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143 results on '"Ullah, Irfan"'

9. Non-binary gender, vulnerable populations and mental health during the COVID-19 pandemic: Data from the COVID-19 MEntal health inTernational for the general population (COMET-G) study

Author

Fountoulakis, Konstantinos N., Vrublevska, Jelena, Abraham, Seri, Adorjan, Kristina, Ahmed, Helal Uddin, Alarcón, Renato D., Arai, Kiyomi, Auwal, Sani Salihu, Berk, Michael, Bjedov, Sarah, Bobes, Julio, Bobes-Bascaran, Teresa, Bourgin-Duchesnay, Julie, Bredicean, Cristina Ana, Bukelskis, Laurynas, Burkadze, Akaki, Abud, Indira Indiana Cabrera, Castilla-Puentes, Ruby, Cetkovich, Marcelo, Colon-Rivera, Hector, Corral, Ricardo, Cortez-Vergara, Carla, Crepin, Piirika, De Berardis, Domenico, Delgado, Sergio Zamora, De Lucena, David, De Sousa, Avinash, Di Stefano, Ramona, Dodd, Seetal, Elek, Livia Priyanka, Elissa, Anna, Erdelyi-Hamza, Berta, Erzin, Gamze, Etchevers, Martin J., Falkai, Peter, Farcas, Adriana, Fedotov, Ilya, Filatova, Viktoriia, Fountoulakis, Nikolaos K., Frankova, Iryna, Franza, Francesco, Frias, Pedro, Galako, Tatiana, Garay, Cristian J., Garcia-Álvarez, Leticia, García-Portilla, Maria Paz, Gonda, Xenia, Gondek, Tomasz M., González, Daniela Morera, Gould, Hilary, Grandinetti, Paolo, Grau, Arturo, Groudeva, Violeta, Hagin, Michal, Harada, Takayuki, Hasan, Tasdik M., Hashim, Nurul Azreen, Hilbig, Jan, Hossain, Sahadat, Iakimova, Rossitza, Ibrahim, Mona, Iftene, Felicia, Ignatenko, Yulia, Irarrazaval, Matias, Ismail, Zaliha, Ismayilova, Jamila, Jacobs, Asaf, Jakovljević, Miro, Jakšić, Nenad, Javed, Afzal, Kafali, Helin Yilmaz, Karia, Sagar, Kazakova, Olga, Khalifa, Doaa, Khaustova, Olena, Koh, Steve, Kosenko, Korneliia, Koupidis, Sotirios A., Lalljee, Alisha, Liewig, Justine, Majid, Abdul, Malashonkova, Evgeniia, Malik, Khamelia, Malik, Najma Iqbal, Mammadzada, Gulay, Mandalia, Bilvesh, Marazziti, Donatella, Marčinko, Darko, Martinez, Stephanie, Matiekus, Eimantas, Mejia, Gabriela, Memon, Roha Saeed, Martínez, Xarah Elenne Meza, Mickevičiūtė, Dalia, Milev, Roumen, Mohammed, Muftau, Molina-López, Alejandro, Morozov, Petr, Muhammad, Nuru Suleiman, Mustač, Filip, Naor, Mika S., Nassieb, Amira, Navickas, Alvydas, Okasha, Tarek, Pandova, Milena, Panfil, Anca-Livia, Panteleeva, Liliya, Papava, Ion, Patsali, Mikaella E., Pavlichenko, Alexey, Pejuskovic, Bojana, Da Costa, Mariana Pinto, Popkov, Mikhail, Popovic, Dina, Raduan, Nor Jannah Nasution, Ramírez, Francisca Vargas, Rancans, Elmars, Razali, Salmi, Rebok, Federico, Rewekant, Anna, Flores, Elena Ninoska Reyes, Rivera-Encinas, María Teresa, Saiz, Pilar, de Carmona, Manuel Sánchez, Martínez, David Saucedo, Saw, Jo Anne, Saygili, Görkem, Schneidereit, Patricia, Shah, Bhumika, Shirasaka, Tomohiro, Silagadze, Ketevan, Sitanggang, Satti, Skugarevsky, Oleg, Spikina, Anna, Mahalingappa, Sridevi Sira, Stoyanova, Maria, Szczegielniak, Anna, Tamasan, Simona Claudia, Tavormina, Giuseppe, Tavormina, Maurilio Giuseppe Maria, Theodorakis, Pavlos N., Tohen, Mauricio, Tsapakis, Eva Maria, Tukhvatullina, Dina, Ullah, Irfan, Vaidya, Ratnaraj, Vega-Dienstmaier, Johann M., Vukovic, Olivera, Vysotska, Olga, Widiasih, Natalia, Yashikhina, Anna, and Smirnova, Daria

Published
2024
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20. The effect of different degrees of lockdown and self-identified gender on anxiety, depression and suicidality during the COVID-19 pandemic: Data from the international COMET-G study.

