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4. 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

5. The relationship between fear of COVID-19 and adherence to personal protective measures in a sample of Iranian healthcare providers: a cross-sectional study

Author

Mohammad-Ehsan Adib, Mojtaba Jafari, and Ali Radfar

Subjects
Personal protective measures, Fear, COVID-19, Adherence, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Studies have been conducted worldwide to investigate the level of adherence to personal protective measures or fear of COVID-19 among healthcare providers. However, few studies have examined the relationship between adherence to personal protective measures and fear of COVID-19. There is also a need for more information on this topic from Iran. This study investigated the relationship between adherence to personal protective measures against COVID-19 and fear of COVID-19 in the healthcare providers at Pastor Hospital of Bam, Iran, in 2022. Methods This cross-sectional study was conducted in August and September 2022 with 199 healthcare providers of Pastor Hospital of Bam, Iran. The study included medical, nursing, and paramedical staff at Pastor Hospital at the time of the study. Incomplete responses and failure to return the questionnaire to the researcher were exclusion criteria. The fear of COVID-19 scale and a checklist of personal protective measures were used to collect data. Descriptive statistics, t-tests, analysis of variance, and Pearson’s correlation coefficient were used to analyze the data. Results Of the 199 participants, 67.3% were female, and their mean age was 31 ± 4.55 years. The mean score for adherence to personal protective measures was 14.46 ± 3.39 (out of 23), and the mean score for fear of COVID-19 was 17.04 ± 4.58 (out of 35). Adherence to personal protective measures was higher among females than males (14.96 ± 2.99 vs. 13.43 ± 3.92, p = 0.003), in individuals who had attended infection control courses than in those who had not (15.57 ± 2.88 vs. 13.30 ± 3.50, p

Published
2024
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6. Rapid intensification of tropical cyclones in the Gulf of Mexico is more likely during marine heatwaves

Author

Soheil Radfar, Hamed Moftakhari, and Hamid Moradkhani

Subjects
Geology, QE1-996.5, Environmental sciences, GE1-350
Abstract

Abstract Tropical cyclones can rapidly intensify under favorable oceanic and atmospheric conditions. This phenomenon is complex and difficult to predict, making it a serious challenge for coastal communities. A key contributing factor to the intensification process is the presence of prolonged high sea surface temperatures, also known as marine heatwaves. However, the extent to which marine heatwaves contribute to the potential of rapid intensification events is not yet fully explored. Here, we conduct a probabilistic analysis to assess how the likelihood of rapid intensification changes during marine heatwaves in the Gulf of Mexico and northwestern Caribbean Sea. Approximately 70% of hurricanes that formed between 1950 and 2022 were influenced by marine heatwaves. Notably, rapid intensification is, on average, 50% more likely during marine heatwaves. As marine heatwaves are on the increase due to climate change, our findings indicate that more frequent rapid intensification events can be expected in the warming climate.

Published
2024
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7. Moderate aerobic training enhances the effectiveness of insulin therapy through hypothalamic IGF1 signaling in rat model of Alzheimer's disease

Author

Forough Radfar, Mehdi Shahbazi, Shahzad Tahmasebi Boroujeni, Elahe Arab Ameri, and Maryam Farahmandfar

Subjects
Alzheimer’s disease, Insulin, Metabolism, Spatial learning and memory, Treadmill exercise, Medicine, Science
Abstract

Abstract Alzheimer's disease (AD) is a neurological condition that is connected with a decline in a person's memory as well as their cognitive ability. One of the key topics of AD research has been the exploration of metabolic causes. We investigated the effects of treadmill exercise and intranasal insulin on learning and memory impairment and the expression of IGF1, BDNF, and GLUT4 in hypothalamus. The animals were put into 9 groups at random. In this study, we examined the impact of insulin on spatial memory in male Wistar rats and analyzed the effects of a 4-week pretreatment of moderate treadmill exercise and insulin on the mechanisms of improved hypothalamic glucose metabolism through changes in gene and protein expression of IGF1, BDNF, and GLUT4. We discovered that rat given Aβ25–35 had impaired spatial learning and memory, which was accompanied by higher levels of Aβ plaque burden in the hippocampus and lower levels of IGF1, BDNF, and GLUT4 mRNA and protein expression in the hypothalamus. Additionally, the administration of exercise training and intranasal insulin results in the enhancement of spatial learning and memory impairments, the reduction of plaque burden in the hippocampus, and the enhancement of the expression of IGF1, BDNF, and GLUT4 in the hypothalamus of rats that were treated with Aβ25–35. Our results show that the improvement of learning and spatial memory due to the improvement of metabolism and upregulation of the IGF1, BDNF, and GLUT4 pathways can be affected by pretreatment exercise and intranasal insulin.

Published
2024
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8. In vitro and in vivo anti-parasitic activity of curcumin nanoemulsion on Leishmania major (MRHO/IR/75/ER)

Author

Keivan Sahebi, Fatemeh Shahsavani, Fatemeh Mehravar, Gholamreza Hatam, Rasoul Alimi, Amirhossein Radfar, Mohammad Saleh Bahreini, Ali Pouryousef, and Aref Teimouri

Subjects
Curcumin, Nanoemulsion, Nanoparticles, Anti-leishmanial activity, Leishmania major, Other systems of medicine, RZ201-999
Abstract

Abstract The present study aimed to assess the anti-leishmanial effects of curcumin nanoemulsion (CUR-NE) against Leishmania major (MRHO/IR/75/ER) in both in vitro and in vivo experiments. CUR-NE was successfully prepared via the spontaneous emulsification method. The in vitro effect of various concentrations of CUR-NE against L. major promastigotes was assessed using the flow cytometry method. In vivo experiments were carried out in BALB/c mice inoculated subcutaneously with 2 × 106 L. major promastigotes. Mice were treated with topical CUR-NE (2.5 mg/ml), intra-lesion injection of CUR-NE (2.5 mg/ml), topical CUR suspension (CUR-S, 2.5 mg/ml), topical NE without CUR (NE-no CUR), amphotericin B as the positive control group, and infected untreated mice as the negative control group. In vitro exposure of promastigotes to CUR-NE showed a dose-dependent anti-leishmanial effect, with a 67.52 ± 0.35% mortality rate at a concentration of 1250 µg/ml and an IC50 of 643.56 µg/ml. In vivo experiments showed that topical CUR-NE and CUR-S significantly decreased the mean lesion size in mice after four weeks from 4.73 ± 1.28 to 2.78 ± 1.28 mm and 4.45 ± 0.88 to 3.23 ± 0.59 mm, respectively (p = 0.001). Furthermore, CUR-NE significantly decreased the parasite load in treated mice compared with the negative control group (p = 0.001). Results from the current study demonstrated the promising activity of CUR-NE against L. major in both in vitro and in vivo experiments. Moreover, CUR-NE was more efficient than CUR-S in healing and reducing parasite burden in mouse models. Future studies should aim to identify molecular mechanisms as well as the pharmacologic and pharmacokinetic aspects of CUR-NE.

Published
2024
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9. Epidemiological aspects of individuals with mental disorders in the referral system: the experience of a Community Mental Health Center in the northeast of Iran

Author

Mahdi Talebi, Shabnam Niroumand, Mobin Gholami, Azadeh Samarghandi, Fatemeh Shaygani, Mahdi Radfar, and Ahmad Nemati

Subjects
Community Mental Health Centers, Mental health, Epidemiology, Referral and consultation, Patient dropouts, Iran, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Background Community Mental Health Centers (CMHCs) offer affordable mental health services in a less stigmatized environment, in a domiciliary setting. This study aimed to shed light on the epidemiological factors of patients attending CMHCs of Mashhad, their referral status, and treatment. Methods This study was conducted over the medical records of patients seen by psychiatrists between January 2014 and December 2021 in Mashhad's CMHC, the northeast of Iran. A detailed questionnaire was used to extract data from medical records about the epidemiological characteristics, diagnosed mental illnesses, referral status, and how often they visited the psychiatrist. The association between epidemiological findings and patient referral (referral system or self-referral) as well as the association between epidemiological findings and the number of psychiatric revisits were examined using the Chi-square test. Results Out of 662 patients, 472 (71%) were female and 190 (29%) were male, with an average age of 29 years. Among the 475 adult patients, 367 (77.3%) were married, with the majority being homemakers (56.4%). Major Depression Disorder (MDD) (32%) and Generalized Anxiety Disorder (GAD) (18.3%) were the most prevalent mental health conditions among patients. The majority of patients (74.9%) were referred to the CMHC of Mashhad from Primary Healthcare centers (PHCs) and psychiatric hospitals. Furthermore, female gender and patients with lower level of education were associated with more referral through from referral system. Of note, 431 patients (65.1%) did not return for a second visit, the ratio of treatment dropout was higher for patients with lower education levels. Conclusions Referral system should be more practical in Iran to enhance health services in CMHCs. It is recommended that PHCs undergo certain modifications to enhance the referral process for patients with mental health conditions, focusing on common mental disorders and individuals with low socioeconomic level.

