Portable and Automated Healthcare Platform Integrated with IoT Technology
Artificial intelligence; Data analysis; IoT; Machine learning; Real time systems; Wearable sensors
The diverse applications of Internet of Things (IoT), Artificial Intelligence (AI) in Bioelectronics - face recognition, pulse wave monitoring and insulin level measurements have been discussed in this chapter. This chapter also emphasizes on the IoT and its applications in biological sensors. The chapter focuses on IoT systems, such as neural networks and other channels that are integrated into a secure healthcare monitoring system in order to make the system operate as a smart model in healthcare sector that determines the priority based on health parameters gathered from the sensor nodes. In this work, the different approaches of IoT with distinct methodologies are also deliberated. 2024 Scrivener Publishing LLC.
Internet of Things in Bioelectronics: Emerging Technologies and Applications, pp. 251-261.
https://doi.org/10.1002/9781394241903.ch12 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208348956&doi=10.1002%2f9781394241903.ch12&partnerID=40&md5=7b6ff698a7d99b163149dbeea79be25b
Preetham Noel P., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Kishorekumar R., Department of Electronics and Communication Engineering, School of Engineering and Technology, Christ University, Bengaluru, India
Statistical Data Analysis of Anticorrosion and Antifouling: Unveiling Insights from Performance and Trends
CA (contact angle); CAH (contact angle hysteresis); MIC (microbiologically influenced corrosion); Polymers; SLIPS (slippery lubricant-infused porous surfaces); WCA (water contact angle)
This chapter provides intended insights about the current antifouling and anticorrosion conventional coatings, each with a distinct quality, that fall short of fully satisfying the contemporary requirements of material preservation in a marine environment. To solve these issues and accomplish the required goals, a number of novel terminologies and procedures have been investigated and several recently published research works were referred to and briefly examined in order to provide appropriate conclusions. In order to explore new methodologies that provide total protection for materials from biofouling and corrosion in maritime environments, this study looks into the complex realm of antifouling and anticorrosion methods. 2024 Scrivener Publishing LLC.
Preetham Noel P.; Kumar K.P.; Mani Joseph P.; Pillai V.J.
Novel Anti-Corrosion and Anti-Fouling Coatings and Thin Films, pp. 459-473.
https://doi.org/10.1002/9781394234318.ch16 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207991010&doi=10.1002%2f9781394234318.ch16&partnerID=40&md5=c92d389c3c35d912a0151ffab9edb01b
Preetham Noel P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Mani Joseph P., Department of Mathematics and Computer Science, Modern College of Business and Science, PC, Muscat, Oman; Pillai V.J., Department of Electronics and Communication Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, India
Photometric identification of objects from galaxy evolution explorer survey and sloan digital sky survey
Dust; Extinction; galaxies:Photometry; galaxy; General; hertzsprungrussell and colour; magnitude diagrams; stars:General; techniques:Photometric
We have used Galaxy Evolution Explorer (GALEX) and Sloan Digital Sky Survey (SDSS) observations to extract seven band photometric magnitudes for over 80 000 objects in the vicinity of the North Galactic Pole. Although these had been identified as stars by the SDSS pipeline, we found through fitting with model spectral energy distributions that most were, in fact, of extragalactic origin. Only about 9 per cent of these objects turned out to be mainsequence stars and about 11 per cent were white dwarfs and red giants collectively, while galaxies and quasars contributed to the remaining 80 per cent of the data. We have classified these objects into different spectral types (for the stars) and into different galactic types (for the galaxies). As part of our fitting procedure, we derive the distance and extinction to each object and the photometric redshift towards galaxies and quasars. This method easily allows for the addition of any number of observations to cover a more diverse range of wavelengths, as well as the addition of any number of model templates. The primary objective of this work is to eventually derive a three-dimensional extinction map of the Milky Way Galaxy. 2013 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.
Preethi K.; Gudennavar S.B.; Bubbly S.G.; Murthy J.; Brosch N.
