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3. Computational Analysis of Flow Around Two Wall-Mounted Trapezoidal Bluff Bodies Arranged in Tandem Position.

Author

Sarkar, Sourav, Gupta, Nishant, Debnath, Koustuv, and Lawrence Raj, Prince Raj

Subjects
DRAG coefficient, STREAMFLOW velocity, REYNOLDS number, DRAG reduction, TURBULENCE
Abstract

Flow around two identical wall-mounted trapezoidal bluff bodies, arranged in tandem, is numerically investigated at a Reynolds number of 750,000. The investigation employs Reynolds-averaged Navier–Stokes (RANS) equations and the k–ω SST turbulence model. The effect due to the change of the angular orientation of the inclined faces (α) of this bluff body and the pitch distance (L/D) on hydrodynamic quantities and turbulence quantities is investigated. Furthermore, the drag coefficient and Strouhal number have also been evaluated to understand the vortex shedding and flow pattern-related phenomena. For d and intermediate types of bluff bodies, the streamwise mean velocity, cross-stream mean velocity, turbulence kinetic energy, and recirculation length decrease with the increase of α or L/D. Significant changes are also observed in the case of Strouhal number. Reduction in drag coefficient and recirculation length is observed with increased L/D at a constant α for d-type bluff bodies. The change of α and L/D also creates the formation of a periodic von Kármán vortex street at downstream of the second bluff body in the case of L/D = 7, making the flows more complex and unstable. The maximum size of the recirculation bubble occurs in the case of L/D = 10 at α = 30 deg. The investigation provides valuable insight into the complex dynamics of tandem configurations of wall-mounted bluff bodies. [ABSTRACT FROM AUTHOR]

Published
2025
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5. Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021

