12,955 results on '"Ignatov, A"'
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202. The Impact of a Delay on the Evolution of Epidemics
- Author
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Ignatov, A. M., Trigger, S. A., and Chernyavskii, E. B.
- Published
- 2022
- Full Text
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203. On Shapley value interpretability in concept-based learning with formal concept analysis
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Ignatov, Dmitry I. and Kwuida, Léonard
- Published
- 2022
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204. Forecasting the Maximum Thickness of Ice Accretions
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Ignatov, R. Yu., Rubinshtein, K. G., and Yusupov, Yu. I.
- Published
- 2022
- Full Text
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205. Correction to: A Note on the Number of (Maximal) Antichains in the Lattice of Set Partitions
- Author
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Ignatov, Dmitry I., primary
- Published
- 2023
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206. A Note on the Number of (Maximal) Antichains in the Lattice of Set Partitions
- Author
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Ignatov, Dmitry I., primary
- Published
- 2023
- Full Text
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207. Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey
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Shalaby, M, Elsheikh, A, Hamed, H, Null, N, Elsheik, A, Sakr, A, Fouad, A, Kassem, A, Elfeki, H, Madbouly, K, Alzahrani, K, Marzouk, K, Ali, M, Helal, M, Elsorogy, M, Farid, M, Di Lorenzo, N, Sileri, P, Wexner, S, Khafagy, W, Adeyeye, A, El-Hussuna, A, Frontali, A, Saklani, A, Lelpo, B, Molena, D, Pandey, D, Karbovnichaya, E, Pata, F, Van Ramshor, G, Gallo, G, Spolverato, G, Pellino, G, Bagaglini, G, Rubio-Perez, I, Negoi, I, Frigerio, I, Juloski, J, Ninkovic, M, Franceschilli, M, Azer, M, Efetov, S, Ippoliti, S, Garoufalia, Z, Fazli, M, Dogjani, A, Cherfa, H, Omar, T, Minoldo, J, Alvarez Gallesio, J, Quesada, M, Bacher, A, Kropshofer, S, Ponholzer, F, Tesik, P, Gehwolf, P, Isci, S, Uranitsch, S, Berchtold, V, Samadov, E, Abualsel, A, Mitul, A, Islam, S, Vanlander, A, Van Praet, C, Van Daele, E, Vanommeslaeghe, H, Stijns, J, Abosi-Appeadu, K, Depuydt, M, Allaeys, M, Yves, V, Colleoni, R, Slavchev, M, Elbahrawy, A, Luc, J, Milford, K, Romic, I, Monti, A, Haydal, A, Klein, M, Ocklind, M, Hadi, S, Alqasaby, A, Elganash, A, Daibes, A, Elsaied, A, Elhattab, A, Lotfy, A, Alnashar, A, Elnour, A, Abdelhalim, A, Abdelhamid, A, Abdellatif, A, Abdelmohsen, A, Abdelrafee, A, Elhawary, A, Zidan, A, Eleshra, A, Elkafoury, A, Ezz, A, Abdelmomen, A, Elkased, A, Fawzy, A, Elkhouly, A, Hemidan, A, Abbas, A, Ismail, A, Attia, A, Farid, A, Elnakash, A, Negida, A, Soliman, A, Taki-Eldin, A, Albadry, A, Sanad, A, Elbatal, A, Elgazar, A, Saleh, A, Fahiem, A, Mohamed, A, Nageeb, A, Elmetwally, A, Alkhalegy, A, El-Wakeel, A, Shemes, A, Fadel, B, Lutfi, B, Ali, D, Abolnasr, K, Gamal, E, Abdallah, E, Ahmed, E, Salem, E, Hamed, E, Elshikh, E, Enad, F, Sarhan, F, Abouelnagah, G, Tagg, G, Atef, G, Shaker, G, Beshir, H, Zakaria, H, Barbary, H, Elgendy, H, Sharaf, H, Elnaghi, H, Elghadban, H, Elzayat, I, Fakhr, I, Sallam, I, Abdelmoneim, I, Elnemr, I, Zewar, K, Elalfy, K, Sabet, K, Mansour, K, Osman, K, Elgaly, M, Shams, M, Abozeid, M, Mohammed, M, Elkatt, M, Samaha, M, Mikhael, M, Khalil, M, Alhendawey, M, Elrefai, M, Gabr, M, Fayed, M, Abdelmaksoud, M, Salem, M, Mohamed Mohamed, M, Nabeeh, M, Elsayed, M, Abdelmonem, M, Eldemery, M, Elmesery, M, Fikry, M, Gharbia, M, Omar, M, Elmoghazy, M, Ghazala, M, Hamed, M, Metwally, M, Arnouse, M, Amen, M, Amary, M, Kandel, M, Abuzeid, M, Rabea, M, Sobh, M, Taman, M, Fathy, M, Moustafa, M, Zuhdy, M, Adel, M, Alaa, M, Alawady, M, Edassy, M, Eldesouki, M, Salim, M, Sanad, M, Khalaf, M, Henes, M, Abdelglil, M, Mohmmed, M, Abdelkhalik, M, Shetiwy, M, Elshazli, M, Hegazy, M, Ahmed, M, Abdelhalim, M, Shahein, M, Sofan, M, Hammad, M, Ahmad, M, Milad, N, Farouk, N, Eldesouky, O, Mohamed, O, Mahadel, O, Gaarour, O, Torky, R, Elhafez, R, Adly, R, Nageeb, R, Hamdi, S, Gamal, S, Emile, S, Regal, S, Abdelrasheed, S, Elzeftawy, S, Khashshan, S, Ashraf, T, Khafagy, T, Nabil, T, Abdelazim, T, Rizk, T, Amr, W, Yousef, Y, Youssef, Y, Castaldi, A, Fiore, A, Hasani, A, Mariani, A, Dagorno, C, Antonio, D, Izzo, G, Addari, G, Mangiameli, G, Rea, L, Pio, L, Paci, M, Andrea, P, De Fatico, G, Elvira, T, Schuldes, A, Rihan, E, Moeslein, G, Lederhuber, H, Botros, I, Jaman, I, Doerner, J, Elseberbihy, J, Sherbiny, K, Ghonim, M, Mikrish, A, Aziz, M, Hatm, M, Archid, R, Gendy, S, Ahmad, S, Charalabopoulos, A, Prodromidou, A, Ioannidis, A, Mpaili, E, Boukorou, G, Papadopoulos, G, Liakakos, T, Styliani, V, Agrawal, A, Jain, A, Rashid, A, Mehraj, A, Brahmachari, S, Lakshmi, H, Vishwakarma, K, Parida, L, Sharma, M, Zaieem, M, Makasarwala, M, Nittala, R, Kumar, S, Vikrantmr, S, Junaid, S, Khuller, S, More, V, Ahmed, A, Alomieri, A, Alhamdany, A, Del, M, Najm, G, Lateef, N, Mcnamara, D, Abdelmageed, M, Majeed, M, Troci, A, Porcu, A, Marano, A, Di Bartolomeo, A, Giani, A, Giardino, A, Canfora, A, Balla, A, Barberis, A, Belli, A, Borasi, A, Manetti, A, Mingoli, A, Morini, A, Maurizi, A, Marra, A, Epifani, A, Iossa, A, Parello, A, Guida, A, Maffioli, A, Scafa, A, Spinelli, A, Matarangolo, A, Picciariello, A, Pirozzi, B, Cirillo, B, Gazia, C, Ratto, C, Foppa, C, Marafante, C, Andrea, C, Tanda, C, Guerci, C, Don, C, Zigiotto, D, Coniglio, D, Sasia, D, Visconti, D, Altomare, D, Guaitoli, E, Botteri, E, Pinotti, E, Martinelli, F, Uggeri, F, Bàmbina, F, Falaschi, F, Costanzo, F, La Torre, F, Milana, F, Abbatini, F, De Lucia, F, Tropeano, F, Colombo, F, Ferrara, F, Litta, F, Carrano, F, Orlando, F, Roscio, F, Selvaggi, F, Giarratano, G, Pagano, G, Lisi, G, Argenio, G, Zancana, G, Cavallaro, G, Frazzetta, G, Mariateresa, G, Sciaudone, G, Vella, I, Siragusa, L, Santurro, L, Ferri, L, Petagna, L, Ferrario, L, Pitoni, L, Pignatelli, M, Angrisani, M, Giugliano, M, Inama, M, Marino, M, Veltri, M, Giuffrida, M, Menna, M, Valente, M, Rottoli, M, Sacchi, M, Uccelli, M, Rho, M, Garino, M, Montuori, M, Campanelli, M, Zese, M, De Falco, N, Cillara, N, Mariani, N, Tamini, N, Adorisio, O, Campennì, P, Venturelli, P, Bernante, P, Sapienza, P, Cianci, P, Marsanic, P, Lapolla, P, Tecchio, P, Familiari, P, Fransvea, P, Bruzzaniti, P, Hassan, R, Pirovano, R, Rimonda, R, Di Saverio, S, Di Carlo, S, Perra, T, Campagnaro, T, Testa, V, Andriola, V, Grappelli, V, Capizzi, V, Chiarella, V, Bellato, V, Yanaga, K, Farouk, M, Uraiqat, A, Almasri, M, Nabwana, A, Siboe, M, W, N, Njoroge, G, Ilkul, J, Obure, R, Palkhi, Y, Alkhayat, A, Ali, A, Malek, A, Abdelsayed, E, Zahra, T, Ayoub, L, Sleilati, F, Aoun, R, Algatanesh, N, Fieturi, N, Ng, J, Díaz, A, Duran, E, Guerrero, J, Jasso, M, Flores, M, Felix, M, Angulo, V, Mejdane, A, Aitali, A, Amal, B, Zentar, A, Bensaad, A, Yacir, E, Jawad, F, Rachid, M, Maliki-Alaoui, M, Ouadii, M, Mohammed, O, Thein, N, Koirala, D, Hilling, D, Pouwels, S, Okunlola, A, Adejumo, A, Akinmade, A, Shittu, A, Oluyomi, A, Abiodun, A, Lawal, B, Odion, C, Popoola, A, Jolayemi, E, Shomoye, E, Wuraola, F, Eke, G, Abiyere, H, Oluwasuyi, I, George, I, Njokanma, I, Aremu, I, Dare, J, Abdur-Rahman, L, Oludara, M, Mohammad, M, Adeoluwa, O, Situ, O, Agbonrofo, P, Kewulere, R, Aliyu, Y, Adebowale, Y, Galala, A, Rao, S, Waleed, A, Inam, A, Shaikh, A, Qureshi, A, Muhammad, A, Kerawala, A, Aslam, M, Mehr, A, Javed, A, Ahmad, F, Majid, H, Ahmed, H, Daudi, I, Akhtar, K, Niaz, K, Anwer, M, Amir, M, Hanif, M, Asif, M, Raza, M, Khokhar, M, Jameel, M, Nasir, M, Shafique, M, Ateeb, M, Nadeem, M, Shah, R, Waqar, S, Shah, S, Waseem, T, Ghafoor, T, Fatima, T, Bashir, U, Gonzales, E, Ruiz, L, Freitas, C, De Sousa, X, Al-Bahrani, A, Portela, C, Elgazar, E, Robles, E, Khan, I, Jarboa, L, Khawar, M, Echevarria, M, Bashah, M, Dawdi, S, Musthafa, S, Ali, S, Ciubotaru, C, Bonci, E, Muresan, M, Bogdan, S, Ioan, T, Zubayraeva, A, Derinov, A, Zakharenko, A, Novikova, A, Bashlachev, A, Kaldarov, A, Stanislav, B, Gorin, D, Puzenko, D, Kazachenko, E, Ashimov, E, Medkova, I, Ignatov, I, Sergeevich, K, Sidorova, L, Kiselev, M, Danilov, M, Aleksandr, O, Rodimov, S, Garmanovs, T, Kitsenko, Y, Valery, N, Japhet, N, Sibiany, A, Saadeldin, A, Abuosba, A, Alawadhi, A, Alharbi, A, Althumali, A, Alghuliga, A, Alotaibi, A, Abduraboh, A, Kateb, A, Sindy, A, Al