Published
2024
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10. 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

11. 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

12. 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

14. The effect of the Yara smartphone application on anxiety, sleep quality, and suicidal thoughts in patients with major depressive disorder in Iran: a randomized controlled trial

Author

Zeinab Soltani, Naser Parizad, Moloud Radfar, Vahid Alinejad, Mohammad Arzanlo, and Mahmonir Haghighi

Subjects
Major depressive disorder, Anxiety, Sleep Quality, Suicide, Thought, Smartphone Apps, Psychiatry, RC435-571
Abstract

Abstract Background Depression is one of the most common mental disorders that leads to anxiety, sleep disturbances, and suicidal thoughts. Due to the high cost of treatment and the reluctance of many patients to seek medical help, major depressive disorder (MDD) is becoming more prevalent. Therefore, alternative methods like smartphone applications can help prevent and improve depression symptoms. The present study aimed to determine the effect of the newly developed Yara smartphone application on anxiety, sleep quality, and suicidal thoughts in patients with MDD. Methods This randomized controlled trial with a pretest-posttest design was conducted on Iranian patients with MDD in 2022. Sixty-four patients were recruited using convenience sampling and randomly assigned to two control and intervention groups. The intervention was conducted using the Yara smartphone application for three months. Data were collected using the Spielberger State-Trait Anxiety Inventory (STAI), Pittsburgh Sleep Quality Index (PSQI), and Beck Scale for Suicidal Ideation (BSSI). Data were first entered into IBM SPSS Statistics for Windows, version 22 (IBM Corp., Armonk, N.Y., USA) and then analyzed using descriptive and analytical statistics. Results There was no statistically significant difference in the mean score of anxiety and sleep quality between the intervention and control groups before the intervention (p ≥ .05). However, this difference in the mean score of anxiety and sleep quality was statistically significant in the two groups after the intervention (p < .05). The results showed no statistically significant difference in the mean score of suicidal thoughts between the two groups before and after the intervention (p ≥ .05). The use of the Yara smartphone application had a significant positive effect on anxiety and sleep quality in depressed patients (p < .001). At the same time, it had no significant effect on suicidal thoughts (p ≥ .05). Conclusion Considering the positive effect of using the Yara smartphone application on reducing anxiety and improving sleep quality in depressed patients, this application can help alleviate the problems of depressed patients alongside existing treatment methods. Thus, this application is recommended for this group of patients in psychiatric clinics and departments. The Yara application's effectiveness was not approved on suicidal thoughts in this study so that further investigation would be necessary. Trial Registration Iranian Registry of Clinical Trial approval code (IRCT# IRCT20131112015390N7).

Published
2024
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15. 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

16. 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

17. Impact of Pulse Duration on the Properties of Laser Hyperdoped Black Silicon

Author

Patrick Mc Kearney, Sören Schäfer, Xiaolong Liu, Simon Paulus, Ingo Lebershausen, Behrad Radfar, Ville Vähänissi, Hele Savin, and Stefan Kontermann

Subjects
atomic layer depositions, black silicon, doping profiles, pulse durations, ultrashort pulse laser hyperdoping, Applied optics. Photonics, TA1501-1820, Optics. Light, QC350-467
Abstract

The impact of three different pulse durations (100 fs, 1, and 10 ps) on the formation of laser hyperdoped black silicon with respect to surface morphology, sub‐bandgap absorptance, the sulfur concentration profile, and the effective minority carrier lifetime after Al2O3 surface passivation is investigated. The current flow behavior is compared through the hyperdoped layer by I–V measurements after hyperdoping with different pulse durations. For conditions that give the same absolute sub‐bandgap absorptance, an increase in pulse duration from 100 fs to 10 ps results in a shallower sulfur concentration profile. Findings are explained by an increasing ablation threshold from 0.19 J cm−2 for a pulse duration of 100 fs to 0.21 J cm−2 for 1 ps and 0.34 J cm−2 for 10 ps. The formation of an equally absorbing layer with a shallower doping profile results in a reduction in contact and/or sheet resistance. Despite the higher local sulfur concentration, the samples show no decrease in carrier lifetime measured by quasi‐steady‐state photoconductance decay on Al2O3 surface‐passivated samples. The investigation shows that longer pulses of up to 10 ps during laser hyperdoping of silicon result in advanced layer properties that promise to be beneficial in a potential device application.

Published
2024
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18. 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

19. 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

21. A review of lean, agile, resilient, and green (LARG) supply chain management in engineering, business and management areas

Author

Fatemeh Khanzadi, Reza Radfar, and Nazanin Pilevari

Subjects
Analysis, QA299.6-433, Business mathematics. Commercial arithmetic. Including tables, etc., HF5691-5716
Abstract

Supply chain management (SCM) that is Lean, Agile, Resilient, and Green (LARG) are required for competitiveness in today's complex, high-demand market. SCM must consider LARG paradigms concurrently, a rarely investigated topic. This study provides a comprehensive review of publications that combine all four LARG principles in engineering, business, and management domains. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, two Scopus and Google Scholar databases were exhaustively examined. Thirty-two manuscripts were selected for a comprehensive review. The year of publication, document type, countries, authors, journals, keywords, and topics was analyzed from 2000 to 2023. Also, each paper's methodology, central topic, findings, limitations, and future recommendations were outlined. Consequently, the current systematic literature review (SLR) revealed that the proposed topic is in its infancy, with promising prospects. By emphasizing the findings of this study, managers and businesses can increase consumer satisfaction and reduce costs.

Published
2024
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22. Designing key performance indicators (KPIs) for decent work in the pharmaceutical supply chain of Iran

Author

Fatemeh Lashgari, Ebrahim Teimoury, Seyed Mohammad Seyedhosseini, and Reza Radfar

Subjects
Analysis, QA299.6-433, Business mathematics. Commercial arithmetic. Including tables, etc., HF5691-5716
Abstract

While decent work has emerged as the central theme of the psychology of work theory and a global concept and directive for promoting social, political, and economic justice, it has garnered increasing scientific and political attention in the past two decades. However, until now, no defined measurement scale for the pharmaceutical supply chain exists. The present study aims to design and validate key performance indicators (KPIs) for 'decent work' in the pharmaceutical industry supply chain of Iran, using the Decent Work Daffi Scale (2017) as a reliable framework with five sub-scales and 15 items, tested and validated. For the validation of the Decent Work Scale, a quantitative survey study was conducted among selected pharmaceutical industry experts with a sample size of 228 individuals in the year 2023. The current study adopted an exploratory factor analysis approach using SPSS software and a confirmatory factor analysis through AMOS version 24 software. In this context, the factor structure, convergent validity, discriminant validity, and Cronbach's alpha coefficients were examined. The results showed that the five-factor structure outperforms the one-factor model with evidence supporting the convergent, discriminant, and predictive validity of the five-factor scale. Thus, the measurement of decent work in the pharmaceutical industry of Iran comprises five sub-scales: occupational safety conditions, access to healthcare, adequate remuneration, Free time and rest, and alignment of organizational values with family and societal values. This scale can serve as a useful tool for industrial and organizational psychology research, as well as for studies on the sustainability of social supply chains.

Published
2024
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23. Designing the pattern of causes and consequences in the implementation of digital marketing strategies in successful Iranian startups with Mixed method Research

Author

sepideh Moradi ziba, Javad Abbasi, Reza Radfar, and Mohammad Ali abdolvand

Subjects
strategy, marketing, digital marketing, startup, social media, artificial intelligence, Business, HF5001-6182
Abstract