Author

Ng, Marie, Gakidou, Emmanuela, Lo, Justin, Abate, Yohannes Habtegiorgis, Abbafati, Cristiana, Abbas, Nasir, Abbasian, Mohammadreza, Abd ElHafeez, Samar, Abdel-Rahman, Wael M, Abd-Elsalam, Sherief, Abdollahi, Arash, Abdoun, Meriem, Abdulah, Deldar Morad, Abdulkader, Rizwan Suliankatchi, Abdullahi, Auwal, Abedi, Armita, Abeywickrama, Hansani Madushika, Abie, Alemwork, Aboagye, Richard Gyan, Abohashem, Shady, Abtahi, Dariush, Abualruz, Hasan, Abubakar, Bilyaminu, Abu Farha, Rana Kamal, Abukhadijah, Hana J, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Adams, Lisa C, Adane, Mesafint Molla, Addo, Isaac Yeboah, Adedokun, Kamoru Ademola, Adegoke, Nurudeen A, Adepoju, Abiola Victor Victor, Adesola, Ridwan Olamilekan, Adeyeoluwa, Temitayo Esther, Adiga, Usha, Adnani, Qorinah Estiningtyas Sakilah, Afaghi, Siamak, Afzal, Saira, Afzal, Muhammad Sohail, Agampodi, Thilini Chanchala, Aghamiri, Shahin, Agostinis Sobrinho, César, Agyemang-Duah, Williams, Ahlstrom, Austin J, Ahmad, Danish, Ahmad, Sajjad, Ahmad, Aqeel, Ahmad, Muayyad M, Ahmad, Fuzail, Ahmad, Noah, Ahmed, Haroon, Ahmed, Muktar Beshir, Ahmed, Ayman, Ahmed, Meqdad Saleh, Ahmed, Mehrunnisha Sharif, Ahmed, Syed Anees, Ajami, Marjan, Akhtar, Samina, Akkaif, Mohammed Ahmed, Akrami, Ashley E, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Al-amer, Rasmieh Mustafa, Alansari, Amani, Al-Ashwal, Fahmi Y, Albashtawy, Mohammed, Aldhaleei, Wafa A, Alemayehu, Bezawit Abeje, Algammal, Abdelazeem M, Alhabib, Khalid F, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Alhuwail, Dari, Ali, Rafat, Ali, Abid, Ali, Waad, Ali, Mohammed Usman, Alif, Sheikh Mohammad, Al-Jabi, Samah W, Aljunid, Syed Mohamed, Alkhatib, Ahmad, Al-Marwani, Sabah, Alomari, Mahmoud A, Alqahtani, Saleh A, Al-Raddadi, Rajaa M Mohammad, Alrawashdeh, Ahmad, Alrimawi, Intima, Alrousan, Sahel Majed, Alshahrani, Najim Z, Al Ta'ani, Omar, Al Ta'ani, Zain, Altaany, Zaid, Altaf, Awais, Al Thaher, Yazan, Alvis-Guzman, Nelson, Al-Wardat, Mohammad, Al-Worafi, Yaser Mohammed, Aly, Safwat, Aly, Hany, Alzahrani, Hosam, Alzoubi, Abdallah, Alzoubi, Karem H, Al-Zubayer, Md. Akib, Amiri, Sohrab, Amu, Hubert, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ananda, Roshan A, Ancuceanu, Robert, Andrei, Catalina Liliana, Anjana, Ranjit Mohan, Ansari, Sumbul, Ansari, Mohammed Tahir, Antony, Catherine M, Anuoluwa, Iyadunni Adesola, Anuoluwa, Boluwatife Stephen, Anvari, Saeid, Anwar, Saleha, Anyasodor, Anayochukwu Edward, Apostol, Geminn Louis Carace, Arab, Juan Pablo, Arabloo, Jalal, Arafat, Mosab, Aravkin, Aleksandr Y, Areda, Demelash, Arifin, Hidayat, Arkew, Mesay, Armocida, Benedetta, Ärnlöv, Johan, Arooj, Mahwish, Artamonov, Anton A, Artanti, Kurnia Dwi, Arumugam, Ashokan, Asghari-Jafarabadi, Mohammad, Ashraf, Tahira, Asiamah-Asare, Bernard Kwadwo Yeboah, Asrat, Anemaw A, Astell-Burt, Thomas, Athari, Seyyed Shamsadin, Atorkey, Prince, Atreya, Alok, Aumoldaeva, Zaure Maratovna, Awad, Hamzeh, Awoke, Mamaru Ayenew, Awotidebe, Adedapo Wasiu, Aychiluhm, Setognal Birara, Azargoonjahromi, Ali, Azimi, Amirali, Aziz, Sadat Abdulla, Aziz, Shahkaar, Azzam, Ahmed Y., Azzolino, Domenico, Azzopardi, Peter S, Babashahi, Mina, Babu, Giridhara Rathnaiah, Badiye, Ashish D, Bagheri, Nasser, Bahurupi, Yogesh, Bai, Ruhai, Baig, Atif Amin, Bakkannavar, Shankar M, Balakrishnan, Senthilkumar, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Banik, Rajon, Bardhan, Mainak, Barqawi, Hiba Jawdat, Barquera, Simon, Barua, Lingkan, Basharat, Zarrin, Bashir, Shahid, Bastan, Mohammad-Mahdi, Basu, Saurav, Bayat, Reza, Bayih, Mulat Tirfie, Beeraka, Narasimha M, Begum, Tahmina, Bello, Umar Muhammad, Bello, Abdulrahman Babatunde, Belo, Luis, Bensenor, Isabela M, Bergami, Maria, Berhe, Kidanemaryam, Berihun, Abiye Assefa, Bhadoria, Ajeet Singh, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhalla, Jaideep Singh, Bharadwaj, Ravi, Bhardwaj, Pankaj, Bhardwaj, Nikha, Bhaskar, Sonu, Bhat, Ajay Nagesh, Bhattacharjee, Priyadarshini, Bhattacharjee, Shuvarthi, Bhatti, Jasvinder Singh, Bhatti, Gurjit Kaur, Bikov, Andras, Bilgin, Cem, Bisignano, Catherine, Biswas, Bijit, Bizzozero Peroni, Bruno, Bjertness, Espen, Bjørge, Tone, Bolla, Srinivasa Rao, Borhany, Hamed, Bosoka, Samuel Adolf, Bouaoud, Souad, Boyko, Edward J, Braithwaite, Dejana, Brazo-Sayavera, Javier, Brenner, Hermann, Britton, Gabrielle, Bryazka, Dana, Bugiardini, Raffaele, Bui, Linh Phuong, Busch, Felix, Bustanji, Yasser, Butt, Nadeem Shafique, Butt, Zahid A, Calina, Daniela, Campos, Luciana Aparecida, Campos-Nonato, Ismael, Cao, Si, Cao, Yin, Capodici, Angelo, Carvalho, Andre F, Carvalho, Márcia, Catapano, Alberico L, Cattafesta, Monica, Cattaruzza, Maria Sofia, Cegolon, Luca, Cembranel, Francieli, Cenko, Edina, Cerin, Ester, Cernigliaro, Achille, Chadwick, Joshua, Chakraborty, Chiranjib, Chan, Raymond N C, Chang, Jung-Chen, Chattu, Vijay Kumar, Chaudhary, Anis Ahmad, Chaurasia, Akhilanand, Chen, Guangjin, Chen, An-Tian, Chen, Haowei, Cheng, Esther T W, Chew, Nicholas WS, Chi, Gerald, Chimoriya, Ritesh, Ching, Patrick R, Choi, Dong-Woo, Chong, Bryan, Chopra, Hitesh, Chopra, Shivani, Chou, Hou In, Choudhari, Sonali Gajanan, Chu, Dinh-Toi, Chung, Sunghyun, Chung, Sheng-Chia, Chutiyami, Muhammad, Cini, Karly I, Cioffi, Iolanda, Cogen, Rebecca M, Collado-Mateo, Daniel, Columbus, Alyssa, Conrad, Nathalie, Criqui, Michael H, Cruz-Martins, Natalia, Cummins, Steven, D'Amico, Emanuele, D'Anna, Lucio, D'Oria, Mario, Dadras, Omid, Dai, Xiaochen, Dalakoti, Mayank, Dandona, Rakhi, Dandona, Lalit, Danpanichkul, Pojsakorn, Darcho, Samuel Demissie, Darvishi Cheshmeh Soltani, Reza, da Silva, Alanna Gomes, Davletov, Kairat, Delgado-Enciso, Ivan, Denova-Gutiérrez, Edgar, Derbew Molla, Meseret, Dergaa, Ismail, Desale, Aragaw Tesfaw, Devanbu, Vinoth Gnana Chellaiyan, Devegowda, Devananda, Dewan, Syed Masudur Rahman, Dhali, Arkadeep, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Dhungel, Bibha, Diaz, Daniel, Dinu, Monica, Dodangeh, Milad, Dohare, Sushil, Dokova, Klara Georgieva, Dolatkhah, Neda, do Prado, Camila Bruneli, Dorostkar, Fariba, Doshi, Ojas Prakashbhai, Doshi, Rajkumar