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1. Evaluation the level of vitamin D and its relationship with clinical symptoms in patients with COVID-19 referred to the medical center in Bam city

Author

Mortazavi, Seyed Mojtaba, Khoshnood, Saeed, Faraji, Reza, Baravati, Rezvan Bagheri, Khalili, Hakime, Radfar, Ali, Jalali, Elham, Nezam Nia, Maria, Akrami, Sousan, and Shirani, Maryam

Subjects
vitamin d, covid-19, outcome, pandemic, Medicine, Public aspects of medicine, RA1-1270, Microbiology, QR1-502
Abstract

Background: Vitamin D is a steroid hormone that protects against viral infections by influencing innate and adaptive immune responses. The effectiveness of vitamin D3 supplementation in COVID-19 is unknown. The study’s goal was to elucidate the relationship between blood vitamin D levels and COVID-19 clinical outcomes by examining the effect of a single high dose of vitamin D3 on the length of hospital stay in patients. Methods: The descriptive, retrospective study was performed from March to May 2021 at a referral center for patients with COVID-19, in Bam, Iran. A checklist consisting of demographic variables was used to gather data, and laboratory assessments of serum 25(OH) D were evaluated and documented. The connection between serum vitamin D and patient clinical outcomes was investigated after patients were given a single oral dose of 200,000 IU of vitamin D3. Results: 71 COVID-19 patients were treated. Radiological results did not change substantially amongst individuals with various levels of 25(OH)D. After a single dosage of vitamin D3, mean blood levels of xyvitamin D increased considerably and the need for intubation and SpO decreased, and as did the respiratory rate in patients requiring hospitalization due to COVID-19. Conclusion: A single administration of 200,000 IU of vitamin D3 significantly reduced the severity of COVID-19.

Published
2024
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3. Advanced Imprinted Materials for In Vitro and In Vivo Application

Author

Radfar, Rahil, Gharibzahedi, Seyed Mohammad Taghi, Altintas, Zeynep, Kalia, Susheel, Series Editor, Haraguchi, Kazutoshi, Editorial Board Member, Celli, Annamaria, Editorial Board Member, Ruiz-Hitzky, Eduardo, Editorial Board Member, Bismarck, Alexander, Editorial Board Member, Thomas, Sabu, Editorial Board Member, Kaith, Balbir Singh, Editorial Board Member, Averous, Luc, Editorial Board Member, Gupta, Bhuvanesh, Editorial Board Member, Njuguna, James, Editorial Board Member, Boufi, Sami, Editorial Board Member, Sabaa, Magdy W., Editorial Board Member, Kumar Mishra, Ajay, Editorial Board Member, Pielichowski, Krzysztof, Editorial Board Member, Habibi, Youssef, Editorial Board Member, Focarete, Maria Letizia, Editorial Board Member, Jawaid, Mohammad, Editorial Board Member, and Altintas, Zeynep, editor

Published
2025
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5. SEPSIGN: early identification of sepsis signs in emergency department

Author

Lafon, Thomas, Cazalis, Marie-Angélique, Hart, Kimberly W., Hennessy, Cassandra, Tazarourte, Karim, Self, Wesley H., Akhavan, Arvin Radfar, Laribi, Saïd, Viglino, Damien, Douplat, Marion, Ginde, Adit A., Tolou, Sophie, Mahler, Simon A., Le Borgne, Pierrick, Claessens, Yann-Erick, Yordanov, Youri, Le Bastard, Quentin, Pancher, Agathe, Ducharme, Jim, Lindsell, Christopher J., and Shapiro, Nathan I.

Published
2024
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13. Evaluating the level of digitalization of the innovation process with artificial intelligence approach in the digital transformation of knowledge-based companies

Author

ali Bagheri, reza radfar, and sepehr ghazinoory

Subjects
digital innovation, adaptive neural fuzzy inference system (anfis), digital transformation, technology-based companies (tbfs), innovation process, digitization, artificial intelligence, Business, HF5001-6182
Abstract

Abstract The purpose of this research is to design a model to evaluate the level of digitization of the innovation process centered on artificial intelligence in knowledge-based companies, so that the digital maturity of the innovation process in an organization can be measured. The results of 188 indicators were distributed in the form of a 5-point Likert questionnaire and by Delphi method in two times among 18 experts in this field. The result of the work was 5 components as input to the model, which was sent in the form of a questionnaire to 230 knowledge-based companies of Pardis Technology Park. 198 companies completed it and sent it back. From this number of samples, 150 data were separated for training data and 48 data as model test based on a random function. In the last stage, i.e. modeling, the adaptive neural-fuzzy inference method was used for the model. The method of grid separation or lookup table (PG) in MATLAB 2023 software was used to evaluate the performance of the model using root mean square, error (RMSE) and relative error (E). This research was able to provide an intelligent model with a very low error. As a result, it was able to achieve effective indicators in the degree of digitization of the innovation process. Extended Abstract Introduction An innovation process, whether in its general form from the stage of idea formation to the stage of entering the market and commercialization, or in each of the parts separately, must be in such a way that it has the most productivity (efficiency and effectiveness combined during the process). Moving in this direction reduces financial, time and human costs. The relevance of innovation to ensure the competitiveness of companies has been confirmed among researchers and professionals (Schiuma, 2012). Also, innovation is a risky process that requires resources, competence, culture and attitudes that cannot even be promoted and managed easily (D'Este et al., 2012). Due to the mismatch in digital skills and awareness, employees are not able to understand the reasons and potential of implementing new technology and displacement. Therefore, the next challenge in the digital ecosystem is to promote and define conditions, roadmaps and management models for implementing digital innovation strategies, for managing digital knowledge and fostering continuous innovation (Nonaka & Takeuch, 2019). Innovation processes rely on external institutions, i.e. innovation intermediaries, research and development laboratories, or innovation centers (Corre & Mischke, 2005). Global and virtual competition, as well as the rapid development of digital technologies and solutions, raise efficiency standards, increase the speed of market dynamics, and reduce product life cycles (Schiuma, 2012). Each actor involved in innovation must be aware of the organization's vision, goals, and strategies in order to effectively contribute and generate value (Lianto et al., 2018; Nonaka & Takeuchi, 2019). To understand and effectively manage technology, codification and exploitation of generated knowledge, specialized skills and new governance models are required (Joshi et al., 2010). Theoretical framework Digital innovation process Completely rebuilding business around new opportunities and new demands is possible through digital technology. Eyring et al., (2022) in an article, mentioned these processes under the title of digitization. Digitization is a useful and effective necessity. It provides a vision of digital assets that offer opportunities to business or even to industry. Digitization sometimes reconceptualizes their products by serving a business model through their artifacts. Digital transformation describes a sometimes broad process of change that may have multiple goals, while innovation focuses on the moment of invention and the implementation of that invention. Innovation may cause fundamental changes and vice versa, but both are not synonymous. Advancement of digitization and digital changes may be started as an innovation action. This may be catalyzed by new business opportunities, but ultimately must reach beyond the innovation function to reshape the entire organization (Globe, 2018). Businesses need a dynamic tool to support their digital innovation management efforts. Artificial intelligence Artificial intelligence is sometimes called machine intelligence, refers to the intelligence shown by machines in various situations, which is in contrast to the natural intelligence in humans; in other words, artificial intelligence refers to systems that can react similar to human intelligent behavior, including understanding complex situations, simulating human thinking processes and reasoning methods and successfully responding to them, learning and having the ability to acquire knowledge and reason to solve problems (Teece et al., 1997). The scientific study of algorithms and statistical models are used by computer systems that use patterns and inference to perform tasks rather than using clear instructions. Machine learning is the science of making computers learn about a specific subject without the need for an explicit program. As a subset of artificial intelligence, machine learning algorithms create a mathematical model based on sample data or "training data" in order to predict or make decisions without overt planning (Du et al., 2019). Research methodology The sampling method was carried out in the form of theoretical saturation at the stage that no new material was obtained from the articles as a new index, and other indices were common in meaning and concept. In this study, Pardis Technology Park companies and its branches, including Azadi Factory and Hi-Wi centers were considered. Because these companies are active within the innovation ecosystem, they are familiar with the literature in this field, which facilitated the completion of the questionnaires. Questionnaires were sent through the Press Line program and within social network groups, and for some through phone and email. All these companies had the approval of knowledge-based company level 1 to 3 and the completers had educational qualifications of at least bachelor's degree to doctorate. After examining the opinions of the experts, it was determined that a consensus was reached by having an average above 4 for all indicators. In adaptive neural fuzzy inference, the network separation method or lookup table (PG) was used in MATLAB 2023 software. In this method, the number of membership functions is 5 functions representing very low, low, medium, high and very high. Research findings A number of 290 articles were selected among the whole, which had the citation higher than 1. The texts of these 290 articles were studied, and finally 149 articles related to the selected topic and the literature and background of this research were used. In fact, the general goal and main question of this research was to model the innovation process centered on artificial intelligence, was carried out successfully with a very low model error during the test. As a result of reviewing these 149 articles and specialized texts, 189 indicators related to the issue of digital innovation were extracted. As the next goal, 42 main and effective indicators were obtained from this literature Conclusion In addition to the main goal and special goals of the research that were achieved through the presentation of the model, by examining the relationships obtained according to the surface diagrams, the sensitivity and impact of each component can be determined. "Variables" in the output, i.e. the degree of digitalization of the innovation process were analyzed. According to the findings and comparing them with the background and findings of previous researches, it can be stated that the sensitivity rate and impact of input 1, that is, the benefit of digital technologies based on artificial intelligence, is more than the other 4 inputs on the digital level of innovation process, in the sense that having these technologies is the main axis. It is less effective to benefit from the output; and if it is not present in the laboratory, from other components. The lowest sensitivity and impact on the output in the model belonging to the third and fourth components, i.e. network and smart learning, were identified next to the component of benefiting from technologies, and this could mean that in the conditions of benefiting from digital technologies, according to the findings of the research, it is suggested to design the intelligent model of innovation in different industries and for each industry separately for future research. In the literature, researchers encountered a wide range of these digital innovation models such as banking, schools and institutions of higher education, health, etc. The second suggestion is that future researches can design a separate model for each of the input components so that the input component of this research is placed in the output position and the indicators determined in this research are used as their input. It is possible to design 5 other models according to the five components of this research, and connecting these models to develop a final and macro model can bring new achievements.

Published
2025
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14. Deep brain stimulation of the anterior cingulate cortex reduces opioid addiction in preclinical studies

Author

Mahdi Fatemizadeh, Esmail Riahi, Gholamreza Hassanzadeh, Anahita Torkaman-Boutorabi, Forough Radfar, and Maryam Farahmandfar

Subjects
Addiction, Deep brain stimulation, Morphine, Anterior cingulate cortex, Medicine, Science
Abstract

Abstract Substance Use Disorder (SUD) is a medical condition where an individual compulsively misuses drugs or alcohol despite knowing the negative consequences. The anterior cingulate cortex (ACC) has been implicated in various types of SUDs, including nicotine, heroin, and alcohol use disorders. Our research aimed to investigate the effects of deep brain stimulation (DBS) in the ACC as a potential therapeutic approach for morphine use disorder. Additionally, we measured c-Fos protein expression as an indicator of neural activity in the nucleus accumbens (NAc) and prefrontal cortex (PFC). Our findings indicate that high-frequency (130 Hz) DBS at different amperages, 150 µA and 200 µA in the ACC during the acquisition phase of morphine conditioned place preference (CPP) inhibited the rewarding properties of morphine. Furthermore, DBS at these intensities during the extinction phase facilitated the extinction and mitigated the reinstatement of morphine CPP triggered by drug priming. Morphine conditioning was associated with impaired novel object conditioning (NOR) and locomotor activity. While DBS in the acquisition and extinction phases at both intensities restored NOR memory, only DBS at 200 µA recovered locomotor activity in the open field test. Treatment with DBS at 200 µA decreased c-Fos protein expression in the NAc and PFC (compared to morphine-only group). In conclusion, our data indicate an intensity-dependent effect of ACC DBS on the acquisition, extinction, and reinstatement of morphine-induced CPP in rats. These findings suggest that ACC DBS could be a potential intervention for the treatment of morphine use disorder.

