# Mihaela Paun

## General Director / CS I - Danubius

### Biografie

Am obținut diploma de licență în Informatică la Universitatea din București în 1998. Apoi, am continuat studiile mele cu un masterat în Informatică la University of Western Ontario în 2000 și un doctorat în Analiză Computațională, Modelare și Statistică Aplicată la Louisiana Tech University în 2006. În prezent, îmi desfășor activitatea ca cercetător principal la Institutul Național de Cercetare și Dezvoltare pentru Științe Biologice.

Interesele mele actuale de cercetare se concentrează în domeniul biostatisticii și biocomputing-ului, calculului cu membrane, calculului de înaltă performanță și analizei datelor de mediu. Am absolvit programul de Analiză și Modelare Computațională (CAM) și am supravegheat și absolvit trei doctoranzi. În 2017, am primit abilitarea în Statistică de la Universitatea de Studii Economice București, unde în prezent coordonez doctoranzii.

În anul 2020, am fost aleasă vicepreședinte al Consiliului Științific al Institutului Național de Cercetare și Dezvoltare pentru Științe Biologice, iar din același an, ocup poziția de director general al institutului.

## Publicatii

Publication | Authors | Date | |
---|---|---|---|

article
## Global Investments In Pandemic Preparedness And Covid-19: Development Assistance And Domestic Spending On Health Between 1990 And 2026 |
Micah Angela E.; Bhangdia Kayleigh; Cogswell Ian E.; Lasher Dylan; Lidral-Porter Brendan; Maddison Emilie R.; Nguyen Trang Nhu Ngoc; Patel Nishali; Pedroza Paola; Solorio Juan; Stutzman Hayley; Tsakalos Golsum; Wang Yifeng; Warriner Wesley; Zhao Yingxi; Zlavog Bianca S.; Abbafati Cristiana; Abbas Jaffar; Abbasi-Kangevari Mohsen; Abbasi-Kangevari Zeinab; Abdelmasseh Michael; Abdulah Deldar Morad; Abedi Aidin; Abegaz Kedir Hussein; Abhilash E. S.; Aboagye Richard Gyan; Abolhassani Hassan; Abrigo Michael R. M.; Ali Hiwa Abubaker; Abu-Gharbieh Eman; Adem Mohammed Hussien; Afzal Muhammad Sohail; Ahmadi Ali; Ahmed Haroon; Rashid Tarik Ahmed; Aji Budi; Akbarialiabad Hossein; Akelew Yibeltal; Al Hamad Hanadi; Alam Khurshid; Alanezi Fahad Mashhour; Alanzi Turki M.; Al-Hanawi Mohammed Khaled; Alhassan Robert Kaba; Aljunid Syed Mohamed; Almustanyir Sami; Al-Raddadi Rajaa M.; Alvis-Guzman Nelson; Alvis-Zakzuk Nelson J.; Amare Azmeraw T.; Ameyaw Edward Kwabena; Amini-Rarani Mostafa; Amu Hubert; Ancuceanu Robert; Andrei Tudorel; Anwar Sumadi Lukman; Appiah Francis; Aqeel Muhammad; Arabloo Jalal; Arab-Zozani Morteza; Aravkin Aleksandr Y.; Aremu Olatunde; Aruleba Raphael Taiwo; Athari Seyyed Shamsadin; Avila-Burgos Leticia; Ayanore Martin Amogre; Azari Samad; Baig Atif Amin; Bantie Abere Tilahun; Barrow Amadou; Baskaran Pritish; Basu Sanjay; Batiha Abdul-Monim Mohammad; Baune Bernhard T.; Berezvai Zombor; Bhardwaj Nikha; Bhardwaj Pankaj; Bhaskar Sonu; Boachie Micheal Kofi; Bodolica Virginia; Botelho Botelho Joao Silva; Braithwaite Dejana; Breitborde Nicholas J. K.; Busse Reinhard; Cahuana-Hurtado Lucero; Catala-Lopez Ferran; Chansa Collins; Charan Jaykaran; Chattu Vijay Kumar; Chen Simiao; Chukwu Isaac Sunday; Dadras Omid; Dandona Lalit; Dandona Rakhi; Dargahi Abdollah; Debela Sisay Abebe; Denova-Gutierrez Edgar; Desye Belay; Dharmaratne Samath Dhamminda; Diao Nancy; Linh Phuong Doan; Dodangeh Milad; dos Santos Wendel Mombaque; Doshmangir Leila; Dube John; Eini Ebrahim; Zaki Maysaa El Sayed; El Tantawi Maha; Enyew Daniel Berhanie; Eskandarieh Sharareh; Asar Mohamad Ezati; Fagbamigbe Adeniyi Francis; Faraon Emerito Jose A.; Fatehizadeh Ali; Fattahi Hamed; Fekadu Ginenus; Fischer Florian; Foigt Nataliya A.; Fowobaje Kayode Raphael; Freitas Alberto; Fukumoto Takeshi; Fullman Nancy; Gaal Peter Andras; Gamkrelidze Amiran; Garcia-Gordillo M. A.; Gebrehiwot Mesfin; Gerema Urge; Ghafourifard Mansour; Ghamari Seyyed-Hadi; Ghanbari Reza; Ghashghaee Ahmad; Gholamrezanezhad Ali; Golechha Mahaveer; Golinelli Davide; Goshu Yitayal Ayalew; Goyomsa Girma Garedew; Guha Avirup; Gunawardane Damitha Asanga; Gupta Bhawna; Hamidi Samer; Harapan Harapan; Hashempour Reza; Hayat Khezar; Heidari Golnaz; Heredia-Pi Ileana; Herteliu Claudiu; Heyi Demisu Zenbaba; Hezam Kamal; Hiraike Yuta; Hlongwa Mbuzeleni Mbuzeleni; Holla Ramesh; Hoque Mohammad Enamul; Hosseinzadeh Mehdi; Hostiuc Sorin; Hussain Salman; Ilesanmi Olayinka Stephen; Immurana Mustapha; Iradukunda Arnaud; Ismail Nahlah Elkudssiah; Isola Gaetano; Merin Linda J.; Jakovljevic Mihajlo; Jalili Mahsa; Janodia Manthan Dilipkumar; Javaheri Tahereh; Jayapal Sathish Kumar; Jemere Digisie Mequanint; Joo Tamas; Joseph Nitin; Jozwiak Jacek Jerzy; Jurisson Mikk; Kaambwa Billingsley; Kadashetti Vidya; Kadel Rajendra; Kadir Dler Hussein; Kalankesh Laleh R.; Kamath Rajesh; Kandel Himal; Kantar Rami S.; Karanth Shama D.; Karaye Ibraheem M.; Karimi Salah Eddin; Kassa Bekalu Getnet; Kayode Gbenga A.; Keikavoosi-Arani Leila; Keshri Vikash Ranjan; Keskin Cumali; Khader Yousef Saleh; Khafaie Morteza Abdullatif; Khajuria Himanshu; Kashani Hamid Reza Khayat; Kifle Zemene Demelash; Kim Hanna; Kim Jihee; Kim Min Seo; Kim Yun Jin; Kisa Adnan; Kohler Stefan; Kompani Farzad; Kosen Soewarta; Laxminarayana Sindhura Lakshmi Koulmane; Koyanagi Ai; Krishan Kewal; Kusuma Dian; Lam Judit; Lamnisos Demetris; Larsson Anders O.; Lee Sang-woong; Lee Shaun Wen Huey; Lee Wei-Chen; Lee Yo Han; Lenzi Jacopo; Lim Lee-Ling; Lorenzovici Laszlo; Lozano Rafael; Machado Machado Vanessa Sintra; Madadizadeh Farzan; Abd El Razek Mohammed Magdy; Mahmoudi Razzagh; Majeed Azeem; Malekpour Mohammad-Reza; Manda Ana Laura; Mansouri Borhan; Mansournia Mohammad Ali; Mantovani Lorenzo Giovanni; Marrugo Arnedo Carlos Alberto; Martorell Miquel; Masoud Ali; Mathews Elezebeth; Maude Richard James; Mechili Enkeleint A.; Nasab Entezar Mehrabi; Joao Mendes Mendes Jose Joao; Meretoja Atte; Meretoja Tuomo J.; Mesregah Mohamed Kamal; Mestrovic Tomislav; Mirica Andreea; Mirrakhimov Erkin M.; Mirutse Mizan Kiros; Mirza Moonis; Mirza-Aghazadeh-Attari Mohammad; Misganaw Awoke; Moccia Marcello; Moghadasi Javad; Mohammadi Esmaeil; Mohammadi Mokhtar; Mohammadian-Hafshejani Abdollah; Mohammadshahi Marita; Mohammed Shafiu; Mohseni Mohammad; Mokdad Ali H.; Monasta Lorenzo; Mossialos Elias; Mostafavi Ebrahim; Isfahani Haleh Mousavi; Mpundu-Kaambwa Christine; Murthy Shruti; Muthupandian Saravanan; Nagarajan Ahamarshan Jayaraman; Naidoo Kovin S.; Naimzada Mukhammad David; Nangia Vinay; Naqvi Atta Abbas; Nayak Biswa Prakash; Ndejjo Rawlance; Nguyen Trang Huyen; Noroozi Nafise; Noubiap Jean Jacques; Nuruzzaman Khan M.; Nzoputam Chimezie Igwegbe; Nzoputam Ogochukwu Janet; Oancea Bogdan; Obi Felix Chukwudi Abrahams; Ogunkoya Abiola; Oh In-Hwan; Okonji Osaretin Christabel; Olagunju Andrew T.; Olagunju Tinuke O.; Olakunde Babayemi Oluwaseun; Bali Ahmed Omar; Onwujekwe Obinna E.; Opio John Nelson; Otoiu Adrian; Otstavnov Nikita; Otstavnov Stanislav S.; Owolabi Mayowa O.; Palicz Tamas; Palladino Raffaele; Pana Adrian; Parekh Tarang; Pasupula Deepak Kumar; Patel Jay; Patton George C.; Paudel Uttam; Paun Mihaela; Pawar Shrikant; Perna Simone; Perumalsamy Navaraj; Petcu Ionela-Roxana; Piracha Zahra Zahid; Poursadeqiyan Mohsen; Pourtaheri Naeimeh; Prada Sergio I.; Rafiei Sima; Raghav Pankaja Raghav; Rahim Fakher; Rahman Mohammad Hifz Ur; Rahman Mosiur; Rahmani Amir Masoud; Ranabhat Chhabi Lal; Raru Temam Beshir; Rashedi Sina; Rashidi Mohammad-Mahdi; Ravangard Ramin; Rawaf Salman; Rawassizadeh Reza; Redwan Elrashdy Moustafa Mohamed; Reiner Robert C. Jr.; Renzaho Andre M. N.; Rezaei Maryam; Rezaei Nazila; Riaz Mavra A.; Buendia Rodriguez Jefferson Antonio; Saad Aly M. A.; Saddik Basema; Sadeghian Saeid; Saeb Mohammad Reza; Saeed Umar; Sahu Maitreyi; Saki Morteza; Salamati Payman; Salari Hedayat; Salehi Sana; Samy Abdallah M.; Sanabria Juan; Sanmarchi Francesco; Santos Joao Vasco; Santric-Milicevic Milena M.; Sao Jose Bruno Piassi; Sarikhani Yaser; Sathian Brijesh; Satpathy Maheswar; Savic Miloje; Sayadi Yaser; Schwendicke Falk; Senthilkumaran Subramanian; Sepanlou Sadaf G.; Servan-Mori Edson; Setshegetso Naomi; Seylani Allen; Shahabi Saeed; Shaikh Masood Ali; Shakhmardanov Murad Ziyaudinovich; Shanawaz Mohd; Sharew Mequannent Melaku Sharew; Sharew Nigussie Tadesse; Sharma Rajesh; Shayan Maryam; Sheikh Aziz; Shenoy Suchitra M.; Shetty Adithi; Shetty Pavanchand H.; Shivakumar K. M.; Lopes Rodrigues Silva Luis Manuel; Simegn Wudneh; Singh Jasvinder A.; Singh Kuldeep; Skhvitaridze Natia; Skryabin Valentin Yurievich; Skryabina Anna Aleksandrovna; Socea Bogdan; Solomon Yonatan; Song Suhang; Stefan Simona Catalina; Suleman Muhammad; Tabares-Seisdedos Rafael; Tat Nathan Y.; Tat Vivian Y.; Tefera Belay Negash; Tichopad Ales; Tobe-Gai Ruoyan; Tovani-Palone Marcos Roberto; Car Lorainne Tudor; Tufa Derara Girma; Vasankari Tommi Juhani; Vasic Milena; Vervoort Dominique; Vlassov Vasily; Bay Vo; Linh Gia Vu; Waheed Yasir; Wamai Richard G.; Wang Cong; Wassie Gizachew Tadesse; Wickramasinghe Nuwan Darshana; Yaya Sanni; Yigit Arzu; Yigit Vahit; Yonemoto Naohiro; Younis Mustafa Z.; Yu Chuanhua; Yunusa Ismaeel; Zaki Leila; Zaman Burhan Abdullah; Zangeneh Alireza; Dehnavi Ali Zare; Zastrozhin Mikhail Sergeevich; Zeng Wu; Zhang Zhi-Jiang; Zuhlke Liesl J.; Zuniga Yves Miel H.; Hay Simon I.; Murray Christopher J. L.; Dieleman Joseph L. | Lancet Global Health, 2023 | |

## AbstractBackground The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings In 2019, at the onset of the COVID-19 pandemic, US$9 center dot 2 trillion (95% uncertainty interval [UI] 9 center dot 1-9 center dot 3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7 center dot 3 trillion (95% UI 7 center dot 2-7 center dot 4) in 2019; 293 center dot 7 times the $24 center dot 8 billion (95% UI 24 center dot 3-25 center dot 3) spent by low-income countries in 2019. That same year, $43 center dot 1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1 center dot 8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and $37 center dot 8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12 center dot 2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health -related COVID-19 response is 252 center dot 2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11-21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained. |
|||

conference
## On The Degree Of Extension Of Some Models Defining Non-Regular Languages |
Mitrana V.; Păun M. | Electronic Proceedings In Theoretical Computer Science, Eptcs, 2023 | |

## AbstractThis work is a survey of the main results reported for the degree of extension of two models defining non-regular languages, namely the context-free grammar and the extended automaton over groups. More precisely, we recall the main results regarding the degree on non-regularity of a context-free grammar as well as the degree of extension of finite automata over groups. Finally, we consider a similar measure for the finite automata with translucent letters and present some preliminary results. This measure could be considered for many mechanisms that extend a less expressive one. © V. Mitrana, M. Păun This work is licensed under the Creative Commons Attribution License. |
|||

article
## Networks Of Splicing Processors: Simulations Between Topologies |
Sanchez Martin Jose Angel; Mitrana Victor; Paun Mihaela | Journal Of Membrane Computing, 2023 | |

## AbstractNetworks of splicing processors are one of the theoretical computational models that take inspiration from nature to efficiently solve problems that our current computational knowledge is not able to. One of the issues restricting/hindering is practical implementation is the arbitrariness of the underlying graph, since our computational systems usually conform to a predefined topology. We propose simulations of networks of splicing processors having arbitrary underlying graphs by networks whose underlying graphs are of a predefined topology: complete, star, and grid graphs. We show that all of these simulations are time efficient in the meaning that they preserve the time complexity of the original network: each computational step in that network is simulated by a fixed number of computational steps in the new topologic networks. Moreover, these simulations do not modify the order of magnitude of the network size. |
|||

article
## Network Analytics For Drug Repurposing In Covid-19 |
Siminea Nicoleta; Popescu Victor; Martin Jose Angel Sanchez; Florea Daniela; Gavril Georgiana; Gheorghe Ana-Maria; Itcus Corina; Kanhaiya Krishna; Pacioglu Octavian; Popa Laura Lona; Trandafir Romica; Tusa Maria Iris; Sidoroff Manuela; Paun Mihaela; Czeizler Eugen; Paun Andrei; Petre Ion | Briefings In Bioinformatics, 2022 | |

