Text Mining-A Comparative Review of Twitter Sentiments Analysis
airline sentiments; decision trees; gaussian naive bayes; gini index; machine learning; multinomial naive bayes; multinomial naive bayes with bagging; Opinion mining; random forest
Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media net-works. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a mar-ginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Nae Bayes, Multinomial Nae Bayes, Multinomial Nae Bayes with Bagging, Logistic Regression and a similar enhancement is observed with Ada-Boost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.. 2024 Bentham Science Publishers.
Patil S.; Subil D.; Nasar N.; Kokatnoor S.A.; Krishnan B.; Kumar S.
Recent Advances in Computer Science and Communications, Vol-17, No. 1, pp. 21-37.
Patil S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Subil D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Nasar N., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, 74, Bangalore, India
Text Summarization Techniques for Kannada Language
Abstractive; Extractive; LSTM; ROUGE; Text processing; TF-IDF
Text Summarization is summarizing the original text document into a shorter description. This short version should retain the meaning and information content of the original text document. A concise summary can help humans quickly understand a large original document better in a short time. Summarization can be used in many text documents, such as reviews of books, movies, newspaper articles, content, and huge documents. Text summarization is broadly classified into extractive Text Summarization (ETS) and Abstractive Text Summarization (ATS). Even though more research works are carried out using extractive methods, meaningful summaries can be attained using abstractive summary techniques, which are more complex. In Indian languages, very few works are carried out in abstract summarization, and there is a high need for research in this area. The paper aims to generate extractive and abstractive summaries of the text by using deep learning and extractive summaries and comparisons between them in the Kannada language. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Yogish D.; Jalapur S.; Yogisha H.K.; Mithun B.N.
Lecture Notes in Networks and Systems, Vol-1121 LNNS, pp. 107-117.
Springer Science and Business Media Deutschland GmbH
Yogish D., CHRIST University, Karnataka, Bengaluru, India; Jalapur S., CHRIST University, Karnataka, Bengaluru, India; Yogisha H.K., Ramaiah Institute of Technology, Karnataka, Bengaluru, India; Mithun B.N., CHRIST University, Karnataka, Bengaluru, India
Text Summarization Using Combination of Sequence-To-Sequence Model with Attention Approach
Attention layer; Decoderencoder; Long-term short memory; Recurrent neural network; Text summarization
In daily life, we come across tons and tons of information which can be related to news articles or any kind of social media posts or customer reviews related to product. It is difficult to read all the content due to time constraint. Being able to develop the software that can identify and automatically extract the important information. There are two types of summarization methods. Extractive text summarization is the method where it picks the important content from the source text and gives same in the form of short summary, and on the other hand, abstractive summarization is the technique where it gets the context of the source text, and based on that context, it regenerates small and crisps summary. In this paper, we use the concept of neural network with attention layer to deal with abstractive text summarization that generates short summary of a long piece of text using review dataset. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes on Data Engineering and Communications Technologies, Vol-141, pp. 283-293.
Bhandarkar P., Department of Data Science, CHRIST University, Bangalore, India; Thomas K.T., Department of Data Science, CHRIST University, Bangalore, India
Text summarization using residual-based temporal attention convolutional neural network
Classifier; Computational complexity; Entropy values; Feature extraction; Improved Initialization strategy-based aquila optimization algorithm; Residual-based temporal attention convolutional neural network
To address the computational complexity and limited to large data Enhanced Residual based Temporal Attention Convolutional Neural Network (ERTACNN) with Improved Initialization strategy-based Aquila Optimization Algorithm (IIAOA) is proposed. Initially the document is pre-processed to get structured data and given to feature extraction. Then the features are selected with Aquila Optimization Algorithm to remove redundant or unrelated features from high-dimensional data, from which the entropy values are calculated and given to proposed classifier. In this classification, the temporal attention mechanism is combined with classifier to compute attention weight and accompanied with important time points for classifying the documents. Finally, the proposed method is implemented in python and evaluated against existing works which achieves 70.34, 55.6 and 72.4 Recall Oriented Understudy for Gisting Evaluation (ROUGE) score than existing approaches. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
International Journal of Information Technology (Singapore)
Springer Science and Business Media B.V.
Rajan R.P., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India; Jose D.V., Department of Computer Science, Christ (Deemed to Be University), Karnataka, Bengaluru, India
Text-Based Sentimental Analysis to Understand User Experience Using Machine Learning Approaches
Google Colab; LSTM; Python; Sentiment Analysis; Twitter
Data Analysis is turning into a driving force in every industry. It is a process in which data is analyzed in multiple ways to come to certain conclusions for the given situation. Sentiment analysis can be said to be a sub-section of data analysis where analysis is carried out on the emotions and opinions of the text. Social media has a plethora of sentiment data in various forms such as tweets, updates on the status, and so forth. Sentiment analysis on the huge volume of data can help in identifying the opinions of the general mass.The primary goal is to find the opinion of customers on the services of the Bangalore airport and to enhance the nature of these services according to the feedback provided. In this paper, we aim to measure customer opinion on services provided by Bangalore Airport through sentiment. Data is collected by a python-based scraper. The tweets are processed to determine whether they are of positive or negative opinion. These opinions are then analyzed to determine the factors which cause the negative opinions and the airport staff are alerted about the same. Various algorithms were used as part of the experimental analysis. LSTM produces more accuracy compared with existing approaches. 2023 IEEE.
