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746 results on '"Hasan, Samiul"'

1. Advance Real-time Detection of Traffic Incidents in Highways using Vehicle Trajectory Data

Author

Roy, Sudipta and Hasan, Samiul

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Applications, Statistics - Machine Learning
Abstract

A significant number of traffic crashes are secondary crashes that occur because of an earlier incident on the road. Thus, early detection of traffic incidents is crucial for road users from safety perspectives with a potential to reduce the risk of secondary crashes. The wide availability of GPS devices now-a-days gives an opportunity of tracking and recording vehicle trajectories. The objective of this study is to use vehicle trajectory data for advance real-time detection of traffic incidents on highways using machine learning-based algorithms. The study uses three days of unevenly sequenced vehicle trajectory data and traffic incident data on I-10, one of the most crash-prone highways in Louisiana. Vehicle trajectories are converted to trajectories based on virtual detector locations to maintain spatial uniformity as well as to generate historical traffic data for machine learning algorithms. Trips matched with traffic incidents on the way are separated and along with other trips with similar spatial attributes are used to build a database for modeling. Multiple machine learning algorithms such as Logistic Regression, Random Forest, Extreme Gradient Boost, and Artificial Neural Network models are used to detect a trajectory that is likely to face an incident in the downstream road section. Results suggest that the Random Forest model achieves the best performance for predicting an incident with reasonable recall value and discrimination capability., Comment: 19 Pages, 4 Tables, 10 Figures

Published
2024

2. Autonomous on-Demand Shuttles for First Mile-Last Mile Connectivity: Design, Optimization, and Impact Assessment

Author

Roy, Sudipta, Dadashev, Gabriel, Yfantis, Lampros, Nahmias-Biran, Bat-hen, and Hasan, Samiul

Subjects
Computer Science - Robotics, Statistics - Applications
Abstract

The First-Mile Last-Mile (FMLM) connectivity is crucial for improving public transit accessibility and efficiency, particularly in sprawling suburban regions where traditional fixed-route transit systems are often inadequate. Autonomous on-Demand Shuttles (AODS) hold a promising option for FMLM connections due to their cost-effectiveness and improved safety features, thereby enhancing user convenience and reducing reliance on personal vehicles. A critical issue in AODS service design is the optimization of travel paths, for which realistic traffic network assignment combined with optimal routing offers a viable solution. In this study, we have designed an AODS controller that integrates a mesoscopic simulation-based dynamic traffic assignment model with a greedy insertion heuristics approach to optimize the travel routes of the shuttles. The controller also considers the charging infrastructure/strategies and the impact of the shuttles on regular traffic flow for routes and fleet-size planning. The controller is implemented in Aimsun traffic simulator considering Lake Nona in Orlando, Florida as a case study. We show that, under the present demand based on 1% of total trips as transit riders, a fleet of 3 autonomous shuttles can serve about 80% of FMLM trip requests on-demand basis with an average waiting time below 4 minutes. Additional power sources have significant effect on service quality as the inactive waiting time for charging would increase the fleet size. We also show that low-speed autonomous shuttles would have negligible impact on regular vehicle flow, making them suitable for suburban areas. These findings have important implications for sustainable urban planning and public transit operations., Comment: 25 Pages, 13 Figures, 1 Table

Published
2024

3. Impact of an Autonomous Shuttle Service on Urban Road Capacity: Experiments by Microscopic Traffic Simulation

Author

Roy, Sudipta, Nahmias-Biran, Bat-hen, and Hasan, Samiul

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

Autonomous vehicles are expected to transform transportation systems with rapid technological advancement. Human mobility would become more accessible and safer with the emergence of driverless vehicles. To this end, autonomous shuttle services are currently introduced in different urban conditions throughout the world. As a result, studies are needed to assess the safety and mobility performance of such autonomous shuttle services. However, calibrating the movement of autonomous shuttles in a simulation environment has been a difficult task due to the absence of any real-world data. This study aims to calibrate autonomous shuttles in a microscopic traffic simulation model and consequently assess the impact of the shuttle service on urban road capacity through simulation experiments. For this analysis, a prototype of an operational shuttle system at Lake Nona, Orlando, Florida is emulated in a microscopic traffic simulator during different times of the day. The movements of autonomous vehicles are calibrated using real-world trajectory data which help replicate the driving behavior of the shuttle in the simulation. The analysis reveals that with increasing frequency of the shuttle service the delay time percentage of the shared road sections increases and traveling speed decreases. It is also found that increasing the speed of shuttles up to 5 mph during off-peak hours and 10 mph during peak hours will improve traffic conditions. The findings from this study will assist policymakers and transportation agencies to revise policies for deploying autonomous shuttles and for planning road infrastructures for shared road-use of autonomous shuttles and human driven vehicles., Comment: 16 Pages, 5 Figures, 6 Tables. Accepted in Transportation Research Board Annual Meeting 2024