Prakashbhai, Dowou, Robert Kokou, Dsouza, Viola Savy, Du, Mi, Dumith, Samuel C, Dumuid, Dorothea, Duncan, Bruce B, Dutta, Sulagna, Dziedzic, Arkadiusz Marian, Ebrahimi, Alireza, Eftekhari, Behrad, Eighaei Sedeh, Ashkan, Ekholuenetale, Michael, Eladl, Mohamed Ahmed, El Arab, Rabie Adel, El-Ashker, Said, Elbarazi, Iffat, El Bayoumy, Ibrahim Farahat, Elgendy, Islam Y, Elhadi, Muhammed, El-Huneidi, Waseem, El-Metwally, Ashraf A, Elmonem, Mohamed A, Elnaem, Mohamed Hassan, Elsheikh, Randa, Elsohaby, Ibrahim, Eltaha, Chadi, Emeto, Theophilus I, Eslami, Maysa, Eze, Ugochukwu Anthony, Fadavian, Heidar, Fagbamigbe, Adeniyi Francis, Fakhradiyev, Ildar Ravisovich, Faraji, Seyed Nooreddin, Farinha, Carla Sofia e Sá, Faris, MoezAlIslam Ezzat Mahmoud, Farooque, Umar, Farrokhpour, Hossein, Fasusi, Samuel Aanuoluwapo, Fazeli, Patrick, Fazylov, Timur, Feizkhah, Alireza, Fekadu, Ginenus, Feng, Xiaoqi, Fernandes, João C, Fernandez-Jimenez, Rodrigo, Ferreira, Nuno, Feyisa, Bikila Regassa, Fischer, Florian, Flood, David, Foigt, Nataliya A, Folayan, Morenike Oluwatoyin, Fomenkov, Artem Alekseevich, Foroumadi, Roham, Fortuna Rodrigues, Celia, Foschi, Matteo, Fotouhi, Maryam, Francis, Kate Louise, Franklin, Richard Charles, Gába, Aleš, Gadanya, Muktar A, Gaidhane, Abhay Motiramji, Galali, Yaseen, Gallus, Silvano, Ganesan, Balasankar, Gangachannaiah, Shivaprakash, Gastélum Espinoza, Wendy Paola, Gebregergis, Miglas Welay, Gebremeskel, Teferi Gebru, Getacher, Lemma, Ghadirian, Fataneh, Ghaffari Jolfayi, Amir, Ghamari, Seyyed-Hadi, Ghazy, Ramy Mohamed, Gil, Artyom Urievich, Gill, Tiffany K, Gnedovskaya, Elena V, Golechha, Mahaveer, Golinelli, Davide, Grivna, Michal, Grover, Ashna, Guan, Zhongyang, Guan, Shi-Yang, Guarducci, Giovanni, Gubari, Mohammed Ibrahim Mohialdeen, Guha, Avirup, Gunawardane, Damitha Asanga, Guo, Zheng, Gupta, Rajeev, Gupta, Anish Kumar, Gupta, Rahul, Gupta, Sapna, Gupta, Vivek Kumar, Gutiérrez-Murillo, Roberth Steven, Guzman-Esquivel, Jose, Hadi, Najah R, Hadian, Zahra, Hamdy, Nadia M, Hameed, Sajid, Hamidi, Samer, Hamiduzzaman, Mohammad, Hanif, Asif, Hanifi, Nasrin, Hankey, Graeme J, Haq, Allie, Harlianto, Netanja I, Haro, Josep Maria, Hartono, Risky Kusuma, Hasan, Faizul, Hashempur, Mohammad Hashem, Hasnain, Md Saquib, Hassan, Amr, Hassan, Nageeb, Hassanipour, Soheil, Hassanzade Rad, Afagh, Havmoeller, Rasmus J, Hay, Simon I, He, Wen-Qiang, Hebert, Jeffrey J, Heidari, Golnaz, Hemmati, Mehdi, Hiraike, Yuta, Hoan, Nguyen Quoc, Hoang, Mai, Holla, Ramesh, Hoogar, Praveen, Hopkins, Ashley Mark, Hossain, Alamgir, Hosseinzadeh, Hassan, Hostiuc, Sorin, Hostiuc, Mihaela, Htay, Zin Wai, Hu, Chengxi, Huang, Junjie, Hundie, Tsegaye Gebreyes, Husseiny, Mohamed Ibrahim, Huynh, Hong-Han, Iavicoli, Ivo, Ibrayeva, Anel, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Imam, Mohammad Tarique, Inbaraj, Leeberk Raja, Inok, Arit, Irham, Lalu Muhammad, Islam, Md. Rabiul, Islam, Sheikh Mohammed Shariful, Islam, Rakibul M, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Isola, Gaetano, Ituka, Mosimah Charles, Iwagami, Masao, Iwu-Jaja, Chinwe Juliana, Iyasu, Assefa N, J, Vinothini, Jacob, Louis, Jaffar, Shabbar, Jahrami, Haitham, Jain, Akhil, Jairoun, Ammar Abdulrahman, Jakovljevic, Mihajlo, Jalloh, Mohamed Lamrana, Javaid, Syed Sarmad, Jayapal, Sathish Kumar, Jayarajah, Umesh, Jayaram, Shubha, Jebai, Rime, Jebasingh, Felix K, Jema, Alelign Tasew, Jokar, Mohammad, Jonas, Jost B, Jose, Jobinse, Joseph, Nitin, Joshua, Charity Ehimwenma, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kaambwa, Billingsley, Kabir, Ali, Kabir, Zubair, Kakkar, Ashish Kumar, Kalra, Sanjay, Kamarajah, Sivesh Kathir, Kanaan, Saddam Fuad, Kankam, Samuel Berchi, Kanmodi, Kehinde Kazeem, Kapoor, Neeti, Karajizadeh, Mehrdad, Karakasis, Paschalis, Karasneh, Reema A, Karimi, Yeganeh, Karimi Behnagh, Arman, Kassebaum, Nicholas J, Kauppila, Joonas H, Kayode, Gbenga A, Kehagias, Dimitrios, Kerr, Jessica A, Keshwani, Ariz, Kesse-Guyot, Emmanuelle, Keykhaei, Mohammad, Khaing, Inn Kynn, Khajuria, Himanshu, Khalili, Pantea, Khalilian, Alireza, Khalis, Mohamed, Khan, Mohammad Jobair, Khan, Maseer, Khan, Nusrat, Khan, Md Abdullah Saeed, Khan, Ajmal, Khan, Moien AB, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khatatbeh, Moawiah Mohammad, Khayamzadeh, Maryam, Khidri, Feriha Fatima, Khorashadizadeh, Fatemeh, Khosla, Atulya Aman, Khosravi, Sepehr, Khosrowjerdi, Mahmood, Khubchandani, Jagdish, Khusun, Helda, Kim, Jinho, Kim, Kwanghyun, Kim, Min Seo, Kim, Yun Jin, Kimokoti, Ruth W, Kisa, Adnan, Kishore, Ladli, Kivimäki, Mika, Kokkorakis, Michail, Kolahi, Ali-Asghar, Kompani, Farzad, Korzh, Oleksii, Kostev, Karel, Koulmane Laxminarayana, Sindhura Lakshmi, Kretchy, Irene Akwo, Krishan, Kewal, Kua, Chong-Han, Kuate Defo, Barthelemy, Kulimbet, Mukhtar, Kulkarni, Vishnutheertha, Kumar, Ashish, Kumar, Vijay, Kumar, G Anil, Kundu, Satyajit, Kunutsor, Setor K, Kurmi, Om P, Kurniasari, Maria Dyah, Kusuma, Dian, Kytö, Ville, Lacey, Ben, Lahariya, Chandrakant, Lai, Daphne Teck Ching, Lai, Hanpeng, Landires, Iván, Larijani, Bagher, Latief, Kamaluddin, La Vecchia, Carlo, Le, Nhi Huu Hanh, Lee, Munjae, Lee, Sang-woong, Lee, Wei-Chen, Lee, Seung Won, Lee, Paul H, Li, Ming-Chieh, Li, Yongze, Li, Weilong, Lim, Stephen S, Lin, Queran, Lin, Jialing, Lindholm, Daniel, Lindstedt, Paulina A, Liu, Simin, Llanaj, Erand, López-Gil, José Francisco, Lorkowski, Stefan, Lucchetti, Giancarlo, Lugo, Alessandra, Lutambi, Angelina M, Lv, Lei, Lytvyak, Ellina, Ma, Zheng Feei, Machoy, Monika, Magaña Gómez, Javier A, Maghbouli, Nastaran, Mahalleh, Mehrdad, Mahmood, Nozad H, Mahmoudi, Elham, Maiti, Rituparna, Makris, Konstantinos Christos C, Malhotra, Kashish, Malik, Ahmad Azam, Malik, Iram, Malta, Deborah Carvalho, Mamun, Abdullah A, Mansourian, Marjan, Manu, Emmanuel, Marateb, Hamid Reza, Marino, Mirko, Marjani, Abdoljalal, Martinez-Piedra, Ramon, Martini, Santi, Martorell, Miquel, Marzouk, Sammer, Masi, Stefano, Masrouri, Soroush, Mathangasinghe, Yasith, Mathur, Manu Raj, Matozinhos, Fernanda Penido, Matthias, Thushara, Mattiello, Rita, Mazidi, Mohsen, McPhail, Steven M, Mechili, Enkeleint