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Causality among Energy Consumption, CO2 Emission, Economic Growth and Trade: A Case of India
The present study attempts to examine the causal nexus between energy consumption, CO2 emissions, economic growth and trade in India using the Perron (1989) unit root test, Gregory and Hansen (1996) cointegration test and vector error-correction model (VECM). The study results exhibit a long-run relationship between energy consumption, CO2 emissions, economic growth and trade in India. The empirical results confirm that energy consumption influences the economic activity in the short run, implying that higher rate of economic growth is driven by consumption demand for energy in the economy. This is also well in consistence with the findings of Paul and Bhattacharya (2004) in the Indian context. Further, the study detects one-way causation that exists from energy use to CO2 emission and trade, and CO2 emissions to economic growth in the short run. 2015 Indian Institute of Foreign Trade. -
Causality and volatility spillovers of banks' stock price returns on BSE Bankex returns
This paper investigates the causal relationships and volatility spillovers between the BSE Bankex index and the stock prices of five major Indian banks (Axis Bank, HDFC Bank, ICICI Bank, Kotak Bank, and SBI). Daily data from January 2, 2018 to March 8, 2023 are used, and statistical techniques such as descriptive statistics, Unit Root test, Cointegration test, Ganger Causality test, OLS regression, and GARCH model are employed. The study finds bidirectional causal relationships between the bank stocks and BSE Bankex returns, suggesting that the movement of the bank stocks significantly affects the overall market returns and vice versa. The study also finds significant volatility spillovers between the bank stocks and BSE Bankex returns, implying that the shocks in the bank stocks affect the market returns and vice versa. The study's outcomes have practical implications for investors and policymakers. Investors can use the results to make informed investment decisions in the Indian stock market, while policymakers can use the findings to monitor the financial stability of the banking sector and design appropriate policy interventions to address any potential financial crises. Overall, the study's findings suggest that policymakers should proactively monitor and manage market risks to safeguard overall financial stability. 2023 Wiley Periodicals LLC. -
Causality between public expenditure and economic growth: The indian case
This study investigates the causal nexus between public expenditure and economic growth in India using cointegration approach and error correction model. The analysis was carried out over the period 1973 to 2012. The Cointegration test result confirms the existence of long-run equilibrium relationship between public expenditure and economic growth in India. The empirical results based on the error-correction model estimate indicates one-way causality runs from economic growth to public expenditure in the short-run and long-run, supporting the Wagner's law of public expenditure. -
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE. -
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics
Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach. 2022 IEEE. -
CeLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer
Cervical cancer is a leading cause of mortality in third world countries. Although there are multiple ways of screening cervical cancer, colposcope image analysis is considered to be standard routine method of diagnosis. Due to factors like lack of skilled personnel and interobserver variability, there is a need for automated diagnostic support for cervical cancer. However, artificial intelligence solutions for medical image analysis done through deep and machine learning models require high quality, non-erroneous and sufficient amount of data. Owing to the lack of such established benchmark datasets for the colposcope images, this work aims at establishing a standard benchmark multi state colposcope image dataset that also contains clinical findings pertaining to each case. In order to establish the quality of the images, mask R-CNN method is used for segmenting the images. Subsequently, a series of IMAGENet pretrained deep learning models are deployed on the dataset to evaluate the performance. The dataset will be made available upon request for strictly research purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Celebration of christmas as a symphony of interfaith in ?tm?nut?pam of St Chavara
This article is an attempt to reflect on the interfaith consciousness of St Kuriakose Elias Chavara, by making an Indian reading of his classical work ?tm?nut?pam, specifically focusing on how the incarnation of Christ is presented and celebrated with an open and inclusive approach. In ?tm?nut?pam, while explaining the episode of the Infancy Narrative, St Chavara addresses Child Jesus with the significant Indian name, Brahman?than, and Jesus is being worshipped by Brahmac?rinis with unique Indian offerings. The addition of an Indian character called S?nti as an aged shepherdess making conversation with Mother Mary makes the narrative Indian. Because of his deep and affective knowledge of Indian culture and religion, and having a moving openness and a dialogical approach to them, St. Chavara could develop a relevant cultural modification of his faith, which will have its unique stamp in the Indian Christian Theology. 2017 Journal of Dharma: Dharmaram Journal of Religions and Philosophies. -
