Does Google Trend Affect Cryptocurrency? An Application of Panel Data Approach
Cryptocurrency; Google trends; Investor attention; Trading volume; Volatility
Cryptocurrency has emerged globally as the most profitable investment asset of the decade. The media exposure and reportage on cryptocurrency are frequent, and it seems that prices of cryptocurrencies could only rise higher. In today's digital world, any individual's first go-to information-seeking platform is the Google search engine. Thus, it is imperative to understand how Google's search trend affects an investable asset and its market as a whole. Researchers have explored varied sentiment measurement proxies such as news coverage, Facebook and Twitter posts, and, most importantly, Google searches. Numerous research studies show increasing interest in Google search volume and its predictive ability to understand investment returns and economic outcomes. In a behavioural finance context, the present research uses Pearson's correlation and panel regression to examine the association of cryptocurrency returns (Bitcoin, Ethereum, and Ripple) and their varied characteristics with the Google search intensity. The study's findings reveal that investors searching for information on Cryptocurrency online drive the price increase in cryptocurrency and push the trading volume up and increase the volatility of the cryptocurrency returns. Furthermore, investor sentiment has a statistically significant impact on cryptocurrencies' trading volume and weekly volatility in periods of high or greedy investor sentiment. The findings imply that the 'price pressure hypothesis' given by Barber and Odean (2008) as a stock market research finding is also present in the cryptocurrency market. 2023 SCMS Group of Educational Institutions. All rights reserved.
SCMS Journal of Indian Management, Vol-20, No. 2, pp. 124-132.
Verma R., Department of Financial Administration, Central University of Punjab, India; Sam S., School of Business and Management, Christ (Deemed-to-be-University), Bangalore, India; Sharma D., Department of Financial Administration, Central University of Punjab, Bhatinda, 151001, India
EPCAEnhanced Principal Component Analysis for Medical Data Dimensionality Reduction
Accuracy; Artificial neural networks; Liver cirrhosis; Machine learning; Medical data; Principal component analysis; Singular value decomposition; Support vector machine
Innovations in technology from thelast one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Nae Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and therebyenhancedthe classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique performed better when compared with other models. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Vinutha M.R.; Chandrika J.; Krishnan B.; Kokatnoor S.A.
Vinutha M.R., Department of Information Science and Engineering, Malnad College of Engineering, Hassan, 573202, India; Chandrika J., Department of Information Science and Engineering, Malnad College of Engineering, Hassan, 573202, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India
Development and Validation of the Multidimensional Psychosocial Risk Screen (MPRS): An Approach towards Primary Prevention
adolescents; Mental health screening; psychosocial risk; scale construction
Background: The prevalence of mental health problems in adolescents has been identified as a global concern. Early screening and identification can offer benefits in terms of primary prevention and reduced healthcare costs. This study aimed to develop a tool to assess the risk of developing mental health problems in adolescents. Methods: The study followed an exploratory sequential design and was divided into five phases. The Multidimensional Psychosocial Risk Screen (MPRS) is a newly developed self-report measure. The various steps in its development and validation have been elaborated. The MPRS was evaluated with a sample of 934 adolescents aged 12-18, spread across the 8th-12th grade. Results: Exploratory and confirmatory factor analyses revealed a robust factor structure. The extracted five factors were named as Parent-Child Relationship (PCR), Self-Concept (SC), Teacher-Student Dynamics (TSD), Social Media Use (SMU), and Peer Interaction (PI). The reliability of the subscales ranged from 0.60 to 0.80. The overall reliability of the scale was good (a = 0.87). Convergent validity of the scale was established using standard measures of risk factors and emotional and behavioural problems. Conclusions: The MPRS can be considered an effective tool with an adequate factor structure and good psychometric properties. It can be beneficial in the early detection of vulnerabilities to mental health problems in adolescents and, therefore, seen as a key element in primary prevention and fostering individualized interventions. 2023 The Author(s).
Indian Journal of Psychological Medicine, Vol-45, No. 3, pp. 220-229.