Monthly Notices of the Royal Astronomical Society, Vol-437, No. 1, pp. 771-776.
All Open Access; Bronze Open Access; Green Open Access
https://doi.org/10.1093/mnras/stt1935 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890063695&doi=10.1093%2fmnras%2fstt1935&partnerID=40&md5=0543f03a7c19a6144ee01b5c144c9f3d
Preethi K., Department of Physics, Christ University, Bangalore 560 029, India; Gudennavar S.B., Department of Physics, Christ University, Bangalore 560 029, India; Bubbly S.G., Department of Physics, Christ University, Bangalore 560 029, India; Murthy J., Indian Institute of Astrophysics, Bangalore 560 034, II Block, Koramangala, India; Brosch N., The Wise Observatory and the School of Physics and Astronomy, The Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
Analysis of Fine Needle Aspiration Images by Using Hybrid Feature Selection and Various Machine Learning Classifiers
Breast cancer; Classification; Feature selection; Fine needle aspiration cytology; Machine learning
Women die of breast cancer most often worldwide. Breast tissue samples can be examined by radiologists, surgeons, and pathologists for evidence of this cancer. Fine needle aspiration cytology (FNAC) can be used to detect this cancer through a visual microscopic examination of breast tissue samples. This sample must be examined by a cytopathologist in order to determine the patient's risk of breast cancer. To determine if a tumor is malignant, the nuclei of the cells must be characterized by their chromatin texture patterns. A machine learning method is used in order to categorize FNA images into two classes, respectively Malignant and Benign. For detecting abnormalities, numerous feature collection methods and machine learning means are applied here. Using features extracted from the FNA image set, UCI machine learning datasets are used to validate the proposed approach. This paper compares three classification methodologies, namely random forests, Naive Bayes, and artificial neural networks, by examining their accuracy, specificity, precision, and sensitivity, respectively. With the ANN and PCA along with the Chi-square selection method, 99.1% of the classifiers are correctly classified. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-462, pp. 383-392.
Springer Science and Business Media Deutschland GmbH
Preethi N., Christ University, Pune, Lavasa, India; Jaisingh W., School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, Bhopal, India
A succinct analysis for deep learning in deep vision and its applications
ConvNet; Data processing; Deep learning; Generative graphical model; Social media analysis; Stochastic hopfield network
Introduction: Deep learning methodologies can achieve forefront results on testing deep vision issues, for instance, picture portrayal, an object area, face affirmation, Natural Language Processing, Visual Data Processing and online life examination. ConvNet, Stochastic Hopfield network with hidden units, generative graphical model and sort of artificial neural network castoff to absorb competent information coding in an unproven way are deep learning plans used in deep vision issues. Objection: This paper gives a succinct survey of without a doubt the most critical Deep learning structures. Deep vision assignments, for instance, object revelation, face affirmation, Natural Language Processing, Visual Data Processing, web-based life examination and their utilization of this task are discussed with a short record of the historic structure, central focuses and impairments. Future headings in arranging Deep learning structures for Deep vision issues and the troubles included are analysed. Method: This paper consists of surveys. In Section two, Deep Learning Approaches and Changes are audited. In section three, we tend to portray the uses of Applications of deep learning in deep vision. In Section four, Deep learning challenges and directions are mentioned. At long last, Section five completes the paper with an outline of the results. Results and Conclusion: Though deep learning can recall a huge proportion of data and info, its feeble cognitive and perception of the data makes it a disclosure answer for certain applications. Deep learning despite everything encounters issues in showing various erratic facts modalities at the equal period. Multimodal profound learning is an extra notable heading in progressing deep learning research. IJCRR.
