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109 results on '"Selvachandran, P."'

6. Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directions

Author

Shinde, Rucha, Patil, Shruti, Kotecha, Ketan, Potdar, Vidyasagar, Selvachandran, Ganeshsree, and Abraham, Ajith

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

Healthcare systems are increasingly incorporating Artificial Intelligence into their systems, but it is not a solution for all difficulties. AI's extraordinary potential is being held back by challenges such as a lack of medical datasets for training AI models, adversarial attacks, and a lack of trust due to its black box working style. We explored how blockchain technology can improve the reliability and trustworthiness of AI-based healthcare. This paper has conducted a Systematic Literature Review to explore the state-of-the-art research studies conducted in healthcare applications developed with different AI techniques and Blockchain Technology. This systematic literature review proceeds with three different paths as natural language processing-based healthcare systems, computer vision-based healthcare systems and acoustic AI-based healthcare systems. We found that 1) Defence techniques for adversarial attacks on AI are available for specific kind of attacks and even adversarial training is AI based technique which in further prone to different attacks. 2) Blockchain can address security and privacy issues in healthcare fraternity. 3) Medical data verification and user provenance can be enabled with Blockchain. 4) Blockchain can protect distributed learning on heterogeneous medical data. 5) The issues like single point of failure, non-transparency in healthcare systems can be resolved with Blockchain. Nevertheless, it has been identified that research is at the initial stage. As a result, we have synthesized a conceptual framework using Blockchain Technology for AI-based healthcare applications that considers the needs of each NLP, Computer Vision, and Acoustic AI application. A global solution for all sort of adversarial attacks on AI based healthcare. However, this technique has significant limits and challenges that need to be addressed in future studies., Comment: 44 Pages

Published
2022

7. Evaluating the Oral Language Skills of English-Stream and French Immersion Students: Are the CLB/NCLC Applicable?

Author

Burchell, Diana, Hipfner-Boucher, Kathleen, Selvachandran, Janani, Cleave, Patricia, and Chen, Xi

Abstract

This study examined the oral language skills of grade-two anglophone children enrolled in French Immersion and English-stream programs. The study had two objectives: (1) to compare performance between the groups on measures of receptive vocabulary, narrative comprehension, and narrative production (i.e., structure and language) in English; and (2) to explore the applicability of the Canadian Language Benchmarks/"Niveaux de compétences linguistiques canadiens" (CLB/NCLC) to assessment of their conversational competency. All children (English-stream n = 27, French Immersion n = 33, aged 7-8 years) were tested in English. In addition, the French Immersion students were tested using equivalent measures in French. The results comparing performance in English revealed no differences between the groups on receptive vocabulary, narrative comprehension and narrative structure. However, the English-stream children outperformed their French Immersion peers in narrative language. Furthermore, CLB/NCLC listening and speaking criteria were applied to conversational samples yielding level scores in English (both groups) and French (French Immersion only). The range of benchmarks that are appropriate for this population is discussed in detail.

Published
2020

11. New approach to bisemiring theory via the bipolar valued neutrosophic normal sets

Author

M. Palanikumar, G. Selvi, Ganeshsree Selvachandran, and Sher Lyn Tan

Subjects
fuzzy set, bipolar valued neutrosophic subbisemiring, bipolar valued neutrosophic bisemiring, homomorphism, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

In this paper, we introduce the notion of bipolar-valued neutrosophic subbisemiring (BVNSBS), level sets of BVNSBS, and bipolar valued neutrosophic normal subbisemiring (BVNNSBS) of a bisemiring. The concept of BVNSBS is a new generalization of subbisemiring over bisemirings. We discussed the theory of (ξ, τ )- BVNSBS and (ξ, τ )-BVNNSBS over bisemirings and presented several illustrative examples to demonstrate the sufficiency and validity of the proposed theorems, lemmas, and propositions.

Published
2023
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12. CD8 T cell-mediated depletion of HBV surface-antigen-expressing, bilineal-differentiated liver carcinoma cells generates highly aggressive escape variants

Author

Na Qiu, Akshaya Srikanth, Medhanie Mulaw, Umesh Tharehalli, Shanthiya Selvachandran, Martin Wagner, Thomas Seufferlein, Katja Stifter, André Lechel, and Reinhold Schirmbeck

Subjects
CD8 T cells, ER-stress, HBV surface antigen, immune escape variants, liver carcinoma, tg mice, Immunologic diseases. Allergy, RC581-607, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

ABSTRACTThe expression of viral antigens in chronic hepatitis B virus (HBV) infection drives continuous liver inflammation, one of the main risk factors to develop liver cancer. HBV developed immune-suppressive functions to escape from the host immune system, but their link to liver tumor development is not well understood. Here, we analyzed if and how HBV surface antigen (HBs) expression in combined hepatocellular-cholangiocarcinoma (cHCC/iCCA) cells influences their antigenicity for CD8 T cells. We randomly isolated liver tumor tissues from AlfpCre+-Trp53fl/fl/Alb-HBs+ tg mice and established primary carcinoma cell lines (pCCL) that showed a bilineal (CK7+/HNF4α+) cHCC/iCCA phenotype. These pCCL uniformly expressed HBs (HBshi), and low levels of MHC-I (MHC-Ilo), and were transiently convertible to a high antigenicity (MHC-Ihi) phenotype by IFN-γ treatment. HBshi/pCCL induced HBs/(Kb/S190–197)-specific CD8 T cells and developed slow-growing tumors in subcutaneously transplanted C57Bl/6J (B6) mice. Interestingly, pCCL-ex cells, established from HBshi/pCCL-induced and re-explanted tumors in B6 but not those in immune-deficient Rag1−/− mice showed major alterations, like an MHC-Ihi phenotype, a prominent growth-biased gene expression signature, a significantly decreased HBs expression (HBslo) and a switch to fast-growing tumors in re-transplanted B6 or PD-1−/− hosts with an unlocked PD-1/PD-L1 control system. CD8 T cell-mediated elimination of HBshi/pCCL, together with the attenuation of the negative restraints of HBs in the tumor cells, like ER-stress, reveals a novel mechanism to unleash highly aggressive HBslo/pCCL-ex immune-escape variants. Under certain conditions, HBs-specific CD8 T-cell responses thus potentiate tumor growth, an aspect that should be considered for therapeutic vaccination strategies against chronic HBV infection and liver tumors.

Published
2023
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14. Special Education Needs in French Immersion: A Parental Perspective of Supports and Challenges

Author

Selvachandran, Janani, Kay-Raining Bird, Elizabeth, DeSousa, Jessica, and Chen, Xi

Abstract

This study collected interview data from parents of five children identified as having special education needs who were attending or had attended a French Immersion program in Toronto, Ontario. The experiences of these families were qualitatively analyzed to uncover critical themes surrounding experiences and beliefs around French Immersion enrolment, educational supports and withdrawal for children with special education needs. The findings showed a relationship between the severity of a special education need and the amount of supports that were needed for a child in an immersion program as might be expected. A lack of accessibility to and availability of supports resulted in parents seeking external resources to help alleviate the learning difficulties of their children. This study highlights an impending need to improve the accessibility of supports in French Immersion in the form of assessments, resources and teacher training.

Published
2022
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16. App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden

Author

Kennedy, Beatrice, Fitipaldi, Hugo, Hammar, Ulf, Maziarz, Marlena, Tsereteli, Neli, Oskolkov, Nikolay, Varotsis, Georgios, Franks, Camilla A., Nguyen, Diem, Spiliopoulos, Lampros, Adami, Hans-Olov, Björk, Jonas, Engblom, Stefan, Fall, Katja, Grimby-Ekman, Anna, Litton, Jan-Eric, Martinell, Mats, Oudin, Anna, Sjöström, Torbjörn, Timpka, Toomas, Sudre, Carole H., Graham, Mark S., du Cadet, Julien Lavigne, Chan, Andrew T., Davies, Richard, Ganesh, Sajaysurya, May, Anna, Ourselin, Sébastien, Pujol, Joan Capdevila, Selvachandran, Somesh, Wolf, Jonathan, Spector, Tim D., Steves, Claire J., Gomez, Maria F., Franks, Paul W., and Fall, Tove

Published
2022
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17. Author Correction: Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom

Author

Nguyen, Long H., Joshi, Amit D., Drew, David A., Merino, Jordi, Ma, Wenjie, Lo, Chun-Han, Kwon, Sohee, Wang, Kai, Graham, Mark S., Polidori, Lorenzo, Menni, Cristina, Sudre, Carole H., Anyane-Yeboa, Adjoa, Astley, Christina M., Warner, Erica T., Hu, Christina Y., Selvachandran, Somesh, Davies, Richard, Nash, Denis, Franks, Paul W., Wolf, Jonathan, Ourselin, Sebastien, Steves, Claire J., Spector, Tim D., and Chan, Andrew T.

