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151. Predictors of Early Introduction of Core and Discretionary Foods in Australian Infants-Results from HSHK Birth Cohort Study

Author

Manohar N, Hayen A, Bhole S, and Arora A

Subjects
0908 Food Sciences, 1111 Nutrition and Dietetics
Abstract

Early introduction of complementary foods can have a detrimental impact on children's long-term health. This study examined the timing and determinants of early introduction of core and discretionary foods among infants in Sydney, Australia. Mothers (n = 1035) from an ongoing population-based birth cohort study were interviewed at 8, 17, 34 and 52 weeks postpartum. The outcome was 'age at which particular core and discretionary food items were first introduced'. Multivariable logistic regression models were used to investigate family and infant-related determinants of early introduction of core (

Published
2020

152. Obesity and dental caries in early childhood: A systematic review and meta-analyses

Author

Manohar N, Hayen A, Fahey P, and Arora A

Subjects
Endocrinology & Metabolism, 11 Medical and Health Sciences, 17 Psychology and Cognitive Sciences
Abstract

Obesity and dental caries in children are significant health problems. The aims of this review are to identify whether children aged 6 years and younger with overweight and/or obesity have higher dental caries experience compared with children with normal weight and, secondly, to identify the common risk factors associated with both conditions. Medline, Embase, and seven other databases were systematically searched followed by lateral searches from reference lists, grey literature, theses, conference proceedings, and contacting field experts. Longitudinal observational studies addressing overweight and/or obesity and dental caries in children aged 6 years and younger were included. A random effects model meta-analyses were applied. Nine studies were included in this review. Children with overweight and obesity had a significantly higher dental caries experience compared with children with normal weight (n = 6). The pooled estimates showed that difference in caries experience between the two groups was statistically significant. Low levels of parental income and education were identified to be associated with both conditions in the sample population. Children with overweight and obesity are more vulnerable to dental caries. Low levels of parental income and education influence the relationship between the two conditions. However, the quality of evidence varied considerably; therefore, findings should be interpreted cautiously.

Published
2019

153. Mapping geographical inequalities in oral rehydration therapy coverage in low-income and middle-income countries, 2000–17