## AbstractTo better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction (PPI) networks integrating the top-ranked host factors, the drug target proteins and directed PPI data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases. |
|||

article
## The Global Burden Of Cancer Attributable To Risk Factors, 2010-19: A Systematic Analysis For The Global Burden Of Disease Study 2019 |
Khanh Bao Tran; Lang Justin J.; Compton Kelly; Xu Rixing; Acheson Alistair R.; Henrikson Hannah Jacqueline; Kocarnik Jonathan M.; Penberthy Louise; Aali Amirali; Abbas Qamar; Abbasi Behzad; Abbasi-Kangevari Mohsen; Abbasi-Kangevari Zeinab; Abbastabar Hedayat; Abdelmasseh Michael; Abd-Elsalam Sherief; Abdelwahab Ahmed Abdelwahab; Abdoli Gholamreza; Abdulkadir Hanan Abdulkadir; Abedi Aidin; Abegaz Kedir Hussein; Abidi Hassan; Aboagye Richard Gyan; Abolhassani Hassan; Absalan Abdorrahim; Abtew Yonas Derso; Ali Hiwa Abubaker; Abu-Gharbieh Eman; Achappa Basavaprabhu; Acuna Juan Manuel; Addison Daniel; Addo Isaac Yeboah; Adegboye Oyelola A.; Adesina Miracle Ayomikun; Adnan Mohammad; Adnani Qorinah Estiningtyas Sakilah; Advani Shailesh M.; Afrin Sumia; Afzal Muhammad Sohail; Aggarwal Manik; Ahinkorah Bright Opoku; Ahmad Araz Ramazan; Ahmad Rizwan; Ahmad Sohail; Ahmadi Sepideh; Ahmed Haroon; Ahmed Luai A.; Ahmed Muktar Beshir; Rashid Tarik Ahmed; Aiman Wajeeha; Ajami Marjan; Akalu Gizachew Taddesse; Akbarzadeh-Khiavi Mostafa; Aklilu Addis; Akonde Maxwell; Akunna Chisom Joyqueenet; Al Hamad Hanadi; Alahdab Fares; Alanezi Fahad Mashhour; Alanzi Turki M.; Alessy Saleh Ali; Algammal Abdelazeem M.; Al-Hanawi Mohammed Khaled; Alhassan Robert Kaba; Ali Beriwan Abdulqadir; Ali Liaqat; Ali Syed Shujait; Alimohamadi Yousef; Alipour Vahid; Aljunid Syed Mohamed; Alkhayyat Motasem; Al-Maweri Sadeq Ali Ali; Almustanyir Sami; Alonso Nivaldo; Alqalyoobi Shehabaldin; Al-Raddadi Rajaa M.; Al-Rifai Rami H. Hani; Al-Sabah Salman Khalifah; Al-Tammemi Alaa B.; Altawalah Haya; Alvis-Guzman Nelson; Amare Firehiwot; Ameyaw Edward Kwabena; Dehkordi Javad Javad Aminian; Amirzade-Iranaq Mohammad Hosein; Amu Hubert; Amusa Ganiyu Adeniyi; Ancuceanu Robert; Anderson Jason A.; Animut Yaregal Animut; Anoushiravani Amir; Anoushirvani Ali Arash; Ansari-Moghaddam Alireza; Ansha Mustafa Geleto; Antony Benny; Antwi Maxwell Hubert; Anwar Sumadi Lukman; Anwer Razique; Anyasodor Anayochukwu Edward; Arabloo Jalal; Arab-Zozani Morteza; Aremu Olatunde; Argaw Ayele Mamo; Ariffin Hany; Aripov Timur; Arshad Muhammad; Al Artaman; Arulappan Judie; Aruleba Raphael Taiwo; Aryannejad Armin; Asaad Malke; Asemahagn Mulusew A.; Asemi Zatollah; Asghari-Jafarabadi Mohammad; Ashraf Tahira; Assadi Reza; Athar Mohammad; Athari Seyyed Shamsadin; Null Maha Mohd Wahbi Atout; Attia Sameh; Aujayeb Avinash; Ausloos Marcel; Avila-Burgos Leticia; Awedew Atalel Fentahun; Awoke Mamaru Ayenew; Awoke Tewachew; Quintanilla Beatriz Paulina Ayala; Ayana Tegegn Mulatu; Ayen Solomon Shitu; Azadi Davood; Null Sina Azadnajafabad; Azami-Aghdash Saber; Azanaw Melkalem Mamuye; Azangou-Khyavy Mohammadreza; Jafari Amirhossein Azari; Azizi Hosein; Azzam Ahmed Y. Y.; Babajani Amirhesam; Badar Muhammad; Badiye Ashish D.; Baghcheghi Nayereh; Bagheri Nader; Bagherieh Sara; Bahadory Saeed; Baig Atif Amin; Baker Jennifer L.; Bakhtiari Ahad; Bakshi Ravleen Kaur; Banach Maciej; Banerjee Indrajit; Bardhan Mainak; Barone-Adesi Francesco; Barra Fabio; Barrow Amadou; Bashir Nasir Z.; Bashiri Azadeh; Basu Saurav; Batiha Abdul-Monim Mohammad; Begum Aeysha; Bekele Alehegn Bekele; Belay Alemayehu Sayih; Belete Melaku Ashagrie; Belgaumi Uzma Iqbal; Bell Arielle Wilder; Belo Luis; Benzian Habib; Berhie Alemshet Yirga; Bermudez Amiel Nazer C.; Bernabe Eduardo; Bhagavathula Akshaya Srikanth; Bhala Neeraj; Bhandari Bharti Bhandari; Bhardwaj Nikha; Bhardwaj Pankaj; Bhattacharyya Krittika; Bhojaraja Vijayalakshmi S.; Bhuyan Soumitra S.; Bibi Sadia; Bilchut Awraris Hailu; Bintoro Bagas Suryo; Biondi Antonio; Birega Mesfin Geremaw Birega; Birhan Habitu Eshetu; Bjorge Tone; Blyuss Oleg; Bodicha Belay Boda Abule; Bolla Srinivasa Rao; Boloor Archith; Bosetti Cristina; Braithwaite Dejana; Brauer Michael; Brenner Hermann; Briko Andrey Nikolaevich; Briko Nikolay Ivanovich; Buchanan Christina Maree; Bulamu Norma B.; Bustamante-Teixeira Maria Teresa; Butt Muhammad Hammad; Butt Nadeem Shafique; Butt Zahid A.; Caetano dos Santos Florentino Luciano; Camera Luis Alberto; Cao Chao; Cao Yin; Carreras Giulia; Carvalho Marcia; Cembranel Francieli; Cerin Ester; Chakraborty Promit Ananyo; Charalampous Periklis; Chattu Vijay Kumar; Chimed-Ochir Odgerel; Chirinos-Caceres Jesus Lorenzo; Cho Daniel Youngwhan; Cho William C. S.; Christopher Devasahayam J.; Chu Dinh-Toi; Chukwu Isaac Sunday; Cohen Aaron J.; Conde Joao; Cortas Sandra; Costa Vera Marisa; Cruz-Martins Natalia; Culbreth Garland T.; Dadras Omid; Dagnaw Fentaw Teshome; Dahlawi Saad M. A.; Dai Xiaochen; Dandona Lalit; Dandona Rakhi; Daneshpajouhnejad Parnaz; Danielewicz Anna; An Thi Minh Dao; Soltani Reza Darvishi Cheshmeh; Darwesh Aso Mohammad; Das Saswati; Davitoiu Dragos Virgil; Esmaeili Elham Davtalab; De la Hoz Fernando Pio; Debela Sisay Abebe; Dehghan Azizallah; Demisse Biniyam; Demisse Fitsum Wolde; DenovaGutiA Edgar; Derakhshani Afshin; Molla Meseret Derbew; Dereje Diriba; Deribe Kalkidan Solomon; Desai Rupak; Desalegn Markos Desalegn; Dessalegn Fikadu Nugusu; Dessalegni Samuel Abebe A.; Dessie Gashaw; Desta Abebaw Alemayehu; Dewan Syed Masudur Rahman; Dharmaratne Samath Dhamminda; Dhimal Meghnath; Dianatinasab Mostafa; Diao Nancy; Diaz Daniel; Digesa Lankamo Ena; Dixit Shilpi Gupta; Doaei Saeid; Linh Phuong Doan; Doku Paul Narh; Dongarwar Deepa; dos Santos Wendel Mombaque; Driscoll Tim Robert; Dsouza Haneil Larson; Durojaiye Oyewole Christopher; Edalati Sareh; Eghbalian Fatemeh; Ehsani-Chimeh Elham; Eini Ebrahim; Ekholuenetale Michael; Ekundayo Temitope Cyrus; Ekwueme Donatus U.; El Tantawi Maha; Elbahnasawy Mostafa Ahmed; Elbarazi Iffat; Elghazaly Hesham; Elhadi Muhammed; El-Huneidi Waseem; Emamian Mohammad Hassan; Bain Luchuo Engelbert; Enyew Daniel Berhanie; Erkhembayar Ryenchindorj; Eshetu Tegegne; Eshrati Babak; Eskandarieh Sharareh; Espinosa-Montero Juan; Etaee Farshid; Etemadimanesh Azin; Eyayu Tahir; Ezeonwumelu Ifeanyi Jude; Ezzikouri Sayeh; Fagbamigbe Adeniyi Francis; Fahimi Saman; Fakhradiyev Ildar Ravisovich; Faraon Emerito Jose A.; Fares Jawad; Farmany Abbas; Farooque Umar; Farrokhpour Hossein; Fasanmi Abidemi Omolara; Fatehizadeh Ali; Fatima Wafa; Fattahi Hamed; Fekadu Ginenus; Feleke Berhanu Elfu; Ferrari Allegra Allegra; Ferrero Simone; Desideri Lorenzo Ferro; Filip Irina; Fischer Florian; Foroumadi Roham; Foroutan Masoud; Fukumoto Takeshi; Gaal Peter Andras; Gad Mohamed M.; Gadanya Muktar A.; Gaipov Abduzhappar; Galehdar Nasrin; Gallus Silvano; Garg Tushar; Fonseca Mariana Gaspar; Gebremariam Yosef Haile; Gebremeskel Teferi Gebru; Gebremichael Mathewos Alemu; Geda Yohannes Fikadu; Gela Yibeltal Yismaw; Gemeda Belete Negese Belete; Getachew Melaku; Getachew Motuma Erena; Ghaffari Kazem; Ghafourifard Mansour; Ghamari Seyyed-Hadi; Nour Mohammad Ghasemi; Ghassemi Fariba; Ghimire Ajnish; Ghith Nermin; Gholamalizadeh Maryam; Navashenaq Jamshid Gholizadeh; Ghozy Sherief; Gilani Syed Amir; Gill Paramjit Singh; Ginindza Themba G.; Gizaw Abraham Tamirat T.; Glasbey James C.; Godos Justyna; Goel Amit; Golechha Mahaveer; Goleij Pouya; Golinelli Davide; Golitaleb Mohamad; Gorini Giuseppe; Goulart Barbara Niegia Garcia; Grosso Giuseppe; Guadie Habtamu Alganeh; Gubari Mohammed Ibrahim Mohialdeen; Gudayu Temesgen Worku; Guerra Maximiliano Ribeiro; Gunawardane Damitha Asanga; Gupta Bhawna; Gupta Sapna; Gupta VeerBala; Gupta Vivek Kumar; Gurara Mekdes Kondale; Guta Alemu; Habibzadeh Parham; Avval Atlas Haddadi; Hafezi-Nejad Nima; Ali Adel Hajj; Haj-Mirzaian Arvin; Halboub Esam S.; Halimi Aram; Halwani Rabih; Hamadeh Randah R.; Hameed Sajid; Hamidi Samer; Hanif Asif; Hariri Sanam; Harlianto Netanja I; Haro Josep Maria; Hartono Risky Kusuma; Hasaballah Ahmed I; Hasan S. M. Mahmudul; Hasani Hamidreza; Hashemi Seyedeh Melika; Hassan Abbas M.; Hassanipour Soheil; Hayat Khezar; Heidari Golnaz; Heidari Mohammad; Heidarymeybodi Zahra; Herrera-Serna Brenda Yuliana; Herteliu Claudiu; Hezam Kamal; Hiraike Yuta; Hlongwa Mbuzeleni Mbuzeleni; Holla Ramesh; Holm Marianne; Horita Nobuyuki; Hoseini Mohammad; Hossain Md Mahbub; Hossain Mohammad Bellal Hossain; Hosseini Mohammad-Salar; Hosseinzadeh Ali; Hosseinzadeh Mehdi; Hostiuc Mihaela; Hostiuc Sorin; Househ Mowafa; Huang Junjie; Hugo Fernando N.; Humayun Ayesha; Hussain Salman; Hussein Nawfal R.; Hwang Bing-Fang; Ibitoye Segun Emmanuel; Iftikhar Pulwasha Maria; Ikuta Kevin S.; Ilesanmi Olayinka Stephen; Ilic Irena M.; Ilic Milena D.; Immurana Mustapha; Innos Kaire; Iranpour Pooya; Irham Lalu Muhammad; Islam Md Shariful; Islam Rakibul M.; Islami Farhad; Ismail Nahlah Elkudssiah; Isola Gaetano; Iwagami Masao; Merin Linda J.; Jaiswal Abhishek; Jakovljevic Mihajlo; Jalili Mahsa; Jalilian Shahram; Jamshidi Elham; Jang Sung-In; Jani Chinmay T.; Javaheri Tahereh; Jayarajah Umesh Umesh; Jayaram Shubha; Jazayeri Seyed Behzad; Jebai Rime; Jemal Bedru; Jeong Wonjeong; Jha Ravi Prakash; Jindal Har Ashish; John-Akinola Yetunde O.; Jonas Jost B.; Joo Tamas; Joseph Nitin; Joukar Farahnaz; Jozwiak Jacek Jerzy; Jarisson Mikk; Kabir Ali; Kacimi Salah Eddine Oussama; Kadashetti Vidya; Kahe Farima; Kakodkar Pradnya Vishal; Kalankesh Leila R.; Kalhor Rohollah; Kamal Vineet Kumar; Kamangar Farin; Kamath Ashwin; Kanchan Tanuj; Kandaswamy Eswar; Kandel Himal; Kang HyeJung; Kanno Girum Gebremeskel; Kapoor Neeti; Kar Sitanshu Sekhar; Karanth Shama D.; Karaye Ibraheem M.; Karch AndrA; Karimi Amirali; Kassa Bekalu Getnet; Katoto Patrick D. M. C.; Kauppila Joonas H.; Kaur Harkiran; Kebede Abinet Gebremickael; Keikavoosi-Arani Leila; Kejela Gemechu Gemechu; Bohan Phillip M. Kemp; Keramati