Published
2024

4. Strengthening Community Resilience by Modeling Transportation and Electric Power Network Interdependencies

Author

Jamal, Tasnuba Binte, Hasan, Samiul, Abdul-Aziz, Omar I., Mozumder, Pallab, and Meyur, Rounak

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

This study presents an agent-based model (ABM) developed to simulate the resilience of a community to hurricane-induced infrastructure disruptions, focusing on the interdependencies between electric power and transportation networks. In this ABM approach, agents represent the components of a system, where interactions within a system shape intra-dependency of a system and interactions among systems shape interdependencies. To study household resilience subject to a hurricane, a library of agents has been created including electric power network, transportation network, wind/flooding hazards, and household agents. The ABM is applied over the household and infrastructure data from a community (Zip code 33147) in Miami-Dade County, Florida. Interdependencies between the two networks are modeled in two ways, (i) representing the role of transportation in fuel delivery to power plants and restoration teams' access, (ii) impact of power outage on transportation network components. Restoring traffic signals quickly is crucial as their outage can slow down traffic and increase the chance of crashes. We simulate three restoration strategies: component based, distance based, and traffic lights based restoration. The model is validated against Hurricane Irma data, showing consistent behavior with varying hazard intensities. Scenario analyses explore the impact of restoration strategies, road accessibility, and wind speed intensities on power restoration. Results demonstrate that a traffic lights based restoration strategy efficiently prioritizes signal recovery without delaying household power restoration time. Restoration of power services will be faster if restoration teams do not need to wait due to inaccessible roads and fuel transportation to power plants is not delayed.

Published
2024

5. Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach

Author

Rashid, Md Mobasshir, Rahman, Rezaur, and Hasan, Samiul

Subjects
Computer Science - Machine Learning
Abstract

Traffic prediction during hurricane evacuation is essential for optimizing the use of transportation infrastructures. It can reduce evacuation time by providing information on future congestion in advance. However, evacuation traffic prediction can be challenging as evacuation traffic patterns is significantly different than regular period traffic. A data-driven traffic prediction model is developed in this study by utilizing traffic detector and Facebook movement data during Hurricane Ian, a rapidly intensifying hurricane. We select 766 traffic detectors from Florida's 4 major interstates to collect traffic features. Additionally, we use Facebook movement data collected during Hurricane Ian's evacuation period. The deep-learning model is first trained on regular period (May-August 2022) data to understand regular traffic patterns and then Hurricane Ian's evacuation period data is used as test data. The model achieves 95% accuracy (RMSE = 356) during regular period, but it underperforms with 55% accuracy (RMSE = 1084) during the evacuation period. Then, a transfer learning approach is adopted where a pretrained model is used with additional evacuation related features to predict evacuation period traffic. After transfer learning, the model achieves 89% accuracy (RMSE = 514). Adding Facebook movement data further reduces model's RMSE value to 393 and increases accuracy to 93%. The proposed model is capable to forecast traffic up to 6-hours in advance. Evacuation traffic management officials can use the developed traffic prediction model to anticipate future traffic congestion in advance and take proactive measures to reduce delays during evacuation.