The purpose of this research is to compile a model of digital marketing strategies in successful Iranian startups. According to its purpose, the research method is practical, and in terms of its implementation, it is mixed (qualitative-quantitative). The statistical population of the research in the qualitative part includes 13 academic and executive experts in the field of startups in the country, who were selected using the purposeful judgment method and the snowball method, and the statistical population in the quantitative part includes the marketing unit of active successful startups. There are 250 people in the country (5 companies) and marketing professors of 5 top universities in Tehran, and the statistical sample was 148 people, 180 distributed and 163 analytical questionnaires were selected using Morgan's table. A semi-structured interview and a researcher-made questionnaire taken from the qualitative section were used to collect information. In the qualitative part, the data theory method of the foundation was used, and the data obtained from the interviews were coded and analyzed in the three main stages of open coding, central coding, and selective coding, and in the quantitative part, the factor analysis of the indicators was done. The results of the qualitative part indicated that 241 primary codes, 46 central codes and 14 selective codes were identified and extracted. The results of the research showed that the causal conditions with 4 variables of network capability, use of artificial intelligence, marketing capability and customer knowledge have an effect on digital marketing strategies, and the results of confirmatory factor analysis showed that the fit indices of the model were confirmed. Extended Abstract Introduction With the growth of ECT technologies including artificial intelligence, GPS, Bluetooth, QR code and other mobile and Internet connected technologies, marketers can offer micro-experiences/activities/services at an ultra-local level and adjust their messaging to effectively target small consumer segments locally (Singh & Keating, 2018). In this regard, digital marketing activities including mobile marketing, search engine based marketing, institutional marketing, email marketing, social network based marketing, etc. have been proposed. (Dwivedi et al, 2020). On the other hand, social networks have also created new challenges and advantages. It is important for companies to act in the field of specialized social networks so that digital marketing is not used by inexperienced experts and companies are safe from adverse effects by paying special attention to aligning their organizational goals with digital marketing solutions (Aswani et al., 2019). Internet technology and the increase in the number of Internet startups have fundamentally affected the world economy. The Internet provides companies with the opportunity to offer their products and services internationally 24 hours a day (Kohler, 2016). Experience shows that in the future, all organizations must operate online in order to remain competitive. The cost of advertising and marketing products and services on the Internet is very low compared to advertising and distribution costs related to traditional methods. Small and medium businesses are an important part of the economy in all countries, but they are heavily influenced by rapid changes in the external environment, especially demographic and economic. These rapid changes affect the marketing department of these businesses to meet the needs and expectations of customers in order to maintain a competitive position in the market (Sedaghati & Seiedin, 2023). Based on this, the current research is looking for an answer to this question: How is the formulation of digital marketing strategies model in successful Iranian startups? Theoretical Framework Digital marketing The term digital marketing has been referred to as a subset of marketing management and advertising management since two decades ago (Kannan, 2017). Digital marketing includes the set of all tools and activities that are used to market products and services on a digital platform (web, mobile internet or other (digital) tools) (Vaziri Gohar & Abdolhosani, 2020).Digital marketing strategiesIn this changing world, there is an urgent need for a strategic vision and a clear strategy map to sustain business growth. As a result, organizations cannot implement digital marketing without a clear definition of the strategy.Create a good quality customer experience.In defining the digital marketing strategy, management should determine the transition plan to identify the ability of human resources to cope with the change. The company must help, accompany its employees during this transformation, and invest in their retention and competitive advantage (Erdmann & Ponzoa, 2021). Javid et al, (2023) investigated the quality of customer relationship management for the development of digital marketing strategies in Bank-e Shahr. The results of the research showed that the variables of infrastructure, organizational environment, customer orientation, human resources, communication management, service quality, management and planning, strategic management, marketing and performance are in a favorable condition in Bank-e Shahr. The use of up-to-date technologies and the implementation of a suitable organizational structure for managing relationships with customers were identified as reasons for this favorability. Hamdi et al, (2023) investigated the identification of dimensions and components of acceptance of customer relationship management social systems by professional users using Web 2.0 technology. The identified factors were extracted in the form of 40 indicators, 8 components and 3 dimensions. Components and indicators of dimensions and components of acceptance of customer relationship management social systems, including three dimensions; organizational, customer, and technological; which organizational dimension has three components (manpower, organizational culture, organizational structure), customer dimension includes three components (value creation, performance expectation, customer satisfaction), and technological has been identified as having two components (social networks, content production). Research methodology According to its purpose, the research method is applicable, and in terms of its implementation, it is mixed (qualitative-quantitative). The statistical population of the research in the qualitative part includes 13 academic and executive experts in the field of startups in the country, who were selected using the purposeful judgment method and the snowball method; and the statistical population in the quantitative part includes the marketing unit of active successful startups in the country (5 companies) and marketing professors of 5 top universities in Tehran; as many as 250 people, and the statistical sample was 148 people, 180 analytical questionnaires distributed and 163 were selected using Morgan's table. A semi-structured interview and a researcher-made questionnaire taken from the qualitative section were used to collect information. Research findings In the qualitative part, the data-based theory method was used, and the data obtained from the interviews were coded and analyzed in the three main stages of open coding, central coding, and selective coding; and in the quantitative part, factor analysis of the indicators was done. The results of the qualitative part indicated that 241 primary codes, 46 central codes and 14 selective codes were identified and extracted. The results of the research showed that causal conditions with 4 variables of network capability, use of artificial intelligence, marketing capability and customer knowledge have an effect on digital marketing strategies; and the results of confirmatory factor analysis showed that the fit indices of the model were confirmed. Conclusion The current research has been conducted with the aim of developing a model of digital marketing strategies in successful Iranian startups. The results of the present study are in agreement with the results of Javid et al, (2023), Saeidi & Hoseinzadeh Naer (2022), Gholipur (2022), Boban et al, (2020), Bozkut & Gligor (2021), Musavirad & Ahmadi (2022), Dehghan (2018). Malek Akhlagh et al, (2021) showed that electronic customer relationship management means the development of traditional customer relationship management with the help of Internet technologies and an attempt to improve and fill the communication gap in which companies' marketing performance helps through examining the created development. Digitalization blurs the boundaries between technology and management and facilitates new business models based on the concepts, methods and tools of the digital environment. According to the results obtained from the research, it is suggested: Strengthening business practices with the aim of increasing competitiveness based on artificial intelligence should be considered, which leads to a major transformation in business. By using artificial intelligence, startups are able to identify, consider and understand their target buyers, and understand which type of product may be most needed at a specific time. With the evolution of big data and technologies, startups will be able to estimate buyer activity and monitor data-driven companies in any catastrophic situation and will obtain significant results that will improve their marketing decisions. Based on marketing capabilities, startups can meet the needs of current and new customers both through the development of new products and through the development of features and applications of existing products in order to ensure stability and survival, and to avoid shocks caused by new waves of competition based on new technologies.

Published
2023
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24. 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

25. 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

29. Author Correction: Genome-wide association study identifies Sjögren’s risk loci with functional implications in immune and glandular cells

Author

Khatri, Bhuwan, Tessneer, Kandice L., Rasmussen, Astrid, Aghakhanian, Farhang, Reksten, Tove Ragna, Adler, Adam, Alevizos, Ilias, Anaya, Juan-Manuel, Aqrawi, Lara A., Baecklund, Eva, Brun, Johan G., Bucher, Sara Magnusson, Eloranta, Maija-Leena, Engelke, Fiona, Forsblad-d’Elia, Helena, Glenn, Stuart B., Hammenfors, Daniel, Imgenberg-Kreuz, Juliana, Jensen, Janicke Liaaen, Johnsen, Svein Joar Auglænd, Jonsson, Malin V., Kvarnström, Marika, Kelly, Jennifer A., Li, He, Mandl, Thomas, Martín, Javier, Nocturne, Gaétane, Norheim, Katrine Brække, Palm, Øyvind, Skarstein, Kathrine, Stolarczyk, Anna M., Taylor, Kimberly E., Teruel, Maria, Theander, Elke, Venuturupalli, Swamy, Wallace, Daniel J., Grundahl, Kiely M., Hefner, Kimberly S., Radfar, Lida, Lewis, David M., Stone, Donald U., Kaufman, C. Erick, Brennan, Michael T., Guthridge, Joel M., James, Judith A., Scofield, R. Hal, Gaffney, Patrick M., Criswell, Lindsey A., Jonsson, Roland, Eriksson, Per, Bowman, Simon J., Omdal, Roald, Rönnblom, Lars, Warner, Blake, Rischmueller, Maureen, Witte, Torsten, Farris, A. Darise, Mariette, Xavier, Alarcon-Riquelme, Marta E., Shiboski, Caroline H., Wahren-Herlenius, Marie, Ng, Wan-Fai, Sivils, Kathy L., Adrianto, Indra, Nordmark, Gunnel, and Lessard, Christopher J.

Published
2023
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30. Gastrointestinal helminths infection of free‐roaming cats (Felis catus) in Southeast Iran

Author

Saeid Reza Nourollahi Fard, Baharak Akhtardanesh, Soheil Sadr, Javad Khedri, Mohammad Hossein Radfar, and Mehdi Shadmehr

Subjects
helminth prevalence, Physaloptera praeputialis, Toxocara cati, zoonotic parasites, Veterinary medicine, SF600-1100
Abstract

Abstract Background Cats in Iran are definitive hosts for several zoonotic intestinal helminths, such as Toxocara cati, Dipylidium caninum, Toxascaris leonina, Physaloptera praeputialis and Diplopylidium nolleri. Objective This study aimed to determine the prevalence of intestinal helminth infection in free‐roaming cats in southeast Iran, a region with a high free‐roaming cat population. Methods From January 2018 to December 2021, 153 cadavers of free‐roaming cats from Southeast Iran were necropsied for intestinal helminth infections. The carcasses were dissected, and the digestive systems were removed. The esophagus, stomach, small intestine, caecum and colon were tightly ligated. All adult helminths were collected, preserved and identified. Results The prevalence of gastrointestinal helminth infections was 80.39% (123/153). Of the cats from Kerman, 73% (73/100) were infected with at least one helminth, including D. caninum 70% (70/100), T. leonina 8% (8/100) and P. praeputialis 17% (17/100). Concurrent infection with two helminth species was found in 16% (16/100) and of three species infections was found in 3% (3/100) of the cats. Of the cats from Zabol, 94.33% (50/53) were infected with at least one of the helminths, including D. caninum 69.81% (37/53), T. leonina 11.32% (6/53), P. praeputialis 37.73% (20/53) and T. cati 5.66% (3/53). Concurrent infection with two helminth species was found in 28.3% (15/53), and three species were found in 1.88% (1/53) of the cats. Helminth infections were more prevalent in older cats. There was no association between sex and infection rate. Conclusion Based on the very high prevalence of zoonotic intestinal helminth infections in free‐roaming cats in southeast Iran, the potential public health risk emphasizes the need for intersectoral collaboration, particularly the provision of health and hygiene education to high‐risk populations, such as pre‐school and school‐age children.

Published
2024
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31. Predicting students' performance using machine learning algorithms and educational data mining (a case study of Shahed University)

Author

Mozhdeh Salari, Reza Radfar, and Mahdi Faghihi

Subjects
student performance prediction, data mining, machine learning, modeling, improving the quality of education, Business, HF5001-6182
Abstract

The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA.