Maryam; Keykhaei Mohammad; Khajuria Himanshu; Khan Abbas; Khan Abdul Aziz Khan; Khan Ejaz Ahmad; Khan Gulfaraz; Khan Md Nuruzzaman; Ab Khan Moien; Khanali Javad; Khatab Khaled; Khatatbeh Moawiah Mohammad; Khatib Mahalaqua Nazli; Khayamzadeh Maryam; Kashani Hamid Reza Khayat; Tabari Mohammad Amin Khazeei; Khezeli Mehdi; Khodadost Mahmoud; Kim Min Seo; Kim Yun Jin; Kisa Adnan; Kisa Sezer; Klugar Miloslav; Klugarova Jitka; Kolahi Ali-Asghar; Kolkhir Pavel; Kompani Farzad; Koul Parvaiz A.; Laxminarayana Sindhura Lakshmi Koulmane; Koyanagi Ai; Krishan Kewal; Krishnamoorthy Yuvaraj; Bicer Burcu Kucuk; Kugbey Nuworza; Kulimbet Mukhtar; Kumar Akshay; Kumar G. Anil; Kumar Narinder; Kurmi Om P.; Kuttikkattu Ambily; La Vecchia Carlo; Lahiri Arista; Lal Dharmesh Kumar; Lam Judit; Lan Qing; Landires Ivan; Larijani Bagher; Lasrado Savita; Lau Jerrald; Lauriola Paolo; Ledda Caterina; Lee Sang-woong; Lee Shaun Wen Huey; Lee Wei-Chen; Lee Yeong Yeh; Lee Yo Han; Legesse Samson Mideksa; Leigh James; Leong Elvynna; Li Ming-Chieh; Lim Stephen S.; Liu Gang; Liu Jue; Lo Chun-Han; Lohiya Ayush; Lopukhov Platon D.; Lorenzovici Laszla; Lotfi Mojgan; Loureiro Joana A.; Lunevicius Raimundas; Madadizadeh Farzan; Mafi Ahmad R.; Magdeldin Sameh; Mahjoub Soleiman; Mahmoodpoor Ata; Mahmoudi Morteza; Mahmoudimanesh Marzieh; Mahumud Rashidul Alam; Majeed Azeem; Majidpoor Jamal; Makki Alaa; Makris Konstantinos Christos; Rad Elaheh Malakan; Malekpour Mohammad-Reza; Malekzadeh Reza; Malik Ahmad Azam; Mallhi Tauqeer Hussain; Mallya Sneha Deepak; Mamun Mohammed A.; Manda Ana Laura; Mansour-Ghanaei Fariborz; Mansouri Borhan; Mansournia Mohammad Ali; Mantovani Lorenzo Giovanni; Martini Santi; Martorell Miquel; Masoudi Sahar; Masoumi Seyedeh Zahra; Matei Clara N.; Mathews Elezebeth; Mathur Manu Raj; Mathur Vasundhara; McKee Martin; Meena Jitendra Kumar; Mehmood Khalid; Nasab Entezar Mehrabi; Mehrotra Ravi; Melese Addisu; Mendoza Walter; Menezes Ritesh G.; Mengesha SIsay Derso; Mensah Laverne G.; Mentis Alexios-Fotios A.; Mera-Mamian Andry Yasmid Mera; Meretoja Tuomo J.; Merid Mehari Woldemariam; Mersha Amanual Getnet; Meselu Belsity Temesgen; Meshkat Mahboobeh; Mestrovic Tomislav; Jonasson Junmei Miao; Miazgowski Tomasz; Michalek Irmina Maria; Mijena Gelana Fekadu Worku; Miller Ted R.; Mir Shabir Ahmad; Mirinezhad Seyed Kazem; Mirmoeeni Seyyedmohammadsadeq; Mirza-Aghazadeh-Attari Mohammad; Mirzaei Hamed; Mirzaei Hamid Reza; Misganaw Abay Sisay; Misra Sanjeev; AbdulmuhsinMohammad Karzan; Mohammadi Esmaeil; Mohammadi Mokhtar; Mohammadian-Hafshejani Abdollah; Mohammadpourhodki Reza; Mohammed Arif; Mohammed Shafiu; Mohan Syam; Mohseni Mohammad; Moka Nagabhishek; Mokdad Ali H.; Molassiotis Alex; Molokhia Mariam; Momenzadeh Kaveh; Momtazmanesh Sara; Monasta Lorenzo; Mons Ute; Al Montasir Ahmed; Montazeri Fateme; Montero Arnulfo; Moosavi Mohammad Amin; Moradi Abdolvahab; Moradi Yousef; Sarabi Mostafa Moradi; Moraga Paula; Morawska Lidia; Morrison Shane Douglas; Morze Jakub; Mosapour Abbas; Mostafavi Ebrahim; Mousavi Seyyed Meysam; Isfahani Haleh Mousavi; Khaneghah Amin Mousavi; Mpundu-Kaambwa Christine; Mubarik Sumaira; Mulita Francesk; Munblit Daniel; Munro Sandra B.; Murillo-Zamora Efran; Musa Jonah; Nabhan Ashraf F.; Nagarajan Ahamarshan Jayaraman; Nagaraju Shankar Prasad; Nagel Gabriele; Naghipour Mohammadreza; Naimzada Mukhammad David; Nair Tapas Sadasivan; Naqvi Atta Abbas; Swamy Sreenivas Narasimha; Narayana Aparna Ichalangod; Nassereldine Hasan; Natto Zuhair S.; Nayak Biswa Prakash; Ndejjo Rawlance; Nduaguba Sabina Onyinye; Negash Wogene Wogene; Nejadghaderi Seyed Aria; Nejati Kazem; Kandel Sandhya Neupane; Huy Van Nguyen Nguyen; Niazi Robina Khan; Noor Nurulamin M.; Noori Maryam; Noroozi Nafise; Nouraei Hasti; Nowroozi Ali; Nunez-Samudio Virginia; Nzoputam Chimezie Igwegbe; Nzoputam Ogochukwu Janet; Oancea Bogdan; Odukoya Oluwakemi Ololade; Oghenetega Onome Bright; Ogunsakin Ropo Ebenezer; Oguntade Ayodipupo Sikiru; Oh In-Hwan; Okati-Aliabad Hassan; Okekunle Akinkunmi Paul; Olagunju Andrew T.; Olagunju Tinuke O.; Olakunde Babayemi Oluwaseun; Olufadewa Isaac Iyinoluwa; Omer Emad; Omonisi Abidemi E. Emmanuel; Ong Sokking; Onwujekwe Obinna E.; Orru Hans; Otstavnov Stanislav S.; Oulhaj Abderrahim; Oumer Bilcha; Owopetu Oluwatomi Funbi; Oyinloye Babatunji Emmanuel; Mahesh P. A.; Padron-Monedero Alicia; Padubidri Jagadish Rao; Pakbin Babak; Pakshir Keyvan; Pakzad Reza; Palicz Tamas; Pana Adrian; Pandey Ashok; Pant Suman; Pardhan Shahina; Park Eun-Kee; Park Seoyeon; Patel Jay; Pati Siddhartha; Paudel Rajan; Paudel Uttam; Paun Mihaela; Toroudi Hamidreza Pazoki; Peng Minjin; Pereira Jeevan; Pereira Renato B.; Perna Simone; Perumalsamy Navaraj; Pestell Richard G.; Pezzani Raffaele; Piccinelli Cristiano; Pillay Julian David; Piracha Zahra Zahid; Pischon Tobias; Postma Maarten J.; Langroudi Ashkan Pourabhari; Pourshams Akram; Pourtaheri Naeimeh; Prashant Akila; Qadir Mirza Muhammad Fahd; Syed Zahiruddin Quazi; Rabiee Mohammad; Rabiee Navid; Radfar Amir; Radhakrishnan Raghu Anekal; Radhakrishnan Venkatraman; Raeisi Mojtaba; Rafiee Ata; Rafiei Alireza; Raheem Nasiru; Rahim Fakher; Rahman Md Obaidur; Rahman Mosiur; Rahman Muhammad Aziz; Rahmani Amir Masoud; Rahmani Shayan; Rahmanian Vahid; Rajai Nazanin; Rajesh Aashish; Ram Pradhum; Ramezanzadeh Kiana; Rana Juwel; Ranabhat Kamal; Ranasinghe Priyanga; Rao Chythra R.; Rao Sowmya J.; Rashedi Sina; Rashidi Amirfarzan; Rashidi Mohammad-Mahdi; Ratan Zubair Ahmed; Rawaf David Laith; Rawaf Salman; Rawal Lal; Rawassizadeh Reza; Razeghinia Mohammad Sadegh; Rehman Ashfaq Ur; Rehman Inayat Ur; Reitsma Marissa B.; Renzaho Andre M. N.; Rezaei Maryam; Rezaei Nima; Rezaei Saeid; Rezaeian Mohsen; Rezapour Aziz; Riad Abanoub; Rikhtegar Reza; Rios-Blancas Maria; Roberts Thomas J.; Rohloff Peter; Romero-Rodriguez Esperanza; Roshandel Gholamreza; Rwegerera Godfrey M.; Manjula S.; Saber-Ayad Maha Mohamed; Saberzadeh-Ardestani Bahar; Sabour Siamak; Saddik Basema; Sadeghi Erfan; Saeb Mohammad Reza; Saeed Umar; Safaei Mohsen; Safary Azam; Sahebazzamani Maryam; Sahebkar Amirhossein; Sahoo Harihar; Sajid Mirza Rizwan; Salari Hedayat; Salehi Sana; Salem Marwa Rashad; Salimzadeh Hamideh; Samodra Yoseph Leonardo; Samy Abdallah M.; Sanabria Juan; Sankararaman Senthilkumar; Sanmarchi Francesco; Santric-Milicevic Milena M.; Saqib Muhammad Arif Nadeem; Sarveazad Arash; Sarvi Fatemeh; Sathian Brijesh; Satpathy Maheswar; Sayegh Nicolas; Schneider Ione Jayce Ceola; Schwarzinger Michael; Sekerija Mario; Senthilkumaran Subramanian; Sepanlou Sadaf G.; Seylani Allen; Seyoum Kenbon; Sha Feng; Shafaat Omid; Shah Pritik A.; Shahabi Saeed; Shahid Izza; Shahrbaf Mohammad Amin; Shahsavari Hamid R.; Shaikh Masood Ali; Shaka Mohammed Feyisso; Shaker Elaheh; Shannawaz Mohammed; Sharew Mequannent Melaku Sharew; Sharifi Azam; Sharifi-Rad Javad; Sharma Purva; Shashamo Bereket Beyene; Sheikh Aziz; Sheikh Mahdi; Sheikhbahaei Sara; Sheikhi Rahim Ali; Sheikhy Ali; Shepherd Peter Robin; Shetty Adithi; Shetty Jeevan K.; Shetty Ranjitha S.; Shibuya Kenji; Shirkoohi Reza; Shirzad-Aski Hesamaddin; Shivakumar K. M.; Shivalli Siddharudha; Shivarov Velizar; Shobeiri Parnian; Varniab Zahra Shokri; Shorofi Seyed Afshin; Shrestha Sunil; Sibhat Migbar Mekonnen; Malleshappa SudeepK Siddappa; Sidemo Negussie Boti; Silva Diego Augusto Santos; Silva Luas Manuel Lopes Rodrigues; Julian Guilherme Silva; Silvestris Nicola; Simegn Wudneh; Singh Achintya Dinesh; Singh Ambrish; Singh Garima; Singh Harpreet; Singh Jasvinder A.; Singh Jitendra Kumar; Singh Paramdeep; Singh Surjit; Sinha Dhirendra Narain; Sinke Abiy H.; Siraj Md Shahjahan; Sitas Freddy; Siwal Samarjeet Singh; Skryabin Valentin Yurievich; Skryabina Anna Aleksandrovna; Socea Bogdan; Soeberg Matthew J.; Sofi-Mahmudi Ahmad; Solomon Yonatan; Soltani-Zangbar Mohammad Sadegh; Song Suhang; Song Yimeng; Sorensen Reed J. D.; Soshnikov Sergey; Sotoudeh Houman; Sowe Alieu; Sufiyan Muawiyyah Babale; Suk Ryan; Suleman Muhammad; Abdulkader Rizwan Suliankatchi; Sultana Saima; Sur Daniel; Szacska Miklas; Tabaeian Seidamir Pasha; Tabares-Seisdedos Rafael; Tabatabaei Seyyed Mohammad; Tabuchi Takahiro; Tadbiri Hooman; Taheri Ensiyeh; Taheri Majid; Soodejani Moslem Taheri; Takahashi Ken; Talaat Iman M.; Tampa Mircea; Tan Ker-Kan; Tat Nathan Y.; Tat Vivian Y.; Tavakoli Arash; Tehrani-Banihashemi Arash; Tekalegn Yohannes; Tesfay Fisaha Haile; Thapar Rekha; Thavamani Aravind; Chandrasekar Viveksandeep Thoguluva; Thomas Nihal; Thomas Nikhil Kenny; Ticoalu Jansje Henny Vera; Tiyuri Amir; Tollosa Daniel Nigusse; Topor-Madry Roman; Touvier Mathilde; Tovani-Palone Marcos Roberto; Traini Eugenio; Mai Thi Ngoc Tran; Tripathy Jaya Prasad; Ukke Gebresilasea Gendisha; Ullah Irfan; Ullah Sana; Unnikrishnan Bhaskaran; Vacante Marco; Vaezi Maryam; Tahbaz Sahel Valadan; Valdez Pascual R.; Vardavas Constantine; Varthya Shoban Babu; Vaziri Siavash; Velazquez Diana Zuleika; Veroux Massimiliano; Villeneuve Paul J.; Violante Francesco S.; Vladimirov Sergey Konstantinovitch; Vlassov Vasily; Vo Bay; Vu Linh Gia; Wadood Abdul Wadood; Waheed Yasir; Walde Mandaras Tariku; Wamai Richard G.; Wang Cong; Wang Fang; Wang Ning; Wang Yu; Ward Paul; Waris Abdul; Westerman Ronny; Wickramasinghe Nuwan Darshana; Woldemariam Melat; Woldu Berhanu; Xiao Hong; Xu Suowen; Xu Xiaoyue; Yadav Lalit; Jabbari Seyed Hossein Yahyazadeh; Yang Lin; Yazdanpanah Fereshteh; Yeshaw Yigizie; Yismaw Yazachew; Yonemoto Naohiro; Younis Mustafa Z.; Yousefi Zabihollah; Yousefian Fatemeh; Yu Chuanhua; Yu Yong; Yunusa Ismaeel; Zahir Mazyar; Zaki Nazar; Zaman Burhan Abdullah; Zangiabadian Moein; Zare Fariba; Zare Iman; Zareshahrabadi Zahra; Zarrintan Armin; Zastrozhin Mikhail Sergeevich; Zeineddine Mohammad A.; Zhang Dongyu; Zhang Jianrong; Zhang Yunquan; Zhang Zhi-Jiang; Zhou Linghui; Zodpey Sanjay; Zoladl Mohammad; Vos Theo; Hay Simon I; Force Lisa M.; Murray Christopher J. L. | Lancet, 2022 | |