Eisa, A, Almulhim, A, Aljawhari, A, Abozeid, A, Saad, A, Alqarni, A, Alwan, A, Alwusaibie, A, Bafaraj, A, Eldeeb, A, Tarabay, A, Alhedaithy, A, Almaghrabi, A, Abed, A, Abdullah, A, Semilan, A, Farag, M, Khudhayr, E, Hussain, M, Abbas, G, Alqudaihi, H, Abualnaja, Y, Shaheen, A, Mubarak, A, Ali, B, Alhazmi, B, Hijazi, B, Abdulrahman, C, Oyedepo, C, Alzamel, H, Tairab, E, Alsuwaimel, M, Hejazi, S, Alnoqaidan, E, Alhussien, F, Jallad, F, Khadwardi, F, Alghamdi, F, Haddad, F, Sauri, F, Alafghani, H, Alfalah, H, Gad, H, Aboelmagid, H, Ibrahim, H, Elzayady, H, Sharafeldin, H, Sembawa, H, Alabbas, H, Abbas, H, Elgamal, H, Alawfi, H, Al-Sadery, H, Abdelmotaleb, H, Al Hassn, I, Mudawi, I, Nekhala, I, Elsanhoury, K, Said, K, Albeshri, K, Albahooth, K, Mohammed, K, Asar, K, Osman, L, Alzamanan, M, Alnabarawi, M, Althobaiti, M, Al Naeb, M, Hassan, M, Abdelhamid, M, Alyami, M, Mirza, M, Sayouh, M, Alkhayat, M, Basendowah, M, Ghunaim, M, Alhussaini, M, Khoj, M, Sbaih, M, Saeed, M, Abdelaziz, N, Malibary, N, Abdo, N, Amer, N, Al Turki, N, Durayb, N, Yassin, N, Akeel, N, Larbi, N, Alsallum, O, Suliman, O, Elsherbiny, O, Abusalem, O, Albalawi, I, Abutalib, R, Alarabi, R, Khan, R, Alazzam, S, Alghamdi, S, Alsawat, S, Salim, S, Alshukr, S, Alzahrani, S, Golea, S, Alowairdhi, T, Salman, U, Abusiam, W, Abualkhair, W, Saber, W, Tashkandi, W, Alhazmi, W, Yassine, W, Alshabi, Y, Ibrahim, Y, Shahin, Y, Aljathlany, Y, Alnahas, Y, Alrashidi, Y, Wali, Z, Ndong, A, Ba, M, Faye, P, Arbutina, D, Milic, L, Cuk, V, Hussein, A, Mccaul, J, Bertels, L, Pohl, L, Arnold, M, Mbatani, N, Oosthuizen, P, Rayamajhi, S, Vosloo, S, Jooma, U, Landaluce-Olavarria, A, Vázquez-Melero, A, Marcos, A, Puerto Puerto, A, De La Hermosa, A, Senent-Boza, A, Ugarte-Sierra, B, Montalbán, B, Martin-Perez, B, Gomez, C, Colás-Ruiz, E, Santos, E, Senra, F, Mora-Guzmán, I, Dziakova, J, Díaz, J, Silva, J, Laina, J, Tallon-Aguilar, L, Di Martino, M, Chacón, M, Frasson, M, Calvo, M, Millan, M, Tejedoe, P, Pérez-Bertólez, S, Turrado-Rodríguez, V, Elsanosi, A, Abdalbakheet, D, Salim, O, Youssef, M, Barbon, C, Bouchrika, A, Maghrebi, H, Loukil, I, Yildiz, A, Dursun, A, Gulcu, B, Calik, B, Eral, B, Yeşilyurt, D, Yakar, F, Akin, F, Kilinc, G, Uslu, G, Tuncer, K, Koc, M, Leventoğlu, S, Sokmen, S, Atici, S, Kaya, T, Dere, Ü, Kırmızı, Y, Ssenono, K, Lule, H, Mbiine, R, Hamza, A, Peediyakkal, S, Varma, G, Mussa, H, Al-Masari, H, Shehata, M, Seiam, M, Nimir, N, Khare, R, Rashid, S, Kazim, S, Gondal, Z, Sherif, A, Ghanem, A, Helmy, A, Ibrahim, A, Elshaer, A, Marzouk, A, Tamburrini, A, Parente, A, Light, A, Diamantopoulou, A, Singh, B, Gurung, B, Frauenfelder, C, Leo, C, Raptis, D, Thakrar, D, Madhuri, T, Tsounaki, E, Garreffa, E, Soggiu, F, Stavrou, G, Ng, H, Tabasi, H, Nasef, H, Kostakis, I, Jeffery, J, Warusavitarne, J, Lund, J, Qurashi, K, Sahnan, K, Tong, K, Orecchia, L, Kaur, M, Zaidi, M, Ganau, M, Curtis, N, Bhatt, N, Machairas, N, Zafar, N, Toma, O, Sarmah, P, Bassuni, M, Davies, J, Shawer, S, Lewis, S, Subramanian, S, Nadeem, U, Njau, A, Tohamy, A, Pakula, A, Simioni, A, Jarvis, B, Skandalakis, G, Hesham, H, Isaiah, I, Villwock, J, Martin, L, Kress, M, Sebelik, M, Lathan, S, Towfigh, S, Holubar, S, Demeester, S, Alshehari, M, Ghabisha, S, Abdulatef, S, Al-Kubati, W, Obadiel, Y, Gots, A, Nakazwe, M, Chipaila, J, Mazingi, D, Shalaby, Mostafa, ElSheikh, Ahmed M., Hamed, Hosam, null, null, Elsheik, Ahmed, Sakr, Ahmad, Fouad, Amgad, Kassem, Amr, Elfeki, Hossam, Madbouly, Khaled, Alzahrani, Khalid H., Marzouk, Khalid, Ali, Mahmoud, Helal, Mohamed Alaa Abdelmoez, Elsorogy, Mohamed, Farid, Mohamed, Di Lorenzo, Nicola, Sileri, Pierpaolo, Wexner, Steven, Khafagy, Wael, Adeyeye, Ademola, El-Hussuna, Alaa, Frontali, Alice, Saklani, Avanish, Lelpo, Benedettao, Molena, Daniela, Pandey, Diwakar, Karbovnichaya, Elena, Pata, Francesco, Van Ramshor, Gabrielle H., Gallo, Gaetano, Spolverato, Gaya, Pellino, Gianluca, Bagaglini, Giulia, Rubio-Perez, Ines, Negoi, Ionut, Frigerio, Isabella, Juloski, Jovan, Ninkovic, Marijana, Franceschilli, Marzia, Azer, Mina, Efetov, Sergey, Ippoliti, Simona, Garoufalia, Zoe, Fazli, Mohammad Rafi, Dogjani, Agron, Cherfa, Harieche Abdennour Abderahim, Omar, Tilioua, Minoldo, Javier, Alvarez Gallesio, José Maria, Quesada, Matias, Bacher, Annica, Kropshofer, Stephan, Ponholzer, Florian, Tesik, Philip, Gehwolf, Philipp, Isci, Sevim, Uranitsch, Stefan, Berchtold, Valeria, Samadov, Elgun, Abualsel, Abdulmenem, Mitul, Ashrarur Rahman, Islam, S. M. Nazmul, Vanlander, Aude, Van Praet, Charles, Van Daele, Elke, Vanommeslaeghe, Hanne, Stijns, Jasper, Abosi-Appeadu, Kessewa, Depuydt, Martijn, Allaeys, Mathias, Yves, Van Nieuwenhove, Colleoni, Ramiro, Slavchev, Mihail, Elbahrawy, Aly, Luc, Jessica G. Y., Milford, Karen, Romic, Ivan, Monti, Alessio, Haydal, Ashraf, Klein, Mads Falk, Ocklind, Miranda E. K., Hadi, Sabah Anwar, Alqasaby, Abdallah, Elganash, Abdelazim, Daibes, Adel Goda Hussein, Elsaied, Adham, Elhattab, Ahmad, Lotfy, Ahmad, Alnashar, Ahmed, Elnour, Ahmed Abd Elbaset Elsayed Abu, Abdelhalim, Ahmed, Abdelhamid, Ahmed, Abdellatif, Ahmed, Abdelmohsen, Ahmed, Abdelrafee, Ahmed, Elhawary, Ahmed Adel, Zidan, Ahmed Azmy, Eleshra, Ahmed, Elkafoury, Ahmed, Ezz, Ahmed, Abdelmomen, Ahmed Ezzat Elghrieb, Elkased, Ahmed Farag, Fawzy, Ahmed, Elkhouly, Ahmed G., Hemidan, Ahmed Gamal Abouelfetouh Ibrahim, Abbas, Ahmed Hosam Eldin Hasan, Ismail, Ahmed Mahmoud Ahmed, Attia, Ahmed Mohamed, Farid, Ahmed Mohammed, Elnakash, Ahmed Mostafa, Negida, Ahmed, Soliman, Ahmed, Taki-Eldin, Ahmed, Albadry, Ali Almahdy Ali, Sanad, Aly, Elbatal, Amira Alsayed Abdelhai, Elgazar, Amr, Saleh, Amr, Fahiem, Andrew, Mohamed, Anwar Yahya A., Nageeb, Ashraf, Elmetwally, Ashraf S., Alkhalegy, Ayman, El-Wakeel, Ayman, Shemes, Ayman, Fadel, Bashir A., Lutfi, Basma Waseem, Ali, Doaa, Abolnasr, Khaled Samir, Gamal, Ehab, Abdallah, Emad, Ahmed, Emad Ali, Salem, Eman Abdalla Mohamed, Hamed, Esmael Ali, Elshikh, Essam, Enad, Farazdaq, Sarhan, Fetoh Alaaeldin Fetoh, Abouelnagah, Galal, Tagg, Gamal Hassan El, Atef, Gehad, Shaker, George Samir Habib, Beshir, Hatem, Zakaria, Hazem M., Barbary, Hesham, Elgendy, Hesham, Sharaf, Hesham, Elnaghi, Hisham, Elghadban, Hosam, Elzayat, Ibrahim, Fakhr, Ibrahim, Sallam, Ibrahim, Abdelmoneim, Ibrahim Tharwat Mohamed, Elnemr, Islam, Zewar, Karem Shahin Mohamed, Elalfy, Khaled, Sabet, Khaled, Mansour, Khaled Yousery Ibrahim, Osman, Khalid Abdalla Abdelgadir, Elgaly, Maher Elesawi Kamel, Shams, Maher, Abozeid, Mahmoud, Mohammed, Mahmoud M., Elkatt, Mahmoud Mohamed, Samaha, Mahmoud Yahia, Mikhael, Marolla Maher Eskander, Khalil, Medhat M. H. A., Alhendawey, Moaaz, Elrefai, Mohamad, Gabr, Mohamed A., Fayed, Mohamed Abdelaziz Mohamed Abdalla M, Abdelmaksoud, Mohamed, Salem, Mohamed Abouelmagd, Mohamed Mohamed, Mohamed Adel, Nabeeh, Mohamed Adel, Elsayed, Mohamed Ahmed Abdelhalim Ahmed, Abdelmonem, Mohamed Ahmed, Ali, Mohamed Anwar Abdel Razik, Eldemery, Mohamed, Elmesery, Mohamed, Fikry, Mohamed, Gharbia, Mohamed, Omar, Mohamed I., Elmoghazy, Mohamed Ibrahim, Ghazala, Mohamed Jomma, Hamed, Mohamed Korayem Fattouh, Metwally, Mohamed, Arnouse, Mohamed Mohamed Hamdy, Amen, Mohamed Mohsen, Amary, Mohamed Mokhtar, Kandel, Mohamed Mosaad, Abuzeid, Mohamed Mostafa, Rabea, Mohamed, Sobh, Mohamed Ramadan, Taman, Mohamed, Fathy, Mohammad, Moustafa, Mohammad Montaser Hassan, Zuhdy, Mohammad, Adel, Mohammed, Alaa, Mohammed, Alawady, Mohammed, Edassy, Mohammed El, Mohammed, Mohammed Mustafa Hassan, Eldesouki, Mohammed Nabil, Salim, Mohammed Said Mahmoud, Sanad, Mohammed, Khalaf, Mohsen George, Henes, Mohsen Michael, Abdelglil, Momen, Mohmmed, Mona Mhmoud, Abdelkhalik, Morsi Mohamed Morsi, Shetiwy, Mosab, Elshazli, Mostafa, Hegazy, Mostafa, Ahmed, Mostafa Mahmoud, Abdelhalim, Mostafa Mohammed, Shahein, Mostafa, Sofan, Mostafa, Hammad, Muhammed Alaa Moukhtar, Ahmad, Mustafa, Milad, Nader, Farouk, Nehal, Eldesouky, Omnia, Mohamed, Omnia Y., Mahadel, Osama Abdel Salam, Gaarour, Osama, Torky, Radwan Abdelsabour, Elhafez, Raheem El-Gohary Abd, Adly, Ramy Magdy, Nageeb, Ramy Mikhael, Hamdi, Salah, Gamal, Sameh, Emile, Sameh Hany, Regal, Samer, Abdelrasheed, Sayed, Elzeftawy, Shady Ahmed, Khashshan, Sohib Mohammed Mohammed, Ashraf, Tamer, Khafagy, Tamer, Nabil, Tamer, Abdelazim, Tarek, Rizk, Tarek Taher, Amr, Wesam, Yousef, Yousef