Gokulapriya R.; Sambandam R.K.; Balamurugan M.
2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023, pp. 402-408.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICACCS57279.2023.10112939 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159771251&doi=10.1109%2fICACCS57279.2023.10112939&partnerID=40&md5=5d0ca4eeb1d8e33b434a4fea7fe54e0c
Gokulapriya R., SOET, CHRIST (Deemed to be University), Dept. of Computer Science and Engineering, Bengaluru, India; Sambandam R.K., SOET, CHRIST (Deemed to be University), Dept. of Computer Science and Engineering, Bengaluru, India; Balamurugan M., SOET, CHRIST (Deemed to be University), Dept. of Computer Science and Engineering, Bengaluru, India
Textile tourism and the challenges of the indigenous handloom sector in Northern Kerala
This study investigates the functioning of indigenous handloom enterprises and their relationship with textile tourism. It also explores the regional textile industry and the challenges weavers encounter in promoting their goods to visitors and exporters. Data was collected using a purposive sampling method, and a structured questionnaire was administered to 120 weavers from four textile weaving centers in Kozhikode, Kerala. The most significant obstacle for weavers and independent producers is the lack of direct communication with customers and the limited access to information provided by manufacturers, corporations, and gallery owners. Firstly, handcrafted items are becoming more accessible and affordable; secondly, the interest of the younger generation is gradually fading, which reduces the number of skilled professionals. The result of this study provides insight into how Khadi Textiles has the potential to contribute positively to socioeconomic achievements and enhance weaver's and destination image. 2024, IGI Global. All rights reserved.
Examining Tourist Behaviors and Community Involvement in Destination Rejuvenation, pp. 194-206.
Nikhilraj K., Christ University, India; Thomas J., Christ University, India
Textual and media-based self-learning modules: Support for achievement in algebra and geometry
Achievement; Algebra; Geometry; Individualised Learning; Media Form; Self-Learning Modules; Textual Form
Owing to the importance of a subject like mathematics in the teaching and learning of science, self-learning often poses a challenge to the educator. The objective of this study is to analyse the enhancement of the textual and the media form of self-learning modules to teach algebra and geometry to eighth graders considering their retention levels. A pre-Test post-Test single-group quasi experimental design was tested and tried out on 49 participants of a school. The 20 modules of self-learning material covering content in the topics of algebra and geometry in the textual and media-Assisted forms of self-learning were administered over three months. The findings of the study revealed the ability of media-Assisted self-learning modules to enhance achievement in the post-Test when compared to the pre-Test. The textual-Assisted learning modules were able to enhance significant difference in the achievements in geometry, but not of algebra. The delayed post-Test results were found to indicate an improved achievement in mathematics. 2022 IGI Global. All rights reserved.
International Journal of Virtual and Personal Learning Environments, Vol-12, No. 1
https://doi.org/10.4018/IJVPLE.285596 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149943543&doi=10.4018%2fIJVPLE.285596&partnerID=40&md5=d400c3650cc9c5d4ca609aaec740a6f0
Kareem J., Christ University, India; Mathew M.M., Christ Junior College, India; David D., St. Mary s School, India
The Routledge Encyclopedia of Indian Writing in English, pp. 427-428.
Tanupriya, Department of English and Cultural Studies, CHRIST (Deemed to be University), Delhi NCR campus, India
The Accountability of Stakeholders in Combating Domestic Violence with Women in India
access to justice; Domestic violence; intimate partner violence; stakeholders; woman
As per the Hindu tradition, women are considered as ardhangini and the western civilisation considers them as better half. Since ancient times, women have been considered as the epitome of love, kindness, care and above all the mother of mankind. But on the contrary, the most terrible and horrifying cruelties are imposed upon her. The predominant type of violence which is inflicted upon a woman is domestic violence also known as intimate partner violence. According to the World Health Organisation, almost one third, i.e., 27% of the women aged between 15 and 49 years, who have been in a relationship report that they have been subjected to some form of physical or sexual violence by their intimate partner. The issue of domestic violence has been addressed at both international and national levels, but still there exists a persistent gap in enforcing and implementing them. And this can be easily proved from the continuous rise in the cases of domestic violence worldwide, especially during the COVID times. The problem that needs to be addressed at hand is the role of several accountable stakeholders involved in the process of providing access to justice to the women affected by domestic violence. According to the Protection of Women from Domestic Violence Act (2005), in India, the several stakeholders include the protection officers, service providers, lawyers, police officers, shelter homes and judges. These are the people with whom the power to protect and help the women are vested, but instead the affected women fall a prey to this system due to various reasons like lack of resources, fewer power and many more which will be further discussed in the article. 2024 selection and editorial matter Dr. Shilpi Sharma and Baidya nath Mukherjee; individual chapters, the contributors.