Published
2022
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18. Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom

Author

Nguyen, Long H., Joshi, Amit D., Drew, David A., Merino, Jordi, Ma, Wenjie, Lo, Chun-Han, Kwon, Sohee, Wang, Kai, Graham, Mark S., Polidori, Lorenzo, Menni, Cristina, Sudre, Carole H., Anyane-Yeboa, Adjoa, Astley, Christina M., Warner, Erica T., Hu, Christina Y., Selvachandran, Somesh, Davies, Richard, Nash, Denis, Franks, Paul W., Wolf, Jonathan, Ourselin, Sebastien, Steves, Claire J., Spector, Tim D., and Chan, Andrew T.

Published
2022
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19. App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden

Author

Beatrice Kennedy, Hugo Fitipaldi, Ulf Hammar, Marlena Maziarz, Neli Tsereteli, Nikolay Oskolkov, Georgios Varotsis, Camilla A. Franks, Diem Nguyen, Lampros Spiliopoulos, Hans-Olov Adami, Jonas Björk, Stefan Engblom, Katja Fall, Anna Grimby-Ekman, Jan-Eric Litton, Mats Martinell, Anna Oudin, Torbjörn Sjöström, Toomas Timpka, Carole H. Sudre, Mark S. Graham, Julien Lavigne du Cadet, Andrew T. Chan, Richard Davies, Sajaysurya Ganesh, Anna May, Sébastien Ourselin, Joan Capdevila Pujol, Somesh Selvachandran, Jonathan Wolf, Tim D. Spector, Claire J. Steves, Maria F. Gomez, Paul W. Franks, and Tove Fall

Subjects
Science
Abstract

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance using daily symptom reports from study participants. Here, the authors show how syndromic surveillance can be used to estimate regional COVID-19 prevalence and to predict later COVID-19 hospital admissions.

Published
2022
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20. Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom

Author

Long H. Nguyen, Amit D. Joshi, David A. Drew, Jordi Merino, Wenjie Ma, Chun-Han Lo, Sohee Kwon, Kai Wang, Mark S. Graham, Lorenzo Polidori, Cristina Menni, Carole H. Sudre, Adjoa Anyane-Yeboa, Christina M. Astley, Erica T. Warner, Christina Y. Hu, Somesh Selvachandran, Richard Davies, Denis Nash, Paul W. Franks, Jonathan Wolf, Sebastien Ourselin, Claire J. Steves, Tim D. Spector, Andrew T. Chan, and COPE Consortium

Subjects
Science
Abstract

The authors show differences in self-reported vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and in the United Kingdom during the initial phase of the COVID-19 vaccine rollout.

Published
2022
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21. Diet and lifestyle behaviour disruption related to the pandemic was varied and bidirectional among US and UK adults participating in the ZOE COVID Study

Author

Mazidi, Mohsen, Leeming, Emily R., Merino, Jordi, Nguyen, Long H., Selvachandran, Somesh, Pujal, Joan Capdavila, Maher, Tyler, Kadé, Kirstin, Murray, Benjamin, Graham, Mark S., Sudre, Carole H., Wolf, Jonathan, Hu, Christina, Drew, David A., Steves, Claire J., Ourselin, Sebastien, Gardner, Christopher, Spector, Tim D., Chan, Andrew T., Franks, Paul W., Gibson, Rachel, and Berry, Sarah E.

Published
2021
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25. A Novel Single Valued Neutrosophic Hesitant Fuzzy Time Series Model: Applications in Indonesian and Argentinian Stock Index Forecasting

Author

Billy Tanuwijaya, Ganeshsree Selvachandran, Le Hoang Son, Mohamed Abdel-Basset, Hiep Xuan Huynh, Van-Huy Pham, and Mahmoud Ismail

Subjects
Single-valued neutrosophic hesitant fuzzy set (SVNHFS), single-valued neutrosophic hesitant fuzzy time series (SVNHFTS), neutrosophic time series (NTS), fuzzy time series (FTS), Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper proposed a novel first-order single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) forecasting model. Our aim is to improve the previously proposed neutrosophic time series (NTS) model by incorporating the degree of the hesitancy using single-valued neutrosophic hesitant fuzzy set (SVNHFS) model instead of single-valued neutrosophic set (SVNS). Our paper's novelty is that we incorporate an algorithm that automatically converts the crisp dataset into the neutrosophic set that eliminates the need for experts' input or opinions in determining the membership in each of the partitioned neutrosophic set. We also incorporate Markov Chain algorithm in the de-neutrosophication process to include the weightage of the repeating neutrosophic logical relationships (NLRs). Our paper's significant contribution is to add to the existing body of knowledge related to fuzzy time series (FTS) by developing a new FTS model based on SVNHFS, one of the improved version of fuzzy sets, since this area of research is still relatively underdeveloped. To determine our proposed model's capability, we apply our proposed SVNHFTS model to three real datasets while also comparing the result to the other FTS models based on improved versions of fuzzy sets. Our datasets include benchmark enrollment data of University of Alabama, IDX Composite (Indonesian composite stock index), and MERVAL index (Argentinian composite stock index). The result shows that our proposed SVNHFTS model outperforms most of the other FTS models in terms of AFE and RMSE, especially the previously proposed NTS model.

Published
2020
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26. Some Results on Single Valued Neutrosophic Hypergroup

Author

S. Rajareega, D. Preethi, J. Vimala, Ganeshsree Selvachandran, and Florentin Smarandache

Subjects
hypergroup, level sets, single valued neutrosophic sets, single valued neutrosophic hypergroup., Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

We introduced the theory of Single valued neutrosophic hypergroup as the initial theory of single valued neutrosophic hyper algebra and also developed some results on single valued neutrosophic hypergroup.

Published
2020
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27. The mental health burden of racial and ethnic minorities during the COVID-19 pandemic.

Author

Long H Nguyen, Adjoa Anyane-Yeboa, Kerstin Klaser, Jordi Merino, David A Drew, Wenjie Ma, Raaj S Mehta, Daniel Y Kim, Erica T Warner, Amit D Joshi, Mark S Graham, Carole H Sudre, Ellen J Thompson, Anna May, Christina Hu, Solveig Jørgensen, Somesh Selvachandran, Sarah E Berry, Sean P David, Maria Elena Martinez, Jane C Figueiredo, Anne M Murray, Alan R Sanders, Karestan C Koenen, Jonathan Wolf, Sebastien Ourselin, Tim D Spector, Claire J Steves, and Andrew T Chan

Subjects
Medicine, Science
Abstract

Racial/ethnic minorities have been disproportionately impacted by COVID-19. The effects of COVID-19 on the long-term mental health of minorities remains unclear. To evaluate differences in odds of screening positive for depression and anxiety among various racial and ethnic groups during the latter phase of the COVID-19 pandemic, we performed a cross-sectional analysis of 691,473 participants nested within the prospective smartphone-based COVID Symptom Study in the United States (U.S.) and United Kingdom (U.K). from February 23, 2021 to June 9, 2021. In the U.S. (n=57,187), compared to White participants, the multivariable odds ratios (ORs) for screening positive for depression were 1·16 (95% CI: 1·02 to 1·31) for Black, 1·23 (1·11 to 1·36) for Hispanic, and 1·15 (1·02 to 1·30) for Asian participants, and 1·34 (1·13 to 1·59) for participants reporting more than one race/other even after accounting for personal factors such as prior history of a mental health disorder, COVID-19 infection status, and surrounding lockdown stringency. Rates of screening positive for anxiety were comparable. In the U.K. (n=643,286), racial/ethnic minorities had similarly elevated rates of positive screening for depression and anxiety. These disparities were not fully explained by changes in leisure time activities. Racial/ethnic minorities bore a disproportionate mental health burden during the COVID-19 pandemic. These differences will need to be considered as health care systems transition from prioritizing infection control to mitigating long-term consequences.

Published
2022
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28. Single-Valued Neutrosophic Hyperrings and Single-Valued Neutrosophic Hyperideals

Author

D. Preethi, S. Rajareega, J.Vimala, Ganeshsree Selvachandran, and F. Smarandache

Subjects
Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

In this paper, we introduced the concepts of Single-valued neutrosophic hyperring and Single-valued neutrosophic hyperideal. The algebraic properties and structural characteristics of the single-val-ued neutrosophic hyperrings and hyperideals are investigated and verified.