All Open Access; Gold Open Access; Green Open Access
Bhardwaj T., Dept. of Psychology, CHRIST (Deemed to be University) Delhi NCR, Uttar Pradesh, Ghaziabad, India; Viju P.D T., Dept. of Psychology, CHRIST (Deemed to be University) Delhi NCR, Uttar Pradesh, Ghaziabad, India
pH-dependent water permeability switching and its memory in MoS2 membranes
Intelligent transport of molecular species across different barriers is critical for various biological functions and is achieved through the unique properties of biological membranes14. Two essential features of intelligent transport are the ability to (1) adapt to different external and internal conditions and (2) memorize the previous state5. In biological systems, the most common form of such intelligence is expressed as hysteresis6. Despite numerous advances made over previous decades on smart membranes, it remains a challenge to create a synthetic membrane with stable hysteretic behaviour for molecular transport711. Here we demonstrate the memory effects and stimuli-regulated transport of molecules through an intelligent, phase-changing MoS2 membrane in response to external pH. We show that water and ion permeation through 1T? MoS2 membranes follows a pH-dependent hysteresis with a permeation rate that switches by a few orders of magnitude. We establish that this phenomenon is unique to the 1T? phase of MoS2, due to the presence of surface charge and exchangeable ions on the surface. We further demonstrate the potential application of this phenomenon in autonomous wound infection monitoring and pH-dependent nanofiltration. Our work deepens understanding of the mechanism of water transport at the nanoscale and opens an avenue for the development of intelligent membranes. 2023, The Author(s), under exclusive licence to Springer Nature Limited.
Hu C.Y.; Achari A.; Rowe P.; Xiao H.; Suran S.; Li Z.; Huang K.; Chi C.; Cherian C.T.; Sreepal V.; Bentley P.D.; Pratt A.; Zhang N.; Novoselov K.S.; Michaelides A.; Nair R.R.
ISSN: 280836; PubMed ID: 37076621; CODEN: NATUA
Hu C.Y., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom, Department of Physics and Astronomy, University of Manchester, Manchester, United Kingdom, College of Chemistry and Chemical Engineering, iChEM, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, China; Achari A., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom; Rowe P., Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom; Xiao H., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom; Suran S., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom; Li Z., School of Chemical Engineering, Dalian University of Technology, Panjin, China; Huang K., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom; Chi C., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom; Cherian C.T., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom, Department of Physics and Electronics, Christ University, Bangalore, India; Sreepal V., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom; Bentley P.D., School of Physics, Engineering and Technology, University of York, York, United Kingdom; Pratt A., School of Physics, Engineering and Technology, University of York, York, United Kingdom; Zhang N., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom, School of Chemical Engineering, Dalian University of Technology, Panjin, China; Novoselov K.S., Department of Physics and Astronomy, University of Manchester, Manchester, United Kingdom, Institute for Functional Intelligent Materials, National University of Singapore, Singapore, Singapore; Michaelides A., Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom; Nair R.R., National Graphene Institute, University of Manchester, Manchester, United Kingdom, Department of Chemical Engineering, University of Manchester, Manchester, United Kingdom
Big data-Industry 4.0 readiness factors for sustainable supply chain management: Towards circularity
Big data; Circular Economy; Digitalization; Industry 4.0; Sustainable Supply Chain
Big data-Industry 4.0 interaction is expected to revolutionize the existing supply chains in recent years. While increased operational efficiency and enhanced decision-making are the primary advantages studied widely, the sustainable aspects of digital supply chain in the circular economy era have received limited attention. The previous literature rarely explores the industry readiness for a digital supply chain. Thus, the present study objectives to explore Big data-Industry 4.0 readiness factors for sustainable supply chain management. A detailed literature analysis was performed to identify a total of seventeen readiness factors for sustainable supply chain management. A team of six experts were consulted to perform the pairwise comparison for the identified potential readiness factors. This study adopts a fuzzy best-worst method to prioritize the readiness factors according to their degree of influence. The results from the study reflect that readiness towards information system infrastructure, Internet stability for developing I4.0 infrastructure, and circular process and awareness are the most significant readiness factors. The potential recommendation of this study includes the increased attention from sustainable supply chain stakeholders on developing infrastructure, including knowledge building exercise and training process focused on circular economy process. The findings from the study will assist sustainable supply chain stakeholders to frame strategies and action plans during the digitalization of supply chains. 2023 Elsevier Ltd