## AbstractBackground Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. |
|||

article
## The Global Burden Of Cancer Attributable To Risk Factors, 2010–19: A Systematic Analysis For The Global Burden Of Disease Study 2019 |
Tran K.B.; Lang J.J.; Compton K.; Xu R.; Acheson A.R.; Henrikson H.J.; Kocarnik J.M.; Penberthy L.; Aali A.; Abbas Q.; Abbasi B.; Abbasi-Kangevari M.; Abbasi-Kangevari Z.; Abbastabar H.; Abdelmasseh M.; Abd-Elsalam S.; Abdelwahab A.A.; Abdoli G.; Abdulkadir H.A.; Abedi A.; Abegaz K.H.; Abidi H.; Aboagye R.G.; Abolhassani H.; Absalan A.; Abtew Y.D.; Abubaker Ali H.; Abu-Gharbieh E.; Achappa B.; Acuna J.M.; Addison D.; Addo I.Y.; Adegboye O.A.; Adesina M.A.; Adnan M.; Adnani Q.E.S.; Advani S.M.; Afrin S.; Afzal M.S.; Aggarwal M.; Ahinkorah B.O.; Ahmad A.R.; Ahmad R.; Ahmad S.; Ahmad S.; Ahmadi S.; Ahmed H.; Ahmed L.A.; Ahmed M.B.; Rashid T.A.; Aiman W.; Ajami M.; Akalu G.T.; Akbarzadeh-Khiavi M.; Aklilu A.; Akonde M.; Akunna C.J.; Al Hamad H.; Alahdab F.; Alanezi F.M.; Alanzi T.M.; Alessy S.A.; Algammal A.M.; Al-Hanawi M.K.; Alhassan R.K.; Ali B.A.; Ali L.; Ali S.S.; Alimohamadi Y.; Alipour V.; Aljunid S.M.; Alkhayyat M.; Al-Maweri S.A.A.; Almustanyir S.; Alonso N.; Alqalyoobi S.; Al-Raddadi R.M.; Al-Rifai R.H.H.; Al-Sabah S.K.; Al-Tammemi A.B.; Altawalah H.; Alvis-Guzman N.; Amare F.; Ameyaw E.K.; Aminian Dehkordi J.J.; Amirzade-Iranaq M.H.; Amu H.; Amusa G.A.; Ancuceanu R.; Anderson J.A.; Animut Y.A.; Anoushiravani A.; Anoushirvani A.A.; Ansari-Moghaddam A.; Ansha M.G.; Antony B.; Antwi M.H.; Anwar S.L.; Anwer R.; Anyasodor A.E.; Arabloo J.; Arab-Zozani M.; Aremu O.; Argaw A.M.; Ariffin H.; Aripov T.; Arshad M.; Artaman A.; Arulappan J.; Aruleba R.T.; Aryannejad A.; Asaad M.; Asemahagn M.A.; Asemi Z.; Asghari-Jafarabadi M.; Ashraf T.; Assadi R.; Athar M.; Athari S.S.; Atout M.M.W.; Attia S.; Aujayeb A.; Ausloos M.; Avila-Burgos L.; Awedew A.F.; Awoke M.A.; Awoke T.; Ayala Quintanilla B.P.; Ayana T.M.; Ayen S.S.; Azadi D.; Azadnajafabad S.; Azami-Aghdash S.; Azanaw M.M.; Azangou-Khyavy M.; Jafari A.A.; Azizi H.; Azzam A.Y.Y.; Babajani A.; Badar M.; Badiye A.D.; Baghcheghi N.; Bagheri N.; Bagherieh S.; Bahadory S.; Baig A.A.; Baker J.L.; Bakhtiari A.; Bakshi R.K.; Banach M.; Banerjee I.; Bardhan M.; Barone-Adesi F.; Barra F.; Barrow A.; Bashir N.Z.; Bashiri A.; Basu S.; Batiha A.-M.M.; Begum A.; Bekele A.B.; Belay A.S.; Belete M.A.; Belgaumi U.I.; Bell A.W.; Belo L.; Benzian H.; Berhie A.Y.; Bermudez A.N.C.; Bernabe E.; Bhagavathula A.S.; Bhala N.; Bhandari B.B.; Bhardwaj N.; Bhardwaj P.; Bhattacharyya K.; Bhojaraja V.S.; Bhuyan S.S.; Bibi S.; Bilchut A.H.; Bintoro B.S.; Biondi A.; Birega M.G.B.; Birhan H.E.; Bjørge T.; Blyuss O.; Bodicha B.B.A.; Bolla S.R.; Boloor A.; Bosetti C.; Braithwaite D.; Brauer M.; Brenner H.; Briko A.N.; Briko N.I.; Buchanan C.M.; Bulamu N.B.; Bustamante-Teixeira M.T.; Butt M.H.; Butt N.S.; Butt Z.A.; Caetano Dos Santos F.L.; Cámera L.A.; Cao C.; Cao Y.; Carreras G.; Carvalho M.; Cembranel F.; Cerin E.; Chakraborty P.A.; Charalampous P.; Chattu V.K.; Chimed-Ochir O.; Chirinos-Caceres J.L.; Cho D.Y.; Cho W.C.S.; Christopher D.J.; Chu D.-T.; Chukwu I.S.; Cohen A.J.; Conde J.; Cortés S.; Costa V.M.; Cruz-Martins N.; Culbreth G.T.; Dadras O.; Dagnaw F.T.; Dahlawi S.M.A.; Dai X.; Dandona L.; Dandona R.; Daneshpajouhnejad P.; Danielewicz A.; Dao A.T.M.; Darvishi Cheshmeh Soltani R.; Darwesh A.M.; Das S.; Davitoiu D.V.; Davtalab Esmaeili E.; De La Hoz F.P.; Debela S.A.; Dehghan A.; Demisse B.; Demisse F.W.; Denova-Gutiérrez E.; Derakhshani A.; Derbew Molla M.; Dereje D.; Deribe K.S.; Desai R.; Desalegn M.D.; Dessalegn F.N.; Dessalegni S.A.A.; Dessie G.; Desta A.A.; Dewan S.M.R.; Dharmaratne S.D.; Dhimal M.; Dianatinasab M.; Diao N.; Diaz D.; Digesa L.E.; Dixit S.G.; Doaei S.; Doan L.P.; Doku P.N.; Dongarwar D.; dos Santos W.M.; Driscoll T.R.; Dsouza H.L.; Durojaiye O.C.; Edalati S.; Eghbalian F.; Ehsani-Chimeh E.; Eini E.; Ekholuenetale M.; Ekundayo T.C.; Ekwueme D.U.; El Tantawi M.; Elbahnasawy M.A.; Elbarazi I.; Elghazaly H.; Elhadi M.; El-Huneidi W.; Emamian M.H.; Engelbert Bain L.; Enyew D.B.; Erkhembayar R.; Eshetu T.; Eshrati B.; Eskandarieh S.; Espinosa-Montero J.; Etaee F.; Etemadimanesh A.; Eyayu T.; Ezeonwumelu I.J.; Ezzikouri S.; Fagbamigbe A.F.; Fahimi S.; Fakhradiyev I.R.; Faraon E.J.A.; Fares J.; Farmany A.; Farooque U.; Farrokhpour H.; Fasanmi A.O.; Fatehizadeh A.; Fatima W.; Fattahi H.; Fekadu G.; Feleke B.E.; Ferrari A.A.; Ferrero S.; Ferro Desideri L.; Filip I.; Fischer F.; Foroumadi R.; Foroutan M.; Fukumoto T.; Gaal P.A.; Gad M.M.; Gadanya M.A.; Gaipov A.; Galehdar N.; Gallus S.; Garg T.; Gaspar Fonseca M.; Gebremariam Y.H.; Gebremeskel T.G.; Gebremichael M.A.; Geda Y.F.; Gela Y.Y.; Gemeda B.N.B.; Getachew M.; Getachew M.E.; Ghaffari K.; Ghafourifard M.; Ghamari S.-H.; Ghasemi Nour M.; Ghassemi F.; Ghimire A.; Ghith N.; Gholamalizadeh M.; Gholizadeh Navashenaq J.; Ghozy S.; Gilani S.A.; Gill P.S.; Ginindza T.G.; Gizaw A.T.T.; Glasbey J.C.; Godos J.; Goel A.; Golechha M.; Goleij P.; Golinelli D.; Golitaleb M.; Gorini G.; Goulart B.N.G.; Grosso G.; Guadie H.A.; Gubari M.I.M.; Gudayu T.W.; Guerra M.R.; Gunawardane D.A.; Gupta B.; Gupta S.; Gupta V.B.; Gupta V.K.; Gurara M.K.; Guta A.; Habibzadeh P.; Haddadi Avval A.; Hafezi-Nejad N.; Hajj Ali A.; Haj-Mirzaian A.; Halboub E.S.; Halimi A.; Halwani R.; Hamadeh R.R.; Hameed S.; Hamidi S.; Hanif A.; Hariri S.; Harlianto N.I.; Haro J.M.; Hartono R.K.; Hasaballah A.I.; Hasan S.M.M.; Hasani H.; Hashemi S.M.; Hassan A.M.; Hassanipour S.; Hayat K.; Heidari G.; Heidari M.; Heidarymeybodi Z.; Herrera-Serna B.Y.; Herteliu C.; Hezam K.; Hiraike Y.; Hlongwa M.M.; Holla R.; Holm M.; Horita N.; Hoseini M.; Hossain Md.M.; Hossain M.B.H.; Hosseini M.-S.; Hosseinzadeh A.; Hosseinzadeh M.; Hostiuc M.; Hostiuc S.; Househ M.; Huang J.; Hugo F.N.; Humayun A.; Hussain S.; Hussein N.R.; Hwang B.-F.; Ibitoye S.E.; Iftikhar P.M.; Ikuta K.S.; Ilesanmi O.S.; Ilic I.M.; Ilic M.D.; Immurana M.; Innos K.; Iranpour P.; Irham L.M.; Islam Md.S.; Islam R.M.; Islami F.; Ismail N.E.; Isola G.; Iwagami M.; Merin J L.; Jaiswal A.; Jakovljevic M.; Jalili M.; Jalilian S.; Jamshidi E.; Jang S.-I.; Jani C.T.; Javaheri T.; Jayarajah U.U.; Jayaram S.; Jazayeri S.B.; Jebai R.; Jemal B.; Jeong W.; Jha R.P.; Jindal H.A.; John-Akinola Y.O.; Jonas J.B.; Joo T.; Joseph N.; Joukar F.; Jozwiak J.J.; Jürisson M.; Kabir A.; Kacimi S.E.O.; Kadashetti V.; Kahe F.; Kakodkar P.V.; Kalankesh L.R.; Kalankesh L.R.; Kalhor R.; Kamal V.K.; Kamangar F.; Kamath A.; Kanchan T.; Kandaswamy E.; Kandel H.; Kang H.; Kanno G.G.; Kapoor N.; Kar S.S.; Karanth S.D.; Karaye I.M.; Karch A.; Karimi A.; Kassa B.G.; Katoto P.D.M.C.; Kauppila J.H.; Kaur H.; Kebede A.G.; Keikavoosi-Arani L.; Kejela G.G.; Kemp Bohan P.M.; Keramati M.; Keykhaei M.; Khajuria H.; Khan A.; Khan A.A.K.; Khan E.A.; Khan G.; Khan Md.N.; Khan M.A.B.; Khanali J.; Khatab K.; Khatatbeh M.M.; Khatib M.N.; Khayamzadeh M.; Khayat Kashani H.R.; Khazeei Tabari M.A.; Khezeli M.; Khodadost M.; Kim M.S.; Kim Y.J.; Kisa A.; Kisa S.; Klugar M.; Klugarová J.; Kolahi A.-A.; Kolkhir P.; Kompani F.; Koul P.A.; Koulmane Laxminarayana S.L.; Koyanagi A.; Krishan K.; Krishnamoorthy Y.; Kucuk Bicer B.; Kugbey N.; Kulimbet M.; Kumar A.; Kumar G.A.; Kumar N.; Kurmi O.P.; Kuttikkattu A.; La Vecchia C.; Lahiri A.; Lal D.K.; Lám J.; Lan Q.; Landires I.; Larijani B.; Lasrado S.; Lau J.; Lauriola P.; Ledda C.; Lee S.-W.; Lee S.W.H.; Lee W.-C.; Lee Y.Y.; Lee Y.H.; Legesse S.M.; Leigh J.; Leong E.; Li M.-C.; Lim S.S.; Liu G.; Liu J.; Lo C.-H.; Lohiya A.; Lopukhov P.D.; Lorenzovici L.; Lotfi M.; Loureiro J.A.; Lunevicius R.; Madadizadeh F.; Mafi A.R.; Magdeldin S.; Mahjoub S.; Mahmoodpoor A.; Mahmoudi M.; Mahmoudimanesh M.; Mahumud R.A.; Majeed A.; Majidpoor J.; Makki A.; Makris K.C.; Malakan Rad E.; Malekpour M.-R.; Malekzadeh R.; Malik A.A.; Mallhi T.H.; Mallya S.D.; Mamun M.A.; Manda A.L.; Mansour-Ghanaei F.; Mansouri B.; Mansournia M.A.; Mantovani L.G.; Martini S.; Martorell M.; Masoudi S.; Masoumi S.Z.; Matei C.N.; Mathews E.; Mathur M.R.; Mathur V.; McKee M.; Meena J.K.; Mehmood K.; Mehrabi Nasab E.; Mehrotra R.; Melese A.; Mendoza W.; Menezes R.G.; Mengesha S.D.; Mensah L.G.; Mentis A.-F.A.; Mera-Mamián A.Y.M.; Meretoja T.J.; Merid M.W.; Mersha A.G.; Meselu B.T.; Meshkat M.; Mestrovic T.; Miao Jonasson J.; Miazgowski T.; Michalek I.M.; Mijena G.F.W.; Miller T.R.; Mir S.A.; Mirinezhad S.K.; Mirmoeeni S.; Mirza-Aghazadeh-Attari M.; Mirzaei H.; Mirzaei H.R.; Misganaw A.S.; Misra S.; Mohammad K.A.; Mohammadi E.; Mohammadi M.; Mohammadian-Hafshejani A.; Mohammadpourhodki R.; Mohammed A.; Mohammed S.; Mohan S.; Mohseni M.; Moka N.; Mokdad A.H.; Molassiotis A.; Molokhia M.; Momenzadeh K.; Momtazmanesh S.; Monasta L.; Mons U.; Al Montasir A.; Montazeri F.; Montero A.; Moosavi M.A.; Moradi A.; Moradi Y.; Moradi Sarabi M.; Moraga P.; Morawska L.; Morrison S.D.; Morze J.; Mosapour A.; Mostafavi E.; Mousavi S.M.; Mousavi Isfahani H.; Mousavi Khaneghah A.; Mpundu-Kaambwa C.; Mubarik S.; Mulita F.; Munblit D.; Munro S.B.; Murillo-Zamora E.; Musa J.; Nabhan A.F.; Nagarajan A.J.; Nagaraju S.P.; Nagel G.; Naghipour M.; Naimzada M.D.; Nair T.S.; Naqvi A.A.; Narasimha Swamy S.; Narayana A.I.; Nassereldine H.; Natto Z.S.; Nayak B.P.; Ndejjo R.; Nduaguba S.O.; Negash W.W.; Nejadghaderi S.A.; Nejati K.; Neupane Kandel S.; Nguyen H.V.N.; Niazi R.K.; Noor N.M.; Noori M.; Noroozi N.; Nouraei H.; Nowroozi A.; Nuñez-Samudio V.; Nzoputam C.I.; Nzoputam O.J.; Oancea B.; Odukoya O.O.; Oghenetega O.B.; Ogunsakin R.E.; Oguntade A.S.; Oh I.-H.; Okati-Aliabad H.; Okekunle A.P.; Olagunju A.T.; Olagunju T.O.; Olakunde B.O.; Olufadewa I.I.; Omer E.; Omonisi A.E.E.; Ong S.; Onwujekwe O.E.; Orru H.; Otstavnov S.S.; Oulhaj A.; Oumer B.; Owopetu O.F.; Oyinloye B.E.; Mahesh P.A.; Padron-Monedero A.; Padubidri J.R.; Pakbin B.; Pakshir K.; Pakzad R.; Palicz T.; Pana A.; Pandey A.; Pandey A.; Pant S.; Pardhan S.; Park E.-C.; Park E.-K.; Park S.; Patel J.; Pati S.; Paudel R.; Paudel U.; Paun M.; Pazoki Toroudi H.; Peng M.; Pereira J.; Pereira R.B.; Perna S.; Perumalsamy N.; Pestell R.G.; Pezzani R.; Piccinelli C.; Pillay J.D.; Piracha Z.Z.; Pischon T.; Postma M.J.; Pourabhari Langroudi A.; Pourshams A.; Pourtaheri N.; Prashant A.; Qadir M.M.F.; Quazi Syed Z.; Rabiee M.; Rabiee N.; Radfar A.; Radhakrishnan R.A.; Radhakrishnan V.; Raeisi M.; Rafiee A.; Rafiei A.; Raheem N.; Rahim F.; Rahman Md.O.; Rahman M.; Rahman M.A.; Rahmani A.M.; Rahmani S.; Rahmanian V.; Rajai N.; Rajesh A.; Ram P.; Ramezanzadeh K.; Rana J.; Ranabhat K.; Ranasinghe P.; Rao C.R.; Rao S.J.; Rashedi S.; Rashidi A.; Rashidi M.; Rashidi M.-M.; Ratan Z.A.; Rawaf D.L.; Rawaf S.; Rawal L.; Rawassizadeh R.; Razeghinia M.S.; Rehman A.U.; Rehman I.U.; Reitsma M.B.; Renzaho A.M.N.; Rezaei M.; Rezaei N.; Rezaei N.; Rezaei N.; Rezaei S.; Rezaeian M.; Rezapour A.; Riad A.; Rikhtegar R.; Rios-Blancas M.; Roberts T.J.; Rohloff P.; Romero-Rodríguez E.; Roshandel G.; Rwegerera G.M.; Manjula S.; Saber-Ayad M.M.; Saberzadeh-Ardestani B.; Sabour S.; Saddik B.; Sadeghi E.; Saeb M.R.; Saeed U.; Safaei M.; Safary A.; Sahebazzamani M.; Sahebkar A.; Sahoo H.; Sajid M.R.; Salari H.; Salehi S.; Salem M.R.; Salimzadeh H.; Samodra Y.L.; Samy A.M.; Sanabria J.; Sankararaman S.; Sanmarchi F.; Santric-Milicevic M.M.; Saqib M.A.N.; Sarveazad A.; Sarvi F.; Sathian B.; Satpathy M.; Sayegh N.; Schneider I.J.C.; Schwarzinger M.; Šekerija M.; Senthilkumaran S.; Sepanlou S.G.; Seylani A.; Seyoum K.; Sha F.; Shafaat O.; Shah P.A.; Shahabi S.; Shahid I.; Shahrbaf M.A.; Shahsavari H.R.; Shaikh M.A.; Shaka M.F.; Shaker E.; Shannawaz M.; Sharew M.M.S.; Sharifi A.; Sharifi-Rad J.; Sharma P.; Shashamo B.B.; Sheikh A.; Sheikh M.; Sheikhbahaei S.; Sheikhi R.A.; Sheikhy A.; Shepherd P.R.; Shetty A.; Shetty J.K.; Shetty R.S.; Shibuya K.; Shirkoohi R.; Shirzad-Aski H.; Shivakumar K.M.; Shivalli S.; Shivarov V.; Shobeiri P.; Shokri Varniab Z.; Shorofi S.A.; Shrestha S.; Sibhat M.M.; Siddappa Malleshappa S.K.; Sidemo N.B.; Silva D.A.S.; Silva L.M.L.R.; Silva Julian G.; Silvestris N.; Simegn W.; Singh A.D.; Singh A.; Singh G.; Singh H.; Singh J.A.; Singh J.K.; Singh P.; Singh S.; Sinha D.N.; Sinke A.H.; Siraj Md.S.; Sitas F.; Siwal S.S.; Skryabin V.Y.; Skryabina A.A.; Socea B.; Soeberg M.J.; Sofi-Mahmudi A.; Solomon Y.; Soltani-Zangbar M.S.; Song S.; Song Y.; Sorensen R.J.D.; Soshnikov S.; Sotoudeh H.; Sowe A.; Sufiyan M.B.; Suk R.; Suleman M.; Suliankatchi Abdulkader R.; Sultana S.; Sur D.; Szócska M.; Tabaeian S.P.; Tabarés-Seisdedos R.; Tabatabaei S.M.; Tabuchi T.; Tadbiri H.; Taheri E.; Taheri M.; Taheri Soodejani M.; Takahashi K.; Talaat I.M.; Tampa M.; Tan K.-K.; Tat N.Y.; Tat V.Y.; Tavakoli A.; Tavakoli A.; Tehrani-Banihashemi A.; Tekalegn Y.; Tesfay F.H.; Thapar R.; Thavamani A.; Thoguluva Chandrasekar V.; Thomas N.; Thomas N.K.; Ticoalu J.H.V.; Tiyuri A.; Tollosa D.N.; Topor-Madry R.; Touvier M.; Tovani-Palone M.R.; Traini E.; Tran M.T.N.; Tripathy J.P.; Ukke G.G.; Ullah I.; Ullah S.; Ullah S.; Unnikrishnan B.; Vacante M.; Vaezi M.; Valadan Tahbaz S.; Valdez P.R.; Vardavas C.; Varthya S.B.; Vaziri S.; Velazquez D.Z.; Veroux M.; Villeneuve P.J.; Violante F.S.; Vladimirov S.K.; Vlassov V.; Vo B.; Vu L.G.; Wadood A.W.; Waheed Y.; Walde M.T.; Wamai R.G.; Wang C.; Wang F.; Wang N.; Wang Y.; Ward P.; Waris A.; Westerman R.; Wickramasinghe N.D.; Woldemariam M.; Woldu B.; Xiao H.; Xu S.; Xu X.; Yadav L.; Yahyazadeh Jabbari S.H.; Yang L.; Yazdanpanah F.; Yeshaw Y.; Yismaw Y.; Yonemoto N.; Younis M.Z.; Yousefi Z.; Yousefian F.; Yu C.; Yu Y.; Yunusa I.; Zahir M.; Zaki N.; Zaman B.A.; Zangiabadian M.; Zare F.; Zare I.; Zareshahrabadi Z.; Zarrintan A.; Zastrozhin M.S.; Zeineddine M.A.; Zhang D.; Zhang J.; Zhang Y.; Zhang Z.-J.; Zhou L.; Zodpey S.; Zoladl M.; Vos T.; Hay S.I.; Force L.M.; Murray C.J.L.; GBD 2019 Cancer Risk Factors Collaborators | The Lancet, 2022 | |

