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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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/). -
Stress strain characteristics of reinforced hollow concrete block masonry melded with mesh reinforcement
Plain Masonry similar to unreinforced concrete, is resilient in compression and weak in tension. Masonry gains strength with age similar to concrete. Inspite of these resemblances, there exist numerous differences between masonry and concrete. The major difference is the regular pattern of horizontal joints(known as bed joints) at specific intervals along the height of walls introduce due to the method of construction of masonry. These bed joints make masonry a direction dependent material possessing orthotropic properties, unlike concrete which is usually regarded as isotropic atleast in the elastic range. Mechanical properties such as compressive strength, tensile strength, flexural strength are a pre-requisite as part of the design of masonry walls. The present study deals with the experimental study to evaluate the mechanical properties of hollow concrete block masonry specimens for varying cement mortar proportions melded with mesh reinforcement at bed joints. Parameters such as compressive strength, modulus of elasticity, failure pattern have been studied and compared for reinforced and unreinforced hollow concrete block prisms. The study showed higher compressive strength and improved elastic modulus for specimens with higher grade of mortar Published under licence by IOP Publishing Ltd. -
Surviving Under Stress: Exploring Zea mays Adaptive Responses to Cadmium Toxicity and Mitigation StrategiesA Review
Cadmium (Cd) toxicity poses a significant threat to Zea mays, disrupting its normal physiological functions and metabolic processes. This chapter summarises current studies on the sources of contamination, Cd intake mechanisms, and the effect of Cd toxicity on critical physiological systems in maize. It then thoroughly investigates Zea mays physiological and adaptive responses to Cd toxicity. The section outlines how Cd inhibits vital metabolic processes, such as photosynthesis and the absorption of nutrients uptake in maize plants, leading to a reduction in biomass, yield, and growth. The adverse impacts on plant growth and development are amplified by anatomical changes brought on by Cd exposure, such as modifications to the roots and leaves. Furthermore, a thorough examination of biochemical modifications is conducted, such as adjustments to protein composition, glucose metabolism, and amino acid levels. The chapter additionally examines how enzymatic activity responds to Cd stress, focusing on modifications in the activity of enzymes involved in antioxidants and metabolism. Under the influence of Cd toxicity, maize plants display a range of intricate adaptive responses. These include upregulation of genes linked to the production of ethylene and the synthesis of peptides that bind metals, such as phytochelatins. The study covers the effectiveness of several mitigation techniques employed to reduce Cd accumulation and improve Cd tolerance in maize crops. These techniques include microbial remediation, phytohormone administration, biostimulant treatments, and designed nanoparticles and mineral ions. With everything considered, this chapter offers insightful information about Zea mays physiological and adaptive responses to endure and mitigate the impact of cadmium toxicity. To ensure sustainable maize production and food security in areas polluted by Cd, it is essential to understand these mechanisms and create appropriate mitigation techniques. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Parental Perspectives on Stress and Challenges in Raising Autistic Children: A Meta-Synthesis
Raising autistic children can be challenging, and the current meta-synthesis explores the stress and challenges the parents encounter across life domains. Database searches (JSTOR, ProQuest, EBSCO, PsycINFO, and Google Scholar) were done using the SPIDER method, and 463 articles published between 2011 and 2021 were reviewed. The meta-synthesis adhered to the PRISMA guidelines and included 28 eligible studies centered on stress in parents of children up to the age of 12 years diagnosed with autism. This comprehensive analysis encompassed a collective participant pool of n-505 individuals. Eight stressors were derived using the line of argument synthesis method, which include parental stress due to emotional impact, diagnosis process, social stigma, financial aspects, work-life balance, lack of resources and social support, marital life, and academic setting. Multiple stressors exert a combined effect of individual and systemic factors across domains of life, leading to parental stress. Interventions must be designed considering the complex nature of the parental stress and its interaction with the environment. Psycho-education for awareness and empowerment contribute to parental well-being. The Author(s), under exclusive licence to Springer Nature India Private Limited 2024. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
Emotional Inhibition and Personality as Predictors of Anxiety and Depression in Young Adults
Purpose: Anxiety and depression have been major contributors to the global burden of disease, and the impact has been exacerbated following the COVID-19 pandemic. Therefore, the aim of this study was to understand the association between emotional suppression and the introverted-extraverted dimension of personality in young people and anxiety and depression. Method: Participants were 152 Indian females between the age group of 18-25 years who provided basic demographic details and completed three questionnaires via a google form. Findings: Results described a significant negative correlation of anxiety r (152) = .500, p <0.01and depression r(152)=.471, p <0.01 with emotional inhibition. There was also a significant positive correlation of anxiety r (152) = .288,p < 0.01 and depression r(152)= .288, p <0.01 with personality. While Emotional inhibition emerged as a significant negative predictor of anxiety (R2= .250) as well as of depression (R2=.222), personality (R2=.243) emerged as a significant predictor of depression. Conclusion/Value: Contrary to popular belief, the results of this study suggest that anxiety and depression are inversely related to emotional inhibition. It restores the complexity of emotions and the need to investigate their role in various pathologies. These findings provide an initial basis for further investigation into the role of emotional expression and suppression in the Indian population. 2024 RJ4All.