International Journal of Current Research and Review, Vol-13, No. 6, pp. 85-92.
https://doi.org/10.31782/IJCRR.2021.13627 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103084031&doi=10.31782%2fIJCRR.2021.13627&partnerID=40&md5=ed84c7b0ba791d0a05f5bd3ce321da51
Preethi N., Department of Data Science, CHRIST (Deemed to be University), Pune, Lavasa, India; Singh W.J., Department of Computer Applications, Kumaraguru College of Technology, Coimbatore, Tamilnadu, India
Discussion on ostracised transgender individuals and entrepreneurship through review of literature
Entrepreneurial motivations; Entrepreneurship; Transgender challenges; Transgender entrepreneurs; Transgender individuals
Transgender individuals are the most deteriorated individuals in society. They face a wide range of trodden lives and setbacks in their everyday life. They encounter challenges and difficulties from the time they violate the social norms, they are also humiliated from their biological families and are sent to live a life of their own. In India, the Mughal period was termed to be the golden years for transgender individuals. It was after colonisation and implementation of the Criminal Tribes Act 1871 transgender individuals were treated brutally and eventually begging and sex work became their only source of income. Alongside, entrepreneurship proved to be a success factor as it brought the shunned women into the mainstream society. Thereby, entrepreneurship increases social capital and thus encourages transgenders in job creation activities. Despite a dire situation, there are transgender individuals who have faced all odds and have proved to set benchmarks in the society in varied fields. There are sporadic transgender individual entrepreneurs in the country who have paved their way into the entrepreneurial world, which is an important area to be explored. The study focuses on literature relating to transgender individuals, challenges faced by transgender individuals, entrepreneurial motivations and also transgender entrepreneurs. 2020 SERSC.
Preethi Sarah J.P.; Karthigai Prakasam C.
International Journal of Advanced Science and Technology, Vol-29, No. 5, pp. 4985-4997.
Science and Engineering Research Support Society
Preethi Sarah J.P., Department of Commerce, CHRIST (Deemed to be University), Bangalore, India; Karthigai Prakasam C., Department of Commerce, CHRIST (Deemed to be University), Bangalore, India
Sartrean Insights on Understanding the Repercussions of Rape Trauma in the Gripping Narratives of Roxanne Gay and Neesha Arter
Embodiment; Emotion; Fundamental Project; Memoirs; Rape; Rape Trauma; Sartre
Women have been subjugated to violence from time immemorial. One of the most horrific forms of violence is sexual violence and rape. Their voice was not heard until the rise of second wave feminism which began around 1970. Women started to write about their experiences in the form of memoirs to bring to light the atrocities of rape and the implications of trauma and its impact. Sexual assault inflicts profound psychological and emotional wounds that give rise to a condition referred to as Rape Trauma. Rape Trauma Syndrome includes of a wide range of physical and psychological signs such as insomnia, nightmare, flashbacks, anxiety, and depression and so on and they last for a long period of time in one?s life. The research uses two memoirs written by American women, Roxanne Gay?s Hunger: A Memoir of my Body (2017) and Neesha Arter?s Controlled: The worst Night of my Life and its Aftermath (2015). The research uses Sartre?s perspective on embodiment, freedom, self to analyse rape trauma. The research uses Jean Paul Sartre?s concept to analyse the immense effects of rape trauma on the lives of the two women as documented in their memoirs. Using the framework provided by Constance L. Mui, the research delineates how rape trauma destroys the fundamental project of the protagonists and how rape trauma annihilates an individual from her own body and isolates her from the world. 2024 Sciedu Press. All rights reserved.
World Journal of English Language, Vol-14, No. 3, pp. 264-273.
https://doi.org/10.5430/wjel.v14n3p264 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191799246&doi=10.5430%2fwjel.v14n3p264&partnerID=40&md5=5f7b61bf754fa209e9e6f6197a6bbd2a
Preethika H.F., Department of English and Cultural Studies, Christ (Deemed to be University), Bangalore, India; Sharon J.
