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1. Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning

Author

Prottasha, Nusrat Jahan, Mahmud, Asif, Sobuj, Md. Shohanur Islam, Bhat, Prakash, Kowsher, Md, Yousefi, Niloofar, and Garibay, Ozlem Ozmen

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning and require extensive training for best performance, often falling short. In this context, we propose a novel method called Semantic Knowledge Tuning (SK-Tuning) for prompt and prefix tuning that employs meaningful words instead of random tokens. This method involves using a fixed LLM to understand and process the semantic content of the prompt through zero-shot capabilities. Following this, it integrates the processed prompt with the input text to improve the model's performance on particular tasks. Our experimental results show that SK-Tuning exhibits faster training times, fewer parameters, and superior performance on tasks such as text classification and understanding compared to other tuning methods. This approach offers a promising method for optimizing the efficiency and effectiveness of LLMs in processing language tasks., Comment: Accepted in Nature Scientific Reports

Published
2024

2. L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs

Author

Kowsher, Md., Sobuj, Md. Shohanur Islam, Mahmud, Asif, Prottasha, Nusrat Jahan, and Bhat, Prakash

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing. Traditional methods, like prompt or prefix tuning, typically rely on arbitrary tokens for training, leading to prolonged training times and generalized token use across various class labels. To address these issues, this paper introduces L-Tuning, an efficient fine-tuning approach designed for classification tasks within the Natural Language Inference (NLI) framework. Diverging from conventional methods, L-Tuning focuses on the fine-tuning of label tokens processed through a pre-trained LLM, thereby harnessing its pre-existing semantic knowledge. This technique not only improves the fine-tuning accuracy and efficiency but also facilitates the generation of distinct label embeddings for each class, enhancing the model's training nuance. Our experimental results indicate a significant improvement in training efficiency and classification accuracy with L-Tuning compared to traditional approaches, marking a promising advancement in fine-tuning LLMs for complex language tasks., Comment: Published in the ICLR TinyPaper track

Published
2023

4. Cytokine Storm among Bangladeshi adults with COVID-19: A prospective cohort study

Author

Sarmin, Monira, Akter, Fhameda, Islam, Anika Nawrin, Mahfuz, Mustafa, Das, Subhasish, Sharifuzzaman, Hasan, S.M. Tafsir, Bhuiyan, Taufiqur Rahman, Rahman, Mustafizur, Gazi, Amran, Matin, Fariha Bushra, Tariqujjaman, Md, Shahrin, Lubaba, Islam, Mofakharul, Mahmud, Asif Mujtaba, Banu, Sayera, Chisti, Mohammod Jobayer, Qadri, Firdausi, Clemens, John David, and Ahmed, Tahmeed

Published
2024
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6. WHO global research priorities for antimicrobial resistance in human health