Mohamed, Youssef, Youssef Abdel Aziz, Castaldi, Antonio, Fiore, Antonio, Hasani, Ariola, Mariani, Aurora, Dagorno, Claire, Antonio, D'Alessandro, Izzo, Giuliano, Addari, Giulio, Mangiameli, Giuseppe, Rea, Lo Dico, Pio, Luca, Paci, Marco, Andrea, Police, De Fatico, GSerena, Elvira, Tartaglia, Schuldes, Alejandro Daniel Lira, Rihan, Eslam, Moeslein, Gabriela, Lederhuber, Hans, Botros, Ibram, Jaman, Ismail, Doerner, Johannes, Elseberbihy, John Rezk Hanna, Sherbiny, Kareem El, Ghonim, Mostafa, Mikrish, Amir, Aziz, Mina, Hatm, Mohamed, Archid, Rami, Gendy, Samuel Elkess Morcos, Ahmad, Sufian, Charalabopoulos, Alexandros, Prodromidou, Anastasia, Ioannidis, Argyrios, Mpaili, Eustratia, Boukorou, Garyfallia, Papadopoulos, Georgios, Liakakos, Theodore, Styliani, Vasileiadou, Agrawal, Abhishek, Jain, Amita, Rashid, Arshad, Mehraj, Asif, Brahmachari, Swagata, Lakshmi, Harish Neelamraju, Vishwakarma, Kushagra, Parida, Lalit, Sharma, Meenakshi, Zaieem, Mohammad, Makasarwala, Murtaza, Nittala, Rigved, Kumar, Sanjeev, Vikrantmr, Sharma, Junaid, Sheikh, Khuller, Somyaa, More, Vinal, Ahmed, Abeer Abdul Hameed, Alomieri, Adil, Alhamdany, Arkan Shubber, Del, Muslim Ka, Najm, Ghadah, Lateef, Nawras Falah, Mcnamara, Deborah, Abdelmageed, Mohammed Elkassaby, Majeed, Mudassar, Troci, Albert, 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Grace, Abiyere, Henry, Oluwasuyi, Ige, George, Ihediwa, Njokanma, Iloba Gabriel, Aremu, Isiaka, Dare, Julius Kolajo, Abdur-Rahman, Lukman, Ahmad, Misbahu Haruna, Oludara, Mobolaji Adewale, Mohammad, Mohammad Aminu, Adeoluwa, Ojajuni, Situ, Oladele, Agbonrofo, Peter, Kewulere, Raji Taofiq, Aliyu, Yakubu, Adebowale, Yusuf, Galala, Ahmed, Rao, Satish, Waleed, Aasma, Inam, Aatif, Shaikh, Abdul Razaque, Qureshi, Ahmad Uzair, Muhammad, Aneeqah Din, Ahmed, Arooj, Kerawala, Asad Ali, Aslam, Mohammad, Mehr, Asma, Javed, Ayesha, Ahmad, Farooq, Majid, Haroon Javaid, Ahmed, Hassan, Daudi, Irfan, Akhtar, Khalid, Niaz, Khurram, Anwer, Mariyah, Amir, Mohammed, Hanif, Muhammad Amir, Asif, Muhammad, Raza, Muhammad Asif, Khokhar, Muhammad Imran, Jameel, Muhammad Khurram, Nasir, Muhammad, Shafique, Muhammad Salman, Ateeb, Mujammad, Nadeem, Munawar, Shah, Rahmat Ullah, Waqar, Shahzad Hussain, Shah, Shahzad Alam, Waseem, Talat, Ghafoor, Tariq, Fatima, Tauseef, Bashir, Umar, Gonzales, Erick Ivan Huaman, 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Alghuliga, Abdullah, Alotaibi, Abdullah, Abduraboh, Abdullah Fayez, Kateb, Abdullah, Sindy, Abdullah, Al Eisa, Abdulmohsen, Alotaibi, Abdulrahman, Almulhim, Abdulrhman, Aljawhari, Adel Ali, Abozeid, Ahmad Mahmoud, Saad, Ahmad, Alqarni, Ahmed, Alwan, Ahmed, Alwusaibie, Ahmed, Bafaraj, Ahmed, Eldeeb, Ahmed, Tarabay, Ahmed, Mohammed, Mahfoudh, Alhedaithy, Alhanouf, Almaghrabi, Alhassan Hesham, Abed, Ali Ibrahim Eldawy, Abdullah, Alqahtani Ali, Semilan, Anmar, Farag, Mohamed, Khudhayr, Essa, Hussain, Marwah, Abbas, Ghanem, Alqudaihi, Heba, Abualnaja, Yousra, Shaheen, Abelnasser, Mubarak, Ashraf Abdelazeem Mohamed, Ali, Bandar Idrees A., Alhazmi, Barrag, Hijazi, Bilal Ahmed, Abdulrahman, Chadi, Oyedepo, Charles Olajide, Alzamel, Heythem, Tairab, Elsanousi Ibrahim Sabir, Alsuwaimel, Munir A., Hejazi, Soha, Alnoqaidan, Emad, Alhussien, Fade Ahmed, Jallad, Fadi Sami, Khadwardi, Faisal, Alghamdi, Faisal Saleh, Haddad, Feras, Sauri, Fozan, Alafghani, Haitham, Alfalah, Haitham, Gad, Hamada, 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Alaa Abdelmoez, Elsorogy, Mohamed, Farid, Mohamed, Di Lorenzo, Nicola, Sileri, Pierpaolo, Wexner, Steven, Khafagy, Wael, Adeyeye, Ademola, El-Hussuna, Alaa, Frontali, Alice, Saklani, Avanish, Lelpo, Benedettao, Molena, Daniela, Pandey, Diwakar, Karbovnichaya, Elena, Pata, Francesco, Van Ramshor, Gabrielle H., Gallo, Gaetano, Spolverato, Gaya, Pellino, Gianluca, Bagaglini, Giulia, Rubio-Perez, Ines, Negoi, Ionut, Frigerio, Isabella, Juloski, Jovan, Ninkovic, Marijana, Franceschilli, Marzia, Azer, Mina, Efetov, Sergey, Ippoliti, Simona, Garoufalia, Zoe, Fazli, Mohammad Rafi, Dogjani, Agron, Cherfa, Harieche Abdennour Abderahim, Omar, Tilioua, Minoldo, Javier, Alvarez Gallesio, José Maria, Quesada, Matias, Bacher, Annica, Kropshofer, Stephan, Ponholzer, Florian, Tesik, Philip, Gehwolf, Philipp, Isci, Sevim, Uranitsch, Stefan, Berchtold, Valeria, Samadov, Elgun, Abualsel, Abdulmenem, Mitul, Ashrarur Rahman, Islam, S. M. Nazmul, Vanlander, Aude, Van Praet, Charles, Van Daele, Elke, Vanommeslaeghe, Hanne, Stijns, Jasper, Abosi-Appeadu, Kessewa, Depuydt, Martijn, Allaeys, Mathias, Yves, Van Nieuwenhove, Colleoni, Ramiro, Slavchev, Mihail, Elbahrawy, Aly, Luc, Jessica G. Y., Milford, Karen, Romic, Ivan, Monti, Alessio, Haydal, Ashraf, Klein, Mads Falk, Ocklind, Miranda E. K., Hadi, Sabah Anwar, Alqasaby, Abdallah, Elganash, Abdelazim, Daibes, Adel Goda Hussein, Elsaied, Adham, Elhattab, Ahmad, Lotfy, Ahmad, Alnashar, Ahmed, Elnour, Ahmed Abd Elbaset Elsayed Abu, Abdelhalim, Ahmed, Abdelhamid, Ahmed, Abdellatif, Ahmed, Abdelmohsen, Ahmed, Abdelrafee, Ahmed, Elhawary, Ahmed Adel, Zidan, Ahmed Azmy, Eleshra, Ahmed, Elkafoury, Ahmed, Ezz, Ahmed, Abdelmomen, Ahmed Ezzat Elghrieb, Elkased, Ahmed Farag, Fawzy, Ahmed, Elkhouly, Ahmed G., Hemidan, Ahmed Gamal Abouelfetouh Ibrahim, Abbas, Ahmed Hosam Eldin Hasan, Ismail, Ahmed Mahmoud Ahmed, Attia, Ahmed Mohamed, Farid, Ahmed Mohammed, Elnakash, Ahmed Mostafa, Negida, Ahmed, Soliman, Ahmed, Taki-Eldin, Ahmed, Albadry, Ali Almahdy Ali, Sanad, Aly, Elbatal, Amira Alsayed Abdelhai, Elgazar, Amr, Saleh, Amr, Fahiem, Andrew, Mohamed, Anwar Yahya A., Nageeb, Ashraf, Elmetwally, Ashraf S., Alkhalegy, Ayman, El-Wakeel, Ayman, Shemes, Ayman, Fadel, Bashir A., Lutfi, Basma Waseem, Ali, Doaa, Abolnasr, Khaled Samir, Gamal, Ehab, Abdallah, Emad, Ahmed, Emad Ali, Salem, Eman Abdalla Mohamed, Hamed, Esmael Ali, Elshikh, Essam, Enad, Farazdaq, Sarhan, Fetoh Alaaeldin Fetoh, Abouelnagah, Galal, Tagg, Gamal Hassan El, Atef, Gehad, Shaker, George Samir Habib, Beshir, Hatem, Zakaria, Hazem M., Barbary, Hesham, Elgendy, Hesham, Sharaf, Hesham, Elnaghi, Hisham, Elghadban, Hosam, Elzayat, Ibrahim, Fakhr, Ibrahim, Sallam, Ibrahim, Abdelmoneim, Ibrahim Tharwat Mohamed, Elnemr, Islam, Zewar, Karem Shahin Mohamed, Elalfy, Khaled, Sabet, Khaled, Mansour, Khaled Yousery Ibrahim, Osman, Khalid Abdalla Abdelgadir, Elgaly, Maher Elesawi Kamel, Shams, Maher, Abozeid, Mahmoud, Mohammed, Mahmoud M., Elkatt, Mahmoud Mohamed, Samaha, Mahmoud Yahia, Mikhael, Marolla Maher Eskander, Khalil, Medhat M. H. A., Alhendawey, Moaaz, Elrefai, Mohamad, Gabr, Mohamed A., Fayed, Mohamed Abdelaziz Mohamed Abdalla M, Abdelmaksoud, Mohamed, Salem, Mohamed Abouelmagd, Mohamed Mohamed, Mohamed Adel, Nabeeh, Mohamed Adel, Elsayed, Mohamed Ahmed Abdelhalim Ahmed, Abdelmonem, Mohamed Ahmed, Ali, Mohamed Anwar Abdel Razik, Eldemery, Mohamed, Elmesery, Mohamed, Fikry, Mohamed, Gharbia, Mohamed, Omar, Mohamed I., Elmoghazy, Mohamed Ibrahim, Ghazala, Mohamed Jomma, Hamed, Mohamed Korayem Fattouh, Metwally, Mohamed, Arnouse, Mohamed Mohamed Hamdy, Amen, Mohamed Mohsen, Amary, Mohamed Mokhtar, Kandel, Mohamed Mosaad, Abuzeid, Mohamed Mostafa, Rabea, Mohamed, Sobh, Mohamed Ramadan, Taman, Mohamed, Fathy, Mohammad, Moustafa, Mohammad Montaser Hassan, Zuhdy, Mohammad, Adel, Mohammed, Alaa, Mohammed, Alawady, Mohammed, Edassy, Mohammed El, Mohammed, Mohammed Mustafa Hassan, Eldesouki, Mohammed Nabil, Salim, Mohammed Said Mahmoud, Sanad, Mohammed, Khalaf, Mohsen George, Henes, Mohsen Michael, Abdelglil, Momen, Mohmmed, Mona Mhmoud, Abdelkhalik, Morsi Mohamed Morsi, Shetiwy, Mosab, Elshazli, Mostafa, Hegazy, Mostafa, Ahmed, Mostafa Mahmoud, Abdelhalim, Mostafa Mohammed, Shahein, Mostafa, Sofan, Mostafa, Hammad, Muhammed Alaa Moukhtar, Ahmad, Mustafa, Milad, Nader, Farouk, Nehal, Eldesouky, Omnia, Mohamed, Omnia Y., Mahadel, Osama Abdel Salam, Gaarour, Osama, Torky, Radwan Abdelsabour, Elhafez, Raheem El-Gohary Abd, Adly, Ramy Magdy, Nageeb, Ramy Mikhael, Hamdi, Salah, Gamal, Sameh, Emile, Sameh Hany, Regal, Samer, Abdelrasheed, Sayed, Elzeftawy, Shady Ahmed, Khashshan, Sohib Mohammed Mohammed, Ashraf, Tamer, Khafagy, Tamer, Nabil, Tamer, Abdelazim, Tarek, Rizk, Tarek Taher, Amr, Wesam, Yousef, Yousef Mohamed, Youssef, Youssef Abdel Aziz, Castaldi, Antonio, Fiore, Antonio, Hasani, Ariola, Mariani, Aurora, Dagorno, Claire, Antonio, D'Alessandro, Izzo, Giuliano, Addari, Giulio, Mangiameli, Giuseppe, Rea, Lo