Enhancing Workplace Efficiency with The Implementation of the Internet of Things to Advance Human Resource Management Practices
Change Management; Data Privacy; Employee Engagement; Employee Satisfaction; Human Resource Management (HRM) Practices; Internet of Things (IoT); Organizational Dynamics; Qualitative and Quantitative Analysis; Real-time Monitoring; Security Concerns; Technological Innovation; Technology Integration; Workplace Culture; Workplace Efficiency
Improving human resource management via the use of the Internet of Things (IoT) is the focus of this research. The primary objective is to enhance productivity in the workplace. The researchers utilized a mix of qualitative (describing) and quantitative (numbers-based) techniques to collect and analyses data. This research shows that key HR KPIs are positively affected by using IoT in HRM. Businesses utilizing IoT for real-time monitoring have better operations and more engaged employees. The study found that state-of-the-art technology, extensive training, and effective change management were needed to overcome people's security concerns and unwillingness to change. The Internet of Things may transform HRM and corporate operations, according to study. According to study, companies should invest in people-focused technologies and services. It emphasizes creating a workplace that embraces new technology while prioritizing security and privacy. In conclusion, the study's results may help organizations navigate HRM and the IoT's changing terrain. It suggests linking HR and technology to improve workplace flexibility and efficiency. 2024 IEEE.
Preeti; Patil S.; Lenka R.M.; Sandhya D.; Lourens M.; Chakravarthi M.K.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/TQCEBT59414.2024.10545155 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204485356&doi=10.1109%2fTQCEBT59414.2024.10545155&partnerID=40&md5=0e5634ad8c579f374b0cab434b1adf38
Preeti, Hr. Manager Frontier Group, India; Patil S., Indira School of Business Studies, Pune, India; Lenka R.M., Symbiosis International (Deemed) University, Symbiosis Institute of Management Studies, Pune, India; Sandhya D., School of Business and Management, Christ University (Yeshwantpur Campus), India; Lourens M., Durban University of Technology, Faculty of Management Sciences, South Africa; Chakravarthi M.K., VIT-AP University, School of Electronics Engineering, Amaravathi, 522237, India
Statistic analysis of IPL match score and winner inning wise using machine learning algorithms
Cricket analytics; Data analysis; IPL score prediction; Machine learning; Predictive modeling
This study explains the statistical analysis of cricket match score prediction using machine learning. According to recent changes in data science and sports, the use of sports-based machine learning and data mining shows the importance of process in outcome performance and prediction. The scope of this research paper is to evaluate current measurements used in the previous work to understand the estimation the ways used to model and analyze data and characterize the variables that govern performance using statistical methods. Actually, this research article will present a reliable statistical tool for data analysis using machine learning algorithms. At present, sports organizations produce enough statistical information on every player, team, match, and season for particular related sports. The first sports researchers were thought to be experts, coaches, team managers, and analysts. Sports organizations want to do statistical analysis of player from their previous data stored on their database using different data mining and machine learning algorithms. Sports data helps coaches and managers in many ways, such as predicting results, analyzing player performance, and skills, and evaluating strategies. Forecasts help managers and organizations make decisions to win teams and competitions. The current evaluation of research shows that primary studies of data mining systems can predict outcomes and evaluate the strengths and weaknesses of each system. Statistical analyses are made for each match for result predictions. Although in many respects this application is very limited. These are prime factors which important to examine machine learning algorithms in these situations to see if the application can give the nearest results in analysis. This research aims to give solutions that will help to make predictions more accurate and precise than previous methods, using more accurate data and machine learning. 2024, Taru Publications. All rights reserved.
Preety; Ray S.K.; Yadav M.; Khare A.; Mishra V.
Journal of Interdisciplinary Mathematics, Vol-27, No. 2, pp. 245-258.