Author

Aanensen, David, Alanio, Alexandre, Alastruey-Izquierdo, Ana, Alemayehu, Tinsae, Al-Hasan, Majdi, Allegaert, Karel, Al-Maani, Amal Saif, Al-Salman, Jameela, Alshukairi, Abeer Nizar, Amir, Afreenish, Applegate, Tanya, Araj, George F, Villalobos, Marlen Arce, Årdal, Christine, Ashiru-Oredope, Diane, Ashley, Elizabeth A, Babin, François-Xavier, Bachmann, Laura H, Bachmann, Till, Baker, Kate Susan, Balasegaram, Manica, Bamford, Colleen, Baquero, Fernando, Barcelona, Laura Isabel, Bassat, Quique, Bassetti, Matteo, Basu, Sulagna, Beardsley, Justin, Vásquez, Grey Benoit, Berkley, James A, Bhatnagar, Anuj K, Bielicki, Julia, Bines, Julie, Bongomin, Felix, Bonomo, Robert A, Bradley, John S, Bradshaw, Catriona, Brett, Ana, Brink, Adrian, Brown, Colin, Brown, Jeremy, Buising, Kirsty, Carson, Carolee, Carvalho, Anna Cristina, Castagnola, Elio, Cavaleri, Marco, Cecchini, Michele, Chabala, Chishala, Chaisson, Richard E, Chakrabarti, Arunaloke, Chandler, Clare, Chandy, Sujith John, Charani, Esmita, Chen, Lisa, Chiara, Francesca, Chowdhary, Anuradha, Chua, Arlene, Chuki, Pem, Chun, Doo Ryeon, Churchyard, Gavin, Cirillo, Daniela, Clack, Lauren, Coffin, Susan E, Cohn, Jennifer, Cole, Michelle, Conly, John, Cooper, Ben, Corso, Alejandra, Cosgrove, Sara E, Cox, Helen, Daley, Charles L, Darboe, Saffiatou, Darton, Tom, Davies, Gerry, de Egea, Viviana, Dedeić-Ljubović, Amela, Deeves, Miranda, Denkinger, Claudia, Dillon, Jo-Anne R, Dramowski, Angela, Eley, Brian, Roberta Esposito, Susanna Maria, Essack, Sabiha Y, Farida, Helmia, Farooqi, Joveria, Feasey, Nicholas, Ferreyra, Cecilia, Fifer, Helen, Finlayson, Heather, Frick, Mike, Gales, Ana Cristina, Galli, Luisa, Gandra, Sumanth, Gerber, Jeffrey S, Giske, Christian, Gordon, Bruce, Govender, Nelesh, Guessennd, Nathalie, Guindo, Ibrehima, Gurbanova, Elmira, Gwee, Amanda, Hagen, Ferry, Harbarth, Stephan, Haze, John, Heim, Jutta, Hendriksen, Rene, Heyderman, Robert Simon, Holt, Kathryn Elizabeth, Hönigl, Martin, Hook, Edward W, Hope, William, Hopkins, Heidi, Hughes, Gwenda, Ismail, Ghada, Issack, Mohammad Iqbal, Jacobs, Jan, Jasovský, Dušan, Jehan, Fyeza, Pearson, Antonieta Jimenez, Jones, Makoto, Joshi, Mohan P, Kapil, Arti, Kariuki, Samuel, Karkey, Abhilasha, Kearns, Gregory L, Keddy, Karen Helena, Khanna, Nina, Kitamura, Akiko, Kolho, Kaija-Leena, Kontoyiannis, Dimitrios P, Kotwani, Anita, Kozlov, Roman S, Kranzer, Katharina, Kularatne, Ranmini, Lahra, Monica M, Langford, Bradley J, Laniado-Laborin, Rafael, Larsson, Joakim, Lass-Flörl, Cornelia, Le Doare, Kirsty, Lee, Hyukmin, Lessa, Fernanda, Levin, Anna S, Limmathurotsakul, Direk, Lincopan, Nilton, Lo Vecchio, Andrea, Lodha, Rakesh, Loeb, Mark, Longtin, Yves, Lye, David Chien, Mahmud, Asif Mujtaba, Manaia, Célia, Manderson, Lenore, Mareković, Ivana, Marimuthu, Kalisvar, Martin, Irene, Mashe, Tapfumanei, Mei, Zeng, Meis, Jacques F, Lyra Tavares De Melo, Flávio Augusto, Mendelson, Marc, Miranda, Angelica Espinosa, Moore, David, Morel, Chantal, Moremi, Nyambura, Moro, Maria Luisa, Moussy, Francis, Mshana, Stephen, Mueller, Arno, Ndow, Francis J, Nicol, Mark, Nunn, Andrew, Obaro, Stephen, Obiero, Christina W, Okeke, Iruka N, Okomo, Uduak, Okwor, Tochi J, Oladele, Rita, Omulo, Sylvia, Ondoa, Pascale, Ortellado de Canese, Juana Medarda, Ostrosky-Zeichner, Luis, Padoveze, Maria Clara, Pai, Madhukar, Park, Benjamin, Parkhill, Julian, Parry, Christopher M, Peeling, Rosanna, Sobreira Vieira Peixe, Luísa Maria, Perovic, Olga, Pettigrew, Melinda M, Principi, Nicola, Pulcini, Céline, Puspandari, Nelly, Rawson, Timothy, Reddy, Denasha Lavanya, Reddy, Kessendri, Redner, Paulo, Rodríguez Tudela, Juan Luis, Rodríguez-Baño, Jesús, Van Katwyk, Susan Rogers, Roilides, Emmanuel, Rollier, Christine, Rollock, Leslie, Ronat, Jean-Baptiste, Ruppe, Etienne, Sadarangani, Manish, Salisbury, David, Salou, Mounerou, Samison, Luc Hervé, Sanguinetti, Maurizio, Sartelli, Massimo, Schellack, Natalie, Schouten, Jeroen, Schwaber, Mitchell J, Seni, Jeremiah, Senok, Abiola, Shafer, William M, Shakoor, Sadia, Sheppard, Donald, Shin, Jong-Hee, Sia, Sonia, Sievert, Dawn, Singh, Ishwar, Singla, Rupak, Skov, Robert Leo, Soge, Olusegun O, Sprute, Rosanne, Srinivasan, Arjun, Srinivasan, Subasree, Sundsfjord, Arnfinn, Tacconelli, Evelina, Tahseen, Sabira, Tangcharoensathien, Viroj, Tängdén, Thomas, Thursky, Karin, Thwaites, Guy, Tigulini de Souza Peral, Renata, Tong, Deborah, Tootla, Hafsah Deepa, Tsioutis, Constantinos, Turner, Katy M, Turner, Paul, Omar, Shaheed Vally, van de Sande, Wendy WJ, van den Hof, Susan, van Doorn, Rogier, Veeraraghavan, Balaji, Verweij, Paul, Wahyuningsih, Retno, Wang, Hui, Warris, Adilia, Weinstock, Hillard, Wesangula, Evelyn, Whiley, David, White, Peter J, Williams, Phoebe, Xiao, Yonghong, Moscoso, Martin Yagui, Yang, Hsu Li, Yoshida, Sachiyo, Yu, Yunsong, Żabicka, Dorota, Zignol, Matteo, Rudan, Igor, Bertagnolio, Silvia, Dobreva, Zlatina, Centner, Chad M, Olaru, Ioana Diana, Donà, Daniele, Burzo, Stefano, Huttner, Benedikt D, Chaillon, Antoine, Gebreselassie, Nebiat, Wi, Teodora, Hasso-Agopsowicz, Mateusz, Allegranzi, Benedetta, Sati, Hatim, Ivanovska, Verica, Kothari, Kavita U, Balkhy, Hanan H, Cassini, Alessandro, Hamers, Raph L, and Weezenbeek, Kitty Van

Published
2024
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8. An RNN Based Approach to Predict Next Word in Bangla Language

Author

Mahmud, Asif, Rony, Md. Nazmul Hasan, Bhowmik, Deba Dip, Kuri, Ratnadip, Rana, A. R. M. Mahmudul Hasan, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Satu, Md. Shahriare, editor, Moni, Mohammad Ali, editor, Kaiser, M. Shamim, editor, and Arefin, Mohammad Shamsul, editor

Published
2023
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9. Automatic Recognition and Categorization of Tomato Leaf Syndrome of Diseases Using Deep Learning Algorithms

Author

Sultana, Irene, Paul, Bijan, Mahmud, Asif, Rafi, Minar Mahmud, Asifuzzaman Jishan, Md., Mahmud, Khan Raqib, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Xie, Juanying, editor, and Rathore, Vijay Singh, editor

Published
2023
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15. Parameter Optimization and Flaw Type Dependent Tensile Properties of 15‐5PH Stainless Steel Manufactured by Laser Powder Bed Fusion.