Published
2023

6. A Co-Simulation Study to Assess the Impacts of Connected and Autonomous Vehicles on Traffic Flow Stability during Hurricane Evacuation

Author

Syed, Zaheen E Muktadi and Hasan, Samiul

Subjects
Physics - Physics and Society
Abstract

Hurricane evacuation has become a major problem for the coastal residents of the United States. Devastating hurricanes have threatened the lives and infrastructure of coastal communities and caused billions of dollars in damage. There is a need for better traffic management strategies to improve the safety and mobility of evacuation traffic. In this study hurricane evacuation traffic was simulated using SUMO a microscopic traffic simulation model. The effects of Connected and Autonomous Vehicles (CAVs) and Autonomous Vehicles (AVs) were evaluated using two approaches. (i) Using the state-of-the-art car-following models available in SUMO and (ii) a co-simulation study by integrating the microscopic traffic simulation model with a separate communication simulator to find the realistic effect of CAVs on evacuation traffic. A road network of I-75 in Florida was created to represent real-world evacuation traffic observed in Hurricane Irma s evacuation periods. Simulation experiments were performed by creating mixed traffic scenarios with 25, 50, 75 and 100 percentages of different vehicle technologies including CAVs or AVs and human-driven vehicles. HDV Simulation results suggest that the CACC car-following model, implemented in SUMO and commonly used in the literature to represent CAVs, produces highly unstable results On the other hand the ACC car following model, used to represent AVs, produces better and more stable results. However, in a co-simulation study, to evaluate the effect of CAVs in the same evacuation traffic scenario, results indicate that with 25 percentage of CAVs the number of potential collisions decrease up to 42.5 percentage.

Published
2023

7. Predicting Real-time Crash Risks during Hurricane Evacuation Using Connected Vehicle Data

Author

Syed, Zaheen E Muktadi and Hasan, Samiul

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Hurricane evacuation, ordered to save lives of people of coastal regions, generates high traffic demand with increased crash risk. To mitigate such risk, transportation agencies need to anticipate highway locations with high crash risks to deploy appropriate countermeasures. With ubiquitous sensors and communication technologies, it is now possible to retrieve micro-level vehicular data containing individual vehicle trajectory and speed information. Such high-resolution vehicle data, potentially available in real time, can be used to assess prevailing traffic safety conditions. Using vehicle speed and acceleration profiles, potential crash risks can be predicted in real time. Previous studies on real-time crash risk prediction mainly used data from infrastructure-based sensors which may not cover many road segments. In this paper, we present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data. Such data contain vehicle location, speed, and acceleration information collected at a very high frequency (less than 30 seconds). To predict potential crash risks, we utilized a dataset collected during the evacuation period of Hurricane Ida on Interstate-10 (I-10) in the state of Louisiana. Multiple machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data in 5-minute intervals. The results indicate that the Gaussian Process Boosting (GPBoost) and Extreme Gradient Boosting (XGBoost) models perform better (recall = 0.91) than other models. The real-time connected vehicle data for crash risks assessment will allow traffic managers to efficiently utilize resources to proactively take safety measures.

Published
2023

8. A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

Author

Jamal, Tasnuba Binte and Hasan, Samiul

Subjects
Statistics - Applications
Abstract

Major disasters such as wildfire, tornado, hurricane, tropical storm, flooding cause disruptions in infrastructure systems such as power outage, disruption to water supply system, wastewater management, telecommunication failures, and transportation facilities. Disruptions in electricity infrastructures has a negative impact on every sector of a region, such as education, medical services, financial, recreation. In this study, we introduce a novel approach to investigate the factors which can be associated with longer restoration time of power service after a hurricane. We consider three types of factors (hazard characteristics, built-environment characteristics, and socio-demographic factors) that might be associated with longer restoration times of power outages during a hurricane. Considering restoration time as the dependent variable and utilizing a comprehensive set of county-level data, we have estimated a Generalized Accelerated Failure Time (GAFT) that accounts for spatial dependence among observations for time to event data. Considering spatial correlation in time to event data has improved the model fit by 12%. Using GAFT model and Hurricane Irma as a case study, we examined: (1) differences in electric power outages and restoration rates among different types of power companies: investor-owned power companies, rural and municipal cooperatives; (2) the relationship between the duration of power outage and power system variables, and socioeconomic attributes. We have found that factors such as maximum sustained wind speed, percentage of customers facing power outage, percentage of customers served by investor-owned power company, median household income, and number of power plants are strongly associated with restoration time. This paper identifies the key factors in predicting the restoration time of hurricane-induced power outages.