Published
2019
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29. Disentangling post-vaccination symptoms from early COVID-19

Author

Liane S. Canas, PhD, Marc F. Österdahl, MRCP, Jie Deng, PhD, Christina Hu, MA, Somesh Selvachandran, MEng, Lorenzo Polidori, MSc, Anna May, MSc, Erika Molteni, PhD, Benjamin Murray, MSc, Liyuan Chen, MSc, Eric Kerfoot, PhD, Kerstin Klaser, PhD, Michela Antonelli, PhD, Alexander Hammers, PhD, Tim Spector, FRCP PhD, Sebastien Ourselin, PhD, Claire Steves, MRCP PhD, Carole H. Sudre, PhD, Marc Modat, PhD, and Emma L. Duncan, FRACP PhD

Subjects
COVID-19 detection, Vaccination, Side-effects, Self-reported symptoms, Mobile technology, Early detection, Medicine (General), R5-920
Abstract

Background: Identifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. Methods: We conducted a prospective observational study in 1,072,313 UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (N=362,770) (other than local symptoms at injection site) and were tested for SARS-CoV-2 (N=14,842), aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models considering UK testing criteria. Findings: Differentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. Most of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). Interpretation: Post-vaccination symptoms per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2 or quarantining, to prevent community spread. Funding: UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Chronic Disease Research Foundation, Zoe Limited.

Published
2021
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32. Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study

Author

Mark S Graham, PhD, Carole H Sudre, PhD, Anna May, MA, Michela Antonelli, PhD, Benjamin Murray, MSc, Thomas Varsavsky, MSc, Kerstin Kläser, MSc, Liane S Canas, PhD, Erika Molteni, PhD, Marc Modat, PhD, David A Drew, PhD, Long H Nguyen, MD, Lorenzo Polidori, MSc, Somesh Selvachandran, MSc, Christina Hu, MA, Joan Capdevila, PhD, Alexander Hammers, ProfPhD, Andrew T Chan, ProfMD, Jonathan Wolf, MA, Tim D Spector, ProfPhD, Claire J Steves, PhD, Sebastien Ourselin, ProfPhD, Cherian Koshy, Amy Ash, Emma Wise, Nathan Moore, Matilde Mori, Nick Cortes, Jessica Lynch, Stephen Kidd, Derek J Fairley, Tanya Curran, James P McKenna, Helen Adams, Christophe Fraser, Tanya Golubchik, David Bonsall, Mohammed O Hassan-Ibrahim, Cassandra S Malone, Benjamin J Cogger, Michelle Wantoch, Nicola Reynolds, Ben Warne, Joshua Maksimovic, Karla Spellman, Kathryn McCluggage, Michaela John, Robert Beer, Safiah Afifi, Sian Morgan, Angela Marchbank, Anna Price, Christine Kitchen, Huw Gulliver, Ian Merrick, Joel Southgate, Martyn Guest, Robert Munn, Trudy Workman, Thomas R Connor, William Fuller, Catherine Bresner, Luke B Snell, Amita Patel, Themoula Charalampous, Gaia Nebbia, Rahul Batra, Jonathan Edgeworth, Samuel C Robson, Angela H Beckett, David M Aanensen, Anthony P Underwood, Corin A Yeats, Khalil Abudahab, Ben EW Taylor, Mirko Menegazzo, Gemma Clark, Wendy Smith, Manjinder Khakh, Vicki M Fleming, Michelle M Lister, Hannah C Howson-Wells, Louise Berry, Tim Boswell, Amelia Joseph, Iona Willingham, Carl Jones, Christopher Holmes, Paul Bird, Thomas Helmer, Karlie Fallon, Julian Tang, Veena Raviprakash, Sharon Campbell, Nicola Sheriff, Victoria Blakey, Lesley-Anne Williams, Matthew W Loose, Nadine Holmes, Christopher Moore, Matthew Carlile, Victoria Wright, Fei Sang, Johnny Debebe, Francesc Coll, Adrian W Signell, Gilberto Betancor, Harry D Wilson, Sahar Eldirdiri, Anita Kenyon, Thomas Davis, Oliver G Pybus, Louis du Plessis, Alex E Zarebski, Jayna Raghwani, Moritz UG Kraemer, Sarah Francois, Stephen W Attwood, Tetyana I Vasylyeva, Marina Escalera Zamudio, Bernardo Gutierrez, M. Estee Torok, William L Hamilton, Ian G Goodfellow, Grant Hall, Aminu S Jahun, Yasmin Chaudhry, Myra Hosmillo, Malte L Pinckert, Iliana Georgana, Samuel Moses, Hannah Lowe, Luke Bedford, Jonathan Moore, Susanne Stonehouse, Chloe L Fisher, Ali R Awan, John BoYes, Judith Breuer, Kathryn Ann Harris, Julianne Rose Brown, Divya Shah, Laura Atkinson, Jack CD Lee, Nathaniel Storey, Flavia Flaviani, Adela Alcolea-Medina, Rebecca Williams, Gabrielle Vernet, Michael R Chapman, Lisa J Levett, Judith Heaney, Wendy Chatterton, Monika Pusok, Li Xu-McCrae, Darren L Smith, Matthew Bashton, Gregory R Young, Alison Holmes, Paul Anthony Randell, Alison Cox, Pinglawathee Madona, Frances Bolt, James Price, Siddharth Mookerjee, Manon Ragonnet-Cronin, Fabricia F. Nascimento, David Jorgensen, Igor Siveroni, Rob Johnson, Olivia Boyd, Lily Geidelberg, Erik M Volz, Aileen Rowan, Graham P Taylor, Katherine L Smollett, Nicholas J Loman, Joshua Quick, Claire McMurray, Joanne Stockton, Sam Nicholls, Will Rowe, Radoslaw Poplawski, Alan McNally, Rocio T Martinez Nunez, Jenifer Mason, Trevor I Robinson, Elaine O'Toole, Joanne Watts, Cassie Breen, Angela Cowell, Graciela Sluga, Nicholas W Machin, Shazaad S Y Ahmad, Ryan P George, Fenella Halstead, Venkat Sivaprakasam, Wendy Hogsden, Chris J Illingworth, Chris Jackson, Emma C Thomson, James G Shepherd, Patawee Asamaphan, Marc O Niebel, Kathy K Li, Rajiv N Shah, Natasha G Jesudason, Lily Tong, Alice Broos, Daniel Mair, Jenna Nichols, Stephen N Carmichael, Kyriaki Nomikou, Elihu Aranday-Cortes, Natasha Johnson, Igor Starinskij, Ana da Silva Filipe, David L Robertson, Richard J Orton, Joseph Hughes, Sreenu Vattipally, Joshua B Singer, Seema Nickbakhsh, Antony D Hale, Louissa R Macfarlane-Smith, Katherine L Harper, Holli Carden, Yusri Taha, Brendan AI Payne, Shirelle Burton-Fanning, Sheila Waugh, Jennifer Collins, Gary Eltringham, Steven Rushton, Sarah O'Brien, Amanda Bradley, Alasdair Maclean, Guy Mollett, Rachel Blacow, Kate E Templeton, Martin P McHugh, Rebecca Dewar, Elizabeth Wastenge, Samir Dervisevic, Rachael Stanley, Emma J Meader, Lindsay Coupland, Louise Smith, Clive Graham, Edward Barton, Debra Padgett, Garren Scott, Emma Swindells, Jane Greenaway, Andrew Nelson, Clare M McCann, Wen C Yew, Monique Andersson, Timothy Peto, Anita Justice, David Eyre, Derrick Crook, Tim J Sloan, Nichola Duckworth, Sarah Walsh, Anoop J Chauhan, Sharon Glaysher, Kelly Bicknell, Sarah Wyllie, Scott Elliott, Allyson Lloyd, Robert Impey, Nick Levene, Lynn Monaghan, Declan T Bradley, Tim Wyatt, Elias Allara, Clare Pearson, Husam Osman, Andrew Bosworth, Esther Robinson, Peter Muir, Ian B Vipond, Richard Hopes, Hannah M Pymont, Stephanie Hutchings, Martin D Curran, Surendra Parmar, Angie Lackenby, Tamyo Mbisa, Steven Platt, Shahjahan Miah, David Bibby, Carmen Manso, Jonathan Hubb, Meera Chand, Gavin Dabrera, Mary Ramsay, Daniel Bradshaw, Alicia Thornton, Richard Myers, Ulf Schaefer, Natalie Groves, Eileen Gallagher, David Lee, David Williams, Nicholas Ellaby, Ian Harrison, Hassan Hartman, Nikos Manesis, Vineet Patel, Chloe Bishop, Vicki Chalker, Juan Ledesma, Katherine A Twohig, Matthew T.G. Holden, Sharif Shaaban, Alec Birchley, Alexander Adams, Alisha Davies, Amy Gaskin, Amy Plimmer, Bree Gatica-Wilcox, Caoimhe McKerr, Catherine Moore, Chris Williams, David Heyburn, Elen De Lacy, Ember Hilvers, Fatima Downing, Giri Shankar, Hannah Jones, Hibo Asad, Jason Coombes, Joanne Watkins, Johnathan M Evans, Laia Fina, Laura Gifford, Lauren Gilbert, Lee Graham, Malorie Perry, Mari Morgan, Matthew Bull, Michelle Cronin, Nicole Pacchiarini, Noel Craine, Rachel Jones, Robin Howe, Sally Corden, Sara Rey, Sara Kumziene-SummerhaYes, Sarah Taylor, Simon Cottrell, Sophie Jones, Sue Edwards, Justin O'Grady, Andrew J Page, Alison E Mather, David J Baker, Steven Rudder, Alp Aydin, Gemma L Kay, Alexander J Trotter, Nabil-Fareed Alikhan, Leonardo de Oliveira Martins, Thanh Le-Viet, Lizzie Meadows, Anna Casey, Liz Ratcliffe, David A Simpson, Zoltan Molnar, Thomas Thompson, Erwan Acheson, Jane AH Masoli, Bridget A Knight, Sian Ellard, Cressida Auckland, Christopher R Jones, Tabitha W Mahungu, Dianne Irish-Tavares, Tanzina Haque, Jennifer Hart, Eric Witele, Melisa Louise Fenton, Ashok Dadrah, Amanda Symmonds, Tranprit Saluja, Yann Bourgeois, Garry P Scarlett, Katie F Loveson, Salman Goudarzi, Christopher Fearn, Kate Cook, Hannah Dent, Hannah Paul, David G Partridge, Mohammad Raza, Cariad Evans, Kate Johnson, Steven Liggett, Paul Baker, Stephen Bonner, Sarah Essex, Ronan A Lyons, Kordo Saeed, Adhyana I.K Mahanama, Buddhini Samaraweera, Siona Silveira, Emanuela Pelosi, Eleri Wilson-Davies, Rachel J Williams, Mark Kristiansen, Sunando Roy, Charlotte A Williams, Marius Cotic, Nadua Bayzid, Adam P Westhorpe, John A Hartley, Riaz Jannoo, Helen L Lowe, Angeliki Karamani, Leah Ensell, Jacqui A Prieto, Sarah Jeremiah, Dimitris Grammatopoulos, Sarojini Pandey, Lisa Berry, Katie Jones, Alex Richter, Andrew Beggs, Angus Best, Benita Percival, Jeremy Mirza, Oliver Megram, Megan Mayhew, Liam Crawford, Fiona Ashcroft, Emma Moles-Garcia, Nicola Cumley, Colin P Smith, Giselda Bucca, Andrew R Hesketh, Beth Blane, Sophia T Girgis, Danielle Leek, Sushmita Sridhar, Sally Forrest, Claire Cormie, Harmeet K Gill, Joana Dias, Ellen E Higginson, Mailis Maes, Jamie Young, Leanne M Kermack, Ravi Kumar Gupta, Catherine Ludden, Sharon J Peacock, Sophie Palmer, Carol M Churcher, Nazreen F Hadjirin, Alessandro M Carabelli, Ellena Brooks, Kim S Smith, Katerina Galai, Georgina M McManus, Chris Ruis, Rose K Davidson, Andrew Rambaut, Thomas Williams, Carlos E Balcazar, Michael D Gallagher, Áine O'Toole, Stefan Rooke, Verity Hill, Kathleen A Williamson, Thomas D Stanton, Stephen L Michell, Claire M Bewshea, Ben Temperton, Michelle L Michelsen, Joanna Warwick-Dugdale, Robin Manley, Audrey Farbos, James W Harrison, Christine M Sambles, David J Studholme, Aaron R Jeffries, Alistair C Darby, Julian A Hiscox, Steve Paterson, Miren Iturriza-Gomara, Kathryn A Jackson, Anita O Lucaci, Edith E Vamos, Margaret Hughes, Lucille Rainbow, Richard Eccles, Charlotte Nelson, Mark Whitehead, Lance Turtle, Sam T Haldenby, Richard Gregory, Matthew Gemmell, Claudia Wierzbicki, Hermione J Webster, Thushan I de Silva, Nikki Smith, Adrienn Angyal, Benjamin B Lindsey, Danielle C Groves, Luke R Green, Dennis Wang, Timothy M Freeman, Matthew D Parker, Alexander J Keeley, Paul J Parsons, Rachel M Tucker, Rebecca Brown, Matthew Wyles, Max Whiteley, Peijun Zhang, Marta Gallis, Stavroula F Louka, Chrystala Constantinidou, Meera Unnikrishnan, Sascha Ott, Jeffrey K.J. Cheng, Hannah E. Bridgewater, Lucy R. Frost, Grace Taylor-Joyce, Richard Stark, Laura Baxter, Mohammad T. Alam, Paul E Brown, Dinesh Aggarwal, Alberto C Cerda, Tammy V Merrill, Rebekah E Wilson, Patrick C McClure, Joseph G Chappell, Theocharis Tsoleridis, Jonathan Ball, David Buck, John A Todd, Angie Green, Amy Trebes, George MacIntyre-Cockett, Mariateresa de Cesare, Alex Alderton, Roberto Amato, Cristina V Ariani, Mathew A Beale, Charlotte Beaver, Katherine L Bellis, Emma Betteridge, James Bonfield, John Danesh, Matthew J Dorman, Eleanor Drury, Ben W Farr, Luke Foulser, Sonia Goncalves, Scott Goodwin, Marina Gourtovaia, Ewan M Harrison, David K Jackson, Dorota Jamrozy, Ian Johnston, Leanne Kane, Sally Kay, Jon-Paul Keatley, Dominic Kwiatkowski, Cordelia F Langford, Mara Lawniczak, Laura Letchford, Rich Livett, Stephanie Lo, Inigo Martincorena, Samantha McGuigan, Rachel Nelson, Steve Palmer, Naomi R Park, Minal Patel, Liam Prestwood, Christoph Puethe, Michael A Quail, Shavanthi Rajatileka, Carol Scott, Lesley Shirley, John Sillitoe, Michael H Spencer Chapman, Scott AJ Thurston, Gerry Tonkin-Hill, Danni Weldon, Diana Rajan, Iraad F Bronner, Louise Aigrain, Nicholas M Redshaw, Stefanie V Lensing, Robert Davies, Andrew Whitwham, Jennifier Liddle, Kevin Lewis, Jaime M Tovar-Corona, Steven Leonard, Jillian Durham, Andrew R Bassett, Shane McCarthy, Robin J Moll, Keith James, Karen Oliver, Alex Makunin, Jeff Barrett, and Rory N Gunson