Law and Emerging Issues: Proceedings of the International Conference on Law and Emerging Issues (ICLEI 2023), pp. 129-140.
https://doi.org/10.4324/9781003428213-14 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196683599&doi=10.4324%2f9781003428213-14&partnerID=40&md5=067a1bafdfc93328f043eac24d5faf9b
Singh M., School of Law, Christ (Deemed to be) University, Delhi-NCR, India, University Institute of Legal Studies, Chandigarh University, Punjab, India; Mathew M., School of Law, Christ (Deemed to be) University, Delhi-NCR, India
The accumulation, antioxidant defences, and secondary metabolite production in common sage (Salvia officinalis L.) under lead toxicity
antioxidants; Pb stress; rosmarinic acid; Sage; Salvia officinalis
The growing levels of lead (Pb) in agricultural soil and water bodies as a result of industrialization and human activity present serious challenges for medicinal plants. The present study investigates the complex responses of Salvia officinalis to toxicity caused by Pb including metal accumulation, translocation dynamics, antioxidant defenses, and the production of secondary metabolites. Pb showed preferential accumulation in the roots, peaking at (1000 ppm Pb) (2484.2 mg kg-1). The reduced levels of total chlorophyll (1.29-fold), protein (1.9-fold), and carbohydrate (2.5-fold) under prolonged exposure to stress demonstrate the toxic impacts of lead. Proline and total phenolic content (TPC) increased concentration-dependently under lead stress, while flavonoids were found to be decreased with the enhancement of lead toxicity. Enzymatic antioxidants (catalase, APX, and SOD) showed notable increase, especially in the 30 days of treatment, demonstrating the plants strong defenses. S. officinaliss adaptive responses were highlighted by concentration-dependent increases in non-enzymatic antioxidants such as total antioxidant capacity (TAC) and DPPH radical scavenging activity. Crucially, under lead stress, S. officinalis showed 1.7-fold increased rosmarinic acid (RA) production, in plants exposed to 200 ppm for 30 days treatment. However, further exposure to lead significantly caused the reduction of RA production. The results add to our knowledge of how sage plants respond to environmental stress and offer important insights for future uses in phytoremediation and the breeding of stress-tolerant plant cultivars. Furthermore, the research highlights about the S. officinaliss potential as a source of bioactive compounds possessing antioxidant qualities, under low levels of lead stress. 2024, Indian journals. All rights reserved.
Medicinal Plants, Vol-16, No. 3, pp. 519-531.
Ramakrishnan R., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India; Nagella P., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be University), Hosur Road, Karnataka, Bengaluru, 560029, India
The aesthetics of corpses in popular culture
Death in Contemporary Popular Culture, pp. 103-115.
Kundu D., School of Business Studies and Social Sciences, Christ (Deemed to Be University), Bangalore, India
The analogy of nanoparticle shapes on the theory of convective heat transfer of AuFe3O4 Casson hybrid nanofluid
casson nanofluid; heat transfer co-efficient; nanoparticle shape; thermal conductivity; volume fraction
In recent times, Au nanoparticles have beencommonly used for delivering the drug especially in the case of hypothermia of tumors, but low absorption of IR light does not solve destruction of tumor cells. However, nanoparticles such as Fe3O4 coated with Au could be used to deliver the drug to a specific spot due to applied external magnetic field. Due to these applications, boundary layer approximation is invoked to simplify the mathematical model. This paper presents the nanoparticle shape analysis and heat transfer features of the AuFe3O4blood hybrid nanofluid flowing past a stretching surface on a magnetohydrodynamic medium. Numerical solutions of nonlinear differential equations are obtained by RKF-45 method with the help of shooting technique. The behavior of emerging parameters is described graphically for velocity and temperature profiles. It is found that the blade-shaped Au and Fe3O4 nanoparticles have better thermal conductance than brick, sphere, cylinder, needle, and platelet shapes. It is also observed that the Lorentz force generated due to magnetic field helps in controlling the flow and enhance the thermal conductivity of hybrid nanofluid. 2021 Wiley Periodicals LLC.