Published
2023

9. Understanding the Loss in Community Resilience due to Hurricanes using Facebook Data

Author

Jamal, Tasnuba Binte and Hasan, Samiul

Subjects
Computer Science - Social and Information Networks, Statistics - Applications
Abstract

Significant negative impacts are observed in productivity, economy, and social wellbeing because of the reduced human activity due to extreme events. Community resilience is an important and widely used concept to understand the impacts of an extreme event to population activity. Resilience is generally defined as the ability of a system to manage shocks and return to a steady state in response to an extreme event. In this study, aggregate location data from Facebook in response to Hurricane Ida are analyzed. Using changes in the number of Facebook users before, during, and after the disaster, community resilience is quantified as a function of the magnitude of impact and the time to recover from the extreme situation. Based on the resilience function, the transient loss of resilience in population activity is measured for the affected communities in Louisiana. The loss in resilience of the affected communities are explained by three types of factors, including disruption in physical infrastructures, disaster conditions due to hurricanes, and socio-economic characteristics. A greater loss in community resilience is associated with factors such as disruptions in power and transportation services and disaster conditions. Socioeconomic disparities in loss of resilience are found with respect to median income of a community. Understanding community resilience using decreased population activity levels due to a disaster and the factors associated with losses in resilience will enable us improve hazard preparedness, enhance disaster management practices, and create better recovery policies towards strengthening infrastructure and community resilience.

Published
2023
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11. Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach

Author

Baghali, Sina, Guo, Zhaomiao, and Hasan, Samiul

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Machine Learning
Abstract

The increasing market penetration of electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Since the electricity demand depends on the travel behavior of EVs, which are inherently uncertain, the forecasting of daily charging demand (CD) will be a challenging task. In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers from conventional vehicles to EVs and forecast the spatiotemporal patterns of daily CD. Our analysis reveals that the travel behavior of EVs and conventional vehicles are similar. Also, the forecasting results indicate that the developed models can generate accurate spatiotemporal patterns of the daily CD., Comment: Accepted in IEEE Power & Energy Society General Meeting (PESGM), 2022

Published
2022

12. A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation

Author

Rahman, Rezaur and Hasan, Samiul

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

Proactive evacuation traffic management largely depends on real-time monitoring and prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic prediction is challenging due to the uncertainties caused by sudden changes in projected hurricane paths and consequently household evacuation behavior. Moreover, modeling spatiotemporal traffic flow patterns requires extensive data over a longer time period, whereas evacuations typically last for 2 to 5 days. In this paper, we present a novel data-driven approach for predicting evacuation traffic at a network scale. We develop a dynamic graph convolution LSTM (DGCN-LSTM) model to learn the network dynamics of hurricane evacuation. We first train the model for non-evacuation period traffic data showing that the model outperforms existing deep learning models for predicting non-evacuation period traffic with an RMSE value of 226.84. However, when we apply the model for evacuation period, the RMSE value increased to 1440.99. We overcome this issue by adopting a transfer learning approach with additional features related to evacuation traffic demand such as distance from the evacuation zone, time to landfall, and other zonal level features to control the transfer of information (network dynamics) from non-evacuation periods to evacuation periods. The final transfer learned DGCN-LSTM model performs well to predict evacuation traffic flow (RMSE=399.69). The implemented model can be applied to predict evacuation traffic over a longer forecasting horizon (6 hour). It will assist transportation agencies to activate appropriate traffic management strategies to reduce delays for evacuating traffic.

Published
2022

13. Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network

Author

Rahman, Rezaur and Hasan, Samiul

Subjects
Computer Science - Machine Learning
Abstract

We present a novel data-driven approach of learning traffic flow patterns of a transportation network given that many instances of origin to destination (OD) travel demand and link flows of the network are available. Instead of estimating traffic flow patterns assuming certain user behavior (e.g., user equilibrium or system optimal), here we explore the idea of learning those flow patterns directly from the data. To implement this idea, we have formulated the traffic-assignment problem as a data-driven learning problem and developed a neural network-based framework known as Graph Convolutional Neural Network (GCNN) to solve it. The proposed framework represents the transportation network and OD demand in an efficient way and utilizes the diffusion process of multiple OD demands from nodes to links. We validate the solutions of the model against analytical solutions generated from running static user equilibrium-based traffic assignments over Sioux Falls and East Massachusetts networks. The validation result shows that the implemented GCNN model can learn the flow patterns very well with less than 2% mean absolute difference between the actual and estimated link flows for both networks under varying congested conditions. When the training of the model is complete, it can instantly determine the traffic flows of a large-scale network. Hence this approach can overcome the challenges of deploying traffic assignment models over large-scale networks and open new directions of research in data-driven network modeling.