Subjects
Public aspects of medicine, RA1-1270
Abstract

Summary: Background: The SARS-CoV-2 variant B.1.1.7 was first identified in December, 2020, in England. We aimed to investigate whether increases in the proportion of infections with this variant are associated with differences in symptoms or disease course, reinfection rates, or transmissibility. Methods: We did an ecological study to examine the association between the regional proportion of infections with the SARS-CoV-2 B.1.1.7 variant and reported symptoms, disease course, rates of reinfection, and transmissibility. Data on types and duration of symptoms were obtained from longitudinal reports from users of the COVID Symptom Study app who reported a positive test for COVID-19 between Sept 28 and Dec 27, 2020 (during which the prevalence of B.1.1.7 increased most notably in parts of the UK). From this dataset, we also estimated the frequency of possible reinfection, defined as the presence of two reported positive tests separated by more than 90 days with a period of reporting no symptoms for more than 7 days before the second positive test. The proportion of SARS-CoV-2 infections with the B.1.1.7 variant across the UK was estimated with use of genomic data from the COVID-19 Genomics UK Consortium and data from Public Health England on spike-gene target failure (a non-specific indicator of the B.1.1.7 variant) in community cases in England. We used linear regression to examine the association between reported symptoms and proportion of B.1.1.7. We assessed the Spearman correlation between the proportion of B.1.1.7 cases and number of reinfections over time, and between the number of positive tests and reinfections. We estimated incidence for B.1.1.7 and previous variants, and compared the effective reproduction number, Rt, for the two incidence estimates. Findings: From Sept 28 to Dec 27, 2020, positive COVID-19 tests were reported by 36 920 COVID Symptom Study app users whose region was known and who reported as healthy on app sign-up. We found no changes in reported symptoms or disease duration associated with B.1.1.7. For the same period, possible reinfections were identified in 249 (0·7% [95% CI 0·6–0·8]) of 36 509 app users who reported a positive swab test before Oct 1, 2020, but there was no evidence that the frequency of reinfections was higher for the B.1.1.7 variant than for pre-existing variants. Reinfection occurrences were more positively correlated with the overall regional rise in cases (Spearman correlation 0·56–0·69 for South East, London, and East of England) than with the regional increase in the proportion of infections with the B.1.1.7 variant (Spearman correlation 0·38–0·56 in the same regions), suggesting B.1.1.7 does not substantially alter the risk of reinfection. We found a multiplicative increase in the Rt of B.1.1.7 by a factor of 1·35 (95% CI 1·02–1·69) relative to pre-existing variants. However, Rt fell below 1 during regional and national lockdowns, even in regions with high proportions of infections with the B.1.1.7 variant. Interpretation: The lack of change in symptoms identified in this study indicates that existing testing and surveillance infrastructure do not need to change specifically for the B.1.1.7 variant. In addition, given that there was no apparent increase in the reinfection rate, vaccines are likely to remain effective against the B.1.1.7 variant. Funding: Zoe Global, Department of Health (UK), Wellcome Trust, Engineering and Physical Sciences Research Council (UK), National Institute for Health Research (UK), Medical Research Council (UK), Alzheimer's Society.