https://doi.org/10.47974/JIM-1819 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188539125&doi=10.47974%2fJIM-1819&partnerID=40&md5=44273e83e4dd30c8839e4fb2646fd185
Preety, Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India; Ray S.K., Department of Mathematics and Statistics, Dr. Akhilesh Das Gupta Institute of Technology & Management, Delhi, India; Yadav M., Department of Information Technology, Galgotias College of Engineering and Technology, Uttar Pradesh, India; Khare A., Department of Computer Science and Engineering, Galgotias University, Uttar Pradesh, India; Mishra V., Department of Computer Science, Christ University, Uttar Pradesh, Ghaziabad, India
A Cognitive Architecture Based Conversation Agent Technology for Secure Communication
Agent Technology; Case Base Reasoning Systems; Decision Support System; JADE; Multi-Agent
This paper outlines a multi-agent system-based approach to provider selection. Suppliers in the supply chain are different and the demand and supply levels are high. Buy agents will find the right supply agent in our approach. First, the multi-layer classification system is used to rationally arrange and overall selection on suppliers and buyers. Secondly, the purchase information is organized by the supplier agent to improve device performance. The assessment process is then used to select the suppliers initially. In addition to selecting the correct provider and maximizing the value of the purchaser, the time negotiating mechanism is implemented. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Communications in Computer and Information Science, Vol-2122 CCIS, pp. 1-12.
Preety, Christ University Delhi NCR, Ghaziabad, India; Singh J., Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, India; Yadav M., Galgotias College of Engineering and Technology, Uttar Pradesh, Greater Noida, India
Role of Green Human Resource Management Practices on Sustainable Performance of the Service Sector Organizations
The prominence revolution took place in the 1980s, and then the Green HRM practises movement blew over the sustainable development literature in the 1990s. This represents an excellent practice because it newlineincorporates sustainable development principles into the operational processes newlineof the human resources department. According to Mishra (2017), Green HRM is a tactical instrument for arranging human resource competences and promoting ideas that motivate businesses to embrace sustainable strategies. newlineThe term human resource management (HRM) is used to describe a set of practices, procedures, and methods that are focused on managing and optimizing the performance of personnel within a business, thereby providing the organization with a competitive advantage. Despite the fact that green human resource management has grown to be a major source of concern, India has only relatively handful of first-hand studies done in this area. While conventional HRM concentrates on functions to provide an organisation a competitive edge, green HRM places an emphasis on the capture and retention of green efforts to meet corporate objectives. newlineGreen HRM in India is plagued by a variety of issues. The study has chosen a few traits from the research at hand that may be utilised to create Green HRM practices for sustainable development. It stands to reason for contemporary businesses to take environmental responsibility seriously (Jackson, Renwick, Jabbour and Camen, 2011). Numerous studies show that environmental newlineinitiatives increase production and provide companies a competitive edge (Bombiak and Marciniuk-Kluska, 2018). Although organizational solutions quotmoved beyond pollution control and the mitigation of environmental degradationquot, Saeed, Jun, Nubuor, Priyankara and Jayasuriya (2018) have highlighted its significance in the New Millennium Era.
Role of green human resource management practices on sustainable performance of the service sector organizations
A Fractional Atmospheric Circulation System under the Influence of a Sliding Mode Controller
atmospheric circulation model; Caputo fractional derivative; CaputoFabrizio fractional derivative; sliding mode controller
The earths surface is heated by the large-scale movement of air known as atmospheric circulation, which works in conjunction with ocean circulation. More than (Formula presented.) variables are involved in the complexity of the weather system. In this work, we analyze the dynamical behavior and chaos control of an atmospheric circulation model known as the Hadley circulation model, in the frame of Caputo and CaputoFabrizio fractional derivatives. The fundamental novelty of this paper is the application of the Caputo derivative with equal dimensionality to models that includes memory. A sliding mode controller (SMC) is developed to control chaos in this fractional-order atmospheric circulation system with uncertain dynamics. The proposed controller is applied to both commensurate and non-commensurate fractional-order systems. To demonstrate the intricacy of the models, we plot some graphs of various fractional orders with appropriate parameter values. We have observed the influence of thermal forcing on the dynamics of the system. The outcome of the analytical exercises is validated using numerical simulations. 2022 by the authors.