Author

Fountoulakis, Konstantinos N., Karakatsoulis, Grigorios N., Abraham, Seri, Adorjan, Kristina, Ahmed, Helal Uddin, Alarcón, Renato D., Arai, Kiyomi, Auwal, Sani Salihu, Berk, Michael, Bjedov, Sarah, Bobes, Julio, Bobes-Bascaran, Teresa, Bourgin-Duchesnay, Julie, Bredicean, Cristina Ana, Bukelskis, Laurynas, Burkadze, Akaki, Abud, Indira Indiana Cabrera, Castilla-Puentes, Ruby, Cetkovich, Marcelo, Colon-Rivera, Hector, Corral, Ricardo, Cortez-Vergara, Carla, Crepin, Piirika, De Berardis, Domenico, Delgado, Sergio Zamora, De Lucena, David, De Sousa, Avinash, Stefano, Ramona Di, Dodd, Seetal, Elek, Livia Priyanka, Elissa, Anna, Erdelyi-Hamza, Berta, Erzin, Gamze, Etchevers, Martin J., Falkai, Peter, Farcas, Adriana, Fedotov, Ilya, Filatova, Viktoriia, Fountoulakis, Nikolaos K., Frankova, Iryna, Franza, Francesco, Frias, Pedro, Galako, Tatiana, Garay, Cristian J., Garcia-Álvarez, Leticia, García-Portilla, Maria Paz, Gonda, Xenia, Gondek, Tomasz M., González, Daniela Morera, Gould, Hilary, Grandinetti, Paolo, Grau, Arturo, Groudeva, Violeta, Hagin, Michal, Harada, Takayuki, Hasan, Tasdik M., Hashim, Nurul Azreen, Hilbig, Jan, Hossain, Sahadat, Iakimova, Rossitza, Ibrahim, Mona, Iftene, Felicia, Ignatenko, Yulia, Irarrazaval, Matias, Ismail, Zaliha, Ismayilova, Jamila, Jacobs, Asaf, Jakovljević, Miro, Jakšić, Nenad, Javed, Afzal, Kafali, Helin Yilmaz, Karia, Sagar, Kazakova, Olga, Khalifa, Doaa, Khaustova, Olena, Koh, Steve, Kopishinskaia, Svetlana, Kosenko, Korneliia, Koupidis, Sotirios A., Kovacs, Illes, Kulig, Barbara, Lalljee, Alisha, Liewig, Justine, Majid, Abdul, Malashonkova, Evgeniia, Malik, Khamelia, Malik, Najma Iqbal, Mammadzada, Gulay, Mandalia, Bilvesh, Marazziti, Donatella, Marčinko, Darko, Martinez, Stephanie, Matiekus, Eimantas, Mejia, Gabriela, Memon, Roha Saeed, Martínez, Xarah Elenne Meza, Mickevičiūtė, Dalia, Milev, Roumen, Mohammed, Muftau, Molina-López, Alejandro, Morozov, Petr, Muhammad, Nuru Suleiman, Mustač, Filip, Naor, Mika S., Nassieb, Amira, Navickas, Alvydas, Okasha, Tarek, Pandova, Milena, Panfil, Anca-Livia, Panteleeva, Liliya, Papava, Ion, Patsali, Mikaella E., Pavlichenko, Alexey, Pejuskovic, Bojana, Da Costa, Mariana Pinto, Popkov, Mikhail, Popovic, Dina, Raduan, Nor Jannah Nasution, Ramírez, Francisca Vargas, Rancans, Elmars, Razali, Salmi, Rebok, Federico, Rewekant, Anna, Flores, Elena Ninoska Reyes, Rivera-Encinas, María Teresa, Saiz, Pilar, de Carmona, Manuel Sánchez, Martínez, David Saucedo, Saw, Jo Anne, Saygili, Görkem, Schneidereit, Patricia, Shah, Bhumika, Shirasaka, Tomohiro, Silagadze, Ketevan, Sitanggang, Satti, Skugarevsky, Oleg, Spikina, Anna, Mahalingappa, Sridevi Sira, Stoyanova, Maria, Szczegielniak, Anna, Tamasan, Simona Claudia, Tavormina, Giuseppe, Tavormina, Maurilio Giuseppe Maria, Theodorakis, Pavlos N., Tohen, Mauricio, Tsapakis, Eva Maria, Tukhvatullina, Dina, Ullah, Irfan, Vaidya, Ratnaraj, Vega-Dienstmaier, Johann M., Vrublevska, Jelena, Vukovic, Olivera, Vysotska, Olga, Widiasih, Natalia, Yashikhina, Anna, Prezerakos, Panagiotis E., and Smirnova, Daria