Published
2021
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33. A Novel Neutrosophic Data Analytic Hierarchy Process for Multi-Criteria Decision Making Method: A Case Study in Kuala Lumpur Stock Exchange

Author

Desmond Jun Yi Tey, Yee Fei Gan, Ganeshsree Selvachandran, Shio Gai Quek, Florentin Smarandache, Le Hoang Son, Mohamed Abdel-Basset, and Hoang Viet Long

Subjects
Single-valued neutrosophic set, analytic hierarchy process (AHP), multi-criteria decision making, neutrosophic AHP, neutrosophic decision making, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper proposes a multi-criteria decision making method called the neutrosophic data analytic hierarchy process (NDAHP) for the single-valued neutrosophic set (SVNS). This method is an extension of the neutrosophic analytic hierarchy process (NAHP) designed to handle actual datasets which consist of crisp values. The proposed NDAHP method uses an objective weighting mechanism whereas all other existing versions of the AHP, fuzzy AHP, and other fuzzy based AHP method in literature such as the NAHP and picture fuzzy AHP uses a subjective weighting mechanism to arrive at the decision. This makes the proposed NDAHP method effective as the weightage of the criteria which forms the input of the evaluation matrix are determined in an objective manner using actual data collected for the problem, and hence will not change according to the opinions of different decision makers which are subjective. The proposed NDAHP method is applied to a multi-criteria decision making problem related to the ranking of the financial performance of five public listed petrochemical companies trading in the main board of the Kuala Lumpur Stock Exchange (KLSE). Actual dataset of 15 financial indices for the five petrochemical companies for 2017 obtained from Yahoo! Finance was used in this paper. Following this, a brief comparative study is conducted to evaluate the performance of our NDAHP algorithm against the results of other existing SVNS-based decision making methods in the literature. The results are compared against actual results obtained from KLSE. To further verify the rankings obtained through each method, the Spearman and Pearson ranking tests are carried out on each of the decision making methods that are studied. It is proved that NDAHP produces the most accurate results, and this was further verified from the results of the Spearman and Pearson ranking tests.

Published
2019
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34. Decision Making Methods for Evaluation of Efficiency of General Insurance Companies in Malaysia: A Comparative Study

Author

Zhao Loon Wang, Jin Kim, Ganeshsree Selvachandran, Florentin Smarandache, Le Hoang Son, Mohamed Abdel-Basset, Pham Huy Thong, and Mahmoud Ismail

Subjects
Single-valued neutrosophic set, analytic hierarchy process (AHP), multi-criteria decision making, neutrosophic data AHP, neutrosophic decision making, efficiency of general insurance companies, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper proposes an integration of two neutrosophic based multi-criteria decision making methods, namely the neutrosophic data analytical hierarchy process (NDAHP) and the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) with maximizing deviation method, both based on the single-valued neutrosophic set (SVNS) to evaluate the efficiency of general insurance companies in Malaysia. The level of efficiency of insurance companies is a subjective and vague matter, as the efficiency can be further branched into operational efficiency, investment efficiency, underwriting efficiency, and risk management efficiency. Hence relying on entirely objective decision making methods based on crisp data might not address the problem effectively, and therefore fuzzy based decision making methods are highly appropriate to be used in this situation. Our proposed decision making algorithm uses an integrated weighting mechanism that takes into consideration both the objective and subjective weights of the data attributes. The objective weighting mechanism handles the actual datasets that were used which consists of crisp values, whereas the subjective weighing mechanism handles the opinions of the experts in the general insurance industry who were surveyed in this study. This makes the proposed method a more holistic approach to evaluate the efficiency of general insurance companies in Malaysia as previous researches in this area are generally based on the actual datasets without consideration of the opinions and evaluations of the industry experts, or vice-versa. The proposed decision making algorithm is applied on actual datasets of management expenses, net commission, net earned premium and the net investment income for 19 selected general insurance companies in Malaysia over a two-year period from 2016 to 2017. The results obtained are then discussed and the possible reasons for the results are analyzed. A comprehensive comparative study of the results obtained via our proposed method and two other commonly used methods are then presented, analyzed and discussed.

Published
2019
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35. Drone Usage for Medicine and Vaccine Delivery during the COVID-19 Pandemic: Attitude of Health Care Workers in Rural Medical Centres

Author

Rohana Sham, Ching Sin Siau, Steven Tan, Dawn Chii Kiu, Hasminulhadi Sabhi, Hui Zhu Thew, Ganeshsree Selvachandran, Shio Gai Quek, Noorsiah Ahmad, and Mohd Hanif Mohd Ramli

Subjects
drone delivery, rural, vaccine, medicine health care worker, attitude, COVID-19, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Rural areas are often difficult to access reliably with medicine and vaccines. This study aimed to examine rural health care workers’ attitude towards drone delivery for medicine and vaccines and the factors that influenced it. Health care workers from four rural health care facilities were sampled. Participants self-reported their demographic information, attitude towards medicine and vaccine delivery using drones, perception of benefits and risks of using drones, and perceived leadership innovativeness through an online or a pen-and-paper questionnaire. A total of 272 health care workers (mean age = 36.19, SD = 8.10) from all of the sites participated in this study. More than half of the study participants agreed or strongly agreed that using a drone to deliver medicine and vaccines is a good idea (54.2%, 95% CI [47.5, 60.8]), a wise idea (54.6%, 95% CI [47.9, 61.2]), and is desirable (52.5%, 95% CI [45.7, 59.0]). Males (β = 0.223), workers from the Obstetrics and Gynaecology department (β = 0.135), a lower perceived delivery risk (β = −0.237), and higher leadership innovativeness (β = 0.336) predicted positive attitudes towards drone usage. Assistant medical officers (β = −0.172) had a negative attitude. There is a need to further understand the roles of occupation and leadership innovativeness in predicting health care workers’ attitude towards drone usage, as these differences could be embedded within their roles in the health care system.

Published
2022
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36. A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models

Author

Madeline Hui Li Lee, Yee Chee Ser, Ganeshsree Selvachandran, Pham Huy Thong, Le Cuong, Le Hoang Son, Nguyen Trung Tuan, and Vassilis C. Gerogiannis

Subjects
electricity consumption, artificial neural network, adaptive neuro-fuzzy inference system, least squares support vector machines, fuzzy time series, fuzzy system, Mathematics, QA1-939
Abstract

Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for different countries. The monthly electricity consumption data for seven countries, namely, Norway, Switzerland, Malaysia, Egypt, Algeria, Bulgaria and Kenya, for 10 years were used in this research. The performance of all of the models was evaluated and compared using error metrics such as the root mean squared error (RMSE), average forecasting error (AFE) and performance parameter (PP). The differences in the results obtained via the different methods are analyzed and discussed, and it is shown that the different models performed better for different countries in different forecasting periods. Overall, it was found that the FTS model performed the best for most of the countries studied compared to the other three models. The research results can allow electricity management companies to have better strategic planning when deciding on the optimal levels of electricity production and supply, with the overall aim of preventing surpluses or shortages in the electricity supply.

Published
2022
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37. Author Correction: Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom

Author

Long H. Nguyen, Amit D. Joshi, David A. Drew, Jordi Merino, Wenjie Ma, Chun-Han Lo, Sohee Kwon, Kai Wang, Mark S. Graham, Lorenzo Polidori, Cristina Menni, Carole H. Sudre, Adjoa Anyane-Yeboa, Christina M. Astley, Erica T. Warner, Christina Y. Hu, Somesh Selvachandran, Richard Davies, Denis Nash, Paul W. Franks, Jonathan Wolf, Sebastien Ourselin, Claire J. Steves, Tim D. Spector, Andrew T. Chan, and COPE Consortium

Subjects
Science
Published
2022
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41. Computing Operational Matrices in Neutrosophic Environments: A Matlab Toolbox

Author

Said Broumi, Le Hoang Son, Assia Bakali, Mohamed Talea, Florentin Smarandache, and Ganeshsree Selvachandran

Subjects
fuzzy set, and intuitionistic fuzzy set by employing a degree of truth (T), a degree of indeterminacy (I), and a degree of falsehood (F) associated with an element of the dataset. One of the most essential problems is studying set-theoretic operators in order to be applied to practical applications. Developing Matlab toolboxes for computing the operational matrices in neutrosophic environments is essential to gain more widely-used of neutrosophic algorithms. In this paper, we propose some computing procedures in Matlab for neutrosophic operational matrices, Neutrosophic set, Matlab Toolbox, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

Neutrosophic set is a generalization of classical set, fuzzy set, and intuitionistic fuzzy set by employing a degree of truth (T), a degree of indeterminacy (I), and a degree of falsehood (F) associated with an element of the dataset. One of the most essential problems is studying set-theoretic operators in order to be applied to practical applications. Developing Matlab toolboxes for computing the operational matrices in neutrosophic environments is essential to gain more widely-used of neutrosophic algorithms. In this paper, we propose some computing procedures in Matlab for neutrosophic operational matrices, especially i) computing the single-valued neutrosophic matrix; ii) determining complement of a single-valued neutrosophic matrix; iii) computing max-min-min and min-max-max of two single-valued neutrosophic matrices; v) computing power of a single-valued neutrosophic matrix; vi) computing additional operation and subtraction of two single-valued neutrosophic matrices; and ix) computing transpose of a single-valued neutrosophic matrix. Numerical examples are given to illustrate their applicability.