Published
2024
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32. 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

38. Some results on commutative BI-Algebras‎

Author

Akefe Radfar, Shahriar Soleymani, and Akbar Rezaei

Subjects
bi-algebra‎, ‎(branchwise) commutative‎, ‎distributive‎, ‎(commutative) ideal, Mathematics, QA1-939
Abstract

‎The notion of a (branchwise) commutative $BI$-algebra is presented, and some related properties are investigated. We show that the class of commutative $BH$-algebras is broader than the class of commutative $BI$-algebras. Moreover, we %show that prove every singular $BI$-algebra is a $BH$-algebra. Also, we define the commutative ideals in $BI$-algebras and characterize the commutative $BI$-algebras in terms of commutative ideals.

Published
2023
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39. Investigating the appearance quality of cosmetic surgery victims and related factors

Author

Parvin Chahardahvali, Zahra Hazrati Somee, Majid Radfar, and Mehdi Forouzesh

Subjects
cosmetic surgery victims, complaint, appearance quality, forensic medicine, Medicine (General), R5-920
Abstract

Background: Appearance quality, which means the perception of each person about their body image and the level of satisfaction with this situation, is one of the important issues in the field of cosmetic surgery, and if it is not fulfilled, it leads to a complaint and a case is raised in the forensic medical commissions of the province. Objectives: This study was aimed to investigate the sociological improvement of appearance quality in cosmetic surgery victims in forensic medical commissions of Tehran province. Materials & Methods: In this cross-sectional study, 276 cosmetic surgery victims whom medical malpractice was confirmed twice in 2018 and 2019 by the Forensic Medical Commissions of Tehran Province were included. A researcher-made questionnaire generated from a qualitative study on 24 individuals was used to assess the appearance quality in terms of satisfaction with appearance and life. Results: The majority of participants were in the 28-37 years age group (43.1%). The mean score of satisfaction with appearance was 11.18±3.60, and the mean score of satisfaction with life was 17.41±6.68. In total, the mean appearance quality was 28.59±8.67. There was a significant relationship between appearance quality and cosmetic surgery complaints (P

Published
2023

40. The effect of drought stress on enzymatic and molecular changes of some antioxidants in parental and mutant bread wheat genotype using RNAseq. data

Author

Meisam Radfar, Seyyede Sanaz Ramezanpour, Hassan Soltanloo, and Leila Kianmehr

Subjects
altered gene expression, enzymatic antioxidants, field capacity, mutant wheat, rna sequencing, Environmental sciences, GE1-350
Abstract

IntroductionWheat (Triticum aestivum L.) is one of the most important grains used in the world and its production is reduced in different regions due to drought stress. the plant antioxidant system can scavenge the reactive oxygen species (ROS) produced under drought. Induction of mutation using gamma ray is one of the common methods for genetic modification and identification of tolerant and resistant mutants. Mutant T65-58-8 is one of these drought tolerant genotypes that has been obtained by irradiation to Tabasi wheat genotype. The wheat plant needs irrigation during the flowering stage and drought stress is very important in this stage. In this study, in the flowering stage, the enzymes superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR) and glutathione peroxidase (GPX) involved in the mechanisms of tolerance to oxidative damage of ROS in the flag leaf were studied and the expression of the genes related to these enzymes was investigated using RNAseq. method.Materials and methodsDrought stress was applied based on field capacity (FC). The experiment was performed as a factorial experiment in a completely randomized design with three replications. Factors studied in this experiment include genotype at two levels (Tabasi parent and Mutant T65-58-8) and drought stress at 5 levels (100% FC or control, 75%, 22%, re-sampling from control pots at 18% FC and sampling after re-irrigation to the pot when had 90% FC). Wheat growth stages can be studied by Zadoks index. SOD activity was measured by Minami and Yoshikawa method, CAT activity by Aebi method, GR activity by Foyer and Halliwell method and GPX activity by Hopkins and Tudhope method. RNA sequencing was performed using Illumina NovaSeq 6000. Gene expression was obtained based on sequencing data by Bowtie2, Tophat2, HTseq-count and Featurecount softwares. After normalization by generating FPKM, Log2FC gene expression was calculated.Results and discussionExamination of superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR) and glutathione peroxidase (GPX) indices showed a significant difference (p

Published
2023
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41. Investigating the quality of Isfahan city rainwater under the influence of air pollution in 2018-2019

Author

Sayyed Ali Shahrezaie, Mahdi Radfar, Rasoul Mirabbasi Najaf Abadi, Mehrdad Moughadas, Nafiseh Sadat Shahrezaie, and Sharareh Mahmoudi

Subjects
rain water quality, suspended particles, air pollution, isfahan city., Environmental technology. Sanitary engineering, TD1-1066
Abstract

Background: Air pollution is one of the most important problems facing people all over the world today, especially in big cities. The ever-increasing population growth, as well as the increase in vehicles and the number of industrial factories in the city of Isfahan, have led to this city being considered one of the largest industrial cities in Iran. Therefore, this city is considered the second most polluted city in Iran after Tehran. The city of Isfahan faces the crisis of air pollution on many days of the year, which directly and indirectly affects all aspects of people's lives in this city. Among these impacts is the quality of rainwater in the area, which joins the surface and groundwater in the region and affects its quality. This cycle will continue and air pollution will affect the quality of drinking water, agriculture, respiratory diseases and, in a word, the health of the people in this region.Materials and methods: In this study, the effects of air pollution in Isfahan on the quality of rainwater in the 10th district of Isfahan city were investigated. The quality of rainwater was studied based on the World Health Organization standard for drinking water, APHA standard for running water, Chinese standard for agricultural use, and Iranian standard for drinking water and agricultural use. To evaluate the air quality of the region, the statistics of the Isfahan Meteorological Organization station were used and the meteorological parameters were analyzed based on the American Environmental Protection Agency standards. Results: The results showed that the EC value of rainwater is more sensitive than pH to determine the suitability of rainwater quality for drinking uses, so when considering pH, many samples have favorable quality, but when considering EC value of the same sample, the quality of rainwater is unfavorable. The results of air quality monitoring based on the amount of PM2.5 on rainy days showed that 5 days were clean, 13 days were acceptable, and 6 days were unsanitary for sensitive groups. Conclusion: Isfahan urban runoff needs to be treated for drinking but it is suitable for agriculture. The results showed that the EC of rainwater is more dependent than pH to the rainfall depth. The results show that rainfall with a relatively small depth caused a significant increase in EC of stormwater. This decrease in the amount of precipitation has led to an increase in the amount of CO and PM2.5 in the atmosphere. The amount of rainfall did not affect the pH of rainwater, while with the increase of rainfall depth, the EC of rainwater has decreased significantly. In general, rainfall depth has a significant effect on EC concentration of rainwater. The results showed that in all the rainy days, the amount of CO, SO2 and O3 pollutants are in a clean state.

Published
2023

42. Context-Aware Transformer Transducer for Speech Recognition

Author

Chang, Feng-Ju, Liu, Jing, Radfar, Martin, Mouchtaris, Athanasios, Omologo, Maurizio, Rastrow, Ariya, and Kunzmann, Siegfried

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

End-to-end (E2E) automatic speech recognition (ASR) systems often have difficulty recognizing uncommon words, that appear infrequently in the training data. One promising method, to improve the recognition accuracy on such rare words, is to latch onto personalized/contextual information at inference. In this work, we present a novel context-aware transformer transducer (CATT) network that improves the state-of-the-art transformer-based ASR system by taking advantage of such contextual signals. Specifically, we propose a multi-head attention-based context-biasing network, which is jointly trained with the rest of the ASR sub-networks. We explore different techniques to encode contextual data and to create the final attention context vectors. We also leverage both BLSTM and pretrained BERT based models to encode contextual data and guide the network training. Using an in-house far-field dataset, we show that CATT, using a BERT based context encoder, improves the word error rate of the baseline transformer transducer and outperforms an existing deep contextual model by 24.2% and 19.4% respectively., Comment: Accepted to ASRU 2021

Published
2021

43. Speech Emotion Recognition Using Quaternion Convolutional Neural Networks

Author

Muppidi, Aneesh and Radfar, Martin

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

Although speech recognition has become a widespread technology, inferring emotion from speech signals still remains a challenge. To address this problem, this paper proposes a quaternion convolutional neural network (QCNN) based speech emotion recognition (SER) model in which Mel-spectrogram features of speech signals are encoded in an RGB quaternion domain. We show that our QCNN based SER model outperforms other real-valued methods in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS, 8-classes) dataset, achieving, to the best of our knowledge, state-of-the-art results. The QCNN also achieves comparable results with the state-of-the-art methods in the Interactive Emotional Dyadic Motion Capture (IEMOCAP 4-classes) and Berlin EMO-DB (7-classes) datasets. Specifically, the model achieves an accuracy of 77.87\%, 70.46\%, and 88.78\% for the RAVDESS, IEMOCAP, and EMO-DB datasets, respectively. In addition, our results show that the quaternion unit structure is better able to encode internal dependencies to reduce its model size significantly compared to other methods., Comment: Published in ICASSP 2021

Published
2021

44. FANS: Fusing ASR and NLU for on-device SLU

Author

Radfar, Martin, Mouchtaris, Athanasios, Kunzmann, Siegfried, and Rastrow, Ariya

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

Spoken language understanding (SLU) systems translate voice input commands to semantics which are encoded as an intent and pairs of slot tags and values. Most current SLU systems deploy a cascade of two neural models where the first one maps the input audio to a transcript (ASR) and the second predicts the intent and slots from the transcript (NLU). In this paper, we introduce FANS, a new end-to-end SLU model that fuses an ASR audio encoder to a multi-task NLU decoder to infer the intent, slot tags, and slot values directly from a given input audio, obviating the need for transcription. FANS consists of a shared audio encoder and three decoders, two of which are seq-to-seq decoders that predict non null slot tags and slot values in parallel and in an auto-regressive manner. FANS neural encoder and decoders architectures are flexible which allows us to leverage different combinations of LSTM, self-attention, and attenders. Our experiments show compared to the state-of-the-art end-to-end SLU models, FANS reduces ICER and IRER errors relatively by 30 % and 7 %, respectively, when tested on an in-house SLU dataset and by 0.86 % and 2 % absolute when tested on a public SLU dataset., Comment: Published in Interspeech 2021