Patil A.; Dwivedi A.; Abdul Moktadir M.; Lakshay
Computers and Industrial Engineering, Vol-178
https://doi.org/10.1016/j.cie.2023.109109 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149638220&doi=10.1016%2fj.cie.2023.109109&partnerID=40&md5=655fa597973507a2da5d5f29d541d9fe
Patil A., Christ University, Bengaluru, India; Dwivedi A., Jindal Global Business School, O.P. Jindal Global University, Sonipat, India; Abdul Moktadir M., Institute of Leather Engineering and Technology, University of Dhaka, Dhaka, 1209, Bangladesh; Lakshay, Department of Mechanical Engineering, Indian Institute of Technology (BHU), Varanasi, India
Memetic Spider Monkey Optimization for Spam Review Detection Problem
big data; golden section search; memetic search; optimization; spam review; swarm intelligence
Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission-fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search to improve the local search ability of SMO is proposed here. The newly developed strategy is titled Memetic SMO (MeSMO). Further, the proposed MeSMO-based clustering approach is applied to solve a big data problem, namely, the spam review detection problem. A customer usually makes decisions to purchase something or make an image of someone based on online reviews. Therefore, there is a good chance that the individuals or companies may write spam reviews to upgrade or degrade the stature or value of a trader/product/company. Therefore, an efficient spam detection algorithm, MeSMO, is proposed and tested over four complex spam datasets. The reported results of MeSMO are compared with the outcomes obtained from the six state-of-art strategies. A comparative analysis of the results proved that MeSMO is a good technique to solve the spam review detection problem and improved precision by 3.68%. 2023 Mary Ann Liebert, Inc., publishers.
https://doi.org/10.1089/big.2020.0188 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153123142&doi=10.1089%2fbig.2020.0188&partnerID=40&md5=6d411d93c49b0a8517cd843b96e16500
Shekhawat S.S., Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India; Sharma H., Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India
Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)
energy depletion; MANET; multicast communication; network lifetime path stability; QSSM-ML algorithm
Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the nodes vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the networks lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the nodes energy use for broadcasting the data from the origin to the target. Moreover, the nodes energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms. 2023 by the authors.
Rajagopal M.; Sivasakthivel R.; Venugopal J.; Sarris I.E.; Loganathan K.
Information (Switzerland), Vol-14, No. 4
https://doi.org/10.3390/info14040244 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153675883&doi=10.3390%2finfo14040244&partnerID=40&md5=7cbcb1aedffbdbe4b7eaa4fffff795c8
Rajagopal M., Department of Lean Operations and Systems, School of Business and Management, CHRIST, Karnataka, Bengaluru, 560001, India; Sivasakthivel R., Department of Computer Science, School of Sciences, CHRIST, Karnataka, Bengaluru, 560001, India; Venugopal J., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Sarris I.E., Department of Mechanical Engineering, University of West Attica, 250 Thivon & P. Ralli Str, Athens, Egaleo, 12244, Greece; Loganathan K., Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India
Does Credit Rating Revisions Affect the Price of Common Stock: A Study of Indian Capital Market
abnormal returns; Credit rating; credit rating agencies; downgrades and upgrades; event study; information efficiency; rating announcements
The current investigation aims to assess the effect of credit assessment changes on the share prices of Indian companies from 2009 to 2019. The data of top 100 companies listed on National Stock Exchange (NSE) across 10 industries stem from CMIE databases. The excess stock return is compared with the market in a 15-day window around credit rating changes. The event effect on share prices is more in the pre-event window compared to the post-event window. Positive abnormal stock returns around upgrades through downgrades are statistically significant compared to upgrades. Credit ratings are not significant across industries, and agency nationality is a critical factor for calculating the intensity of price reaction. 2021 K. J. Somaiya Institute of Management.
Business Perspectives and Research, Vol-11, No. 2, pp. 190-209.
Dawar G., Christ University, NCR Campus, Uttar Pradesh, Ghaziabad, India; Bhatia S., Indira Gandhi Delhi Technical University for Women, Delhi, India; Bindal J.P., Ingram Micro India Pvt Ltd, India
An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches
Green Channel; Image Quality; Noise Filtering; Pre-Processing; Quality Score; Retinal Image
Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed schemes accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively. 2023 Baqiyatallah University of Medical Sciences. All rights reserved.