Published
2017
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43. Deflection of Steel Reinforced Concrete Beam Prestressed With CFRP Bar

Author

Selvachandran P., Anandakumar S., and Muthuramu K.L.

Subjects
Cracked Moment of Inertia, Deformability, Effective Moment of Inertia, Neutral axis, Partial Prestressig Ratio, Yielding point, Mining engineering. Metallurgy, TN1-997, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Carbon Fiber Reinforced polymer (CFRP) bars are weak in yielding property which results in sudden failure of structure at failure load. Inclusion of non-pretensioned steel reinforcement in the tension side of CFRP based prestressed concrete beam will balance the yielding requirements of member and it will show the definite crack failure pattern before failure. Experimental investigation has been carried out to study the deflection behavior of partially prestressed beam. Experimental works includes four beam specimens stressed by varying degree of prestressing. The Partial Prestressing Ratio (PPR) of specimen is considered for experimental works in the range of 0.6 to 0.8. A new deflection model is recommended in the present study considering the strain contribution of CFRP bar and steel reinforcement for the fully bonded member. New deflection model converges to experimental results with the error of less than 5% .

Published
2017
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44. Perception, acceptance and willingness of older adults in Malaysia towards online shopping: a study using the UTAUT and IRT models

Author

Soh, Pick Yan, Heng, Hui Bao, Selvachandran, Ganeshsree, Anh, Le Quynh, Chau, Hoang Thi Minh, Son, Le Hoang, Abdel-Baset, Mohamed, Manogaran, Gunasekaran, and Varatharajan, R.

Abstract

With the confluence of information technology era and the progressively aging society, the internet usage rate by older adults in Malaysia is growing at a substantial rate. Therefore, older adults are becoming an increasingly significant potential target market for electronic commerce. However, previous researchers have focused mainly on the youth market and paid less attention to the online behaviours of older adults. To bridge the gap, the objective of this research is to increase a better understanding of how the factors affecting the perception, acceptance and willingness of older adults in Malaysia towards online shopping. To this end, this study is developed by integrating the unified theory of acceptance and use of technology (UTAUT) and innovation resistance theory (IRT). They are applied to an original dataset of 200 responses from respondents that were collected through a survey.

Published
2024
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46. Safety of Nonsteroidal Anti-inflammatory Drugs in Major Gastrointestinal Surgery: A Prospective, Multicenter Cohort Study