Published
2025
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17. Development of a Selective Wet-Chemical Etchant for 3D Structuring of Silicon via Nonlinear Laser Lithography

Author

Borra, Mona Zolfaghari, Radfar, Behrad, Nasser, Hisham, Çolakoğlu, Tahir, Tokel, Onur, Turnalı, Ahmet, Demirtaş, Merve, Ustunel, Hande, Toffoli, Daniele, İlday, F. Ömer, Turan, Raşit, Pavlov, Ihor, and Bek, Alpan

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science, Physics - Optics
Abstract

Recently-demonstrated high-quality three-dimensional (3D) subsurface laser processing inside crystalline silicon (c-Si) wafers opens a door to a wide range of novel applications in multidisciplinary research areas. Using this technique, a novel maskless micro-pillars with precise control on the surface reflection and coverage are successfully fabricated by etching the laser processed region of c-Si wafer. To achieve this, a particular selective wet chemical etching is developed to follow subsurface laser processing of c-Si to reveal the desired 3D structures with smooth surfaces. Here, we report the development of a novel chromium-free chemical etching recipe based on copper nitrate, which yields substantially smooth surfaces at high etch rate and selectivity on the both laser-processed Si surface and subsurface, i.e., without significant etching of the unmodified Si. Our results show that the etch rate and surface morphology are interrelated and strongly influenced by the composition of the adopted etching solution. After an extensive compositional study performed at room temperature, we identify an etchant with a selectivity of over 1600 times for laser-modified Si with respect to unmodified Si. We also support our findings using density functional theory calculations of HF and Cu adsorption energies, indicating significant diversity on the c-Si and laser-modified surfaces.

Published
2023

18. Determination and Prioritization of Public Library Services in Disasters

Author

Yaghoub Norouzi, hamidreza Radfar, and Nayereh Jafarifar

Subjects
service model design, public library services, disasters, crisis management, Bibliography. Library science. Information resources
Abstract

Purpose: This study aims to identify and prioritize the services provided by public libraries during disasters, focusing on their roles before, during, and after such events.Methodology: The research, applied in purpose and descriptive-survey in method, utilized the fuzzy Delphi technique and the Best Worst Method (BWM) to analyze the opinions of 62 public library librarians and 11 library science experts in Iran.Findings: The resulting model outlines 33 services categorized into pre-disaster (14 services), during-disaster (12 services), and post-disaster (7 services). Services during disasters were deemed the most critical, followed by pre-disaster and post-disaster services. Key services include implementing mobile health ambassador libraries, forming voluntary campaigns among librarians, and establishing specialized groups to assess immediate library needs.Conclusion: Public libraries hold a vital role in disaster response and recovery due to their mission of equal access and community support. This study proposes a localized, actionable model of library services to enhance disaster management in Iran.Value: By addressing the unique needs of communities during crises, public libraries can significantly contribute to disaster preparedness and recovery, reinforcing their role as safe havens and resource hubs.

Published
2024
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19. Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers

Author

Strimel, Grant P., Xie, Yi, King, Brian, Radfar, Martin, Rastrow, Ariya, and Mouchtaris, Athanasios

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Streaming speech recognition architectures are employed for low-latency, real-time applications. Such architectures are often characterized by their causality. Causal architectures emit tokens at each frame, relying only on current and past signal, while non-causal models are exposed to a window of future frames at each step to increase predictive accuracy. This dichotomy amounts to a trade-off for real-time Automatic Speech Recognition (ASR) system design: profit from the low-latency benefit of strictly-causal architectures while accepting predictive performance limitations, or realize the modeling benefits of future-context models accompanied by their higher latency penalty. In this work, we relax the constraints of this choice and present the Adaptive Non-Causal Attention Transducer (ANCAT). Our architecture is non-causal in the traditional sense, but executes in a low-latency, streaming manner by dynamically choosing when to rely on future context and to what degree within the audio stream. The resulting mechanism, when coupled with our novel regularization algorithms, delivers comparable accuracy to non-causal configurations while improving significantly upon latency, closing the gap with their causal counterparts. We showcase our design experimentally by reporting comparative ASR task results with measures of accuracy and latency on both publicly accessible and production-scale, voice-assistant datasets., Comment: Accepted to ICML 2023

Published
2023

20. End-to-end spoken language understanding using joint CTC loss and self-supervised, pretrained acoustic encoders

Author

Wang, Jixuan, Radfar, Martin, Wei, Kai, and Chung, Clement

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information. Popular end-to-end (E2E) SLU models utilize sequence-to-sequence automatic speech recognition (ASR) models to extract textual embeddings as input to infer semantics, which, however, require computationally expensive auto-regressive decoding. In this work, we leverage self-supervised acoustic encoders fine-tuned with Connectionist Temporal Classification (CTC) to extract textual embeddings and use joint CTC and SLU losses for utterance-level SLU tasks. Experiments show that our model achieves 4% absolute improvement over the the state-of-the-art (SOTA) dialogue act classification model on the DSTC2 dataset and 1.3% absolute improvement over the SOTA SLU model on the SLURP dataset., Comment: ICASSP 2023

Published
2023

21. Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition

Author

Chang, Feng-Ju, Alexandridis, Anastasios, Swaminathan, Rupak Vignesh, Radfar, Martin, Mallidi, Harish, Omologo, Maurizio, Mouchtaris, Athanasios, King, Brian, and Maas, Roland

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

To achieve robust far-field automatic speech recognition (ASR), existing techniques typically employ an acoustic front end (AFE) cascaded with a neural transducer (NT) ASR model. The AFE output, however, could be unreliable, as the beamforming output in AFE is steered to a wrong direction. A promising way to address this issue is to exploit the microphone signals before the beamforming stage and after the acoustic echo cancellation (post-AEC) in AFE. We argue that both, post-AEC and AFE outputs, are complementary and it is possible to leverage the redundancy between these signals to compensate for potential AFE processing errors. We present two fusion networks to explore this redundancy and aggregate these multi-channel (MC) signals: (1) Frequency-LSTM based, and (2) Convolutional Neural Network based fusion networks. We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion. Our experimental results on commercial virtual assistant tasks demonstrate that using the AFE output and two post-AEC signals with fusion networks offers up to 25.9% word error rate (WER) relative improvement over the model using the AFE output only, at the cost of <= 2% parameter increase.

Published
2023

22. Global Predictability of Marine Heatwave Induced Rapid Intensification of Tropical Cyclones

Author

Soheil Radfar, Ehsan Foroumandi, Hamed Moftakhari, Hamid Moradkhani, Gregory R. Foltz, and Alex Sen Gupta

Subjects
tropical cyclones, rapid intensification, marine heatwaves, machine learning, prediction, global warming, Environmental sciences, GE1-350, Ecology, QH540-549.5
Abstract

Abstract Prediction of the rapid intensification (RI) of tropical cyclones (TCs) is crucial for improving disaster preparedness against storm hazards. These events can cause extensive damage to coastal areas if occurring close to landfall. Available models struggle to provide accurate RI estimates due to the complexity of underlying physical mechanisms. This study provides new insights into the prediction of a subset of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves (MHWs). MHWs could provide sufficient energy to supercharge TCs. Preconditioning by MHW led to RI of recent destructive TCs, Otis (2023), Doksuri (2023), and Ian (2022), with economic losses exceeding $150 billion. Here, we analyze the TC best track and sea surface temperature data from 1981 to 2023 to identify hotspot regions for compound events, where MHWs and RI of tropical cyclones occur concurrently or in succession. Building upon this, we propose an ensemble machine learning model for RI forecasting based on storm and MHW characteristics. This approach is particularly valuable as RI forecast errors are typically largest in favorable environments, such as those created by MHWs. Our study offers insight into predicting MHW TCs, which have been shown to be stronger TCs with potentially higher destructive power. Here, we show that using MHW predictors instead of the conventional method of using sea surface temperature reduces the false alarm rate by 30%. Overall, our findings contribute to coastal hazard risk awareness amidst unprecedented climate warming causing more frequent MHWs.

Published
2024
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23. Bioactive compound encapsulation: Characteristics, applications in food systems, and implications for human health

Author

Alieh Rezagholizade-shirvan, Mahya Soltani, Samira Shokri, Ramin Radfar, Masoumeh Arab, and Ehsan Shamloo

Subjects
Encapsulation systems, Bioactive compounds, Nutritional benefits, Nanocarriers, Functional foods, Nutrition. Foods and food supply, TX341-641, Food processing and manufacture, TP368-456
Abstract

Nanotechnology plays a pivotal role in food science, particularly in the nanoencapsulation of bioactive compounds, to enhance their stability, bioavailability, and therapeutic potential. This review aims to provide a comprehensive analysis of the encapsulation of bioactive compounds, emphasizing the characteristics, food applications, and implications for human health. This work offers a detailed comparison of polymers such as sodium alginate, gum Arabic, chitosan, cellulose, pectin, shellac, and xanthan gum, while also examining both conventional and emerging encapsulation techniques, including freeze-drying, spray-drying, extrusion, coacervation, and supercritical anti-solvent drying. The contribution of this review lies in highlighting the role of encapsulation in improving system stability, controlling release rates, maintaining bioactivity under extreme conditions, and reducing lipid oxidation. Furthermore, it explores recent technological advances aimed at optimizing encapsulation processes for targeted therapies and functional foods. The findings underline the significant potential of encapsulation not only in food supplements and functional foods but also in supportive medical treatments, showcasing its relevance to improving human health in various contexts.

Published
2024
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25. Sub-8-bit quantization for on-device speech recognition: a regularization-free approach

Author

Zhen, Kai, Radfar, Martin, Nguyen, Hieu Duy, Strimel, Grant P., Susanj, Nathan, and Mouchtaris, Athanasios

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency. Among existing QAT methods, one major drawback is that the quantization centroids have to be predetermined and fixed. To overcome this limitation, we introduce a regularization-free, "soft-to-hard" compression mechanism with self-adjustable centroids in a mu-Law constrained space, resulting in a simpler yet more versatile quantization scheme, called General Quantizer (GQ). We apply GQ to ASR tasks using Recurrent Neural Network Transducer (RNN-T) and Conformer architectures on both LibriSpeech and de-identified far-field datasets. Without accuracy degradation, GQ can compress both RNN-T and Conformer into sub-8-bit, and for some RNN-T layers, to 1-bit for fast and accurate inference. We observe a 30.73% memory footprint saving and 31.75% user-perceived latency reduction compared to 8-bit QAT via physical device benchmarking., Comment: Accepted for publication at IEEE SLT'22

Published
2022

26. ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition

Author

Radfar, Martin, Barnwal, Rohit, Swaminathan, Rupak Vignesh, Chang, Feng-Ju, Strimel, Grant P., Susanj, Nathan, and Mouchtaris, Athanasios

Subjects
Computer Science - Sound, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

The recurrent neural network transducer (RNN-T) is a prominent streaming end-to-end (E2E) ASR technology. In RNN-T, the acoustic encoder commonly consists of stacks of LSTMs. Very recently, as an alternative to LSTM layers, the Conformer architecture was introduced where the encoder of RNN-T is replaced with a modified Transformer encoder composed of convolutional layers at the frontend and between attention layers. In this paper, we introduce a new streaming ASR model, Convolutional Augmented Recurrent Neural Network Transducers (ConvRNN-T) in which we augment the LSTM-based RNN-T with a novel convolutional frontend consisting of local and global context CNN encoders. ConvRNN-T takes advantage of causal 1-D convolutional layers, squeeze-and-excitation, dilation, and residual blocks to provide both global and local audio context representation to LSTM layers. We show ConvRNN-T outperforms RNN-T, Conformer, and ContextNet on Librispeech and in-house data. In addition, ConvRNN-T offers less computational complexity compared to Conformer. ConvRNN-T's superior accuracy along with its low footprint make it a promising candidate for on-device streaming ASR technologies., Comment: This paper was presented in Interspeech 2022