Dico, Pio, Luca, Paci, Marco, Andrea, Police, De Fatico, GSerena, Elvira, Tartaglia, Schuldes, Alejandro Daniel Lira, Rihan, Eslam, Moeslein, Gabriela, Lederhuber, Hans, Botros, Ibram, Jaman, Ismail, Doerner, Johannes, Elseberbihy, John Rezk Hanna, Sherbiny, Kareem El, Ghonim, Mostafa, Mikrish, Amir, Aziz, Mina, Hatm, Mohamed, Archid, Rami, Gendy, Samuel Elkess Morcos, Ahmad, Sufian, Charalabopoulos, Alexandros, Prodromidou, Anastasia, Ioannidis, Argyrios, Mpaili, Eustratia, Boukorou, Garyfallia, Papadopoulos, Georgios, Liakakos, Theodore, Styliani, Vasileiadou, Agrawal, Abhishek, Jain, Amita, Rashid, Arshad, Mehraj, Asif, Brahmachari, Swagata, Lakshmi, Harish Neelamraju, Vishwakarma, Kushagra, Parida, Lalit, Sharma, Meenakshi, Zaieem, Mohammad, Makasarwala, Murtaza, Nittala, Rigved, Kumar, Sanjeev, Vikrantmr, Sharma, Junaid, Sheikh, Khuller, Somyaa, More, Vinal, Ahmed, Abeer Abdul Hameed, Alomieri, Adil, Alhamdany, Arkan Shubber, Del, Muslim Ka, Najm, Ghadah, Lateef, Nawras Falah, Mcnamara, Deborah, Abdelmageed, Mohammed Elkassaby, Majeed, Mudassar, Troci, Albert, Porcu, Alberto, Marano, Alessandra, Di Bartolomeo, Alessandro, Giani, Alessandro, Giardino, Alessandro, Canfora, Alfonso, Balla, Andrea, Barberis, Andrea, Belli, Andrea, Borasi, Andrea, Manetti, Andrea, Mingoli, Andrea, Morini, Andrea, Maurizi, Angela, Marra, Angelo Alessandro, Epifani, Angelo Gabriele, Iossa, Angelo, Parello, Angelo, Guida, Anna, Maffioli, Anna, Scafa, Anthony Kevin, Spinelli, Antonino, Matarangolo, Antonio, Picciariello, Arcangelo, Pirozzi, Brunella, Cirillo, Bruno, Gazia, Carlo, Ratto, Carlo, Foppa, Caterina, Marafante, Chiara, Andrea, Chierici, Tanda, Cinzia, Guerci, Claudio, Don, Cristine, Zigiotto, Daniele, Coniglio, Denise, Sasia, Diego, Visconti, Diego, Altomare, Donato F., Guaitoli, Eleonora, Botteri, Emanuele, Pinotti, Enrico, Martinelli, Fabio, Uggeri, Fabio, Bàmbina, Fabrizio, Falaschi, Federica, Costanzo, Federico, La Torre, Filippo, Milana, Flavio, Abbatini, Francesca, De Lucia, Francesca, Tropeano, Francesca Paola, Colombo, Francesco, Ferrara, Francesco, Litta, Francesco, Carrano, Francesco Maria, Orlando, Francesco, Roscio, Francesco, Selvaggi, Francesco, Giarratano, Gabriella, Pagano, Gianluca, Lisi, Giorgio, Argenio, Giulio, Zancana, Giuseppa, Cavallaro, Giuseppe, Frazzetta, Giuseppe, Mariateresa, Grasso, Sciaudone, Guido, Vella, Ivan, Siragusa, Leandro, Santurro, Letizia, Ferri, Lorenzo, Petagna, Lorenzo, Ferrario, Luca, Pitoni, Ludovica, Pignatelli, Marcello Filograna, Angrisani, Marco, Giugliano, Marco, Inama, Marco, Marino, Marco V., Veltri, Marco, Giuffrida, Maria Carmela, Menna, Maria Paola, Valente, Marina, Rottoli, Matteo, Sacchi, Matteo, Uccelli, Matteo, Rho, Maurizio, Garino, Mauro, Montuori, Mauro, Campanelli, Michela, Zese, Monica, De Falco, Nadia, Cillara, Nicola, Mariani, Nicolò Maria, Tamini, Nicolò, Adorisio, Ottavio, Campennì, Paola, Venturelli, Paolina, Bernante, Paolo, Sapienza, Paolo, Cianci, Pasquale, Marsanic, Patrizia, Lapolla, Pierfrancesco, Tecchio, Piero, Familiari, Pietro, Fransvea, Pietro, Bruzzaniti, Placido, Hassan, Redan, Pirovano, Riccardo, Rimonda, Roberto, Di Saverio, Salomone, Di Carlo, Sara, Perra, Teresa, Campagnaro, Tommaso, Testa, Valentina, Andriola, Valeria, Grappelli, Virgilio Michael Ambrosi, Capizzi, Vita, Chiarella, Vito, Bellato, Vittoria, Yanaga, Katsuhiko, Farouk, Mohamed, Uraiqat, Ahmad, Almasri, Mahmoud, Nabwana, Ambrose, Siboe, Mark M. W., W, Njoroge P., Njoroge, Githu, Ilkul, Jh., Obure, Ralph Ombati, Palkhi, Yusuf, Alkhayat, Ali, Ali, Ali Sayed, Malek, Amgad Nashaat Abdel, Abdelsayed, Emad Fahim, Zahra, Tarek, Ayoub, Larissa, Sleilati, Fadi, Aoun, Rany, Algatanesh, Nassib, Fieturi, Nura Ahmed, Ng, Jen Siang, Díaz, Andrés Vega, Duran, Erik Efrain Sosa, Guerrero, José Eaazim Flores, Jasso, Manuel Meza, Flores, Manuel SSalas, Felix, Marcos José Serrato, Angulo, Victor Manuel Pinto, Mejdane, Abdelhadi, Aitali, Abdelmounaim, Amal, Benzakour, Zentar, Aziz, Bensaad, Ahmed, Yacir, El Alami, Jawad, Fassi Fihri Mohamed, Rachid, Mohamed Ghassane, Maliki-Alaoui, Mohamed, Ouadii, Mouaqit, Mohammed, Ouazni, Thein, Nyan, Koirala, Dinesh Prasad, Hilling, Denise, Pouwels, Sjaak, Okunlola, Abiodun Idowu, Adejumo, Adeyinka, Akinmade, Akinola, Shittu, Asimiyu Adekunle, Oluyomi, Ayodele Samuel, Abiodun, Azeez Lateef, Lawal, Bashir, Odion, Clement, Popoola, Ademola, Jolayemi, Edward, Shomoye, El-Zaki, Wuraola, Funmilola Olanike, Eke, Grace, Abiyere, Henry, Oluwasuyi, Ige, George, Ihediwa, Njokanma, Iloba Gabriel, Aremu, Isiaka, Dare, Julius Kolajo, Abdur-Rahman, Lukman, Ahmad, Misbahu Haruna, Oludara, Mobolaji Adewale, Mohammad, Mohammad Aminu, Adeoluwa, Ojajuni, Situ, Oladele, Agbonrofo, Peter, Kewulere, Raji Taofiq, Aliyu, Yakubu, Adebowale, Yusuf, Galala, Ahmed, Rao, Satish, Waleed, Aasma, Inam, Aatif, Shaikh, Abdul Razaque, Qureshi, Ahmad Uzair, Muhammad, Aneeqah Din, Ahmed, Arooj, Kerawala, Asad Ali, Aslam, Mohammad, Mehr, Asma, Javed, Ayesha, Ahmad, Farooq, Majid, Haroon Javaid, Ahmed, Hassan, Daudi, Irfan, Akhtar, Khalid, Niaz, Khurram, Anwer, Mariyah, Amir, Mohammed, Hanif, Muhammad Amir, Asif, Muhammad, Raza, Muhammad Asif, Khokhar, Muhammad Imran, Jameel, Muhammad Khurram, Nasir, Muhammad, Shafique, Muhammad Salman, Ateeb, Mujammad, Nadeem, Munawar, Shah, Rahmat Ullah, Waqar, Shahzad Hussain, Shah, Shahzad Alam, Waseem, Talat, Ghafoor, Tariq, Fatima, Tauseef, Bashir, Umar, Gonzales, Erick Ivan Huaman, Ruiz, Luis Angel Garcia, Freitas, Carla, De Sousa, Xavier, Al-Bahrani, Ahmed, Portela, Carlos Antonio Sanchez, Elgazar, Elsayed Aly, Robles, Eloy Morasen, Khan, Irfan Jan, Jarboa, Lutfi, Khawar, Mahwish, Echevarria, Miguel Jose Pinto, Bashah, Moataz M., Dawdi, Salahaldeen, Musthafa, Shameel, Ali, Syed Muhammad, Ciubotaru, Cezar, Bonci, Eduard-Alexandru, Muresan, Mihai-Stefan, Bogdan, Stoica, Ioan, Tanase, Zubayraeva, Albina, Derinov, Aleksandr, Zakharenko, Alexander, Novikova, Anastasia, Bashlachev, Andrey, Kaldarov, Ayrat, Stanislav, Berelavichus, Gorin, David, Puzenko, Dmitriy, Kazachenko, Ekaterina, Ashimov, Erkin, Medkova, Iuliia, Ignatov, Ivan, Sergeevich, Kochetkov Viktor, Sidorova, Lyudmila, Kiselev, Michail, Danilov, Michail, Aleksandr, Ogoreltsev, Rodimov, Sergey, Garmanovs, Tatiana, Kitsenko, Yury, Valery, Nekoval, Japhet, Ntezamizero, Sibiany, Abdulrahman, Saadeldin, Abdelhalim, Abuosba, Abdelrahman, Alawadhi, Abdulbari Mohammed, Alharbi, Abdulhamid, Althumali, Abdullah, Alghuliga, Abdullah, Alotaibi, Abdullah, Abduraboh, Abdullah Fayez, Kateb, Abdullah, Sindy, Abdullah, Al Eisa, Abdulmohsen, Alotaibi, Abdulrahman, Almulhim, Abdulrhman, Aljawhari, Adel Ali, Abozeid, Ahmad Mahmoud, Saad, Ahmad, Alqarni, Ahmed, Alwan, Ahmed, Alwusaibie, Ahmed, Bafaraj, Ahmed, Eldeeb, Ahmed, Tarabay, Ahmed, Mohammed, Mahfoudh, Alhedaithy, Alhanouf, Almaghrabi, Alhassan Hesham, Abed, Ali Ibrahim Eldawy, Abdullah, Alqahtani Ali, Semilan, Anmar, Farag, Mohamed, Khudhayr, Essa, Hussain, Marwah, Abbas, Ghanem, Alqudaihi, Heba, Abualnaja, Yousra, Shaheen, Abelnasser, Mubarak, Ashraf Abdelazeem Mohamed, Ali, Bandar Idrees A., Alhazmi, Barrag, Hijazi, Bilal Ahmed, Abdulrahman, Chadi, Oyedepo, Charles Olajide, Alzamel, Heythem, Tairab, Elsanousi Ibrahim Sabir, Alsuwaimel, Munir A., Hejazi, Soha, Alnoqaidan, Emad, Alhussien, Fade Ahmed, Jallad, Fadi Sami, Khadwardi, Faisal, Alghamdi, Faisal Saleh, Haddad, Feras, Sauri, Fozan, Alafghani, Haitham, Alfalah, Haitham, Gad, Hamada, Aboelmagid, Hamdy Haggag Ebrahim, Ibrahim, Hamed, Elzayady, Hany M., Sharafeldin, Hatem Abdelrahman Ahmed, Sembawa, Hatem A., Alabbas, Haytham, Abbas, Hazem, Elgamal, Hesham, Alawfi, Homoud, Al-Sadery, Humood, Abdelmotaleb, Hussien Ali, Al Hassn, Ibrahim, Mudawi, Ishag M., Nekhala, Islam, Elsanhoury, Kareem, Said, Khalid Babieker, Albeshri, Khalid A., Albahooth, Khalid, Mohammed, Khalid Fathelrahman Bakier, Asar, Khalid Mohammad Ibrahim, Osman, Luqman, Alzamanan, Mahdi, Alnabarawi, Mahmoud, Althobaiti, Majid, Elsayed, Mohamed Abdelmoneim, Al Naeb, Mohamed, Hassan, Mohamed Salah Eldin, Abdelhamid, Mohamed Sayed, Alyami, Mohammad, Mirza, Mohammad Amin, Sayouh, Mohammad, Alkhayat, Mohammed Amer, Basendowah, Mohammed, Ghunaim, Mohammed, Alhussaini, Mohammed Khalid, Khoj, Mohammed, Sbaih, Mohammed, Saeed, Muhammad Ahmad, Ali, Muhammad Zulfiqar, Abdelaziz, Nabil Yassin Tammam, Malibary, Nadim, Abdo, Nael, Amer, Nasser