International Journal on Recent and Innovation Trends in Computing and Communication, Vol-11, No. 5, pp. 170-180.
Auricle Global Society of Education and Research
https://doi.org/10.17762/ijritcc.v11i5.6603 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164580442&doi=10.17762%2fijritcc.v11i5.6603&partnerID=40&md5=ff7951e87b79733fa46c450fd1c339a5
Saju B., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Rajesh R., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India
Coronal Elemental Abundances During A-Class Solar Flares Observed by Chandrayaan-2 XSM
Corona, abundances; Flares, FIP bias; Spectroscopy, X-ray
The abundances of low first ionization potential (FIP) elements are three to four times higher in the closed loop active corona than in the photosphere, known as the FIP effect. Observations suggest that the abundances vary in different coronal structures. Here, we use the soft X-ray spectroscopic measurements from the Solar X-ray Monitor (XSM) onboard the Chandrayaan-2 orbiter to study the FIP effect in multiple A-class flares observed during the minimum of Solar Cycle 24. Using time-integrated spectral analysis, we derive the average temperature, emission measure, and the abundances of four elements Mg, Al, Si, and S. We find that the temperature and emission measure scales with the sub-class of flares while the measured abundances show an intermediate FIP bias for the lower A-flares (e.g. A1), while for the higher A-flares, the FIP bias is near unity. To investigate it further, we perform a time-resolved spectral analysis for a sample of the A-class flares and examine the evolution of temperature, emission measure, and abundances. We find that the abundances drop from the coronal values towards their photospheric values in the impulsive phase of the flares and, after the impulsive phase, they quickly return to the usual coronal values. The transition of the abundances from the coronal to photospheric values in the impulsive phase of the flares indicates the injection of fresh unfractionated material from the lower solar atmosphere to the corona due to chromospheric evaporation. However, explaining the quick recovery of the abundances from the photospheric to coronal values in the decay phase of the flare is challenging. 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Nama L.; Mondal B.; Narendranath S.; Paul K.T.
Springer Science and Business Media B.V.
Nama L., Department of Physics and Electronics, Christ (Deemed to be University), Hosur Main Road, Bangalore, India; Mondal B., Physical Research Laboratory, Navrangpura, Gujarat, Ahmedabad, India; Narendranath S., Space Astronomy Group, U R Rao Satellite Centre, ISRO, Bengaluru, India; Paul K.T., Department of Physics and Electronics, Christ (Deemed to be University), Hosur Main Road, Bangalore, India
Murraya koenigii extract blended nanocellulose-polyethylene glycol thin films for the sustainable synthesis of antibacterial food packaging
Antimicrobial; Antioxidant; Food packaging; Murraya koenigii; Nanocellulose
Non-biodegradable plastics are a worldwide problem that have a negative impact on all living things, including humans. Nanocellulose, an excellent biopolymer is known for their increasing uses in food, healthcare, cosmetics, and various other fields. Nanocellulose is readily biodegradable, bioderived, and useful for creating innovative bioplastics that are employed in the production of food packaging and wound dressing. Curry leaves (Murraya koenigii) belongs to the rutaceae family and has many health benefits. Synthesis of Murraya koenigii incorporated nanocellulose thin films, and its characterisation using FT-IR, and XRD is discussed in detail. The source of nanocellulose in this study is sugar cane bagasse, an easily available agricultural residue in Kerala. Also, a biocompatible plasticizer is utilised to produce antibacterial packaging for food. The synthesised nanocomposites showed non-toxicity against THP1-derived macrophage cells and significant antibacterial activity against gram positive and gram-negative bacteria suggesting the possible application as a viable alternative for food packaging materials. 2023 Elsevier B.V.
Madhavan A.; Reshmy R.; Arun K.B.; Philip E.; Sindhu R.; Nair B.G.; Awasthi M.K.; Pandey A.; Binod P.