Published
2022

30. The global epidemiology and health burden of the autism spectrum: findings from the Global Burden of Disease Study 2021

Author

Santomauro, Damian F, Erskine, Holly E, Mantilla Herrera, Ana M, Miller, Paul Anthony, Shadid, Jamileh, Hagins, Hailey, Addo, Isaac Yeboah, Adnani, Qorinah Estiningtyas Sakilah, Ahinkorah, Bright Opoku, Ahmed, Ayman, Alhalaiqa, Fadwa Naji, Ali, Mohammed Usman, Al-Marwani, Sabah, Almazan, Joseph Uy, Almustanyir, Sami, Alvi, Farrukh Jawad, Amer, Yasser Sami Abdel Dayem, Ameyaw, Edward Kwabena, Amiri, Sohrab, Andrei, Catalina Liliana, Angappan, Dhanalakshmi, Antony, Catherine M, Aravkin, Aleksandr Y, Ashraf, Tahira, Ayuso-Mateos, Jose L, Barrow, Amadou, Batra, Kavita, Bemanalizadeh, Maryam, Bhagavathula, Akshaya Srikanth, Bhaskar, Sonu, Bhatti, Jasvinder Singh Singh, Bolla, Srinivasa Rao, Britton, Gabrielle, Castaldelli-Maia, Joao Mauricio, Catalá-López, Ferrán, Caye, Arthur, Chattu, Vijay Kumar, Chong, Yuen Yu, Ciobanu, Liliana G, Cortese, Samuele, Cruz-Martins, Natalia, Dachew, Berihun Assefa, Dai, Xiaochen, Darwish, Amira Hamed, Dashti, Mohsen, de la Torre-Luque, Alejandro, Diaz, Daniel, Ding, Delaney D, Dy, Angel Belle Cheng, Dziedzic, Arkadiusz Marian, Ebrahimi Meimand, Sepideh, El Meligy, Omar Abdelsadek Abdou, El Sayed, Iman, Elgar, Frank J, Fagbamigbe, Adeniyi Francis, Faris, Pawan Sirwan, Faro, Andre, Ferreira, Nuno, Filip, Irina, Fischer, Florian, Gandhi, Aravind P, Ganesan, Balasankar, Gebregergis, Miglas Welay, Gebrehiwot, Mesfin, Ghaderi Yazdi, Bardiya, Ghasemi, Mohammad-Reza, Ghasemzadeh, Afsaneh, Gunturu, Sasidhar, Gupta, Veer Bala, Gupta, Vivek Kumar, Halim, Sobia Ahsan, Hall, Brian J, Han, Fulei, Haro, Josep Maria, Hasaballah, Ahmed I, Hay, Simon I, Hedley, Darren, Helfer, Bartosz, Hossain, Md Mahbub, Hwang, Bing-Fang, Ibrahim, Umar Idris, Ilaghi, Mehran, Islam, Md Rabiul, Islam, Sheikh Mohammed Shariful, Iyer, Mahalaxmi, Jaggi, Khushleen, Jahrami, Haitham, Jamshidi, Elham, Khaleghi, Ali, Khan, Abdul Aziz, Khan, Mohammad Jobair, Khidri, Feriha Fatima, Kim, Kwanghyun, Koh, Hyun Yong, Kumar, Manasi, Landires, Iván, Le, Long Khanh Dao, Lee, Seung Won, Li, Zhihui, Lim, Stephen S, Martinez-Raga, Jose, Marzo, Roy Rillera, Matthew, Indu Liz, Maugeri, Andrea, Mestrovic, Tomislav, Mitchell, Philip B, Mohammed, Salahuddin, Mokdad, Ali H, Monasta, Lorenzo, Montazeri, Fateme, Mrejen, Matías, Mughal, Faraz, Murray, Christopher J L, Myung, Woojae, Nauman, Javaid, Newton, Charles Richard James, Nguyen, Huong Lan Thi, Nri-Ezedi, Chisom Adaobi, Nwatah, Vincent Ebuka, Oladunjoye, Adeolu Olufunso, Olufadewa, Isaac Iyinoluwa, Ordak, Michal, Otstavnov, Nikita, Palma-Alvarez, Raul Felipe, Parikh, Romil R, Park, Seoyeon, Pasovic, Maja, Patel, Jay, Pereira, Marcos, Pereira, Maria Odete, Phillips, Michael R, Polanczyk, Guilherme V, Pourfridoni, Mohammad, Puvvula, Jagadeesh, Radfar, Amir, Rahim, Fakher, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rahmati, Masoud, Ratan, Zubair Ahmed, Rhee, Taeho Gregory, Ronfani, Luca, Roy, Priyanka, Saddik, Basema Ahmad, Saghazadeh, Amene, Sakshaug, Joseph W, Salehi, Sana, Samuel, Vijaya Paul, Sankararaman, Senthilkumar, Saravanan, Aswini, Satpathy, Maheswar, Schumacher, Austin E, Schwebel, David C, Šekerija, Mario, Shafiee, Arman, Shahabi, Saeed, Shamim, Muhammad Aaqib, Silva, João Pedro, Solomon, Yonatan, Sumpaico-Tanchanco, Lourdes Bernadette C, Swain, Chandan Kumar, Tabarés-Seisdedos, Rafael, Temsah, Mohamad-Hani, Tromans, Samuel Joseph, Tzivian, Lilian, Varma, Ravi Prasad, Vinueza Veloz, Andres Fernando, Vinueza Veloz, Maria Fernanda, Walde, Mandaras Tariku, Waqas, Muhammad, Wickramasinghe, Nuwan Darshana, Yesodharan, Renjulal, Yon, Dong Keon, Youm, Yoosik, Zaman, Burhan Abdullah, Zeng, Youjie, Zielińska, Magdalena, Whiteford, Harvey A, Brugha, Traolach, Scott, James G, Vos, Theo, and Ferrari, Alize J

Published
2025
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32. Compute Cost Amortized Transformer for Streaming ASR

Author

Xie, Yi, Macoskey, Jonathan, Radfar, Martin, Chang, Feng-Ju, King, Brian, Rastrow, Ariya, Mouchtaris, Athanasios, and Strimel, Grant P.

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization. Our architecture creates sparse computation pathways dynamically at inference time, resulting in selective use of compute resources throughout decoding, enabling significant reductions in compute with minimal impact on accuracy. The fully differentiable architecture is trained end-to-end with an accompanying lightweight arbitrator mechanism operating at the frame-level to make dynamic decisions on each input while a tunable loss function is used to regularize the overall level of compute against predictive performance. We report empirical results from experiments using the compute amortized Transformer-Transducer (T-T) model conducted on LibriSpeech data. Our best model can achieve a 60% compute cost reduction with only a 3% relative word error rate (WER) increase.

Published
2022

33. Global, regional, and national progress towards the 2030 global nutrition targets and forecasts to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Author