Published
2021

45. Information Security Research in Iran: A Scientometric Analysis

Author

Norouzi, Yaghoub, Radfar, Hamedreza, and JafarFar, Nayere

Subjects
HL. Databases and database Networking., HZ. None of these, but in this section.
Abstract

This research has examined studies conducted in the field of information security in Iran with a scientometric approach to provide a comprehensive view of the status of this field. The present applied study, which is a survey-cross-sectional, has been done using co-occurrence analysis and scientometric methods. All the scientific papers published in the field of information security between the years 2001-2023 and indexed in the Islamic World Science Citation Center (ISC) were examined (1,114 articles). Amin Police University is at the top of science production in the field of information security in Iran with the publication of 92 articles. The journal of Societal Security Studies Scientific with the publication of 35 articles, has the most published articles in the field of information security. Bagheri (with 9 articles), Ebrahimi Atani (with 8 articles) and Tork Ladani (with 7 articles) were recognized as the three top scientists in the field of information security in Iran. The keyword "security" with the highest frequency of repetition was recognized as the most frequent concept. In terms of co-occurrence, the two keywords "security" and "border" were the most frequent. The word co-occurrence map showed the existence of 4 clusters in the field of security, information security, sense of security, and sustainable security. "Terrorism" keyword, having the farthest distance from other keywords, was less related to other keywords. After 2017, the downward trend of scientific publications in the field of information security was observed in Iran. The formation of a forum by specialized associations to exchange opinions and opinions of the authors of works in high-quality research publications can be vital in the development of this field. The clusters of national security, stable security, and crime were recognized as immature clusters of the scientific map of information security in Iranian articles. Conducting research to develop knowledge in the field of these concepts is recommended.

Published
2023

46. Multi-Channel Transformer Transducer for Speech Recognition

Author

Chang, Feng-Ju, Radfar, Martin, Mouchtaris, Athanasios, and Omologo, Maurizio

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

Multi-channel inputs offer several advantages over single-channel, to improve the robustness of on-device speech recognition systems. Recent work on multi-channel transformer, has proposed a way to incorporate such inputs into end-to-end ASR for improved accuracy. However, this approach is characterized by a high computational complexity, which prevents it from being deployed in on-device systems. In this paper, we present a novel speech recognition model, Multi-Channel Transformer Transducer (MCTT), which features end-to-end multi-channel training, low computation cost, and low latency so that it is suitable for streaming decoding in on-device speech recognition. In a far-field in-house dataset, our MCTT outperforms stagewise multi-channel models with transformer-transducer up to 6.01% relative WER improvement (WERR). In addition, MCTT outperforms the multi-channel transformer up to 11.62% WERR, and is 15.8 times faster in terms of inference speed. We further show that we can improve the computational cost of MCTT by constraining the future and previous context in attention computations.

Published
2021

47. The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input

Author

He, Bradley and Radfar, Martin

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

In order to evaluate the performance of the attention based neural ASR under noisy conditions, the current trend is to present hours of various noisy speech data to the model and measure the overall word/phoneme error rate (W/PER). In general, it is unclear how these models perform when exposed to a cocktail party setup in which two or more speakers are active. In this paper, we present the mixtures of speech signals to a popular attention-based neural ASR, known as Listen, Attend, and Spell (LAS), at different target-to-interference ratio (TIR) and measure the phoneme error rate. In particular, we investigate in details when two phonemes are mixed what will be the predicted phoneme; in this fashion we build a model in which the most probable predictions for a phoneme are given. We found a 65% relative increase in PER when LAS was presented with mixed speech signals at TIR = 0 dB and the performance approaches the unmixed scenario at TIR = 30 dB. Our results show the model, when presented with mixed phonemes signals, tend to predict those that have higher accuracies during evaluation of original phoneme signals., Comment: 5 pages, 3 figures

Published
2021

49. 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, Halboub, Esam S, Halimi, Aram, Hall, Brian J, Haller, Sebastian, Halwani, Rabih, Hamadeh, Randah R, Hamdy, Nadia M, Hameed, Sajid, Hamidi, Samer, Hammoud, Ahmad, Hanif, Asif, Hanifi, Nasrin, Haq, Zaim Anan, Haque, Md Rabiul, Harapan, Harapan, Hargono, Arief, Haro, Josep Maria, Hasaballah, Ahmed I, Hasan, Ikramul, Hasan, Mohammad Jahid, Hasan, S M Mahmudul, Hasani, Hamidreza, Hasanian, Mohammad, Hashmeh, Nadim, Hasnain, Md Saquib, Hassan, Amr, Hassan, Ikrama, Hassan Zadeh Tabatabaei, Mahgol Sadat, Hassani, Shokoufeh, Hassanipour, Soheil, Hassankhani, Hadi, Haubold, Johannes, Havmoeller, Rasmus J, Hay, Simon I, Hebert, Jeffrey J, Hegazi, Omar E, Hegena, Tadele Yohannes, Heidari, Golnaz, Heidari, Mohammad, Helfer, Bartosz, Hemmati, Mehdi, Henson, Claire A, Herbert, Molly E, Herteliu, Claudiu, Heuer, Austin, Hezam, Kamal, Hinneh, Thomas Kwadwo, Hiraike, Yuta, Hoan, Nguyen Quoc, Holla, Ramesh, Hon, Julia, Hoque, Mohammad Enamul, Horita, Nobuyuki, Hossain, Sahadat, Hosseini, Seyed Ehsan, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hoven, Hanno, Hsairi, Mohamed, Hsu, Johnathan M, Hu, Chengxi, Huang, Junjie, Huda, Md Nazmul, Hulland, Erin N, Hultström, Michael, Hushmandi, Kiavash, Hussain, Javid, Hussein, Nawfal R, Huynh, Chantal K, Huynh, Hong-Han, Ibitoye, Segun Emmanuel, Idowu, Oluwatope Olaniyi, Ihler, Audrey L, Ikeda, Nayu, Ikuta, Kevin S, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Imam, Mohammad Tarique, Immurana, Mustapha, Inbaraj, Leeberk Raja, Irham, Lalu Muhammad, Isa, Mustafa Alhaji, Islam, Md Rabiul, Ismail, Faisal, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Isola, Gaetano, Iwagami, Masao, Iwu, Chidozie C D, Iwu-Jaja, Chinwe Juliana, J, Vinothini, Jaafari, Jalil, Jacob, Louis, Jacobsen, Kathryn H, Jadidi-Niaragh, Farhad, Jahankhani, Kasra, Jahanmehr, Nader, Jahrami, Haitham, Jain, Akhil, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Jaiswal, Abhishek, Jakovljevic, Mihajlo, Jalilzadeh Yengejeh, Reza, Jamora, Roland Dominic G, Jatau, Abubakar Ibrahim, Javadov, Sabzali, Javaheri, Tahereh, Jayaram, Shubha, Jeganathan, Jayakumar, Jeswani, Bijay Mukesh, Jiang, Heng, Johnson, Catherine O, Jokar, Mohammad, Jomehzadeh, Nabi, Jonas, Jost B, Joo, Tamas, Joseph, Abel, Joseph, Nitin, Joshi, Vivek, Joshua, Charity Ehimwenma, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kaambwa, Billingsley, Kabir, Ali, Kabir, Zubair, Kadashetti, Vidya, Kahn, Ethan M, Kalani, Rizwan, Kaliyadan, Feroze, Kalra, Sanjay, Kamath, Rajesh, Kanagasabai, Thanigaivelan, Kanchan, Tanuj, Kandel, Himal, Kanmiki, Edmund Wedam, Kanmodi, Kehinde Kazeem, Kansal, Sushil Kumar, Kapner, Daniel John, Kapoor, Neeti, Karagiannidis, Efstratios, Karajizadeh, Mehrdad, Karakasis, Paschalis, Karanth, Shama D, Karaye, Ibraheem M, Karch, André, Karim, Asima, Karimi, Hanie, Karmakar, Shilpi, Kashoo, Faizan Zaffar, Kasraei, Hengameh, Kassahun, Woldeteklehaymanot Dagne, Kassebaum, Nicholas J, Kassel, Molly B, Katikireddi, Srinivasa Vittal, Kauppila, Joonas H, Kawakami, Norito, Kaydi, Neda, Kayode, Gbenga A, Kazemi, Foad, Keiyoro, Peter Njenga, Kemmer, Laura, Kempen, John H, Kerr, Jessica A, Kesse-Guyot, Emmanuelle, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khajuria, Himanshu, Khalaji, Amirmohammad, Khalil, Mariam, Khalilian, Alireza, Khamesipour, Faham, Khan, Asaduzzaman, Khan, M Nuruzzaman, Khan, Maseer, Khan, Mohammad Jobair, Khan, Moien AB, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khatatbeh, Haitham, Khatatbeh, Moawiah Mohammad, Khatib, Mahalaqua Nazli, Khavandegar, Armin, Khayat Kashani, Hamid Reza, Khidri, Feriha Fatima, Khodadoust, Elaheh, Khormali, Moein, Khorrami, Zahra, Khosla, Atulya Aman, Khosrowjerdi, Mahmood, Khreis, Haneen, Khusun, Helda, Kifle, Zemene Demelash, Kim, Kwanghyun, Kim, Min Seo, Kim, Yun Jin, Kimokoti, Ruth W, Kisa, Adnan, Kisa, Sezer, Knibbs, Luke D, Knudsen, Ann Kristin Skrindo, Koh, David S Q, Kolahi, Ali-Asghar, Kompani, Farzad, Kong, Jianqiu, Koren, Gerbrand, Korja, Miikka, Korshunov, Vladimir Andreevich, Korzh, Oleksii, Kosen, Soewarta, Kothari, Nikhil, Koul, Parvaiz A, Koulmane Laxminarayana, Sindhura Lakshmi, Krishan, Kewal, Krishnamoorthy, Vijay, Krishnamoorthy, Yuvaraj, Krishnan, Bindu, Krohn, Kris J, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kuddus, Md Abdul, Kuddus, Mohammed, Kugbey, Nuworza, Kuitunen, Ilari, Kulimbet, Mukhtar, Kulkarni, Vishnutheertha, Kumar, Ashish, Kumar, Nithin, Kumar, Vijay, Kundu, Satyajit, Kurmi, Om P, Kusnali, Asep, Kusuma, Dian, Kutluk, Tezer, La Vecchia, Carlo, Ladan, Muhammad Awwal, Laflamme, Lucie, Lahariya, Chandrakant, Lai, Daphne Teck Ching, Lal, Dharmesh Kumar, Lallukka, Tea, Lám, Judit, Lan, Qing, Lan, Tuo, Landires, Iván, Lanfranchi, Francesco, Langguth, Berthold, Lansingh, Van Charles, Laplante-Lévesque, Ariane, Larijani, Bagher, Larsson, Anders O, Lasrado, Savita, Lauriola, Paolo, Le, Huu-Hoai, Le, Long Khanh Dao, Le, Nhi Huu Hanh, Le, Thao Thi Thu, Leasher, Janet L, Ledda, Caterina, Lee, Munjae, Lee, Paul H, Lee, Seung Won, Lee, Shaun Wen Huey, Lee, Yo Han, LeGrand, Kate E, Leigh, James, Leong, Elvynna, Lerango, Temesgen L, Lescinsky, Haley, Leung, Janni, Li, Ming-Chieh, Li, Wang-Zhong, Li, Wei, Li, Yichong, Li, Zhihui, Ligade, Virendra S, Lim, Lee-Ling, Lim, Stephen S, Lin, Ro-Ting, Lin, Shuzhi, Liu, Chaojie, Liu, Gang, Liu, Jinli, Liu, Jue, Liu, Richard T, Liu, Shiwei, Liu, Wei, Liu, Xiaofeng, Liu, Xuefeng, Livingstone, Katherine M, Llanaj, Erand, Lohiya, Ayush, López-Bueno, Rubén, Lopukhov, Platon D, Lorkowski, Stefan, Lotufo, Paulo A, Lozano, Rafael, Lubinda, Jailos, Lucchetti, Giancarlo, Luo, Lisha, lv, Hengliang, M Amin, Hawraz Ibrahim, Ma, Zheng Feei, Maass, Kelsey Lynn, Mabrok, Mahmoud, Machairas, Nikolaos, Machoy, Monika, Mafhoumi, Asma, Magdy Abd El Razek, Mohammed, Maghazachi, Azzam A, Mahadeshwara Prasad, D R, Maharaj, Sandeep B, Mahmoud, Mansour Adam, Mahmoudi, Elham, Majeed, Azeem, Makram, Omar Mohamed, Makris, Konstantinos Christos, Malasala, Satyaveni, Maled, Venkatesh, Malhotra, Kashish, 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Samer H, Murray, Christopher J L, and Gakidou, Emmanuela