Heat Transfer, Vol-51, No. 3, pp. 2586-2603.
https://doi.org/10.1002/htj.22415 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121330835&doi=10.1002%2fhtj.22415&partnerID=40&md5=725ff35d957de7bfe84103ab4503c7b3
Puneeth V., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, India; Baby A.K., Department of Sciences and Humanities, CHRIST (Deemed to be University), Bangalore, India; Manjunatha S., Department of Sciences and Humanities, CHRIST (Deemed to be University), Bangalore, India
THE ANALYSIS OF THE FLOW OF BLOOD IN A STENOSED ARTERY THROUGH SIMULATION: A COMPARISON AMONG VARIOUS NON-NEWTONIAN MODELS
atherosclerosis; Coronary artery; low-density lipoprotein; rheology; stenosis
This paper focuses on the dual quality of blood, Newtonian and non-Newtonian, in particular by exploring the energy curves. Careful investigation of the dual property of blood has been made by considering two different geometries to represent a stenosed arterial segment. We present a cautious assessment of non-Newtonian blood rheology impacts in arterial stream simulations by coupling the Newtonian and non-Newtonian models. The flow of energy through the two flow dimensions is meticulously investigated using velocity (kinetic energy), pressure, and wall shear stress (pressure energy). Besides, the proper implementation of an interface boundary condition (IBC) was emphasized to ensure consistency with the flow conditions downstream of a backward-facing step. The integration of the Newtonian and non-Newtonian models adjoins the novelty of the current research. The energy curves are obtained by implementing five different non-Newtonian models to designate a suitable non-Newtonian model for blood flow investigations. The combination of the non-Newtonian models enforced in this research is novel and particular attention is paid to the energy curves obtained. The conclusion was to elect the Carreau model as a suitable non-Newtonian rheological model for the blood flow study. This study was able to finalize the fact that the coupling of Newtonian and non-Newtonian models is necessary to obtain accurate results. For the sinusoidal waveform considered for the velocity, Carreau and the Power law models yield better results, eliminating the other non-Newtonian models from the list. With a better inlet condition imposed in the form of the Fourier series for pressure and velocity, the Carreau model yields the best results. 2024 World Scientific Publishing Company.
Shankar Narayan S.; Bhattacharjee A.; Saha S.; Puneeth V.; Singhal A.; Sayfutdinovna Abdullaeva B.
Journal of Mechanics in Medicine and Biology, Vol-24, No. 7
Shankar Narayan S., Department of Mathematics and Statistics, Ramaiah University of Applied Sciences, Bengaluru, 560058, India; Bhattacharjee A., Department of Mathematics, Dayananda Sagar University, Bengaluru, 560058, India; Saha S., Department of Mathematics, Vellore Institute of Technology, Vellore, 632014, India; Puneeth V., Department of Computational Science, CHRIST (Deemed to be University), Ghaziabad, 201003, India; Singhal A., Department of Computational Science, CHRIST (Deemed to be University), Ghaziabad, 201003, India; Sayfutdinovna Abdullaeva B., Doctor of Pedagogical Sciences, Vice-Rector for Scientific Affairs, Tashkent State Pedagogical University, Tashkent, Uzbekistan
The application of artificial intelligence in education: Opportunities and challenges
The chapter investigates the challenges of implementing AI in education, including ethical and privacy concerns, the impact on teacher and student roles, bias and fairness in AI-based systems, and technical challenges. Furthermore, it discusses AI's potential policy and practice implications in education and outlines future research directions. The chapter concludes by emphasizing the transformative nature of AI in education and its capacity to enhance teaching and learning experiences, improve educational outcomes, and foster equitable access to quality education. 2024, IGI Global. All rights reserved.
Educational Perspectives on Digital Technologies in Modeling and Management, pp. 282-292.
Singha S., Kristu Jayanti College (Autonomous), India; Singha R., Christ University, India
The Association between Accounting determined and market determined measures of risk: Evidences from Indian Pharmaceutical Industry.
http://www.indianjournals.com/ijor.aspx?target=ijor:ajrbem&volume=3&issue=12&article=003#aff002
Asian Journal of Research in Business Economics and Management
The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*
The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project. 2022. The Author(s). Published by the American Astronomical Society.
Price-Whelan A.M.; Lim P.L.; Earl N.; Starkman N.; Bradley L.; Shupe D.L.; Patil A.A.; Corrales L.; Brasseur C.E.; Nhe M.; Donath A.; Tollerud E.; Morris B.M.; Ginsburg A.; Vaher E.; Weaver B.A.; Tocknell J.; Jamieson W.; Van Kerkwijk M.H.; Robitaille T.P.; Merry B.; Bachetti M.; Gther H.M.; Aldcroft T.L.; Alvarado-Montes J.A.; Archibald A.M.; Bi A.; Bapat S.; Barentsen G.; Baz J.; Biswas M.; Boquien M.; Burke D.J.; Cara D.; Cara M.; Conroy K.E.; Conseil S.; Craig M.W.; Cross R.M.; Cruz K.L.; D'Eugenio F.; Dencheva N.; Devillepoix H.A.R.; Dietrich J.P.; Eigenbrot A.D.; Erben T.; Ferreira L.; Foreman-Mackey D.; Fox R.; Freij N.; Garg S.; Geda R.; Glattly L.; Gondhalekar Y.; Gordon K.D.; Grant D.; Greenfield P.; Groener A.M.; Guest S.; Gurovich S.; Handberg R.; Hart A.; Hatfield-Dodds Z.; Homeier D.; Hosseinzadeh G.; Jenness T.; Jones C.K.; Joseph P.; Kalmbach J.B.; Karamehmetoglu E.; Ka?uszy?ski M.; Kelley M.S.P.; Kern N.; Kerzendorf W.E.; Koch E.W.; Kulumani S.; Lee A.; Ly C.; Ma Z.; MacBride C.; Maljaars J.M.; Muna D.; Murphy N.A.; Norman H.; O'Steen R.; Oman K.A.; Pacifici C.; Pascual S.; Pascual-Granado J.; Patil R.R.; Perren G.I.; Pickering T.E.; Rastogi T.; Roulston B.R.; Ryan D.F.; Rykoff E.S.; Sabater J.; Sakurikar P.; Salgado J.; Sanghi A.; Saunders N.; Savchenko V.; Schwardt L.; Seifert-Eckert M.; Shih A.Y.; Jain A.S.; Shukla G.; Sick J.; Simpson C.; Singanamalla S.; Singer L.P.; Singhal J.; Sinha M.; Sipocz B.M.; Spitler L.R.; Stansby D.; Streicher O.; umak J.; Swinbank J.D.; Taranu D.S.; Tewary N.; Tremblay G.R.; De Val-Borro M.; Van Kooten S.J.; Vasovi? Z.; Verma S.; De Miranda Cardoso J.V.; Williams P.K.G.; Wilson T.J.; Winkel B.; Wood-Vasey W.M.; Xue R.; Yoachim P.; Zhang C.; Zonca A.