Author

Ayers, Nicolas, Lucas, Cameron, Mahmud, Asif, Kljestan, Nemanja, Knezevic, Marko, and Sohn, Yongho

Subjects
LASER fusion, STRESS concentration, STAINLESS steel, STEEL manufacture, PRECIPITATION hardening
Abstract

Careful consideration of processing parameters to mitigate defect formation is crucial to ensure reliability of components produced by laser powder bed fusion (LPBF). In this work, additive manufacturing parameter optimization and flaw type dependent tensile properties of 15‐5 precipitation hardened (PH) stainless steel produced by LPBF is examined to understand the correlation among processing parameters, flaw types, and corresponding tensile mechanical behavior. It is reported that LPBF samples with keyhole (KH) pores exhibit superior mechanical properties compared to those induced with lack‐of‐fusion (LOF) flaws, for a given relative density level. Diminished mechanical properties in samples with LOF flaws is correlated to the flaws' irregular shapes and sharp corners, causing stress concentration. Minimal improvements to the mechanical properties of LOF‐induced samples, despite increasing relative density, indicate even small amounts of LOF flaws cause stress concentration and crack initiation. Conversely, KH pores exhibit high circularity and lower curvature values, and samples with KH pores demonstrate a strong correlation between tensile properties and relative density. Findings from this study suggest that when suboptimal LPBF processing is inevitable, favoring excessive energy input to induce KH pores over LOF flaws can lead to more predictable mechanical behavior of LPBF components. [ABSTRACT FROM AUTHOR]

Published
2024
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19. WHO global research priorities for antimicrobial resistance in human health