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2024
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50. 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

Author

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, Al-Eyadhy, Ayman, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhalaiqa, Fadwa Alhalaiqa Naji, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Akhtar, Ali, Beriwan Abdulqadir, Ali, Hassam, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Zahid, Alian Samakkhah, Shohreh, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aligol, Mohammad, Alimi, Rasoul, Aliyi, Ahmednur Adem, Al-Jumaily, Adel, Aljunid, Syed Mohamed, Almahmeed, Wael, Al-Marwani, Sabah, Al-Maweri, Sadeq Ali Ali, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Omar, Alomari, Mahmoud A, Alonso, Nivaldo, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Sabah, Salman Khalifah, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Alvi, Farrukh Jawad, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Alzoubi, Karem H, Amare, Azmeraw T, Ameyaw, Edward Kwabena, Amhare, Abebe Feyissa, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Aminian Dehkordi, Javad, Amiri, Sohrab, Amu, Hubert, Amugsi, Dickson A, Amzat, Jimoh, Ancuceanu, Robert, Anderlini, Deanna, Andrade, Pedro Prata, Andrei, Catalina Liliana, Andrei, Tudorel, Angappan, Dhanalakshmi, Anil, Abhishek, Anjum, Afifa, Antony, Catherine M, Antriyandarti, Ernoiz, Anuoluwa, Iyadunni Adesola, Anwar, Sumadi Lukman, Anyasodor, Anayochukwu Edward, Appiah, Seth Christopher Yaw, Aqeel, Muhammad, Arabloo, Jalal, Arabzadeh Bahri, Razman, Arab-Zozani, Morteza, Arafat, Mosab, Araújo, Ana Margarida, Aravkin, Aleksandr Y, Aremu, Abdulfatai, Ariffin, Hany, Aripov, Timur, Armocida, Benedetta, Arooj, Mahwish, Artamonov, Anton A, Artanti, Kurnia Dwi, Arulappan, Judie, Aruleba, Idowu Thomas, Aruleba, Raphael Taiwo, Arumugam, Ashokan, Asaad, Malke, Asgary, Saeed, Ashemo, Mubarek Yesse, Ashraf, Muhammad, Asika, Marvellous O, Athari, Seyyed Shamsadin, Atout, Maha Moh'd Wahbi, Atreya, Alok, Attia, Sameh, Aujayeb, Avinash, Avan, Abolfazl, Awotidebe, Adedapo Wasiu, Ayala Quintanilla, Beatriz Paulina, Ayanore, Martin Amogre, Ayele, Getnet Melaku, Ayuso-Mateos, Jose L, Ayyoubzadeh, Seyed Mohammad, Azadnajafabad, Sina, Azhar, Gulrez Shah, Aziz, Shahkaar, Azzam, Ahmed Y, Babashahi, Mina, Babu, Abraham Samuel, Badar, Muhammad, Badawi, Alaa, Badiye, Ashish D, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bah, Sulaiman, Bahadorikhalili, Saeed, Bai, Jianjun, Bai, Ruhai, Baker, Jennifer L, Bakkannavar, Shankar M, Bako, Abdulaziz T, Balakrishnan, Senthilkumar, Balogun, Saliu A, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Bandyopadhyay, Soham, Banik, Biswajit, Banik, Palash Chandra, Bansal, Hansi, Barati, Shirin, Barchitta, Martina, Bardhan, Mainak, Barker-Collo, Suzanne Lyn, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barr, Ronald D, Barrero, Lope H, Basharat, Zarrin, Bashir, Asma'u I J, Bashiru, Hameed Akande, Baskaran, Pritish, Basnyat, Buddha, Bassat, Quique, Basso, João Diogo, Basu, Saurav, Batra, Kavita, Batra, Ravi, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Beaney, Thomas, Bedi, Neeraj, Begum, Tahmina, Behboudi, Emad, Behnoush, Amir Hossein, Beiranvand, Maryam, Bejarano Ramirez, Diana Fernanda, Belgaumi, Uzma Iqbal, Bell, Michelle L, Bello, Aminu K, Bello, Muhammad Bashir, Bello, Olorunjuwon Omolaja, Belo, Luis, Beloukas, Apostolos, Bendak, Salaheddine, Bennett, Derrick A, Bensenor, Isabela M, Benzian, Habib, Berezvai, Zombor, Berman, Adam E, Bermudez, Amiel Nazer C, Bettencourt, Paulo J G, Beyene, Habtamu B, Beyene, Kebede A, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhalla, Ashish, Bhandari, Dinesh, 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, Binmadi, Nada, Bintoro, Bagas Suryo, Biondi, Antonio, Bisignano, Catherine, Bisulli, Francesca, Biswas, Atanu, Biswas, Raaj Kishore, Bitaraf, Saeid, Bjørge, Tone, Bleyer, Archie, Boampong, Mary Sefa, Bodolica, Virginia, Bodunrin, Aadam Olalekan, Bolarinwa, Obasanjo Afolabi, Bonakdar Hashemi, Milad, Bonny, Aime, Bora, Kaustubh, Bora Basara, Berrak, Borodo, Safiya Bala, Borschmann, Rohan, Botero Carvajal, Alejandro, Bouaoud, Souad, Boudalia, Sofiane, Boyko, Edward J, Bragazzi, Nicola Luigi, Braithwaite, Dejana, Brenner, Hermann, Britton, Gabrielle, Browne, Annie J, Brunoni, Andre R, Bulamu, Norma B, Bulto, Lemma N, Buonsenso, Danilo, Burkart, Katrin, Burns, Richard A, Burugina Nagaraja, Sharath, Busse, Reinhard, Bustanji, Yasser, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Cai, Tianji, Calina, Daniela, Cámera, Luis Alberto, Campos, Luciana Aparecida, Campos-Nonato, Ismael R, Cao, Chao, Cardenas, Carlos Alberto, 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, Cembranel, Francieli, Cenderadewi, Muthia, Cercy, Kelly M, Cerin, Ester, Cevik, Muge, Chacón-Uscamaita, Pamela R Uscamaita, Chahine, Yaacoub, Chakraborty, Chiranjib, Chan, Jeffrey Shi Kai, Chang, Chin-Kuo, Charalampous, Periklis, Charan, Jaykaran, Chattu, Vijay Kumar, Chatzimavridou-Grigoriadou, Victoria, Chavula, Malizgani Paul, Cheema, Huzaifa Ahmad, Chen, An-Tian, Chen, Haowei, Chen, Lingxiao, Chen, Meng Xuan, Chen, Simiao, Cherbuin, Nicolas, Chew, Derek S, Chi, Gerald, Chirinos-Caceres, Jesus Lorenzo, Chitheer, Abdulaal, Cho, So Mi Jemma, Cho, William C S, Chong, Bryan, Chopra, Hitesh, Choudhary, Rahul, Chowdhury, Rajiv, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Chung, Eric, Chung, Eunice, Chung, Sheng-Chia, Cini, Karly I, Clark, Cain C T, Coberly, Kaleb, Columbus, Alyssa, Comfort, Haley, Conde, Joao, Conti, Sara, Cortesi, Paolo