All Open Access; Gold Open Access; Green Open Access
Price-Whelan A.M., Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, 10010, NY, United States; Lim P.L., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Earl N., Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, 61801, IL, United States; Starkman N., David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada; Bradley L., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Shupe D.L., IPAC, MC 100-22, Caltech, 1200 E. California Boulevard, Pasadena, 91125, CA, United States; Patil A.A., David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada, Dunlap Institute for Astronomy & Astrophysics, University of Toronto, 50 St George Street, Toronto, ON M5S 3H4, Canada; Corrales L., University of Michigan, Department of Astronomy, 1085 S. University Avenue, Ann Arbor, 48109, MI, United States; Brasseur C.E., School of Physics and Astronomy, University of St Andrews, North Haugh, Street, Andrews, KY16 9SS, United Kingdom; Nhe M., Astroparticle Physics, TU Dortmund, Otto-Hahn-Stra 4a, Dortmund, D-44227, Germany; Donath A., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States; Tollerud E., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Morris B.M., Center for Space and Habitability, University of Bern, Gesellschaftsstrasse 6, Bern, 3012, Switzerland; Ginsburg A., Department of Astronomy, University of Florida, P.O. Box 112055, Gainesville, FL, United States; Vaher E., Lund Observatory, Department of Astronomy and Theoretical Physics, Lund University, Box 43, Lund, SE-22100, Sweden; Weaver B.A., NSF's National Optical-Infrared Astronomy Research Laboratory, 950 North Cherry Avenue, Tucson, AZ 85719, United States; Tocknell J., School of Mathematical and Physical Sciences, Macquarie University, Sydney, 2109, NSW, Australia, Astronomy, Astrophysics and Astrophotonics Research Centre, Macquarie University, Sydney, 2109, NSW, Australia, Australian Astronomical Optics, Macquarie University, Sydney, 2109, NSW, Australia; Jamieson W., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Van Kerkwijk M.H., David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada; Robitaille T.P., Aperio Software Ltd., Insight House, Riverside Business Park, Stoney Common Road, Essex, Stansted, CM24 8PL, United Kingdom; Merry B., South African Radio Astronomy Observatory, 2 Fir Street, Black River Park, Observatory 7925, South Africa; Bachetti M., Istituto Nazionale di Astrofisica/Osservatorio Astronomico di Cagliari, via della Scienza 5 (CA), Selargius, I-09047, Italy; Gther H.M., MIT Kavli Institute for Astrophysics and Space Research, 77 Massachusetts Avenue, Cambridge, 02139, MA, United States; Aldcroft T.L., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States; Alvarado-Montes J.A., School of Mathematical and Physical Sciences, Macquarie University, Sydney, 2109, NSW, Australia, Astronomy, Astrophysics and Astrophotonics Research Centre, Macquarie University, Sydney, 2109, NSW, Australia; Archibald A.M., Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom; Bi A., Konkoly Observatory, Research Centre for Astronomy and Earth Sciences (ELKH), Konkoly Thege Mikl 15-17, Budapest, H-1121, Hungary, MTA CSFK Lendet Near-Field Cosmology Research Group, Budapest, Hungary; Bapat S., School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Kamand (H.P.), Mandi, 175005, India; Barentsen G., Bay Area Environmental Research Institute, P.O. Box 25, Moffett Field, 94035, CA, United States; Baz J., CIEMAT Astroparticle Physics, Madrid, Spain; Biswas M., Savitribai Phule Pune University, Maharashtra, Pune, India; Boquien M., Centro de Astronom (CITEVA), Universidad de Antofagasta, Avenida Angamos 601, Antofagasta, Chile; Burke D.J., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States; Cara D., Wake Forest University, 1834 Wake Forest Road, Winston-Salem, 27109, NC, United States; Cara M., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Conroy K.E., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Conseil S., Aix Marseille University, CNRS, CNES, LAM, Marseille, France; Craig M.W., Department of Physics and Astronomy, Minnesota State University Moorhead, Moorhead, 56563, MN, United States; Cross R.M.; Cruz K.L., Hunter College, Physics and Astronomy, 695 Park Avenue, New York, 10065, NY, United States, American Museum of Natural History, Department of Astrophysics, Central Park West at 79th Street, New York, 10024, NY, United States, Flatiron Institute, 162 Fifth Avenue, New York, 10010, NY, United States; D'Eugenio F., Cavendish Laboratory, Kavli Institute for Cosmology, University of Cambridge, Madingley Rise, Cambridge, CB3 0HA, United Kingdom; Dencheva N., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Devillepoix H.A.R., School of Earth and Planetary Sciences, Curtin University, Perth, 6845, WA, Australia; Dietrich