A, Mehboob, Riffat, Mehmood, Asim, Mehndiratta, Man Mohan, Mehrabani-Zeinabad, Kamran, Mekene Meto, Tesfahun, Meles, Hadush Negash, Mendoza, Walter, Menezes, Ritesh G, Mengistie, Emiru Ayalew, Meo, Sultan Ayoub, Mestrovic, Tomislav, Mettananda, Sachith, Mettananda, Chamila Dinushi Kukulege, Micheletti Gomide Nogueira de Sá, Ana Carolina, Miller, Ted R, Mini, GK, Mirrakhimov, Erkin M, Misganaw, Awoke, Mittal, Madhukar, Mohamed, Ahmed Ismail, Mohamed, Mona Gamal, Mohamed, Nouh Saad, Mohamed, Jama, Mohammad, Taj, Mohammad-Alizadeh-Charandabi, Sakineh, Mohammadzadeh, Ibrahim, Mohammed, Shafiu, Mohammed, Mustapha, Mokdad, Ali H, Mondello, Stefania, Moni, Mohammad Ali, Moradi, Maryam, Morrison, Shane Douglas, Mossialos, Elias, Motappa, Rohith, Mulita, Francesk, Mullany, Erin C, Munkhsaikhan, Yanjinlkham, Murillo-Zamora, Efren, Musa, Sani, Mustafa, Ghulam, Muthu, Sathish, Myung, Woojae, Naghavi, Pirouz, Naghavi, Mohsen, Naik, Ganesh R, Naik, Hiten, Nambi, Gopal, Nangia, Vinay, Nansseu, Jobert Richie, Nascimento, Gustavo G, Nassar, Mahmoud, Natto, Zuhair S, Nauman, Javaid, Naureen, Zakira, Navaratna, Samidi Nirasha Kumari, Nayak, Biswa Prakash, Nayon, Md Fahad Shahariar, Nazri-Panjaki, Athare, Negahdary, Masoud, Negoi, Ruxandra Irina, Negoi, Ionut, Nejadghaderi, Seyed Aria, Nematollahi, Soroush, Nepal, Samata, Netsere, Henok Biresaw, Ngunjiri, Josephine W, Nguyen, Cuong Tat, Nguyen, Dang, Nguyen, Trang, Nguyen, Duc Hoang, Nguyen, Phuong The, Niazi, Robina Khan, Nieddu, Luciano, Niknam, Mahdieh, Nikoobar, Ali, Nkeck, Jan Rene, Nomura, Shuhei, Noor, Syed Toukir Ahmed, Noreen, Mamoona, Noroozi, Masoud, Nawsherwan, Noubiap, Jean Jacques, Nouri, Mehran, Nri-Ezedi, Chisom Adaobi, Nugen, Fred, Nurrika, Dieta, Nzoputam, Ogochukwu Janet, O'Connell, Erin M, Oancea, Bogdan, Oguta, James Odhiambo, Oh, In-Hwan, Okati-Aliabad, Hassan, Okekunle, Akinkunmi Paul, Okonji, Osaretin Christabel, Olagunju, Andrew T, Olalusi, Oladotun Victor, Olanrewaju, Timothy Olusegun, Olasupo, Omotola O, Oliveira, Gláucia Maria Moraes, Oliveira, Arão Belitardo, Oluwafemi, Yinka Doris, Omar, Hany A, Omar Bali, Ahmed, Opitz, Marcel, Ordak, Michal, Ortiz, Alberto, Osborne, Augustus, Osman, Wael M S, Osman, Alaa A M, Osuagwu, Uchechukwu Levi, Otoiu, Adrian, Oumer, Abdu, Ouyahia, Amel, Owolabi, Mayowa O, Owusu, Irene Amoakoh, Oyebola, Kolapo, P A, Mahesh Padukudru, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Panda, Sujogya Kumar, Panda-Jonas, Songhomitra, Pandey, Anamika, Pandi-Perumal, Seithikurippu R, Pardhan, Shahina, Parekh, Utsav, Parija, Pragyan Paramita, Parikh, Romil R, Park, Eun-Cheol, Pashaei, Ava, Passera, Roberto, Patel, Hemal M, Pathan, Aslam Ramjan, Patoulias, Dimitrios, Patton, George C, Paudel, Susan, Pazoki Toroudi, Hamidreza, Pensato, Umberto, Peprah, Prince, Pereira, Gavin, Pereira, Marcos, Perianayagam, Arokiasamy, Perico, Norberto, Perna, Simone, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Pham, Hoang Nhat, Polibin, Roman V, Popovic, Djordje S, Pourghazi, Farzad, Pourshams, Akram, Pradhan, Jalandhar, Pradhan, Pranil Man Singh, Prasad, Manya, Prashant, Akila, Prates, Elton Junio Sady, Putra, I Gusti Ngurah Edi, Puvvula, Jagadeesh, Qattea, Ibrahim, Qiu, Jia-Yong, Radhakrishnan, Venkatraman, Radojčić, Maja R, Raggi, Catalina, Rahman, Muhammad Aziz, Rahman, Fryad Majeed, Rahman, Mohammad Hifz Ur, Rahman, Mosiur, Rahmani, Saeed, Rahmanian, Vahid, Rahmawaty, Setyaningrum, Rai, Rajesh Kumar, Raimondo, Ivano, Raj, Jeffrey Pradeep, Rajput, Prashant, Ramadan, Mahmoud Mohammed, Ramasamy, Chitra, Ramasamy, Shakthi Kumaran, Ramazanu, Sheena, Rana, Kritika, Ranabhat, Chhabi Lal, Rao, Mithun, Rao, Sowmya J, Rashedi, Sina, Rashidi, Mohammad-Mahdi, Rasouli-Saravani, Ashkan, Rathish, Devarajan, Rauniyar, Santosh Kumar, Rautalin, Ilari, Rawaf, David Laith, Rawaf, Salman, Redwan, Elrashdy M. Moustafa Mohamed, Rege, Sanika, Reis-Mendes, Ana, Remuzzi, Giuseppe, Rezaei, Nazila, Rezaeian, Mohsen, Rezazadeh, Hossein, Rhee, Taeho Gregory, Rocha-Gomes, João Rocha, Rodrigues, Mónica, Rodrigues da Silva, Thales Philipe, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Rohloff, Peter, Romadlon, Debby Syahru, Rony, Moustaq Karim Khan, Roshandel, Gholamreza, Rout, Himanshu Sekhar, Roy, Nitai, Rwegerera, Godfrey M, Saad, Aly M A, Saber-Ayad, Maha Mohamed, Sabet, Cameron John, Sadarangani, Kabir P, Saddik, Basema Ahmad, Sadeghi, Masoumeh, Saeb, Mohammad Reza, Saeed, Umar, Saeedi Moghaddam, Sahar, Safi, Sher Zaman, Saghazadeh, Amene, Sagoe, Dominic, Sahebkar, Amirhossein, Saheb Sharif-Askari, Fatemeh, Sahoo, Soumya Swaroop, Sajid, Mirza Rizwan, Salaroli, Luciane B, Saleh, Mohamed A, Salem, Marwa Rashad, Salimi, Sohrab, Samodra, Yoseph Leonardo, Samuel, Vijaya Paul, Samy, Abdallah M, Santhekadur, Prasanna K, Santric-Milicevic, Milena M, Saqib, Muhammad Arif Nadeem, Saraswati, Ushasi, Saravanan, Aswini, Sari, Dianis Wulan, Sarkar, Tanmay, Sarmadi, Mohammad, Sarode, Sachin C, Sarode, Gargi Sachin, Sassano, Michele, Sathian, Brijesh, Saya, Ganesh Kumar, Schinckus, Christophe, Schmidt, Maria Inês, Schuermans, Art, Schutte, Aletta Elisabeth, Sebastian, Sneha Annie, Selvaraj, Siddharthan, Semreen, Mohammad H, Sendekie, Ashenafi Kibret, Sengupta, Pallav, Senol, Yigit Can, Senthilkumaran, Subramanian, Sepanlou, Sadaf G, Sethi, Yashendra, Seylani, Allen, Shafie, Mahan, Shah, Sweni, Shah, Syed Mahboob, Shahid, Samiah, Shahrahmani, Fatemeh, Shahwan, Moyad Jamal, Sham, Sunder, Shamim, Muhammad Aaqib, Shams-Beyranvand, Mehran, Shamsi, Anas, Shamsutdinova, Alfiya, Shan, Dan, Shanawaz, Mohd, Shannawaz, Mohammed, Sharath, Medha, Sharfaei, Sadaf, Sharifan, Amin, Sharma, Anupam, Sharma, Ujjawal, Sharma, Manoj, Sharma, Vishal, Sheida, Fateme, Sheikhy, Ali, Shenoy, Rekha Raghuveer, Shetty, Pavanchand H, Shibuya, Kenji, Shiferaw, Desalegn, Shin, Min-Jeong, Shiri, Rahman, 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Zheng, David X, Zheng, Peng, Zhong, Anthony, Zhong, Claire Chenwen, Zhou, Jiayan, Zhu, Bin, Zhumagaliuly, Abzal, Zielińska, Magdalena, Zoghi, Ghazal, Zou, Zhiyong, Zweck, Elric, Zyoud, Sa'ed H, Murray, Christopher J L, Sawyer, Susan M, and Vollset, Stein Emil