Angelo, Costa, Vera Marisa, Cousin, Ewerton, Cowden, Richard G, Criqui, Michael H, Cruz-Martins, Natália, Culbreth, Garland T, Cullen, Patricia, Cunningham, Matthew, da Silva e Silva, Daniel, Dadana, Sriharsha, Dadras, Omid, Dai, Zhaoli, Dalal, Koustuv, Dalli, Lachlan L, Damiani, Giovanni, D'Amico, Emanuele, Daneshvar, Sara, Darwesh, Aso Mohammad, Das, Jai K, Das, Saswati, Dash, Nihar Ranjan, Dashti, Mohsen, Dávila-Cervantes, Claudio Alberto, Davis Weaver, Nicole, Davletov, Kairat, De Leo, Diego, Debele, Aklilu Tamire, Degenhardt, Louisa, Dehbandi, Reza, Deitesfeld, Lee, Delgado-Enciso, Ivan, Delgado-Ortiz, Laura, Demant, Daniel, Demessa, Berecha Hundessa, Demetriades, Andreas K, Deng, Xinlei, Denova-Gutiérrez, Edgar, Deribe, Kebede, Dervenis, Nikolaos, Des Jarlais, Don C, Desai, Hardik Dineshbhai, Desai, Rupak, Deuba, Keshab, Devanbu, Vinoth Gnana Chellaiyan, Dey, Sourav, Dhali, Arkadeep, Dhama, Kuldeep, Dhimal, Mandira Lamichhane, Dhimal, Meghnath, Dhingra, Sameer, Dias da Silva, Diana, Diaz, Daniel, Dima, Adriana, Ding, Delaney D, Dirac, M Ashworth, Dixit, Abhinav, Dixit, Shilpi Gupta, Do, Thanh Chi, Do, Thao Huynh Phuong, do Prado, Camila Bruneli, Dodangeh, Masoud, Dokova, Klara Georgieva, Dolecek, Christiane, Dorsey, E Ray, dos Santos, Wendel Mombaque, Doshi, Rajkumar, Doshmangir, Leila, Douiri, Abdel, Dowou, Robert Kokou, Driscoll, Tim Robert, Dsouza, Haneil Larson, Dube, John, Dumith, Samuel C, Dunachie, Susanna J, Duncan, Bruce B, Duraes, Andre Rodrigues, Duraisamy, Senbagam, Durojaiye, Oyewole Christopher, Dutta, Sulagna, Dzianach, Paulina Agnieszka, Dziedzic, Arkadiusz Marian, Ebenezer, Oluwakemi, Eboreime, Ejemai, Ebrahimi, Alireza, Echieh, Chidiebere Peter, Ed-Dra, Abdelaziz, Edinur, Hisham Atan, Edvardsson, David, Edvardsson, Kristina, Efendi, Defi, Efendi, Ferry, Eghdami, Shayan, Eikemo, Terje Andreas, Eini, Ebrahim, Ekholuenetale, Michael, Ekpor, Emmanuel, Ekundayo, Temitope Cyrus, El Arab, Rabie Adel, El Morsi, Doaa Abdel Wahab, El Sayed Zaki, Maysaa, El Tantawi, Maha, Elbarazi, Iffat, Elemam, Noha Mousaad, Elgar, Frank J, Elgendy, Islam Y, ElGohary, Ghada Metwally Tawfik, Elhabashy, Hala Rashad, Elhadi, Muhammed, Elmeligy, Omar Abdelsadek Abdou, Elshaer, Mohammed, Elsohaby, Ibrahim, Emami Zeydi, Amir, Emamverdi, Mehdi, Emeto, Theophilus I, Engelbert Bain, Luchuo, Erkhembayar, Ryenchindorj, Eshetie, Tesfahun C, Eskandarieh, Sharareh, Espinosa-Montero, Juan, Estep, Kara, Etaee, Farshid, Eze, Ugochukwu Anthony, Fabin, Natalia, Fadaka, Adewale Oluwaseun, Fagbamigbe, Adeniyi Francis, Fahimi, Saman, Falzone, Luca, Farinha, Carla Sofia e Sá, Faris, MoezAlIslam Ezzat Mahmoud, Farjoud Kouhanjani, Mohsen, Faro, Andre, Farrokhpour, Hossein, Fatehizadeh, Ali, Fattahi, Hamed, Fauk, Nelsensius Klau, Fazeli, Pooria, Feigin, Valery L, Fekadu, Ginenus, Fereshtehnejad, Seyed-Mohammad, Feroze, Abdullah Hamid, Ferrante, Daniela, Ferrara, Pietro, Ferreira, Nuno, Fetensa, Getahun, Filip, Irina, Fischer, Florian, Flavel, Joanne, Flaxman, Abraham D, Flor, Luisa S, Florin, Bobirca Teodor, Folayan, Morenike Oluwatoyin, Foley, Kristen Marie, Fomenkov, Artem Alekseevich, Force, Lisa M, Fornari, Carla, Foroutan, Behzad, Foschi, Matteo, Francis, Kate Louise, Franklin, Richard Charles, Freitas, Alberto, Friedman, Joseph, Friedman, Sara D, Fukumoto, Takeshi, Fuller, John E, Gaal, Peter Andras, Gadanya, Muktar A, Gaihre, Santosh, Gaipov, Abduzhappar, Gakidou, Emmanuela, Galali, Yaseen, Galehdar, Nasrin, Gallus, Silvano, Gan, Quan, Gandhi, Aravind P, Ganesan, Balasankar, Garg, Jalaj, Gau, Shuo-Yan, Gautam, Prem, Gautam, Rupesh K, Gazzelloni, Federica, Gebregergis, Miglas W, Gebrehiwot, Mesfin, Gebremariam, Tesfay Brhane, Gerema, Urge, Getachew, Motuma Erena, Getachew, Tamirat, Gething, Peter W, Ghafourifard, Mansour, Ghahramani, Sulmaz, Ghailan, Khalid Yaser, Ghajar, Alireza, Ghanbarnia, Mohammad Javad, Ghasemi, MohammadReza, Ghasemzadeh, Afsaneh, Ghassemi, Fariba, Ghazy, Ramy Mohamed, Ghimire, Sailaja, Gholamian, Asadollah, Gholamrezanezhad, Ali, Ghorbani Vajargah, Pooyan, Ghozali, Ghozali, Ghozy, Sherief, Ghuge, Arun Digambarrao, Gialluisi, Alessandro, Gibson, Ruth Margaret, Gil, Artyom Urievich, Gill, Paramjit Singh, Gill, Tiffany K, Gillum, Richard F, Ginindza, Themba G, Girmay, Alem, Glasbey, James C, Gnedovskaya, Elena V, Göbölös, Laszlo, Goel, Amit, Goldust, Mohamad, Golechha, Mahaveer, Goleij, Pouya, Golestanfar, Arefeh, Golinelli, Davide, Gona, Philimon N, Goudarzi, Houman, Goudarzian, Amir Hossein, Goyal, Anmol, Greenhalgh, Scott, Grivna, Michal, Guarducci, Giovanni, Gubari, Mohammed Ibrahim Mohialdeen, Gudeta, Mesay Dechasa, Guha, Avirup, Guicciardi, Stefano, Gunawardane, Damitha Asanga, Gunturu, Sasidhar, Guo, Cui, Gupta, Anish Kumar, Gupta, Bhawna, Gupta, Indarchand Ratanlal, Gupta, Rajat Das, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vijai Kumar, Gupta, Vivek Kumar, Gutiérrez, Reyna Alma, Habibzadeh, Farrokh, Habibzadeh, Parham, Hachinski, Vladimir, Haddadi, Mohammad, Haddadi, Rasool, Haep, Nils, Hajj Ali, Adel, Halboub, Esam S, Halim, Sobia Ahsan, Hall, Brian J, Haller, Sebastian, Halwani, Rabih, Hamadeh, Randah R, Hamagharib Abdullah, Kanaan, Hamidi, Samer, Hamiduzzaman, Mohammad, Hammoud, Ahmad, Hanifi, Nasrin, Hankey, Graeme J, Hannan, Md Abdul, Haque, Md Nuruzzaman, Harapan, Harapan, Haro, Josep Maria, Hasaballah, Ahmed I, Hasan, Faizul, Hasan, Ikramul, Hasan, M Tasdik, Hasani, Hamidreza, Hasanian, Mohammad, Hasanpour- Dehkordi, Ali, Hassan, Abbas M, Hassan, Amr, Hassanian-Moghaddam, Hossein, Hassanipour, Soheil, Haubold, Johannes, Havmoeller, Rasmus J, Hay, Simon I, Hbid, Youssef, Hebert, Jeffrey