Author

Wiens, K. E. (Kirsten E.), Lindstedt, P. A. (Paulina A.), Blacker, B. F. (Brigette F.), Johnson, K. B. (Kimberly B.), Baumann, M. M. (Mathew M.), Schaeffer, L. E. (Lauren E.), Abbastabar, H. (Hedayat), Abd-Allah, F. (Foad), Abdelalim, A. (Ahmed), Abdollahpour, I. (Ibrahim), Abegaz, K. H. (Kedir Hussein), Abejie, A. N. (Ayenew Negesse), Abreu, L. G. (Lucas Guimaraes), Abrigo, M. R. (Michael R. M.), Abualhasan, A. (Ahmed), Accrombessi, M. M. (Manfred Mario Kokou), Acharya, D. (Dilaram), Adabi, M. (Maryam), Adamu, A. A. (Abdu A.), Adebayo, O. M. (Oladimeji M.), Adedoyin, R. A. (Rufus Adesoji), Adekanmbi, V. (Victor), Adetokunboh, O. O. (Olatunji O.), Adhena, B. M. (Beyene Meressa), Afarideh, M. (Mohsen), Ahmad, S. (Sohail), Ahmadi, K. (Keivan), Ahmed, A. E. (Anwar E.), Ahmed, M. B. (Muktar Beshir), Ahmed, R. (Rushdia), Akalu, T. Y. (Temesgen Yihunie), Alahdab, F. (Fares), Al-Aly, Z. (Ziyad), Alam, N. (Noore), Alam, S. (Samiah), Alamene, G. M. (Genet Melak), Alanzi, T. M. (Turki M.), Alcalde-Rabanal, J. E. (Jacqueline Elizabeth), Ali, B. A. (Beriwan Abdulqadir), Alijanzadeh, M. (Mehran), Alipour, V. (Vahid), Aljunid, S. M. (Syed Mohamed), Almasi, A. (Ali), Almasi-Hashiani, A. (Amir), Al-Mekhlafi, H. M. (Hesham M.), Altirkawi, K. A. (Khalid A.), Alvis-Guzman, N. (Nelson), Alvis-Zakzuk, N. J. (Nelson J.), Amini, S. (Saeed), Amit, A. M. (Arianna Maever L.), Andrei, C. L. (Catalina Liliana), Anjomshoa, M. (Mina), Anoushiravani, A. (Amir), Ansari, F. (Fereshteh), Antonio, C. A. (Carl Abelardo T.), Antony, B. (Benny), Antriyandarti, E. (Ernoiz), Arabloo, J. (Jalal), Aref, H. M. (Hany Mohamed Amin), Aremu, O. (Olatunde), Armoon, B. (Bahram), Arora, A. (Amit), Aryal, K. K. (Krishna K.), Arzani, A. (Afsaneh), Asadi-Aliabadi, M. (Mehran), Atalay, H. T. (Hagos Tasew), Athari, S. S. (Seyyed Shamsadin), Athari, S. M. (Seyyede Masoume), Atre, S. R. (Sachin R.), Ausloos, M. (Marcel), Awoke, N. (Nefsu), Quintanilla, B. P. (Beatriz Paulina Ayala), Ayano, G. (Getinet), Ayanore, M. A. (Martin Amogre), Aynalem, Y. A. (Yared Asmare), Azari, S. (Samad), Azzopardi, P. S. (Peter S.), Babaee, E. (Ebrahim), Babalola, T. K. (Tesleem Kayode), Badawi, A. (Alaa), Bairwa, M. (Mohan), Bakkannavar, S. M. (Shankar M.), Balakrishnan, S. (Senthilkumar), Bali, A. G. (Ayele Geleto), Banach, M. (Maciej), Banoub, J. A. (Joseph Adel Mattar), Barac, A. (Aleksandra), Barnighausen, T. W. (Till Winfried), Basaleem, H. (Huda), Basu, S. (Sanjay), Bay, V. D. (Vo Dinh), Bayati, M. (Mohsen), Baye, E. (Estifanos), Bedi, N. (Neeraj), Beheshti, M. (Mahya), Behzadifar, M. (Masoud), Behzadifar, M. (Meysam), Bekele, B. B. (Bayu Begashaw), Belayneh, Y. M. (Yaschilal Muche), Bell, M. L. (Michelle L.), Bennett, D. A. (Derrick A.), Berbada, D. A. (Dessalegn Ajema), Bernstein, R. S. (Robert S.), Bhat, A. G. (Anusha Ganapati), Bhattacharyya, K. (Krittika), Bhattarai, S. (Suraj), Bhaumik, S. (Soumyadeep), Bhutta, Z. A. (Zulfiqar A.), Bijani, A. (Ali), Bikbov, B. (Boris), Birihane, B. M. (Binyam Minuye), Biswas, R. K. (Raaj Kishore), Bohlouli, S. (Somayeh), Bojia, H. A. (Hunduma Amensisa), Boufous, S. (Soufiane), Brady, O. J. (Oliver J.), Bragazzi, N. L. (Nicola Luigi), Briko, A. N. (Andrey Nikolaevich), Briko, N. I. (Nikolay Ivanovich), Britton, G. B. (Gabrielle B.), Nagaraja, S. B. (Sharath Burugina), Busse, R. (Reinhard), Butt, Z. A. (Zahid A.), Camera, L. A. (Luis Alberto), Campos-Nonato, I. R. (Ismael R.), Cano, J. (Jorge), Car, J. (Josip), Cardenas, R. (Rosario), Carvalho, F. (Felix), Castaneda-Orjuela, C. A. (Carlos A.), Castro, F. (Franz), Chanie, W. F. (Wagaye Fentahun), Chatterjee, P. (Pranab), Chattu, V. K. (Vijay Kumar), Chichiabellu, T. Y. (Tesfaye Yitna), Chin, K. L. (Ken Lee), Christopher, D. J. (Devasahayam J.), Chu, D.-T. (Dinh-Toi), Cormier, N. M. (Natalie Maria), Costa, V. M. (Vera Marisa), Culquichicon, C. (Carlos), Daba, M. S. (Matiwos Soboka), Damiani, G. (Giovanni), Dandona, L. (Lalit), Dandona, R. (Rakhi), Dang, A. K. (Anh Kim), Darwesh, A. M. (Aso Mohammad), Darwish, A. H. (Amira Hamed), Daryani, A. (Ahmad), Das, J. K. (Jai K.), Das Gupta, R. (Rajat), Dash, A. P. (Aditya Prasad), Davey, G. (Gail), Davila-Cervantes, C. A. (Claudio Alberto), Davis, A. C. (Adrian C.), Davitoiu, D. V. (Dragos Virgil), De la Hoz, F. P. (Fernando Pio), Demis, A. B. (Asmamaw Bizuneh), Demissie, D. B. (Dereje Bayissa), Demissie, G. D. (Getu Debalkie), Demoz, G. T. (Gebre Teklemariam), Denova-Gutierrez, E. (Edgar), Deribe, K. (Kebede), Desalew, A. (Assefa), Deshpande, A. (Aniruddha), Dharmaratne, S. D. (Samath Dhamminda), Dhillon, P. (Preeti), Dhimal, M. (Meghnath), Dhungana, G. P. (Govinda Prasad), Diaz, D. (Daniel), Dipeolu, I. O. (Isaac Oluwafemi), Djalalinia, S. (Shirin), Doyle, K. E. (Kerrie E.), Dubljanin, E. (Eleonora), Duko, B. (Bereket), Duraes, A. R. (Andre Rodrigues), Kalan, M. E. (Mohammad Ebrahimi), Edinur, H. A. (Hisham Atan), Effiong, A. (Andem), Eftekhari, A. (Aziz), El Nahas, N. (Nevine), El Sayed, I. (Iman), Zaki, M. E. (Maysaa El Sayed), El Tantawi, M. (Maha), Elema, T. B. (Teshome Bekele), Elhabashy, H. R. (Hala Rashad), El-Jaafary, S. I. (Shaimaa I.), Elkout, H. (Hajer), Elsharkawy, A. (Aisha), Elyazar, I. R. (Iqbal R. F.), Endalamaw, A. (Aklilu), Endalew, D. A. (Daniel Adane), Eskandarieh, S. (Sharareh), Esteghamati, A. (Alireza), Esteghamati, S. (Sadaf), Etemadi, A. (Arash), Ezekannagha, O. (Oluchi), Fareed, M. (Mohammad), Faridnia, R. (Roghiyeh), Farzadfar, F. (Farshad), Fazlzadeh, M. (Mehdi), Feigin, V. L. (Valery L.), Fereshtehnejad, S.-M. (Seyed-Mohammad), Fernandes, E. (Eduarda), Filip, I. (Irina), Fischer, F. (Florian), Foigt, N. A. (Nataliya A.), Folayan, M. O. (Morenike Oluwatoyin), Foroutan, M. (Masoud), Franklin, R. C. (Richard Charles), Fukumoto, T. (Takeshi), Gad, M. M. (Mohamed M.), Gayesa, R. T. (Reta Tsegaye), Gebre, T. (Teshome), Gebremedhin, K. B. (Ketema Bizuwork), Gebremeskel, G. G. (Gebreamlak Gebremedhn), Gesesew, H. A. (Hailay Abrha), Gezae, K. E. (Kebede Embaye), Ghadiri, K. (Keyghobad), Ghashghaee, A. (Ahmad), Ghimire, P. R. (Pramesh Raj), Gill, P. S. (Paramjit Singh), Gill, T. K. (Tiffany K.), Ginindza, T. G. (Themba G.), Gomes, N. G. (Nelson G. M.), Gopalani, S. V. (Sameer Vali), Goulart, A. C. (Alessandra C.), Goulart, B. N. (Barbara Niegia Garcia), Grada, A. (Ayman), Gubari, M. I. (Mohammed Ibrahim Mohialdeen), Gugnani, H. C. (Harish Chander), Guido, D. (Davide), Guimaraes, R. A. (Rafael Alves), Guo, Y. (Yuming), Gupta, R. (Rajeev), Hafezi-Nejad, N. (Nima), Haile, D. H. (Dessalegn H.), Hailu, G. B. (Gessessew Bugssa), Haj-Mirzaian, A. (Arvin), Haj-Mirzaian, A. (Arya), Hamadeh, R. R. (Randah R.), Hamidi, S. (Samer), Handiso, D. W. (Demelash Woldeyohannes), Haririan, H. (Hamidreza), Hariyani, N. (Ninuk), Hasaballah, A. I. (Ahmed I.), Hasan, M. M. (Md Mehedi), Hasanpoor, E. (Edris), Hasanzadeh, A. (Amir), Hassankhani, H. (Hadi), Hassen, H. Y. (Hamid Yimam), Hegazy, M. I. (Mohamed I.), Heibati, B. (Behzad), Heidari, B. (Behnam), Hendrie, D. (Delia), Henry, N. J. (Nathaniel J.), Herteliu, C. (Claudiu), Heydarpour, F. (Fatemeh), de Hidru, H. D. (Hagos Degefa), Hird, T. R. (Thomas R.), Hoang, C. L. (Chi Linh), Rad, E. H. (Enayatollah Homaie), Hoogar, P. (Praveen), Hoseini, M. (Mohammad), Hossain, N. (Naznin), Hosseini, M. (Mostafa), Hosseinzadeh, M. (Mehdi), Househ, M. (Mowafa), Hsairi, M. (Mohamed), Hu, G. (Guoqing), Hussen, M. M. (Mohammedaman Mama), Ibitoye, S. E. (Segun Emmanuel), Igumbor, E. U. (Ehimario U.), Ilesanmi, O. S. (Olayinka Stephen), Ilic, M. D. (Milena D.), Imani-Nasab, M. H. (Mohammad Hasan), Iqbal, U. (Usman), Irvani, S. S. (Seyed Sina Naghibi), Islam, S. M. (Sheikh Mohammed Shariful), Iwu, C. J. (Chinwe Juliana), Izadi, N. (Neda), Jaca, A. (Anelisa), Jahanmehr, N. (Nader), Jakovljevic, M. (Mihajlo), Jalali, A. (Amir), Jayatilleke, A. U. (Achala Upendra), Jha, R. P. (Ravi Prakash), Jha, V. (Vivekanand), Ji, J. S. (John S.), Jonas, J. B. (Jost B.), Jozwiak, J. J. (Jacek Jerzy), Kabir, A. (Ali), Kabir, Z. (Zubair), Kahsay, A. (Amaha), Kalani, H. (Hamed), Kanchan, T. (Tanuj), Matin, B. K. (Behzad Karami), Karch, A. (Andre), Karim, M. A. (Mohd Anisul), Karki, H. K. (Hamidreza Karimi-Sari Surendra), Kasaeian, A. (Amir), Kasahun, G. G. (Gebremicheal Gebreslassie), Kasahun, Y. C. (Yawukal Chane), Kasaye, H. K. (Habtamu Kebebe), Kassa, G. G. (Gebrehiwot G.), Kassa, G. M. (Getachew Mullu), Kayode, G. A. (Gbenga A.), Karyani, A. K. (Ali Kazemi), Kebede, M. M. (Mihiretu M.), Keiyoro, P. N. (Peter Njenga), Kelbore, A. G. (Abraham Getachew), Kengne, A. P. (Andre Pascal), Ketema, D. B. (Daniel Bekele), Khader, Y. S. (Yousef Saleh), Khafaie, M. A. (Morteza Abdullatif), Khalid, N. (Nauman), Khalilov, R. (Rovshan), Khan, E. A. (Ejaz Ahmad), Khan, J. (Junaid), Khan, M. N. (Md Nuruzzaman), Khan, M. S. (Muhammad Shahzeb), Khatab, K. (Khaled), Khater, A. M. (Amir M.), Khater, M. M. (Mona M.), Khayamzadeh, M. (Maryam), Khazaei, M. (Mohammad), Khazaei, S. (Salman), Khosravi, M. H. (Mohammad Hossein), Khubchandani, J. (Jagdish), Kiadaliri, A. (Ali), Kim, Y. J. (Yun Jin), Kimokoti, R. W. (Ruth W.), Kisa, A. (Adnan), Kisa, S. (Sezer), Kissoon, N. (Niranjan), Shivakumar, K. M. (K. M.), Kochhar, S. (Sonali), Kolola, T. (Tufa), Komaki, H. (Hamidreza), Kosen, S. (Soewarta), Koul, P. A. (Parvaiz A.), Koyanagi, A. (Ai), Kraemer, M. U. (Moritz U. G.), Krishan, K. (Kewal), Kugbey, N. (Nuworza), Kumar, G. A. (G. Anil), Kumar, M. (Manasi), Kumar, P. (Pushpendra), Kusuma, V. K. (Vivek Kumar Dian), La Vecchia, C. (Carlo), Lacey, B. (Ben), Lad, S. D. (Sheetal D.), Lal, D. K. (Dharmesh Kumar), Lam, F. (Felix), Lami, F. H. (Faris Hasan), Lamichhane, P. (Prabhat), Lansingh, V. C. (Van Charles), Lasrado, S. (Savita), Laxmaiah, A. (Avula), Lee, P. H. (Paul H.), LeGrand, K. E. (Kate E.), Leili, M. (Mostafa), Lenjebo, T. L. (Tsegaye Lolaso), Leshargie, C. T. (Cheru Tesema), Levine, A. J. (Aubrey J.), Li, S. (Shanshan), Linn, S. (Shai), Liu, S. (Shiwei), Liu, S. (Simin), Lodha, R. (Rakesh), Longbottom, J. (Joshua), Lopez, J. C. (Jaifred Christian F.), Abd El Razek, H. M. (Hassan Magdy), Abd El Razek, M. M. (Muhammed Magdy), Prasad, D. R. (D. R. Mahadeshwara), Mahasha, P. W. (Phetole Walter), Mahotra, N. B. (Narayan B.), Majeed, A. (Azeem), Malekzadeh, R. (Reza), Malta, D. C. (Deborah Carvalho), Mamun, A. A. (Abdullah A.), Manafi, N. (Navid), Manda, A. L. (Ana Laura), Manohar, N. D. (Narendar Dawani Dawanu), Mansournia, M. A. (Mohammad Ali), Mapoma, C. C. (Chabila Christopher), Maravilla, J. C. (Joemer C.), Martinez, G. (Gabriel), Martini, S. (Santi), Martins-Melo, F. R. (Francisco Rogerlandio), Masaka, A. (Anthony), Massenburg, B. B. (Benjamin Ballard), Mathur, M. R. (Manu Raj), Mayala, B. K. (Benjamin K.), Mazidi, M. (Mohsen), McAlinden, C. (Colm), Meharie, B. G. (Birhanu Geta), Mehndiratta, M. M. (Man Mohan), Mehta, K. M. (Kala M.), Mekonnen, T. C. (Tefera Chane), Meles, G. G. (Gebrekiros Gebremichael), Memiah, P. T. (Peter T. N.), Memish, Z. A. (Ziad A.), Mendoza, W. (Walter), Menezes, R. G. (Ritesh G.), Mereta, S. T. (Seid Tiku), Meretoja, T. J. (Tuomo J.), Mestrovic, T. (Tomislav), Miazgowski, B. (Bartosz), Mihretie, K. M. (Kebadnew Mulatu), Miller, T. R. (Ted R.), Mini, G. K. (G. K.), Mirrakhimov, E. M. (Erkin M.), Moazen, B. (Babak), Mohajer, B. (Bahram), Mohamadi-Bolbanabad, A. (Amjad), Mohammad, D. K. (Dara K.), Mohammad, K. A. (Karzan Abdulmuhsin), Mohammad, Y. (Yousef), Mezerji, N. M. (Naser Mohammad Gholi), Mohammadibakhsh, R. (Roghayeh), Mohammadifard, N. (Noushin), Mohammed, J. A. (Jemal Abdu), Mohammed, S. (Shafiu), Mohebi, F. (Farnam), Mokdad, A. H. (Ali H.), Molokhia, M. (Mariam), Monasta, L. (Lorenzo), Moodley, Y. (Yoshan), Moore, C. E. (Catrin E.), Moradi, G. (Ghobad), Moradi, M. (Masoud), Moradi-Joo, M. (Mohammad), Moradi-Lakeh, M. (Maziar), Moraga, P. (Paula), Morales, L. (Linda), Velasquez, I. M. (Ilais Moreno), Mosapour, A. (Abbas), Mouodi, S. (Simin), Mousavi, S. M. (Seyyed Meysam), Mozaffor, M. (Miliva), Muchie, K. F. (Kindie Fentahun), Mulaw, G. F. (Getahun Fentaw), Munro, S. B. (Sandra B.), Muriithi, M. K. (Moses K.), Murray, C. J. (Christoper J. L.), Murthy, G. V. (G. V. S.), Musa, K. I. (Kamarul Imran), Mustafa, G. (Ghulam), Muthupandian, S. (Saravanan), Nabhan, A. F. (Ashraf F.), Naderi, M. (Mehdi), Nagarajan, A. J. (Ahamarshan Jayaraman), Naidoo, K. S. (Kovin S.), Naik, G. (Gurudatta), Najafi, F. (Farid), Nangia, V. (Vinay), Nansseu, J. R. (Jobert Richie), Nascimento, B. R. (Bruno Ramos), Nazari, J. (Javad), Ndwandwe, D. E. (Duduzile Edith), Negoi, I. (Ionut), Netsere, H. B. (Henok Biresaw), Ngunjiri, J. W. (Josephine W.), Nguyen, C. T. (Cuong Tat), Nguyen, H. L. (Huong Lan Thi), Nguyen, T. H. (Trang Huyen), Nigatu, D. (Dabere), Nigatu, S. G. (Solomon Gedlu), Ningrum, D. N. (Dina Nur Anggraini), Nnaji, C. A. (Chukwudi A.), Nojomi, M. (Marzieh), Nong, V. M. (Vuong Minh), Norheim, O. F. (Ole F.), Noubiap, J. J. (Jean Jacques), Motlagh, S. N. (Soraya Nouraei), Oancea, B. (Bogdan), Ogah, O. S. (Okechukwu Samuel), Ogbo, F. A. (Felix Akpojene), Oh, I.