Author

Bertagnolio, Silvia, Dobreva, Zlatina, Centner, Chad M, Olaru, Ioana Diana, Donà, Daniele, Burzo, Stefano, Huttner, Benedikt D, Chaillon, Antoine, Gebreselassie, Nebiat, Wi, Teodora, Hasso-Agopsowicz, Mateusz, Allegranzi, Benedetta, Sati, Hatim, Ivanovska, Verica, Kothari, Kavita U, Balkhy, Hanan H, Cassini, Alessandro, Hamers, Raph L, Weezenbeek, Kitty Van, Aanensen, David, Alanio, Alexandre, Alastruey-Izquierdo, Ana, Alemayehu, Tinsae, Al-Hasan, Majdi, Allegaert, Karel, Al-Maani, Amal Saif, Al-Salman, Jameela, Alshukairi, Abeer Nizar, Amir, Afreenish, Applegate, Tanya, Araj, George F, Villalobos, Marlen Arce, Årdal, Christine, Ashiru-Oredope, Diane, Ashley, Elizabeth A, Babin, François-Xavier, Bachmann, Laura H, Bachmann, Till, Baker, Kate Susan, Balasegaram, Manica, Bamford, Colleen, Baquero, Fernando, Barcelona, Laura Isabel, Bassat, Quique, Bassetti, Matteo, Basu, Sulagna, Beardsley, Justin, Vásquez, Grey Benoit, Berkley, James A, Bhatnagar, Anuj K, Bielicki, Julia, Bines, Julie, Bongomin, Felix, Bonomo, Robert A, Bradley, John S, Bradshaw, Catriona, Brett, Ana, Brink, Adrian, Brown, Colin, Brown, Jeremy, Buising, Kirsty, Carson, Carolee, Carvalho, Anna Cristina, Castagnola, Elio, Cavaleri, Marco, Cecchini, Michele, Chabala, Chishala, Chaisson, Richard E, Chakrabarti, Arunaloke, Chandler, Clare, Chandy, Sujith John, Charani, Esmita, Chen, Lisa, Chiara, Francesca, Chowdhary, Anuradha, Chua, Arlene, Chuki, Pem, Chun, Doo Ryeon, Churchyard, Gavin, Cirillo, Daniela, Clack, Lauren, Coffin, Susan E, Cohn, Jennifer, Cole, Michelle, Conly, John, Cooper, Ben, Corso, Alejandra, Cosgrove, Sara E, Cox, Helen, Daley, Charles L, Darboe, Saffiatou, Darton, Tom, Davies, Gerry, de Egea, Viviana, Dedeić-Ljubović, Amela, Deeves, Miranda, Denkinger, Claudia, Dillon, Jo-Anne R, Dramowski, Angela, Eley, Brian, Roberta Esposito, Susanna Maria, Essack, Sabiha Y, Farida, Helmia, Farooqi, Joveria, Feasey, Nicholas, Ferreyra, Cecilia, Fifer, Helen, Finlayson, Heather, Frick, Mike, Gales, Ana Cristina, Galli, Luisa, Gandra, Sumanth, Gerber, Jeffrey S, Giske, Christian, Gordon, Bruce, Govender, Nelesh, Guessennd, Nathalie, Guindo, Ibrehima, Gurbanova, Elmira, Gwee, Amanda, Hagen, Ferry, Harbarth, Stephan, Haze, John, Heim, Jutta, Hendriksen, Rene, Heyderman, Robert Simon, Holt, Kathryn Elizabeth, Hönigl, Martin, Hook, Edward W, Hope, William, Hopkins, Heidi, Hughes, Gwenda, Ismail, Ghada, Issack, Mohammad Iqbal, Jacobs, Jan, Jasovský, Dušan, Jehan, Fyeza, Pearson, Antonieta Jimenez, Jones, Makoto, Joshi, Mohan P, Kapil, Arti, Kariuki, Samuel, Karkey, Abhilasha, Kearns, Gregory L, Keddy, Karen Helena, Khanna, Nina, Kitamura, Akiko, Kolho, Kaija-Leena, Kontoyiannis, Dimitrios P, Kotwani, Anita, Kozlov, Roman S, Kranzer, Katharina, Kularatne, Ranmini, Lahra, Monica M, Langford, Bradley J, Laniado-Laborin, Rafael, Larsson, Joakim, Lass-Flörl, Cornelia, Le Doare, Kirsty, Lee, Hyukmin, Lessa, Fernanda, Levin, Anna S, Limmathurotsakul, Direk, Lincopan, Nilton, Lo Vecchio, Andrea, Lodha, Rakesh, Loeb, Mark, Longtin, Yves, Lye, David Chien, Mahmud, Asif Mujtaba, Manaia, Célia, Manderson, Lenore, Mareković, Ivana, Marimuthu, Kalisvar, Martin, Irene, Mashe, Tapfumanei, Mei, Zeng, Meis, Jacques F, Lyra Tavares De Melo, Flávio Augusto, Mendelson, Marc, Miranda, Angelica Espinosa, Moore, David, Morel, Chantal, Moremi, Nyambura, Moro, Maria Luisa, Moussy, Francis, Mshana, Stephen, Mueller, Arno, Ndow, Francis J, Nicol, Mark, Nunn, Andrew, Obaro, Stephen, Obiero, Christina W, Okeke, Iruka N, Okomo, Uduak, Okwor, Tochi J, Oladele, Rita, Omulo, Sylvia, Ondoa, Pascale, Ortellado de Canese, Juana Medarda, Ostrosky-Zeichner, Luis, Padoveze, Maria Clara, Pai, Madhukar, Park, Benjamin, Parkhill, Julian, Parry, Christopher M, Peeling, Rosanna, Sobreira Vieira Peixe, Luísa Maria, Perovic, Olga, Pettigrew, Melinda M, Principi, Nicola, Pulcini, Céline, Puspandari, Nelly, Rawson, Timothy, Reddy, Denasha Lavanya, Reddy, Kessendri, Redner, Paulo, Rodríguez Tudela, Juan Luis, Rodríguez-Baño, Jesús, Van Katwyk, Susan Rogers, Roilides, Emmanuel, Rollier, Christine, Rollock, Leslie, Ronat, Jean-Baptiste, Ruppe, Etienne, Sadarangani, Manish, Salisbury, David, Salou, Mounerou, Samison, Luc Hervé, Sanguinetti, Maurizio, Sartelli, Massimo, Schellack, Natalie, Schouten, Jeroen, Schwaber, Mitchell J, Seni, Jeremiah, Senok, Abiola, Shafer, William M, Shakoor, Sadia, Sheppard, Donald, Shin, Jong-Hee, Sia, Sonia, Sievert, Dawn, Singh, Ishwar, Singla, Rupak, Skov, Robert Leo, Soge, Olusegun O, Sprute, Rosanne, Srinivasan, Arjun, Srinivasan, Subasree, Sundsfjord, Arnfinn, Tacconelli, Evelina, Tahseen, Sabira, Tangcharoensathien, Viroj, Tängdén, Thomas, Thursky, Karin, Thwaites, Guy, Tigulini de Souza Peral, Renata, Tong, Deborah, Tootla, Hafsah Deepa, Tsioutis, Constantinos, Turner, Katy M, Turner, Paul, Omar, Shaheed Vally, van de Sande, Wendy WJ, van den Hof, Susan, van Doorn, Rogier, Veeraraghavan, Balaji, Verweij, Paul, Wahyuningsih, Retno, Wang, Hui, Warris, Adilia, Weinstock, Hillard, Wesangula, Evelyn, Whiley, David, White, Peter J, Williams, Phoebe, Xiao, Yonghong, Moscoso, Martin Yagui, Yang, Hsu Li, Yoshida, Sachiyo, Yu, Yunsong, Żabicka, Dorota, Zignol, Matteo, Bertagnolio, Silvia, Dobreva, Zlatina, Centner, Chad