Mohammed, Al Turki, Neamat Ahmed Ali, Durayb, Norah, Yassin, Nouf, Akeel, Nouf, Larbi, Noureddine, Alsallum, Ofays, Suliman, Omar AAbu, Elsherbiny, Osama, Abusalem, Osama, Albalawi, Ibrahim Altedlawi, Abutalib, Raid Abdullah, Alarabi, Rayan, Khan, Roaa Ghazi, Alazzam, Saleh, Alghamdi, Saleh, Alsawat, Salem, Salim, Sami, Alshukr, Sarah, Alzahrani, Saud, Golea, Smain, Alowairdhi, Tumadher, Salman, Usama, Abusiam, Wael, Abualkhair, Wael, Saber, Wael, Tashkandi, Wail, Alhazmi, Waleed, Tashkandi, Waleed, Yassine, Wassim Abou, Alshabi, Yaser Ahmad, Ibrahim, Yaser, Shahin, Yasser, Ibrahim, Yassin, Aljathlany, Yousef, Alnahas, Yousef, Alrashidi, Yousef, Wali, Zubair, Ndong, Abdourahmane, Ba, Mamadou, Faye, Papa Mamadou, Arbutina, Dragana, Milic, Ljiljana, Cuk, Vladica, Hussein, Abdinafic Mohamud, Mccaul, Jeannie, Bertels, Laurie, Pohl, Linda, Arnold, Marion, Mbatani, Nomonde, Oosthuizen, Pj, Rayamajhi, Shreya, Vosloo, Susan, Jooma, Uzair, Landaluce-Olavarria, Aitor, Vázquez-Melero, Alba, Marcos, Alberto, Puerto Puerto, Alejandro, De La Hermosa, Alicia Ruiz, Senent-Boza, Ana, Ugarte-Sierra, Bakarne, Montalbán, Beatriz Cros, Martin-Perez, Beatriz, Gomez, Caroina Gonzalez, Colás-Ruiz, Enrique, Santos, Esther Garcia, Senra, Fatima, Mora-Guzmán, Ismael, Dziakova, Jana, Díaz, Jeancarlos J. Trujillo, Silva, Jesús, Laina, Juan Luis Blas, Tallon-Aguilar, Luis, Di Martino, Marcello, Chacón, Mario Franco, Frasson, Matteo, Calvo, Mikel Prieto, Millan, Monica, Tejedoe, Patricia, Pérez-Bertólez, Sonia, Turrado-Rodríguez, Víctor, Elsanosi, Abdelrhman Azhari Mohammed, Abdalbakheet, Duaa, Ahmed, Mohamed, Salim, Omer El Faroug H., Youssef, Mohamed, Barbon, Carlotta, Bouchrika, Amal, Maghrebi, Houcine, Loukil, Issam, Yildiz, Alp, Dursun, Ayberk, Gulcu, Baris, Calik, Bulent, Eral, Burak, Yeşilyurt, Değercan, Yakar, Fatih, Akin, Furkan Atakan, Kilinc, Gizem, Uslu, Gülberk, Tuncer, Korhan, Koc, Mehmet Ali, Leventoğlu, Sezai, Sokmen, Selman, Atici, Semra Demirli, Kaya, Tayfun, Dere, Ümit Akın, Kırmızı, Yasemin, Ssenono, Kavuma Daniel, Lule, Herman, Mbiine, Ronald, Hamza, Ahmed, Ali, Shabeer, Peediyakkal, Saidalavi Padinhare, Varma, Gopala Pillay, Mussa, Haidar Aal, Al-Masari, Hayder Makki, Shehata, Mina, Seiam, Moham, Aziz, Muhammad Akram Abdul, Nimir, Nessrein, Khare, Ritu, Rashid, Shahid, Kazim, Shuiab, Gondal, Zafar, Sherif, Ahmed Elshawadfy, Ghanem, Ahmed, Helmy, Ahmed Hazem I., Ibrahim, Ahmed, Elshaer, Ahmed Mohammed, Marzouk, Ahmed Msm, Tamburrini, Alessandro Paolo, Parente, Alessandro, Light, Alexander, Diamantopoulou, Angela, Singh, Baljit, Gurung, Binay, Frauenfelder, Claire, Leo, Cosimo Alex, Raptis, Dimitri, Thakrar, Dixa, Madhuri, Thumuluru Kavitha, Tsounaki, Efthymia, Garreffa, Emanuele, Soggiu, Fiammetta, Stavrou, George, Ng, Hwei Jene, Tabasi, Hani, Nasef, Hazem, Kostakis, Ioannis D., Jeffery, James, Warusavitarne, Janindra, Lund, Jon, Qurashi, Kamran, Sahnan, Kapil, Tong, Kin Seng, Orecchia, Luca, Kaur, Mandeep, Zaidi, Mariam, Ganau, Mario, Hassan, Mohamed Ali Gad, Curtis, Nathan, Bhatt, Nikita, Machairas, Nikolaos, Zafar, Noman, Toma, Omar, Sarmah, Panchali, Bassuni, Majid, Davies, Justin, Shawer, Sami, Shawer, Sherif, Lewis, Sophia, Subramanian, Sivaraman, Ahmad, Suhaib, Nadeem, Uqba, Njau, Aidan, Tohamy, Aley Eldin, Pakula, Andrea M., Simioni, Andrea, Jarvis, Bennie L., Skandalakis, Georgios P., Hesham, Hosai Todd, Isaiah, Isaac A., Villwock, Jennifer, Martin, Linda W., Kress, Melissa, Sebelik, Merry, Lathan, Sanaz, Towfigh, Shirin, Holubar, Stefan D., Demeester, Steve, Alshehari, Mohammed Mohammed Hasan, Ghabisha, Saif Ali, Abdulatef, Shehab Ahmed Ali, Al-Kubati, Waheeb, Obadiel, Yasser Abdurabo, Gots, Alexander, Nakazwe, Mildred, Chipaila, Jackson, and Mazingi, Dennis
- Abstract
BackgroundSARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice.MethodsThis is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure.ResultsNine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout.There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management.ResultsNine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout.There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits
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- 2024
208. Neural networks for intelligent multilevel control of artificial and natural objects based on data fusion: A survey.
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Tianxing Man, Vasiliy Osipov, Nataly Zhukova, Alexey Nikolayevich Subbotin, and Dmitry I. Ignatov
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- 2024
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209. PhoneDepth: A Dataset for Monocular Depth Estimation on Mobile Devices.
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Fausto Tapia Benavides, Andrey Ignatov, and Radu Timofte
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- 2022
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210. LAN: Lightweight Attention-based Network for RAW-to-RGB Smartphone Image Processing.
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Daniel Wirzberger Raimundo, Andrey Ignatov, and Radu Timofte
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- 2022
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211. IPTV Monitoring via GPM3 for A1 and Vivacom.
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Hristo Hristov, Stoyan Cheresharov, Vladimir Valkanov, and Angel Ignatov
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- 2022
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212. Bioinspired Cellular Nonlinear Networks working on the edge of chaos.
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Angela Slavova and Ventsislav Ignatov
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- 2022
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213. PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks.
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Andrey Ignatov, Grigory Malivenko, Radu Timofte, Yu Tseng, Yu-Syuan Xu, Po-Hsiang Yu, Cheng-Ming Chiang, Hsien-Kai Kuo, Min-Hung Chen, Chia-Ming Cheng, and Luc Van Gool
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- 2022
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214. MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning.
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Andrey Ignatov, Anastasia Sycheva, Radu Timofte, Yu Tseng, Yu-Syuan Xu, Po-Hsiang Yu, Cheng-Ming Chiang, Hsien-Kai Kuo, Min-Hung Chen, Chia-Ming Cheng, and Luc Van Gool
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- 2022
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215. Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challenge: Report.
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Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou 0015, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li 0103, Xingdong Zhang, Jiawei Zhang 0002, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu 0017, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu 0003, Zhenzhong Chen, Gen Li 0008, Yuanfan Zhang, Lei Sun 0009, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu 0040, Qiang Liu 0026, Dahai Yu, Ao Li 0007, Lei Luo 0003, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu 0031, Garas Gendy, Nabil Sabor, Jingchao Hou, and Guanghui He
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- 2022
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216. Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report.
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Andrey Ignatov, Radu Timofte, Cheng-Ming Chiang, Hsien-Kai Kuo, Yu-Syuan Xu, Man-Yu Lee, Allen Lu, Chia-Ming Cheng, Chih-Cheng Chen, Jia-Ying Yong, Hong-Han Shuai, Wen-Huang Cheng, Zhuang Jia, Tianyu Xu, Yijian Zhang, Long Bao, Heng Sun, Diankai Zhang, Si Gao, Shaoli Liu, Biao Wu, Xiaofeng Zhang, Chengjian Zheng, Kaidi Lu, Ning Wang 0020, Xiao Sun, Haodong Wu, Xuncheng Liu, Weizhan Zhang, Caixia Yan, Haipeng Du, Qinghua Zheng, Qi Wang, Wangdu Chen, Ran Duan, Mengdi Sun, Dan Zhu, Guannan Chen, Hojin Cho, Steve Kim, Shijie Yue, Chenghua Li, Zhengyang Zhuge, Wei Chen, Wenxu Wang, Yufeng Zhou, Xiaochen Cai, Hengxing Cai, Kele Xu, Li Liu 0036, Zehua Cheng, Wenyi Lian, and Wenjing Lian
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- 2022
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217. Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 Challenge: Report.