Arndt, Michael Benjamin, Abate, Yohannes Habtegiorgis, Abbasi-Kangevari, Mohsen, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdulah, Deldar Morad, Abdulkader, Rizwan Suliankatchi, Abidi, Hassan, Abiodun, Olumide, Aboagye, Richard Gyan, Abolhassani, Hassan, Abtew, Yonas Derso, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Acuna, Juan Manuel, Adamu, Kidist, Adane, Denberu Eshetie, Addo, Isaac Yeboah, Adeyinka, Daniel Adedayo, Adnani, Qorinah Estiningtyas Sakilah, Afolabi, Aanuoluwapo Adeyimika, Afrashteh, Fatemeh, Afzal, Saira, Agodi, Antonella, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Sajjad, Ahmad, Tauseef, Ahmadi, Ali, Ahmed, Ali, Ahmed, Luai A A, Ajami, Marjan, Aji, Budi, Akbarialiabad, Hossein, Akonde, Maxwell, Al Hamad, Hanadi, Al Thaher, Yazan, Al-Aly, Ziyad, Alhabib, Khalid F, Alhassan, Robert Kaba, Ali, Beriwan Abdulqadir, Ali, Syed Shujait, Alimohamadi, Yousef, Aljunid, Syed Mohamed, Al-Mekhlafi, Hesham M, Almustanyir, Sami, Alomari, Mahmoud A, Al-Tammemi, Alaa B, Altirkawi, Khalid A, Alvis-Guzman, Nelson, Alvis-Zakzuk, Nelson J, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amiri, Sohrab, Amu, Hubert, Amugsi, Dickson A, Anagaw, Tadele Fentabel Fentabil, Ancuceanu, Robert, Angappan, Dhanalakshmi, Ansari-Moghaddam, Alireza, Antriyandarti, Ernoiz, Anvari, Davood, Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Aravkin, Aleksandr Y, Ariffin, Hany, Aripov, Timur, Arkew, Mesay, Armocida, Benedetta, Arumugam, Ashokan, Aryastami, Ni Ketut, Asaad, Malke, Asemi, Zatollah, Asemu, Mulu Tiruneh, Asghari-Jafarabadi, Mohammad, Astell-Burt, Thomas, Athari, Seyyed Shamsadin, Atomsa, Gamechu Hunde, Atorkey, Prince, Atout, Maha Moh'd Wahbi, Aujayeb, Avinash, Awoke, Mamaru Ayenew, Azadnajafabad, Sina, Azevedo, Rui M S, B, Darshan B, Badiye, Ashish D, Baghcheghi, Nayereh, Bagheri, Nasser, Bagherieh, Sara, Baig, Atif Amin, Baker, Jennifer L, Balasubramanian, Madhan, Baltatu, Ovidiu Constantin, Banach, Maciej, Banik, Palash Chandra, Barchitta, Martina, Bärnighausen, Till Winfried, Barr, Ronald D, Barrow, Amadou, Barua, Lingkan, Bashiri, Azadeh, Baskaran, Pritish, Basu, Saurav, Bekele, Alehegn, Belay, Sefealem Assefa, Belgaumi, Uzma Iqbal, Bell, Shelly L, Belo, Luis, Bennett, Derrick A, Bensenor, Isabela M, Beressa, Girma, Bermudez, Amiel Nazer C, Beyene, Habtamu B, Bhagavathula, Akshaya Srikanth, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhaskar, Sonu, Bhattacharjee, Natalia V, Bhutta, Zulfiqar A, Bitaraf, Saeid, Bodolica, Virginia, Bonakdar Hashemi, Milad, Braithwaite, Dejana, Butt, Muhammad Hammad, Butt, Zahid A, Calina, Daniela, Cámera, Luis Alberto, Campos, Luciana Aparecida, Cao, Chao, Cárdenas, Rosario, Carvalho, Márcia, Castañeda-Orjuela, Carlos A, Catapano, Alberico L, Cattaruzza, Maria Sofia, Cembranel, Francieli, Cerin, Ester, Chadwick, Joshua, Chalek, Julian, Chandrasekar, Eeshwar K, Charan, Jaykaran, Chattu, Vijay Kumar, Chauhan, Kirti, Chien, Ju-Huei, Chitheer, Abdulaal, Choudhari, Sonali Gajanan, Chowdhury, Enayet Karim, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Chung, Sheng-Chia, Claro, Rafael M, Columbus, Alyssa, Cortese, Samuele, Cruz-Martins, Natalia, Dabo, Bashir, Dadras, Omid, Dai, Xiaochen, D'Amico, Emanuele, Dandona, Lalit, Dandona, Rakhi, Darban, Isaac, Darmstadt, Gary L, Darwesh, Aso Mohammad, Darwish, Amira Hamed, Das, Jai K, Das, Saswati, Davletov, Kairat, De la Hoz, Fernando Pio, Debele, Aklilu Tamire, Demeke, Dessalegn, Demissie, Solomon, Denova-Gutiérrez, Edgar, Desai, Hardik Dineshbhai, Desta, Abebaw Alemayehu, Dharmaratne, Samath Dhamminda, Dhimal, Meghnath, Dias da Silva, Diana, Diaz, Daniel, Diress, Mengistie, Djalalinia, Shirin, Doaei, Saeid, Dongarwar, Deepa, Dsouza, Haneil Larson, Edalati, Sareh, Edinur, Hisham Atan, Ekholuenetale, Michael, Ekundayo, Temitope Cyrus, Elbarazi, Iffat, Elgendy, Islam Y, Elhadi, Muhammed, Elmeligy, Omar Abdelsadek Abdou, Eshetu, Habitu Birhan, Espinosa-Montero, Juan, Esubalew, Habtamu, Etaee, Farshid, Etafa, Werku, Fagbamigbe, Adeniyi Francis, Fakhradiyev, Ildar Ravisovich, Falzone, Luca, Farinha, Carla Sofia e Sá, Farmer, Sam, Fasanmi, Abidemi Omolara, Fatehizadeh, Ali, Feigin, Valery L, Feizkhah, Alireza, Feng, Xiaoqi, Ferrara, Pietro, Fetensa, Getahun, Fischer, Florian, Fitzgerald, Ryan, Flood, David, Foigt, Nataliya A, Folayan, Morenike Oluwatoyin, Fowobaje, Kayode Raphael, Franklin, Richard Charles, Fukumoto, Takeshi, Gadanya, Muktar A, Gaidhane, Abhay Motiramji, Gaihre, Santosh, Gakidou, Emmanuela, Galali, Yaseen, Galehdar, Nasrin, Gardner, William M, Garg, Priyanka, Gebremeskel, Teferi Gebru, Gerema, Urge, Getacher, Lemma, Getachew, Motuma Erena, Getawa, Solomon, Ghaffari, Kazem, Ghamari, Seyyed-Hadi, Ghasemi Nour, Mohammad, Ghassemi, Fariba, Ghith, Nermin, Gholamalizadeh, Maryam, Gholami, Ali, Gholamrezanezhad, Ali, Ghozy, Sherief, Gill, Paramjit Singh, Gill, Tiffany K, Glasbey, James C, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Goudarzi, Houman, Grivna, Michal, Guadie, Habtamu Alganeh, Gubari, Mohammed Ibrahim Mohialdeen, Gudayu, Temesgen Worku, Guha, Avirup, Gunawardane, Damitha Asanga, Gupta, Anish Kumar, Gupta, Bhawna, Gupta, Rahul, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Hagins, Hailey, Haj-Mirzaian, Arvin, Handal, Alexis J, Hanif, Asif, Hankey, Graeme J, Harapan, Harapan, Hargono, Arief, Haro, Josep Maria, Hasaballah, Ahmed I, Hasan, Md Mehedi, Hasani, Hamidreza, Hashi, Abdiwahab, Hassanipour, Soheil, Havmoeller, Rasmus J, Hay, Simon I, Hayat, Khezar, He, Jiawei, Heidari-Foroozan, Mahsa, Herteliu, Claudiu, Hessami, Kamran, Heyi, Demisu Zenbaba, Hezam, Kamal, Hiraike, Yuta, Holla, Ramesh, Hoogar, Praveen, Hossain, Sheikh Jamal, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hoveidamanesh, Soodabeh, Huang, Junjie, Humphrey, Kyle Matthew, Hussain, Salman, Hussien, Foziya Mohammed, Hwang, Bing-Fang, Iacoviello, Licia, Iftikhar, Pulwasha Maria, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Immurana, Mustapha, Inbaraj, Leeberk Raja, Iravanpour, Farideh, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Isola, Gaetano, Iwagami, Masao, Iwu, Chidozie Declan, J, Linda Merin, Jacob, Louis, Jahrami, Haitham, Jakovljevic, Mihajlo, Jamshidi, Elham, Janodia, Manthan Dilipkumar, Jayanna, Krishnamurthy, Jayapal, Sathish Kumar, Jayaram, Shubha, Jebai, Rime, Jema, Alelign Tasew, Jeswani, Bijay Mukesh, Jonas, Jost B, Joseph, Abel, Joseph, Nitin, Joshua, Charity Ehimwenma, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kaambwa, Billingsley, Kabir, Ali, Kabir, Zubair, Kadashetti, Vidya, Kamal, Vineet Kumar, Kamble, Bhushan Dattatray, Kandel, Himal, Kapoor, Neeti, Karaye, Ibraheem M, Katoto, Patrick DMC, Kauppila, Joonas H, Kaur, Harkiran, Kayode, Gbenga A, Kebede, Worku Misganaw, Kebira, Jemal Yusuf, Keflie, Tibebeselassie S, Kerr, Jessica A, Keykhaei, Mohammad, Khader, Yousef Saleh, Khajuria, Himanshu, Khalid, Nauman, Khammarnia, Mohammad, Khan, M Nuruzzaman, Khan, Moien AB, Khan, Taimoor, Khan, Yusra H, Khanali, Javad, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khatatbeh, Moawiah Mohammad, Khateri, Sorour, Khatib, Mahalaqua Nazli, Khayat Kashani, Hamid Reza, Khubchandani, Jagdish, Kifle, Zemene Demelash, Kim, Gyu Ri, Kimokoti, Ruth W, Kisa, Adnan, Kisa, Sezer, Kompani, Farzad, Kondlahalli, Shivakumar KM Marulasiddaiah, Koohestani, Hamid Reza, Korzh, Oleksii, Koulmane Laxminarayana, Sindhura Lakshmi, Koyanagi, Ai, Krishan, Kewal, Krishnamoorthy, Vijay, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kuddus, Mohammed, Kumar, G Anil, Kumar, Manasi, Kumar, Nithin, Kurmanova, Almagul, Kurmi, Om P, Kusuma, Dian, La Vecchia, Carlo, Lacey, Ben, Lal, Dharmesh Kumar, Larsson, Anders O, Latief, Kamaluddin, Ledda, Caterina, Lee, Paul H, Lee, Sang-woong, Lee, Wei-Chen, Lee, Yo Han, Lenzi, Jacopo, Li, Ming-Chieh, Li, Wei, Ligade, Virendra S, Lim, Stephen S, Lindstedt, Paulina A, Lo, Chun-Han, Lo, Justin, Lodha, Rakesh, Loreche, Arianna Maever, Lorenzovici, László, Lorkowski, Stefan, Madadizadeh, Farzan, Madureira-Carvalho, Áurea M, Mahajan, Preetam Bhalchandra, Makris, Konstantinos Christos, Malakan Rad, Elaheh, Malik, Ahmad Azam, Mallhi, Tauqeer Hussain, Malta, Deborah Carvalho, Manguerra, Helena, Marjani, Abdoljalal, Martini, Santi, Martorell, Miquel, Masrie, Awoke, Mathews, Elezebeth, Maugeri, Andrea, Mazaheri, Maryam, Mediratta, Rishi P, Mehndiratta, Man Mohan, Melaku, Yohannes Adama, Mendoza, Walter, Menezes, Ritesh G, Mensah, George A, Mentis, Alexios-Fotios A, Meretoja, Tuomo J, Mestrovic, Tomislav, Miazgowski, Tomasz, Miller, Ted R, Mini, GK, Mirghafourvand, Mojgan, Mirica, Andreea, Mirrakhimov, Erkin M, Mirza, Moonis, Misra, Sanjeev, Mithra, Prasanna, Mohammad, Karzan Abdulmuhsin, Mohammadian-Hafshejani, Abdollah, Mohammed, Shafiu, Mohseni, Mohammad, Mokdad, Ali H, Monasta, Lorenzo, Moni, Mohammad Ali, Moradi, Maryam, Moradi, Yousef, Morrison, Shane Douglas, Mougin, Vincent, Mubarik, Sumaira, Mueller, Ulrich Otto, Mulita, Francesk, Munblit, Daniel, Murillo-Zamora, Efren, Murray, Christopher J L, Mustafa, Ghulam, Nagarajan, Ahamarshan Jayaraman, Nangia, Vinay, Narasimha Swamy, Sreenivas, Natto, Zuhair S, Naveed, Muhammad, Nayak, Biswa Prakash, Nejadghaderi, Seyed Aria, Nguefack-Tsague, Georges, Ngunjiri, Josephine W, Nguyen, Phuong The, Nguyen, QuynhAnh P, Niazi, Robina Khan, Nnaji, Chukwudi A, Noor, Nurulamin M, Noubiap, Jean Jacques, Nri-Ezedi, Chisom Adaobi, Nurrika, Dieta, Nwatah, Vincent Ebuka, Oancea, Bogdan, Obamiro, Kehinde O, Oghenetega, Onome Bright, Ogunsakin, Ropo Ebenezer, Okati-Aliabad, Hassan, Okekunle, Akinkunmi Paul, Okello, Daniel Micheal, Okonji, Osaretin Christabel, Olagunju, Andrew T, Olana, Diriba Dereje, Oliveira, Gláucia Maria Moraes, Olusanya, Bolajoko Olubukunola, Olusanya, Jacob Olusegun, Ong, Sok King, Ortega-Altamirano, Doris V, Ortiz, Alberto, Ostojic, Sergej M, Otoiu, Adrian, Oumer, Abdu, Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Pana, Adrian, Panda-Jonas, Songhomitra, Pandey, Anamika, Pandi-Perumal, Seithikurippu R, Papadopoulou, Paraskevi, Pardhan, Shahina, Pasovic, Maja, Patel, Jay, Pathan, Aslam Ramjan, Paudel, Deepak, Pawar, Shrikant, Pepito, Veincent Christian Filipino, Pereira, Gavin, Pereira, Marcos, Perico, Norberto, Perna, Simone, Petcu, Ionela-Roxana, Petermann-Rocha, Fanny Emily, Piracha, Zahra Zahid, Plakkal, Nishad, Pourtaheri, Naeimeh, Radfar, Amir, Radhakrishnan, Venkatraman, Raggi, Catalina, Raghav, Pankaja, Rahim, Fakher, Rahimi-Movaghar, Vafa, Rahman, Azizur, Rahman, Md Mosfequr, Rahman, Md Obaidur, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rahmanian, Vahid, Rahmawaty, Setyaningrum, Rai, Rajesh Kumar, Raimondo, Ivano, Rajaa, Sathish, Rajput, Prashant, Ram, Pradhum, Ramasamy, Shakthi Kumaran, Ramazanu, Sheena, Rao, Chythra R, Rao, Indu Ramachandra, Rao, Sowmya J, Rasali, Drona Prakash, Rashid, Ahmed Mustafa, Rashidi, Mohammad-Mahdi, Ratan, Zubair Ahmed, Rawaf, Salman, Rawal, Lal, Redwan, Elrashdy M Moustafa Mohamed, Remuzzi, Giuseppe, Rengasamy, Kannan RR, Renzaho, Andre M N, Rezaee, Malihe, Rezaei, Nazila, Rezaeian, Mohsen, Riad, Abanoub, Rickard, Jennifer, Rodriguez, Alina, Rodriguez, Jefferson Antonio Buendia, Roever, Leonardo, Rohloff, Peter, Roy, Bedanta, Rwegerera, Godfrey M, S N, Chandan, Saad, Aly M A, Saber-Ayad, Maha Mohamed, Sabour, Siamak, Sachdeva Dhingra, Mamta, Saddik, Basema Ahmad, Sadeghi, Erfan, Sadeghi, Malihe, Sadeghian, Saeid, Saeed, Umar, Saeedi Moghaddam, Sahar, Safi, Sher Zaman, Saheb Sharif-Askari, Fatemeh, Sahebkar, Amirhossein, Sahoo, Harihar, Sahoo, Soumya Swaroop, Sajid, Mirza Rizwan, Salem, Marwa Rashad, Samy, Abdallah M, Sanabria, Juan, Sanjeev, Rama Krishna, Sankararaman, Senthilkumar, Santos, Itamar S, Santric-Milicevic, Milena M, Saraswathy, Sivan Yegnanarayana Iyer, Sargazi, Saman, Sarikhani, Yaser, Satpathy, Maheswar, Sawhney, Monika, Saya, Ganesh Kumar, Sayeed, Abu, Scarmeas, Nikolaos, Schlaich, Markus P, Schneider, Rachel D, Schutte, Aletta Elisabeth, Senthilkumaran, Subramanian, Sepanlou, Sadaf G, Serban, Dragos, Seylani, Allen, Shafie, Mahan, Shah, Pritik A, Shahbandi, Ataollah, Shaikh, Masood Ali, Shama, Adisu Tafari T, Shams-Beyranvand, Mehran, Shanawaz, Mohd, Sharew, Mequannent Melaku, Shetty, Pavanchand H, Shiri, Rahman, Shivarov, Velizar, Shorofi, Seyed Afshin, Shuval, Kerem, Sibhat, Migbar Mekonnen, Silva, Luís Manuel Lopes Rodrigues, Singh, Jasvinder A, Singh, Narinder Pal, Singh, Paramdeep, Singh, Surjit, Skryabina, Anna Aleksandrovna, Smith, Amanda E, Solomon, Yonatan, Song, Yi, Sorensen, Reed J D, Stanaway, Jeffrey D, Sufiyan, Mu'awiyyah Babale, Suleman, Muhammad, Sun, Jing, Sunuwar, Dev Ram, Szeto, Mindy D, Tabarés-Seisdedos, Rafael, Tabatabaeizadeh, Seyed-Amir, Tabatabai, Shima, Taheri Soodejani, Moslem, Tamuzi, Jacques Lukenze JL, Tan, Ker-Kan, Tarigan, Ingan Ukur, Tariku, Zerihun, Tariqujjaman, Md, Tarkang, Elvis Enowbeyang, Tat, Nathan Y, Taye, Birhan Tsegaw, Taylor, Heather Jean, Tefera, Yibekal Manaye, Tehrani-Banihashemi, Arash, Temsah, Mohamad-Hani, Teramoto, Masayuki, Thangaraju, Pugazhenthan, Thapar, Rekha, Thiyagarajan, Arulmani, Thrift, Amanda G, Tichopad, Ales, Ticoalu, Jansje Henny Vera, Tillawi, Tala, Tiruye, Tenaw Yimer, Tonelli, Marcello, Topor-Madry, Roman, Touvier, Mathilde, Tovani-Palone, Marcos Roberto, Tran, Mai Thi Ngoc, Ullah, Sana, Undurraga, Eduardo A, Unnikrishnan, Bhaskaran, Ushula, Tolassa Wakayo, Vahabi, Seyed Mohammad, Vakilian, Alireza, Valadan Tahbaz, Sahel, Valizadeh, Rohollah, Van den Eynde, Jef, Varthya, Shoban Babu, Vasankari, Tommi Juhani, Venketasubramanian, Narayanaswamy, Verma, Madhur, Veroux, Massimiliano, Vervoort, Dominique, Vlassov, Vasily, Vollset, Stein Emil, Vukovic, Rade, Waheed, Yasir, Wang, Cong, Wang, Fang, Wassie, Molla Mesele, Weerakoon, Kosala Gayan, Wei, Melissa Y, Werdecker, Andrea, Wickramasinghe, Nuwan Darshana, Wolde, Asrat Arja, Wubetie, Gedif Ashebir, Wulandari, Ratna Dwi, Xu, Rongbin, Xu, Suowen, Xu, Xiaoyue, Yadav, Lalit, Yamagishi, Kazumasa, Yang, Lin, Yano, Yuichiro, Yaya, Sanni, Yazdanpanah, Fereshteh, Yehualashet, Sisay Shewasinad, Yiğit, Arzu, Yiğit, Vahit, Yon, Dong Keon, Yu, Chuanhua, Yuan, Chun-Wei, Zamagni, Giulia, Zaman, Sojib Bin, Zanghì, Aurora, Zangiabadian, Moein, Zare, Iman, Zastrozhin, Michael, Zigler, Bethany, Zoladl, Mohammad, Zou, Zhiyong, Kassebaum, Nicholas J, and Reiner, Robert C, Jr