-H. (In-Hwan), Olagunju, A. T. (Andrew T.), Olagunju, T. O. (Tinuke O.), Olusanya, B. O. (Bolajoko Olubukunola), Olusanya, J. O. (Jacob Olusegun), Onwujekwe, O. E. (Obinna E.), Oren, E. (Eyal), Ortega-Altamirano, D. V. (Doris V.), Osarenotor, O. (Osayomwanbo), Osei, F. B. (Frank B.), Owolabi, M. O. (Mayowa O.), Mahesh, P. A. (P. A.), Padubidri, J. R. (Jagadish Rao), Pakhale, S. (Smita), Patel, S. K. (Sangram Kishor), Paternina-Caicedo, A. J. (Angel J.), Pathak, A. (Ashish), Patton, G. C. (George C.), Paudel, D. (Deepak), Paulos, K. (Kebreab), Pepito, V. C. (Veincent Christian Filipino), Pereira, A. (Alexandre), Perico, N. (Norberto), Pervaiz, A. (Aslam), Pescarini, J. M. (Julia Moreira), Piroozi, B. (Bakhtiar), Pirsaheb, M. (Meghdad), Postma, M. J. (Maarten J.), Pourjafar, H. (Hadi), Pourmalek, F. (Farshad), Pourshams, A. (Akram), Poustchi, H. (Hossein), Prada, S. I. (Sergio I.), Prasad, N. (Narayan), Preotescu, L. (Liliana), Quintana, H. (Hedley), Rabiee, N. (Navid), Radfar, A. (Amir), Rafiei, A. (Alireza), Rahim, F. (Fakher), Rahimi-Movaghar, A. (Afarin), Rahimi-Movaghar, V. (Vafa), Rahman, M. H. (Mohammad Hifz Ur), Rahman, M. A. (Muhammad Aziz), Rahman, S. (Shafiur), Rajati, F. (Fatemeh), Rana, S. M. (Saleem Muhammad), Ranabhat, C. L. (Chhabi Lal), Rasella, D. (Davide), Rawaf, D. L. (David Laith), Rawaf, S. (Salman), Rawal, L. (Lal), Rawasia, W. F. (Wasiq Faraz), Renjith, V. (Vishnu), Renzaho, A. M. (Andre M. N.), Resnikoff, S. (Serge), Reta, M. A. (Melese Abate), Rezaei, N. (Negar), Rezai, M. S. (Mohammad Sadegh), Riahi, S. M. (Seyed Mohammad), Ribeiro, A. I. (Ana Isabel), Rickard, J. (Jennifer), Rios-Blancas, M. (Maria), Roever, L. (Leonardo), Ronfani, L. (Luca), Roro, E. M. (Elias Merdassa), Ross, J. M. (Jennifer M.), Rubagotti, E. (Enrico), Rubino, S. (Salvatore), Saad, A. M. (Anas M.), Sabde, Y. D. (Yogesh Damodar), Sabour, S. (Siamak), Sadeghi, E. (Ehsan), Safari, Y. (Yahya), Safari-Faramani, R. (Roya), Sagar, R. (Rajesh), Sahebkar, A. (Amirhossein), Sahraian, M. A. (Mohammad Ali), Sajadi, S. M. (S. Mohammad), Salahshoor, M. R. (Mohammad Reza), Salam, N. (Nasir), Salamati, P. (Payman), Salem, H. (Hosni), Salem, M. R. (Marwa Rashad), Salimi, Y. (Yahya), Salimzadeh, H. (Hamideh), Samy, A. M. (Abdallah M.), Sanabria, J. (Juan), Santric-Milicevic, M. M. (Milena M.), Jose, B. P. (Bruno Piassi Sao), Saraswathy, S. Y. (Sivan Yegnanarayana Iyer), Sarkar, K. (Kaushik), Sarker, A. R. (Abdur Razzaque), Sarrafzadegan, N. (Nizal), Sartorius, B. (Benn), Sathian, B. (Brijesh), Sathish, T. (Thirunavukkarasu), Sawhney, M. (Monika), Saxena, S. (Sonia), Schwebel, D. C. (David C.), Senbeta, A. M. (Anbissa Muleta), Senthilkumaran, S. (Subramanian), Sepanlou, S. G. (Sadaf G.), Servan-Mori, E. (Edson), Shabaninejad, H. (Hosein), Shafieesabet, A. (Azadeh), Shaikh, M. A. (Masood Ali), Shalash, A. S. (Ali S.), Shallo, S. A. (Seifadin Ahmed), Shams-Beyranvand, M. (Mehran), Shamsi, M. (MohammadBagher), Shamsizadeh, M. (Morteza), Shannawaz, M. (Mohammed), Sharafi, K. (Kiomars), Sharifi, H. (Hamid), Shehata, H. S. (Hatem Samir), Sheikh, A. (Aziz), Shetty, B. S. (B. Suresh Kumar), Shibuya, K. (Kenji), Shiferaw, W. S. (Wondimeneh Shibabaw), Shifti, D. M. (Desalegn Markos), Shigematsu, M. (Mika), Il Shin, J. (Jae), Shiri, R. (Rahman), Shirkoohi, R. (Reza), Siabani, S. (Soraya), Siddiqi, T. J. (Tariq Jamal), Silva, D. A. (Diego Augusto Santos), Singh, A. (Ambrish), Singh, J. A. (Jasvinder A.), Singh, N. P. (Narinder Pal), Singh, V. (Virendra), Sisay, M. M. (Malede Mequanent), Skiadaresi, E. (Eirini), Sobhiyeh, M. R. (Mohammad Reza), Sokhan, A. (Anton), Soltani, S. (Shahin), Somayaji, R. (Ranjani), Soofi, M. (Moslem), Sorrie, M. B. (Muluken Bekele), Soyiri, I. N. (Ireneous N.), Sreeramareddy, C. T. (Chandrashekhar T.), Sudaryanto, A. (Agus), Sufiyan, M. B. (Mu'awiyyah Babale), Suleria, H. A. (Hafiz Ansar Rasul), Sultana, M. (Marufa), Sunguya, B. F. (Bruno Fokas), Sykes, B. L. (Bryan L.), Tabares-Seisdedos, R. (Rafael), Tabuchi, T. (Takahiro), Tadesse, D. B. (Degena Bahrey), Tarigan, I. U. (Ingan Ukur), Tasew, A. A. (Aberash Abay), Tefera, Y. M. (Yonatal Mesfin), Tekle, M. G. (Merhawi Gebremedhin), Temsah, M.-H. (Mohamad-Hani), Tesfay, B. E. (Berhe Etsay), Tesfay, F. H. (Fisaha Haile), Tessema, B. (Belay), Tessema, Z. T. (Zemenu Tadesse), Thankappan, K. R. (Kavumpurathu Raman), Thomas, N. (Nihal), Toma, A. (Alemayehu), Topor-Madry, R. (Roman), Tovani-Palone, M. R. (Marcos Roberto), Traini, E. (Eugenio), Tran, B. X. (Bach Xuan), Tran, K. B. (Khanh Bao), Ullah, I. (Irfan), Unnikrishnan, B. (Bhaskaran), Usman, M. S. (Muhammad Shariq), Uzochukwu, B. S. (Benjamin S. Chudi), Valdez, P. R. (Pascual R.), Varughese, S. (Santosh), Violante, F. S. (Francesco S.), Vollmer, S. (Sebastian), Hawariat, F. G. (Feleke Gebremeskel W.), Waheed, Y. (Yasir), Wallin, M. T. (Mitchell Taylor), Wang, Y. (Yafeng), Wang, Y.-P. (Yuan-Pang), Weaver, M. (Marcia), Weji, B. G. (Bedilu Girma), Weldesamuel, G. T. (Girmay Teklay), Welgan, C. A. (Catherine A.), Werdecker, A. (Andrea), Westerman, R. (Ronny), Wiangkham, T. (Taweewat), Wiysonge, C. S. (Charles Shey), Wolde, H. F. (Haileab Fekadu), Wondafrash, D. Z. (Dawit Zewdu), Wonde, T. E. (Tewodros Eshete), Worku, G. T. (Getasew Taddesse), Wu, A.-M. (Ai-Min), Xu, G. (Gelin), Yadollahpour, A. (Ali), Jabbari, S. H. (Seyed Hossein Yahyazadeh), Yamada, T. (Tomohide), Yatsuya, H. (Hiroshi), Yeshaneh, A. (Alex), Yilgwan, C. S. (Christopher Sabo), Yilma, M. T. (Mekdes Tigistu), Yip, P. (Paul), Yisma, E. (Engida), Yonemoto, N. (Naohiro), Yoon, S.-J. (Seok-Jun), Younis, M. Z. (Mustafa Z.), Yousefifard, M. (Mahmoud), Yousof, H. S. (Hebat-Allah Salah A.), Yu, C. (Chuanhua), Yusefzadeh, H. (Hasan), Zadey, S. (Siddhesh), Zaidi, Z. (Zoubida), Bin Zaman, S. (Sojib), Zamani, M. (Mohammad), Zandian, H. (Hamed), Zepro, N. B. (Nejimu Biza), Zerfu, T. A. (Taddese Alemu), Zhang, Y. (Yunquan), Zhao, X. G. (Xiu-Ju George), Ziapour, A. (Arash), Zodpey, S. (Sanjay), Zuniga, Y. M. (Yves Miel H.), Hay, S. I. (Simon I.), Reiner, R. C. (Robert C., Jr.), Wiens, K. E. (Kirsten E.), Lindstedt, P. A. (Paulina A.), Blacker, B. F. (Brigette F.), Johnson, K. B. (Kimberly B.), Baumann, M. M. (Mathew M.), Schaeffer, L. E. (Lauren E.), Abbastabar, H. (Hedayat), Abd-Allah, F. (Foad), Abdelalim, A. (Ahmed), Abdollahpour, I. (Ibrahim), Abegaz, K. H. (Kedir Hussein), Abejie, A. N. (Ayenew Negesse), Abreu, L. G. (Lucas Guimaraes), Abrigo, M. R. (Michael R. M.), Abualhasan, A. (Ahmed), Accrombessi, M. M. (Manfred Mario Kokou), Acharya, D. (Dilaram), Adabi, M. (Maryam), Adamu, A. A. (Abdu A.), Adebayo, O. M. (Oladimeji M.), Adedoyin, R. A. (Rufus Adesoji), Adekanmbi, V. (Victor), Adetokunboh, O. O. (Olatunji O.), Adhena, B. M. (Beyene Meressa), Afarideh, M. (Mohsen), Ahmad, S. (Sohail), Ahmadi, K. (Keivan), Ahmed, A. E. (Anwar E.), Ahmed, M. B. (Muktar Beshir), Ahmed, R. (Rushdia), Akalu, T. Y. (Temesgen Yihunie), Alahdab, F. (Fares), Al-Aly, Z. (Ziyad), Alam, N. (Noore), Alam, S. (Samiah), Alamene, G. M. (Genet Melak), Alanzi, T. M. (Turki M.), Alcalde-Rabanal, J. E. (Jacqueline Elizabeth), Ali, B. A. (Beriwan Abdulqadir), Alijanzadeh, M. (Mehran), Alipour, V. (Vahid), Aljunid, S. M. (Syed Mohamed), Almasi, A. (Ali), Almasi-Hashiani, A. (Amir), Al-Mekhlafi, H. M. (Hesham M.), Altirkawi, K. A. (Khalid A.), Alvis-Guzman, N. (Nelson), Alvis-Zakzuk, N. J. (Nelson J.), Amini, S. (Saeed), Amit, A. M. (Arianna Maever L.), Andrei, C. L. (Catalina Liliana), Anjomshoa, M. (Mina), Anoushiravani, A. (Amir), Ansari, F. (Fereshteh), Antonio, C. A. (Carl Abelardo T.), Antony, B. (Benny), Antriyandarti, E. (Ernoiz), Arabloo, J. (Jalal), Aref, H. M. (Hany Mohamed Amin), Aremu, O. (Olatunde), Armoon, B. (Bahram), Arora, A. (Amit), Aryal, K. K. (Krishna K.), Arzani, A. (Afsaneh), Asadi-Aliabadi, M. (Mehran), Atalay, H. T. (Hagos Tasew), Athari, S. S. (Seyyed Shamsadin), Athari, S. M. (Seyyede Masoume), Atre, S. R. (Sachin R.), Ausloos, M. (Marcel), Awoke, N. (Nefsu), Quintanilla, B. P. (Beatriz Paulina Ayala), Ayano, G. (Getinet), Ayanore, M. A. (Martin Amogre), Aynalem, Y. A. (Yared Asmare), Azari, S. (Samad), Azzopardi, P. S. (Peter S.), Babaee, E. (Ebrahim), Babalola, T. K. (Tesleem Kayode), Badawi, A. (Alaa), Bairwa, M. (Mohan), Bakkannavar, S. M. (Shankar M.), Balakrishnan, S. (Senthilkumar), Bali, A. G. (Ayele Geleto), Banach, M. (Maciej), Banoub, J. A. (Joseph Adel Mattar), Barac, A. (Aleksandra), Barnighausen, T. W. (Till Winfried), Basaleem, H. (Huda), Basu, S. (Sanjay), Bay, V. D. (Vo Dinh), Bayati, M. (Mohsen), Baye, E. (Estifanos), Bedi, N. (Neeraj), Beheshti, M. (Mahya), Behzadifar, M. (Masoud), Behzadifar, M. (Meysam), Bekele, B. B. (Bayu Begashaw), Belayneh, Y. M. (Yaschilal Muche), Bell, M. L. (Michelle L.), Bennett, D. A. (Derrick A.), Berbada, D. A. (Dessalegn Ajema), Bernstein, R. S. (Robert S.), Bhat, A. G. (Anusha Ganapati), Bhattacharyya, K. (Krittika), Bhattarai, S. (Suraj), Bhaumik, S. (Soumyadeep), Bhutta, Z. A. (Zulfiqar A.), Bijani, A. (Ali), Bikbov, B. (Boris), Birihane, B. M. (Binyam Minuye), Biswas, R. K. (Raaj Kishore), Bohlouli, S. (Somayeh), Bojia, H. A. (Hunduma Amensisa), Boufous, S. (Soufiane), Brady, O. J. (Oliver J.), Bragazzi, N. L. (Nicola Luigi), Briko, A. N. (Andrey Nikolaevich), Briko, N. I. (Nikolay Ivanovich), Britton, G. B. (Gabrielle B.), Nagaraja, S. B. (Sharath Burugina), Busse, R. (Reinhard), Butt, Z. A. (Zahid A.), Camera, L. A. (Luis Alberto), Campos-Nonato, I. R. (Ismael R.), Cano, J. (Jorge), Car, J. (Josip), Cardenas, R. (Rosario), Carvalho, F. (Felix), Castaneda-Orjuela, C. A. (Carlos A.), Castro, F. (Franz), Chanie, W. F. (Wagaye Fentahun), Chatterjee, P. (Pranab), Chattu, V. K. (Vijay Kumar), Chichiabellu, T. Y. (Tesfaye Yitna), Chin, K. L. (Ken Lee), Christopher, D. J. (Devasahayam J.), Chu, D.-T. (Dinh-Toi), Cormier, N. M. (Natalie Maria), Costa, V. M. (Vera Marisa), Culquichicon, C. (Carlos), Daba, M. S. (Matiwos Soboka), Damiani, G. (Giovanni), Dandona, L. (Lalit), Dandona, R. (Rakhi), Dang, A. K. (Anh Kim), Darwesh, A. M. (Aso Mohammad), Darwish, A. H. (Amira Hamed), Daryani, A. (Ahmad), Das, J. K. (Jai K.), Das Gupta, R. (Rajat), Dash, A. P. (Aditya Prasad), Davey, G. (Gail), Davila-Cervantes, C. A. (Claudio Alberto), Davis, A. C. (Adrian C.), Davitoiu, D. V. (Dragos Virgil), De la Hoz, F. P. (Fernando Pio), Demis, A. B. (Asmamaw Bizuneh), Demissie, D. B. (Dereje Bayissa), Demissie, G. D. (Getu Debalkie), Demoz, G. T. (Gebre Teklemariam), Denova-Gutierrez, E. (Edgar), Deribe, K. (Kebede), Desalew, A. (Assefa), Deshpande, A. (Aniruddha), Dharmaratne, S. D. (Samath Dhamminda), Dhillon, P. (Preeti), Dhimal, M. (Meghnath), Dhungana, G. P. (Govinda Prasad), Diaz, D. (Daniel), Dipeolu, I. O. (Isaac Oluwafemi), Djalalinia, S. (Shirin), Doyle, K. E. (Kerrie E.), Dubljanin, E. (Eleonora), Duko, B. (Bereket), Duraes, A. R. (Andre Rodrigues), Kalan, M. E. (Mohammad Ebrahimi), Edinur, H. A. (Hisham Atan), Effiong, A. (Andem), Eftekhari, A. (Aziz), El Nahas, N. (Nevine), El Sayed, I. (Iman), Zaki, M. E. (Maysaa El Sayed), El Tantawi, M. (Maha), Elema, T. B. (Teshome Bekele), Elhabashy, H. R. (Hala Rashad), El-Jaafary, S. I. (Shaimaa I.), Elkout, H. (Hajer), Elsharkawy, A. (Aisha), Elyazar, I. R. (Iqbal R. F.), Endalamaw, A. (Aklilu), Endalew, D. A. (Daniel Adane), Eskandarieh, S. (Sharareh), Esteghamati, A. (Alireza), Esteghamati, S. (Sadaf), Etemadi, A. (Arash), Ezekannagha, O. (Oluchi), Fareed, M. (Mohammad), Faridnia, R. (Roghiyeh), Farzadfar, F. (Farshad), Fazlzadeh, M. (Mehdi), Feigin, V. L. (Valery L.), Fereshtehnejad, S.