## AbstractBackground: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). Interpretation: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Funding: Bill & Melinda Gates Foundation. © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license |
|||

article
## Accepting Multiple Splicing Systems |
Sanchez Couso Jose Ramon; Arroyo Fernando; Mitrana Victor; Paun Andrei; Paun Mihaela | Journal Of King Saud University-Computer And Information Sciences, 2022 | |

## AbstractWe introduce an accepting splicing system based on a type of splicing, multiple splicing, which has never considered so far for accepting systems. This type of splicing differs from the usual operation in that several (not necessarily distinct) rules can be applied simultaneously to the same string. We first consider accepting multiple splicing systems where the number of splicing sites is a predefined constant. We prove that this model is computationally complete, if the constant is 2, by simulating a 2-tag system. Moreover, we show that the simulation is time-complexity preserving, and discuss also the descriptional complexity of the accepting splicing system given by our construction. We then consider the accepting multiple splicing systems where the number of sites has either an upper bound or a lower bound. The computational power of these systems is also investigated. We finally discuss some open problems. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. |
|||

article
## How Accurate Is The Remote Sensing Based Estimate Of Water Physico-Chemical Parameters In The Danube Delta (Romania)? |
Necula Marian; Tusa Iris Maria; Sidoroff Manuela Elisabeta; Itcus Corina; Florea Daniela; Amarioarei Alexandru; Paun Andrei; Pacioglu Octavian; Paun Mihaela | Annals Of Forest Research, 2022 | |

## AbstractThe current paper estimated the physico-chemical properties of water in the Danube Delta (Romania), based on Sentinel 2 remote sensing data. Eleven sites from the Danube Delta were sampled in spring and autumn for three years (2018-2020) and 21 water physico-chemical parameters were measured in laboratory. Several families of machine learning algorithms, translated into hundreds of models with different parameterizations for each machine learning algorithm, based on remote sensing data input from Sentinel 2 spectral bands, were employed to find the best models that predicted the values measured in laboratory. This was a novel approach, reflected in the types of selected models that minimised the values of performance metrics for the tested parameters. For alkalinity, calcium, chloride, carbon dioxide, hardness, potassium, sodium, ammonium, dissolved oxygen, sulphates, and suspended matter the results were promising, with an overall percentage bias of the estimates of +/- 10% from the observed values. For copper, magnesium, nitrites, nitrates, turbidity and zinc the estimates were fairly accurate, with percentage biases in the interval +/- 10% - 20%, whereas for detergents, led, and phosphates the percentage bias was higher than 20%. Overall, the results of the current study showed fairly good estimates between remote sensing based estimates and laboratory measured values for most water physico-chemical parameters. |
|||

article
## Increasing Rdi Outputs Through The Competitive Research Funding Operational Programme With Impact On The Emerging Market |
Dobrota Carmen; Rosu Maria-Magdalena; Puiu Andreea-Ionela; Milea Eduard C.; Paun Mihaela | Romanian Statistical Review, 2022 | |

## AbstractConsidering the broad impact of applied research on the economy, RDI funding evaluations are required both in terms of the number of allocated resources and the management of these resources. RDI efficacy depends on human resources productivity and the financing instruments established through national priorities. This paper offers an analysis of the RDI investments in Romania allocated through the Competitiveness Operational Programme 2014-2020, covering the European Structural and Investment Funds, namely the European regional development fund. The analysis of the funded projects highlighted the common trends among the beneficiaries of RDI projects, their options in managing resources in relation to the eligibility of costs, and their national distribution between the seven development regions of Romania. The amount of funding was discussed in terms of the smart specialization domains established by the national strategy. The conclusions of the study, correlated with the objectives set by the SNCDI 2014-2020, are relevant for the management of the future funding instruments allocated to RDI by the ERDF in the period 2021-2027. |
|||

article
## New Methodological Approach To Classify Educational Institutions-A Case Study On Romanian High Schools |
Necula Marian; Rosu Maria-Magdalena; Firescu Alexandra-Maria; Basu Cecilia; Ardelean Andreea; Milea Eduard C.; Paun Mihaela | Mathematics, 2022 | |

## AbstractSince 2021, the National Evaluation exam in Romania (the exam aimed to assess 14- to 15-year-old students' knowledge at the end of lower secondary education and just before high school) has presented a novel examination structure that resembles PISA tests. The current investigation analyses the 2021 National Evaluation exam results compared to the results obtained in the previous two years (2019-2020) as an evaluation of upper education institutions' effectiveness in Romania. The results put forward the same conclusions as proposed by extant literature on Bucharest high schools. Even though the educational institutions show apparent progress and great adaptability to change, a more in-depth analysis reveals great inequality between educational institutions. As in the case of Bucharest, nationally there are only a small number of top-performing high schools in Romania, with the majority of high schools ranking in the lowest category as conceptualised in the study. The current investigation puts together a novel methodology for classification based on the main instruments proposed in literature: a letter grade classification and Turner's f-index. The results and the methodological proposal are especially relevant considering the latest PISA (2018) conclusions on Romania characterising the national educational system as underperforming. |
|||

conference
## Network Controllability Analysis For Drug Repurposing In Covid-19 |
Nicoleta Siminea; Victor Popescu; Jose Angel Sanchez Martin; Ana-Maria Dobre; Daniela Florea; Geor-giana Gavril; Corina Ițcuș; Krishna Kanhaiya; Octavian Pacioglu; Laura Ioana Popa; Romica Trandafir; Maria Iris Tușa; Manuela Sidoroff; Mihaela Păun; Eugen Czeizler; Andrei Păun; Ion Petre | The 29Th Conference On Inteligent Systems For Molecular Biology, Joint With The 20Th European Conference On Computational Biology, 2021 | |

## Abstract |
|||

article
## Simulations Between Three Types Of Networks Of Splicing Processors |
Sanchez Couso Jose Ramon; Sanchez Martin Jose Angel; Mitrana Victor; Paun Mihaela | Mathematics, 2021 | |

## AbstractNetworks of splicing processors (NSP for short) embody a subcategory among the new computational models inspired by natural phenomena with theoretical potential to handle unsolvable problems efficiently. Current literature considers three variants in the context of networks managed by random-context filters. Despite the divergences on system complexity and control degree over the filters, the three variants were proved to hold the same computational power through the simulations of two computationally complete systems: Turing machines and 2-tag systems. However, the conversion between the three models by means of a Turing machine is unattainable because of the huge computational costs incurred. This research paper addresses this issue with the proposal of direct and efficient simulations between the aforementioned paradigms. The information about the nodes and edges (i.e., splicing rules, random-context filters, and connections between nodes) composing any network of splicing processors belonging to one of the three categories is used to design equivalent networks working under the other two models. We demonstrate that these new networks are able to replicate any computational step performed by the original network in a constant number of computational steps and, consequently, we prove that any outcome achieved by the original architecture can be accomplished by the constructed architectures without worsening the time complexity. |
|||

article
## Nonlinear Parsimonious Forest Modeling Assuming Normal Distribution Of Residuals |
Strimbu Bogdan M.; Amarioarei Alexandru; Paun Mihaela | European Journal Of Forest Research, 2021 | |

## AbstractTo avoid the transformation of the dependent variable, which introduces bias when back-transformed, complex nonlinear forest models have the parameters estimated with heuristic techniques, which can supply erroneous values. The solution for accurate nonlinear models provided by Strimbu et al. (Ecosphere 8:e01945, 2017) for 11 functions (i.e., power, trigonometric, and hyperbolic) is not based on heuristics but could contain a Taylor series expansion. Therefore, the objectives of the present study are to present the unbiased estimates for variance following the transformation of the predicted variable and to identify an expansion of the Taylor series that does not induce numerical bias for mean and variance. We proved that the Taylor series expansion present in the unbiased expectation of mean and variance depends on the variance. We illustrated the new modeling approach on two problems, one at the ecosystem level, namely site productivity, and one at individual tree level, namely stem taper. The two models are unbiased, more parsimonious, and more precise than the existing less parsimonious models. This study focuses on research methods, which could be applied in similar studies of other species, ecosystem, as well as in behavioral sciences and econometrics. |
|||

article
## Dna-Guided Assembly For Fibril Proteins |
Amarioarei Alexandru; Spencer Frankie; Barad Gefry; Gheorghe Ana-Maria; Itcus Corina; Tusa Iris; Prelipcean Ana-Maria; Paun Andrei; Paun Mihaela; Rodriguez-Paton Alfonso; Trandafir Romica; Czeizler Eugen | Mathematics, 2021 | |