Premakumari R.N.; Baishya C.; Veeresha P.; Akinyemi L.
https://doi.org/10.3390/sym14122618 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144838051&doi=10.3390%2fsym14122618&partnerID=40&md5=56e3e2e6c62e95a159e8f14e90f52c28
Premakumari R.N., Department of Studies and Research in Mathematics, Tumkur University, Tumkur, 572103, India; Baishya C., Department of Studies and Research in Mathematics, Tumkur University, Tumkur, 572103, India; Veeresha P., Center for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Akinyemi L., Department of Mathematics, Lafayette College, Easton, 18042, PA, United States
Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
CNN; Galaxy; Parallel CNN; ResNet; Star; VGG16
This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE.
Premanand N.; Tarun V.G.; Pawar S.; Jawakar D.N.; Utsav; Deepa S.; Jayapriya J.; Vinay M.
2023 International Conference on Data Science and Network Security, ICDSNS 2023
Premanand N., Christ University, Department of Computer Science, Bengaluru, India; Tarun V.G., Christ University, Department of Computer Science, Bengaluru, India; Pawar S., Christ University, Department of Computer Science, Bengaluru, India; Jawakar D.N., Christ University, Department of Computer Science, Bengaluru, India; Utsav, Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India
Impact of AI Technology Disruption on Turnover Intention of Employees in Digital Marketing
AI technology disruption; digital marketing industry; job insecurity; job mobility; turnover intention
The rapid integration of AI technology into the digital marketing sector has prompted a need to understand its effects on employee perspectives and behaviors. This study investigates how AI adoption influences job insecurity, turnover intention, and job mobility among digital marketing professionals. Addressing concerns about AI rendering roles obsolete is crucial for fostering a supportive work environment. Turnover intention, influenced by AI adoption and potential job dissatisfaction, offers insights into employees' commitment to the industry. Job mobility, influenced by growth prospects and alignment with AI-driven workplaces, sheds light on career aspirations. Our study involving 303 employees of digital marketing industry in India reveals that AI disruption significantly impacts turnover intention, with job insecurity mediating this effect. Additionally, mistreatment by superiors increases turnover intention. Overall, this research underscores the profound impact of AI technology on employees' attitudes, behaviors, and career decisions in digital marketing, providing valuable insights into their perceptions and engagement 2024 IEEE.
Premnath H., Christ (Deemed to Be University), Pune, India; Lohani R., Christ (Deemed to Be University), Pune, India
Connected k-forcing sets of graphs and splitting graphs
Connected k-forcing number; K-forcing number; Zeroforcing number
The notion of k-forcing number of a graph was introduced by Amos et al. For a given graph G and a given subset I of the vertices of the graph G, the vertices in I are known as initially colored black vertices and the vertices in V (G) ? I are known as not initially colored black vertices or white vertices. The set I is a k-forcing set of a graph G if all vertices in G eventually colored black after applying the following color changing rule: If a black colored vertex is adjacent to at most k-white vertices, then the white vertices change to be colored black. The cardinality of a smallest k-forcing set is known as the k-forcing number Zk (G) of the graph G. If the sub graph induced by the vertices in I are connected, then I is called the connected k-forcing set. The minimum cardinality of such a set is called the connected k-forcing number of G and is denoted by Zck (G). This manuscript is intended to study the connected k-forcing number of graphs and the splitting graphs. 2020 the author(s).
Journal of Mathematical and Computational Science, Vol-10, No. 3, pp. 656-680.
https://doi.org/10.28919/jmcs/4446 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085742286&doi=10.28919%2fjmcs%2f4446&partnerID=40&md5=253abf45a55e32889618c952f29db5b6
Premodkmuar K.P., Research Center of Mathematics, St. Josephs College, Devagiri, Calicut, Kerala, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, India; Chacko B., Research Center of Mathematics, St. Josephs College, Devagiri, Calicut, Kerala, India
Graphs with large diameter and their two distance forcing number
2-distance forcing number; 2-distance forcing set; diameter
For a given simple graph G = (V,E), the two distance forcing number Z2d (G) is defined as the minimum cardinality among all Z2d-sets in G. This paper examine about Z2d (G) of some graphs with large diameter. Also we determine the 2-distance forcing number of some complement graphs. 2021 the author(s).