J.P., Universits-Sternwarte Mchen, Scheinerstr, Mchen, 181679, Germany; Eigenbrot A.D., National Solar Observatory, 3665 Discovery Drive, Boulder, 80303, CO, United States; Erben T., Argelander-Institut f Astronomie (University of Bonn), Auf dem Hel 71, Bonn, D-53121, Germany; Ferreira L., Centre for Astronomy and Particle Physics, University of Nottingham, Nottingham, United Kingdom; Foreman-Mackey D., Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, 10010, NY, United States; Fox R.; Freij N., Bay Area Environmental Research Institute, P.O. Box 25, Moffett Field, 94035, CA, United States, Lockheed Martin Solar & Astrophysics Laboratory, Org. A021S, Bldg. 252, 3251 Hanover Street, Palo Alto, 94304, CA, United States; Garg S., Department of Physics, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan, Institute of Cosmic Ray Research, The University of Tokyo, Chiba, Kashiwa City, 277-8582, Japan; Geda R., Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, 08544, NJ, United States; Glattly L., ScienceBetter Consulting, LLC, 4 West 101st Street, No. 29, New York, 10025, NY, United States; Gondhalekar Y., BITS Pilani/Computer Science, Goa, Sancoale, 403726, India; Gordon K.D., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Grant D., University of Oxford, Department of Physics, Keble Road, Oxford, OX1 3RH, United Kingdom, University of Bristol, HH Wills Physics Laboratory, Tyndall Avenue, Bristol, BS8 1TL, United Kingdom; Greenfield P., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Groener A.M., Drexel University, Department of Physics, 816 Disque Hall 32 S. 32nd Street, Philadelphia, 19104, PA, United States; Guest S., RAL Space, STFC Rutherford Appleton Laboratory, Harwell, Didcot, OX11 0QX, United Kingdom; Gurovich S., Instituto de Astronomia Teorica y Experimental - Observatorio Astronomico Cordoba, Cdoba, Argentina; Handberg R., Stellar Astrophysics Centre, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, Aarhus C, DK-8000, Denmark; Hart A.; Hatfield-Dodds Z., Australian National University, Canberra, Australia; Homeier D., Aperio Software Ltd., Insight House, Riverside Business Park, Stoney Common Road, Essex, Stansted, CM24 8PL, United Kingdom, Fderkreis Planetarium Gtingen, Gtingen, Germany; Hosseinzadeh G., Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, 85721-0065, AZ, United States; Jenness T., Vera C. Rubin Observatory, University of Arizona, 950 N. Cherry Avenue, Tucson, 85719, AZ, United States; Jones C.K., The Malone Center for Healthcare in Engineering, Johns Hopkins University, Baltimore, 21218-2608, MD, United States; Joseph P., Indian Institute of Astrophysics, Bangalore, 560034, India, Department of Physics, CHRIST (Deemed to Be University), Bangalore, 560029, India; Kalmbach J.B., DIRAC Institute, Department of Astronomy, University of Washington, Box 351580, Seattle, 98195, WA, United States; Karamehmetoglu E., Aarhus University, Department of Physics and Astronomy, Ny Munkegade 120, Aarhus C, DK-8000, Denmark; Ka?uszy?ski M., Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, Bartycka 18, Warsaw, 00-716, Poland, AkondLab, Walbrzyska 6-8, Wroclaw, Poland; Kelley M.S.P., Department of Astronomy, University of Maryland, 4296 Stadium Drive, College Park, 20742-0001, MD, United States; Kern N., Massachusetts Institute of Technology, Department of Physics, 182 Memorial Dr, Cambridge, 02139, MA, United States; Kerzendorf W.E., Department of Physics & Astronomy/Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, 48824, MI, United States; Koch E.W., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States; Kulumani S., Johns Hopkins University, Applied Physics Laboratory, Laurel, MD, United States; Lee A., Laboratoire Photonique, Numique et Nanosciences UMR 5298, Universitde Bordeaux, Institut d'Optique, CNRS, Talence, F-33400, France; Ly C., Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, 85721-0065, AZ, United States; Ma Z., Department of Astronomy, University of Massachusetts Amherst, 710 North Pleasant Street, Amherst, 01003, MA, United States; MacBride C., Astrophysics Research Centre, School of Mathematics and Physics, Queen's University Belfast, Belfast, BT7 1NN, United Kingdom; Maljaars J.M., Science & Technology Corporation, Delft, Netherlands; Muna D., Department of Physics and Astronomy, University of Texas at San Antonio, San Antonio, 78249, TX, United States; Murphy N.A., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States; Norman H., Winter Way, Lerum, Sweden; O'Steen R., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Oman K.A., Institute for Computational Cosmology, Durham University, South Road, Durham, DH1 3LE, United Kingdom, Department of Physics, Durham University, South Road, Durham, DH1 3LE, United Kingdom; Pacifici C., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States; Pascual