-M. (Seyed-Mohammad), Fernandes, E. (Eduarda), Filip, I. (Irina), Fischer, F. (Florian), Foigt, N. A. (Nataliya A.), Folayan, M. O. (Morenike Oluwatoyin), Foroutan, M. (Masoud), Franklin, R. C. (Richard Charles), Fukumoto, T. (Takeshi), Gad, M. M. (Mohamed M.), Gayesa, R. T. (Reta Tsegaye), Gebre, T. (Teshome), Gebremedhin, K. B. (Ketema Bizuwork), Gebremeskel, G. G. (Gebreamlak Gebremedhn), Gesesew, H. A. (Hailay Abrha), Gezae, K. E. (Kebede Embaye), Ghadiri, K. (Keyghobad), Ghashghaee, A. (Ahmad), Ghimire, P. R. (Pramesh Raj), Gill, P. S. (Paramjit Singh), Gill, T. K. (Tiffany K.), Ginindza, T. G. (Themba G.), Gomes, N. G. (Nelson G. M.), Gopalani, S. V. (Sameer Vali), Goulart, A. C. (Alessandra C.), Goulart, B. N. (Barbara Niegia Garcia), Grada, A. (Ayman), Gubari, M. I. (Mohammed Ibrahim Mohialdeen), Gugnani, H. C. (Harish Chander), Guido, D. (Davide), Guimaraes, R. A. (Rafael Alves), Guo, Y. (Yuming), Gupta, R. (Rajeev), Hafezi-Nejad, N. (Nima), Haile, D. H. (Dessalegn H.), Hailu, G. B. (Gessessew Bugssa), Haj-Mirzaian, A. (Arvin), Haj-Mirzaian, A. (Arya), Hamadeh, R. R. (Randah R.), Hamidi, S. (Samer), Handiso, D. W. (Demelash Woldeyohannes), Haririan, H. (Hamidreza), Hariyani, N. (Ninuk), Hasaballah, A. I. (Ahmed I.), Hasan, M. M. (Md Mehedi), Hasanpoor, E. (Edris), Hasanzadeh, A. (Amir), Hassankhani, H. (Hadi), Hassen, H. Y. (Hamid Yimam), Hegazy, M. I. (Mohamed I.), Heibati, B. (Behzad), Heidari, B. (Behnam), Hendrie, D. (Delia), Henry, N. J. (Nathaniel J.), Herteliu, C. (Claudiu), Heydarpour, F. (Fatemeh), de Hidru, H. D. (Hagos Degefa), Hird, T. R. (Thomas R.), Hoang, C. L. (Chi Linh), Rad, E. H. (Enayatollah Homaie), Hoogar, P. (Praveen), Hoseini, M. (Mohammad), Hossain, N. (Naznin), Hosseini, M. (Mostafa), Hosseinzadeh, M. (Mehdi), Househ, M. (Mowafa), Hsairi, M. (Mohamed), Hu, G. (Guoqing), Hussen, M. M. (Mohammedaman Mama), Ibitoye, S. E. (Segun Emmanuel), Igumbor, E. U. (Ehimario U.), Ilesanmi, O. S. (Olayinka Stephen), Ilic, M. D. (Milena D.), Imani-Nasab, M. H. (Mohammad Hasan), Iqbal, U. (Usman), Irvani, S. S. (Seyed Sina Naghibi), Islam, S. M. (Sheikh Mohammed Shariful), Iwu, C. J. (Chinwe Juliana), Izadi, N. (Neda), Jaca, A. (Anelisa), Jahanmehr, N. (Nader), Jakovljevic, M. (Mihajlo), Jalali, A. (Amir), Jayatilleke, A. U. (Achala Upendra), Jha, R. P. (Ravi Prakash), Jha, V. (Vivekanand), Ji, J. S. (John S.), Jonas, J. B. (Jost B.), Jozwiak, J. J. (Jacek Jerzy), Kabir, A. (Ali), Kabir, Z. (Zubair), Kahsay, A. (Amaha), Kalani, H. (Hamed), Kanchan, T. (Tanuj), Matin, B. K. (Behzad Karami), Karch, A. (Andre), Karim, M. A. (Mohd Anisul), Karki, H. K. (Hamidreza Karimi-Sari Surendra), Kasaeian, A. (Amir), Kasahun, G. G. (Gebremicheal Gebreslassie), Kasahun, Y. C. (Yawukal Chane), Kasaye, H. K. (Habtamu Kebebe), Kassa, G. G. (Gebrehiwot G.), Kassa, G. M. (Getachew Mullu), Kayode, G. A. (Gbenga A.), Karyani, A. K. (Ali Kazemi), Kebede, M. M. (Mihiretu M.), Keiyoro, P. N. (Peter Njenga), Kelbore, A. G. (Abraham Getachew), Kengne, A. P. (Andre Pascal), Ketema, D. B. (Daniel Bekele), Khader, Y. S. (Yousef Saleh), Khafaie, M. A. (Morteza Abdullatif), Khalid, N. (Nauman), Khalilov, R. (Rovshan), Khan, E. A. (Ejaz Ahmad), Khan, J. (Junaid), Khan, M. N. (Md Nuruzzaman), Khan, M. S. (Muhammad Shahzeb), Khatab, K. (Khaled), Khater, A. M. (Amir M.), Khater, M. M. (Mona M.), Khayamzadeh, M. (Maryam), Khazaei, M. (Mohammad), Khazaei, S. (Salman), Khosravi, M. H. (Mohammad Hossein), Khubchandani, J. (Jagdish), Kiadaliri, A. (Ali), Kim, Y. J. (Yun Jin), Kimokoti, R. W. (Ruth W.), Kisa, A. (Adnan), Kisa, S. (Sezer), Kissoon, N. (Niranjan), Shivakumar, K. M. (K. M.), Kochhar, S. (Sonali), Kolola, T. (Tufa), Komaki, H. (Hamidreza), Kosen, S. (Soewarta), Koul, P. A. (Parvaiz A.), Koyanagi, A. (Ai), Kraemer, M. U. (Moritz U. G.), Krishan, K. (Kewal), Kugbey, N. (Nuworza), Kumar, G. A. (G. Anil), Kumar, M. (Manasi), Kumar, P. (Pushpendra), Kusuma, V. K. (Vivek Kumar Dian), La Vecchia, C. (Carlo), Lacey, B. (Ben), Lad, S. D. (Sheetal D.), Lal, D. K. (Dharmesh Kumar), Lam, F. (Felix), Lami, F. H. (Faris Hasan), Lamichhane, P. (Prabhat), Lansingh, V. C. (Van Charles), Lasrado, S. (Savita), Laxmaiah, A. (Avula), Lee, P. H. (Paul H.), LeGrand, K. E. (Kate E.), Leili, M. (Mostafa), Lenjebo, T. L. (Tsegaye Lolaso), Leshargie, C. T. (Cheru Tesema), Levine, A. J. (Aubrey J.), Li, S. (Shanshan), Linn, S. (Shai), Liu, S. (Shiwei), Liu, S. (Simin), Lodha, R. (Rakesh), Longbottom, J. (Joshua), Lopez, J. C. (Jaifred Christian F.), Abd El Razek, H. M. (Hassan Magdy), Abd El Razek, M. M. (Muhammed Magdy), Prasad, D. R. (D. R. Mahadeshwara), Mahasha, P. W. (Phetole Walter), Mahotra, N. B. (Narayan B.), Majeed, A. (Azeem), Malekzadeh, R. (Reza), Malta, D. C. (Deborah Carvalho), Mamun, A. A. (Abdullah A.), Manafi, N. (Navid), Manda, A. L. (Ana Laura), Manohar, N. D. (Narendar Dawani Dawanu), Mansournia, M. A. (Mohammad Ali), Mapoma, C. C. (Chabila Christopher), Maravilla, J. C. (Joemer C.), Martinez, G. (Gabriel), Martini, S. (Santi), Martins-Melo, F. R. (Francisco Rogerlandio), Masaka, A. (Anthony), Massenburg, B. B. (Benjamin Ballard), Mathur, M. R. (Manu Raj), Mayala, B. K. (Benjamin K.), Mazidi, M. (Mohsen), McAlinden, C. (Colm), Meharie, B. G. (Birhanu Geta), Mehndiratta, M. M. (Man Mohan), Mehta, K. M. (Kala M.), Mekonnen, T. C. (Tefera Chane), Meles, G. G. (Gebrekiros Gebremichael), Memiah, P. T. (Peter T. N.), Memish, Z. A. (Ziad A.), Mendoza, W. (Walter), Menezes, R. G. (Ritesh G.), Mereta, S. T. (Seid Tiku), Meretoja, T. J. (Tuomo J.), Mestrovic, T. (Tomislav), Miazgowski, B. (Bartosz), Mihretie, K. M. (Kebadnew Mulatu), Miller, T. R. (Ted R.), Mini, G. K. (G. K.), Mirrakhimov, E. M. (Erkin M.), Moazen, B. (Babak), Mohajer, B. (Bahram), Mohamadi-Bolbanabad, A. (Amjad), Mohammad, D. K. (Dara K.), Mohammad, K. A. (Karzan Abdulmuhsin), Mohammad, Y. (Yousef), Mezerji, N. M. (Naser Mohammad Gholi), Mohammadibakhsh, R. (Roghayeh), Mohammadifard, N. (Noushin), Mohammed, J. A. (Jemal Abdu), Mohammed, S. (Shafiu), Mohebi, F. (Farnam), Mokdad, A. H. (Ali H.), Molokhia, M. (Mariam), Monasta, L. (Lorenzo), Moodley, Y. (Yoshan), Moore, C. E. (Catrin E.), Moradi, G. (Ghobad), Moradi, M. (Masoud), Moradi-Joo, M. (Mohammad), Moradi-Lakeh, M. (Maziar), Moraga, P. (Paula), Morales, L. (Linda), Velasquez, I. M. (Ilais Moreno), Mosapour, A. (Abbas), Mouodi, S. (Simin), Mousavi, S. M. (Seyyed Meysam), Mozaffor, M. (Miliva), Muchie, K. F. (Kindie Fentahun), Mulaw, G. F. (Getahun Fentaw), Munro, S. B. (Sandra B.), Muriithi, M. K. (Moses K.), Murray, C. J. (Christoper J. L.), Murthy, G. V. (G. V. S.), Musa, K. I. (Kamarul Imran), Mustafa, G. (Ghulam), Muthupandian, S. (Saravanan), Nabhan, A. F. (Ashraf F.), Naderi, M. (Mehdi), Nagarajan, A. J. (Ahamarshan Jayaraman), Naidoo, K. S. (Kovin S.), Naik, G. (Gurudatta), Najafi, F. (Farid), Nangia, V. (Vinay), Nansseu, J. R. (Jobert Richie), Nascimento, B. R. (Bruno Ramos), Nazari, J. (Javad), Ndwandwe, D. E. (Duduzile Edith), Negoi, I. (Ionut), Netsere, H. B. (Henok Biresaw), Ngunjiri, J. W. (Josephine W.), Nguyen, C. T. (Cuong Tat), Nguyen, H. L. (Huong Lan Thi), Nguyen, T. H. (Trang Huyen), Nigatu, D. (Dabere), Nigatu, S. G. (Solomon Gedlu), Ningrum, D. N. (Dina Nur Anggraini), Nnaji, C. A. (Chukwudi A.), Nojomi, M. (Marzieh), Nong, V. M. (Vuong Minh), Norheim, O. F. (Ole F.), Noubiap, J. J. (Jean Jacques), Motlagh, S. N. (Soraya Nouraei), Oancea, B. (Bogdan), Ogah, O. S. (Okechukwu Samuel), Ogbo, F. A. (Felix Akpojene), Oh, I.-H. (In-Hwan), Olagunju, A. T. (Andrew T.), Olagunju, T. O. (Tinuke O.), Olusanya, B. O. (Bolajoko Olubukunola), Olusanya, J. O. (Jacob Olusegun), Onwujekwe, O. E. (Obinna E.), Oren, E. (Eyal), Ortega-Altamirano, D. V. (Doris V.), Osarenotor, O. (Osayomwanbo), Osei, F. B. (Frank B.), Owolabi, M. O. (Mayowa O.), Mahesh, P. A. (P. A.), Padubidri, J. R. (Jagadish Rao), Pakhale, S. (Smita), Patel, S. K. (Sangram Kishor), Paternina-Caicedo, A. J. (Angel J.), Pathak, A. (Ashish), Patton, G. C. (George C.), Paudel, D. (Deepak), Paulos, K. (Kebreab), Pepito, V. C. (Veincent Christian Filipino), Pereira, A. (Alexandre), Perico, N. (Norberto), Pervaiz, A. (Aslam), Pescarini, J. M. (Julia Moreira), Piroozi, B. (Bakhtiar), Pirsaheb, M. (Meghdad), Postma, M. J. (Maarten J.), Pourjafar, H. (Hadi), Pourmalek, F. (Farshad), Pourshams, A. (Akram), Poustchi, H. (Hossein), Prada, S. I. (Sergio I.), Prasad, N. (Narayan), Preotescu, L. (Liliana), Quintana, H. (Hedley), Rabiee, N. (Navid), Radfar, A. (Amir), Rafiei, A. (Alireza), Rahim, F. (Fakher), Rahimi-Movaghar, A. (Afarin), Rahimi-Movaghar, V. (Vafa), Rahman, M. H. (Mohammad Hifz Ur), Rahman, M. A. (Muhammad Aziz), Rahman, S. (Shafiur), Rajati, F. (Fatemeh), Rana, S. M. (Saleem Muhammad), Ranabhat, C. L. (Chhabi Lal), Rasella, D. (Davide), Rawaf, D. L. (David Laith), Rawaf, S. (Salman), Rawal, L. (Lal), Rawasia, W. F. (Wasiq Faraz), Renjith, V. (Vishnu), Renzaho, A. M. (Andre M. N.), Resnikoff, S. (Serge), Reta, M. A. (Melese Abate), Rezaei, N. (Negar), Rezai, M. S. (Mohammad Sadegh), Riahi, S. M. (Seyed Mohammad), Ribeiro, A. I. (Ana Isabel), Rickard, J. (Jennifer), Rios-Blancas, M. (Maria), Roever, L. (Leonardo), Ronfani, L. (Luca), Roro, E. M. (Elias Merdassa), Ross, J. M. (Jennifer M.), Rubagotti, E. (Enrico), Rubino, S. (Salvatore), Saad, A. M. (Anas M.), Sabde, Y. D. (Yogesh Damodar), Sabour, S. (Siamak), Sadeghi, E. (Ehsan), Safari, Y. (Yahya), Safari-Faramani, R. (Roya), Sagar, R. (Rajesh), Sahebkar, A. (Amirhossein), Sahraian, M. A. (Mohammad Ali), Sajadi, S. M. (S. Mohammad), Salahshoor, M. R. (Mohammad Reza), Salam, N. (Nasir), Salamati, P. (Payman), Salem, H. (Hosni), Salem, M. R. (Marwa Rashad), Salimi, Y. (Yahya), Salimzadeh, H. (Hamideh), Samy, A. M. (Abdallah M.), Sanabria, J. (Juan), Santric-Milicevic, M. M. (Milena M.), Jose, B. P. (Bruno Piassi Sao), Saraswathy, S. Y. (Sivan Yegnanarayana Iyer), Sarkar, K. (Kaushik), Sarker, A. R. (Abdur Razzaque), Sarrafzadegan, N. (Nizal), Sartorius, B. (Benn), Sathian, B. (Brijesh), Sathish, T. (Thirunavukkarasu), Sawhney, M. (Monika), Saxena, S. (Sonia), Schwebel, D. C. (David C.), Senbeta, A. M. (Anbissa Muleta), Senthilkumaran, S. (Subramanian), Sepanlou, S. G. (Sadaf G.), Servan-Mori, E. (Edson), Shabaninejad, H. (Hosein), Shafieesabet, A. (Azadeh), Shaikh, M. A. (Masood Ali), Shalash, A. S. (Ali S.), Shallo, S. A. (Seifadin Ahmed), Shams-Beyranvand, M. (Mehran), Shamsi, M. (MohammadBagher), Shamsizadeh, M. (Morteza), Shannawaz, M. (Mohammed), Sharafi, K. (Kiomars), Sharifi, H. (Hamid), Shehata, H. S. (Hatem Samir), Sheikh, A. (Aziz), Shetty, B. S. (B. Suresh Kumar), Shibuya, K. (Kenji), Shiferaw, W. S. (Wondimeneh Shibabaw), Shifti, D. M. (Desalegn Markos), Shigematsu, M. (Mika), Il Shin, J. (Jae), Shiri, R. (Rahman), Shirkoohi, R. (Reza), Siabani, S. (Soraya), Siddiqi, T. J. (Tariq Jamal), Silva, D. A. (Diego Augusto Santos), Singh, A. (Ambrish), Singh, J. A. (Jasvinder A.), Singh, N. P. (Narinder Pal), Singh, V. (Virendra), Sisay, M. M. (Malede Mequanent), Skiadaresi, E. (Eirini), Sobhiyeh, M. R. (Mohammad Reza), Sokhan, A. (Anton), Soltani, S. (Shahin), Somayaji, R. (Ranjani), Soofi, M. (Moslem), Sorrie, M. B. (Muluken Bekele), Soyiri, I. N. (Ireneous N.), Sreeramareddy, C. T. (Chandrashekhar T.), Sudaryanto, A. (Agus), Sufiyan, M. B. (Mu'awiyyah Babale), Suleria, H. A. (Hafiz Ansar Rasul), Sultana, M. (Marufa), Sunguya, B. F. (Bruno Fokas), Sykes, B. L. (Bryan L.), Tabares-Seisdedos, R. (Rafael), Tabuchi, T. (Takahiro), Tadesse, D. B. (Degena Bahrey), Tarigan, I. U. (Ingan Ukur), Tasew, A. A. (Aberash Abay), Tefera, Y. M. (Yonatal Mesfin), Tekle, M. G. (Merhawi Gebremedhin), Temsah, M.-H. (Mohamad-Hani), Tesfay, B. E. (Berhe Etsay), Tesfay, F. H. (Fisaha Haile), Tessema, B. (Belay), Tessema, Z. T. (Zemenu Tadesse), Thankappan, K. R. (Kavumpurathu Raman), Thomas, N. (Nihal), Toma, A. (Alemayehu), Topor-Madry, R. (Roman), Tovani-Palone, M. R. (Marcos Roberto), Traini, E. (Eugenio), Tran, B. X. (Bach Xuan), Tran, K. B. (Khanh Bao), Ullah, I. (Irfan), Unnikrishnan, B. (Bhaskaran), Usman, M. S. (Muhammad Shariq), Uzochukwu, B. S. (Benjamin S. Chudi), Valdez, P. R. (Pascual R.), Varughese, S. (Santosh), Violante, F. S. (Francesco S.), Vollmer, S. (Sebastian), Hawariat, F. G. (Feleke Gebremeskel W.), Waheed, Y. (Yasir), Wallin, M. T. (Mitchell Taylor), Wang, Y. (Yafeng), Wang, Y.-P. (Yuan-Pang), Weaver, M. (Marcia), Weji, B. G. (Bedilu Girma), Weldesamuel, G. T. (Girmay Teklay), Welgan, C. A. (Catherine A.), Werdecker, A. (Andrea), Westerman, R. (Ronny), Wiangkham, T. (Taweewat), Wiysonge, C. S. (Charles Shey), Wolde, H. F. (Haileab Fekadu), Wondafrash, D. Z. (Dawit Zewdu), Wonde, T. E. (Tewodros Eshete), Worku, G. T. (Getasew Taddesse), Wu, A.-M. (Ai-Min), Xu, G. (Gelin), Yadollahpour, A. (Ali), Jabbari, S. H. (Seyed Hossein Yahyazadeh), Yamada, T. (Tomohide), Yatsuya, H. (Hiroshi), Yeshaneh, A. (Alex), Yilgwan, C. S. (Christopher Sabo), Yilma, M. T. (Mekdes Tigistu), Yip, P. (Paul), Yisma, E. (Engida), Yonemoto, N. (Naohiro), Yoon, S.-J. (Seok-Jun), Younis, M. Z. (Mustafa Z.), Yousefifard, M. (Mahmoud), Yousof, H. S. (Hebat-Allah Salah A.), Yu, C. (Chuanhua), Yusefzadeh, H. (Hasan), Zadey, S. (Siddhesh), Zaidi, Z. (Zoubida), Bin Zaman, S. (Sojib), Zamani, M. (Mohammad), Zandian, H. (Hamed), Zepro, N. B. (Nejimu Biza), Zerfu, T. A. (Taddese Alemu), Zhang, Y. (Yunquan), Zhao, X. G. (Xiu-Ju George), Ziapour, A. (Arash), Zodpey, S. (Sanjay), Zuniga, Y. M. (Yves Miel H.), Hay, S. I. (Simon I.), and Reiner, R. C. (Robert C., Jr.)