Abstract

Overweight and obesity is a global epidemic. Forecasting future trajectories of the epidemic is crucial for providing an evidence base for policy change. In this study, we examine the historical trends of the global, regional, and national prevalence of adult overweight and obesity from 1990 to 2021 and forecast the future trajectories to 2050.

Published
2025
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6. Tranexamic acid versus placebo to prevent bleeding in patients with haematological malignancies and severe thrombocytopenia (TREATT): a randomised, double-blind, parallel, phase 3 superiority trial

Author

Estcourt, Lise J, McQuilten, Zoe K, Bardy, Peter, Cole-Sinclair, Merrole, Collins, Graham P., Crispin, Philip J., Curnow, Elinor, Curnow, Jennifer, Degelia, Amber, Dyer, Claire, Friebe, Adam, Floro, Lajos, Grand, Effie, Hudson, Cara, Jones, Gail, Joseph, Joanne, Kallmeyer, Charlotte, Karakantza, Marina, Kerr, Paul, Last, Sara, Lobo-Clarke, Maria, Lumley, Matthew, McMullin, Mary F, Medd, Patrick G., Morton, Suzy M., Mumford, Andrew D., Mushkbar, Maria, Parsons, Joseph, Powter, Gillian, Sekhar, Mallika, Smith, Laura, Soutar, Richard, Stevenson, William S., Subramoniapillai, Elango, Szer, Jeff, Thomas, Helen, Waters, Neil A., Wei, Andrew H., Westerman, David A., Wexler, Sarah A., Wood, Erica M., Stanworth, Simon J., Abioye, Adrienne, Afghan, Rabia, Ai, Sylvia Ai, Akanni, Magbor, Alajangi, Rajesh, Alam, Usmaan, Al-Bubseeree, Bahaa, Alderson, Sophie, Alderson, Craig, Ali, Sayed, Ali, Kabir, Alighan, Rookmeen, Allam, Rebecca Allam, Allen, Tania, Al-Sakkaf, Wesam, Ames, Kate, Anderson, Jacqueline, Andrews, Colin, Angel, Ann-Marie, Anlya, Manuela Anlya, Ansari, Farah, Appleby, Rowan, Arnold, Claire, Asbjornsdottir, Hulda, Asfaw, Biruk, Atkins, Elissa, Atkinson, Leela, Aubrey, Clare, Ayesha, Noor, Babbola, Lola, Badcock, David, Badcock, Samuel, Baggio, Diva, Bailiff, Ben, Baines, Kizzy, Baker, Holly, Baker, Victoria, Ball, Lindsay, Ball, Martin, Balquin, Irwin, Banks, Emma, Banos, George, Barnett, Jaytee, Barrie, Claire, Barron, Claire, Barton, Rebecca, Bason, Nina, Batta, Bindu, Bautista, Dianne, Bayley, Angela, Bayly, Emma, Baynes, Fionnuala, Bazargan, Ali, Bazeley, Rachel, Beadle, Yvonne, Beardsmore, Claire, Beattie, Kate, Beattie, Kate, Bedford, Caroline, Behal, Rachna, Behan, Daniel, Bejan, Lilihna, Bell, Sarah, Bell, Karen, Bell, Louise, Bell, Kaitlyn, Benjamin, Reuben, Bennett, Sam, Benson, Gary, Benson, Warwick, Bent, Cameron, Bergin, Krystal, Berry, Alex, Besenyei, Stephanie, Besley, Caroline, Betteridge, Scott, Beveridge, Leigh, Bhattacharyya, Abir, Billen, Annelies, Bilmon, Ian, Binns, Emma, Birt, Mark, Bishop, David, Blanco, Andrea, Bleby, Lisa, Blemnerhet, Richard, Blombery, Piers, Blyth, Emily, Blythe, Nicola, Boal, Lauren, Boden, Ali, Bokhari, Syed W.I., Bongetti, Elisa, Booth, Stephen, Borley, Jayne, Bowen, David, Bowers, Dawn, Boyd, Stephen, Bradley, Sarah, Bradman, Helen, Bretag, Peta, Brillante, Maria, Brockbank, Rachel, Brough, Yasmin, Brown, Ellen, Brown, Jo, Brown, Eleanor, Brown, Claire, Brown, Jenny, Brown, Susan, Browning, Joe, Brownsdon, Alex, Bruce, David, Brydon-Hill, Ruth, Buckwell, Andrea, Burgess, Dannielle, Burke, Glenda, Burley, Kate, Burney, Claire, Burns, David, Burrows, Samuel, burton, Kieran, Butler, Jason, Cambalova, Lenka, Camozzi, Maria C., Campbell, Philip, Campfield, Karen, Campion, Victoria, Cargo, Catherine, Carmona, Julia, Carney, Dennis, Casan, Joshua, Cashman, Helen, Catt, Lorraine, Cattell, Michael, Cavill, Megan, Chadbone, Rachel, Chaganti, Sridhar, Chai, Yee, Chai, Khai Li, Chang, Joshua, Chapman, Judith, Chapman, Oliver G., Chapter, Tamika, Charlton, Andrew, Chau, Celina, Chauhan, Saleena, Chavda, Nikesh, Chen, Frederick, Chen, Melody, Chen, Meng Xi, Chen, Melanie, Chen, Melissa, Cheok, Kathleen, Cheung, Mai, Chidgey, Luke, Chmielokliec, Karolina, Choi, Philip, Choi, Jae, Chok, Anne, Chopra, Ruchika, Christopherson, Louise, Chu, Vicky, Chua, Chong Chyn, Chudakou, Pavel, Chugh, Vidushi, Chung, Chi, Clark, Erin, Clarke, Peter, Clarke, Kathleen, Clay, Jennifer, Clayton, Laura, Clements, Mitch, Clemmens, Jonathan, Clifford, Ruth, Collett, Dave, Collins, Maia, Collyer, Emily, Connolly, Maureen, Cook, Mark, Coombs, Sarah, Coppell, Jason, Cornwell, Sophie, Corrigan, Claire, Coughlin, Elizabeth, Couling, Jennifer, Cousins, Tony, Cowan, Catriona, Cox, Christine, Cox, Catherine, Coyle, Luke, Craig, Emily, Creasey, Thomas, Croan, Laura, Croft, Jane, Crosbie, Nicola, Crowe, Josephine, Crowther, Helen, Crozier, Jane, Culleton, Naomi, Cullis, Jonathan, Cumming, Anita, Cummins, Michelle, Cunningham, Adam, Curley, Cameron, Curtis, Samantha, Cuthbert, Robert, Cuthill, Kirsty, Dahahayake, Dinusha A, Dang, Amy, Davies, Marc, Davies, Ceri, Dawson, Emily, Day, Tom, De Abrew, Kanchana, De Lavallade, Hugues, De Silva, Neelaskshi, Dean, Georgina, Deane, Christopher, Demosthenous, Lisa, Desai, Amisha, Desborough, Michael, Devanny, Ian, Dhanapal, Jay, Dhani, Sundip, Di Martino, Vicky, Dickens, Emmy, DiCorleto, Carmen, Dinnett, Louise, Dirisan, Divya, Dixon, Karen, Dixon, Kiri, Doal, Inderjit, Dobivh, J, Docanto, Maria, Doecke, Helve, Donaldson, David, Donaldson, Kylee, Donohoe, Carrie, Douglas, Ashley, Doung, Stephen, Downer, Susan, D'Rozario, James, Drummond, Malcolm, Drummond, Mark, Drummond, Samantha, Drysdale, Elizabeth, D'Souza, Ross, D'Souza, Eugene, Dunn, Alex, Dutton, David, Dyson, Martin, Ediriwicurena, Kushani, Edleston, Sharon, Edwards, Dawn, Edwards, Morgan, Edwards, Anita, Eise, Nicole, Ellis, Steven, Ellis, Hayley, Elmonley, Shareef, Enstone, Rosemarie, Eordogh, 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Kennedy, Glen, Kennedy, Nola, Kenny, Angela, Kenworthy, Zoe, Kerridge, Ian, Kesavan, Murali, Khafizi, Angelika, Khakwani, Muhammad, Khalid, Amna, Khamly, Kate, Khan, Anjum, Khan, Dalia, Khan, Mojid, Khan, Lubna, Khoo, Mona, Khwaja, Asim, Kim, Grace, King, Andrew, King, Vicky, King, Donna, Kinsella, Francesca, Kipp, David, Kirandeep, Pachoo, Kirui, Laura C., Kishore, Bhuvan, Knectlhi, Christopher, Knot, Amy, Knot, Armit, Ko, Cathy, Kolaric, Caitlin, Koo, Ray, Kotadia, Mary, Kothari, Jaimal, Kottaridis, Panagiotis D., Kuiluinathan, Gajan, Kulasekararaj, Austin, Kwan, John, Kwok, Marwan, Kwok, Phillip, Kwok, Fiona, Laane, Kristiina, Lad, Deena, Laird, Jennifer, Lam, Ada, Lane, Mary, Lanenco, Monica, Lang, Susan, Langridge, Alex, Langton, Catherine, Lannon, Michelle, Latif, Annie, Latimer, Maya, Latter, Ruth, Lau, I-Jun, Lawless, Sarah, Lawless, Theresa, Leach, Mike, Leaney, Sarah, Leary, Heather, Leavy, James, LeBlanc, Abbey, Lee, Vivienne, Lee, Edwin, Lee, Jenny, Lee, Tamara, Leischkie, Marian, Leitinger, Emma, Leon, Christopher, Leonard, Jayne, Lewis, David, Lewis, Ian, Lewis, Tania, Lim, Daniel, Littlewood, Kelly, Liu, Dara, Loh, Joanna, Lokare, Anand, Lokare, Anand, Lomas, Oliver, Lovell, Richard, Lowe, Theresa, Lowry, Lisa, Lubowiecki, Marcin, Lumb, Rebecca, Lynch, Gail, Macaulay, Amanda, MacDonald, Lyndsey, MacDonald-Burn, Jill, Macmillan, Margaret, Maddock, Karen, Mahaliyana, Tomas, Mahon, Cassandra, Maidment, Alison, Maier, Susie, Mairos, Michelle, Majid, Mahseeman, Mak, Ka L, Mak, Anne, Malendrayogau, Arunthrthy, Malham, Hana, Malyon, Felicity, Mandadapu, Vineela, Mandel, Laura, Mant, Sarah, Manton, Ruth, Maouche, Nadjoua, Maqbool, Muhammad G., Marchant, Gregory, Marinho, Mariana, Marks, David, Marner, Mike, Marr, Helen, Marshall, Gillian, Martin, Siobhan, Martin, Abigail, Marzolini, Maria, Mason, Kiara, Massie, Jonathan, Masson, Rebecca, Mathavan, Vidya, Mathew, Siju, Mathie, Judith, Mattocks, Lehenta, Maybury, Bernard, Mayer, Georgina, McAlister, Chyrelle, 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Smith, Sophie, Smith, Sarit, Smith, Claire, Smith, Alastair, Smith, Neil, Snowden, Katherine, Solis, Joel, Somios, Denise, Soo, Jade, Spanevello, Michelle, Spaulding, Madeleie, Spence, Laura, Spillane, Liz, Spiteri, Alisha, Sprigg, Naomi, Springett, Sally, Stafford, Lynn, Stainthorp, Katherine, Stark, Kate, Steeden, Louise, Stephen, Ella, Stephenson, Aisling, Stewart, Andrew, Stewart, Orla, Stobie, Emma, Stokes, Chelsea, Streater, Jacqui, Suddens, Charlie-Marie, Suntharalingam, Surenthini, Surana, Narinder, Sutherland, Robyn, Sutherland, Antony, Sutton, David, Sweeney, Connor, Sweet, Reilly, Szucs, Aniko P, Taheri, Leila E., Tailor, Hinesh, Tam, Constantine, Tam, Constantine, Tambakis, George, Tamplin, Mary, Tan, Chee, Tan, Sui, Tan, Joanne, Tan, Zhi, Taran, Tatiana, Tarpey, Fiona, Taseka, Angela, Tasker, Suzy, Tatarczuch, Maciej, Tayabali, Sarrah, Taylor, Hannah, Taylor, Robert, Taylor, Melaine, Taylor-Moore, Ella, Teasdale, Lesley, Tebbet, Elizabeth, Tedjasepstra, Aditya, Tedjaseputra, Aditya, Tepkumkun, Oummy, Terpstra, Andrew, Thomas, Wayne, Thomas, Shanice, Thompson, Rachel, Thornton, Thomas, Thorp, Bronwyn, Thrift, Moi Yap, Thwaites, Phillipa, Timbres, Jasmine, Tindall, Lauren, Tiong, Ing Soo, Tippler, Nicole, Todd, Tony, Todd, Shirley, Toghill, Neil, Tomlinson, Eve, Tooth, Jacinta, Topp, M., Trail, Nicola, Tran, Nguyen, Tran, Elizabeth, Tran, Vi, Treder, Bona, Tribbeck, Michelle, Trochowski, Siobhan, Truslove, Maria, Tse, Tsun, Tseu, Bing, Tucker, David, Turner, Kelly, Turner, Dianne, Turner, Kelly, Turner, Herleen, Turner, Gillian, Twohig, Julie, Tylee, Thomas, Uhe, Micheleine, Underhill, Lauren, V, Joanne, Van der Vliet, Georgina, Van Tonder, Tina, VanderWeyden, Carrie, Varghese, Jerry, Vaughan, Lachlan, Veale, David, Vickaryyous, Nicky, Vince, Kathryn, Von Welligh, Jacoba, Vora, Sona, Vora, Sona, Wadehra, Karan, Walker, Rebecca, Walker, Stephen, Wallace, Roslyn, Wallniosve, Stephanie, Wallwork, S., Walmsley, Zoe, Walters, Fiona, Wang, Joyce, Wang, Angela, Wang, Chen, Wanyika, Mercy, Warcel, Dana, Wardrobe, Katrina, Warnes, Kristian, Waterhouse, Christopher, Waterworth, Adam, Watson, Caroline, Watson, Edmund, Watts, Emily, Weaver, Emma, Weber, Nicholas, Webley, Kaytie, Welford, Anna, Wells, Matt, Westbury, Sarah, Westcott, Jackie, Western, Robyn, Weston, Julia, White, Jessica, White, Phillipa, Whitehead, Anna, Whitehouse, James, Wieringa, Samantha, Willan, John, Williams, Sandra, Williams, Bethany, Williamson, Stephanie, Willoughby, Brett, Wilmot, Gail, Wilmott, Rosalind, Wilson, Joanna, Wilson, Emma, Wilson, Suzy, Wilson, Heather, Wilson, Caroline, Wilson, Tanya, Wilton, Margaret, Wiltshire, Paula, Wincup, Joanne, Wolf, Julia, Wong, Henna, Wong, Cyndi, Wong, Daniel, Wong, Jonathan, Wong, Shi Qin, Wood, Sarah, Wood, Henry, Wooding, Jackie, Woolley, Kelly, Wright, Myles, Wright, Myles, Wynn-Williams, Roland, Yannakou, Costas, Yeoh, Zhi Han, Yeoh, Zhi Han, Yeung, David, Young, Agnes, Yuen, Flora, Yuen, Agnes, Zaja, Oliver, Zhang, Xiao-Yin, and Zhang, Mei

Abstract

Bleeding is common in patients with haematological malignancies undergoing intensive therapy. We aimed to assess the effect of tranexamic acid on preventing bleeding and the need for platelet transfusions.

Published
2025
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7. Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal

Author

Budhathoki, Bhumi Raj, Adhikari, Tirtha Raj, Shu, Lele, Shrestha, Suraj, Awasthi, Ram Prasad, Dawadi, Binod, Baniya, Binod, and Dhital, Yam Prasad

Abstract

Hydrological information is essential for planning water resource projects and for simulating hydrological models to calibrate and generate streamflow data. This study evaluates the performance of distributed and semi-distributed hydrological models in the Tamakoshi River Basin (TRB), Nepal. The models assessed include the Spatial Process in Hydrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS). The models were evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) ranging from 0.62 to 0.77, Coefficient of Determination (R2) from 0.76 to 0.77, and Percentage Bias (P Bias) from 26.16 % to −3.54 % during daily calibration. All three models were successfully calibrated and validated on a daily and monthly basis using observed discharge data at the Busti station in the TRB. The SPHY model performed better during the calibration period compared to the validation period, while the HBV and HEC-HMS models showed better performance during validation than calibration. Among the statistical indicators, the coefficient of determination closely matched, but NSE and P Bias showed some deviations. This comparative analysis of calibrated and validated models is valuable for filling gaps in hydrological data records and for selecting appropriate hydrological models for planning, managing, and developing water resource projects in the TRB.