## AbstractCurrent advances in computational modelling and simulation have led to the inclusion of computer scientists as partners in the process of engineering of new nanomaterials and nanodevices. This trend is now, more than ever, visible in the field of deoxyribonucleic acid (DNA)-based nanotechnology, as DNA's intrinsic principle of self-assembly has been proven to be highly algorithmic and programmable. As a raw material, DNA is a rather unremarkable fabric. However, as a way to achieve patterns, dynamic behavior, or nano-shape reconstruction, DNA has been proven to be one of the most functional nanomaterials. It would thus be of great potential to pair up DNA's highly functional assembly characteristics with the mechanic properties of other well-known bio-nanomaterials, such as graphene, cellulos, or fibroin. In the current study, we perform projections regarding the structural properties of a fibril mesh (or filter) for which assembly would be guided by the controlled aggregation of DNA scaffold subunits. The formation of such a 2D fibril mesh structure is ensured by the mechanistic assembly properties borrowed from the DNA assembly apparatus. For generating inexpensive pre-experimental assessments regarding the efficiency of various assembly strategies, we introduced in this study a computational model for the simulation of fibril mesh assembly dynamical systems. Our approach was based on providing solutions towards two main circumstances. First, we created a functional computational model that is restrictive enough to be able to numerically simulate the controlled aggregation of up to 1000s of elementary fibril elements yet rich enough to provide actionable insides on the structural characteristics for the generated assembly. Second, we used the provided numerical model in order to generate projections regarding effective ways of manipulating one of the the key structural properties of such generated filters, namely the average size of the openings (gaps) within these meshes, also known as the filter's aperture. This work is a continuation of Amarioarei et al., 2018, where a preliminary version of this research was discussed. |
|||

article
## Hairpin Completions And Reductions: Semilinearity Properties |
Bordihn Henning; Mitrana Victor; Paun Andrei; Paun Mihaela | Natural Computing, 2021 | |

## AbstractThis paper is part of the investigation of some operations on words and languages with motivations coming from DNA biochemistry, namely three variants of hairpin completion and three variants of hairpin reduction. Since not all the hairpin completions or reductions of semilinear languages remain semilinear, we study sufficient conditions for semilinear languages to preserve their semilinearity property after applying the non-iterated hairpin completion or hairpin reduction. A similar approach is then applied to the iterated variants of these operations. Along these lines, we define the hairpin reduction root of a language and show that the hairpin reduction root of a semilinear language is not necessarily semilinear except the universal language. A few open problems are finally discussed. |
|||

article
## Efficient Synthetic Generation Of Ecological Data With Preset Spatial Association Of Individuals |
Strimbu Bogdan M.; Paun Andrei; Amarioarei Alexandru; Paun Mihaela; Strimbu Victor F. | Canadian Journal Of Forest Research, 2021 | |

## AbstractMany experiments cannot feasibly be conducted as factorials. Simulations using synthetically generated data are viable alternatives to such factorial experiments. The main objective of the present research is to develop a methodology and platform to synthetically generate spatially explicit forest ecosystems represented by points with a predefined spatial pattern. Using algorithms with polynomial complexity and parameters that control the number of clusters, the degree of clusterization, and the proportion of nonrandom trees, we show that spatially explicit forest ecosystems can be generated time efficiently, which enables large factorial simulations. The proposed method was tested on 1200 synthetically generated forest stands, each of 25 ha, using 10 spatial indices: Clark-Evans aggregation index; Ripley's K; Besag's L; Morisita's dispersion index; Greig-Smith index; the size dominance index of Hui; index of nonrandomness of Pielou; directional index and mean directional index of Corral-Rivas; and size differentiation index of Von Gadow. The size of individual trees was randomly generated aiming at variograms such as real forests. We obtained forest stands with the expected spatial arrangement and distribution of sizes in less than 1 h. To ensure replicability of the study, we have provided free, fully functional software that executes the stated tasks. |
|||

article
## Students' Perception Of Online Education In The Covid-19 Pandemic Framework |
Buzatu Andreea-Raluca; Cojoc Cristian; Cotovici Ecaterina; Spirache Miruna Cristiana; Trandafir Romica; Paun Mihaela | Romanian Statistical Review, 2020 | |

## AbstractDue to the wide worldwide spread the COVID-19 pandemic has reached at the beginning of 2020, many countries have imposed strict measures of social distancing, the result of which was a sudden shift towards the online environment for most institutions of each state. This study explores students' perception of the quality of online education during the COVID-19 pandemic, right after the shift from traditional face-to-face learning to online education. Using an online questionnaire, feedback from the respondents regarding their perception of online education, sources of information used and preventive behavior is collected. A total of 238 students from different levels and fields participated in the study which concludes with a general opinion reflecting that although in favor of online education, students are unsure if the quality of it matches the quality of the traditional face-to-face education. |
|||

article
## Networks Of Uniform Splicing Processors: Computational Power And Simulation |
Gomez-Canaval Sandra; Mitrana Victor; Paun Mihaela; Sanchez Martin Jose Angel; Sanchez Couso Jose Ramon | Mathematics, 2020 | |

## AbstractWe investigated the computational power of a new variant of network of splicing processors, which simplifies the general model such that filters remain associated with nodes but the input and output filters of every node coincide. This variant, callednetwork of uniform splicing processors, might be implemented more easily. Although the communication in the new variant seems less powerful, the new variant is sufficiently powerful to be computationally complete. Thus, nondeterministic Turing machines were simulated by networks of uniform splicing processors whose size depends linearly on the alphabet of the Turing machine. Furthermore, the simulation was time efficient. We argue that the network size can be decreased to a constant, namely six nodes. We further show that networks with only two nodes are able to simulate 2-tag systems. After these theoretical results, we discuss a possible software implementation of this model by proposing a conceptual architecture and describe all its components. |
|||

article
## Development Of Nonlinear Parsimonious Forest Models Using Efficient Expansion Of The Taylor Series: Applications To Site Productivity And Taper |
Amarioarei Alexandru; Paun Mihaela; Strimbu Bogdan | Forests, 2020 | |

## AbstractThe parameters of nonlinear forest models are commonly estimated with heuristic techniques, which can supply erroneous values. The use of heuristic algorithms is partially rooted in the avoidance of transformation of the dependent variable, which introduces bias when back-transformed to original units. Efforts were placed in computing the unbiased estimates for some of the power, trigonometric, and hyperbolic functions since only few transformations of the predicted variable have the corrections for bias estimated. The approach that supplies unbiased results when the dependent variable is transformed without heuristic algorithms, but based on a Taylor series expansion requires implementation details. Therefore, the objective of our study is to investigate the efficient expansion of the Taylor series that should be included in applications, such that numerical bias is not present. We found that five functions require more than five terms, whereas the arcsine, arccosine, and arctangent did not. Furthermore, the Taylor series expansion depends on the variance. We illustrated the results on two forest modeling problems, one at the stand level, namely site productivity, and one at individual tree level, namely taper. The models that are presented in the paper are unbiased, more parsimonious, and they have a RMSE comparable with existing less parsimonious models. |
|||

article
## Dna Origami Design And Implementation: The Romanian Map |
Popa Laura Ioana; Dobre Ana-Maria; Itcus Corina; Amarioarei Alexandru; Paun Andrei; Paun Mihaela; Pop Felician; Tusa Iris; Minh-Kha Nguyen; Kuzyk Anton; Czeizler Eugen | Romanian Biotechnological Letters, 2020 | |

## AbstractSince its introduction in the early 2000s, DNA origami had a big impact on the development of nanotechnology by gathering numerous applications. During this time, many tools were designed and used to generate arbitrary shapes capable of self-assembly which make this technique more approachable. In this paper, we have created the map of Romania at nanoscale dimensions by using a new open-source software - PERDIX. For this purpose, we used a scaffold strand with a length of 6959 nucleotides and 162 staple strands with a variable length ranging between 20 and 63 nucleotides. All the computational tools that were used in this experiment are open-source and user-friendly. |
|||

article
## On The Group Memory Complexity Of Extended Finite Automata Over Groups |
Arroyo Fernando; Mitrana Victor; Paun Andrei; Paun Mihaela; Sanchez Couso Jose Ramon | Journal Of Logical And Algebraic Methods In Programming, 2020 | |

## AbstractWe define and investigate a complexity measure defined for extended finite automata over groups (EFA). Roughly, an EFA is a finite automaton augmented with a register storing an element of a group, initially the identity element. When a transition is performed, not only the state, but the register contents are updated. A word is accepted if, after reading completely the word, the automaton reached a final state, and the register returned to the identity element. The group memory complexity of an EFA over a group is a function from N to N which associates with each n the value 0, if there is no word of length n accepted by the automaton, or the minimal integer c such that for every word x of length n accepted by the automaton, there is a computation on x such that the number of transitions labeled by non-neutral element of the group used in that computation is at most c. We prove that a language is regular if and only if it is accepted by an EFA with a finite group memory complexity. In particular, any EFA over a group such that all its finitely generated subgroups are finite accepts a regular language. We then provide examples of EFA over some groups that accept non-regular languages and have a sublinear group memory complexity, namely a function in O(root n) or O(log n). There are non-regular languages such that any EFA over some group that accepts that language has a group memory complexity in Omega(n). (C) 2020 Elsevier Inc. All rights reserved. |
|||

conference
## How Complex Is To Solve A Hard Problem With Accepting Splicing Systems |
Victor Mitrana; Andrei Paun; Mihaela Paun | 4Th International Conference On Complexity, Future Information Systems And Risk, Crete, Greece, Proceedings Of The 4Th International Conference On Complexity, Future Information Systems And Risk (Complexis), 2019 | |

## AbstractWe define a variant of accepting splicing system that can be used as a problem solver. A condition for halting the computation on a given input as well as a condition for making a decision as soon as the computation has stopped is considered. An algorithm based on this accepting splicing system that solves a well-known NP-complete problem, namely the 3-colorability problem is presented. We discuss an efficient solution in terms of running time and additional resources (axioms, supplementary symbols, number of splicing rules. More precisely, for a given graph with n vertices and m edges, our solution runs in O (nm) time, and needs O (mn(2)) other resources. Two variants of this algorithm of a reduced time complexity at an exponential increase of the other resources are finally discussed. |
|||

conference
## A Multi-Agent Model For Cell Population |
Fernando Arroyo; Victor Mitrana; Andrei Păun; Mihaela Păun | Agents And Multi-Agent Systems: Technologies And Applications 2019, Smart Innovation, Systems And Technologies (Book Series), 2019 | |

## AbstractAn intriguing problem in computer science is the formal description of dynamics in cell populations. We propose here a multi-agent-based model that could be used in this respect. The model proposed in this paper consists of biological entities (cells) as agents and a biochemical environment. Both are represented by multisets of symbols. The environment evolution is regulated by multiset Lindenmayer rules depending on the current state of all agents, while the evolution of each agent, which depends on the environment current state, is defined by means of multiset patterns. We discuss some algorithmic problems related to the dynamics of the proposed multi-agent model: infinite and stationary evolution, environment, and agent reachability. |
|||

conference
## Simulation Of One Dimensional Staged Dna Tile Assembly By The Signal-Passing Hierarchical Tam |
Barad Gefry; Amarioarei Alexandru; Paun Mihaela; Dobre Ana Maria; Itcus Corina; Tusa Iris; Trandafir Romica; Czeizler Eugen | Knowledge-Based And Intelligent Information & Engineering Systems (Kes 2019), 2019 | |

## AbstractThe Tile Assembly Model, and its many variants, is one of the most fundamental algorithmic assembly formalism within DNA nanotechnology. Most of the research in this field is focused on the complexity of assembling different shapes and patterns. In many cases, the assembly process is intrinsically deterministic and the final product is unique, while the assembly process might evolve through several possible assembly strategies. In this study we consider the controlled assembly of one dimensional tile structures according to predefined assembly graphs. We provide algorithmic approaches for developing such controlled assembly protocols, using the signal-passing Tile Assembly Model, as well as probabilistic approaches for investigating the assembly of such tile-based one-dimensional structures. As a byproduct, we build a generalized TAS (tile assembly system) which generate specific non-local non-associative algebraic computations and we assamble n x n squares using only one tile, which is a better efficiency compared to the staged assembly model. (C) 2019 The Authors. Published by Elsevier B.V. |
|||

conference
## Further Properties Of Self-Assembly By Hairpin Formation |
Henning Bordihn; Victor Mitrana; Andrei Păun; Mihaela Păun | International Conference On Unconventional Computation And Natural Computation, Unconventional Computation And Natural Computation, Ucnc 2019, 2019 | |

## AbstractWe continue the investigation of three operations on words and languages with motivations coming from DNA biochemistry, namely unbounded and bounded hairpin completion and hairpin lengthening. We first show that each of these operations can be used for replacing the third step, the most laborious one, of the solution to the CNF-SAT reported in [28]. As not all the bounded/unbounded hairpin completion or lengthening of semilinear languages remain semilinear, we study sufficient conditions for semilinear languages to preserve their semilinearity property after applying once either the bounded or unbounded hairpin completion, or lengthening. A similar approach is then started for the iterated variants of the three operations. A few open problems are finally discussed. |
|||

conference
## Dna-Guided Assembly Of Nanocellulose Meshes |
Alexandru Amărioarei; Gefry Barad; Eugen Czeizler; Ana-Maria Dobre; Corina Iţcuş; Victor Mitrana; Andrei Păun; Mihaela Păun; Frankie Spencer; Romică Trandafir; Iris Tuşa | International Conference On Theory And Practice Of Natural Computing, Tpnc 2018: Theory And Practice Of Natural Computing, 2018 | |

## Abstract |
|||

article
## A Posteriori Bias Correction Of Three Models Used For Environmental Reporting |
Bogdan M Strimbu; Alexandru Amarioarei; John Paul McTague; Mihaela M Paun | Forestry, Forestry: An International Journal Of Forest Research, 2018 | |

## AbstractA plethora of forest models were developed by transforming the dependent variable, which introduces bias if appropriate corrections are not applied when back-transformed. Many recognized models are still biased and the original data sets are no longer available, which suggests ad hoc bias corrections. The present research presents a procedure for bias correction in the absence of needed information from summary statistics. Additionally, we developed a realistic correction of the square root transformation based on a truncated normal distribution. The transformations considered in this study are the logarithm, the square root and arcsine square root. Using simulated data we found that uncorrected back-transformation created biases by as much as 100 percent. The generated data revealed that depending on available information, that bias can still be present after correction. In addition to generated data we corrected the site index of Douglas-fir and ponderosa pine in Oregon USA, tree volume of 27 species from Romania, stand merchantable volume for longleaf pine in Louisiana and East Texas USA, and canopy fuel weight in Washington USA. Using only the available information, the unbiased back-transformed estimates can change from <= 1 percent (i.e. the site index and canopy fuel weight) to >= 1/3 (tree and stand volume). |
|||

article
## A Parsimonious Approach For Modeling Uncertainty Within Complex Nonlinear Relationships |
Bogdan M. Strimbu; Alexandru Amarioarei; Mihaela Paun | Ecosphere, Ecosphere, An Esa Open Access Journal, 2018 | |

## AbstractAdvancements in information technology led environmental scientists to the illusion that efforts should be mainly focused on developing models that reduce uncertainty rather than on models adjusted to the existing uncertainty. As a result, environmental relationships are represented by non-parsimonious and suboptimal models, which in many instances could be even wrong. The objective of this research was to provide scientists focused on modeling ecosystem processes with a procedure that supplies parsimonious correct results. The procedure transforms the response variable to achieve a linear model and the normality of the residuals. After the parameters of the transformed model are estimated, the bias induced by back-transforming is corrected. We have computed the bias corrections for 11 of the most popular functions from the power, trigonometric, and hyperbolic families by considering the truncated normal distribution, when necessary. Using generated data, we have shown that the proposed procedure supplies unbiased results. We have identified a sample size artifact of data generation such that when the variance increases the truncation of distribution starts altering the corrections of predicted values, sometimes by more than 50% from the actual values. Our results indicate that uncertainty, measured by variance, impacts the analysis in a non-intuitive way when the defining domain of the response variable is restricted. The subtle way of influencing the development of complex nonlinear models by uncertainty advocates the usage of parsimonious linear models, which are less sensitive to the method of processing data. Finally, ecosystem processes should be modeled with strategies that consider not only processes and computation aspects, but also uncertainty, in particularly reducing variance to levels with no significant impact on the results. |
|||

article
## Small Networks Of Polarized Splicing Processors Are Universal |
Henning Bordihn; Victor Mitrana; Maria C. Negru; Andrei Păun; Mihaela Păun | Natural Computing, 2018 | |