S., Departamento de Fica de la Tierra y Astrofica, Fac. CC. Ficas, Universidad Complutense de Madrid, Madrid, E-28040, Spain, Instituto de Fica de Partulas y Del Cosmos, IPARCOS, Fac. CC. Ficas, Universidad Complutense de Madrid, Madrid, E-28040, Spain; Pascual-Granado J., Instituto de Astrofica de Andaluc (CSIC), Glorieta de la Astronom s/n., Granada, E-18008, Spain; Patil R.R., Syracuse University, Syracuse, NY, United States; Perren G.I., Instituto de Astrofica de la Plata, IALP (CONICET-UNLP), La Plata, 1900, Argentina; Pickering T.E., MMT Observatory, Tucson, AZ, United States; Rastogi T.; Roulston B.R., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States, Department of Astronomy, Boston University, 725 Commonwealth Avenue, Boston, 02215, MA, United States; Ryan D.F., University of Applied Sciences and Arts Northwest Switzerland, Bahnhofstrasse 6, Windisch, 5210, Switzerland, American University, 4400 Massachusetts Avenue, NW, Washington, 20016, DC, United States; Rykoff E.S., SLAC National Accelerator Laboratory, Menlo Park, 94025, CA, United States; Sabater J., STFC UK Astronomy Technology Centre, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, United Kingdom; Sakurikar P., International Institute of Information Technology, Hyderabad, India; Salgado J., SKAO Observatory, Jodrell Bank, Cheshire, United Kingdom; Sanghi A., Department of Astronomy, The University of Texas at Austin, 2515 Speedway, C1400, Austin, 78712, TX, United States; Saunders N., Institute for Astronomy, University of Hawai'i at M?noa, 2680 Woodlawn Drive, Honolulu, 96822, HI, United States, American Museum of Natural History, 200 Central Park West, Manhattan, 10024, NY, United States; Savchenko V., Department of Astronomy, University of Geneva, Chemin d'ogia 16, Versoix, CH-1290, Switzerland, EPFL SCITAS, Station 9, Lausanne, CH-1015, Switzerland; Schwardt L., South African Radio Astronomy Observatory, 2 Fir Street, Black River Park, Observatory 7925, South Africa; Seifert-Eckert M.; Shih A.Y., NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, 20771, MD, United States; Jain A.S.; Shukla G., Maulana Azad National Institute of Technology, Bhopal, India; Sick J., J. Sick Codes Inc., Penetanguishene, ON, Canada; Simpson C., Gemini Observatory-NOIRLab, 670 N. A'ohoku Place, Hilo, 96720, HI, United States; Singanamalla S., Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States; Singer L.P., Astroparticle Physics Laboratory, NASA Goddard Space Flight Center, Code 661, Greenbelt, 20771, MD, United States; Singhal J.; Sinha M., Centre for Astrophysics & Supercomputing, Swinburne University of Technology, Hawthorn, 3122, VIC, Australia, ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia; Sipocz B.M., IPAC, MC 100-22, Caltech, 1200 E. California Boulevard, Pasadena, 91125, CA, United States; Spitler L.R., School of Mathematical and Physical Sciences, Macquarie University, Sydney, 2109, NSW, Australia, Astronomy, Astrophysics and Astrophotonics Research Centre, Macquarie University, Sydney, 2109, NSW, Australia, Australian Astronomical Optics, Macquarie University, Sydney, 2109, NSW, Australia; Stansby D., Mullard Space Science Laboratory, University College London, Holmbury Street, Mary, Surrey, RH5 6NT, United Kingdom; Streicher O., Leibniz Institute for Astrophysics Potsdam, An der Sternwarte 16, Potsdam, 14482, Germany; umak J.; Swinbank J.D., ASTRON, Oude Hoogeveensedijk 4, Dwingeloo, 7991 PD, Netherlands; Taranu D.S., Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, 08544, NJ, United States; Tewary N., Bharati Vidyapeeth's College of Engineering/Electronics and Communication, Department, A-4 Block, Baba Ramdev Marg, Shiva Enclave, Paschim Vihar, New Delhi, 110063, India; Tremblay G.R., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States; De Val-Borro M., Planetary Science Institute, 1700 East Fort Lowell, Tucson, 85719, AZ, United States; Van Kooten S.J., Southwest Research Institute, 1050 Walnut Street, Ste 300, Boulder, 80302, CO, United States; Vasovi? Z., Faculty of Physics, University of Belgrade, Studentski trg 12-16, Belgrade, Serbia; Verma S., Atal Bihari Vajpayee Indian Institute of Information Technology and Management, MP, Gwalior, India; De Miranda Cardoso J.V., The Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong; Williams P.K.G., Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, 02138, MA, United States, American Astronomical Society, 1667 K Street, NW Suite 800, Washington, 20006, DC, United States; Wilson T.J., Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, 21218, MD, United States, School of Physics, University of Exeter, Stocker Road, Exeter, EX4 4QL, United Kingdom; Winkel B., Max-Planck-Institut f Radioastronomie, Auf dem Hel 69, Bonn, D-53121, Germany; Wood-Vasey W.M., Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), Physics and Astronomy Department, University of Pittsburgh, Pittsburgh, 15260, PA, United States; Xue R., National Radio Astronomy Observatory, 520 Edgemont Road, Charlottesville, 22903, VA, United States; Yoachim P., Department of Astronomy, University of Washington, Seattle, 98195, WA, United States; Zhang C., Purple Mountain Observatory, CAS, Qixia District, Nanjing, 210023, China; Zonca A., San Diego Supercomputer Center, University of California, 9500 Gilman Drive, La Jolla, San Diego, 92093, CA, United States