Published
2025
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9. A high-performance core laboratory GFAP/UCH-L1 test for the prediction of intracranial injury after mild traumatic brain injury.

Author

Welch, Robert D., Bazarian, Jeffrey J., Chen, James Y., Chandran, Raj, Datwyler, Saul A., McQuiston, Beth, and Caudle, Krista

Abstract

A glial fibrillary acidic protein (GFAP) and ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1) blood biomarker panel can reliably eliminate the need to perform a head computed tomography (CT) scan in selected patients with traumatic brain injury (TBI). Currently, this FDA cleared panel can be run both on a core laboratory platform or a hand-held single-sample point of care platform. This study examined test characteristics of the panel as analyzed on a core lab-based fast high-throughput platform. This secondary analysis of clinical data and banked blood samples obtained for the ALERT-TBI study included patients ≥18 years old with nonpenetrating head injury, a presenting Glasgow Coma Scale score 9–15, and a head CT was indicated. Included were patients with a GCS 13–15 who had sufficient banked blood for analysis. Test characteristics of the biomarker panel were determined relative to head CT findings for traumatic intracranial injury. Among the 1899 included subjects, mean age was 49.1 yrs. (18 to 98 yrs), 56.5 % male, and 70.6 % were Caucasian. The most common mechanism of injury was a fall (51.9 %) and 94.1 % presented with a GCS of 15. Head CT was positive for traumatic intracranial injury in 120 patients (6.3 %) of which the biomarker panel was a false negative in four patients. Sensitivity (95 % confidence interval) of the biomarker panel was 96.7 (91.7, 98.7), specificity 40.1 (37.8, 42.4), negative predictive value 99.4 (98.6, 99.8), and the negative likelihood ratio was 0.08 (0.03, 0.22). The biomarker panel, measured on this core lab-based fast high-throughput platform, had high sensitivity and negative predictive values. The core laboratory platform has the advantage of speed and the ability to analyze multiple samples simultaneously suggesting additional utility when there is high need for CT imaging such as mass casualty or emergency department volume overload situations. [ABSTRACT FROM AUTHOR]

Published
2025
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11. Propeller Slipstream Effects on the Aerodynamics Characteristics of a Transition Micro Air Vehicle.

Author

Raj, A. Vinoth and Kumar, C. Senthil

Subjects
WIND tunnel testing, AERODYNAMIC stability, MICRO air vehicles, AERODYNAMICS, PROPELLERS
Abstract

This study investigates the influence of propeller slipstream on the aerodynamic characteristics of a Transition Micro Air Vehicle (TMAV). The TMAV under consideration comprises a cylindrical body, planar wing, and X-tails. Wind tunnel testing and numerical simulations were performed on TMAV configurations both with and without a propeller at various advance ratios (J = 0.45, 0.55, 0.65, and 0.75). The angle of attack ranged from -8° to +8° in increments of 4°, and from +8° to +16° in increments of 1°. The findings indicate that propeller slipstream significantly alters the flow field around the TMAV components, leading to a reduction in overall aerodynamic performance and stability. Specifically, the slipstream downwash decreased the lift and drag of the port wing and certain tails, while the slipstream upwash increased the lift and drag of the starboard wing and other tails, resulting in earlier stall occurrences under slipstream conditions. Furthermore, it was observed that aerodynamic performance improves as the propeller advance ratio decreases. The data obtained from this study elucidate the effects of propeller slipstream on the aerodynamic performance of wing and X-tail combined MAVs. Currently, there is a lack of literature addressing the effects of propeller slipstream on the aerodynamics of wing and X-tail combined MAV configurations. This study provides valuable insights into the aerodynamic behavior of this TMAV under the influence of propeller slipstream. [ABSTRACT FROM AUTHOR]

Published
2025
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12. Closing the Gap in Global Disparities in Hypertension Control.

Author

Mishra, Shiva Raj, Satheesh, Gautam, Khanal, Vishnu, Nguyen, Tu N., Picone, Dean, Chapman, Niamh, and Lindley, Richard I.

Abstract

The article "Closing the Gap in Global Disparities in Hypertension Control" discusses the challenges in achieving blood pressure control globally, with over 1.2 billion people not achieving control. Disparities exist across income levels, with low-income countries having lower control rates. The article suggests strategies such as regular monitoring, improving access to medication, utilizing digital health technologies, and implementing community-based solutions to address these disparities and improve hypertension control rates. [Extracted from the article]

Published
2025
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13. Five- Year Outcomes in Low-Risk Patients Undergoing Surgery in the PARTNER 3 Trial.

Author

Thourani, Vinod H., Leon, Martin B., Makkar, Raj, Ascione, Guido, Szeto, Wilson Y., Madhavan, Mahesh V., Kodali, Susheel K., Hahn, Rebecca T., Pibarot, Philippe, Malaisrie, S. Chris, Kapadia, Samir R., Russo, Mark J., Herrmann, Howard C., Babaliaros, Vasilis, Guyton, Robert, Genereux, Philippe, Cohen, David J., Park, Brandon, Clarke, Sarah, and Gunnarsson, Marissa

Abstract

Surgery remains an important treatment for low-risk patients with severe symptomatic aortic stenosis (AS). This study evaluated 5-year outcomes in low-risk patients undergoing isolated surgical aortic valve replacement (SAVR) or SAVR with concomitant procedures within the randomized Placement of Aortic Transcatheter Valves (PARTNER) 3 trial. In the PARTNER 3 trial, 454 patients underwent surgery for severe, symptomatic, trileaflet AS and were followed up for 5 years. Patients were stratified into those undergoing isolated SAVR (n = 334; 73.6%) vs concomitant SAVR (n = 120; 26.4%). Short- and long-term morbidity was adjudicated by a Clinical Events Committee. Hemodynamic valve performance was evaluated by an echocardiographic core laboratory. Patient-reported health status was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ). The mean age of participants was 73.6 ± 6.1 years; 71.1% of these patients were male. The median SAVR implant size was 23 mm overall. Five-year all-cause mortality (with vital status sweep) was 9.0% for all patients (8.5% isolated SAVR; 10.2% concomitant SAVR; P =.58), comparable to a recent analysis of low-risk isolated SAVR-treated patients in the STS database (overall mortality, 7.1%). The average 5-year mean gradient was 11.7 ± 5.6 mm Hg overall. Reintervention rates were low in both groups (isolated SAVR, 2.3% vs concomitant SAVR, 5.0%; P =.21), and most patients (isolated SAVR, 87.9%; concomitant SAVR, 86.1%) were alive with no evidence of bioprosthetic valve failure at 5 years. SAVR in low-risk patients in the PARTNER 3 trial demonstrated excellent 5-year outcomes. Five-year mortality was similar in patients undergoing isolated SAVR vs concomitant SAVR. This result was comparable to recently published national SAVR outcomes, thus demonstrating the generalizability of these findings. [ABSTRACT FROM AUTHOR]

Published
2025
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14. Surgical Management of Moderate to Severe Epistaxis in Hereditary Hemorrhagic Telangiectasia: Systematic Review and Meta-Analysis.

Author

Benaim, Ezer H., Kallenberger, Ethan M., Mirmozaffari, Yasine, Klatt-Cromwell, Cristine, Ebert Jr., Charles S., Kimple, Adam J., Senior, Brent A., Kasthuri, Raj S., and Thorp, Brian D.

Subjects
HEREDITARY hemorrhagic telangiectasia, PATIENT satisfaction, LASER therapy, NOSEBLEED, PATIENT reported outcome measures
Abstract

Background: Epistaxis is one of the most common and debilitating symptoms of hereditary hemorrhagic telangiectasia (HHT), significantly impacting patients' quality of life. While various medical and surgical interventions exist for managing epistaxis in patients with HHT, patients with moderate to severe epistaxis are high health-care utilizers who frequently need surgical treatment. Objective: To compare the efficacy, complications, and patient-reported outcomes for common surgical interventions utilized in treating epistaxis in patients with HHT Methods: Studies were identified in PubMed, Embase, Scopus, Redalyc, and LILACS databases and uploaded to Covidence. A systematic review following PRISMA guidelines was conducted on studies evaluating outcomes in adults with HHT with moderate to severe epistaxis who had undergone surgical interventions. We compared the respective outcomes for pre-operative and post-operative epistaxis severity/intensity, need for further interventions or transfusions, estimated blood loss, length of surgery, complications, and patient satisfaction. Results: Twenty studies with a total of 546 patients were included. The most common surgeries studied were nasal closure and laser photocoagulation. Seven studies recorded the change in epistaxis severity score and observed a significant reduction postoperatively (3.91, [95% CI 2.73-5.09]). Eleven studies found a decrease in the number of transfusions and a rise in hemoglobin levels post-operatively. Common complications reported were partial dehiscence of a nasal closure, septal perforation, and continued bleeding requiring re-operation. Most patients report improved quality of life and satisfaction with surgical intervention. Conclusion: Surgery can significantly reduce the severity of epistaxis in patients with HHT and improve quality of life. Further studies should focus on head-to-head comparisons of procedures and standardization of outcome measures. [ABSTRACT FROM AUTHOR]

Published
2025
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15. Late Clinical Outcomes of Balloon-Expandable Valves in Small Annuli: Results From the PARTNER Trials.