Sustainable Chemistry and Pharmacy, Vol-32
https://doi.org/10.1016/j.scp.2023.101021 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149967433&doi=10.1016%2fj.scp.2023.101021&partnerID=40&md5=6a6f5e39d08b5d1c5d4703a317e3a9a1
Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Reshmy R., Post Graduate and Research Department of Chemistry, Bishop Moore College, Kerala, Mavelikara, 690 110, India, Department of Science and Humanities, Providence College of Engineering, Kerala, Chengannur, 689122, India; Arun K.B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Philip E., Post Graduate and Research Department of Chemistry, Bishop Moore College, Kerala, Mavelikara, 690 110, India; Sindhu R., Department of Food Technology, TKM Institute of Technology, Kollam, 689 122, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi, 712 100, China; Pandey A., Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, 226 029, India, Centre for Innovation and Translational Research, CSIR- Indian Institute for Toxicology Research (CSIR-IITR), 31 MG Marg, Lucknow, 226 001, India; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Trivandrum, 695 019, India
Analysis of the Effectiveness of a Two-Stage Three-Phase Grid-Connected Inverter for Photovoltaic Applications
Boost voltage; DC-AC converter; distortion; Maximum power; Solar cell; Total harmonic
This paper proposes a two-stage three-phase grid-connected inverter for photovoltaic applications. The proposed inverter topology consists of a DC-DC boost converter and a three-phase grid-connected inverter. The DC-DC boost converter is used to boost the low voltage DC output of the PV array to a high voltage DC level that is suitable for feeding into the grid-connected inverter. The three-phase grid-connected inverter is used to convert the high voltage DC output of the boost converter into a three-phase AC output that is synchronized with the grid voltage. The proposed inverter topology offers several advantages over traditional single-stage inverters. Firstly, the DC-DC boost converter allows for the use of a smaller, more efficient inverter in the second stage, reducing the overall cost of the system. Secondly, the use of a boost converter allows for the maximum power point tracking of the PV array, which can increase the overall efficiency of the system. The proposed inverter topology offers improved control of the grid current, reducing the impact of the PV system on the grid. The proposed topology has been simulated using MATLAB/Simulink and the results show that the system is capable of delivering a high-quality three-phase AC output with low harmonic distortion. The Author(s). Publisher: University of Tehran Press.
Ezhilvannan P.; Krishnan S.; Kumar B.H.; Janardhan K.; Ramachandran S.
Journal of Solar Energy Research, Vol-8, No. 2, pp. 1471-1483.
https://doi.org/10.22059/jser.2023.357025.1285 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159462796&doi=10.22059%2fjser.2023.357025.1285&partnerID=40&md5=bca8d0c97ec5c5f472bcc5043b4bac27
Ezhilvannan P., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Tirupati, India; Krishnan S., Department of Electrical and Electronics Engineering, Christ University, Bangalore, India; Kumar B.H., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Tirupati, India; Janardhan K., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Tirupati, India; Ramachandran S., Department of Electrical and Electronics Engineering, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Tirupati, India
Design and Development of Multi-Sensor ADEP for Bore Wells Integrated with IoT Enabled Monitoring Framework
Bore Wells; Cloud; Depth Sensor Probe; Groundwater; Hydrological and Geological Features; Water Level; Water Quality; WSN
Typically, about 51% of the groundwater satisfies the drinking water worldwide and is regarded as the major source for the purpose of irrigation. Moreover, the monitoring and assessment of groundwater over bore wells is essential to identify the effect of seasonal changes, precipitations, and the extraction of water. Hence, there is a need to design a depth sensor probe for bore wells so as to analyze/monitor the quality of underground water thereby estimating any geophysical variations like landslides/earthquakes. Once the depth sensor probe is designed, the data is collected over wireless sensor network (WSN) medium and is stored in cloud for further monitoring and analyzing purposes. WSN is the major promising technologies that offer the real-time monitoring opportunities for geographical areas. The wireless medium in turn senses and gathers data like rainfall, movement, vibration, moisture, hydrological and geological aspects of soil that helps in better understanding of landslide or earthquake disasters. In this paper, the design and development of geophysical sensor probe for the deep bore well so as to monitor and collect the data like geological and hydrological conditions. The data collected is then transmitted by wireless network to analyze the geological changes which can cause natural disaster and water quality assessment. 2023 The Authors. Published by AnaPub Publications. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Sankaran K.S.; Lekha J.; Jenath M.; Easwaran B.