The asymmetric relationship between foreign direct investment, oil prices and carbon emissions: evidence from Gulf Cooperative Council economies
Asymmetry; carbon emissions; F1; F3; G2; G3; green industry; sustainability
We investigate the asymmetric nonlinear link between foreign direct investment, oil prices, and CO2 emissions for the Gulf Cooperation Council nations, using foreign direct investment and oil price data. As foreign direct investment is positively associated with carbon emissions in the long run and oil prices have positive, significant effects on CO2 emissions, our findings support the pollution-haven hypothesis. Furthermore, these variables have an asymmetric nonlinear relationship, which corresponds to the theoretical expectations of the pollution-haven hypothesis. We also find that negative changes in foreign direct investment have positive, significant impacts on carbon emissions in the short run, implying that foreign enterprises utilize green technologies in their manufacturing processes in the short run. In the long run, however, negative changes in oil prices are positively associated with carbon emissions. These findings should help Gulf Cooperation Council economies focus on policies that encourage foreign direct investment in green rather than dirty industries in order to ensure environmental sustainability. 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Cogent Economics and Finance, Vol-10, No. 1
Ashraf S., College of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates; P J., Department of Economics, Christ University, Bangalore, India; Umar Z., College of Business, Zayed University, United Arab Emirates
The behaviour of macro and micro economic variables and the impact on systematic risk of non-banking finance companies
The reforms initiated by the Government of India during 1990s have brought fundamental changes in the structure and functioning of Banking and Non-Banking Institutions, their business models and the products and services offered by them. Global economic developments have altered the macro economic conditions of the respective nations and make the nations and their economies vulnerable to economic shocks in the form of systematic risk associated with their business activities. Macro-economic factors and micro economic factors have had effect on the risk level, assessing risk, measuring and managing risk has become paramount for Non-Banking financial Institutions. Individual influence of factors on the systematic risk as there is weak relationship with Beta but combined effect of factors is very positive on the systematic risk of the companies. Indian Institute of Finance.
Finance India, Vol-32, No. 1, pp. 121-138.
Appala Raju M., Christ University, Department of Management Studies, House Road, Bangalore, 560029, Karnataka, India
Financial Sector; Government Deficit; Inflation; Leverage; Macro Economic Factors; Systematic Risk; Systemic
The reforms initiated by the Indian Government during 1990 have brought in drastic changes in the financial sector functioning so as to make competitive with financial markets worldwide. The Indian financial Sector has expanded and acquired greater depth to get itself competitive with new participants in the market. The new liberal business policies introduced by the government due to financial reforms have brought in fundamental changes in the structure and functioning of Banking and Non Banking Institutions, their business models and the products and services offered by them. Global economic developments have altered the macro economic conditions of the respective nations and make the nations and their economies vulnerable to economic shocks in the form of systematic risk associated with their business activities. Macro economic factors at broader level and micro economic factors at the firm level have had effect on the risk level, assessing risk, measuring and managing risk has become paramount for banks and others Non Banking financial Institutions alike. Individual influence of factors on the systematic risk as there is weak relationship with Beta but combined effect of factors is very positive on the systematic risk of the companies.
International Journal of Applied Business and Economic Research, Vol-14, No. 6, pp. 4345-4367.
Raju M.A., Department of Management Studies, Christ University, India