## AbstractIn this paper, we consider the computational power of a new variant of networks of splicing processors in which each processor as well as the data navigating throughout the network are now considered to be polarized. While the polarization of every processor is predefined (negative, neutral, positive), the polarization of data is dynamically computed by means of a valuation mapping. Consequently, the protocol of communication is naturally defined by means of this polarization. We show that networks of polarized splicing processors (NPSP) of size 2 are computationally complete, which immediately settles the question of designing computationally complete NPSPs of minimal size. With two more nodes we can simulate every nondeterministic Turing machine without increasing the time complexity. Particularly, we prove that NPSP of size 4 can accept all languages in NP in polynomial time. Furthermore, another computational model that is universal, namely the 2-tag system, can be simulated by NPSP of size 3 preserving the time complexity. All these results can be obtained with NPSPs with valuations in the set as well. We finally show that Turing machines can simulate a variant of NPSPs and discuss the time complexity of this simulation. |
|||

article
## Classification Of Romanian Salt Water Lakes By Statistical Methods |
Amarioarei A.; Itcus C.; Tusa I.; Sidoroff M.; Paun M. | Journal Of Environmental Protection And Ecology, 2018 | |

## AbstractInvestigation of the lake systems can provide a variety of information that can lead to the development of general concepts about how lakes function and respond to environmental changes. The purpose of this study is to assess the current classification of therapeutic lakes based on supervised learning methods applied to several biochemical characteristics of such lakes. In order to classify the therapeutic lakes in a separate class, a dataset consisting of 45 observations from 9 different basins and from three different altitude categories was analysed using clustering and classification methods. |
|||

article
## A Scalar Measure Tracing Tree Species Composition In Space Or Time |
Bogdan M.Strimbu; Mihaela Paun; Cristian Montes; Sorin C.Popescu | Physica A-Statistical Mechanics And Its Applications, 2018 | |

## AbstractThe tree species composition of a forest ecosystem is commonly represented with weights that measure the importance of one species with respect to the other species. Inclusion of weight in practical applications is difficult because of the inherent multidimensional perspective on composition. Scalar indices overcome the multidimensional challenges, and, consequently, are commonly present in complex ecosystem modeling. However, scalar indices face two major issues, namely non-uniqueness and non-measurability, which limit their ability to be generalized. The objective of this study is to identify the conditions for developing a univariate true measure of composition from weights. We argue that six conditions define a scalar measure of species mixture: (1) usefulness, (2) all species have equal importance, (3) all individuals have the same importance, (4) the measurements expressing importance of an individual are consistent and appropriate, (5) the function measuring composition is invertible, and (6) the function is a true-measure. We support our argument by formally proving all the conditions. To illustrate the applicability of the scalar measure we develop a rectilinear-based measure, and apply it in yield modeling and assessment of ecosystem dynamics. (C) 2018 Elsevier B.V. All rights reserved. |
|||

conference
## Biostatistical Methods Related To Modasys Project |
Mihaela Paun | Workshop 2018 Algonano: Metode Algoritmice Și Computaționale În Bio-Medicină Și Nanotehnologie, 2018 | |

## Abstract |
|||

conference
## Computational Approaches For The Programmed Assembly Of Nanocellulose Meshes |
Alexandru Amarioarei; Frankie Spencer; Trandafir Romica; Gefry Barad; Ana Maria Dobre; Corina Itcus; Iris Tusa; Mihaela Paun; Andrei Paun and Eugen Czeizler | 3Rd International Workshop On Verification Of Engineered Molecular Devices And Programs, Oxford, United Kingdom, 2018 | |

## Abstract |
|||

article
## One Dimensional Dna Tiles Self Assembly Model Simulation |
Amarioarei Alexandru; Barad Gefry; Czeizler Elena; Czeizler Eugen; Dobre Ana-Maria; Itcus Corina; Paun Andrei; Paun Mihaela; Trandafir Romica; Tusa Iris | International Journal Of Unconventional Computing, 2018 | |

## AbstractThe TAM (Model Tile Assembly Model) is a mathematical paradigm for modeling DNA self-assembling according to various given shapes, using DNA-tiles (rectangular shape) with sticky ends on each of the four edges that bound together on various shapes desired by the researcher. Although there are various models in the literature, the focus in this manuscript is on a rule based model, specifically the authors present an overview of the one-dimensional hierarchical self-assembly model of DNA tiles. The authors also present the evolution of number of tiles in partial assemblies, the average assembly size and of the number of partial assemblies of sizes 2 through 10 over the total running time. All simulations were run using the NFSim simulator on a preset period of time. |
|||

conference
## Towards Probabilistic Networks Of Polarized Evolutionary Processors |
Fernando Arroyo ; Sandra Gomez-Canaval ; Victor Mitrana ; Mihaela Paun ; Jose Ramon Sanchez-Couso | International Conference On High Performance Computing & Simulation (Hpcs), Proceedings 2018 International Conference On High Performance Computing & Simulation (Hpcs), 2018 | |

## AbstractThe aim of this paper is to discuss two possible ways of introducing some features based on probabilistic concepts and methods in networks of polarized evolutionary processors (NPEP). We associate probabilities with rules in every node such that together with the communication protocol, which is based on the compatibility between the polarization of each node and data navigating through the network, might facilitate the study of biological phenomena as well as software simulations or hardware implementations. The probability associated with rules may be a priori defined and fixed or may be computed dynamically. Probabilities will also appear when communicating data between nodes; these probabilities may be statically or dynamically defined. This note also proposes the study of the impact of these characteristics and see how these new features reduce the gap between the formal model and its practical applicability. Introducing probabilities in NPEP is aimed to decrease the exponential expansion of the number of strings which appear in the computations used to solve NP-problems in a polynomial time. A decreasing of the exponential expansion of this number is achieved with a loss of certainty of the final result which is reached with some error probability. |
|||

article
## 3D Dna Origami Map Structure Simulation |
Itcus Corina; Amarioarei Alexandru; Czeizler Eugen; Dobre Ana-Maria; Mitrana Victor; Negre Florentina; Paun Andrei; Paun Mihaela; Sidoroff Manuela Elisabeta; Trandafir Romica; Tusa Iris | Romanian Journal Of Information Science And Technology, 2018 | |

## AbstractThis paper presents the latest trends and approaches used for constructing nanoscale structures of 2D objects through DNA folding based on the DNA origami technology developed by Rothemund. The Rothemund method has been used in the construction of various shapes, such as the development of the nanoscale structure for the United States map. Following the steps of Rothemund's technique, we simulate the construction of the Romanian map nanoscale 2D structure, embedding the number 100 into it. |
|||

article
## Phytoremediation Research - How Romania Is Placed Worldwide |
Amarioarei Alexandru; Itcus Corina; Paun Mihaela | Romanian Biotechnological Letters, 2017 | |

## AbstractIn the last few decades, due to the global industrialization and population expansion the level of pollutants has largely increased, being one of the main environmental problems faced worldwide. Consequently, phytoremediation research had been gathering more and more interest. A study of data selected from Scopus is presented by the authors, identifying trends in publication number, collaboration and impact. The trends are identified at the global level followed by a discussion of how Romania performs with respect to the identified trends. When evaluating the research produced by the Romanian institutions, two characteristics are considered: phytoremediation potential of the plants in the Danube Delta and the coastal region and the phytoremediation research temporal evolution and international impact. The paper presents a quantitative analysis without adding variable weights to citations and publications based on the publications ranks offering an overview of the national research status in this research area. |
|||

conference
## Networks Of Polarized Splicing Processors |
Henning Bordihn; Victor Mitrana; Andrei Păun; Mihaela Păun | International Conference On Theory And Practice Of Natural Computing, Tpnc 2017: Theory And Practice Of Natural Computing, Theory And Practice Of Natural Computing, Tpnc 2017, 2017 | |

## AbstractIn this paper, we consider the computational power of a new variant of networks of splicing processors in which each processor as well as the data navigating throughout the network are now considered to be polarized. While the polarization of every processor is predefined (negative, neutral, positive), the polarization of data is dynamically computed by means of a valuation mapping. Consequently, the protocol of communication is naturally defined by means of this polarization. We show that networks of polarized splicing processors (NPSP) of size 2 are computationally complete, which immediately settles the question of designing computationally complete NPSPs of minimal size. We prove that NPSP of size 4 can accept all languages in NP in polynomial time. All these results can be obtained with NPSPs with valuations in the set {-1, 0, 1} as well. We finally show that Turing machines can simulate a variant of NPSPs and discuss the time complexity of these simulations. |
|||

article
## Improvements On Contours Based Segmentation For Dna Microarray Image Processing |
Yang Lia; Andrei Păun; Mihaela Păun | Theoretical Computer Science, 2017 | |

## Abstractthis paper we present an improvement of the Segment Based Contours (SBC) method by implementing a higher order of finite difference schemes in the partial differential equation used in our mathematical model. Two methods are presented: one is a 4th order method and the other a 8th order method. The 4th order method could be applied to segment both the cDNA microarray images and the Affymetrix GeneChips, while the 8th order method could only be applied to processing the cDNA microarray images, due to the limitation of the current image resolution. Additionally, we provide both the mathematical derivations for the partial. differential equations (their 4th or 8th order approximations) as well as the validation trough simulations of the microarray images by using real images as seeds for the Nykter's 2006 methodology. We conclude by showing that both the 4th order method as well as the 8th order one are superior to the SBC and the widely used GOGAC method implemented in the Affymetrix standard processing package for microarrays. (C) 2017 Elsevier B.V. All rights reserved. |
|||

article
## Measuring Funded Research Performance For Multidisciplinary Research In The Danube Basin |
Sidoroff M.; Paraschiv M.; Amarioarei A.; Paun M. | Journal Of Environmental Protection And Ecology, 2016 | |

## AbstractEvaluation of funded research, by measuring the outcomes of the grants publications, journals, and citations is not always done comparatively and publicised. Bibliometric indicators were employed and applied to the 2009-2014 publications authored by academicians funded by European research grants that are indexed in the Web of Science. Citation based approaches, such as the h-index or the impact factor, have been widely used to evaluate researchers or journals. In this study we use the aforementioned H-index to evaluate the funded research grants and to provide a ranking in terms of the most successful ones. The ranking results can be used by evaluators in determining how successful an applicant has potential to be, but also by researchers to choose their publication outlets, or to pursue future collaborations. |
|||

article
## Cellulase-Mediated Hydrolysis Applied On Several Danube Delta Bioresources |
Paraschiv M.; Manole C.; Tusa I.; Paun M.; Tcacenco L. | Journal Of Environmental Protection And Ecology, 2016 | |

## AbstractThe paper deals with the cellulase-mediated hydrolysis applied to several residual biomass resulting after extraction of biologically active principles from three medicinal plants: Melissa officinalis L., Melilotus officinalis L., Viola tricolor L. The yield of hydrolysis to hexoses was determined based on the amount of free glucose in the reaction mixture identified by spectrophotometric analysis, and total conversion of biomass residues was calculated. It was found that biomass resulted from Viola tricolor L. exhibits best glucose amount (10.35%) after 1 h of enzymatic hydrolysis, while that sourced from Melissa officinalis L. gives the highest total conversion of solid biomass (82.13%). |
|||

article
## A Failure Index For Hpc Applications |
Paun Andrei; Chandler Clayton; Leangsuksun Chokchai Box; Paun Mihaela | Journal Of Parallel And Distributed Computing, 2016 | |

## AbstractThis paper conducts an examination of log files originating from High Performance Computing (HPC) applications with known reliability problems. The results of this study further the maturation and adoption of meaningful metrics representing HPC system and application failure characteristics. Quantifiable metrics representing the reliability of HPC applications are foundational for building an application resilience methodology critical in the realization of exascale supercomputing. In this examination, statistical inequality methods originating from the study of economics are applied to health and status information contained in HPC application log files. The main result is the derivation of a new failure index metric for HPC a normalized representation of parallel application volatility and/or resiliency to complement existing reliability metrics such as mean time between failure (MTBF), which aims for a better presentation of HPC application resilience. This paper provides an introduction to a Failure Index (FI) for HPC reliability and takes the reader through a use-case wherein the H is used to expose various run-time fluctuations in the failure rate of applications running on a collection of HPC platforms. (C) 2016 Elsevier Inc. All rights reserved. |
|||

article
## Twenty Years Of Research On Water Management Issues In The Danube Macro-Region — Past Developments And Future Directions |
Feldbacher E.; Paun M.; Reckendorfer W.; Sidoroff M.; Stanica A.; Strimbu B.; Tusa I.; Vulturescu V.; Hein T. | Science Of The Total Environment, 2016 | |

## AbstractThe Danube River–Danube Delta–Black Sea (DBS) region has witnessed major political, social and economic changes during the past three decades, which have profoundly affected the riverine, coastal and marine systems, their water management situation and the development of related research programmes. We reviewed the research activities in the DBS system of the past twenty years to determine the main funding bodies and to assess key research areas and how they varied over time and geographic region. As data basis we used a metadatabase filled with 478 projects addressing environmental and water management issues in the Danube River Basin, covering also the Danube Delta and the north-western Black Sea. As overall outcome extensive research efforts in the field of water management could be proven for the past two decades, despite the tumultuous times of political and economic transformations. One of the main findings was that EU funded projects played a key role for the development of transboundary research collaboration and were also the scientifically most productive one's. Historically, nutrient pollution was the main problem addressed, shifting to pollution in a broader sense and hydromorphological alterations in recent years. The newly arising challenges of climate change impacts and sediment management became important research questions in the last years, too. Most research was performed in the thematic field of navigation, followed by restoration and biodiversity issues. To meet all of the already identified and newly emerging challenges in the DBS System, cross-border and integrated (river-delta-sea) research activities are of major importance and have to be further promoted. We thus suggest drawing up a regional DBS Research Agenda linked to key challenges in water management to strengthen research collaboration and advance targeted scientific projects, an approach fostering also the scientific capacity in the region. © 2015 Elsevier B.V. |
|||

article
## Twenty Years Of Research On Water Management Issues In The Danube Macro-Region - Past Developments And Future Directions |
Feldbacher Eva; Paun Mihaela; Reckendorfer Walter; Sidoroff Manuela; Stanica Adrian; Strimbu Bogdan; Tusa Iris; Vulturescu Viorel; Heina Thomas | Science Of The Total Environment, 2016 | |

## AbstractThe Danube River-Danube Delta-Black Sea (DBS) region has witnessed major political, social and economic changes during the past three decades, which have profoundly affected the riverine, coastal and marine systems, their water management situation and the development of related research programmes. We reviewed the research activities in the DBS system of the past twenty years to determine the main funding bodies and to assess key research areas and how they varied over time and geographic region. As data basis we used a metadatabase filled with 478 projects addressing environmental and water management issues in the Danube River Basin, covering also the Danube Delta and the north-western Black Sea. As overall outcome extensive research efforts in the field of water management could be proven for the past two decades, despite the tumultuous times of political and economic transformations. One of the main findings was that EU funded projects played a key role for the development of transboundary research collaboration and were also the scientifically most productive one's. Historically, nutrient pollution was the main problem addressed, shifting to pollution in a broader sense and hydromorphological alterations in recent years. The newly arising challenges of climate change impacts and sediment management became important research questions in the last years, too. Most research was performed in the thematic field of navigation, followed by restoration and biodiversity issues. To meet all of the already |
|||

article
## Scientometric Indicators As A Way To Classify Brands For Customer’S Information |
Tusa I.; Paun M. | Journal Of Economic Development, Environment And People, 2015 | |