Published
2022
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27. Results of the COVID-19 mental health international for the general population (COMET-G) study

Author

Fountoulakis, Konstantinos N, Karakatsoulis, Grigorios, Abraham, Seri, Adorjan, Kristina, Ahmed, Helal Uddin, Alarcón, Renato D., Arai, Kiyomi, Auwal, Sani Salihu, Berk, Michael, Bjedov, Sarah, Bobes, Julio, Bobes-Bascaran, Teresa, Bourgin-Duchesnay, Julie, Bredicean, Cristina Ana, Bukelskis, Laurynas, Burkadze, Akaki, Abud, Indira Indiana Cabrera, Castilla-Puentes, Ruby, Cetkovich, Marcelo, Colon-Rivera, Hector, Corral, Ricardo, Cortez-Vergara, Carla, Crepin, Piirika, De Berardis, Domenico, Zamora Delgado, Sergio, De Lucena, David, De Sousa, Avinash, Stefano, Ramona Di, Dodd, Seetal, Elek, Livia Priyanka, Elissa, Anna, Erdelyi-Hamza, Berta, Erzin, Gamze, Etchevers, Martin J., Falkai, Peter, Farcas, Adriana, Fedotov, Ilya, Filatova, Viktoriia, Fountoulakis, Nikolaos K., Frankova, Iryna, Franza, Francesco, Frias, Pedro, Galako, Tatiana, Garay, Cristian J., Garcia-Álvarez, Leticia, García-Portilla, Maria Paz, Gonda, Xenia, Gondek, Tomasz M., González, Daniela Morera, Gould, Hilary, Grandinetti, Paolo, Grau, Arturo, Groudeva, Violeta, Hagin, Michal, Harada, Takayuki, Hasan, M. Tasdik, Hashim, Nurul Azreen, Hilbig, Jan, Hossain, Sahadat, Iakimova, Rossitza, Ibrahim, Mona, Iftene, Felicia, Ignatenko, Yulia, Irarrazaval, Matias, Ismail, Zaliha, Ismayilova, Jamila, Jacobs, Asaf, Jakovljević, Miro, Jakšić, Nenad, Javed, Afzal, Kafali, Helin Yilmaz, Karia, Sagar, Kazakova, Olga, Khalifa, Doaa, Khaustova, Olena, Koh, Steve, Kopishinskaia, Svetlana, Kosenko, Korneliia, Koupidis, Sotirios A., Kovacs, Illes, Kulig, Barbara, Lalljee, Alisha, Liewig, Justine, Majid, Abdul, Malashonkova, Evgeniia, Malik, Khamelia, Malik, Najma Iqbal, Mammadzada, Gulay, Mandalia, Bilvesh, Marazziti, Donatella, Marčinko, Darko, Martinez, Stephanie, Matiekus, Eimantas, Mejia, Gabriela, Memon, Roha Saeed, Martínez, Xarah Elenne Meza, Mickevičiūtė, Dalia, Milev, Roumen, Mohammed, Muftau, Molina-López, Alejandro, Morozov, Petr, Muhammad, Nuru Suleiman, Mustač, Filip, Naor, Mika S., Nassieb, Amira, Navickas, Alvydas, Okasha, Tarek, Pandova, Milena, Panfil, Anca-Livia, Panteleeva, Liliya, Papava, Ion, Patsali, Mikaella E., Pavlichenko, Alexey, Pejuskovic, Bojana, Pinto Da Costa, Mariana, Popkov, Mikhail, Popovic, Dina, Raduan, Nor Jannah Nasution, Ramírez, Francisca Vargas, Rancans, Elmars, Razali, Salmi, Rebok, Federico, Rewekant, Anna, Flores, Elena Ninoska Reyes, Rivera-Encinas, María Teresa, Saiz, Pilar, de Carmona, Manuel Sánchez, Martínez, David Saucedo, Saw, Jo Anne, Saygili, Görkem, Schneidereit, Patricia, Shah, Bhumika, Shirasaka, Tomohiro, Silagadze, Ketevan, Sitanggang, Satti, Skugarevsky, Oleg, Spikina, Anna, Mahalingappa, Sridevi Sira, Stoyanova, Maria, Szczegielniak, Anna, Tamasan, Simona Claudia, Tavormina, Giuseppe, Tavormina, Maurilio Giuseppe Maria, Theodorakis, Pavlos N., Tohen, Mauricio, Tsapakis, Eva Maria, Tukhvatullina, Dina, Ullah, Irfan, Vaidya, Ratnaraj, Vega-Dienstmaier, Johann M., Vrublevska, Jelena, Vukovic, Olivera, Vysotska, Olga, Widiasih, Natalia, Yashikhina, Anna, Prezerakos, Panagiotis E., and Smirnova, Daria

Published
2022
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28. A multiscale finite element modeling for predicting the surface integrity induced by thermo-mechanical loads during high-speed milling of Ti-6Al-4V.