Author

Hahn, Rebecca T., Pibarot, Philippe, Abbas, Amr, Makkar, Raj, Thourani, Vinod H., Généreux, Philippe, Kodali, Susheel, Kapadia, Samir, Babaliaros, Vasilis, Ternacle, Julien, Theron, Alexis, Cristell, Nicole, Clarke, Sarah, Zhao, Yanglu, Alu, Maria, Madhavan, Mahesh V., Cohen, David J., Leipsic, Jonathon, Webb, John, and Mack, Michael J.

Abstract

Short-term clinical outcomes after transcatheter aortic valve replacement (TAVR) are similar in individuals with small or large annuli. The longer term impact of prosthesis-patient mismatch (PPM) and mean gradient (MG) post-TAVR in these patients remains controversial. The aim of this study was to investigate 5-year outcomes in patients with small vs large annuli. Patients from the PARTNER (Placement of Aortic Transcatheter Valves) 2 SAPIEN 3 intermediate-risk registry and the PARTNER 3 low-risk randomized controlled trial were grouped according to small (≤430 mm2) or large (>430 mm2) annular size. The primary endpoint was a composite of all-cause death, disabling stroke, or heart failure hospitalization. In addition, the relationships between both PPM and post-TAVR MG and clinical outcomes were analyzed. In total, 1,355 patients were included: 476 with small annuli (376.7 ± 41.9 mm2) and 879 with large annuli (518.3 ± 58.0 mm2). Patients with small annuli were older (age 79.6 ± 7.1 years vs 78.7 ± 7.8 years; P = 0.047), were more often female (75.0% vs 16.2%; P < 0.0001), had higher baseline Society of Thoracic Surgeons scores (4.3% ± 1.93% vs 4.0% ± 1.93%; P < 0.0001), and had higher left ventricular ejection fractions (66.3% ± 15.82% vs 59.7% ± 13.68%; P < 0.0001). Primary endpoint rates were similar at 1 year (7.8% vs 8.0%; P = 0.94) and 5 years (36.3% vs 35.8%; P = 0.83). Bioprosthetic valve failure was infrequent at 5 years in both groups (2.9% vs 2.1%; P = 0.46). Among female patients, outcomes were similar for small vs large annuli (primary endpoint; 33.6% vs 34.2%; P = 0.90). Among patients with small annuli, there was no association between 5-year outcomes and any severity of PPM (P = 0.22) or 30-day MG (P for nonlinearity = 0.96). Five-year clinical outcomes were excellent and comparable between patients with small vs large aortic annuli. Outcomes in patients with small annuli were not affected by 30-day MG or PPM. [Display omitted] [ABSTRACT FROM AUTHOR]

Published
2025
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20. Mechanism of action and translation to the clinic of detalimogene voraplasmid (EG-70): A novel, investigational, non-viral immunotherapy for non-muscle-invasive bladder cancer (NMIBC).

Author

Narayan, Vikram M., Lotan, Yair, Goulet, Marie-Line, Dauphinee, Shauna, Veilleux, Daniel, Louis, Kristine S, Lazure, David, Stevenson, Sarah, Bilimoria, Darius, Zamzameer, Fazmina, Chen, Ximin, Sublemontier, Sébastien, Amirkhani, Sahar, Fleet, Carlos, Pruthi, Raj, Cheung, Anthony, Sullivan, James C., Packiam, Vignesh T., and Kamat, Ashish M.

Published
2025
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30. Sino-Japanese Reactive Diplomacy Through BRI and FOIP in Bangladesh: Economic Implications.

Author

Akon, Md. Saifullah, Raj, S. M. Rabby, Aktar, Md. Nadim, and Abdullah-Al-Mamun

Subjects
BELT & Road Initiative, ECONOMIC impact, ECONOMIC expansion, DIPLOMACY
Abstract

This study delves into the realm of Sino-Japanese reactive diplomacy within the context of Bangladesh. It primarily examines their engagements through the Belt and Road Initiative (BRI) and the Free and Open Indo-Pacific (FOIP) vision. The study evaluates these diplomatic efforts' economic upshots in a continuously changing regional context. The contest between China and Japan for influence in Bangladesh has grown in the last few years due to the country's constant economic growth. This study investigates how both nations use reactive diplomacy, reacting to each other's actions and changing tactics to support Bangladesh's economic growth. The Chinese government has invested significantly in infrastructure-related projects in Bangladesh as a part of the BRI, and Japan's FOIP strategy strongly emphasises connectedness and high-quality infrastructure. This dichotomy of approaches leads to an intricate web of interactions with local stakeholders, reflecting diverse economic implications. However, the study emphasises how crucial it is to examine Bangladesh's strategy for navigating this diplomatic rivalry—that is, how to balance the economic gains from bilateral investments while avoiding undue reliance or political entanglements. [ABSTRACT FROM AUTHOR]

Published
2025
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31. Influence of Porosity on Vibration of Porous FG Plates Resting on an Arbitrarily Orthotropic Winkler-Pasternak Foundation by PDDO.

Author

Yang, Yongyu, Wang, Xiaoqi, Zhao, Hang, Wang, Chao, Cheng, Changzheng, and Das, Raj

Abstract

This paper studies the vibration responses of porous functionally graded (FG) thin plates with four various types of porous distribution based on the physical neutral plane by employing the peridynamic differential operator (PDDO). It is assumed that density and elastic modulus continuously vary along the transverse direction following the power law distribution for porous FG plates. The governing differential equation of free vibration for a porous rectangular FG plate and its associated boundary conditions are expressed by a Lévy-type solution based on nonlinear von Karman plate theory. Dimensionless frequencies and mode shapes are obtained after solving the characteristic equations established by PDDO. The results of the current method are validated through comparison with existing literature. The effects of geometric parameters, material properties, elastic foundation, porosity distribution, and boundary conditions on the frequency are investigated and discussed in detail. The highest fundamental dimensionless frequency occurs under SCSC boundary conditions, while the lowest is under SFSF boundary conditions. The porous FG plate with the fourth pore type, featuring high density of porosity at the top and low at the bottom, exhibits the highest fundamental frequency under SSSS, SFSF, and SCSC boundary conditions. The dimensionless frequency increases with an increase in the elastic foundation stiffness coefficient. [ABSTRACT FROM AUTHOR]

Published
2025
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34. Guideline concordant care for acute low back pain: A mixed-methods analysis of determinants of implementation.

Author

Minick, Kate I., Krueger, Ashley, Millward, Amelia, Veale, Kristy, Kamerath, Joseph, Woodfield, Devyn, Cook, Preston, Fowles, Timothy R., Bledsoe, Joseph, Balls, Adam, Srivastava, Raj, and Knighton, Andrew J.

Abstract

We conducted an explanatory, sequential mixed-methods study to measure variation in the use of imaging and physical therapy (PT) for acute low back pain (LBP) and to identify implementation determinants that might explain variation in use across 22 EDs and 27 urgent cares in urban and rural locations within a community-based health system. We described the patient population and measured concordance with LBP guideline recommendations on imaging and PT referral from January–June 2023. We conducted key informant interviews with physicians and advanced practice providers (APPs), n = 30, from these 49 sites between July – September 2023 and performed content analysis to identify implementation determinants to guideline concordance. From January–June 30, 2023, 1047 Intermountain Health employed or affiliated physicians and APPs at the 22 adult EDs and 27 adult UCs cared for 8047 patient encounters involving acute LBP with no red flags. 29% of acute LBP patient encounters included an imaging order (ED: 43%; UC: 18%) and 5% included a PT order (ED: 7%; UC: 4%). 17 ED and 13 UC physicians and APPs participated in semi-structured interviews. Their patient encounters represent 6% of the overall study population (ED: 5%; UC: 7%) with order rates and patient population characteristics similar to the full study population. ED and UC clinicians were generally familiar with LBP guideline recommendations but varied significantly in their knowledge and beliefs of the appropriate application of guidelines in evaluation and treatment plans. Guideline concordance for use of imaging and PT varied substantially across physicians and advance practice providers providing care at EDs and UC centers within a community-based health system. Implementation strategies that address barriers identified by this study, including varied understanding of the PT discipline, complex workflows for placing PT referrals, the medico-legal assurance that imaging provides, and the lack of feedback loops in ED and UC centers should be tested in future hybrid implementation-effectiveness trials to increase concordance to LBP guidelines and minimize harm related to overuse of imaging and underuse of conservative first-line treatment approaches. [ABSTRACT FROM AUTHOR]

Published
2025
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35. Rates of repeat EGD in nondiabetic adults with glucagon-like peptide-1 receptor agonist prescription: a retrospective matched cohort study.

Author

Elangovan, Abbinaya, Aggarwal, Pearl, Kaelber, David C., and Shah, Raj

Abstract

Data on endoscopic outcomes in adults without diabetes who are taking glucagon-like peptide-1 receptor agonists (GLP-1RAs) are limited. Therefore, we compared repeat EGD in this population with those not taking GLP-1RAs using a retrospective matched case-control study. Using the TriNetX health research platform (Cambridge, Mass, USA), we analyzed adults with a body mass index ≥27 kg/m2 without diabetes who underwent diagnostic EGD. The study group included individuals with ≥3 GLP-1RA prescriptions and undergoing EGD ≥30 days after the initial GLP-1RA prescription. The control group included individuals who were never prescribed GLP-1RAs but had an EGD after the prescription of other weight loss medications. Outcomes were compared using the risk ratio (RR) in matched cohorts. No significant difference in repeat EGD (5.4% vs 4.2%; RR, 1.28; 95% confidence interval [CI],.95-1.71) or new diagnosis of gastroparesis (1.1% vs.6%; RR; 2.00; 95% CI,.94-4.27) was noted between the groups. GLP-1RAs may not substantially increase the risk of repeat endoscopy in individuals without diabetes. [ABSTRACT FROM AUTHOR]

Published
2025
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36. A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction.