Published
2022

16. Analyzing the Travel and Charging Behavior of Electric Vehicles -- A Data-driven Approach

Author

Baghali, Sina, Hasan, Samiul, and Guo, Zhaomiao

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract

The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs. The simulation results show that the proposed modeling can effectively estimate the daily CD pattern based on travel behavior of EVs, and simple machine learning techniques can forecast the travel parameters with acceptable accuracy., Comment: Accepted in IEEE Kansas Power and Energy Conference 2021

Published
2021
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18. Spatio-Temporal Mobility Patterns of On-demand Ride-hailing Service Users

Author

Zhang, Jiechao, Hasan, Samiul, Yan, Xuedong, and Liu, Xiaobing

Subjects
Computer Science - Social and Information Networks
Abstract

Understanding individual mobility behavior is critical for modeling urban transportation. It provides deeper insights on the generative mechanisms of human movements. Emerging data sources such as mobile phone call detail records, social media posts, GPS observations, and smart card transactions have been used before to reveal individual mobility behavior. In this paper, we report the spatio-temporal mobility behaviors using large-scale data collected from a ride-hailing service platform. Based on passenger-level travel information, we develop an algorithm to identify users' visited places and the category of those places. To characterize temporal movement patterns, we reveal the differences in trip generation characteristics between commuting and non-commuting trips and the distribution of gap time between consecutive trips. To understand spatial mobility patterns, we observe the distribution of the number of visited places and their rank, the spatial distribution of residences and workplaces, and the distributions of travel distance and travel time. Our analysis highlights the differences in mobility patterns of the users of ride-hailing services, compared to the findings of existing mobility studies based on other data sources. It shows the potential of developing high-resolution individual-level mobility models that can predict the demand of emerging mobility services with high fidelity and accuracy.

Published
2020

26. Crisis Communication Patterns in Social Media during Hurricane Sandy

Author

Sadri, Arif Mohaimin, Hasan, Samiul, Ukkusuri, Satish V., and Cebrian, Manuel

Subjects
Computer Science - Social and Information Networks
Abstract

Hurricane Sandy was one of the deadliest and costliest of hurricanes over the past few decades. Many states experienced significant power outage, however many people used social media to communicate while having limited or no access to traditional information sources. In this study, we explored the evolution of various communication patterns using machine learning techniques and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ~52M tweets coming from ~13M users between October 14, 2012 and November 12, 2012. We run topic model on ~763K tweets from top 4,029 most frequent users who tweeted about Sandy at least 100 times. We identified 250 well-defined communication patterns based on perplexity. Conversations of most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. We also present each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real time user needs in future crises.

Published
2017

27. Understanding Information Spreading in Social Media during Hurricane Sandy: User Activity and Network Properties

Author

Sadri, Arif Mohaimin, Hasan, Samiul, Ukkusuri, Satish V., and Cebrian, Manuel

Subjects
Computer Science - Social and Information Networks
Abstract

Many people use social media to seek information during disasters while lacking access to traditional information sources. In this study, we analyze Twitter data to understand information spreading activities of social media users during hurricane Sandy. We create multiple subgraphs of Twitter users based on activity levels and analyze network properties of the subgraphs. We observe that user information sharing activity follows a power-law distribution suggesting the existence of few highly active nodes in disseminating information and many other nodes being less active. We also observe close enough connected components and isolates at all levels of activity, and networks become less transitive, but more assortative for larger subgraphs. We also analyze the association between user activities and characteristics that may influence user behavior to spread information during a crisis. Users become more active in spreading information if they are centrally placed in the network, less eccentric, and have higher degrees. Our analysis provides insights on how to exploit user characteristics and network properties to spread information or limit the spreading of misinformation during a crisis event.