## Abstract |
|||

conference
## Environmental Research Output Assessment In The Danube Region |
Paun M. | Symposium Strategia Ue Pentru Regiunea Dunării “Cercetări Integrate În Bazinul Dunării” , Romanian Academy Of Science, 2015 | |

## Abstract |
|||

book, book chapter
## Strategia De Dezvoltate A Romaniei In Urmatorii 20 De Ani |
Sidoroff M.; Paun M. | Editura Academiei, 2015 | |

## Abstract |
|||

book, book chapter
## Towards The Integrated Management Of The Danube River – Danube Delta – Black Sea System: Proposal For A Strategic Research And Innovation Agenda |
Sidoroff M.; Paun M.; Litescu S. | Geoecomar, 2015 | |

## Abstract |
|||

book, book chapter
## Towards The Integrated Management Of The Danube River – Danube Delta – Black Sea System: Collaboration Of The Two Eusdr Flagship Distributed Research Infrastructures |
Sidoroff M.; Paun M.; Litescu S. | Geoecomar, 2015 | |

## Abstract |
|||

book, book chapter
## Owards The Integrated Management Of The Danube River – Danube Delta – Black Sea System: Proposal For The Development Of Human Capital |
Sidoroff M.; Paun M.; Litescu S. | Geoecomar, 2015 | |

## Abstract |
|||

article
## Environmental Research Assessment |
Tusa I.; Sidoroff M.; Paun M. | Journal Of Environmental Protection And Ecology, 2015 | |

## AbstractThe paper summarises the results of the 478 projects from the FP7-ENVIRONMENT proiect, DANube macroregion: Capacity building and Excellence in River Systems (basin, delta and sea) DANCERS database considering different attributes recorded for these projects. An analysis is performed on the recorded data and the significant characteristics are presented. The analysis performs a research output assessment in order to quantify the success of the projects. Trends are identified not only by thematic area and region of implementation, but also by coordinating country region and level of financing. A need to homogenising the level of financing as well as the level of funding efforts among the EU Strategy for the Danube Region (EUSDR) countries and not only, transpires at the end of the analysis. A concentrated effort towards inter and intra disciplinary collaboration as well as within and between institutional collaboration and knowledge sharing is also apparent. |
|||

article
## Hpc Application In Cloud Environment |
Paun M.; Leangsuksun C.; Nassar R.; Thanakornworakij T. | Romanian Journal Of Information Science And Technology, 2015 | |

## AbstractHigh Performance Computing applications on Cloud are of significance because of cost-effectiveness and elasticity. Reliability analysis of HPC applications on Cloud is an important areof study to better utilize infrastructure while dealing fault tolerant issues in a Cloud environment. In this work, we present a reliability model of a Cloud system under four scenarios: 1) Hardware components fail independently and software components fail independently; 2) software components fail independently and hardware components are correlated in failure; 3) correlated software failure and independent hardware failure; 4) dependent software and hardware failures. Moreover, we propose an optimal checkpoint placement technique based on reliability information for each scenario. Results show that if failure of the nodes and/or software in the system possesses a degree of dependency, the system becomes less reliable, which means that the failure rate increases and the mean time to failure decreases. Also, an increase in the number of nodes decreases there liability of the system. Moreover, the optimal checkpoint interval decreases when the reliability of the system decreases. |
|||

conference
## Danubius-Ri: The International Centre For Advanced Studies On River-Sea Systems |
Litescu S.; Paun M. | “Learning Week” Event, Organized By The European Commission Of The Romanian Representation At The European Commission, Brussels, 2015 | |

## Abstract |
|||

article
## Segmenting Microarray Images Using A Contour-Based Method |
Paun Mihaela; Li Yang; Cheng Yuan; Tusa Iris; Paun Andrei | Theoretical Computer Science, 2015 | |

## AbstractIn this work we describe a new segmentation technique for the Affymetrix microarray images. We prove that our method can offer better predictions on the gene levels as opposed to the standard Affymetrix segmentation implemented in the Affymetrix GeneChip Operating Software (GCOS). To check the accuracy and show the benefits of the new segmentation method we use a previously implemented methodology to simulate microarray images with realistic features. Using such an artificial image provides us with the actual levels for each spot and each gene investigated in the microarray. Using this information we then proceed to segment the same image twice (with GCOS and our new method). The two segmentations will produce two sets of gene levels that are then compared to the known gene levels (known since the moment of generating the artificial image). Using this methodology we are able to show statistically (using 50 replicates of the same steps of generating the image, segmenting, comparing the results) that in some cases our new method greatly outperforms the GCOS implemented segmentation method, while in the rest of the cases performs in similar fashion. (C) 2015 Elsevier B.V. All rights reserved. |
|||

article
## The Influence Of Movie'S Quality On Its Performance: Evidence Based On Oscar Awards |
Zhuang Weiling; Babin Barry; Xiao Qian; Paun Mihaela | Managing Service Quality, 2014 | |

## AbstractPurpose - The purpose of this paper is to develop and empirically test a new framework that shows how different signals of movie quality along with key control variables affect consumers' post-consumption evaluations, critics' reviews (CR), and movie box office revenues. Design/methodology/approach - The data set consists of a sample of 332 movies released between 2000 and 2008. Regression was used to test the study hypotheses. Findings - The results suggest that the three signals of movie quality exhibit different effects on three movie performance measures. Of the three cues, the peripheral quality signal is positive related to movie box, moviegoers' evaluations (ME), and CR. Furthermore, star performance quality is positive related to both ME and CR. Surprisingly, overall quality signal does not display any influence on movie performances. Research limitations/implications - The primary limitation is the use of cross-sectional study design and future research should apply for time-series technique to test the relationships between movie quality signals and movie performances. Practical implications - The findings suggest that consumers and critics evaluate movie qualities based on various movie quality signals. Furthermore, the characteristics of movies also have mixed impacts on movie performances. Movie studios may take these findings into account to produce better movies. Originality/value - This study proposes and empirically tests the impacts of three groups of movie signals peripheral quality signal, star performance quality signal, and overall quality signal on motion picture performance. This study contributes to service quality literature and signal theory by categorizing different Academy Awards into three groups of quality signals and by empirically testing the proposed relationships. |
|||

conference
## Metrics And Statistical Methods For Evaluating Biodiversity And Biological Data For Large Rivers And Deltas |
Paun M.; Tusa I.; Sidoroff M.; Paun A. | General Assembly Meeting – Dancers, Vienna, Austria, 2014 | |

## Abstract |
|||

article
## Reliability-Aware Performance Model For Optimal Gpu-Enabled Cluster Environment |
Laosooksathit Supada; Nassar Raja; Leangsuksun Chokchai; Paun Mihaela | Journal Of Supercomputing, 2014 | |

## AbstractGiven that the reliability of a very large-scaled system is inversely related to the number of computing elements, fault tolerance has become a major concern in high performance computing including the most recent deployments with graphic processing units (GPUs). Many fault tolerance strategies, such as the checkpoint/restart mechanism, have been studied to mitigate failures within such systems. However, fault tolerance mechanisms generate additional costs and these may cause a significant performance drop if it is not used carefully. This paper presents a novel fault tolerance scheduling model that explores the interplay between the GPGPU application performance and the reliability of a large GPU system. This work focuses on the checkpoint scheduling model that aims to minimize fault tolerance costs. Additionally, a GPU performance analysis is conducted. Furthermore, the effect of a checkpoint/restart mechanism on the application performance is thoroughly studied and discussed. |
|||

article
## Biochemical Networks Discrete Modeling Inspired By Membrane Systems |
Jack John; Paun Andrei; Paun Mihaela | Applications Of Membrane Computing In Systems And Synthetic Biology, 2014 | |

## AbstractThe ideas expressed in this work pertain to biochemical modeling. We explore our technique, the Nondeterministic Waiting Time algorithm, for modeling molecular signaling cascades. The algorithm is presented with pseudocode along with an explanation of its implementation. We discuss several important extensions including: (i) a heap with special maintenance functions for sorting reaction waiting times, (ii) a nondeterminstic component for handling reaction competition, and (iii) a memory enhancement allowing slower reactions to compete with faster reactions. Several example systems are provided for comparisons between modeling with systems of ordinary differential equations, the Gillespie Algorithm, and our Nondeterministic Waiting Time Algorithm. Our algorithm has a unique ability to exhibit behavior similar to the solutions to systems of ordinary differential equations for certain models and parameter choices, but it also has the nondeterministic component which yields results similar stochastic methods (e.g., the Gillespie Algorithm). There are several extensions for the current work discussed at the end of the chapter. |
|||

book
## Applications Of Membrane Computing In Systems And Synthetic Biology - Chapter 6 – Biochemical Networks Discrete Modeling Inspired By Membrane Systems |
J. Jack; A.Paun; M. Paun | Springer, 2014 | |

## Abstract |
|||

conference
## Nirdsb Assessment And Approach To Data Gathering |
Paun M.; Sidoroff M. | Geo Workshop, Novi Sad, Serbia, 2013 | |

## Abstract |
|||

article
## Reliability Model Of A System Of K Nodes With Simultaneous Failures For High-Performance Computing Applications |
Thanakornworakij Thanadech; Nassar Raja; Leangsuksun Chokchai Box; Paun Mihaela | International Journal Of High Performance Computing Applications, 2013 | |

## AbstractA high-performance computing (HPC) system, which is composed of a large number of components, is prone to failure. To maximize HPC system utilization, one should understand the failure behavior and the reliability of the system. Studies in the literature show that the time to failure of a node is best described by a Weibull distribution. In this study, we consider, without loss of generality, the Weibull as the distribution of time to failure and develop a reliability model for a system of k nodes where nodes can fail simultaneously. From this model, we develop expressions for the probability of failure of the system at any time t, for the failure rate, and for the mean time to failure. Also, we validate the model by using failure data from the Blue Gene/L logs obtained from the Lawrence Livermore National Laboratory. Results show that if failures of the components (nodes) in the system possess a degree of dependency, the system becomes less reliable, which means that the failure rate increases and the mean time to failure decreases. Also, an increase in the number of nodes decreases the reliability of the system. |
|||

article
## Sensitivity Of Forest Plan Value To Parameters Of Simulated Annealing |
Strimbu Bogdan M.; Paun Mihaela | Canadian Journal Of Forest Research, 2013 | |

## AbstractSimulated annealing (SA) is a heuristic technique popular in forest planning, providing solutions close to optimality in reduced computation time. The present study challenges the common approach used to establish the parameters of SA that mimic physical processes by proving that slow cooling or large initial temperatures do not necessarily lead to optimal solutions. The study has two objectives: (1) to identify the parameters (i.e., initial temperature and annealing rate) that could supply close to optimal results with reduced experimentation time and (2) to assess the impact of parameters determining SA performances. Using three forest inventory data sets from British Columbia, we investigated the influence of initial temperature, annealing rate, and numbers of runs on forest planning solutions using a replicated completely randomized design organized as a factorial experiment within a repeated-measures framework. The optimal solution seems to be little influenced by the number of runs; our findings indicate that the combination of initial temperature and rate of annealing is critical in obtaining superior results. Furthermore, the selection of the SA parameters seems to be dependent on the harvest age, which indicates that the parameters should be selected considering whether or not a stand is harvested more than once during the planning period. |
|||

conference
## A Reliability Model For Cloud Computing For High Performance Computing Applications |
Thanakornworakij T.; Nassar R.F.; Leangsuksun C.; Pǎun M. | Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2013 | |

## AbstractWith virtualization technology, Cloud computing utilizes resources more efficiently. A physical server can deploy many virtual machines and operating systems. However, with the increase in software and hardware components, more failures are likely to occur in the system. Hence, one should understand failure behavior in the Cloud environment in order to better utilize the cloud resources. In this work, we propose a reliability model and estimate the mean time to failure and failure rate based on a system of k nodes and s virtual machines under four scenarios. Results show that if the failure of the hardware and/or the software in the system exhibits a degree of dependency, the system becomes less reliable, which means that the failure rate of the system increases and the mean time to failure decreases. Additionally, an increase in the number of nodes decreases the reliability of the system. © 2013 Springer-Verlag. |
|||

article
## A Novel Dynamic Layer-By-Layer Assembled Nano-Scale Biointerface: Functionality Tests With Platelet Adhesion And Aggregate Morphology Influenced By Adenosine Diphosphate |
Watson Melanie G.; Lopez Juan M.; Paun Mihaela; Jones Steven A. | Journal Of Thrombosis And Thrombolysis, 2013 | |

## AbstractAn improved biointerface was developed, dynamic layer-by-layer self-assembly surface (d-LbL), and utilized as a biologically-active substrate for platelet adhesion and aggregation. Possible clinical applications for this research include improved anti-coagulation surfaces. This work demonstrated the functionality of d-LbL biointerfaces in the presence of platelet-rich-plasma (PRP) with the addition of 20 mu M adenosine diphosphate (ADP), a thrombus activator. The surface morphology of the experimental control, plain PRP, was compared to PRP containing additional ADP (PRP + ADP) and resulted in an expected increase of platelet adhesions along the fibrinogen d-LbL substrate. The d-LbL process was used to coat glass slides with fibrinogen, Poly (sodium 4-styrene-sulfonate), and Poly (diallydimethlyammonium chloride). Slides were exposed to PRP under flow and static conditions with and without 20 mu M of ADP. Fluorescence microscopy (FM), phase contrast microscopy (PCM), atomic force microscopy (AFM), and field emission-scanning electron microscopy (FE-SEM) were used to evaluate platelet adhesions under the influence of varied shear conditions. PCM images illustrated differences between the standard LbL and d-LbL substrates. FM images provided percent surface coverage values. For high-shear conditions, percent surface coverage values increased when using ADP whereas plain PRP exposure displayed no significant increase. AFM scans also displayed higher mean peak height values and unique surface characteristics for PRP + ADP as opposed to plain PRP. FE-SEM images revealed platelet adhesions along the biointerface and unique characteristics of the d-LbL surface. In conclusion, PRP + ADP was more effective at increasing platelet aggregation, especially under high shear conditions, providing further validation of the improved biointerface. |
|||

conference
## An Economic Model For Maximizing Profit Of A Cloud Service Provider |
T. Thanakornworakij; R. Nassar; C. Leangsuksun; M. Paun | 7Th International Conference On Availability, Reliability And Security (“Ares”), University Of Economics In Prague, Czech Republic, 2012 | |

## Abstract |
|||

article
## P Systems With Proteins On Membranes: A Survey |
Paun Andrei; Paun Mihaela; Rodriguez-Paton Alfonso; Sidoroff Manuela | International Journal Of Foundations Of Computer Science, 2011 | |

## AbstractThe paper is a survey of the recent model of P systems with proteins on membranes introduced by Paun and Popa in 2006. This model can be viewed as an extension of the highly successful paper of (Paun and Paun 2002) describing P systems based on symport/antiport. The previous model represented an important change of direction from strings to objects in the area of P systems. The main drawback of the model from 2002 was the massive parallelism that is not seen in real life. The 2006 model was a step in controlling the parallelism the same way it is done in nature in symporters and antiporters: these processes take place through protein channels embedded at the level of the membrane which can only be used by a molecule at a time, thus yielding a sequentiality with respect to the number of such proteins embedded in the membrane. |