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Andrey Ignatov, Radu Timofte, Jin Zhang, Feng Zhang, Gaocheng Yu, Zhe Ma, Hongbin Wang, Minsu Kwon, Haotian Qian, Wentao Tong, Pan Mu, Ziping Wang, Guangjing Yan, Brian Lee, Lei Fei, Huaijin G. Chen, Hyebin Cho, Byeongjun Kwon, Munchurl Kim, Mingyang Qian, Huixin Ma, Yanan Li 0006, Xiaotao Wang, and Lei Lei
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- 2022
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218. Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report.
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Andrey Ignatov, Radu Timofte, Shuai Liu 0009, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Ziyao Yi, Yan Xiang, Zibin Liu, Shaoqing Li, Keming Shi, Dehui Kong, Ke Xu, Minsu Kwon, Yaqi Wu, Jiesi Zheng, Zhihao Fan, Xun Wu, Feng Zhang, Albert No, Minhyeok Cho, Zewen Chen, Xiaze Zhang, Ran Li, Juan Wang, Zhiming Wang, Marcos V. Conde, Ui-Jin Choi, Georgy Perevozchikov, Egor I. Ershov, Zheng Hui, Mengchuan Dong, Xin Lou, Wei Zhou 0037, Cong Pang, Haina Qin, and Mingxuan Cai
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- 2022
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219. Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report.
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Andrey Ignatov, Grigory Malivenko, Radu Timofte, Lukasz Treszczotko, Xin Chang, Piotr Ksiazek, Michal Lopuszynski, Maciej Pioro, Rafal Rudnicki, Maciej Smyl, Yujie Ma, Zhenyu Li 0007, Zehui Chen, Jialei Xu, Xianming Liu, Junjun Jiang, XueChao Shi, Difan Xu, Yanan Li 0006, Xiaotao Wang, Lei Lei, Ziyu Zhang, Yicheng Wang, Zilong Huang, Guozhong Luo, Gang Yu 0002, Bin Fu, Jiaqi Li 0007, Yiran Wang 0005, Zihao Huang 0001, Zhiguo Cao 0001, Marcos V. Conde, Denis Sapozhnikov, Byeong Hyun Lee, Dongwon Park, Seongmin Hong, Joonhee Lee, Seunggyu Lee, and Se Young Chun
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- 2022
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220. Triclustering in Big Data Setting
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Egurnov, Dmitry, Ignatov, Dmitry I., Tochilkin, Dmitry, Missaoui, Rokia, editor, Kwuida, Léonard, editor, and Abdessalem, Talel, editor
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- 2022
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221. The Shores of the Northern Pacific Contact Zones
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Ignatov, E. I., Afanas’ev, V. V., Uba, V., Litvin, Yuri, Series Editor, Jiménez-Franco, Abigail, Series Editor, Mukherjee, Soumyajit, Series Editor, and Chaplina, Tatiana, Series Editor
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- 2022
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222. Zinc Oxide and Gallium Nitride Nanoparticles Application in Biomedicine: A Review
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Cojocari, Ștefan, Ignatov, O., Jian, M., Cobzac, V., Braniște, T., Monaico, E. V., Taran, A., Nacu, V., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Tiginyanu, Ion, editor, Sontea, Victor, editor, and Railean, Serghei, editor
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- 2022
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223. Decision Stream: Cultivating Deep Decision Trees
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Ignatov, Dmitry and Ignatov, Andrey
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Computer Science - Learning - Abstract
Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at the same time being their major shortcoming: the recursive nodes partitioning leads to geometric reduction of data quantity in the leaf nodes, which causes an excessive model complexity and data overfitting. In this paper, we present a novel architecture - a Decision Stream, - aimed to overcome this problem. Instead of building a tree structure during the learning process, we propose merging nodes from different branches based on their similarity that is estimated with two-sample test statistics, which leads to generation of a deep directed acyclic graph of decision rules that can consist of hundreds of levels. To evaluate the proposed solution, we test it on several common machine learning problems - credit scoring, twitter sentiment analysis, aircraft flight control, MNIST and CIFAR image classification, synthetic data classification and regression. Our experimental results reveal that the proposed approach significantly outperforms the standard decision tree learning methods on both regression and classification tasks, yielding a prediction error decrease up to 35%.
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- 2017
224. Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks
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Ignatov, Dmitry Yu., Filippov, Alexander N., Ignatov, Andrey D., and Zhang, Xuecang
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Mathematics - Optimization and Control ,Computer Science - Networking and Internet Architecture - Abstract
The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous functioning requires automatic screening and permanent optimization with processing of the huge amount of data by high-performance distributed systems. We propose new meta-algorithm of large homogeneous network analysis, its decomposition into alternative sets of loosely connected subnets, and parallel optimization of the most independent elements. This algorithm is based on a network-specific correlation function, Simulated Annealing technique, and is adapted to work in the computer cluster. On the example of large wireless network, we show that proposed algorithm essentially increases speed of parallel optimization. The elaborated general approach can be used for analysis and optimization of the wide range of networks, including such specific types as artificial neural networks or organized in networks physiological systems of living organisms., Comment: Article is published in "Proceedings of ISP RAS" under Creative Commons Attribution (CC BY 4.0) license - https://creativecommons.org/licenses/by/4.0/ Original copy of article is uploaded
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- 2017
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225. The Cylindrical Drift Chamber of the MEG II experiment
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Chiappini, M., Baldini, A.M., Benmansour, H., Cavoto, G., Cei, F., Chiarello, G., Corvaglia, A., Cuna, F., Maso, G. Dal, Francesconi, M., Galli, L., Grancagnolo, F., Grassi, M., Hildebrandt, M., Ignatov, F., Meucci, M., Miccoli, A., Molzon, W., Nicolò, D., Oya, A., Palo, D., Panareo, M., Papa, A., Raffaelli, F., Renga, F., Schwendimann, P., Signorelli, G., Tassielli, G.F., Uchiyama, Y., Venturini, A., Vitali, B., and Voena, C.
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- 2023
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226. High-entropy Fe-Cr-Ni-Co-(Cu) coatings produced by vacuum electro-spark deposition for marine and coastal applications
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Kuptsov, K.A., Antonyuk, M.N., Sheveyko, A.N., Bondarev, A.V., Ignatov, S.G., Slukin, P.V., Dwivedi, P., Fraile, A., Polcar, T., and Shtansky, D.V.
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- 2023
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227. Tris(trifluoromethyl)germyl biphenyl conjugated molecular system with ferrocenyl substituent: Confirmation of photoinduced intramolecular charge transfer to the germanium center
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Ermolaev, Nikolai L., Fukin, Georgy K., Shavyrin, Andrei S., Lopatin, Mikhail A., Kuznetsova, Olga V., Kryzhkov, Denis I., Ignatov, Stanislav K., Chuhmanov, Evgeniy P., Berberova, Nadezhda T., and Pashchenko, Konstantin P.
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- 2023
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228. Morphological evaluation of the amniotic membrane decellularization
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Olga Ignatov, Adrian Cociug, Oleg Pascal, and Viorel Nacu
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amniotic membrane ,decellularization ,morphology ,Medicine - Abstract
Background: Biological materials derived from decellularized tissues could be a good basis for progress in regenerative medicine while maintaining the main components of the extracellular matrix. A promising scaffold for tissue-engineered is the human amniotic membrane. It is one of the oldest biomaterials used for scaffolds. Material and methods: 3 placentas were obtained through Human Tissue Bank. Under sterile condition human amniotic membrane was collected. The human amniotic membrane was treated with 0.5% of sodium dodecyl sulphate (SDS), 1% Triton for 24 and 5 hours. Amniotic membrane decellularization was also carried out in combination with ultrasound bath for 20 minutes 3 times. For morphological and structure evaluation of human amniotic membrane the scanning electron microscopy of native amniotic membrane and histology of decellularized and native amniotic membrane were performed. Results: The human amniotic membrane decellularization process with 0.5% SDS solution and 1% Triton solution showed that decellularization for 24 hours is too aggressive for human amniotic membrane structure. The decellularization for 5h with 1% Triton solution was incomplete. Conclusions: The method of decellularization with 0.5% SDS solution is more suitable for amniotic membrane decellularization and can be performed in only 5 hours. The use of ultrasound bath did not have a significant effect on the obtained results.
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- 2022
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229. Influence of production specialisation level on efficiency of technical service and safety of buses operation
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A. S. Denisov, E. V. Feklin, and A. V. Ignatov
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maintenance and repair ,bus ,cooperation ,specialisation ,centralised specialised production ,efficiency ,safety ,marginal utility theory ,technical availability factor ,service ,Transportation engineering ,TA1001-1280 - Abstract
Introduction. The focus is on improving the maintenance and repair system of city buses on the scale of large cities in particular and regions in general. Due to the increase in the number of rolling stock, the load on the existing technical service organisations increases, which in turn leads to their overload. Consequently, the efficiency and safety indicators of bus operation are reduced. The proposed solution is to develop a system of centralised specialised production.Materials and methods. The system of maintenance and repair is considered from the point of view of the marginal utility theory. Dependences of total and marginal utility on the quantity of goods (services) are presented. The term ‘specialization’ is described from the point of view of the motor transport industry. The necessity of combining universal and specialised posts is justified.Results. The results of the experimental study on ten passenger enterprises are presented. The results of calculations of the efficiency indicators of the current repair area are given. The dependence of annual costs and profit of the current repair zone of ten posts on the level of specialisation is reflected. The dependence of the coefficient of technical readiness of buses on the scale of parks in Russia is analysed. The natural factors, influencing the coefficient of technical readiness are presented. An adequate mathematical model with physical variables of the dependence of the coefficient of technical availability on the scale of the enterprise and the level of specialisation was obtained with further processing and verification of the results.Discussion and conclusions. The results of the study are intended for the enterprises of passenger transport. The data obtained will allow to develop a system of cooperation of technical service production facilities in order to improve the performance of requests for maintenance and repair of rolling stock.
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- 2022
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230. Object-Attribute Biclustering for Elimination of Missing Genotypes in Ischemic Stroke Genome-Wide Data
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Ignatov, Dmitry I., Khvorykh, Gennady V., Khrunin, Andrey V., Nikolić, Stefan, Shaban, Makhmud, Petrova, Elizaveta A., Koltsova, Evgeniya A., Takelait, Fouzi, Egurnov, Dmitrii, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, van der Aalst, Wil M. P., editor, Batagelj, Vladimir, editor, Buzmakov, Alexey, editor, Ignatov, Dmitry I., editor, Kalenkova, Anna, editor, Khachay, Michael, editor, Koltsova, Olessia, editor, Kutuzov, Andrey, editor, Kuznetsov, Sergei O., editor, Lomazova, Irina A., editor, Loukachevitch, Natalia, editor, Makarov, Ilya, editor, Napoli, Amedeo, editor, Panchenko, Alexander, editor, Pardalos, Panos M., editor, Pelillo, Marcello, editor, Savchenko, Andrey V., editor, and Tutubalina, Elena, editor
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- 2021
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231. DaNetQA: A Yes/No Question Answering Dataset for the Russian Language
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Glushkova, Taisia, Machnev, Alexey, Fenogenova, Alena, Shavrina, Tatiana, Artemova, Ekaterina, Ignatov, Dmitry I., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, van der Aalst, Wil M. P., editor, Batagelj, Vladimir, editor, Ignatov, Dmitry I., editor, Khachay, Michael, editor, Koltsova, Olessia, editor, Kutuzov, Andrey, editor, Kuznetsov, Sergei O., editor, Lomazova, Irina A., editor, Loukachevitch, Natalia, editor, Napoli, Amedeo, editor, Panchenko, Alexander, editor, Pardalos, Panos M., editor, Pelillo, Marcello, editor, Savchenko, Andrey V., editor, and Tutubalina, Elena, editor
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- 2021
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232. Multilingual hope speech detection: A Robust framework using transfer learning of fine-tuning RoBERTa model
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Muhammad Shahid Iqbal Malik, Anna Nazarova, Mona Mamdouh Jamjoom, and Dmitry I. Ignatov
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Transfer learning ,Russian ,XLM-RoBERTa ,Hope speech ,Translation-based ,Multi-lingual ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Hope Speech Detection (HSD) from social media is a new direction for promoting and supporting positive content to encourage harmony and positivity in society. As users of social media belong to different linguistic communities, hope speech detection is rarely studied as a multilingual task considering low-resource languages. Moreover, prior studies explored only monolingual techniques, and the Russian language is not addressed. This study tackles the issue of Multi-lingual Hope Speech Detection (MHSD) in English and Russian languages using the transfer learning paradigm with fine-tuning approach. We explore joint multi-lingual and translation-based approaches to tackle the task of multilingualism, where the latter approach adopts the translation mechanism to transform all content into one language and then classify them. The joint multi-lingual method handles it by designing a universal classifier for various languages. We explore the strengths of the Robustly Optimized BERT Pre-Training Approach (RoBERTa) that showed a benchmark in capturing the semantics and contextual information within the content. The proposed framework consists of several stages: 1) data preprocessing, 2) representation of data using RoBERTa models, 3) fine-tuning phase, and 4) classification of hope speech into two labels. A new Russian corpus for hope speech detection is built, containing YouTube comments. Several experiments are conducted in English and Russian languages by using semi-supervised bilingual English and Russian datasets. The findings show that the proposed framework demonstrated benchmark performance and outperformed the baselines. Furthermore, the translation-based approach (Russian-RoBERTa) offered the best performance by achieving 94% accuracy and 80.24% f1-score.