M, Olaru, Ioana Diana, Donà, Daniele, Burzo, Stefano, Huttner, Benedikt D, Chaillon, Antoine, Gebreselassie, Nebiat, Wi, Teodora, Hasso-Agopsowicz, Mateusz, Allegranzi, Benedetta, Sati, Hatim, Ivanovska, Verica, Kothari, Kavita U, Balkhy, Hanan H, Cassini, Alessandro, Hamers, Raph L, Weezenbeek, Kitty Van, Aanensen, David, Alanio, Alexandre, Alastruey-Izquierdo, Ana, Alemayehu, Tinsae, Al-Hasan, Majdi, Allegaert, Karel, Al-Maani, Amal Saif, Al-Salman, Jameela, Alshukairi, Abeer Nizar, Amir, Afreenish, Applegate, Tanya, Araj, George F, Villalobos, Marlen Arce, Årdal, Christine, Ashiru-Oredope, Diane, Ashley, Elizabeth A, Babin, François-Xavier, Bachmann, Laura H, Bachmann, Till, Baker, Kate Susan, Balasegaram, Manica, Bamford, Colleen, Baquero, Fernando, Barcelona, Laura Isabel, Bassat, Quique, Bassetti, Matteo, Basu, Sulagna, Beardsley, Justin, Vásquez, Grey Benoit, Berkley, James A, Bhatnagar, Anuj K, Bielicki, Julia, Bines, Julie, Bongomin, Felix, Bonomo, Robert A, Bradley, John S, Bradshaw, Catriona, Brett, Ana, Brink, Adrian, Brown, Colin, Brown, Jeremy, Buising, Kirsty, Carson, Carolee, Carvalho, Anna Cristina, Castagnola, Elio, Cavaleri, Marco, Cecchini, Michele, Chabala, Chishala, Chaisson, Richard E, Chakrabarti, Arunaloke, Chandler, Clare, Chandy, Sujith John, Charani, Esmita, Chen, Lisa, Chiara, Francesca, Chowdhary, Anuradha, Chua, Arlene, Chuki, Pem, Chun, Doo Ryeon, Churchyard, Gavin, Cirillo, Daniela, Clack, Lauren, Coffin, Susan E, Cohn, Jennifer, Cole, Michelle, Conly, John, Cooper, Ben, Corso, Alejandra, Cosgrove, Sara E, Cox, Helen, Daley, Charles L, Darboe, Saffiatou, Darton, Tom, Davies, Gerry, de Egea, Viviana, Dedeić-Ljubović, Amela, Deeves, Miranda, Denkinger, Claudia, Dillon, Jo-Anne R, Dramowski, Angela, Eley, Brian, Roberta Esposito, Susanna Maria, Essack, Sabiha Y, Farida, Helmia, Farooqi, Joveria, Feasey, Nicholas, Ferreyra, Cecilia, Fifer, Helen, Finlayson, Heather, Frick, Mike, Gales, Ana Cristina, Galli, Luisa, Gandra, Sumanth, Gerber, Jeffrey S, Giske, Christian, Gordon, Bruce, Govender, Nelesh, Guessennd, Nathalie, Guindo, Ibrehima, Gurbanova, Elmira, Gwee, Amanda, Hagen, Ferry, Harbarth, Stephan, Haze, John, Heim, Jutta, Hendriksen, Rene, Heyderman, Robert Simon, Holt, Kathryn Elizabeth, Hönigl, Martin, Hook, Edward W, Hope, William, Hopkins, Heidi, Hughes, Gwenda, Ismail, Ghada, Issack, Mohammad Iqbal, Jacobs, Jan, Jasovský, Dušan, Jehan, Fyeza, Pearson, Antonieta Jimenez, Jones, Makoto, Joshi, Mohan P, Kapil, Arti, Kariuki, Samuel, Karkey, Abhilasha, Kearns, Gregory L, Keddy, Karen Helena, Khanna, Nina, Kitamura, Akiko, Kolho, Kaija-Leena, Kontoyiannis, Dimitrios P, Kotwani, Anita, Kozlov, Roman S, Kranzer, Katharina, Kularatne, Ranmini, Lahra, Monica M, Langford, Bradley J, Laniado-Laborin, Rafael, Larsson, Joakim, Lass-Flörl, Cornelia, Le Doare, Kirsty, Lee, Hyukmin, Lessa, Fernanda, Levin, Anna S, Limmathurotsakul, Direk, Lincopan, Nilton, Lo Vecchio, Andrea, Lodha, Rakesh, Loeb, Mark, Longtin, Yves, Lye, David Chien, Mahmud, Asif Mujtaba, Manaia, Célia, Manderson, Lenore, Mareković, Ivana, Marimuthu, Kalisvar, Martin, Irene, Mashe, Tapfumanei, Mei, Zeng, Meis, Jacques F, Lyra Tavares De Melo, Flávio Augusto, Mendelson, Marc, Miranda, Angelica Espinosa, Moore, David, Morel, Chantal, Moremi, Nyambura, Moro, Maria Luisa, Moussy, Francis, Mshana, Stephen, Mueller, Arno, Ndow, Francis J, Nicol, Mark, Nunn, Andrew, Obaro, Stephen, Obiero, Christina W, Okeke, Iruka N, Okomo, Uduak, Okwor, Tochi J, Oladele, Rita, Omulo, Sylvia, Ondoa, Pascale, Ortellado de Canese, Juana Medarda, Ostrosky-Zeichner, Luis, Padoveze, Maria Clara, Pai, Madhukar, Park, Benjamin, Parkhill, Julian, Parry, Christopher M, Peeling, Rosanna, Sobreira Vieira Peixe, Luísa Maria, Perovic, Olga, Pettigrew, Melinda M, Principi, Nicola, Pulcini, Céline, Puspandari, Nelly, Rawson, Timothy, Reddy, Denasha Lavanya, Reddy, Kessendri, Redner, Paulo, Rodríguez Tudela, Juan Luis, Rodríguez-Baño, Jesús, Van Katwyk, Susan Rogers, Roilides, Emmanuel, Rollier, Christine, Rollock, Leslie, Ronat, Jean-Baptiste, Ruppe, Etienne, Sadarangani, Manish, Salisbury, David, Salou, Mounerou, Samison, Luc Hervé, Sanguinetti, Maurizio, Sartelli, Massimo, Schellack, Natalie, Schouten, Jeroen, Schwaber, Mitchell J, Seni, Jeremiah, Senok, Abiola, Shafer, William M, Shakoor, Sadia, Sheppard, Donald, Shin, Jong-Hee, Sia, Sonia, Sievert, Dawn, Singh, Ishwar, Singla, Rupak, Skov, Robert Leo, Soge, Olusegun O, Sprute, Rosanne, Srinivasan, Arjun, Srinivasan, Subasree, Sundsfjord, Arnfinn, Tacconelli, Evelina, Tahseen, Sabira, Tangcharoensathien, Viroj, Tängdén, Thomas, Thursky, Karin, Thwaites, Guy, Tigulini de Souza Peral, Renata, Tong, Deborah, Tootla, Hafsah Deepa, Tsioutis, Constantinos, Turner, Katy M, Turner, Paul, Omar, Shaheed Vally, van de Sande, Wendy WJ, van den Hof, Susan, van Doorn, Rogier, Veeraraghavan, Balaji, Verweij, Paul, Wahyuningsih, Retno, Wang, Hui, Warris, Adilia, Weinstock, Hillard, Wesangula, Evelyn, Whiley, David, White, Peter J, Williams, Phoebe, Xiao, Yonghong, Moscoso, Martin Yagui, Yang, Hsu Li, Yoshida, Sachiyo, Yu, Yunsong, Żabicka, Dorota, and Zignol, Matteo