Author

Abbas, Z., Abburu, S., Abd Ghaffar, M. K., Abdelhadi, M., Abdikadir, H. R., Abdulmajid, A., Abid, H., Abid, A., Abuhussein, N., Abul, M. H., Acquaah, F., Acres, M., Adams, R., Adams, R. D., Adebayo, A. A., Adeleja, I., Adjei, H., Afzal, Z., Agarwal, V., Ahern, N., Ahmad, K., Ahmad, S., Ahmed, M., Ahmed, H., Ahmed, N., Ahmed, S., Ahmed, F., Ahn, J. S., Aidoo-Micah, G. E., Aildasani, L., Aithie, J. M., Akhtar, S., Ali, S., Ali, A., Ali, B., Ali, M., Ali, H., Alizadeh, M., Allan, C., Allen, J. L. Y., Allot, R., Al-Mousawi, A., Al-Obaedi, O., Al-Robeye, A., Amajuoyi, A., Amin, H., Amin, O., Amphlett, A. H., Anandarajah, C., Anderson, L., Anderson, L. B., Anderson, S. M., Ang, A., Angelov, S., Anilkumar, A., Anim-Addo, N., Ansari, N., Antoniou, I., Archer, C. H., Arif, T., Asbjoernsen, C. A., Ashfaq, U., Ashken, L., Ashraf, S. F., Ashraf, S., Ashton, A. J., Ashwood, J., Aslanyan, A., Asmadi, A., Assadullah, S., Atayi, A., Atraszkiewicz, B. A., Attalla, M., Austreng, L., Auyoung, E., Avery, P., Axelson, T., Aziz, H., Aziz, N., Baker, A. N., Bakewell, Z. R., Bakhsh, A., Balaji, S., Balian, V., Bamgbose, F. A., Barai, I., Barnes, J., Barrow, T. R., Barthorpe, A. E., Bartlett, J., Bartlett, R. D., Barton, E. C., Bassam, N., Bassett, J., Bassiony, S., Bath, M. F., Batho, A., Batt, E., Bazeer, H. Z., Beckett, J., Beecroft, S., Behar, N., Bell, N., Bell, L., Bell, A., Bemand, T. P., Bergara, T., Bernstein, I., Bethell, G. S., Bhanderi, S., Bhangu, A., Bhaskaran, G., Bhatt, N., Bhatti, M., Bhome, R., Bhudia, R., Bingham, G., Blege, H. K., Blessed, R., Bloomer, J., Bloomfield, T., Blore, C. D., Bolton, W., Bolton, L., Bonsu, S., Bookless, L. R., Bose, R., Botchey, S., Boulton, A. J., Boxall, N., Boyle, J., Braganza, L., Brathwaite-Shirley, C., Bravo, M., Brecher, J., Bremner, R. H., Brennan, C., Brennan, E., Brennan, K., Brent, G., Brewer, C. F., Brewster, O., Bright, M., Brown, D., Brown, E., Brown, F. S., Brown, E. J., Broyd, A., Brzyska, K., Buakuma, P., Buchan, A. M., Buckle, R. T., Bucko, A. M., Bulley, F., Bullman, L. M., Bullock, N. P., Burgess, A., Burke, J., Burke, D., Burke, E., Burney, L. J., Callan, R., Campbell, J., Canning, N., Canning, E., Cao, Y., Cardwell, A. E., Carr, L., Carr, R., Carroll, A. F., Carter, D., Carthew, L., Chamberlain, M., Chan, N., Chan, C., Chandan, N., Chapman, S. J., Charalambos, M. A., Charalambous, G., Charania, A., Charavanamuttu, V., Chaudhary, M., Chaudhry, F. I., Chaudhry, W. W., Cheema, H., Chen, J. H., Chen, X., Chen, M., Cheng, K., Chervenkoff, J., Cheskes, L., Cheung, F., Chew, L. S., Chew, L., Chhabra, A., Chhina, C., Chilima, C. P., Chillarge, G., Chilvers, N. J., Chin, H., Chin, R., Chisholm, E. G., Chitnis, A. R., Chiu, S. M., Chong, B. F., Chong, J., Choo, K. P., Chrastek, D., Chua, E. Y., Chung, A., Claireaux, H. A., Clark, I. J., Clarke, A. K., Cleere, J., Clement, K. D., Clesham, K., Coates, A., Cody, A., Cody, N., Coffey, D., Coffey, C. 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V., Holton, P., Holyoak, H., Horne, L., Horst, C., Horth, D., Hoskins, T. C., Howells, L., Hu, L., Huang, H. C., Hudson-Phillips, S., Hughes, F., Hughes, B. A., Hughes, R. K., Hulley, K., Hung, G., Hurst, P. C., Husnoo, S. B., Hussain, N., Hussain, O., Ibrahim, I., Ibrahim, A., Ingham, R., Ingram, E., Iqbal, S., Iqbal, A., Isaac, A., Jackson, H. R., Jackson, S., Jacob, L., Jafree, D. J., Jaitley, A., Jalota, P., Jamal, N., Jathanna, N., Jawad, A. S., Jayakody, N., Jenkin, S. L., Jenvey, C., Jewell, P. D., Jhala, H., Jindal, A., Johnston, A., Johnston, J., Johnstone, M., Jordan, H. E. M., Joshi, K. R., Joshi, D., Joyce, H. L., Joyner, C., Jubainville, C. L., Jull, P., Kadicheeni, M., Kahar, A., Kalra, N., Kanabar, S., Kane, T., Karia, M., Karia, P., Karsan, R. B., Karunakaran, P., Kaushal, A., Kazmi, Z., Keane, P., Keane, C. P., Keane, N., Kee, J. Y., Keeling, R. E., Keelty, N., Keevil, H., Kelly, M., Kelly, M. E., Kelly, N., Kennedy, E. D., Kennedy, H. R., Kerai, A., Kerr, A. L., Khajuria, A., Khalid, H., Khan, T., Khan, M., Khan, S., Khan, U., Khan, A., Khangura, J., Khanijau, R., Khatri, C., Khattak, M., Khetarpal, A. A., Khokhar, H. A., Khonat, Z., Khonsari, P., Kiff, R., Kim, S., Kim, J. W., Kimani, L., King, M., Kishore, A., Kisyov, I., Kitt, H., Knight, C. L., Kong, C. Y., Kong, C., Kosasih, S. R., Koshy, R. M., Kotecha, D., Koumpa, F., Kow, K., Koysombat, K., Kreibich, A., Kretzmer, L., Kumar, A. N., Kumaran, G., Kwan, M. L., Kwang, P., Lakhani, M., Lakhani, S. M., Lakshmipathy, G., Lalor, P., Lamont, J., Lankage, C. M., Lavery, J., Lazenby, D., Ledsam, A., Lee, A. H. Y., Lee, S., Lees, D. M., Lek, C., Leong, S., Leslie, K. E., Leung, W., Lewis, T., Li, N., Li, M. M., Liew, Y., Liew, W., Lim, K., Lim, J., Lim, D., Lim, A. E., Lim, S. J., Lim, S., Lim, E., Linton, A., Liu, S., Liu, C., Livesey, A., Lo, T., Lockey, J. W., Logan, A. E., Loke, W., Long, F., Lopes, S., Lotfallah, A., Lou, C. N., Loughran, D., Loveday, J., Low, J. Y. L., Lu, Q., Lua Boon Xuan, J., de Carvalho, J. Lucas, Luhishi, A., Luk, C. Y., Lunawat, S., Lwin, K. N., Lykoudis, P. M., Lynch, A. S., Lynne, S., Lyons, R., Maamari, R., MacAskill, A., MacDonald, J., Mackin, S., Maclennan, D., Mah, J., Mahboob, S., Maheswaran, Y., Mahmood, J., Majid, S., Major, C., Malaj, M., Malik, A., Mallick, S., Malys, M. K., Manson, R., Mansoor, S., Maple, N., Marchal, S. T., Markham, R. M., Marsden, M., Marsh, A., Marshall, D. C., Martin, A. L., Martin, R., Maru, D., Mason, J. D., Masood, M., Mastan, A., Matheson, J., Matthams, J., Matthews, B. W., Matthews, J. H., Maxwell-Armstrong, C., Mazan, K., Mazumdar, E., McAleer, S., McAleer, E., McAllister, R., McAuley, D., McBride, A., McCabe, G., McCance, E., McCann, M., McClymont, L. F., McCormack, D. R., McCrann, C., McDowell, M., McEnhill, P. M., McFarlane, H., McGalliard, R. J., McGarrigle, C., McGarvie, S., Mcgenity, C., McGowan, C., McGrath, A., McGregor, R. J., McIntyre, C. J., Mckean, E., McKelvey, L. L., McKerr, C. N., McKevitt, K. L., McLaughin, C., McLean, R. C., McLure, S. W., McMenamin, M., McMullan, R., McNamee, L., McRobbie, H. D., Meek, J., Mehdi, A., Mehta, J. K., Menon, A., Mian, A., Mills, E. D., Mills, M., Mills, H., Milne, S., Minhas, M., Miranda, B. H., Mirdavoudi, V., Mirza, M., Mishra, A., Mistry, S., Mistry, B. D., Mitchell, H., Mitha, N., Mithrakumar, P., Mitrasinovic, S., Mittapalli, D., Mogan, Y. P., Mohamud, M., Mohan, M., Mohan, K., Mohite, A., Momoh, Z., Moody, N., Moon, R. D. C., Moradzadeh, J., Morgan, F., Morgan, C., Morley, R., Morris, F., Morris, S., Morrison, P., Morrison, C. J., Mortimer, A., Murkin, C., Murphy, L., Murray, S. E., Murtaza, A., Mushtaq, J., Nachiappan, R., Nadanakumaran, K., Naqib, S., Narain, A., Naran, P., Narang, Y., Narayan, P., Narramore, R., Narwani, V., Navayogaarajah, V., Naveed, H., Nayee, H., Nehikhare, I., Nelaj, S., Neo, Y. N., Neophytou, C., Nepogodiev, D., Nesargikar, P. N., Ng, K., Ng, J. C. K., Ng, G. S., Ng, J. Q., Ng, A. Y. L., Ng, S., Ng, L., Nicholls, K., Nixon, G., Norris, J. M., North, A. S., Norton, J., Ntala, C., O’Bryan, M., O’Carroll, O., O’Connell, C., O’Connor, A., O’Connor, S., O’Flynn, L. D., O’Kane, A., O’Loughlin, R. A., O’Neill, S., O’Neill, E. M., O’Reilly, D., O’Sullivan, D. A., O’Sullivan, K., Obute, R. D., Odeleye, A., Omar, A., Omara, S., Omer, H. M., Ong, K. K., Oremule, B., Osei-Kuffour, D., Osman, S., Owasil, R., Owczarek, S., Williams, R. P., Paine, H. R., Pal, S., Palkhi, E., Palmer, C., Pandey, A., Pandey, G., Paraoan, V., Park, J. H., Parker, O., Parker, J., Parkin, J., Parsons, S., Parthiban, S., Patel, P., Patel, M., Patel, T., Patel, S., Patel, N., Patel, J. B., Patel, V., Patel, B. Y., Patel, B., Patel, B. A., Patel, K., Paul, J., Pearce, J., Pearse, R. J., Peck, F. S., Perera, M., Pericleous, A., Peroos, S., Peters, M., Petra, H., Petrarca, M., Pezas, T. P., Phan, P. N., Phillips, C., Pickard, J., Pinto, R., Piquet, A., Pitts-Tucker, T., Pizzolato, A., Player, C., Ponweera, A., Poo, S. X., Pope, S., Prabhudesai, A., Prakash, E., Preece, R., Prest-Smith, J., Priestland, R., Prys-Jones, O., Ptacek, I. B., Puan, L., Punj, R., Punjabi, N., Qamar, M. A., Qureshi, S., Qureshi, U., Qureshi, A., Rabinthiran, S., Radotra, A., Rafiq, N. M., Raghuvir, V., Raghvani, T., Rajan, N., Raji, K., Raman, K. P., Ramjeeawon, N., Ramnarine, A., Rampal, R., Ramsay, N., Ramtoola, T., Rangan, T., Rangedara, A., Raphael, M., Rashid, S., Rashid, M., Rasiah, M. G., Ratnakumaran, R., Rattan, G., Ratu, S. G., Raut, P., Reakes, T., Redgrave, N. A., Reed, A., Reeder, C., Reehal, R. S., Rees, C., Reeves, T., Reid, N. B., Reid, R., Reid, K. G., Remedios, J., Rhema, I. A., Rinkoff, S., Roberts, E. J., Roberts, A. W., Roberts, H. F., Roberts, C., Robertson, K. L., Robertson, V., Robertson, D. T., Robinson, M., Robinson, C., Robson, J., Rocke, A. S., Rogers, J. E., Rogers, S., Rojoa, D., Rookes, C. W., Rosen O’Sullivan, H., Ross, T., Ross, H., Rothwell, L., Roy, C. S. D., Ruiz, E. M., Russell, G., Ryan, M., Sabine, L. M., Sagar, R., Sagmeister, M., Sahathevan, A., Sait, M. S., Sajjad, U., Salam, G. J., Sale, T., Salem, M., Salih, A. E., Salmon, D., Sanders, J. A., Sandhu, K. K., Sandhu, S., Sangal, P., Sarvanandan, T., Sarwar, S., Sasapu, K., Satterthwaite, L., Schulz, T. M., Scotcher, S. E., Seager, E., Seedat, M., Segall, E., Sellathurai, J., Selvachandran, H., Semana, A. D., Semnani, S. A., Semple, E., Seneviratne, N., Sethi, R. K., Shafi, A. M. A., Shafiq, N. M., Shah, A., Shah, J. P., Shah, R., Shah, S., Shaheen, H., Shahid, S., Shahidi, S., Shakweh, E., Shanahan, D., Sharifpour, M., Shatkar, V., Shaunak, R., Sheldon, A., Shepherd, R., Shepherd, P., Sherif, M. A., Sherliker, G. X. J., Sheth, S., Shoa, M., Shufflebotham, H., Shuker, B. A., Shukla, A., Shumon, S., Shurovi, B. N., Shuttleworth, R. H., Siddiqui, M., Sii, S., Sim, N. K., Sim, P., Sim, D., Simpson, R., Simpson, A., Singagireson, S., Singh, B., Singh, K., Singh, R., Singh, S., Sinha, Y., Sirakaya, M., Sitta, O., Slade, R., Smith, N., Smith, D. N. E., Smith, A. C. D., Sng, S., Soo, Y. H., Soon, W. C., Sorah, T., Spence, O., Spencer, T., Springford, L. R., Sreh, A., Srikantharajah, M., Sritharan, P., Stanger, S. A., Stanley, G. H., Stather, P. W., Steel, M., Stein, J., Stevens, S., Stewart, G. E., Stezaker, S., Stoddart, M. T., Stokes, S., Stone, E. J., Stott, G. D., Strange, E., Street, A. N., Sukkari, M., Sukumar, S., Suleman, Y. N., Sullivan, J. A. L., Sun, E., Sundar-Singh, M., Suresh, S., Suresh, R. S., Syeed, J. A., Sykes, M. C., Szczap, A., Tahir, M., Tahmina, A., Tai, A., Talukdar, S. S., Tan, Y. H., Tan, R., Tan, E. T., Tan, D., Tan, Y., Tan, S., Tan, E. S. M., Tay, A. Y., Tayeh, S., Tear, A. K., Telfer, R., Teng, V., Teoh, P. J., Thacoor, A., Thakker, C. E., Thakur, H., Tharakan, R. G., Tharmachandirar, T., Theodoreson, M. D., Theodoropoulou, K., Thethi, R., Thevathasan, A. A., Thirumal, V., Thomas, G., Thomas, D., Thompson, O. D., Thompson, J. D., Tilston, T. W., Toale, C., Toh, C., Toner, E., Tongo, F., Tonkins, M., Topham, C., Torlot, G. E., Torrance, H. D., Trail, M., Traynor, B. P., Trecarten, S., Trimble, A., Trist, A. J., Tsui, A. Y., Tung, L., Turaga, S., Turley, H., Turnbull, J. A., Turner, L., Turner, M., Turner, E. J. H., Turner, J., Ungcharoen, N., Uppal, E., Valli, A., Vanmali, P., Varley, R., Varma, R. K., Varma, D., Varma, N., Vaughan, R., Venn, M., Ventre, C. M., Verma, K., Verma, S., Vernon, O. K., Vithanage, N. A., Vivekanantham, S., Wadanamby, S., Waldron, R. M., Walford, R. A., Wali, A., Wall, C., Walsh, S. L., Wan, J. C., Wang, S., Wang, A., Ward, N., Ward, T., Ward, A. E., Warren, N., Warwick, H. L., Watson, N., Watson, R. P., Weaver, R., Webb, E., Weinberg, D., Wells, M., Weston, C., Wetherall, N., Whacha, C., Whatling, E. A., Whewell, H., White, A., White, C. J., White, U., Whitehurst, K., Whitham, R. D. J., Whittingham, H., Wijesekera, M., Wild, J. R. L., Wilkinson, D., Williams, M., Williams, M. R., Williams, P., Wills, J., Wilson, H. C. P., Wilson, H., Wilson, R., Wiltshire, J. J., Winarski, A., Wing, V. C., Wingfield, L. R., Winslow, F., Woin, E., Wong, V., Wong, E., Wood, A. D., Woodcock, N., Woodward, H., Woon, E., Wright, A., Wright, E. V., Wye, J., Wylam, D., Wylie, J., Wynell-Mayow, W. M., Xiao, C., Xu, G. X., Xylas, D., Yan, A., Yang, T., Yates, J. A., Yener, A., Yim, N., Yoganathan, S., Yong, C. S., Yong, N., Yousif, A., Yow, L., Yuen, R., Zegeye, M. I., Zhao, J., Ziff, O., Ziprin, P., Zuhair, M., and STARSurg Collaborative