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- 2023
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233. Improved prediction of MHC-peptide binding using protein language models
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Nasser Hashemi, Boran Hao, Mikhail Ignatov, Ioannis Ch. Paschalidis, Pirooz Vakili, Sandor Vajda, and Dima Kozakov
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MHC class I ,deep learning ,transformers ,natural language processing ,cellular immune system ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.
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- 2023
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234. Contextual Embeddings based on Fine-tuned Urdu-BERT for Urdu threatening content and target identification
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Muhammad Shahid Iqbal Malik, Uswa Cheema, and Dmitry I. Ignatov
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Threatening content ,Target identification ,Fine-tuned BERT ,Urdu ,Twitter ,Text representation ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Identification of threatening text on social media platforms is a challenging task. Contrary to the high-resource languages, the Urdu language has very limited such approaches and the benchmark approach has an issue of inappropriate data annotation. Therefore, we present robust threatening content and target identification as a hierarchical classification model for Urdu tweets. This study investigates the potential of the Urdu-BERT (Bidirectional Encoder Representations from Transformer) language model to learn universal contextualized representations aiming to showcase its usefulness for binary classification tasks of threatening content and target identification. We propose to exploit a pre-trained Urdu-BERT as a transfer learning model after fine-tuning its parameters on a newly designed Urdu corpus from the Twitter platform. The proposed dataset contains 2,400 tweets manually annotated as threatening or non-threatening at the first level and threatening tweets are further categorized into individual or group at the second level. The performance of fine-tuned Urdu-BERT is compared with the benchmark study and other feature models. Experimental results show that the fine-tuned Urdu-BERT model achieves state-of-the-art performance by obtaining 87.5% accuracy and 87.8% F1-score for threatening content identification and 82.5% accuracy and 83.2% F1-score for target identification task. Furthermore, the proposed model outperforms the benchmark study.
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- 2023
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235. Xanthomonas Phage PBR31: Classifying the Unclassifiable
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Rashit I. Tarakanov, Peter V. Evseev, Ha T. N. Vo, Konstantin S. Troshin, Daria I. Gutnik, Aleksandr N. Ignatov, Stepan V. Toshchakov, Konstantin A. Miroshnikov, Ibrahim H. Jafarov, and Fevzi S.-U. Dzhalilov
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Xanthomonas phage PBR31 ,bacteriophage taxonomy ,temperate phages ,Xanthomonas campestris pv. campestris ,phage control ,Microbiology ,QR1-502 - Abstract
The ability of bacteriophages to destroy bacteria has made them the subject of extensive research. Interest in bacteriophages has recently increased due to the spread of drug-resistant bacteria, although genomic research has not kept pace with the growth of genomic data. Genomic analysis and, especially, the taxonomic description of bacteriophages are often difficult due to the peculiarities of the evolution of bacteriophages, which often includes the horizontal transfer of genes and genomic modules. The latter is particularly pronounced for temperate bacteriophages, which are capable of integration into the bacterial chromosome. Xanthomonas phage PBR31 is a temperate bacteriophage, which has been neither described nor classified previously, that infects the plant pathogen Xanthomonas campestris pv. campestris. Genomic analysis, including phylogenetic studies, indicated the separation of phage PBR31 from known classified bacteriophages, as well as its distant relationship with other temperate bacteriophages, including the Lederbervirus group. Bioinformatic analysis of proteins revealed distinctive features of PBR31, including the presence of a protein similar to the small subunit of D-family DNA polymerase and advanced lysis machinery. Taxonomic analysis showed the possibility of assigning phage PBR31 to a new taxon, although the complete taxonomic description of Xanthomonas phage PBR31 and other related bacteriophages is complicated by the complex evolutionary history of the formation of its genome. The general biological features of the PBR31 phage were analysed for the first time. Due to its presumably temperate lifestyle, there is doubt as to whether the PBR31 phage is appropriate for phage control purposes. Bioinformatics analysis, however, revealed the presence of cell wall-degrading enzymes that can be utilised for the treatment of bacterial infections.
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- 2024
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236. Phytopathogenic Curtobacterium flaccumfaciens Strains Circulating on Leguminous Plants, Alternative Hosts and Weeds in Russia
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Anna D. Tokmakova, Rashit I. Tarakanov, Anna A. Lukianova, Peter V. Evseev, Lyubov V. Dorofeeva, Alexander N. Ignatov, Fevzi S.-U. Dzhalilov, Sergei A. Subbotin, and Konstantin A. Miroshnikov
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Curtobacterium ,soybean ,common bean ,bacterial wilt ,tan spot ,genetic diversity ,Botany ,QK1-989 - Abstract
Many bacterial plant pathogens have a broad host range important for their life cycle. Alternate hosts from plant families other than the main (primary) host support the survival and dissemination of the pathogen population even in absence of main host plants. Metabolic peculiarities of main and alternative host plants can affect genetic diversity within and between the pathogen populations isolated from those plants. Strains of Gram-positive bacterium Curtobacterium flaccumfaciens were identified as being causal agents of bacterial spot and wilt diseases on leguminous plants, and other crop and weed plants, collected in different regions of Russia. Their biochemical properties and susceptibility to copper compounds have been found to be relatively uniform. According to conventional PCR assays, all of the isolates studied were categorised as pathovar Curtobacterim flaccumfaciens pv. flaccumfaciens, a pathogen of legumes. However, the strains demonstrated a substantial diversity in terms of virulence on several tested host plants and different phylogenetic relationships were revealed by BOX-PCR and alanine synthase gene (alaS) sequencing.
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- 2024
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237. Temperature-Induced Restructuring of Mycolic Acid Bilayers Modeling the Mycobacterium tuberculosis Outer Membrane: A Molecular Dynamics Study
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Alexander V. Vasyankin, Sergey V. Panteleev, Ilya S. Steshin, Ekaterina A. Shirokova, Alexey V. Rozhkov, Grigory D. Livshits, Eugene V. Radchenko, Stanislav K. Ignatov, and Vladimir A. Palyulin
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tuberculosis ,mycobacterium ,cell wall ,outer membrane ,temperature induced conformational changes ,diffusion constants ,Organic chemistry ,QD241-441 - Abstract
The emergence of new drug-resistant strains of the tuberculosis pathogen Mycobacterium tuberculosis (Mtb) is a new challenge for modern medicine. Its resistance capacity is closely related to the properties of the outer membrane of the Mtb cell wall, which is a bilayer membrane formed by mycolic acids (MAs) and their derivatives. To date, the molecular mechanisms of the response of the Mtb outer membrane to external factors and, in particular, elevated temperatures have not been sufficiently studied. In this work, we consider the temperature-induced changes in the structure, ordering, and molecular mobility of bilayer MA membranes of various chemical and conformational compositions. Using all-atom long-term molecular dynamics simulations of various MA membranes, we report the kinetic parameters of temperature-dependent changes in the MA self-diffusion coefficients and conformational compositions, including the apparent activation energies of these processes, as well as the characteristic times of ordering changes and the features of phase transitions occurring over a wide range of elevated temperatures. Understanding these effects could be useful for the prevention of drug resistance and the development of membrane-targeting pharmaceuticals, as well as in the design of membrane-based materials.
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- 2024
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238. Simulation model of 177 MWe coal-fired double power unit
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Radenkov Vasil and Ignatov Borislav
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Environmental sciences ,GE1-350 - Abstract
Model-based studies are widely used in the analysis and optimization of complex energy conversion processes in large thermal power plants. The creation and validation of a model of a power unit utilizing Bulgarian lignite enables us to study in detail the redistribution of heat flows in that unit. Along with the ability to optimize the thermodynamic cycle of energy generation, this research method enables us to conduct many study tasks that cannot be realized by conducting expensive and complex industrial experiments. The current article presents the creation of thermodynamic simulation model of a 4th power unit firing lignite coal in Maritsa East 2 TPP. Validation of the numerical solution was performed with data from the "Operating Instructions for the boilers”, design heat balance calculations for steam turbine installation and performance test results onsite on two different loads.
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- 2024
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239. Characterisation of New Foxunavirus Phage Murka with the Potential of Xanthomonas campestris pv. campestris Control
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Peter V. Evseev, Rashit I. Tarakanov, Ha T. N. Vo, Natalia E. Suzina, Anna A. Vasilyeva, Alexander N. Ignatov, Konstantin A. Miroshnikov, and Fevzi S.-U. Dzhalilov
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Xanthomonas bacteriophages ,cabbage ,Brassicae ,biocontrol ,bacteriophage taxonomy ,Microbiology ,QR1-502 - Abstract
Phages of phytopathogenic bacteria are considered to be promising agents for the biological control of bacterial diseases in plants. This paper reports on the isolation and characterisation of a new Xanthomonas campestris pv. campestris phage, Murka. Phage morphology and basic kinetic characteristics of the infection were determined, and a phylogenomic analysis was performed. The phage was able to lyse a reasonably broad range (64%, 9 of the 14 of the Xanthomonas campestris pv. campestris strains used in the study) of circulating strains of the cabbage black rot pathogen. This lytic myovirus has a DNA genome of 44,044 bp and contains 83 predicted genes. Taxonomically, it belongs to the genus Foxunavirus. This bacteriophage is promising for use as a possible means of biological control of cabbage black rot.
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- 2024
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240. Selectivity of Ethylaluminium Dichloride–Proton Donor Complex Catalysts from the C4 Fraction in Initiating the Oligomerization of Isobutylene
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Zaikov, G. E., Artsis, M. I., Andreev, D. S., and Ignatov, A. V.
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- 2022
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241. Allelic Polymorphism of Anthrax Pathogenicity Factor Genes as a Means of Estimating Microbiological Risks Associated with Climate Change
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Goncharova, Y. O., Bogun, A. G., Bahtejeva, I. V., Titareva, G. M., Mironova, R. I., Kravchenko, T. B., Ostarkov, N. A., Brushkov, A. V., Timofeev, V. S., and Ignatov, S. G.
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- 2022
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242. Brownian Motion of a Dust Molecule
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Ignatov, A. M.
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- 2022
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243. Fast Perceptual Image Enhancement
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de Stoutz, Etienne, Ignatov, Andrey, Kobyshev, Nikolay, Timofte, Radu, and Van Gool, Luc
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Computer Science - Computer Vision and Pattern Recognition - Abstract
The vast majority of photos taken today are by mobile phones. While their quality is rapidly growing, due to physical limitations and cost constraints, mobile phone cameras struggle to compare in quality with DSLR cameras. This motivates us to computationally enhance these images. We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras. However, the neural models employed require large amounts of computational resources and are not lightweight enough to run on mobile devices. We build upon the prior work and explore different network architectures targeting an increase in image quality and speed. With an efficient network architecture which does most of its processing in a lower spatial resolution, we achieve a significantly higher mean opinion score (MOS) than the baseline while speeding up the computation by 6.3 times on a consumer-grade CPU. This suggests a promising direction for neural-network-based photo enhancement using the phone hardware of the future.