Abstract

The WHO research agenda for antimicrobial resistance (AMR) in human health has identified 40 research priorities to be addressed by the year 2030. These priorities focus on bacterial and fungal pathogens of crucial importance in addressing AMR, including drug-resistant pathogens causing tuberculosis. These research priorities encompass the entire people-centred journey, covering prevention, diagnosis, and treatment of antimicrobial-resistant infections, in addition to addressing the overarching knowledge gaps in AMR epidemiology, burden and drivers, policies and regulations, and awareness and education. The research priorities were identified through a multistage process, starting with a comprehensive scoping review of knowledge gaps, with expert inputs gathered through a survey and open call. The priority setting involved a rigorous modified Child Health and Nutrition Research Initiative approach, ensuring global representation and applicability of the findings. The ultimate goal of this research agenda is to encourage research and investment in the generation of evidence to better understand AMR dynamics and facilitate policy translation for reducing the burden and consequences of AMR.

Published
2024

22. Estimation of VMT using heteroskedastic log-linear regression models.

Author

Mahmud, Asif, Hamilton, Ian, Gayah, Vikash V., and Porter, Richard J.

Subjects
*LOG-linear models, *REGRESSION analysis, *TRANSPORTATION engineering, *PREDICTION models, *LOGARITHMS
Abstract

Vehicle miles traveled (VMT) is an essential input for many aspects of transportation engineering, and an accurate estimation of VMT is critical for practicing engineers. Linear regression models are a popular method to estimate VMT as they provide insight into the relationships between VMT and other external factors. In linear regression models the prediction of the response variable has a non-zero probability of resulting in a negative value. For this reason, the natural logarithm of VMT is often used as the response variable to force a positive outcome. However, these log-linear regression (LLR) models provide median VMT estimate instead of the mean estimate. To overcome this limitation of LLR models, this study proposes using heteroskedastic LLR and count data methods to estimate VMT. These methods are found to have better performance than LLR models in terms of data fit and prediction accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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25. A Combined Feature Extraction Method for Automated Face Recognition in Classroom Environment

Author

Shafiqul Islam, Md., Mahmud, Asif, Akter Papeya, Azmina, Sultana Onny, Irin, Uddin, Jia, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Thampi, Sabu M., editor, Krishnan, Sri, editor, Corchado Rodriguez, Juan Manuel, editor, Das, Swagatam, editor, Wozniak, Michal, editor, and Al-Jumeily, Dhiya, editor

Published
2018
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26. Frequency of nodal metastasis in differentiated thyroid carcinoma

Author

null M. Abdullah Al Mamun, null Muhammad Ariful Islam, null M. Hasibur Rahman, null M. Saiful Islam, and null Mahmud Asif Rifat

Subjects
General Engineering
Abstract

Background: Thyroid cancer is the most common endocrine malignancy. Moreover, within thyroid cancer, differentiated thyroid carcinoma (DTC) is the most common variety, with the incidence rising over the past decade. Often, most of the cases present with multicentric disease and presenting with lymph node metastases at the time of diagnosis. Nodal metastasis has prognostic importance, and it may guide surgeons regarding further management. Considering this scientific fact, the study was performed to see the frequency of lymph node metastasis in DTC among the patients admitted to a tertiary care hospital. Methods: This cross-sectional study was conducted in the department of otolaryngology and head-neck surgery, Sher-E-Bangla Medical College Hospital, for 9 months after the acceptance of the protocol. A total of 50 patients (in all age groups) who attended the relevant department due to thyroid malignancies were approached and interviewed. Thorough history taking, physical examination, and relevant investigation were done and recorded into separate case record forms. Informed written consent was taken from each subject. Following collection, data were coded and inputted into statistical software. Data analysis was done with SPSS 21 according to the objective of the study. Data were presented in the form of tables, and charts. Results: Among the 50 patients, the mean age of the patients was 47.86±15.69 SD (years) with minimum and maximum ages of 14 and 78 years respectively. The male-female ratio was 1:4 (20% male vs 80% female). Papillary carcinoma was the most frequent (88%) followed by follicular (10%). Nodal metastasis was most common in the papillary variety, about 54.55% of cases, whereas 20% were present in the follicular variety. The majority of the patients underwent total thyroidectomy with neck dissection (90%) and the remaining cases were managed by lobectomy (10%). Conclusions: This study concluded that nodal metastasis was present in 54.55% of cases of papillary carcinoma.

Published
2023
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27. Assessment of Bacteriological Pattern and Antimicrobial Susceptibility in Deep Neck Space Abscess

Author

Muhammad Ariful Islam, Md. Hasibur Rahman, Md. Saiful Islam, Mahmud Asif Rifat, and Md. Abdullah Al Mamun

Subjects
Automotive Engineering
Abstract

Background: Deep neck abscess is a common clinical entity in developing countries like ours. Despite the widespread use of antibiotics, deep neck infections do not disappear and remain one of the most difficult emergencies encountered in daily clinical practice. The extent and severity of the illness could become life-threatening. Therefore, coping with deep neck abscess remain a challenge to otolaryngologists. This study aimed to analyze the bacteriological pattern and antimicrobial susceptibility in deep neck space abscesses. Material Methods: It was a cross-sectional observational study. 50 patients with deep neck space abscesses fulfilling the inclusion and exclusion criteria admitted to the department of ENT Head Neck Surgery, Rangpur Medical College Hospital, Rangpur, from 1st July 2017 to 30th December 2017 were enrolled in this study. Pus from deep neck space abscess was collected by either aspiration or incision and drainage with proper aseptic measure and sent by sterile test tube to microbiology department immediately. Data were collected by detailed history taking and clinical examination investigations with informed written consent and analyzed by SPSS (version 20). Results: In this study most commonly involved deep neck spaces were Submandibular (38%), Peritonsillar (32%), Retropharyngeal (14%), and parapharyngeal (8%) spaces. Streptococcus viridans was the most prominent organism 14 (28%) followed by Klebsiella pneumonia 9(18%) and Staph. aureus 4 (8%). The most effective antibiotic was Ceftriaxone 34(79%) followed by Cefuroxime 30 (70%) and Erythromycin 23(54%). Aerobic organisms were highly sensitive to Cefuroxime (83%) and Ceftriaxone (83%) followed by Erythromycin (48%). Anaerobic organisms were sensitive to Clindamycin (100%), Metronidazole (100%), and Erythromycin (100%) followed by Ceftriaxone (75%). Conclusion: The most frequently isolated organism in deep neck space abscesses were Streptococcus viridans and Staphylococcus aureus and sensitivity results showed the majority of isolates are susceptible to Ceftriaxone and Cefuroxime.