Published
2024
Full Text
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34. Substance use policy and practice in the COVID-19 pandemic: Learning from early pandemic responses through internationally comparative field data

Author

Aronowitz, Shoshana V, Carroll, Jennifer J, Hansen, Helena, Jauffret-Roustide, Marie, Parker, Caroline Mary, Suhail-Sindhu, Selena, Albizu-Garcia, Carmen, Alegria, Margarita, Arrendondo, Jaimie, Baldacchino, Alexander, Bluthenthal, Ricky, Bourgois, Philippe, Burraway, Joshua, Chen, Jia-shin, Ekhtiari, Hamed, Elkholy, Hussien, Farhoudian, Ali, Friedman, Joseph, Jordan, Ayana, Kato, Lindsey, Knight, Kelly, Martinez, Carlos, McNeil, Ryan, Murray, Hayley, Namirembe, Sarah, Radfar, Ramin, Roe, Laura, Sarang, Anya, Scherz, China, Teck, Joe Tay Wee, Textor, Lauren, and Oanh, Khuat Thi Hai

Subjects
Social Work, Human Society, Drug Abuse (NIDA only), Behavioral and Social Science, Clinical Research, Substance Misuse, Generic health relevance, Good Health and Well Being, Humans, Drug Users, Pandemics, COVID-19, Substance-Related Disorders, Public Policy, Harm Reduction, Harm reduction, drug policy, overdose, substance use, Public Health and Health Services, Public Health, Epidemiology, Public health, Policy and administration
Abstract

The COVID-19 pandemic has created an unprecedented natural experiment in drug policy, treatment delivery, and harm reduction strategies by exposing wide variation in public health infrastructures and social safety nets around the world. Using qualitative data including ethnographic methods, questionnaires, and semi-structured interviews with people who use drugs (PWUD) and Delphi-method with experts from field sites spanning 13 different countries, this paper compares national responses to substance use during the first wave of the COVID-19 pandemic. Field data was collected by the Substance Use x COVID-19 (SU x COVID) Data Collaborative, an international network of social scientists, public health scientists, and community health practitioners convened to identify and contextualise health service delivery models and social protections that influence the health and wellbeing of PWUD during COVID-19. Findings suggest that countries with stronger social welfare systems pre-COVID introduced durable interventions targeting structural drivers of health. Countries with fragmented social service infrastructures implemented temporary initiatives for PWUD led by non-governmental organisations. The paper summarises the most successful early pandemic responses seen across countries and ends by calling for greater systemic investments in social protections for PWUD, diversion away from criminal-legal systems toward health interventions, and integrated harm reduction, treatment and recovery supports for PWUD.

Published
2022

35. Presenting a Hybrid Model based on the Machine Learning for the Classification of Banking and Insurance Industry Common Customers

Author

Hamidreza Amirhassankhani, Abbas Toloie Eshlaghy, Reza Radfar, and Alireza pourebrahimi

Subjects
genetic algorithm, classification, support vector machine, insurance, bank, Management. Industrial management, HD28-70
Abstract

Global competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%.

Published
2024

36. A neural prosody encoder for end-ro-end dialogue act classification

Author

Wei, Kai, Knox, Dillon, Radfar, Martin, Tran, Thanh, Muller, Markus, Strimel, Grant P., Susanj, Nathan, Mouchtaris, Athanasios, and Omologo, Maurizio

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful for DAC. Despite their importance, little research has explored neural approaches to integrate prosodic features into end-to-end (E2E) DAC models which infer dialogue acts directly from audio signals. In this work, we propose an E2E neural architecture that takes into account the need for characterizing prosodic phenomena co-occurring at different levels inside an utterance. A novel part of this architecture is a learnable gating mechanism that assesses the importance of prosodic features and selectively retains core information necessary for E2E DAC. Our proposed model improves DAC accuracy by 1.07% absolute across three publicly available benchmark datasets.

Published
2022

37. Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding

Author

Fu, Xuandi, Chang, Feng-Ju, Radfar, Martin, Wei, Kai, Liu, Jing, Strimel, Grant P., and Sathyendra, Kanthashree Mysore

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

End-to-end Spoken Language Understanding (E2E SLU) has attracted increasing interest due to its advantages of joint optimization and low latency when compared to traditionally cascaded pipelines. Existing E2E SLU models usually follow a two-stage configuration where an Automatic Speech Recognition (ASR) network first predicts a transcript which is then passed to a Natural Language Understanding (NLU) module through an interface to infer semantic labels, such as intent and slot tags. This design, however, does not consider the NLU posterior while making transcript predictions, nor correct the NLU prediction error immediately by considering the previously predicted word-pieces. In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system. In this work, we propose a streamable multi-task semantic transducer model to address these considerations. Our proposed architecture predicts ASR and NLU labels auto-regressively and uses a semantic decoder to ingest both previously predicted word-pieces and slot tags while aggregating them through a fusion network. Using an industry scale SLU and a public FSC dataset, we show the proposed model outperforms the two-stage E2E SLU model for both ASR and NLU metrics., Comment: Accepted at ICASSP 2022

Published
2022

40. Genome-wide association study identifies Sjögrens risk loci with functional implications in immune and glandular cells.

Author

Khatri, Bhuwan, Tessneer, Kandice, Rasmussen, Astrid, Aghakhanian, Farhang, Reksten, Tove, Adler, Adam, Alevizos, Ilias, Anaya, Juan-Manuel, Aqrawi, Lara, Baecklund, Eva, Brun, Johan, Bucher, Sara, Eloranta, Maija-Leena, Engelke, Fiona, Forsblad-dElia, Helena, Glenn, Stuart, Hammenfors, Daniel, Imgenberg-Kreuz, Juliana, Jensen, Janicke, Johnsen, Svein, Jonsson, Malin, Kvarnström, Marika, Kelly, Jennifer, Li, He, Mandl, Thomas, Martín, Javier, Nocturne, Gaétane, Norheim, Katrine, Palm, Øyvind, Skarstein, Kathrine, Stolarczyk, Anna, Taylor, Kimberly, Teruel, Maria, Theander, Elke, Venuturupalli, Swamy, Wallace, Daniel, Grundahl, Kiely, Hefner, Kimberly, Radfar, Lida, Lewis, David, Stone, Donald, Kaufman, C, Brennan, Michael, Guthridge, Joel, James, Judith, Scofield, R, Gaffney, Patrick, Criswell, Lindsey, Jonsson, Roland, Eriksson, Per, Bowman, Simon, Omdal, Roald, Rönnblom, Lars, Warner, Blake, Rischmueller, Maureen, Witte, Torsten, Farris, A, Mariette, Xavier, Alarcon-Riquelme, Marta, Shiboski, Caroline, Wahren-Herlenius, Marie, Ng, Wan-Fai, Sivils, Kathy, Adrianto, Indra, Nordmark, Gunnel, and Lessard, Christopher

Subjects
Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Sjogrens Syndrome
Abstract

Sjögrens disease is a complex autoimmune disease with twelve established susceptibility loci. This genome-wide association study (GWAS) identifies ten novel genome-wide significant (GWS) regions in Sjögrens cases of European ancestry: CD247, NAB1, PTTG1-MIR146A, PRDM1-ATG5, TNFAIP3, XKR6, MAPT-CRHR1, RPTOR-CHMP6-BAIAP6, TYK2, SYNGR1. Polygenic risk scores yield predictability (AUROC = 0.71) and relative risk of 12.08. Interrogation of bioinformatics databases refine the associations, define local regulatory networks of GWS SNPs from the 95% credible set, and expand the implicated gene list to >40. Many GWS SNPs are eQTLs for genes within topologically associated domains in immune cells and/or eQTLs in the main target tissue, salivary glands.