J, Hegazi, Omar E, Heidari, Golnaz, Heidari, Mohammad, Heidari-Foroozan, Mahsa, Heidari-Soureshjani, Reza, Helfer, Bartosz, Herteliu, Claudiu, Hesami, Hamed, Hettiarachchi, Dineshani, Heyi, Demisu Zenbaba, Hezam, Kamal, Hiraike, Yuta, Hoffman, Howard J, Holla, Ramesh, Horita, Nobuyuki, Hossain, Md Belal, Hossain, Md Mahbub, Hossain, Sahadat, Hosseini, Mohammad-Salar, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hostiuc, Mihaela, Hostiuc, Sorin, Hsairi, Mohamed, Hsieh, Vivian Chia-rong, Hu, Chengxi, Huang, Junjie, Huda, Md Nazmul, Hugo, Fernando N, Hultström, Michael, Hussain, Javid, Hussain, Salman, Hussein, Nawfal R, Huy, Le Duc, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Idowu, Oluwatope Olaniyi, Ijo, Desta, Ikuta, Kevin S, Ilaghi, Mehran, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Immurana, Mustapha, Inbaraj, Leeberk Raja, Iradukunda, Arnaud, Iravanpour, Farideh, Iregbu, Kenneth Chukwuemeka, Islam, Md Rabiul, Islam, Mohammad Mainul, Islam, Sheikh Mohammed Shariful, Islami, Farhad, Ismail, Nahlah Elkudssiah, Isola, Gaetano, Iwagami, Masao, Iwu, Chidozie C D, Iwu-Jaja, Chinwe Juliana, Iyer, Mahalaxmi, J, Linda Merin, Jaafari, Jalil, Jacob, Louis, Jacobsen, Kathryn H, Jadidi-Niaragh, Farhad, Jafarinia, Morteza, Jaggi, Khushleen, Jahankhani, Kasra, Jahanmehr, Nader, Jahrami, Haitham, Jain, Akhil, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Jakovljevic, Mihajlo, Jalilzadeh Yengejeh, Reza, Jamshidi, Elham, Jani, Chinmay T, Janko, Mark M, Jatau, Abubakar Ibrahim, Jayapal, Sathish Kumar, Jayaram, Shubha, Jeganathan, Jayakumar, Jema, Alelign Tasew, Jemere, Digisie Mequanint, Jeong, Wonjeong, Jha, Anil K, Jha, Ravi Prakash, Ji, John S, Jiang, Heng, Jin, Yingzhao, Jin, Yinzi, Johnson, Olatunji, Jomehzadeh, Nabi, Jones, Darwin Phan, Joo, Tamas, Joseph, Abel, Joseph, Nitin, Joshua, Charity Ehimwenma, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kaambwa, Billingsley, Kabir, Ali, Kabir, Hannaneh, Kabir, Zubair, Kadashetti, Vidya, Kahe, Farima, Kakodkar, Pradnya Vishal, Kalani, Rizwan, Kalankesh, Leila R, Kaliyadan, Feroze, Kalra, Sanjay, Kamath, Ashwin, Kamireddy, Arun, Kanagasabai, Thanigaivelan, Kandel, Himal, Kanmiki, Edmund Wedam, Kanmodi, Kehinde Kazeem, Kantar, Rami S, Kapoor, Neeti, Karajizadeh, Mehrdad, Karami Matin, Behzad, Karanth, Shama D, Karaye, Ibraheem M, Karim, Asima, Karimi, Hanie, Karimi, Salah Eddin, Karimi Behnagh, Arman, Karkhah, Samad, Karna, Ajit K, Kashoo, Faizan Zaffar, Kasraei, Hengameh, Kassaw, Nigussie Assefa, Kassebaum, Nicholas J, Kassel, Molly B, Katamreddy, Adarsh, Katikireddi, Srinivasa Vittal, Katoto, Patrick DMC, Kauppila, Joonas H, Kaur, Navjot, Kaydi, Neda, Kayibanda, Jeanne Françoise, Kayode, Gbenga A, Kazemi, Foad, Kazemian, Sina, Kazeminia, sara, Keikavoosi-Arani, Leila, Keller, Cathleen, Kempen, John H, Kerr, Jessica A, Kesse-Guyot, Emmanuelle, Keykhaei, Mohammad, Khadembashiri, Mohamad Mehdi, Khadembashiri, Mohammad Amin, Khafaie, Morteza Abdullatif, Khajuria, Himanshu, Khalafi, Mohammad, Khalaji, Amirmohammad, Khalid, Nauman, Khalil, Ibrahim A, Khamesipour, Faham, Khan, Asaduzzaman, Khan, Gulfaraz, Khan, Ikramullah, Khan, Imteyaz A, Khan, Maseer, Khan, Moien AB, Khan, Taimoor, Khan suheb, Mahammed Ziauddin, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khatami, Fatemeh, Khavandegar, Armin, Khayat Kashani, Hamid Reza, Kheirallah, Khalid A, Khidri, Feriha Fatima, Khodadoust, Elaheh, Khormali, Moein, Khosrowjerdi, Mahmood, Khubchandani, Jagdish, Khusun, Helda, Kifle, Zemene Demelash, Kim, Grace, Kim, Jihee, Kimokoti, Ruth W, Kinzel, Kasey E, Kiross, Girmay Tsegay, Kisa, Adnan, Kisa, Sezer, Kiss, Juniper Boroka, Kivimäki, Mika, Klu, Desmond, Knudsen, Ann Kristin Skrindo, Kolahi, Ali-Asghar, Kompani, Farzad, Koren, Gerbrand, Kosen, Soewarta, Kostev, Karel, Kotnis, Ashwin Laxmikant, Koul, Parvaiz A, Koulmane Laxminarayana, Sindhura Lakshmi, Koyanagi, Ai, Kravchenko, Michael A, Krishan, Kewal, Krishna, Hare, Krishnamoorthy, Vijay, Krishnamoorthy, Yuvaraj, Krohn, Kris J, Kuate Defo, Barthelemy, Kubeisy, Connor M, Kucuk Bicer, Burcu, Kuddus, Md Abdul, Kuddus, Mohammed, Kuitunen, Ilari, Kujan, Omar, Kulimbet, Mukhtar, Kulkarni, Vishnutheertha, Kumar, Ashish, Kumar, Harish, Kumar, Nithin, Kumar, Rahul, Kumar, Shiv, Kumari, Madhulata, Kurmanova, Almagul, Kurmi, Om P, Kusnali, Asep, Kusuma, Dian, Kutluk, Tezer, Kuttikkattu, Ambily, Kyei, Evans F, Kyriopoulos, Ilias, La Vecchia, Carlo, Ladan, Muhammad Awwal, Laflamme, Lucie, Lahariya, Chandrakant, Lahmar, Abdelilah, Lai, Daphne Teck Ching, Laksono, Tri, Lal, Dharmesh Kumar, Lalloo, Ratilal, Lallukka, Tea, Lám, Judit, Lamnisos, Demetris, Lan, Tuo, Lanfranchi, Francesco, Langguth, Berthold, Lansingh, Van Charles, Laplante-Lévesque, Ariane, Larijani, Bagher, Larsson, Anders O, Lasrado, Savita, Latief, Kamaluddin, Latif, Mahrukh, Latifinaibin, Kaveh, Lauriola, Paolo, Le, Long Khanh Dao, Le, Nhi Huu Hanh, Le, Thao Thi Thu, Le, Trang Diep Thanh, Lee, Munjae, Lee, Paul H, Lee, Sang-woong, Lee, Seung Won, Lee, Wei-Chen, Lee, Yo Han, Legesse, Samson Mideksa, Leigh, James, Lenzi, Jacopo, Leong, Elvynna, Lerango, Temesgen L, Li, Ming-Chieh, Li, Wei, Li, Xiaopan, Li, Yichong, Li, Zhihui, Libra, Massimo, Ligade, Virendra S, Likaka, Andrew Tiyamike Makhiringa, Lim, Lee-Ling, Lin, Ro-Ting, Lin, Shuzhi, Lioutas, Vasileios-Arsenios, Listl, Stefan, Liu, Jue, Liu, Simin, Liu, Xiaofeng, Livingstone, Katherine M, Llanaj, Erand, Lo, Chun-Han, Loreche, Arianna Maever, Lorenzovici, László, Lotfi, Mojgan, Lotfizadeh, Masoud, Lozano, Rafael, Lubinda, Jailos, Lucchetti, Giancarlo, 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