Journal of Machine and Computing, Vol-3, No. 2, pp. 144-158.
All Open Access; Hybrid Gold Open Access
https://doi.org/10.53759/7669/jmc202303014 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159767263&doi=10.53759%2f7669%2fjmc202303014&partnerID=40&md5=d5bbf2231b2ab1ad98d31ca0071c1df0
Sankaran K.S., Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Chennai, India; Lekha J., Department of Data Science, Christ Deemed to be University, Lavasa Campus, Maharashtra, Pune, India; Jenath M., Electronics and Communication Engineering, Sri Sairam Egineering College, Chennai, India; Easwaran B., Texila American University, 405 A, Greater lubinda Road, Lilayi, Lusaka, Zambia
Building Global Teaching Capacity Among Pre-Service Teachers: Epistemological and Positional Framing in an Internationally Paired, Authentic Practicum
Australian and Indian preservice teachers; cross-cultural teaching competence; epistemological and positioning framework; international paired practicum
Building the capacity of pre-service teachers to work in globalized cross-cultural environments is essential to cope with the challenges of the 21st century. This study establishes the value of internationally paired, authentically collaborative practicums with strong epistemological and positional framing in pursuing such capacity development. It was conducted among 90 pre-service teachers from three different universities in Australia and India who participated in a three-week paired practicum in three schools in India. The practicum included the collaborative production of an integrated Australian and Indian combined theme presented in a whole school forum. Mixed methods and a design-based research approach yielded data affirming that such a model did indeed provide pre-service teachers with the confidence to teach in increasingly diverse classrooms and contexts, while also identifying which aspects of this practicum model were most influential in this regard. 2021 European Association for International Education.
Kurup P.M.; Nethsinghe R.; Li X.; Cherian J.; Yang Y.
Journal of Studies in International Education, Vol-27, No. 2, pp. 336-357.
Kurup P.M., La Trobe University, Australia; Nethsinghe R., University of Canberra, Australia; Li X., La Trobe University, Australia; Cherian J., Christ University, India; Yang Y., University of Melbourne, Australia
Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images
data science; disease diagnosis; image processing; Lung cancer; machine learning; predictive models
In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-The-Art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%. 2023 World Scientific Publishing Company.
Vishwa Kiran S.; Kaur I.; Thangaraj K.; Saveetha V.; Kingsy Grace R.; Arulkumar N.
International Journal of Image and Graphics, Vol-23, No. 3
Vishwa Kiran S., Department of AI and ML, BMS Institute of Technology and Management, Karnataka, Bangalore, 560064, India; Kaur I., Department of CSE, Ajay Kumar Garg Engineering College, Uttar Pradesh, Ghaziabad, 201009, India; Thangaraj K., Department of IT, Sona College of Technology, Tamil Nadu, Salem, 636005, India; Saveetha V., Department of IT, Dr. N. G. P Institute of Technology, Tamil Nadu, Coimbatore, 641048, India; Kingsy Grace R., Department of CSE, Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, 641022, India; Arulkumar N., Department of Computer Science, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India
An improved frequent pattern tree: the child structured frequent pattern tree CSFP-tree
CSFP-tree; CSFP-tree mining; Data mining; FP-tree; Frequent itemset mining; Improved FP-tree
Frequent itemsets are itemsets that occur frequently in a dataset. Frequent itemset mining extracts specific itemsets with supports higher than or equal to a minimum support threshold. Many mining methods have been proposed but Apriori and FP-growth are still regarded as two prominent algorithms. The performance of the frequent itemset mining depends on many factors; one of them is searching the nodes while constructing the tree. This paper introduces a new prefix-tree structure called child structured frequent pattern tree (CSFP-tree), an FP-tree attached with a child search subtree to each node. The experimental results reveal that the CSFP-tree is superior to the FP-tree and its new variations for any kind of datasets. 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Pattern Analysis and Applications, Vol-26, No. 2, pp. 437-454.