Published
2023
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31. Screening for Insecticidal Potential of the Chickpea Plant Cicer Arietinum (L.) Against Three Stored Grain Pests Callosobruchus Chinensis (L.), Sitophilus Oryzae (L.) And Tribolium Castaneum (Herbst) Adults Under Laboratory Conditions

Author

Toukir Mahmud Asif, Meherun Nesa, Kazi Mahnaz Mehrin, Nelufa Yasmin, Alimul Islam, Nishat Fatema, and Nurul Islam

Subjects
Insect Science, Animal Science and Zoology, Forestry, Plant Science, Aquatic Science, Ecology, Evolution, Behavior and Systematics
Abstract

Petroleum ether (PE), chloroform (CHCl3), and methanol (CH3OH) extracts of the whole chickpea plant Cicer arietinum (L.) have been tested against adult beetles of Callosobruchus chinensis (L.), Sitophilus oryzae (L.) and Tribolium castaneum (Herbst) through dose-mortality and repellent activity tests. In dose mortality tests, the PE extracts of C. arietinum gave LD50 values of 3.466, 3.292, 2.721, 2.398, 1.887, 1.663 and 1.428 mg/cm2 against C. chinensis and 4.297, 3.415, 2.246, 1.725, 1.384, 0.984 and 0.722 mg/cm2 against S. oryzae both after 12, 18, 24, 30, 36, 42 and 48 h of exposures, respectively. The CHCl3 extracts gave LD50 values of 5.242, 4.844, 3.472, 2.945, 2.540, 1.875, 1.295 and 1.053 mg/cm2 against C. chinensis after 6, 12, 18, 24, 30, 36, 42 and 48 h of exposures, respectively. The CH3OH extracts, on the other hand, yielded LD50 values of 0.923, 0.757, 0.649, 0.515, 0.400, 0.303, 0.247 and 0.197 mg/cm2 against C. chinensis, 4.227, 3.528, 3.454, 2.933, 2.493, 2.187, 1.938 and 1.724 mg/cm2 against S. oryzae and 3.485, 3.176, 2.851, 1.531, 1.113, 0.756, 0.534 and 0.478 mg/cm2 against T. castaneum adults after 6, 12, 18, 24, 30, 36, 42 and 48 h of exposures, respectively. However, the PE extracts of C. arietinum did not show any mortality against T. castaneum and the CHCl3 extracts were inactive against S. oryzae and T. castaneum adults. In repellency tests, all three organic extracts did not show any repellency against the test beetles. According to their intensity of activities, the extracts could be arranged in the following descending order: CH3OH extracts against C. chinensis > CH3OH extracts against T. castaneum > PE extracts against S. oryzae > CHCl3 extracts against C. chinensis > PE extracts against C. chinensis > CH3OH extracts against S. oryzae adults. The present results indicate that the test plant C. arietinum contains bioactive compounds and could be used as a source of control agents for the stored product pests. J Bio-Sci. 30(1): 91-100, 2022 (June)

Published
2023
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32. Crash classification based on manner of collision: a comparative analysis.

Author

Mahmud, Asif, Sengupta, Agnimitra, and Gayah, Vikash V.

Subjects
*DISCRETE choice models, *STATISTICAL learning, *SUPPORT vector machines, *MACHINE learning, *COMPARATIVE studies, *STATISTICAL models, *TRAFFIC accidents
Abstract

Traffic crashes vary in the manner in which the collision occurs (collision type), and countermeasures to reduce crashes might vary significantly based on this collision type. The inherent complexity in their mechanism has motivated this study to identify significant factors influencing collision types, with the goal of better countermeasure deployment. The objective of this work is to compare the performances of statistical and machine learning (ML) models in classifying crashes based on collision type, and assess their generalizability and interpretability. Discrete choice models, Bayesian classifiers, tree-based algorithms, and support vector machines are among the data-driven methods considered for comparison. Results indicate that tree-based algorithms perform consistently well and offer a higher interpretability, with out-of-distribution robustness. However, while ML models provide a flexible framework for modeling large data volumes, statistical models provide additional interpretability on the effect of critical variables on crash mechanisms – which is relevant from a safety management standpoint. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Tracheostomy in Non-malignant Disorders

Author

Liton, Md Assaduzzaman, primary, Islam, Rashedul, additional, Rifat, Mahmud Asif, additional, Osmany, Husne Qumer, additional, Ghosh, Uzzal Chandra, additional, and Fakir, Md Abu Yusuf, additional

Published
2023
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39. Cyber Physical Security (CPS) Extension to Air Traffic Management (ATM) Testbed

Author

Datta, Koushik, Chatterji, Gano, Zeng, Daniel, Mahmud, Asif, and Wendt, Nathan

Subjects
Aircraft Communications And Navigation
Abstract

The Air Traffic Management (ATM) Testbed is being developed at NASA to enable benefit, impact, safety and cost assessments for accelerating the deployment of Concept and Technologies (C&T) in the National Airspace System (NAS). Today, C&T introduction into the NAS takes decades. The primary reason for this is an inability to assess the operational impact of the interaction between the proposed C&T and operationally deployed systems (Realistic Technologies) in terms of NAS-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on airline fleet operations. Transition of C&T to operations requires mathematical modeling and simulation, Human-in-the-Loop (HITL) testing and shadow-mode evaluation driven by operational data. Whereas interaction with the operational system during testing and stages of deployment is not permissible due to safety concerns, it is certainly possible to create a simulation environment that closely mimics the NAS using the same operational systems/hardware for enabling such assessments. This presentation focuses on a proposed Cyber Physical Security extension to the ATM Testbed for creating a modeling and simulation architecture to study how well the Air Traffic Management system will perform and analyze effectiveness of mitigating security measures against particular cyber-attack scenarios.

Published
2019

42. Withholding and withdrawal of life-sustaining treatments in low-middle-income versus high-income Asian countries and regions