Author

G., Jini Mol and Bosco Raj T., Ajith

Subjects
CONVOLUTIONAL neural networks, MACHINE learning, WEIGHT training, DEVELOPING countries, GENETIC algorithms
Abstract

Cardiac data modeling remains challenging in emerging nations across Asia and Africa. This research proposes an ensemble classification method leveraging machine learning (ML) to predict cardiac problems, providing physicians with actionable insights for personalized diagnoses and treatments. An ensemble classification method for modelling cardiac temporal data is presented in this research. The minimax scalar transform is applied to first denoise the input data, and then the ENN-smote approach is applied to address the issue of an imbalanced dataset. Secondly, we employ a standard deep learning (DL) methodology. To identify the irregularities in the cardiac data pattern, a gated recurrent unit (GRU) classifier and a one-dimensional convolutional neural network (1D-CNN) are introduced. A typical genetic algorithm (GA) is used to optimize the suggested GRU network in order to pass over the local minima. This aids with 1D-CNN weight training. GA methodically optimizes the model's GRU parameters. The data processed were finally used by the hybrid 1D-CNN-Optimized GRU network to predict cardiovascular illness. The suggested method attained a training accuracy of over 97% and a test accuracy of over 96% on the dataset. The proposed model's overall accuracy is 99%. This is completely evaluated against other deep learning algorithms. [ABSTRACT FROM AUTHOR]

Published
2025
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37. Prior football or rugby exposure and white matter signal abnormalities in professional male mixed martial arts fighters.

Author

Esagoff, Aaron I., Gifford, Mia K., Narapareddy, Bharat R., Sair, Haris I., Luna, Licia P., Raj, Divyaansh, Shan, Guogen, Peters, Matthew, and Bernick, Charles

Abstract

Background: White matter signal abnormalities have been associated with traumatic brain injury (TBI) and repetitive head impacts (RHI) in contact sports (e.g. American football, rugby). However, previous studies of mixed martial arts (MMA) fighters from the Professional Fighters Brain Health Study have not found greater white matter signal abnormalities in fighters versus controls. Objective: This study aims to explore the varying white matter effects of football/rugby and MMA by analyzing how football/rugby history in mixed martial arts fighters may relate to white matter signal abnormalities, helping to further our understanding of sport-specific brain health risks. Methods: Baseline visits for 90 active, professional, male mixed martial arts fighters and 27 unexposed male controls were cross-sectionally analyzed. Wilcoxon and Kruskal–Wallis tests compared demographics and white matter signal abnormalities, and multivariable regression models examined the associations between football/rugby history and white matter signal abnormality burden in fighters, adjusting for age, education, race, fights, MRI scanner, and supratentorial volume. Results: 37/90 fighters had football/rugby history (mean: 4 years; range: 1–12 years). White matter signal abnormalities were significantly greater in fighters with football/rugby history compared to fighters without football/rugby history (Wilcoxon, p = 0.0190). Football/rugby history was significantly associated with white matter signal abnormality burden >75th percentile (OR: 12, CI: 3.3–61, p < 0.001) and >50th percentile (OR: 3.2, CI: 1.2–9.4, p = 0.024) in fighters. Years of football/rugby were also significantly associated with white matter signal abnormalities. Conclusion: Our findings expand on previous literature by demonstrating a significant relationship between white matter signal abnormalities (WMSAs) and football/rugby history but not MMA. Furthermore, our study suggests an added risk for WMSAs in MMA fighters with a history of football/rugby. Future research should further evaluate WMSAs in contact sports, helping to inform athletes, regulatory bodies, and healthcare providers of the potential brain health risks of contact sports. [ABSTRACT FROM AUTHOR]

Published
2025
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39. Preliminary safety, antitumor activity, and circulating tumor DNA (ctDNA) changes with RMC-9805, an oral, RAS(ON) G12D-selective tri-complex inhibitor in patients with KRAS G12D pancreatic ductal adenocarcinoma (PDAC) from a phase 1 study in advanced...

Author

Spira, Alexander I., Papadopoulos, Kyriakos P., Kim, Dae Won, Parikh, Aparna Raj, Barve, Minal A., Powderly II, John D., Starodub, Alexander, Strickler, John H, Li, Bob T., Oberstein, Paul Eliezer, Hassan, Faisal, Yang, Michelle, McCleland, Mark, Lally, Satwant, Lin, Wei, Sohoni, Sophia, and Hong, David S.

Published
2025
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40. Efficacy and safety of cabozantinib for advanced gastrointestinal (GI) neuroendocrine tumors (NET) after progression on prior therapy: Subgroup analysis of the phase 3 CABINET trial (Alliance A021602).

Author

Strosberg, Jonathan R., Zemla, Tyler J., Geyer, Susan Michelle, Knopp, Michael V., Behr, Spencer, Pulsipher, Sydney, Acoba, Jared David, Shergill, Ardaman, Wolin, Edward M., Halfdanarson, Thorvardur Ragnar, Konda, Bhavana, Trikalinos, Nikolaos, Tawfik, Bernard, Raj, Nitya Prabhakar, Shaheen, Shagufta, Vijayvergia, Namrata, Dasari, Arvind, O'Reilly, Eileen M., Meyerhardt, Jeffrey A., and Chan, Jennifer A.

Published
2025
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44. Genomic profiling of matched tumor samples in patients with rectal cancer undergoing total neoadjuvant therapy.

Author

Rosen, Daniel B, Smart, Alicia, Parikh, Aparna Raj, Cerrato, Grace, Niemierko, Andrzej, Wo, Jennifer Yon-Li, Roberts, Hannah Johnson, Allen, Jill N., Blaszkowsky, Lawrence Scott, clark, jeffrey William, Caughey, Bennett Adam, Ricciardi, Rocco, Berger, David L., Efstathiou, Jason A., Willers, Henning, Miyamoto, David T, Koenig, Julie L., Kunitake, Hiroko, Hong, Theodore S., and Kamran, Sophia C.

Published
2025
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45. Phase 1/2 study of XTX101, a tumor-activated, Fc-enhanced anti-CTLA-4 monoclonal antibody, in combination with atezolizumab in patients with advanced solid tumors and in MSS CRC.

Author

Hecht, Joel R, Davar, Diwakar, Fakih, Marwan, Bekaii-Saab, Tanios S., DeVito, Nicholas C., Knecht, John George, Vandross, Andrae Lavon, Perez, Cesar Augusto, Bessudo, Alberto, Gupta, Anurag, Patel, Ekta, Fantini, Damiano, Paramasivan, Sattanathan, Crowe, David, Duncan, Meghan, McConnell, Scott, Luptakova, Katarina, and Parikh, Aparna Raj

Published
2025
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47. Association of Year-to-Year Lipid Variability With Risk of Cognitive Decline and Dementia in Community-Dwelling Older Adults.

Author

Zhou, Zhen, Moran, Chris, Murray, Anne M., Zoungas, Sophia, Magnussen, Costan, Chong, Trevor T.-J., Shah, Raj C., Sheets, Kerry M., Nelson, Mark, Zhu, Chao, Tonkin, Andrew M., Talic, Stella, Ernst, Michael E., Orchard, Suzanne G., McNeil, John J., Wolfe, Rory, Woods, Robyn L., Neumann, Johannes T., Qiu, Peng, and Ryan, Joanne

Published
2025
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49. AI-POWERED MARK RECOGNITION IN ASSESSMENT AND ATTAINMENT CALCULATION.

Author

Annrose, Sam, A. Shibin, Samnath, S., Raj, Jennings Rousseau, and Shanjeya Bhaarath, I. R.

Subjects
OUTCOME-based education, ARTIFICIAL intelligence, COMPUTER vision, DEEP learning, EDUCATIONAL accreditation
Abstract

The cornerstone of a quality education system is Outcome-Based Education (OBE), which revolves around predefined outcomes or goals. Based on the best practices of NEP 2020, all the education institutions and accreditation authorities rely on Outcome-based teaching, learning, and evaluation. To measure the defined outcome of each course, it is essential to enter the question-wise marks in the student exam portal for CO attainment calculation. Traditional methods adopt the manual entry of marks. This is time-consuming and error-prone, leading to additional work for each examiner after evaluation. This project uses computer vision and AI to automate the identification and calculation of marks from the corrected handwritten exam answer sheets. It extracts information like roll numbers, question numbers, and marks, calculates total marks, and exports data to Excel sheets, hence reduces manual effort and improves efficiency. Deep neural network-based pre-trained models are used for the effective identification of handwritten marks. We have trained and tested the proposed system through continuous assessment and internal examination answer sheets available in our institution. [ABSTRACT FROM AUTHOR]

Published
2025
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