Published
2017

28. Analyzing Social Interaction Networks from Twitter for Planned Special Events

Author

Sadri, Arif Mohaimin, Hasan, Samiul, Ukkusuri, Satish V., and Lopez, Juan Esteban Suarez

Subjects
Computer Science - Social and Information Networks
Abstract

The complex topology of real networks allows its actors to change their functional behavior. Network models provide better understanding of the evolutionary mechanisms being accountable for the growth of such networks by capturing the dynamics in the ways network agents interact and change their behavior. Considerable amount of research efforts is required for developing novel network modeling techniques to understand the structural properties such networks, reproducing similar properties based on empirical evidence, and designing such networks efficiently. First, we demonstrate how to construct social interaction networks using social media data and then present the key findings obtained from the network analytics. We analyze the characteristics and growth of such interaction networks, examine the network properties and derive important insights based on the theories of network science literature. We also discuss the application of such networks as a useful tool to effectively disseminate targeted information during planned special events. We observed that the degree-distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interactions. While the network elements and average user degree grow linearly each day, densities of such networks tend to become zero. Largest connected components exhibit higher connectivity (density) when compared with the whole graph. Network radius and diameter become stable over time evidencing the small-world property. We also observe increased transitivity and higher stability of the power-law exponents as the networks grow. Data is specific to the Purdue University community and two large events, namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue University as part of Indiana Primary Election 2016., Comment: 20 pages, 6 figures, 1 table. arXiv admin note: text overlap with arXiv:1704.01706

Published
2017

29. Joint Inference of User Community and Interest Patterns in Social Interaction Networks

Author

Sadri, Arif Mohaimin, Hasan, Samiul, and Ukkusuri, Satish V.

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

Online social media have become an integral part of our social beings. Analyzing conversations in social media platforms can lead to complex probabilistic models to understand social interaction networks. In this paper, we present a modeling approach for characterizing social interaction networks by jointly inferring user communities and interests based on social media interactions. We present several pattern inference models: i) Interest pattern model (IPM) captures population level interaction topics, ii) User interest pattern model (UIPM) captures user specific interaction topics, and iii) Community interest pattern model (CIPM) captures both community structures and user interests. We test our methods on Twitter data collected from Purdue University community. From our model results, we observe the interaction topics and communities related to two big events within Purdue University community, namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue University as part of Indiana Primary Election 2016. Constructing social interaction networks based on user interactions accounts for the similarity of users' interactions on various topics of interest and indicates their community belonging further beyond connectivity. We observed that the degree-distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interactions. We also discuss the application of such networks as a useful tool to effectively disseminate specific information to the target audience towards planning any large-scale events and demonstrate how to single out specific nodes in a given community by running network algorithms., Comment: 18 pages, 8 figures, 1 table

Published
2017

35. Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Author

Wright, Naomi Jane, Leather, Andrew J.M., Ade-Ajayi, Niyi, Sevdalis, Nick, Davies, Justine, Poenaru, Dan, Ameh, Emmanuel, Ademuyiwa, Adesoji, Lakhoo, Kokila, Smith, Emily Rose, Douiri, Abdel, Elstad, Maria, Sim, Marcus, Riboni, Cristiana, Martinez-Leo, Bruno, Akhbari, Melika, Tabiri, Stephen, Mitul, Ashrarur, Aziz, Dayang Anita Abdul, Fachin, Camila, Niyukuri, Alliance, Arshad, Muhammad, Ibrahim, Fowzia, Moitt, Natalie, Doheim, Mohamed Fahmy, Thompson, Hannah, Ubhi, Harmony, Williams, Isabelle, Hashim, Sophia, Philipo, Godfrey Sama, Herrera, Laura, Yunus, Aayenah, Vervoort, Dominique, Parker, Samuel, Benaskeur, Yousra-Imane, Alser, Osaid H., Adofo-Ansong, Nana, Alhamid, Ahmad, Salem, Hosni khairy, Saleh, Mahmoud, Elrais, Safa Abdal, Abukhalaf, Sadi, Shinondo, Patricia, Nour, Ibrahim, Aydin, Emrah, Vaitkiene, Agota, Naranjo, Kelly, Dube, Andile Maqhawe, Ngwenya, Sodumisa, Yacoub, Mina A., