are the antecedents of the adoption of P2P lending, except for facilitating conditions and price value. In addition, gender moderates the relationship between performance expectancy, hedonic motivation, personal innovativeness, and intention to adopt P2P lending. The study also throws light on the perceptions of both users and non users in terms of the antecedents. The study's conclusions significantly impact the P2P lending industry and provide practical insights for developers, platforms and regulators to improve and enhance the service. The study suggests looking at other moderating factors like age, voluntariness, experience, and actual usage behaviour for further research. Overall, the research contributes to the academic literature by confirming the predictive power of the extended unified theory of acceptance and use of technology (UTAUT). It highlights personal innovativeness after performance expectancy and motivation as an important factor in predicting the usage of P2P lending. Finally, the study lists managerial implications in the domains of technological adoption, which will assist in the P2P lending long-term success in India.