Abstract

Background: Oral rehydration solution (ORS) is a form of oral rehydration therapy (ORT) for diarrhoea that has the potential to drastically reduce child mortality; yet, according to UNICEF estimates, less than half of children younger than 5 years with diarrhoea in low-income and middle-income countries (LMICs) received ORS in 2016. A variety of recommended home fluids (RHF) exist as alternative forms of ORT; however, it is unclear whether RHF prevent child mortality. Previous studies have shown considerable variation between countries in ORS and RHF use, but subnational variation is unknown. This study aims to produce high-resolution geospatial estimates of relative and absolute coverage of ORS, RHF, and ORT (use of either ORS or RHF) in LMICs. Methods: We used a Bayesian geostatistical model including 15 spatial covariates and data from 385 household surveys across 94 LMICs to estimate annual proportions of children younger than 5 years of age with diarrhoea who received ORS or RHF (or both) on continuous continent-wide surfaces in 2000–17, and aggregated results to policy-relevant administrative units. Additionally, we analysed geographical inequality in coverage across administrative units and estimated the number of diarrhoeal deaths averted by increased coverage over the study period. Uncertainty in the mean coverage estimates was calculated by taking 250 draws from the posterior joint distribution of the model and creating uncertainty intervals (UIs) with the 2·5th and 97·5th percentiles of those 250 draws. Findings: While ORS use among children with diarrhoea increased in some countries from 2000 to 2017, coverage remained below 50% in the majority (62·6%; 12 417 of 19 823) of second administrative-level units and an estimated 6 519 000 children (95% UI 5 254 000–7 733 000) with diarrhoea were not treated with any form of ORT in 2017. Increases in ORS use corresponded with declines in RHF in many locations, resulting in relatively constant overall O

Published
2020

154. Global, Regional, and National Levels and Trends in Burden of Oral Conditions from 1990 to 2017: A Systematic Analysis for the Global Burden of Disease 2017 Study

Author

Bernabe, E., Bernabe, E., Marcenes, W., Hernandez, C. R., Bailey, J., Abreu, L. G., Alipour, V, Amini, S., Arabloo, J., Arefi, Z., Arora, A., Ayanore, M. A., Baernighausen, T. W., Chan, T. H., Bijani, A., Cho, D. Y., Chu, D. T., Crowe, C. S., Demoz, G. T., Demsie, D. G., Forooshani, Z. S. Dibaji, Du, M., El Tantawi, Maha, Fischer, F., Folayan, Morenike O., Futran, N. D., Geramo, Y. C. D., Haj-Mirzaian, A., Hariyani, N., Hasanzadeh, A., Hassanipour, S., Hay, S., I, Hole, M. K., Hostiuc, S., Ilić, Milena D., James, S. L., Kalhor, R., Kemmer, L., Keramati, M., Khader, Y. S., Kisa, S., Kisa, A., Koyanagi, A., Lalloo, R., Le Nguyen, Q., London, S. D., Manohar, N. D., Massenburg, B. B., Mathur, M. R., Meles, H. G., Mestrović, T., Mohammadian-Hafshejani, A., Mohammadpourhodki, R., Mokdad, A. H., Morrison, S. D., Nazari, J., Nguyen, T. H., Nguyen, C. T., Nixon, M. R., Olagunju, T. O., Pakshir, K., Pathak, M., Rabiee, N., Rafiei, A., Ramezanzadeh, K., Rios-Blancas, M. J., Roro, E. M., Sabour, S., Samy, A. M., Sawhney, M., Schwendicke, F., Shaahmadi, F., Shaikh, M. A., Stein, C., Tovani-Palone, M. R., Tran, B. X., Unnikrishnan, B., Vu, G. T., Vuković, Ana, Warouw, T. S. S., Zaidi, Z., Zhang, Z. J., Kassebaum, N. J., Bernabe, E., Bernabe, E., Marcenes, W., Hernandez, C. R., Bailey, J., Abreu, L. G., Alipour, V, Amini, S., Arabloo, J., Arefi, Z., Arora, A., Ayanore, M. A., Baernighausen, T. W., Chan, T. H., Bijani, A., Cho, D. Y., Chu, D. T., Crowe, C. S., Demoz, G. T., Demsie, D. G., Forooshani, Z. S. Dibaji, Du, M., El Tantawi, Maha, Fischer, F., Folayan, Morenike O., Futran, N. D., Geramo, Y. C. D., Haj-Mirzaian, A., Hariyani, N., Hasanzadeh, A., Hassanipour, S., Hay, S., I, Hole, M. K., Hostiuc, S., Ilić, Milena D., James, S. L., Kalhor, R., Kemmer, L., Keramati, M., Khader, Y. S., Kisa, S., Kisa, A., Koyanagi, A., Lalloo, R., Le Nguyen, Q., London, S. D., Manohar, N. D., Massenburg, B. B., Mathur, M. R., Meles, H. G., Mestrović, T., Mohammadian-Hafshejani, A., Mohammadpourhodki, R., Mokdad, A. H., Morrison, S. D., Nazari, J., Nguyen, T. H., Nguyen, C. T., Nixon, M. R., Olagunju, T. O., Pakshir, K., Pathak, M., Rabiee, N., Rafiei, A., Ramezanzadeh, K., Rios-Blancas, M. J., Roro, E. M., Sabour, S., Samy, A. M., Sawhney, M., Schwendicke, F., Shaahmadi, F., Shaikh, M. A., Stein, C., Tovani-Palone, M. R., Tran, B. X., Unnikrishnan, B., Vu, G. T., Vuković, Ana, Warouw, T. S. S., Zaidi, Z., Zhang, Z. J., and Kassebaum, N. J.