Springer Science and Business Media Deutschland GmbH
Jamsheela O., EMEA College of Arts and Science, Kerala, Kondotty, India; Raju G., Christ University Yeshwantpur Campus, Bengaluru, India
Subspace spanning graph topological spaces of graphs
d-closed spanning graphs; Spanning graph topology; subspace spanning graph topology
A collection of spanning subgraphs TS, of a graph G is said to be a spanning graph topology if it satisfies the three axioms: (Formula Presented) where, n = |V (G)|, the collection is closed under any union and finite intersection. Let (X, T ) be a topological space in point set topology and Y ? X then, TY = {U ? Y: U ? T } is a topological space called a subspace topology or relative topology defined by T on Y. In this paper we discusses the subspace spanning graph topology defined by the graph topology TS on a spanning subgraph H of G. 2023, Proyecciones. All Rights Reserved.
Proyecciones, Vol-42, No. 2, pp. 479-488.
Aniyan A., CHRIST (Deemed to be University), India; Naduvath S., CHRIST (Deemed to be University), India
Phytochemical Composition, Bioactive Compounds, and Antioxidant Properties of Different Parts of Andrographis macrobotrys Nees
Andrographis; antioxidants; bioactive compounds; GS-MS analysis
Andrographis macrobotrys Nees is an ethnomedicinal plant belonging to the family Acanthaceae, distributed in the moist deciduous and semi-evergreen forests of the southern Western Ghats of India. The objective of this research was to determine the phytochemical composition and bioactive chemical components using gas chromatography and mass spectrometry (GC-MS) and to check the antioxidant potential of the plant part extracts. A. macrobotrys roots, stems, and leaves were obtained from the species natural habitat in the Western Ghats, India. The bioactive compounds were extracted using a Soxhlet extractor at 5560 C for 8 h in methanol. Identification analysis of A. macrobotrys bioactive compound was performed using GC-MS. Quantitative estimation of phytochemicals was carried out, and the antioxidant capacity of the plant extracts was determined by 2,2?-diphenyl-1-picrylhydrazyl radical scavenging (DPPH) and ferric reducing assays (FRAP). A. macrobotrys has a higher concentration of phenolics in its stem extract than in its root or leaf extracts (124.28 mg and 73.01 mg, respectively), according to spectrophotometric measurements. GC-MS analysis revealed the presence of phytochemicals such as azulene, 2,4-di-tert-butylphenol, benzoic acid, 4-ethoxy-ethyl ester, eicosane, 3-heptadecanol, isopropyl myristate, hexadecanoic acid methyl ester, hexadecanoic acid, 1-butyl-cyclohexanol, 9,12-octadecadienoic acid, alpha-monostearin, and 5-hydroxy-7,8-dimethoxyflavone belonging to various classes of flavonoids, terpenoids, phenolics, fatty acids, and aromatic compounds. Significant bioactive phytochemicals include 2,4-di-tert-butylphenol, 2-methoxy-4-vinylphenol, 5-hydroxy-7,8-dimethoxyflavone, azulene, salvigenin, squalene, and tetrapentacontane. In addition, the antioxidant capability of each of the three extracts was assessed. The stem extract demonstrated impressive DPPH scavenging and ferric reduction activities, with EC50 values of 79 mg/mL and 0.537 0.02 OD at 0.2 mg/mL, respectively. The results demonstrated the importance of A. macrobotrys as a source of medicine and antioxidants. 2023 by the authors.
Dalawai D.; Murthy H.N.; Dewir Y.H.; Sebastian J.K.; Nag A.