Journal of Mathematical and Computational Science, Vol-11, No. 2, pp. 1810-1837.
https://doi.org/10.28919/jmcs/5366 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103372662&doi=10.28919%2fjmcs%2f5366&partnerID=40&md5=14a13a1fa4dfdc6864ee049e460aa928
Premodkumar K.P., Department and Research Center of Mathematics, St. Josephs College, Devagiri, Calicut, Kerala, India; Dominic C., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, India; Chacko B., Department and Research Center of Mathematics, St. Josephs College, Devagiri, Calicut, Kerala, India
2-distance forcing number; Diameter; Graph; K-forcing number
Motivated from the graph parameters namely zero forcing number, k-forcing number and the connected k-forcing number, in this article, we introduce a new parameter known as the 2-distance forcing number. Assume that each vertex of a graph G = (V (G), E(G)) is colored as either white or black. Consider the set Z2d of black colored vertices of the graph G. The color change rule changes the color of a white vertex v to black if the white vertex v is the only 2-distance white neighbor of a black vertex u. The set Z2d is called a two distance forcing set of G if all vertices of the graph G will be turned black after limited applications of the color change rule. The 2-distance forcing number of G, denoted by Z2d (G), is the minimum of | Z2d | over all 2-distance forcing sets Z2d ? V (G). This manuscript is intended to study the 2-distance forcing number of some graphs. We find the exact value of the 2-distance forcing number of graphs such as the pineapple graph, gear graph, jelly fish graph, helm graph, sunflower graph, comet graph and the n-pan graph. 2020 the author(s).
Journal of Mathematical and Computational Science, Vol-10, No. 6, pp. 2233-2248.
https://doi.org/10.28919/jmcs/4842 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090662321&doi=10.28919%2fjmcs%2f4842&partnerID=40&md5=a2c9db9e7c968b4f1c8380890520770a
Premodkumar K.P., P.G. Department and Research Center of Mathematics, St. Josephs College, Devagiri, Calicut, Kerala, India; Dominic C., Department of Mathematics, CHRIST(Deemed to be University), Karnataka, India; Chacko B., P.G. Department and Research Center of Mathematics, St. Josephs College, Devagiri, Calicut, Kerala, India
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
A new optimal design and analysis method based on MADM for MEMS products development
Design flow; MADM; MEMS; Microelectromechanical systems; Optimal design; Power sensor; RF MEMS
Abstract This paper presents an optimum design method to support the total micro-electromechanical systems (MEMS) product/device optimization, and its evaluation at the conceptual stage itself using the multiple attribute decision making method. In the traditional MEMS product development cycle, simulation and design using software tools are very important due to the knowledge limitation and complexity in design, fabrication, and packaging processes. The available tools are time consuming and relay on trial and error to achieve an optimum solution. The proposed method simplifies the relationship between parameters of design, fabrication, materials, packaging, and the performance of the MEMS product. The methodology is explained with the help of design flow diagram and time chart. A MEMS-based radio frequency (RF) power sensor is designed and the methodology is demonstrated. The proposed sensitivity analysis method is more effective and less time consuming than traditional techniques. Sensitivity analysis is carried out by varying the thickness of the signal conductor. The results of RF power sensor with insertion loss 0.428 dB, reflection loss 25.956 and voltage standing wave ratio of 1.106 at 1.5 GHz are reported. Springer-Verlag London Limited 2012.
International Journal of Advanced Manufacturing Technology, Vol-63, No. 46000, pp. 851-861.
https://doi.org/10.1007/s00170-012-3969-7 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870061773&doi=10.1007%2fs00170-012-3969-7&partnerID=40&md5=4aade606715a7e9baa06cf81c30d1a5d
Prince A.A., Electrical and Electronics Engineering Department, BITS-Pilani K.K. Birla Goa Campus, Zuarinagar, Goa 403726, India; Jose I., Electronics and Communication Engineering Department, Christ University, Bangalore 560060, India; Agrawal V.P., Department of Mechanical Engineering, Thapar University, Patiala 147004, India