Abstract

Government and nongovernmental organizations need national and global estimates on the descriptive epidemiology of common oral conditions for policy planning and evaluation. The aim of this component of the Global Burden of Disease study was to produce estimates on prevalence, incidence, and years lived with disability for oral conditions from 1990 to 2017 by sex, age, and countries. In addition, this study reports the global socioeconomic pattern in burden of oral conditions by the standard World Bank classification of economies as well as the Global Burden of Disease Socio-demographic Index. The findings show that oral conditions remain a substantial population health challenge. Globally, there were 3.5 billion cases (95% uncertainty interval [95% UI], 3.2 to 3.7 billion) of oral conditions, of which 2.3 billion (95% UI, 2.1 to 2.5 billion) had untreated caries in permanent teeth, 796 million (95% UI, 671 to 930 million) had severe periodontitis, 532 million (95% UI, 443 to 622 million) had untreated caries in deciduous teeth, 267 million (95% UI, 235 to 300 million) had total tooth loss, and 139 million (95% UI, 133 to 146 million) had other oral conditions in 2017. Several patterns emerged when the World Bank's classification of economies and the Socio-demographic Index were used as indicators of economic development. In general, more economically developed countries have the lowest burden of untreated dental caries and severe periodontitis and the highest burden of total tooth loss. The findings offer an opportunity for policy makers to identify successful oral health strategies and strengthen them; introduce and monitor different approaches where oral diseases are increasing; plan integration of oral health in the agenda for prevention of noncommunicable diseases; and estimate the cost of providing universal coverage for dental care.

Published
2020

155. Obesity and dental caries in early childhood: A systematic review and meta-analyses.

Author

Manohar N, Hayen A, Fahey P, Arora A, Manohar N, Hayen A, Fahey P, and Arora A

Abstract

Obesity and dental caries in children are significant health problems. The aims of this review are to identify whether children aged 6 years and younger with overweight and/or obesity have higher dental caries experience compared with children with normal weight and, secondly, to identify the common risk factors associated with both conditions. Medline, Embase, and seven other databases were systematically searched followed by lateral searches from reference lists, grey literature, theses, conference proceedings, and contacting field experts. Longitudinal observational studies addressing overweight and/or obesity and dental caries in children aged 6 years and younger were included. A random effects model meta-analyses were applied. Nine studies were included in this review. Children with overweight and obesity had a significantly higher dental caries experience compared with children with normal weight (n = 6). The pooled estimates showed that difference in caries experience between the two groups was statistically significant. Low levels of parental income and education were identified to be associated with both conditions in the sample population. Children with overweight and obesity are more vulnerable to dental caries. Low levels of parental income and education influence the relationship between the two conditions. However, the quality of evidence varied considerably; therefore, findings should be interpreted cautiously.

Published
2020

156. A Computer-Based Application for Speech Recognition in Multi-Speaker Environment to Assist Hearing Impaired People

Author

Chalana D A, Aishwarya Anegundi, Manohar N, Vinay G, Vinuta V Pawale, and Rudraswamy S B

Subjects
Hearing aid, Microphone, Computer science, Hearing loss, business.industry, Speech recognition, medicine.medical_treatment, Client–server model, Transmission (telecommunications), Multithreading, medicine, medicine.symptom, business, Range (computer programming), Graphical user interface
Abstract

In this paper, a computer-based application has been proposed that facilitates speech recognition in a multi-speaker environment. It identifies the speaker, recognizes the speech and displays the text output obtained from speech to text conversion along with the identified speaker’s name tag on the computer screen. The proposed system provides three functionalities: i) Speech Recognition ii) Speaker Identification iii) Many to one transmission-reception of converted speech to text using the client-server model and multi-threading concept. The system consists of transmission and reception capable devices like computers, a computer application that connects various devices using WiFi technology and microphone to obtain speech input from the users. All the functionalities are implemented as a computer application with a user-friendly graphical user interface(GUI). The application has two scenarios: i) Near and ii) Far. In the case when the speakers are in the range of the user’s microphone, the user opts for near scenario else user opts for the far scenario. The proposed system is intended mainly for people suffering from hearing loss. Although hearing aid technology is greatly improved, it still utilizes an impaired person’s inner, outer and middle ear including hearing nerve to support natural hearing.

Published
2019
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158. Enhanced ergonomic design of driver seat.

Author

Manohar, N. Janaki, Krishnan, N. Muthu, Kumar, A. Rahul, Dhinakaran, V, Kumaresan, G, and Raju, Ramesh

Subjects
*AUTOMOBILE seats, *AUTOMOBILE occupants, *ENGINEERING design, *DESIGN
Abstract

The essential component of the vehicle is the driver seat. The expectation of the customer is increasing continuously to get a comfortable position for driving. The design of seat in an automobile has always been a challenging feat for engineers due to its complexity. The design engineer has to consider the fact that it must satisfy specific design objectives. The primary goals to be considered for design are safety, comfort, and small space. Driver seat recommendations of an automobile preferably of passenger cars are reviewed concerning Indian anthropometry. In this paper, the importance is given to fit parameters related to anthropometric measurements and particular attention of support parameters that deal with the posture of the occupant. The feel parameter is also taken into account during the designing process of the seat. The RULA analysis of the designed seat is carried out to check the comfortability of the driver. The design is done by taking account the people which accommodate the members of the population who lie between the 5th percentile female to 95th percentile male values of Indian manikin. [ABSTRACT FROM AUTHOR]

Published
2020
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159. Feasibility and Economical Analysis of Pumped Hydro Storage System for STES Campus, Lonavala

Author

Sarika V. Tade, Aishwarya V. Khedkar, Vilas N. Ghate, and Manohar N. Kalgunde

Subjects
Pumped-storage hydroelectricity, Waste management, Water storage tank, business.industry, Electric potential energy, Environmental science, Electricity, business, Energy storage, Renewable energy
Abstract

Electrical energy utilization and involvement of renewable energy sector is increasing day by day. For rapidly increasing renewable sector necessity of storage device is becoming very important. Various energy storage systems are available for storing the electricity. Pumped hydro storage system (PHS) is one of the most promising storage systems for storage of bulk power. Due to geographical conditions and naturally availability of water during rainy days, it is feasible to implement a PHS unit on Sinhgad Technical Education Society (STES) campus, Lonavala. The already available water storage tank can store water which can generate electricity at about 180 KW. Technical and economical calculations are done to ensure the feasibility of the PHS unit at Lonavala campus and presented in this paper.

Published
2018
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161. Determinants of early initiation of breastfeeding in Ethiopia: A population-based study using the 2016 demographic and health survey data

Author

John, JR, Mistry, SK, Kebede, G, Manohar, N, Arora, A, John, JR, Mistry, SK, Kebede, G, Manohar, N, and Arora, A

Abstract

Background: Timely breastfeeding initiation is a simple but important measure that has protective effects on infants and mothers. This study aims to determine the predictors of early breastfeeding initiation among mothers residing in Ethiopia. Methods: This study employed the 2016 Ethiopian Demographic and Health Survey data. A total of 5546 children born during the last 24 months at the time of survey were included for analysis from nine regional states and two city administration areas. Socio-demographic and socio-economic factors including individual, household and community-level factors were examined of their significance against the outcome variable of early initiation of breastfeeding using a mixed-effect logistic regression model. Results: The proportion of infants who had timely initiation of breastfeeding was 74.3% (n = 3064). In the multivariate logistic regression analysis, mothers who delivered with assistance of one or more health professionals had 68% (AOR 1.68; 95% CI: 1.23, 2.29) higher odds of initiating timely breastfeeding. In addition, mothers delivering by a caesarean section had 86% reduced odds of early breastfeeding initiation (AOR 0.14; 95% CI: 0.09, 0.22) when compared to mothers who had vaginal delivery. In terms of socio-demographic factors, the odds of early breastfeeding initiation were more than two and half times higher particularly for mothers residing particularly in Oromiya (AOR 2.58; 95% CI: 1.84, 3.63) and Southern Nations Nationalities and Peoples (SNNP) (AOR 2.75; 95% CI: 1.86, 4.05). In addition, timely breastfeeding initiation was also significantly associated with wealth index with wealthier mothers having 43% higher odds compared to mothers of poorest households (AOR 1.43; 95% CI: 1.07, 1.92). Other factors such as age, gender and birth order of the infant also had significant associations with early breastfeeding initiation. Conclusion: Early breastfeeding initiation in Ethiopia is inextricably associated with various soc

Published
2019

162. Obesity and dental caries in early childhood: A systematic review protocol

Author

Manohar, N, Hayen, A, Arora, A, Manohar, N, Hayen, A, and Arora, A

Abstract

© 2019 Joanna Briggs Institute. Unauthorized reproduction of this article is prohibited. The objectives of this review are to examine whether overweight/obese children experience more dental caries compared with non-overweight/non-obese children, and to identify common risk factors associated with both conditions. Introduction: Systematic reviews have shown that any evidence on a link between overweight and/or obesity and dental caries remains inconclusive. This relationship has not been assessed for children under six years of age with primary dentition. Therefore, an updated systematic review of this topic is necessary as its findings will be important for young children, clinicians, researchers and policy makers. Inclusion criteria: Studies examining children under six years of age and with complete primary dentition at the time of dental caries assessment will be included. The exposure of interest is the overweight and/or obesity status of children under six years of age. The outcome is dental caries in children with complete primary dentition. There will be no restriction on setting, date or language. Methods: MEDLINE, Web of Science, Cochrane Central Register of Controlled Trials, Embase, PsycINFO, ProQuest Central, Scopus, CINAHL, and Google Scholar will be searched for eligible studies. The electronic database search will be supplemented by OpenGrey and Grey Literature Report databases, ProQuest Dissertations and Theses Global, and the International Association for Dental Research conference websites. Two reviewers will independently screen and select studies, assess methodological quality and extract data. Meta-analysis will be performed, if possible, and the Grading of Recommendations Assessment Development and Evaluation (GRADE) Summary of Findings presented.

Published
2019

163. Utilization of Solar Energy with Pumped Hydro Storage Based on Standalone Photovolatic Power Generation

Author

Shashikant Golande and Manohar N. Kalgunde

Subjects
Pumped-storage hydroelectricity, Electricity generation, business.industry, Photovoltaic system, Environmental science, business, Solar energy, Process engineering, Sizing, Energy storage, Renewable energy, Power (physics)
Abstract

Simulation model for photovoltaic system (PV System) is described in this paper. It also describes feasible study of standalone hybrid solar system with pumped storage for Remote Island. Energy storage is the most important part for continuous reliable power supply. The pumped hydro storage system proposed which has capacity to store energy effectively with minimum loss. The system reliability and feasibility has been examined. The sizing methods and economic models are developed and finally applied in real projects. The pumped hydro storage (PHS) becomes most cost effective like increasing storage capacity and days of autonomy. Therefore solar energy system (SES) with pumped hydro storage is technically feasible and has practically potential to supply continuous power in Remote Island.

Published
2017
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164. Regularized LMS and diffusion adaptation LMS with graph filters for non-stationary data

Author

Manohar N. Murthi, May Zar Lin, and Kamal Premaratne

Subjects
Diffusion adaptation, Frequency response, Signal processing, Stationary process, Computer science, 0202 electrical engineering, electronic engineering, information engineering, Graph (abstract data type), 020206 networking & telecommunications, 02 engineering and technology, Laplace operator, Regularization (mathematics), Wireless sensor network, Algorithm
Abstract

In sensor networks, adaptive algorithms such as diffusion adaptation LMS are commonly used to learn and track non-stationary signals. When such signals have similarities across certain nodes as captured by a graph, then Laplacian Regularized (LR) LMS and diffusion adaptation LR LMS can be utilized for the respective centralized and distributed estimation cases. In this paper, we re-examine these adaptive methods, and use graph signal processing notions to augment the algorithms with an additional graph filtering step for regularization. Moreover, we demonstrate how to design these graph filters, leading to performance improvements over existing methods in both the centralized and distributed cases. Furthermore, we analyze the stability and convergence of our methods and illustrate how the empirical performance is captured by the theoretical results which unveil the bias and variance tradeoff.

Published
2017
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165. Energy storage using pumped hydro storage based on standalone photovoltaic power generation system

Author

Manohar N. Kalgunde and Shashikant Golande

Subjects
Pumped-storage hydroelectricity, business.industry, Photovoltaic power generation, Environmental science, Power generation system, Solar energy, business, Grid, Sizing, Energy storage, Automotive engineering, Power (physics)
Abstract

A standalone solar energy system (SES) is the most important solution particularly in remote areas without utility grid access while energy storage is the most important part while achieving continuous and reliable power supply. This paper presents detailed study of pumped hydro storage (PHS) system based on standalone photovoltaic power generation system. That study examines a detailed analysis and performance of system produced. A mathematical model of major components should be developed. A system sizing, simulation has been carried out and system performance has been examined.