Published
2022

41. Selecting the Appropriate Credit Guarantee Model for New Technology-based Firms (NTBFs) in Iran using the Analytical Network Process (ANP)

Author

Mohammad Mahdi Faridvand, Mahdi Elyasi, and Reza Radfar

Subjects
credit guarantee models, innovation financing, new technology-based firms (ntbfs), analytical network process (anp), Management. Industrial management, HD28-70
Abstract

ObjectiveAccording to the studies conducted on New Technology-Based Firms (NTBFs), the biggest problem of these companies is their access to financial resources. Studies show that among the indirect intervention policies, credit guarantee schemes have been considered by governments for facilitating the financing of these enterprises due to the need for less budget and a higher leverage effect. The purpose of this article is to present the appropriate credit guarantee model for new technology-based Firms. MethodsIn this study, which used a combination of qualitative and quantitative methods, firstly, by reviewing the literature, credit guarantee models, key components, and main criteria for choosing the appropriate credit guarantee model were identified and validated by the opinion of an expert group. Finally, the most appropriate model was selected using the Multiple Criteria Decision-Making Model and Analytical Network Process (ANP). Also, to form the grounds for policy implications for credit guarantee programs in Iran, an in-depth study of credit guarantee programs in the country and a comparison of the constituent components of credit guarantee programs with the classified models for credit guarantee in the world was carried out. ResultsThe findings of this study indicate a lack of effective policy formulation and a deficiency in the precise and targeted selection of a local credit guarantee model for emerging technology-based firms in Iran. The central outcomes of this study revolve around the identification of credit guarantee models, the delineation of key components, and the establishment of criteria for choosing the most suitable credit guarantee model. Furthermore, the research entails the ranking of both these criteria and the various models under consideration. According to the insights gathered from experts in this domain, the research findings highlight the primary criteria for selecting a credit guarantee model. These criteria, ranked in order of significance include: facilitating the provision of credit services, assessing the consequences of the credit guarantee system, effective risk management, and governance of the credit guarantee system. This ranking reflects the experts' assessment of the most pivotal factors when evaluating credit guarantee programs. The criterion of facilitating the provision of credit services has the most weight (importance). Also, policy implications such as: "Government support policies regarding the development of guarantee tools", " creating a centralized and reliable credit risk database" and "Governance and functional changes in the country's credit guarantee institutions" were proposed to create a local credit guarantee system for NTBFs. ConclusionIn this research, utilizing the ANP methodology, the selection of an apt credit guarantee model for the nation aligns with the specific requirements of emerging technology-based firms, the prevailing economic and institutional landscape, and global best practices and successful models. This approach holds substantial promise for achieving significant success, not only in shaping effective policies but also in their successful implementation within the realm of financing for these companies. Reforming the institutional and functional structure of the country's credit guarantee institutions in the short term, as well as the formation of new credit guarantee institutions according to successful credit guarantee models in the world and their development in the long run can play a significant role in the financing of NTBFs.

Published
2023
Full Text
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47. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author

Brauer, Michael, Roth, Gregory A, Aravkin, Aleksandr Y, Zheng, Peng, Abate, Kalkidan Hassen, Abate, Yohannes Habtegiorgis, Abbafati, Cristiana, Abbasgholizadeh, Rouzbeh, Abbasi, Madineh Akram, Abbasian, Mohammadreza, Abbasifard, Mitra, Abbasi-Kangevari, Mohsen, Abd ElHafeez, Samar, Abd-Elsalam, Sherief, Abdi, Parsa, Abdollahi, Mohammad, Abdoun, Meriem, Abdulah, Deldar Morad, Abdullahi, Auwal, Abebe, Mesfin, Abedi, Aidin, Abedi, Armita, Abegaz, Tadesse M, Abeldaño Zuñiga, Roberto Ariel, Abiodun, Olumide, Abiso, Temesgen Lera, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Abualruz, Hasan, Abubakar, Bilyaminu, Abu-Gharbieh, Eman, Abukhadijah, Hana Jihad Jihad, Aburuz, Salahdein, Abu-Zaid, Ahmed, Adane, Mesafint Molla, Addo, Isaac Yeboah, Addolorato, Giovanni, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Aden, Bashir, Adetunji, Juliana Bunmi, Adeyeoluwa, Temitayo Esther, Adha, Rishan, Adibi, Amin, Adnani, Qorinah Estiningtyas Sakilah, Adzigbli, Leticia Akua, Afolabi, Aanuoluwapo Adeyimika, Afolabi, Rotimi Felix, Afshin, Ashkan, Afyouni, Shadi, Afzal, Muhammad Sohail, Afzal, Saira, Agampodi, Suneth Buddhika, Agbozo, Faith, Aghamiri, Shahin, Agodi, Antonella, Agrawal, Anurag, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Noah, Ahmad, Shahzaib, Ahmad, Tauseef, Ahmed, Ali, Ahmed, Anisuddin, Ahmed, Ayman, Ahmed, Luai A, Ahmed, Muktar Beshir, Ahmed, Safoora, Ahmed, Syed Anees, Ajami, Marjan, Akalu, Gizachew Taddesse, Akara, Essona Matatom, Akbarialiabad, Hossein, Akhlaghi, Shiva, Akinosoglou, Karolina, Akinyemiju, Tomi, Akkaif, Mohammed Ahmed, Akkala, Sreelatha, Akombi-Inyang, Blessing, Al Awaidy, Salah, Al Hasan, Syed Mahfuz, Alahdab, Fares, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Alam, Nazmul, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albakri, Almaza, AlBataineh, Mohammad T, Aldhaleei, Wafa A, Aldridge, Robert W, Alemayohu, Mulubirhan Assefa, Alemu, Yihun Mulugeta, Al-Fatly, Bassam, Al-Gheethi, Adel Ali Saeed, Al-Habbal, Khairat, Alhabib, Khalid F, Alhassan, Robert Kaba, Ali, Abid, Ali, Amjad, Ali, Beriwan Abdulqadir, Ali, Iman, Ali, Liaqat, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Waad, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aljunid, Syed Mohamed, Alla, François, Al-Marwani, Sabah, Al-Mekhlafi, Hesham M, Almustanyir, Sami, Alomari, Mahmoud A, Alonso, Jordi, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Raddadi, Rajaa M, Alrawashdeh, Ahmad, Al-Rifai, Rami Hani, Alrousan, Sahel Majed, Al-Sabah, Salman Khalifah, Alshahrani, Najim Z, Altaany, Zaid, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvis-Guzman, Nelson, Alvis-Zakzuk, Nelson J, Alwafi, Hassan, Al-Wardat, Mohammad Sami, Al-Worafi, Yaser Mohammed, Aly, Hany, Aly, Safwat, Alzoubi, Karem H, Al-Zyoud, Walid, Amaechi, Uchenna Anderson, Aman Mohammadi, Masous, Amani, Reza, Amiri, Sohrab, Amirzade-Iranaq, Mohammad Hosein, Ammirati, Enrico, Amu, Hubert, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderlini, Deanna, Anderson, Jason A, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Anenberg, Susan C, Angappan, Dhanalakshmi, Angus, Colin, Anil, Abhishek, Anil, Sneha, Anjum, Afifa, Anoushiravani, Amir, Antonazzo, Ippazio Cosimo, Antony, Catherine M, Antriyandarti, Ernoiz, Anuoluwa, Boluwatife Stephen, Anvari, Davood, Anvari, Saeid, Anwar, Saleha, Anwar, Sumadi Lukman, Anwer, Razique, Anyabolo, Ekenedilichukwu Emmanuel, Anyasodor, Anayochukwu Edward, Apostol, Geminn Louis Carace, Arabloo, Jalal, Arabzadeh Bahri, Razman, Arafat, Mosab, Areda, Demelash, Aregawi, Brhane Berhe, Aremu, Abdulfatai, Armocida, Benedetta, Arndt, Michael Benjamin, Ärnlöv, Johan, Arooj, Mahwish, Artamonov, Anton A, Artanti, Kurnia Dwi, Aruleba, Idowu Thomas, Arumugam, Ashokan, Asbeutah, Akram M, Asgary, Saeed, Asgedom, Akeza Awealom, Ashbaugh, Charlie, Ashemo, Mubarek Yesse, Ashraf, Tahira, Askarinejad, Amir, Assmus, Michael, Astell-Burt, Thomas, Athar, Mohammad, Athari, Seyyed Shamsadin, Atorkey, Prince, Atreya, Alok, Aujayeb, Avinash, Ausloos, Marcel, Avila-Burgos, Leticia, Awoke, Andargie Abate, Ayala Quintanilla, Beatriz Paulina, Ayatollahi, Haleh, Ayestas Portugal, Carlos, Ayuso-Mateos, Jose L, Azadnajafabad, Sina, Azevedo, Rui M S, Azhar, Gulrez Shah, Azizi, Hosein, Azzam, Ahmed Y, Backhaus, Insa Linnea, Badar, Muhammad, Badiye, Ashish D, Bagga, Arvind, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bahrami