Published
2017
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47. Predicting Sustainable Farm Performance—Using Hybrid Structural Equation Modelling with an Artificial Neural Network Approach

Author

Naeem Hayat, Abdullah Al Mamun, Noorul Azwin Md Nasir, Ganeshsree Selvachandran, Noorshella Binti Che Nawi, and Quek Shio Gai

Subjects
conservative agriculture practices, environmental performance, yield performance, financial performance, sustainable farm performance, Agriculture
Abstract

The adoption of innovative technology has always been a complex issue. The agriculture sectors of developing countries are following unsustainable farming policies. The currently adopted intensive farming practices need to replace with conservative agriculture practices (CAPs). However, the adoption of CAPs has remained low since its emergence and reports have suggested that the use of CAPs is scant for sustainable farm performance. This article aims to study three scenarios: Firstly, the influence of personal and CAPs level factors on the intention to adopt CAPs; secondly, the influence intention to adopt CAPs, facilitating conditions and voluntariness of use on the actual use of CAPs; and thirdly, the impact of the actual use of CAPs on sustainable farm performance. This study is based on survey data collected by structured interviews of rice farmers in rural Pakistan, which consists of 336 samples. The final analysis is performed using two methods: (1) a well-established and conventional way of Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart PLS 3.0, and (2) a frontier technology of computing using an artificial neural network (ANN), which is generated through a deep learning algorithm to achieve maximum possible accuracy. The results reveal that profit orientation and environment attitude as behavioural inclination significantly predicts the intention to adopt CAPs. The perception of effort expectancy can significantly predict the intention to adopt CAPs. Low intention to adopt CAPs caused by the low-level trust on extension, low-performance expectancy, and low social influence for the CAPs. The adoption of CAPs is affected by facilitating conditions, voluntary use of CAPs, and the intention to adopt CAPs. Lastly, the use of CAPs can positively and significantly forecast the perception of sustainable farm performance. Thus, it is concluded that right policies are required to enhance the farmers’ trust on extension and promote social and performance expectation for CAPs. Besides, policy recommendations can be made for sustainable agriculture development in developing and developed countries.

Published
2020
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48. Similarity Measure of Lattice Ordered Multi-Fuzzy Soft Sets Based on Set Theoretic Approach and Its Application in Decision Making

Author

Sabeena Begam S, Vimala J, Ganeshsree Selvachandran, Tran Thi Ngan, and Rohit Sharma

Subjects
soft set, fuzzy soft set, multi-fuzzy set, multi-fuzzy soft set, ℒℳℱ??, similarity measure of ℒℳℱ??, Mathematics, QA1-939
Abstract

Many effective tools in fuzzy soft set theory have been proposed to handle various complicated problems in different fields of our real life, especially in decision making. Molodtsov’s soft set theory has been regarded as a newly emerging mathematical tool to deal with uncertainty and vagueness. Lattice ordered multi-fuzzy soft set (LMFSS) has been applied in forecasting process. However, similarity measure is not used in this application. In our research, similarity measure of LMFSS is proposed to calculate the similarity between two LMFSSs. Moreover, some of its properties are introduced and proved. Finally, an application of LMFSS in decision making using similarity measure is analysed.

Published
2020
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49. Entropy Measures for Plithogenic Sets and Applications in Multi-Attribute Decision Making

Author

Shio Gai Quek, Ganeshsree Selvachandran, Florentin Smarandache, J. Vimala, Son Hoang Le, Quang-Thinh Bui, and Vassilis C. Gerogiannis

Subjects
neutrosophic set, plithogenic set, fuzzy set, entropy, similarity measure, information measure, Mathematics, QA1-939
Abstract

Plithogenic set is an extension of the crisp set, fuzzy set, intuitionistic fuzzy set, and neutrosophic sets, whose elements are characterized by one or more attributes, and each attribute can assume many values. Each attribute has a corresponding degree of appurtenance of the element to the set with respect to the given criteria. In order to obtain a better accuracy and for a more exact exclusion (partial order), a contradiction or dissimilarity degree is defined between each attribute value and the dominant attribute value. In this paper, entropy measures for plithogenic sets have been introduced. The requirements for any function to be an entropy measure of plithogenic sets are outlined in the axiomatic definition of the plithogenic entropy using the axiomatic requirements of neutrosophic entropy. Several new formulae for the entropy measure of plithogenic sets are also introduced. The newly introduced entropy measures are then applied to a multi-attribute decision making problem related to the selection of locations.

Published
2020
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50. On Bipolar Fuzzy Gradation of Openness

Author

Subhadip Roy, Jeong-Gon Lee, Syamal Kumar Samanta, Anita Pal, and Ganeshsree Selvachandran

Subjects
bipolar gradation of openness, bipolar gradation of closedness, bipolar fuzzy topology, bipolar gradation preserving map, Mathematics, QA1-939
Abstract

The concept of bipolar fuzziness is of relatively recent origin where in addition to the presence of a property, which is done in fuzzy theory, the presence of its counter-property is also taken into consideration. This seems to be much natural and realistic. In this paper, an attempt has been made to incorporate this bipolar fuzziness in topological perspective. This is done by introducing a notion of bipolar gradation of openness and to redefine the bipolar fuzzy topology. Furthermore, a notion of bipolar gradation preserving map is given. A concept of bipolar fuzzy closure operator is also introduced and its characteristic properties are studied. A decomposition theorem involving our bipolar gradation of openness and Chang type bipolar fuzzy topology is established. Finally, some categorical results of bipolar fuzzy topology (both Chang type and in our sense) are proved.

Published
2020
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