Published
2017
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166. Convergence Analysis of Iterated Belief Revision in Complex Fusion Environments

Author

Kamal Premaratne, Nitesh V. Chawla, Manohar N. Murthi, and Thanuka L. Wickramarathne

Subjects
business.industry, Belief revision, Network topology, Range (mathematics), Iterated function, Asynchronous communication, Signal Processing, Convergence (routing), Probability mass function, Artificial intelligence, Electrical and Electronic Engineering, Special case, business, Mathematics
Abstract

We study convergence of iterated belief revision in complex fusion environments, which may consist of a network of soft (i.e., human or human-based) and hard (i.e., conventional physics-based) sensors and where agent communications may be asynchronous and the link structure may be dynamic. In particular, we study the problem in which network agents exchange and revise belief functions (which generalize probability mass functions) and are more geared towards handling the uncertainty pervasive in soft/hard fusion environments. We focus on belief revision in which agents utilize a generalized fusion rule that is capable of generating a rational consensus. It includes the widely used weighted average consensus as a special case. By establishing this fusion scheme as a pool of paracontracting operators, we derive general convergence criteria that are relevant for a wide range of applications. Furthermore, we analyze the conditions for consensus for various social networks by simulating several network topologies and communication patterns that are characteristic of such networks.

Published
2014
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167. Stable 1-Norm Error Minimization Based Linear Predictors for Speech Modeling

Author

Manohar N. Murthi, Marc Moonen, Søren Holdt Jensen, Mads Græsbøll Christensen, Daniele Giacobello, and Tobias Lindstrøm Jensen

Subjects
Code-excited linear prediction, Mathematical optimization, Acoustics and Ultrasonics, Speech coding, Cauchy distribution, Linear prediction, Speech enhancement, Computational Mathematics, Norm (mathematics), Convex optimization, Computer Science (miscellaneous), Electrical and Electronic Engineering, Numerical range, Mathematics
Abstract

In linear prediction of speech, the 1-norm error minimization criterion has been shown to provide a valid alternative to the 2-norm minimization criterion. However, unlike 2-norm minimization, 1-norm minimization does not guarantee the stability of the corresponding all-pole filter and can generate saturations when this is used to synthesize speech. In this paper, we introduce two new methods to obtain intrinsically stable predictors with the 1-norm minimization. The first method is based on constraining the roots of the predictor to lie within the unit circle by reducing the numerical range of the shift operator associated with the particular prediction problem considered. The second method uses the alternative Cauchy bound to impose a convex constraint on the predictor in the 1-norm error minimization. These methods are compared with two existing methods: the Burg method, based on the 1-norm minimization of the forward and backward prediction error, and the iteratively reweighted 2-norm minimization known to converge to the 1-norm minimization with an appropriate selection of weights. The evaluation gives proof of the effectiveness of the new methods, performing as well as unconstrained 1-norm based linear prediction for modeling and coding of speech.

Published
2014
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169. A study on protection issues in presence of distributed generation

Author

Amol A. Kalage, Manohar N. Kalgunde, and Dhananjay M. Chahyal

Subjects
Electric power system, business.industry, Computer science, Distributed computing, Distributed generation, business, Fault (power engineering), Distributed power generation, Grid, Energy (signal processing), Renewable energy, Power (physics)
Abstract

Distributed generation (DG) is very effective way to absorb the local clusters of renewable energy pockets. The locally formed grids serve the best purpose of supporting the main grid and to reach the far customer to be supplied by the energy. The conventional generation systems when integrated with DG leads to many issues for the protection aspect of the system. This paper presents the analysis and discuss the various probable fault issues while integration. The study is done with and without DG systems. The attempt of probable solutions to be implemented can also to be discussed. The classical method of fault calculation using the power world simulator is also tried for the effective and accurate analysis. The obtained results are presented and discussed.

Published
2017
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171. Recognition and classification of animals based on texture features through parallel computing

Author

Hemantha Kumar G, Manohar N, Sharath Kumar Y H, Subrahmanya S, and Bharathi R K

Subjects
Computer science, business.industry, Parallel algorithm, 020206 networking & telecommunications, Pattern recognition, 02 engineering and technology, Parallel computing, Texture (music), Machine learning, computer.software_genre, Support vector machine, Task (computing), ComputingMethodologies_PATTERNRECOGNITION, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Artificial intelligence, business, computer
Abstract

In this work, we proposed an efficient system for animal recognition and classification based on texture features which are obtained from the local appearance and texture of animals. The classification of animals are done by training and subsequently testing two different machine learning techniques, namely k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM). Computer-assisted technique when applied through parallel computing makes the work efficient by reducing the time taken for the task of animal recognition and classification. Here we propose a parallel algorithm for the same. Experimentation is done for about 30 different classes of animals containing more than 3000 images. Among the different classifiers, k-Nearest Neighbor classifiers have achieved a better accuracy.

Published
2016
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172. Retrieving Sparse Patterns Using a Compressed Sensing Framework: Applications to Speech Coding Based on Sparse Linear Prediction

Author

Mads Græsbøll Christensen, Manohar N. Murthi, Marc Moonen, Daniele Giacobello, and Søren Holdt Jensen

Subjects
business.industry, Computer science, Applied Mathematics, Speech coding, Signal compression, 020206 networking & telecommunications, Linear prediction, Pattern recognition, 02 engineering and technology, Sparse approximation, Linear predictive coding, Compressed sensing, Computer Science::Sound, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Electrical and Electronic Engineering, business, Data compression, Sparse matrix
Abstract

Encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application in the context of speech coding based on sparse linear prediction. In particular, a compressed sensing method can be devised to compute a sparse approximation of speech in the residual domain when sparse linear prediction is involved. We compare the method of computing a sparse prediction residual with the optimal technique based on an exhaustive search of the possible nonzero locations and the well known Multi-Pulse Excitation, the first encoding technique to introduce the sparsity concept in speech coding. Experimental results demonstrate the potential of compressed sensing in speech coding techniques, offering high perceptual quality with a very sparse approximated prediction residual.

Published
2010
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173. On Predictive Coding for Erasure Channels Using a Kalman Framework

Author

Manohar N. Murthi, Thomas Arildsen, Soren Vang Andersen, and Søren Holdt Jensen

Subjects
erasure channels, Iterative method, Computer science, Quantization (signal processing), linear predictive coding, Data_CODINGANDINFORMATIONTHEORY, Kalman filter, Linear predictive coding, Soft-decision decoder, Adaptive coding, Control theory, Signal Processing, differential pulse code modulation, joint source-channel coding, Erasure, Codec, quantization, Electrical and Electronic Engineering, Kalman filtering, Algorithm, Encoder, Decoding methods, Computer Science::Information Theory, Communication channel
Abstract

Udgivelsesdato: NOV We present a new design method for robust low-delay coding of autoregressive sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well.

Published
2009
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174. Regenerative cooperative diversity with path selection and equal power consumption in wireless networks

Author

Kefei Lu, Jing Liu, Manohar N. Murthi, and Xiaodong Cai

Subjects
Routing protocol, Computer science, business.industry, Wireless network, Applied Mathematics, Computer Science Applications, law.invention, Cooperative diversity, Diversity combining, Relay, law, Bit error rate, Fading, Electrical and Electronic Engineering, business, Selection (genetic algorithm), Computer network
Abstract

Recently developed cooperative protocol with distributed path selection provides a simple and practical means of achieving full cooperative diversity in wireless networks. While the best path selection method can significantly improve bit error rate (BER) performance, it may cause unequal power consumption among relay nodes, which may reduce the lifetime of energy-constrained networks. A path selection method under the equal power constraint has been developed for the amplifyand- forward (AF) protocol, but there is no such method for the decode-and-forward (DF) protocol. In this paper, we develop a distributed path selection method with an equal power constraint for the DF protocol. We also analyze the BER performance of our path-selection method. Numerical results demonstrate that the proposed method can guarantee equal power consumption, while achieving full diversity as the best path selection method and providing significant performance gain relative to noncooperative communication.

Published
2009
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175. Transmission Rate Allocation in Multisensor Target Tracking Over a Shared Network

Author

Kamal Premaratne, Xingzhe Fan, M.C. Ranasingha, and Manohar N. Murthi

Subjects
Computer science, Network packet, Quality of service, Bandwidth (signal processing), Real-time computing, General Medicine, Kalman filter, Sensor fusion, Computer Science Applications, Human-Computer Interaction, Packet switching, Control and Systems Engineering, Resource allocation, Resource management, Electrical and Electronic Engineering, Software, Information Systems, Data transmission
Abstract

In a multisensor target tracking application running on a shared network, at what bit rates should the sensors send their measurements to the tracking fusion center? Clearly, the sensors cannot use arbitrary rates in a shared network, and a standard network rate control algorithm may not provide rates amenable to effective target tracking. For Kalman filter-based multisensor target tracking, we derive a utility function that captures the tracking quality of service as a function of the sensor bit rates. We incorporate this utility function into a network rate resource allocation framework, deriving a distributed rate control algorithm for a shared network that is suitable for current best effort packet networks, such as the Internet. In simulation studies, the new rate control algorithm engenders significantly better tracking performance than a standard rate control method, while the ordinary data transfer flows continue to effectively operate while using their standard rate control methods.

Published
2009
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176. Normalized Queueing Delay: Congestion Control Jointly Utilizing Delay and Marking

Author

Kamal Premaratne, T. Dilusha Wickramarathna, Xingzhe Fan, Mingyu Chen, and Manohar N. Murthi

Subjects
CUBIC TCP, Queueing theory, Computer Networks and Communications, Computer science, business.industry, Network delay, TCP tuning, Throughput, H-TCP, TCP congestion-avoidance algorithm, Computer Science Applications, Network congestion, TCP Westwood plus, TCP Friendly Rate Control, HSTCP, Electrical and Electronic Engineering, business, Software, Computer network
Abstract

Depending upon the type of feedback that is primarily used as a congestion measure, congestion control methods can be generally classified into two categories: marking/loss-based or delay-based. While both marking and queueing delay provide information about the congestion state of a network, they have been largely treated with separate control strategies. In this paper, we propose the notion of the normalized queueing delay, which serves as a congestion measure by combining both delay and marking information. Utilizing normalized queueing delay (NQD), we propose an approach to congestion control that allows a source to scale its rate dynamically to prevailing network conditions through the use of a time-variant set-point. In ns-2 simulation studies, an NQD-enabled FAST TCP demonstrates a significant link utilization improvement over FAST TCP under certain conditions. In addition, we propose another NQD-based controller D + M TCP (Delay+Marking TCP) that achieves quick convergence to fair and stable rates with nearly full link utilization. Therefore, NQD is a suitable candidate as a congestion measure for practical congestion control.

Published
2009
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177. Gaussian Mixture Kalman Predictive Coding of Line Spectral Frequencies

Author

Subasingha Shaminda Subasingha, Manohar N. Murthi, and Soren Vang Andersen

Subjects
Acoustics and Ultrasonics, Quantization (signal processing), Speech recognition, Speech coding, Vector quantization, Linear prediction, Kalman filter, Mixture model, symbols.namesake, symbols, Electrical and Electronic Engineering, Gaussian process, Mathematics, Data compression
Abstract

Gaussian mixture model (GMM)-based predictive coding of line spectral frequencies (LSFs) has gained wide acceptance. In such coders, each mixture of a GMM can be interpreted as defining a linear predictive transform coder. In this paper, we use Kalman filtering principles to model each of these linear predictive transform coders to present GMM Kalman predictive coding. In particular, we show how suitable modeling of quantization noise leads to an adaptive a posteriori GMM that defines a signal-adaptive predictive coder that provides improved coding of LSFs in comparison with the baseline recursive GMM predictive coder. Moreover, we show how running the GMM Kalman predictive coders to convergence can be used to design a stationary GMM Kalman predictive coding system which again provides improved coding of LSFs but now with only a modest increase in run-time complexity over the baseline. In packet loss conditions, this stationary GMM Kalman predictive coder provides much better performance than the recursive GMM predictive coder, and in fact has comparable mean performance to a memoryless GMM coder. Finally, we illustrate how one can utilize Kalman filtering principles to design a postfilter which enhances decoded vectors from a recursive GMM predictive coder without any modifications to the encoding process.