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- 2018
244. COMET Phase-I Technical Design Report
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The COMET Collaboration, Abramishvili, R., Adamov, G., Akhmetshin, R. R., Allin, A., Angélique, J. C., Anishchik, V., Aoki, M., Aznabayev, D., Bagaturia, I., Ban, G., Ban, Y., Bauer, D., Baygarashev, D., Bondar, A. E., Cârloganu, C., Carniol, B., Chau, T. T., Chen, J. K., Chen, S. J., Cheung, Y. E., da Silva, W., Dauncey, P. D., Densham, C., Devidze, G., Dornan, P., Drutskoy, A., Duginov, V., Eguchi, Y., Epshteyn, L. B., Evtoukhovich, P., Fayer, S., Fedotovich, G. V., Finger Jr, M., Finger, M., Fujii, Y., Fukao, Y., Gabriel, J. L., Gay, P., Gillies, E., Grigoriev, D. N., Gritsay, K., Hai, V. H., Hamada, E., Hashim, I. H., Hashimoto, S., Hayashi, O., Hayashi, T., Hiasa, T., Ibrahim, Z. A., Igarashi, Y., Ignatov, F. V., Iio, M., Ishibashi, K., Issadykov, A., Itahashi, T., Jansen, A., Jiang, X. S., Jonsson, P., Kachelhoffer, T., Kalinnikov, V., Kaneva, E., Kapusta, F., Katayama, H., Kawagoe, K., Kawashima, R., Kazak, N., Kazanin, V. F., Kemularia, O., Khvedelidze, A., Koike, M., Kormoll, T., Kozlov, G. A., Kozyrev, A. N., Kravchenko, M., Krikler, B., Kumsiashvili, G., Kuno, Y., Kuriyama, Y., Kurochkin, Y., Kurup, A., Lagrange, B., Lai, J., Lee, M. J., Li, H. B., Litchfield, R. P., Li, W. G., Loan, T., Lomidze, D., Lomidze, I., Loveridge, P., Macharashvili, G., Makida, Y., Mao, Y. J., Markin, O., Matsuda, Y., Melkadze, A., Melnik, A., Mibe, T., Mihara, S., Miyamoto, N., Miyazaki, Y., Idris, F. Mohamad, Azmi, K. A. Mohamed Kamal, Moiseenko, A., Moritsu, M., Mori, Y., Motoishi, T., Nakai, H., Nakai, Y., Nakamoto, T., Nakamura, Y., Nakatsugawa, Y., Nakazawa, Y., Nash, J., Natori, H., Niess, V., Nioradze, M., Nishiguchi, H., Noguchi, K., Numao, T., O'Dell, J., Ogitsu, T., Ohta, S., Oishi, K., Okamoto, K., Okamura, T., Okinaka, K., Omori, C., Ota, T., Pasternak, J., Paulau, A., Picters, D., Ponariadov, V., Quémener, G., Ruban, A. A., Rusinov, V., Sabirov, B., Sakamoto, H., Sarin, P., Sasaki, K., Sato, A., Sato, J., Semertzidis, Y. K., Shigyo, N., Shoukavy, Dz., Slunecka, M., Stöckinger, D., Sugano, M., Tachimoto, T., Takayanagi, T., Tanaka, M., Tang, J., Tao, C. V., Teixeira, A. M., Tevzadze, Y., Thanh, T., Tojo, J., Tolmachev, S. S., Tomasek, M., Tomizawa, M., Toriashvili, T., Trang, H., Trekov, I., Tsamalaidze, Z., Tsverava, N., Uchida, T., Uchida, Y., Ueno, K., Velicheva, E., Volkov, A., Vrba, V., Abdullah, W. A. T. Wan, Warin-Charpentier, P., Wong, M. L., Wong, T. S., Wu, C., Xing, T. Y., Yamaguchi, H., Yamamoto, A., Yamanaka, M., Yamane, T., Yang, Y., Yano, T., Yao, W. C., Yeo, B., Yoshida, H., Yoshida, M., Yoshioka, T., Yuan, Y., Yudin, Yu. V., Zdorovets, M. V., Zhang, J., Zhang, Y., and Zuber, K.
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Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
The Technical Design for the COMET Phase-I experiment is presented in this paper. COMET is an experiment at J-PARC, Japan, which will search for neutrinoless conversion of muons into electrons in the field of an aluminium nucleus ($\mu-e$ conversion, $\mu^- N \to e^- N$); a lepton flavor violating process. The experimental sensitivity goal for this process in the Phase-I experiment is $3.1\times10^{-15}$, or 90 % upper limit of branching ratio of $7\times 10^{-15}$, which is a factor of 100 improvement over the existing limit. The expected number of background events is 0.032. To achieve the target sensitivity and background level, the 3.2 kW 8 GeV proton beam from J-PARC will be used. Two types of detectors, CyDet and StrECAL, will be used for detecting the \mue conversion events, and for measuring the beam-related background events in view of the Phase-II experiment, respectively. Results from simulation on signal and background estimations are also described., Comment: A minor correction applied in Eq. 3
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- 2018
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245. PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
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Ignatov, Andrey, Timofte, Radu, Van Vu, Thang, Luu, Tung Minh, Pham, Trung X, Van Nguyen, Cao, Kim, Yongwoo, Choi, Jae-Seok, Kim, Munchurl, Huang, Jie, Ran, Jiewen, Xing, Chen, Zhou, Xingguang, Zhu, Pengfei, Geng, Mingrui, Li, Yawei, Agustsson, Eirikur, Gu, Shuhang, Van Gool, Luc, de Stoutz, Etienne, Kobyshev, Nikolay, Nie, Kehui, Zhao, Yan, Li, Gen, Tong, Tong, Gao, Qinquan, Hanwen, Liu, Michelini, Pablo Navarrete, Dan, Zhu, Fengshuo, Hu, Hui, Zheng, Wang, Xiumei, Deng, Lirui, Meng, Rang, Qin, Jinghui, Shi, Yukai, Wen, Wushao, Lin, Liang, Feng, Ruicheng, Wu, Shixiang, Dong, Chao, Qiao, Yu, Vasu, Subeesh, Madam, Nimisha Thekke, Kandula, Praveen, Rajagopalan, A. N., Liu, Jie, and Jung, Cheolkon
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
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- 2018
246. AI Benchmark: Running Deep Neural Networks on Android Smartphones
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Ignatov, Andrey, Timofte, Radu, Chou, William, Wang, Ke, Wu, Max, Hartley, Tim, and Van Gool, Luc
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Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.
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- 2018
247. Strong neutron pairing in core+4n nuclei
- Author
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Revel, A., Marques, F. M., Sorlin, O., Aumann, T., Caesar, C., Holl, M., Panin, V., Vandebrouck, M., Wamers, F., Alvarez-Pol, H., Atar, L., Avdeichikov, V., Beceiro-Novo, S., Bemmerer, D., Benlliure, J., Bertulani, C. A., Boillos, J. M., Boretzky, K., Borge, M. J. G., Caamano, M., Casarejos, E., Catford, W. N., Cederkäll, J., Chartier, M., Chulkov, L., Cortina-Gil, D., Cravo, E., Crespo, R., Pramanik, U. Datta, Fernandez, P. Diaz, Dillmann, I., Elekes, Z., Enders, J., Ershova, O., Estrade, A., Farinon, F., Fraile, L. M., Freer, M., Galaviz, D., Geissel, H., Gernhauser, R., Golubev, P., Göbel, K., Hagdahl, J., Heftrich, T., Heil, M., Heine, M., Heinz, A., Henriques, A., Hufnagel, A., Ignatov, A., Johansson, H. T., Jonson, B., Kahlbow, J., Kalantar-Nayestanaki, N., Kanungo, R., Kelic-Heil, A., Knyazev, A., Kroll, T., Kurz, N., Labiche, M., Langer, C., Bleis, T. Le, Lemmon, R., Lindberg, S., Machado, J., Marganiec, J., Movsesyan, A., Nacher, E., Najafi, M., Nikolskii, E., Nilsson, T., Nociforo, C., Paschalis, S., Perea, A., Petri, M., Pietri, S., Plag, R., Reifarth, R., Ribeiro, G., Rigollet, C., Roder, M., Rossi, D., Savran, D., Scheit, H., Simon, H., Syndikus, I., Taylor, J. T., Tengblad, O., Thies, R., Togano, Y., Velho, P., Volkov, V., Wagner, A., Weick, H., Wheldon, C., Wilson, G., Winfield, J. S., Woods, P., Yakorev, D., Zhukov, M., Zilges, A., and Zuber, K.
- Subjects
Nuclear Experiment - Abstract
The emission of neutron pairs from the neutron-rich $N\!=\!12$ isotones $^{18}$C and $^{20}$O has been studied by high-energy nucleon knockout from $^{19}$N and $^{21}$O secondary beams, populating unbound states of the two isotones up to 15~MeV above their two-neutron emission thresholds. The analysis of triple fragment-$n$-$n$ correlations shows that the decay $^{19}$N$(-1p)^{18}$C$^*\!\rightarrow^{16}$C+$n$+$n$ is clearly dominated by direct pair emission. The two-neutron correlation strength, the largest ever observed, suggests the predominance of a $^{14}$C core surrounded by four valence neutrons arranged in strongly correlated pairs. On the other hand, a significant competition of a sequential branch is found in the decay $^{21}$O$(-1n)^{20}$O$^*\!\rightarrow^{18}$O+$n$+$n$, attributed to its formation through the knockout of a deeply-bound neutron that breaks the $^{16}$O core and reduces the number of pairs., Comment: 6 pages, 4 figures, accepted for publication in Phys. Rev. Lett
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- 2018
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248. The design of the MEG II experiment
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Baldini, A. M., Baracchini, E., Bemporad, C., Berg, F., Biasotti, M., Boca, G., Cattaneo, P. W., Cavoto, G., Cei, F., Chiappini, M., Chiarello, G., Chiri, C., Cocciolo, G., Corvaglia, A., de Bari, A., De Gerone, M., D'Onofrio, A., Francesconi, M., Fujii, Y., Galli, L., Gatti, F., Grancagnolo, F., Grassi, M., Grigoriev, D. N., Hildebrandt, M., Hodge, Z., Ieki, K., Ignatov, F., Iwai, R., Iwamoto, T., Kaneko, D., Kasami, K., Kettle, P. -R., Khazin, B. I., Khomutov, N., Korenchenko, A., Kravchuk, N., Libeiro, T., Maki, M., Matsuzawa, N., Mihara, S., Milgie, M., Molzon, W., Mori, Toshinori, Morsani, F., Mtchedilishvili, A., Nakao, M., Nakaura, S., Nicoló, D., Nishiguchi, H., Nishimura, M., Ogawa, S., Ootani, W., Panareo, M., Papa, A., Pepino, A., Piredda, G., Popov, A., Raffaelli, F., Renga, F., Ripiccini, E., Ritt, S., Rossella, M., Rutar, G., Sawada, R., Signorelli, G., Simonetta, M., Tassielli, G. F., Uchiyama, Y., Usami, M., Venturini, M., Voena, C., Yoshida, K., Yudin, Yu. V., and Zhang, Y.
- Subjects
Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
The MEG experiment, designed to search for the mu+->e+ gamma decay at a 10^-13 sensitivity level, completed data taking in 2013. In order to increase the sensitivity reach of the experiment by an order of magnitude to the level of 6 x 10-14 for the branching ratio, a total upgrade, involving substantial changes to the experiment, has been undertaken, known as MEG II. We present both the motivation for the upgrade and a detailed overview of the design of the experiment and of the expected detector performance., Comment: 61 pages and 97 figures
- Published
- 2018
- Full Text
- View/download PDF
249. Effects of electromagnetic waves on parameters, hydration and in vitro antimicrobial activity of the Brassica oleracea L. var. italica Plenck. and water
- Author
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Ignatov, Ignat, primary, Huether, Fabio, additional, P. Popova, Teodora, additional, I. Ignatov, Alexander, additional, T. Iliev, Mario, additional, and Stoyanov, Chavdar, additional
- Published
- 2024
- Full Text
- View/download PDF
250. Impact of the Import Substitution Program on the Competitiveness of Industrial and Transport Enterprises
- Author
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Mikalyt, Sergey, primary, Dubrovina, Tatiana, additional, and Ignatov, Stanislav, additional
- Published
- 2022
- Full Text
- View/download PDF
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