https://doi.org/10.3390/life13051166 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160217195&doi=10.3390%2flife13051166&partnerID=40&md5=95be5acab5631770dc76163800e99046
Dalawai D., Department of Botany, Karnatak University, Dharwad, 580003, India; Murthy H.N., Department of Botany, Karnatak University, Dharwad, 580003, India, Department of Horticultural Science, Chungbuk National University, Cheongju, 28644, South Korea; Dewir Y.H., Plant Production Department, College of Food & Agriculture Sciences, King Saud University, Riyadh, 11451, Saudi Arabia; Sebastian J.K., Department of Life Sciences, Christ University, Bangalore, 560029, India; Nag A., Department of Life Sciences, Christ University, Bangalore, 560029, India
A ratiometric fluorescent sensor based on dual-emissive carbon dot for the selective detection of Cd2+
Biomass-derived; Cadmium; Carbon quantum dots; Pollutant; Ratiometric fluorescence sensor
Cadmium (Cd2+), a heavy metal ion used in numerous industries, has toxic adverse effects on the environment; it is crucial to develop a quick and reliable method for Cd2+ determination. Fluorescent biomass-derived carbon quantum dots (CD) with rich carboxyl groups on the surface were synthesized using water amaranth leaves by hydrothermal method with a 12.1% quantum yield. The surface of CD was further modified with 1-pyrene carboxaldehyde (PC) to synthesize pyrene carboxaldehyde-carbon quantum dots (PC-CD). This study developed a fluorescent ratiometric nanosensor using a covalently functionalized CD with pyrene derivative and demonstrates highly selective identification capability towards Cd2+ over competing metal ions. The Nano sensor has significant selectivity towards Cd2+ in an excellent linear range of 0-70 ?M with a detection limit as low as 15 nM and demonstrates excellent water solubility and biocompatibility. Transmission electron spectroscopy (TEM), Fourier Transform infrared spectroscopy (FT-IR), and X-ray photon spectroscopy (XPS) were used to identify the surface functionalization of PC-CD. Finally, the developed ratiometric sensor was used for detecting Cd2+ metal ions from various water effluents. 2023 Elsevier Ltd.
Keerthana P.; Kumar Das A.; Bharath M.; Ghosh M.; Varghese A.
Journal of Environmental Chemical Engineering, Vol-11, No. 2
https://doi.org/10.1016/j.jece.2023.109325 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147733950&doi=10.1016%2fj.jece.2023.109325&partnerID=40&md5=beb7d905c2acdeeac3d56406830b6f03
Keerthana P., Department of Chemistry, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India; Kumar Das A., Department of Chemistry, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India; Bharath M., Department of Chemistry, Ashoka University, Haryana, 131029, India; Ghosh M., Department of Chemistry, Ashoka University, Haryana, 131029, India; Varghese A., Department of Chemistry, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India
Comparison of the effect of suction-injection-combination on Rayleigh-Bard convection in the case of asymmetric boundaries with those of symmetric ones
The effect of suction-injection-combination (SIC) on the linear and weakly nonlinear stability of Rayleigh-Bard convection is considered in the paper for the cases of symmetric and asymmetric boundary conditions. Using the Maclaurin series with an appropriate number of terms, expression for eigenfunctions is obtained. The linear theory corroborates the results obtained using the chosen eigenfunctions in the limiting case of the no-SIC effect by matching accurately with the exact values concerning the critical Rayleigh number (Rac) and the wave number (?c). It is found that the effect of SIC is to stabilize the system in the case of symmetric boundaries irrespective of SIC being pro-gravity or anti-gravity. However, the effect of SIC is to stabilize/destabilize the system depending on SIC being pro-gravity or anti-gravity in the case of the asymmetric boundaries. We also noted a similar effect in the case of ?c wherein a maximum error of order 10 ? 4 was observed. The main novelty of the present work is studying the influence of SIC on the nonlinear dynamics of the considered problem. It is shown that the effect of SIC is to hasten the onset of chaos. Using various indicators (the largest Lyapunov exponent, the time series solution, the amplitude spectrum, and the phase-space plots), the dynamical behavior of the system is analyzed and the influence of SIC on the dynamics is recorded. The change due to the boundary effect and the SIC on the size of convection rolls and the trapping region where the dynamical system evolves within a bound is highlighted in the paper. 2023 Author(s).
Kanchana C.; Siddheshwar P.G.; Pez L.M.; Laroze D.
https://doi.org/10.1063/5.0146657 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159786556&doi=10.1063%2f5.0146657&partnerID=40&md5=fffa2703afc2330ee0d1d9b189a2f081
Kanchana C., Instituto de Alta Investigaci, CEDENNA, Universidad de Tarapac Casilla 7 D, Arica, Chile; Siddheshwar P.G., Center for Mathematical Needs, Department of Mathematics, CHRIST(Deemed to be University), Bengaluru, 560029, India; Pez L.M., Departamento de Fica, FACI, Universidad de Tarapac Casilla 7 D, Arica, 1000000, Chile; Laroze D., Instituto de Alta Investigaci, CEDENNA, Universidad de Tarapac Casilla 7 D, Arica, Chile