Taghanaki, Pegah, Bai, Ruhai, Baig, Atif Amin, Baker, Jennifer L, Bakkannavar, Shankar M, Balasubramanian, Madhan, Baltatu, Ovidiu Constantin, Bam, Kiran, Bandyopadhyay, Soham, Banik, Biswajit, Banik, Palash Chandra, Banke-Thomas, Aduragbemi, Bansal, Hansi, Barchitta, Martina, Bardhan, Mainak, Bardideh, Erfan, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barrero, Lope H, Barrow, Amadou, Barteit, Sandra, Basharat, Zarrin, Basiru, Afisu, Basso, João Diogo, Bastan, Mohammad-Mahdi, Basu, Sanjay, Batchu, Sai, Batra, Kavita, Batra, Ravi, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Beaney, Thomas, Behnoush, Amir Hossein, Beiranvand, Maryam, Béjot, Yannick, Bekele, Alehegn, Belgaumi, Uzma Iqbal, Bell, Arielle Wilder, Bell, Michelle L, Bello, Muhammad Bashir, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bendak, Salaheddine, Bennett, Derrick A, Bennitt, Fiona B, Bensenor, Isabela M, Benzian, Habib, Beran, Azizullah, Berezvai, Zombor, Bernabe, Eduardo, Bernstein, Robert S, Bettencourt, Paulo J G, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhandari, Dinesh, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhaskar, Sonu, Bhat, Ajay Nagesh, Bhat, Vivek, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhatti, Rajbir, Bhuiyan, Mohiuddin Ahmed, Bhutta, Zulfiqar A, Bikbov, Boris, Bishai, Jessica Devin, Bisignano, Catherine, Biswas, Atanu, Biswas, Bijit, Biswas, Raaj Kishore, Bjørge, Tone, Boachie, Micheal Kofi, Boakye, Hosea, Bockarie, Moses John, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bogale, Eyob Ketema, Bolla, Srinivasa Rao, Boloor, Archith, Bonakdar Hashemi, Milad, Boppana, Sri Harsha, Bora Basara, Berrak, Borhany, Hamed, Botero Carvajal, Alejandro, Bouaoud, Souad, Boufous, Soufiane, Bourne, Rupert, Boxe, Christopher, Braithwaite, Dejana, Brant, Luisa C, Brar, Amanpreet, Breitborde, Nicholas J K, Breitner, Susanne, Brenner, Hermann, Briko, Andrey Nikolaevich, Britton, Gabrielle, Brown, Colin Stewart, Browne, Annie J, Brunoni, Andre R, Bryazka, Dana, Bulamu, Norma B, Bulto, Lemma N, Buonsenso, Danilo, Burkart, Katrin, Burns, Richard A, Busse, Reinhard, Bustanji, Yasser, Butt, Nadeem Shafique, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Cagney, Jack, Cahuana-Hurtado, Lucero, Calina, Daniela, Cámera, Luis Alberto, Campos, Luciana Aparecida, Campos-Nonato, Ismael R, Cao, Chao, Cao, Fan, Cao, Yubin, Capodici, Angelo, Cárdenas, Rosario, Carr, Sinclair, Carreras, Giulia, Carrero, Juan J, Carugno, Andrea, Carvalho, Felix, Carvalho, Márcia, Castaldelli-Maia, Joao Mauricio, Castañeda-Orjuela, Carlos A, Castelpietra, Giulio, Catalá-López, Ferrán, Catapano, Alberico L, Cattaruzza, Maria Sofia, Caye, Arthur, Cederroth, Christopher R, Cegolon, Luca, Cenderadewi, Muthia, Cercy, Kelly M, Cerin, Ester, Chadwick, Joshua, Chakraborty, Chiranjib, Chakraborty, Promit Ananyo, Chakraborty, Sandip, Chan, Jeffrey Shi Kai, Chan, Raymond N C, Chandan, Joht Singh, Chandika, Rama Mohan, Chaturvedi, Pankaj, Chen, An-Tian, Chen, Catherine S, Chen, Haowei, Chen, Meng Xuan, Chen, Mingling, Chen, Simiao, Cheng, Ching-Yu, Cheng, Esther T W, Cherbuin, Nicolas, Chi, Gerald, Chichagi, Fatemeh, Chimed-Ochir, Odgerel, Chimoriya, Ritesh, Ching, Patrick R, Chirinos-Caceres, Jesus Lorenzo, Chitheer, Abdulaal, Cho, William C S, Chong, Bryan, Chopra, Hitesh, Chowdhury, Rajiv, Christopher, Devasahayam J, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Chung, Eric, Chung, Sheng-Chia, Chutiyami, Muhammad, Cioffi, Iolanda, Cogen, Rebecca M, Cohen, Aaron J, Columbus, Alyssa, Conde, Joao, Corlateanu, Alexandru, Cortese, Samuele, Cortesi, Paolo Angelo, Costa, Vera Marisa, Costanzo, Simona, Criqui, Michael H, Cruz, Jessica A, Cruz-Martins, Natália, Culbreth, Garland T, da Silva, Alanna Gomes, Dadras, Omid, Dai, Xiaochen, Dai, Zhaoli, Daikwo, Patience Unekwuojo, Dalli, Lachlan L, Damiani, Giovanni, D'Amico, Emanuele, D'Anna, Lucio, Darwesh, Aso Mohammad, Das, Jai K, Das, Subasish, Dash, Nihar Ranjan, Dashti, Mohsen, Dávila-Cervantes, Claudio Alberto, Davis Weaver, Nicole, Davitoiu, Dragos Virgil, De la Hoz, Fernando Pio, de la Torre-Luque, Alejandro, De Leo, Diego, Debopadhaya, Shayom, Degenhardt, Louisa, Del Bo', Cristian, Delgado-Enciso, Ivan, Delgado-Saborit, Juana Maria, Demoze, Chalachew Kassaw, Denova-Gutiérrez, Edgar, Dervenis, Nikolaos, Dervišević, Emina, Desai, Hardik Dineshbhai, Desai, Rupak, Devanbu, Vinoth Gnana Chellaiyan, Dewan, Syed Masudur Rahman, Dhali, Arkadeep, Dhama, Kuldeep, Dhane, Amol S, Dhimal, Mandira Lamichhane, Dhimal, Meghnath, Dhingra, Sameer, Dhulipala, Vishal R, Dhungana, Raja Ram, Dias da Silva, Diana, Diaz, Daniel, Diaz, Luis Antonio, Diaz, Michael J, Dima, Adriana, Ding, Delaney D, Dinu, Monica, Djalalinia, Shirin, Do, Thanh Chi, Do, Thao Huynh Phuong, do Prado, Camila Bruneli, Dodangeh, Masoud, Dohare, Sushil, Dokova, Klara Georgieva, Dong, Wanyue, Dongarwar, Deepa, D'Oria, Mario, Dorostkar, Fariba, Dorsey, E Ray, Doshi, Rajkumar, Doshmangir, Leila, Dowou, Robert Kokou, Driscoll, Tim Robert, Dsouza, Ashel Chelsea, Dsouza, Haneil Larson, Dumith, Samuel C, Duncan, Bruce B, Duraes, Andre Rodrigues, Duraisamy, Senbagam, Dushpanova, Anar, Dzianach, Paulina Agnieszka, Dziedzic, Arkadiusz Marian, Ebrahimi, Alireza, Echieh, Chidiebere Peter, Ed-Dra, Abdelaziz, Edinur, Hisham Atan, Edvardsson, David, Edvardsson, Kristina, Efendi, Ferry, Eftekharimehrabad, Aziz, Eini, Ebrahim, Ekholuenetale, Michael, Ekundayo, Temitope Cyrus, El Arab, Rabie Adel, El Sayed Zaki, Maysaa, El-Dahiyat, Faris, Elemam, Noha Mousaad, Elgar, Frank J, ElGohary, Ghada Metwally Tawfik, Elhabashy, Hala Rashad, Elhadi, Muhammed, Elmehrath, Ahmed O, Elmeligy, Omar Abdelsadek Abdou, Elshaer, Mohammed, Elsohaby, Ibrahim, Emeto, Theophilus I, Esfandiari, Negin, Eshrati, Babak, Eslami, Majid, Esmaeili, Sayed Vahid, Estep, Kara, Etaee, Farshid, Fabin, Natalia, Fagbamigbe, Adeniyi Francis, Fagbule, Omotayo Francis, Fahimi, Saman, Falzone, Luca, Fareed, Mohammad, Farinha, Carla Sofia e Sá, Faris, MoezAlIslam Ezzat Mahmoud, Faris, Pawan Sirwan, Faro, Andre, Fasina, Folorunso Oludayo, Fatehizadeh, Ali, Fauk, Nelsensius Klau, Fazylov, Timur, Feigin, Valery L, Feng, Xiaoqi, Fereshtehnejad, Seyed-Mohammad, Feroze, Abdullah Hamid, Ferrara, Pietro, Ferrari, Alize J, Ferreira, Nuno, Fetensa, Getahun, Feyisa, Bikila Regassa, Filip, Irina, Fischer, Florian, Fitriana, Ida, Flavel, Joanne, Flohr, Carsten, Flood, David, Flor, Luisa S, Foigt, Nataliya A, Folayan, Morenike Oluwatoyin, Force, Lisa M, Fortuna, Daniela, Foschi, Matteo, Franklin, Richard Charles, Freitas, Alberto, Friedman, Sara D, Fux, Blima, G, Sridevi, Gaal, Peter Andras, Gaihre, Santosh, Gajdács, Márió, Galali, Yaseen, Gallus, Silvano, Gandhi, Aravind P, Ganesan, Balasankar, Ganiyani, Mohammad Arfat, Garcia, Vanessa, Gardner, William M, Garg, Ravindra K, Gautam, Rupesh K, Gebi, Tilaye Gebru, Gebregergis, Miglas W, Gebrehiwot, Mesfin, Gebremariam, Tesfay B B, Gebremeskel, Teferi Gebru, Gerema, Urge, Getacher, Lemma, Getahun, Genanew K a, Getie, Molla, Ghadirian, Fataneh, Ghafarian, Sadegh, Ghaffari Jolfayi, Amir, Ghailan, Khalid Yaser, Ghajar, Alireza, Ghasemi, MohammadReza, Ghasempour Dabaghi, Ghazal, Ghasemzadeh, Afsaneh, Ghassemi, Fariba, Ghazy, Ramy Mohamed, Gholami, Ali, Gholamrezanezhad, Ali, Gholizadeh, Nasim, Ghorbani, Mahsa, Gil, Artyom Urievich, Gil, Gabriela Fernanda, Gilbertson, Nora M, Gill, Paramjit Singh, Gill, Tiffany K, Gindaba, Ebisa Zerihun, Girmay, Alem, Glasbey, James C, Gnedovskaya, Elena V, Göbölös, Laszlo, Godinho, Myron Anthony, Goel, Amit, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Gomes, Nelson G M, Gopalani, Sameer Vali, Gorini, Giuseppe, Goudarzi, Houman, Goulart, Alessandra C, Gouravani, Mahdi, Goyal, Anmol, Graham, Simon Matthew, Grivna, Michal, Grosso, Giuseppe, Guan, Shi-Yang, Guarducci, Giovanni, Gubari, Mohammed Ibrahim Mohialdeen, Guha, Avirup, Guicciardi, Stefano, Gulati, Snigdha, Gulisashvili, David, Gunawardane, Damitha Asanga, Guo, Cui, Gupta, Anish Kumar, Gupta, Bhawna, Gupta, Mohak, Gupta, Rahul, Gupta, Rajat Das, Gupta, Rajeev, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vijai Kumar, Gupta, Vivek Kumar, Habibzadeh, Farrokh, Habibzadeh, Parham, Hadaro, Tesfahun Simon, Hadian, Zahra, Haep, Nils, Haghi-Aminjan, Hamed, Haghmorad, Dariush, Hagins, Hailey, Haile, Demewoz, Hailu, Alemayehu, Hajj Ali, Adel, 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Samer H, Murray, Christopher J L, and Gakidou, Emmanuela