Author

Ullah, Irfan, Akinlabi, Esther T., Songmene, Victor, Kouam, Jules, and Sadeghifar, Morteza

Subjects
MECHANICAL loads, FINITE element method, MANUFACTURED products, PLANT cuttings, GRAIN size, CUTTING force, GRAIN
Abstract

High-speed milling (HSM) of Ti-6Al-4V is subjected to complex thermo-mechanical loads, leading to alteration in metallurgical conditions within the cutting deformation zones, adversely impacting the mechanical performances of manufactured products. Hence, inclusive insight into microstructural alterations within the Adiabatic Shear Band (ASB) and the milled surface becomes essential for better service performance. This study first developed a Finite Element (FE) milling model to simulate the machining process of Ti-6Al-4V. The proposed FE model is validated through experimental results regarding cutting forces (CFs), cutting temperature (CT), and chip geometry, where the absolute relative error between simulations and experiments was less than 15 %. Secondly, Zenner-Holloman (Z-H) and Hall-Petch (H-P) equations were incorporated into a user-defined subroutine to simulate dynamic recrystallization (DRX) for grain size and microhardness prediction. Simulation results revealed that the grains became finer in the ASB than on the milled surface. In particular, when the cutting speed and feed rate were increased to 350 m/min and 0.30 mm/tooth, the grain size in the ASB decreased from 14 to 0.68 and 0.44 µm, while in the topmost milled surface, it reduced to 7.06 and 6.75 µm, respectively. Conversely, microhardness exhibited an inverse correlation with grain size and increased with cutting speed and feed rate. Furthermore, the impact of increased plastic strain and temperature on the grains during chip segmentation was also examined. Finally, the proposed FE model validity was established by comparing simulated results with experimental data using advanced characterization techniques. This research significantly contributes to a comprehensive understanding of microstructural evolution and its implications for the mechanical performance of machined titanium components. [ABSTRACT FROM AUTHOR]

Published
2024
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50. GPU accelerated grouped magnetic resonance fingerprinting using clustering techniques.

Author

Ullah, Irfan, Hassan, Abdul Moiz, Saad, Rana Muhammad, and Omer, Hammad

Subjects
*MAGNETIC resonance imaging, *IMAGE reconstruction, *PARALLEL processing, *IMAGE reconstruction algorithms
Abstract

Magnetic Resonance Fingerprinting (MRF) is a new quantitative technique of Magnetic Resonance Imaging (MRI). Conventionally, MRF requires sequential correlation of the acquired MRF signals with all the signals of (a large sized) MRF dictionary. This is a computationally intensive matching process and is a major challenge in MRF image reconstruction. This paper introduces the use of clustering techniques (to reduce the effective size of MRF dictionary) by splitting MRF dictionary into multiple small sized MRF dictionary components called MRF signal groups. The proposed method has been further optimized for parallel processing to reduce the computation time of MRF pattern matching. A multi-core GPU based parallel framework has been developed that enables the MRF algorithm to process multiple MRF signals simultaneously. Experiments have been performed on human head and phantom datasets. The results show that the proposed method accelerates the conventional MRF (MATLAB based) reconstruction time up to 25× with single-core CPU implementation, 300× with multi- core CPU implementation and 1035× with the proposed multi-core GPU based framework by keeping the SNR of the resulting images in a clinically acceptable range. Furthermore, experimental results show that the memory requirements of MRF dictionary get significantly reduced (due to efficient memory utilization) in the proposed method. • MRF is a new quantitative technique of acquiring and processing MRI data. • Major limitations of the MRF in its clinical utilization include long reconstruction time and significant memory requirement. • A new technique utilizing parallel architecture to reduce computation time as well as effective memory utilization of MRF. • Proposed method introduces clustering techniques to reduce the effective memory requirements of MRF dictionary. • Reduction in the memory requirements reduces the computation complexity, hence reduce the computation time of MRF. [ABSTRACT FROM AUTHOR]

Published
2023
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