Celebrity endorsements the interplay between intellectual property law and the consumer protection act, 2019
The ambit of Copyright law has expanded over time, leading to development of newer concepts such as, Personality Rights. These rights are vested in individuals who have acquired an identifiable persona in the eyes of the public. There are two important facets to personality rights-Right to Publicity &Right to Privacy. When such identifiable identities use their acquired celebrity status to promote goods and services of a company to attract more consumers, it can be understood as Celebrity Endorsements. This is the most common source of marketing used by major companies to increase sales and garner goodwill and reputation. However, this source of communicating necessary information to the public becomes dangerous when celebrities promote false or misleading advertisements. To counter such issues, the Consumer Protection Act, 2019 introduced provisions tohold celebrities endorsing such products or services to be liable for injury suffered by consumers. The Law mandates that in order to ensure that such misleading advertisements arent promoted, the celebrities must conduct due diligence of the products before endorsing them. However, the question remains that to what extent can celebrities, who are not directly involved in production or manufacturing, be held liable for exploiting their personality rights? This paper aims at addressing the newly created legal interlink between personality rights via celebrity endorsements and protection of consumer interests. 2020, National Institute of Science Communication and Information Resources. All rights reserved. -
Cellular agriculture research progress and prospects: Insights from bibliometric analysis
World agriculture is facing a daunting task to feed the burgeoning population against multiple production and environmental threats. The alarming growth in population vis-vis current food production is expected to increase the global food insecurity levels. Inter alia, cellular agriculture an incipient technology is being considered as a potential alternative to cater for the growing demand for food and nutrition. The technology aims to develop edible agricultural products including meat with reduced environmental footprint against conventional farm production. In this context, an attempt has been made to review the progress of cellular agriculture research in four decades (19812020) through a bibliometric analysis and to suggest a roadmap for future research. The study sourced data from the Web of Science during October 2020. Using keywords, the database showed 212 searches pertaining to cellular agriculture from 135 journals worldwide. Of the journals, seven had at least five published articles and 33 had two articles each. Subsequently, the bibliographic coupling among the identified journals was carried out. It is found that the Journals: Appetite, Meat Science, and Journal of Agricultural and Environmental Ethics had the largest circles corresponding to their respective number of publications coupled with notable linkages with other journals. Also, a detailed analysis was performed on categories, growth trend, keywords, institutions, regions and leading researchers of cellular agriculture. The findings indicate that the Appetite Journal followed by the Journal of Agricultural and Environmental Ethics had published a significant percentage of articles on cellular agriculture, and Environmental Science and Technology was identified as the highly cited journal. The USA, England and the Netherlands were identified as the progressive regions in cellular agriculture research. The bibliometric analysis points to sluggish progress in cellular agriculture research and production despite its potential benefits. Future research should focus on the cost-effectiveness of the technology, consumer willingness to buy, development of food safety protocols on its merit and regional policy governance coupled with popularising its paybacks in the context of ensuring food security. 2021 The Author(s) -
Centrality measures-based sensitivity analysis and entropy of nonzero component graphs
The nonzero component graph of a finite-dimensional vector space over a finite field is a graph whose vertices are the nonzero vectors in the vector space, and any two vertices are adjacent if the corresponding linear combination contains a common basis vector. In this paper, we discuss the centrality measures and entropy of the nonzero component graph and also analyze the sensitivity of the graph using the centrality measures. 2024 World Scientific Publishing Company. -
Centring African indigenous knowledge: Afro-feminist perspectives on women's empowerment
This chapter explores the Afro-feminist perspective of the significance of African indigenous knowledge in the context of women's emancipation. The recognition of gender inequities in Africa prompts a need for the incorporation of intersectionality in feminist discussions that include a wide range of cultural contexts. The chapter emphasizes the significance of intergenerational learning in preserving knowledge and empowering older women via examining power relations, colonial legacies, and the integration of Western-traditional medicine. This chapter examines the impact of indigenous community and feminist organization involvement on legislative progress, focusing on protecting indigenous women. Global connections, cross-cultural discussions, and unity facilitate the empowerment of Afro-feminism. These elements surpass geographical boundaries and incorporate indigenous traditions. 2024, IGI Global. All rights reserved. -
Ceramic-Polymer-Carbon Composite Coating on the Truncated Octahedron-Shaped LNMO Cathode for High Capacity and Extended Cycling in High-Voltage Lithium-Ion Batteries
Long-term electrochemical cycle life of the LiNi0.5Mn1.5O4 (LNMO) cathode with liquid electrolytes (LEs) and the inadequate knowledge of the cell failure mechanism are the eloquent Achilles heel to practical applications despite their large promise to lower the cost of lithium-ion batteries (LIBs). Herein, a strategy for engineering the cathode-LE interface is presented to enhance the cycle life of LIBs. The direct contact between cathode-active particles and LE is controlled by encasing sol-gel-synthesized truncated octahedron-shaped LNMO particles by an ion-electron-conductive (ambipolar) hybrid ceramic-polymer electrolyte (IECHP) via a simple slot-die coating. The IECHP-coated LNMO cathode demonstrated negligible capacity fading in 250 cycles and a capacity retention of ?90% after 1000 charge-discharge cycles, significantly exceeding that of the uncoated LNMO cathode (a capacity retention of ?57% after 980 cycles) in 1 M LiPF6 in EC:DMC at 1 C rate. The difference in stability between the two types of cathodes after cycling is examined by focused ion beam scanning electron microscopy and time-of-flight secondary ion mass spectrometry. These studies revealed that the pristine LNMO produces an inactive layer on the cathode surface, reducing ionic transport between the cathode and the electrolyte and increasing the interface resistance. The IECHP coating successfully overcomes these limitations. Therefore, the present work underlines the adaptability of IECHP-coated LNMO as a high-voltage cathode material in a 1 M LiPF6 electrolyte for prolonged use. The proposed strategy is simple and affordable for commercial applications. 2024 The Authors. Published by American Chemical Society. -
Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models
In recent years, cerebral stroke has ascended as a paramount concern in global public health. Proactive strategies emphasizing metabolic control over salient risk factors present a superior approach compared to relying solely on physiological indicators, which may not delineate clear preventive directives. In this research, we present the SPX-CerebroPredict modela novel machine learning framework designed to classify imbalanced cerebral stroke data for clinical diagnostics. The study delves into feature selection methodologies, employing both information gain and principal component analysis (PCA). To address the class imbalance dilemma, the Synthetic Minority Over-sampling Technique (SMOTE) was harnessed. The empirical evaluation, conducted on the cerebral stroke prediction dataset from Kagglecomprising 43,400 medical records with 783 stroke instancespitted well-established algorithms such as support vector machine, logistic regression, decision tree, random forest, XGBoost, and K-nearest neighbor against one another. The results evince that our SPX-CerebroPredict model, integrating SMOTE, PCA, and XGBoost, surpasses its contemporaries, achieving an impressive accuracy rate of 95%. This discovery underscores the models potential for clinical applicability in cerebral stroke diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ceria doped titania nano particles: Synthesis and photocatalytic activity
Ceria (0.5, 1 and 2 mol%) doped titania nano catalysts were prepared by combustion synthesis method, using titanium isopropoxide as the starting material. The prepared catalysts were characterized by X-ray diffraction (XRD), Energy dispersive X-ray analysis (EDX), Scanning electron microscopy (SEM) and Infra red spectroscopy (FTIR). Total acidity of the prepared catalysts were determined by temperature programmed desorption of ammonia (TPD - NH3). XRD pattern of 1% ceria doped titania obtained by calcinations at 873 K indicated that the samples were crystalline with a mixture of anatase and rutile phase. No peaks corresponding to cerium oxide were observed XRD patterns indicating that the amount of cerium is negligible on the surface of titania catalyst. The photo catalytic activity was evaluated for the degradation of methylene blue (MB) under visible light irradiation. The degradation rates of MB on cerium doped TiO2 samples were higher than that of pure TiO2. The introduction of structural defects (cationic ceria dopant) into the titania crystal lattice leads to the change of band gap energy. As a result, the excitation energy is expanded from UV light of anatase TiO2 to visible light for ceria doped titania. 2016 Elsevier Ltd. -
Cerium-doped Co3O4 spinel structures synthesized by modified combustion route as an excellent material for electrochemical applications
This work shed light on the impact of cerium doping on the structural and electrochemical features of Co3-xCexO4(x = 0, 0.02, 0.04) synthesized via a facile and cost-effective modified combustion route. The structural, morphological and compositional investigations unveiled the formation of nanocrystalline structures with promising morphologies. BET and XPS methodologies explored the materials' porosity and electronic state of the materials. The electrochemical performance of the synthesized materials was evaluated by Cyclic Voltammetry (CV) at various scan rates, Galvanostatic Charge-Discharge (GCD) at different current densities, and Electrochemical Impedance Spectroscopic (EIS) techniques. GCD studies depicted an exquisite specific capacitance of 498 Fg-1 for Co2.98Ce0.02O4 at a current density of 1 Ag-1 and it displayed a capacitance retention of 95 % for over 2000 GCD cycles further it retains up to 90 % even after 3000 GCD cycles at a current density of 1Ag-1 juxtapose to other compositions. Our work emphasizes the importance of the material for energy storage applications. 2024 Elsevier Ltd and Techna Group S.r.l. -
Certain results on trans-paraSasakian 3-manifolds
Let M be a trans-paraSasakian 3-manifold. In this paper, the necessary and sufficient condition for the Reeb vector field of a trans-paraSasakian 3-manifold to be harmonic is obtained. Also, it is proved that the Ricci operator of M is invariant along the Reeb flow if and only if M is a paracosymplectic manifold, an ?-paraSasakian manifold or a space of negative constant sectional curvature. 2022 Walter de Gruyter GmbH, Berlin/Boston. -
Certain types of metrics on almost coKler manifolds
In this paper, we study an almost coKler manifold admitting certain metrics such as ? -Ricci solitons, satisfying the critical point equation (CPE) or Bach flat. First, we consider a coKler 3-manifold (M,g) admitting a ? -Ricci soliton (g,X) and we show in this case that either M is locally flat or X is an infinitesimal contact transformation. Next, we study non-coKler (?, ?) -almost coKler metrics as CPE metrics and prove that such a g cannot be a solution of CPE with non-trivial function f. Finally, we prove that a (?, ?) -almost coKler manifold (M,g) is coKler if either M admits a divergence free Cotton tensor or the metric g is Bach flat. In contrast to this, we show by a suitable example that there are Bach flat almost coKler manifolds which are non-coKler. 2021, Fondation Carl-Herz and Springer Nature Switzerland AG. -
Certificate Generation and Validation Using Blockchain
Verifying academic credentials is a standard procedure for employers when making job offers. After the interview procedure is complete, the employer takes a long time to supply the offer letter. The employer must have the certificate authenticated by the organization that issued it to confirm its originality. While confirming the authenticity of a certificate, the employer takes a long time. The selection procedure takes longer overall because of the long process involved in certificate verification. Blockchain offers a verified distributed ledger with a cryptography technique to combat academic certificate forgery to address this issue. The blockchain also offers a standard platform for document storage, access, and minimization of verification time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Challenges and concerns of assisted reproductive treatments: A systematic review
India, once a highly populated country with a Total fertility rate of 5.7 1(year 1960) now has one of the least fertility rates of the world around 2.3 1 (year 2015). In just one decade, with the rising economy, improving life expectancy and lifestyle, we have embraced a new disease Infertility 2. There are numerous reasons for rising infertility amongst Indians, some related to life style changes, some infections and some are occupational hazards. As a remedy to this new disease, hospitals in India were quick enough to learn Assisted Reproductive Technologies from foreign countries and practice the same in our home country. There are many ART clinics in every city however; this solution to the problem of infertility is a problem in itself. The paper uses a systematic review process to unravel the causes of infertility and highlights the concerns revolving around infertility treatments and finally presents suggestions to policy makers. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Challenges and Issues in Health Care and Clinical Studies Using Deep Learning
Deep learning is a subset of machine learning, which has more than three layers of neural networks. Neural networks resemble the functioning of human behavior in nature. These neural networks are capable of producing results with single layers, but multiple layers help in producing accurate results with increased precision rate. Deep learning supports a number of artificial intelligence (AI)-based applications and services, which helps in increased automated devices, data analysis, and many more physical tasks in various fields. Deep learning technology has become part of human day-to-day life. It is involved in every aspect of daily routine like voice-based searches, operating a device, baking transactions, and many more. Deep learning allows the healthcare industry to examine data quickly without compromising accuracy. Deep learning uses mathematical models designed to work almost like the human brain. Multiple layers of networking and technology enable unmatched computing capability and the ability to traverse and analyze through vast sets of data that would have previously been lost, forgotten, or missed. 2024 Taylor & Francis Group, LLC.