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2024
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48. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

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Naghavi, Mohsen, Ong, Kanyin Liane, Aali, Amirali, Ababneh, Hazim S, Abate, Yohannes Habtegiorgis, Abbafati, Cristiana, Abbasgholizadeh, Rouzbeh, Abbasian, Mohammadreza, Abbasi-Kangevari, Mohsen, Abbastabar, Hedayat, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed, Abdollahi, Mohammad, Abdollahifar, Mohammad-Amin, Abdoun, Meriem, Abdulah, Deldar Morad, Abdullahi, Auwal, Abebe, Mesfin, Abebe, Samrawit Shawel, Abedi, Aidin, Abegaz, Kedir Hussein, Abhilash, E S, Abidi, Hassan, Abiodun, Olumide, Aboagye, Richard Gyan, Abolhassani, Hassan, Abolmaali, Meysam, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abtahi, Dariush, Abu Rumeileh, Samir, Abualruz, Hasan, Abubakar, Bilyaminu, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Accrombessi, Manfred Mario Kokou, Adal, Tadele Girum, Adamu, Abdu A, Addo, Isaac Yeboah, Addolorato, Giovanni, Adebiyi, Akindele Olupelumi, Adekanmbi, Victor, Adepoju, Abiola Victor, Adetunji, Charles Oluwaseun, Adetunji, Juliana Bunmi, Adeyeoluwa, Temitayo Esther, Adeyinka, Daniel Adedayo, Adeyomoye, Olorunsola Israel, Admass, Biruk Adie Adie, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afolabi, Aanuoluwapo Adeyimika, Afzal, Muhammad Sohail, Afzal, Saira, Agampodi, Suneth Buddhika, Agasthi, Pradyumna, Aggarwal, Manik, Aghamiri, Shahin, Agide, Feleke Doyore, Agodi, Antonella, Agrawal, Anurag, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Shahzaib, Ahmad, Tauseef, Ahmadi, Keivan, Ahmadzade, Amir Mahmoud, Ahmed, Ali, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Mehrunnisha Sharif, Ahmed, Meqdad Saleh, Ahmed, Muktar Beshir, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Akara, Essona Matatom, Akbarialiabad, Hossein, Akinosoglou, Karolina, Akinyemiju, Tomi, Akkaif, Mohammed Ahmed, Akyirem, Samuel, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Alahdab, Fares, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Alam, Manjurul, Alam, Noore, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Al-Azzam, Sayer, Albakri, Almaza, Albashtawy, Mohammed, AlBataineh, Mohammad T, Alcalde-Rabanal, Jacqueline Elizabeth, Aldawsari, Khalifah A, Aldhaleei, Wafa A, Aldridge, Robert W, Alema, Haileselasie Berhane, Alemayohu, Mulubirhan Assefa, Alemi, Sharifullah, Alemu, Yihun Mulugeta, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhalaiqa, Fadwa Alhalaiqa Naji, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Amjad, Ali, Liaqat, Ali, Mohammed Usman, Ali, Rafat, Ali, Shahid, Ali, Syed Shujait Shujait, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Alikhani, Reyhaneh, Alimohamadi, Yousef, Aliyi, Ahmednur Adem, Aljasir, Mohammad A M, Aljunid, Syed Mohamed, Alla, François, Allebeck, Peter, Al-Marwani, Sabah, Al-Maweri, Sadeq Ali Ali, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Louay, Almidani, Omar, Alomari, Mahmoud A, Al-Omari, Basem, Alonso, Jordi, Alqahtani, Jaber S, Alqalyoobi, Shehabaldin, Alqutaibi, Ahmed Yaseen, Al-Sabah, Salman Khalifah, Altaany, Zaid, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvis-Guzman, Nelson, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Aly, Safwat, Alzoubi, Karem H, Amani, Reza, Amare, Azmeraw T, Amegbor, Prince M, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Amiri, Sohrab, Amirzade-Iranaq, Mohammad Hosein, Amu, Hubert, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderlini, Deanna, Anderson, David B, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Angus, Colin, Anil, Abhishek, Anil, Sneha, Anoushiravani, Amir, Ansari, Hossein, Ansariadi, Ansariadi, Ansari-Moghaddam, Alireza, Antony, Catherine M, Antriyandarti, Ernoiz, Anvari, Davood, Anvari, Saeid, Anwar, Saleha, Anwar, Sumadi Lukman, Anwer, Razique, Anyasodor, Anayochukwu Edward, Aqeel, Muhammad, Arab, Juan Pablo, Arabloo, Jalal, Arafat, Mosab, Aravkin, Aleksandr Y, Areda, Demelash, Aremu, Abdulfatai, Aremu, Olatunde, Ariffin, Hany, Arkew, Mesay, Armocida, Benedetta, Arndt, Michael Benjamin, Ärnlöv, Johan, Arooj, Mahwish, Artamonov, Anton A, Arulappan, Judie, Aruleba, Raphael Taiwo, Arumugam, Ashokan, Asaad, Malke, Asadi-Lari, Mohsen, Asgedom, Akeza Awealom, Asghariahmadabad, Mona, Asghari-Jafarabadi, Mohammad, Ashraf, Muhammad, Aslani, Armin, Astell-Burt, Thomas, Athar, Mohammad, Athari, Seyyed Shamsadin, Atinafu, Bantalem Tilaye Tilaye, Atlaw, Habtamu Wondmagegn, Atorkey, Prince, Atout, Maha Moh'd Wahbi, Atreya, Alok, Aujayeb, Avinash, Ausloos, Marcel, Avan, Abolfazl, Awedew, Atalel Fentahun, Aweke, Amlaku Mulat, Ayala Quintanilla, Beatriz Paulina, Ayatollahi, Haleh, Ayuso-Mateos, Jose L, Ayyoubzadeh, Seyed Mohammad, Azadnajafabad, Sina, Azevedo, Rui M S, Azzam, Ahmed Y, B, Darshan B, Babu, Abraham Samuel, Badar, Muhammad, Badiye, Ashish D, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bah, Sulaiman, Bahadorikhalili, Saeed, Bahmanziari, Najmeh, Bai, Ruhai, Baig, Atif Amin, Baker, Jennifer L, Bako, Abdulaziz T, Bakshi, Ravleen Kaur, Balakrishnan, Senthilkumar, Balasubramanian, Madhan, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Bandyopadhyay, Soham, Banik, Palash Chandra, Bansal, Hansi, Bansal, Kannu, Barbic, Franca, Barchitta, Martina, Bardhan, Mainak, Bardideh, Erfan, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barrero, Lope H, Barrow, Amadou, Barteit, Sandra, Barua, Lingkan, Basharat, Zarrin, Bashiri, Azadeh, Basiru, Afisu, Baskaran, Pritish, Basnyat, Buddha, Bassat, Quique, Basso, João Diogo, Basting, Ann V L, Basu, Sanjay, Batra, Kavita, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Beaney, Thomas, Bedi, Neeraj, Beghi, Massimiliano, Behboudi, Emad, Behera, Priyamadhaba, Behnoush, Amir Hossein, Behzadifar, Masoud, Beiranvand, Maryam, Bejarano Ramirez, Diana Fernanda, Béjot, Yannick, Belay, Sefealem Assefa, Belete, Chalie Mulu, Bell, Michelle L, Bello, Muhammad Bashir, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bender, Rose Grace, Bensenor, Isabela M, Beran, Azizullah, Berezvai, Zombor, Berhie, Alemshet Yirga, Berice, Betyna N, Bernstein, Robert S, Bertolacci, Gregory J, Bettencourt, Paulo J G, Beyene, Kebede A, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhalla, Ashish, Bhandari, Dinesh, Bhangdia, Kayleigh, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhardwaj, Prarthna V, Bhargava, Ashish, Bhaskar, Sonu, Bhat, Vivek, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh, Bhatti, Manpreet S, Bhatti, Rajbir, Bhutta, Zulfiqar A, Bikbov, Boris, Bishai, Jessica Devin, Bisignano, Catherine, Bisulli, Francesca, Biswas, Atanu, Biswas, Bijit, Bitaraf, Saeid, Bitew, Bikes Destaw, Bitra, Veera R, Bjørge, Tone, Boachie, Micheal Kofi, Boampong, Mary Sefa, Bobirca, Anca Vasilica, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bogale, Eyob Ketema, Bogale, Kassawmar Angaw, Bohlouli, Somayeh, Bolarinwa, Obasanjo Afolabi, Boloor, Archith, Bonakdar Hashemi, Milad, Bonny, Aime, Bora, Kaustubh, Bora Basara, Berrak, Borhany, Hamed, Borzutzky, Arturo, Bouaoud, Souad, Boustany, Antoine, Boxe, Christopher, Boyko, Edward J, Brady, Oliver J, Braithwaite, Dejana, Brant, Luisa C, Brauer, Michael, Brazinova, Alexandra, Brazo-Sayavera, Javier, Breitborde, Nicholas J K, Breitner, Susanne, Brenner, Hermann, Briko, Andrey Nikolaevich, Briko, Nikolay Ivanovich, Britton, Gabrielle, Brown, Julie, Brugha, Traolach, Bulamu, Norma B, Bulto, Lemma N, Buonsenso, Danilo, Burns, Richard A, Busse, Reinhard, Bustanji, Yasser, Butt, Nadeem Shafique, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Calina, Daniela, Cámera, Luis Alberto, Campos, Luciana Aparecida, Campos-Nonato, Ismael R, Cao, Chao, Cao, Yin, Capodici, Angelo, Cárdenas, Rosario, Carr, Sinclair, Carreras, Giulia, Carrero, Juan J, Carugno, Andrea, Carvalheiro, Cristina G, Carvalho, Felix, 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2024
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49. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

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Schumacher, Austin E, Kyu, Hmwe Hmwe, Aali, Amirali, Abbafati, Cristiana, Abbas, Jaffar, Abbasgholizadeh, Rouzbeh, Abbasi, Madineh Akram, Abbasian, Mohammadreza, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed, Abdollahi, Mohammad, Abdoun, Meriem, Abdullahi, Auwal, Abdurehman, Ame Mehadi, Abebe, Mesfin, Abedi, Aidin, Abedi, Armita, Abegaz, Tadesse M, Abeldaño Zuñiga, Roberto Ariel, Abhilash, E S, Abiodun, Olugbenga Olusola, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abrigo, Michael R M, Abtahi, Dariush, Abu Rumeileh, Samir, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Acuna, Juan Manuel, Adair, Tim, Addo, Isaac Yeboah, Adebayo, Oladimeji M, Adegboye, Oyelola A, Adekanmbi, Victor, Aden, Bashir, Adepoju, Abiola Victor, Adetunji, Charles Oluwaseun, Adeyeoluwa, Temitayo Esther, Adeyomoye, Olorunsola Israel, Adha, Rishan, Adibi, Amin, Adikusuma, Wirawan, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afework, Abel, Afolabi, Aanuoluwapo Adeyimika, Afraz, Ali, Afyouni, Shadi, Afzal, Saira, Agasthi, Pradyumna, Aghamiri, Shahin, Agodi, Antonella, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Tauseef, Ahmadi, Keivan, Ahmadzade, Amir Mahmoud, Ahmadzade, Mohadese, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Muktar Beshir, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Ajumobi, Olufemi, Akalu, Gizachew Taddesse, Akara, Essona Matatom, Akinosoglou, Karolina, Akkala, Sreelatha, Akyirem, Samuel, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Homsi, Ammar, Al Qadire, Mohammad, Ala, Moein, Aladelusi, Timothy Olukunle, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Al-Aly, Ziyad, Alam, Khurshid, Alam, Manjurul, Alam, Zufishan, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albashtawy, Mohammed, AlBataineh, Mohammad T, Aldridge, Robert W, Alemi, Sharifullah, 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2024
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50. Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

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Bhattacharjee, Natalia V, Schumacher, Austin E, Aali, Amirali, Abate, Yohannes Habtegiorgis, Abbasgholizadeh, Rouzbeh, Abbasian, Mohammadreza, Abbasi-Kangevari, Mohsen, Abbastabar, Hedayat, Abd ElHafeez, Samar, Abd-Elsalam, Sherief, Abdollahi, Mohammad, Abdollahifar, Mohammad-Amin, Abdoun, Meriem, Abdullahi, Auwal, Abebe, Mesfin, Abebe, Samrawit Shawel, Abiodun, Olumide, Abolhassani, Hassan, Abolmaali, Meysam, Abouzid, Mohamed, Aboye, Girma Beressa, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abrigo, Michael R M, Abtahi, Dariush, Abualruz, Hasan, Abubakar, Bilyaminu, Abu-Gharbieh, Eman, Abu-Rmeileh, Niveen ME, Adal, Tadele Girum Girum, Adane, Mesafint Molla, Adeagbo, Oluwafemi Atanda Adeagbo, Adedoyin, Rufus Adesoji, Adekanmbi, Victor, Aden, Bashir, Adepoju, Abiola Victor, Adetokunboh, Olatunji O, Adetunji, Juliana Bunmi, Adeyinka, Daniel Adedayo, Adeyomoye, Olorunsola Israel, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afolabi, Rotimi Felix, Afyouni, Shadi, Afzal, Muhammad Sohail, Afzal, Saira, Aghamiri, Shahin, Agodi, Antonella, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahlstrom, Austin J, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Tauseef, Ahmed, Ali, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Meqdad Saleh, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Akalu, Gizachew Taddesse, Akbarialiabad, Hossein, Akinyemi, Rufus Olusola, Akkaif, Mohammed Ahmed, Akkala, Sreelatha, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Qadire, Mohammad, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Alam, Khurshid, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albakri, Almaza, Albashtawy, Mohammed, AlBataineh, Mohammad T, Alemi, Hediyeh, Alemi, Sharifullah, Alemu, Yihun Mulugeta, Al-Eyadhy, Ayman, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhajri, Noora, Alhalaiqa, Fadwa Alhalaiqa Naji, Alhassan, Robert Kaba, Ali, Abid, Ali, Beriwan Abdulqadir, Ali, Liaqat, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Alif, Sheikh Mohammad, Aligol, Mohammad, Alijanzadeh, Mehran, Aljasir, Mohammad A M, Aljunid, Syed Mohamed, Al-Marwani, Sabah, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Omar, Alomari, Mahmoud A, Al-Omari, Basem, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Raddadi, Rajaa M, Al-Sabah, Salman Khalifah, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Aluh, Deborah Oyine, Alvi, Farrukh Jawad, Alvis-Guzman, Nelson, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Aly, Safwat, Alzoubi, Karem H, Ameyaw, Edward Kwabena, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Amini-Rarani, Mostafa, Amiri, Sohrab, Ampomah, Irene Gyamfuah, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderlini, Deanna, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Anil, Abhishek, Anil, Sneha, Ansar, Adnan, Ansari-Moghaddam, Alireza, Antony, Catherine M, Antriyandarti, Ernoiz, Anvari, Saeid, ANWAR, 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