Published
2009
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181. An overview and design of Dynamic Voltage Restorer to improve power quality in microgrid

Author

Prasad A. Raut and Manohar N. Kalgunde

Subjects
Engineering, Switched-mode power supply, business.industry, Voltage sag, Electrical engineering, Electronic engineering, Voltage regulation, Power factor, Voltage regulator, Voltage optimisation, business, Switched-mode power supply applications, Constant power circuit
Abstract

Power quality is one of major concerns in the present era. It has become important, especially, with the introduction of sophisticated devices, whose performance is very sensitive to the quality of power supply. A dynamic voltage restorer (DVR) based on photovoltaic (PV) generation/battery units is proposed to improve voltage quality in a micro-grid. The restorer is connected with the grid by a rectifier, which is in series with the point of common coupling (PCC). Power quality problem is an occurrence manifested as a nonstandard voltage, current or frequency that results in a failure of end use equipments. One of the major problems dealt here is the voltage sag. To solve this problem, custom power devices are used. One of those devices is the Dynamic Voltage Restorer (DVR), which is the most efficient and effective modern custom power device used in power distribution networks. Its appeal includes lower cost, smaller size, and its fast dynamic response to the disturbance. This paper introduces power quality problems and overview of Dynamic Voltage Restorer so that young electrical engineers come to know about such a modern custom power device for power quality improvement in future era.

Published
2015
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182. Mobile adaptive networks for pursuing multiple targets

Author

Kamal Premaratne, Manohar N. Murthi, and May Zar Lin

Subjects
Steady state (electronics), Computer science, Control theory, Distributed computing, Adaptive system, Node (networking), Mobile computing, Stability (learning theory), Adaptive learning
Abstract

We examine the design of self-organizing mobile adaptive networks with multiple targets in which the network nodes form distinct clusters to learn about and purse multiple targets, all while moving in a cohesive collision-free manner. We build upon previous distributed diffusion-based adaptive learning networks that focused on a single target to examine the case with multiple targets in which the nodes do not know the number of targets, and exchange local information with their neighbors in their learning objectives. In particular, we design a method allowing the nodes to switch the target they are tracking thereby engendering the formation of distinct stable learning groups that can split up and purse their distinct targets over time. We provide analytical mean stability and steady state mean-square deviation results along with simulations that demonstrate the efficacy of the proposed method.

Published
2015
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183. Determinants of breastfeeding initiation among mothers in Sydney, Australia: Findings from a birth cohort study

Author

Arora, A, Manohar, N, Hayen, A, Bhole, S, Eastwood, J, Levy, S, Scott, JA, Arora, A, Manohar, N, Hayen, A, Bhole, S, Eastwood, J, Levy, S, and Scott, JA

Abstract

© 2017 The Author(s). Background: Breastfeeding has short-term and long-term benefits for both the infant and the mother. The objective of this study was to identify the incidence of breastfeeding initiation among women in South Western Sydney, and the factors associated with the initiation of breastfeeding. Methods: Child and Family Health Nurses recruited mother-infant dyads (n=1035) to the Healthy Smiles Healthy Kids birth study in South Western Sydney, an ethnically and socio-economically diverse area, at the first post-natal home visit. A sample of 935 women completed a structured, interviewer-administered questionnaire at 8weeks. Multivariate logistic regression analysis was used to identify those factors independently associated with the initiation of breastfeeding. Results: In total, 92% of women (n=860) commenced breastfeeding in hospital. Women who completed a university degree were more likely to initiate breastfeeding compared to those who did not complete high school (AOR=7.16, 95% CI 2.73, 18.79). Vietnamese women had lower odds of breastfeeding initiation compared to Australian born women (AOR=0.34. 95% CI 0.13, 0.87). Women who had more than one child were less likely to breastfeed than those who had one child (AOR=0.38, 95% CI 0.19, 0.79). Women who gave birth via a caesarean section were less likely to breastfeed their baby compared to those who had a vaginal delivery (AOR=0.27, 95% CI 0.14, 0.52). Women who drank alcohol during pregnancy had 72% lower odds to breastfeed compared to those who did not drink alcohol during pregnancy (AOR=0.28, 95% CI 0.11, 0.71). Women who reported that their partner preferred breastfeeding were more likely to initiate breastfeeding (AOR=11.77, 95% CI 5.73, 24.15) and women who had chosen to breastfeed before pregnancy had more than 2.5 times the odds of breastfeeding their baby compared to those women who made their decision either during pregnancy or after labour (AOR=2.80, 95% CI 1.31, 5.97). Conclusions: Women wi

Published
2017

184. Determinants of breastfeeding initiation among mothers in Sydney, Australia: findings from a birth cohort study

Author

Arora, A., Manohar, N., Hayen, A., Bhole, S., Eastwood, J., Levy, S., Scott, Jane, Arora, A., Manohar, N., Hayen, A., Bhole, S., Eastwood, J., Levy, S., and Scott, Jane

Abstract

Background: Breastfeeding has short-term and long-term benefits for both the infant and the mother. The objective of this study was to identify the incidence of breastfeeding initiation among women in South Western Sydney, and the factors associated with the initiation of breastfeeding. Methods: Child and Family Health Nurses recruited mother-infant dyads (n = 1035) to the Healthy Smiles Healthy Kids birth study in South Western Sydney, an ethnically and socio-economically diverse area, at the first post-natal home visit. A sample of 935 women completed a structured, interviewer-administered questionnaire at 8 weeks. Multivariate logistic regression analysis was used to identify those factors independently associated with the initiation of breastfeeding. Results: In total, 92% of women (n = 860) commenced breastfeeding in hospital. Women who completed a university degree were more likely to initiate breastfeeding compared to those who did not complete high school (AOR = 7.16, 95% CI 2.73, 18.79). Vietnamese women had lower odds of breastfeeding initiation compared to Australian born women (AOR = 0.34. 95% CI 0.13, 0.87). Women who had more than one child were less likely to breastfeed than those who had one child (AOR = 0.38, 95% CI 0.19, 0.79). Women who gave birth via a caesarean section were less likely to breastfeed their baby compared to those who had a vaginal delivery (AOR = 0.27, 95% CI 0.14, 0.52). Women who drank alcohol during pregnancy had 72% lower odds to breastfeed compared to those who did not drink alcohol during pregnancy (AOR = 0.28, 95% CI 0.11, 0.71). Women who reported that their partner preferred breastfeeding were more likely to initiate breastfeeding (AOR = 11.77, 95% CI 5.73, 24.15) and women who had chosen to breastfeed before pregnancy had more than 2.5 times the odds of breastfeeding their baby compared to those women who made their decision either during pregnancy or after labour (AOR = 2.80, 95% CI 1.31, 5.97). Conclusions: Women with

Published
2017

190. Belief theoretic methods for soft and hard data fusion

Author

Marco A. Pravia, Manohar N. Murthi, Kamal Premaratne, Matthias Scheutz, Thanuka L. Wickramarathne, and Sandra Kübler

Subjects
Reliability theory, Data processing, Computer science, business.industry, Reliability (computer networking), SIGNAL (programming language), Bayesian probability, Probabilistic logic, Inference, computer.software_genre, Sensor fusion, Machine learning, Text mining, Data mining, Artificial intelligence, business, computer
Abstract

In many contexts, one is confronted with the problem of extracting information from large amounts of different types soft data (e.g., text) and hard data (from e.g., physics-based sensing systems). In handling hard data, signal and data processing offers a wealth of methods related to modeling, estimation, tracking, and inference tasks. However, soft data present several challenges that necessitate the development of new data processing methods. For example, with suitable statistical natural language processing (NLP) methods, text can be converted into logic statements that are associated with various forms of associated uncertainty related to the credibility of the statement, the reliability of the text source, and so forth. In combining or fusing soft data with either soft or hard data, one must deploy methods that can suitably preserve and update the uncertainty associated with the data, thereby providing uncertainty bounds related to any inferences regarding semantics. Since standard Bayesian probabilistic approaches have problems with suitably handling uncertain logic statements, there is an emerging need for new methods for processing heterogeneous data. In this paper, we describe a framework for fusing soft and hard data based on the Dempster-Shafer (DS) belief theoretic approach which is well-suited to the task of capturing the types of models and uncertain rules that are more typical of soft data. Since the effectiveness of traditional DS methods has been hampered by high computational requirements, we base the processing framework on our new conditional approach to DS theoretic evidence updating and fusion. We address the issue of laying the foundation for a theoretically justifiable, and computationally efficient framework for fusing soft and hard data taking into account the inherent data uncertainty such as reliability and credibility. Moreover, we present an illustrative example that highlights the potential for the DS conditional approach for fusing heterogeneous data.

Published
2011
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191. Quantization for classification accuracy in high-rate quantizers

Author

Manohar N. Murthi and Behzad Mohammadi Dogahe

Subjects
Kullback–Leibler divergence, Signal reconstruction, business.industry, media_common.quotation_subject, Replica, Quantization (signal processing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Fidelity, Conditional probability, Pattern recognition, Data_CODINGANDINFORMATIONTHEORY, Artificial intelligence, business, Encoder, Algorithm, Decoding methods, media_common, Mathematics
Abstract

Quantization of signals is required for many transmission, storage and compression applications. The original signal is quantized at the encoder side. At the decoder side, a replica of the original signal that should resemble the original signal in some sense is recovered. Present quantizers make an effort to reduce the distortion of the signal in the sense of reproduction fidelity. Consider scenarios in which signals are generated from multiple classes. The encoder focuses on the task of quantizing the data without any regards to the class of the signal. The quantized signal reaches the decoder where not only the recovery of the signal should take place but also a decision is to be made on the class of the signal based on the quantized version of the signal only. In this paper, we study the design of such scalar quantizer that is optimized for the task of classification at the decoder. We define the distortion to be the symmetric Kullback-Leibler (KL) divergence measure between the conditional probabilities of class given the signal before and after quantization. A high-rate analysis of the quantizer is presented and the optimum point density of the quantizer for minimizing the symmetric KL divergence is derived. The performance of this method on synthetically generated data is examined and observed to be superior in the task of classification of signals at the decoder.

Published
2011
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193. Focal elements generated by the Dempster-Shafer theoretic conditionals: A complete characterization

Author

Kamal Premaratne, Thanuka L. Wickramarathne, and Manohar N. Murthi

Subjects
Structure (mathematical logic), business.industry, Process (engineering), Computation, Dempster–Shafer theory, Proposition, Material implication, Artificial intelligence, Propositional calculus, business, Upper and lower bounds, Mathematics
Abstract

Incorporation of soft evidence into the fusion process poses considerable challenges, including issues related to the material implications of propositional logic statements, contradictory evidence, and non-identical scopes of sources providing soft evidence. The conditional approach to Dempster-Shafer (DS) theoretic evidence updating and fusion provides a promising avenue for overcoming these challenges. However, the computation of the Fagin-Halpern (FH) conditionals utilized in the conditional evidence updating strategies is non-trivial because of the lack of a method to identify the conditional focal elements directly. The work in this paper presents a complete characterization of the conditional focal elements via a necessary and sufficient condition that identifies the explicit structure of a proposition that will remain a focal element after conditioning. We illustrate the resulting computational advantage via several experiments.

Published
2010
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199. Estimation of frame independent and enhancement components for speech communication over packet networks

Author

Marc Moonen, Søren Holdt Jensen, Manohar N. Murthi, Daniele Giacobello, and Mads Græsbøll Christensen

Subjects
Computer science, Speech recognition, Speech coding, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Speech synthesis, Data_CODINGANDINFORMATIONTHEORY, 010103 numerical & computational mathematics, computer.software_genre, 01 natural sciences, 030507 speech-language pathology & audiology, 03 medical and health sciences, Packet loss, 0101 mathematics, Fast packet switching, Voice over IP, business.industry, Network packet, Linear predictive coding, Speech processing, Speech enhancement, Bit rate, 0305 other medical science, business, computer, Decoding methods, Communication channel
Abstract

In this paper, we describe a new approach to cope with packet loss in speech coders. The idea is to split the information present in each speech packet into two components, one to independently decode the given speech frame and one to enhance it by exploiting interframe dependencies. The scheme is based on sparse linear prediction and a redefinition of the analysis-by-synthesis process. We presentMean Opinion Scores for the presented coder with different degrees of packet loss and show that it performs similarly to frame dependent coders for low packet loss probability and similarly to frame independent coders for high packet loss probability. We also present ideas on how to make the coder work synergistically with the channel loss estimate.

Published
2010
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200. A Kalman filtering approach to GMM predictive coding of LSFS for packet loss conditions

Author

Manohar N. Murthiy, Subasingha Shaminda Subasingha, and Soren Vang Andersen

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Minimum mean square error, Robustness (computer science), Packet loss, Speech recognition, Speech coding, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Vector quantization, Data_CODINGANDINFORMATIONTHEORY, Kalman filter, Mixture model, Decoding methods, Mathematics
Abstract

Gaussian Mixture Model (GMM)-based vector quantization of Line Spectral Frequencies (LSFs) has gained wide acceptance in speech coding. In predictive coding of LSFs, the GMM approach utilizing Kalman filtering principles to account for quantization noise has been shown to perform better than a baseline GMM Recursive Coder approaches for both clean and packet loss conditions at roughly the same complexity. However, the GMM Kalman based predictive coder was not specifically designed for operation in packet loss conditions. In this paper, we show how an initial GMM Kalman predictive coder can be utilized to obtain a robust GMM predictive coder specifically designed to operate in packet loss. In particular, we demonstrate how one can define sets of encoding and decoding modes, and design special Kalman encoding and decoding gains for each set. With this framework, GMM predictive coding design can be viewed as determining the special Kalman gains that minimize the expected minimum mean squared error at the decoder in packet loss conditions. The simulation results demonstrate that the proposed robust Kalman predictive coder achieves better performance than the baseline GMM predictive coders.

Published
2009
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