Author

Phua, Jason, Joynt, Gavin M., Nishimura, Masaji, Deng, Yiyun, Myatra, Sheila Nainan, Chan, Yiong Huak, Binh, Nguyen Gia, Tan, Cheng Cheng, Faruq, Mohammad Omar, Arabi, Yaseen M., Wahjuprajitno, Bambang, Liu, Shih-Feng, Hashemian, Seyed Mohammad Reza, Kashif, Waqar, Staworn, Dusit, Palo, Jose Emmanuel, Koh, Younsuck, Gomersall, Charles D., Du, Bin, Divatia, Jigeeshu V., Tu, Mei-Lien, Patjanasoontorn, Boonsong, Khatun, U. H. Shahera, Mannan, Md Abdul, Uddin, Mirza Nazim, Manzoor, Raghib, Maniruzzaman, Mohammad, Ahsan, A. S. M. Areef, Fatema, Kaniz, Ahmed, Fatema, Nooruzzaman, A. R. M., Mahmud, Asif Mujtaba, Hossain Shahid, S. M., Tamanna, Rownak Jahan, Islam, Md Sayedul, Alam, A. K. M. Shamsul, Karim, Abdul, Mahmud, Mohammad Rashed, Chowdhury, Rashed, Faruk, Mohammad, Karim, Md Rezaul, Iqbal, Sarwar, Huda, Qumrul, Du, Bin, Qiu, Haibo, Zhang, Xiangyu, Dong, Yun, Wan, Yong, Gan, Cihai, Zhang, Yawei, Gao, Peiyang, Chen, Hong, Jia, Chao, Xiao, Xianhua, Xiang, Nutao, Li, Jingsong, Wang, Yushan, Tang, Yaoqing, Li, Jianguo, An, Youzhong, Ma, Xiaochun, Wang, Xue, Hu, Zhenjie, Qin, Tiehe, Chow, Fu Loi, Watt, Chi Lung, Wong, K. K., Yan, Wing-Wa, Buckley, Tom, Lai, Kang Yiu, Young, Karl, Ching, Chi Keung, Law, Kin-Ip, Divatia, Jigeeshu Vasishtha, Kapadia, Farhad N., Ramakrishnan, Nagarajan, Sircar, Mrinal, Amin, Pravin, Singh, Charu, Chawla, Rajesh, Mani, Raj Kumar, Nayyar, Ashwani, Shah, Jignesh, Tampubolon, Oloan, Singh, Tinni T., Jamaati, Hamidreza, Fallahian, Farahnaz, Radpay, Badiozaman, Zali, Alireza, Bagheri, Ahmad, Tada, Keiichi, Nakamura, Toshiaki, Sanui, Masamitsu, Fujino, Yuji, Shiragami, Gotaroh, Kotani, Joji, Miyasho, Kohji, Morisaki, Hiroshi, Eguchi, Yutaka, Takeda, Shinhiro, Nishimura, Shinya, Nishida, Osamu, Tanigawa, Koichi, Takada, Koji, Oda, Sigeto, Fukuoka, Toshio, Sawamoto, Toru, Hashimoto, Satoru, Sobue, Kazuya, Nishiyama, Kingo, Morita, Kiyoshi, Taniguchi, Hiroyuki, Sumita, Shinzo, Tong, Jenny May Geok, Hashmi, Madiha, Awan, Dreema, King, Larry S., Al-Dorzi, Hasan M., Sadat, Musharaf, Al-Hameed, Fahad M., Tan, Chee Keat, Tee, Augustine, Ooi, E-Lin, Tan, Addy Y. H., Chan, Yeow, Ho, Benjamin, Johan, Azman, Soh, Chai Rick, Shin, Cheung Soo, Jun, Jong Hun, Kwak, Sang Hyun, Suh, Gee Young, Lim, Chae-Man, Tu, Mei-Lien, Chou, Shao-Ting, Liang, Yu-Feng, Wei, Yu-Feng, Cheng, Kuo-Chen, Chatrkaw, Phornlert, Permpikul, Chairat, Pothirat, Chaicharn, Nguyen, Dang Tuan, Le, Duc Nhan, and ACME Study Investigators and the Asian Critical Care Clinical Trials Group

Published
2016
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44. Pattern of Lymph Node Metastasis in Oral Cancer

Author

Partha Pratim Chowdhury, Mahmud Asif Rifat, Mohammad Zaid Hossain, Tanvir Mostafa, Mostafa Kamal Arefin, Abu Yusuf Fakir, S K Nurul Fattah Rumi, Husne Qumer Osmany, and Hashim Reza Roctim

Subjects
Oncology, medicine.medical_specialty, business.industry, Internal medicine, medicine, Cancer, Lymph node metastasis, business, medicine.disease
Abstract

Background: Oral cancer is a common neoplasm worldwide which has a increased incidence and mortality rate over the past decades. In spite of skilled surgical and radio therapeutic modalities it is characterized by poor prognosis and a low survival rate. Lymph node metastasis is an important negative prognostic factor in oral cancer. In this study, pattern of cervical lymph node metastasis in oral carcinoma has been described. Such information may contribute to the understanding of oral cancer management plan. Methods: This cross-sectional observational study was conducted in the Otolaryngology and Head-Neck surgery department of Dhaka Medical College Hospital. The study period was from January 2018 to June 2018. A total of 50 patients were selected by purposive sampling technique. Data were collected by study physician himself. Then these collected data were recorded in structured case report forms. Clinical examination and relevant investigations were done. After planned surgical procedure post operative histopathological reports were collected. All collected questionnaire were checked very carefully. Data were processed and analyzed with the help of computer program SPSS and Microsoft excel. Quantitative data were expressed as mean and standard deviation and qualitative data as frequency and percentage. Comparison were done by tabulation and graphical presentation in the form of tables, pie chart, graphs, bar diagrams, histogram & charts etc. Result: Overall demographic features of 50 patients revealed that, the maximum incidence was seen in the age group 31-50 years (54.0%), mean age of the patient was 49.6 ±9.2 year. Male and female ratio was 4.5:1. In this study most of the tumors were well and moderately differentiated, 34.0% and 62.0% respectively. Aetiology and predisposing factors revealed that the most common were cigarette smoking, betal quid and alcohol consumption, present in 52.0%, 28.0% and 22.0% of patients respectively. Post operative histopathology reports showed that 24 patients (48.0%) had neck node metastasis among which 59.4% were with tumor size >1 cm. Tumors with the depth of invasion >3 mm had a very high risk of metastasis (P

Published
2021
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45. Molecular Epidemiology and Genetic Diversity of Multidrug-Resistant Mycobacterium tuberculosis Isolates in Bangladesh

Author

Rahman, S. M. Mazidur, primary, Rahman, Arfatur, additional, Nasrin, Rumana, additional, Ather, Md. Fahim, additional, Ferdous, Sara Sabrina, additional, Ahmed, Shahriar, additional, Uddin, Mohammad Khaja Mafij, additional, Khatun, Razia, additional, Sarker, Mohammad Shahnewaz, additional, Mahmud, Asif Mujtaba, additional, Rahman, Md. Mojibur, additional, and Banu, Sayera, additional

Published
2022
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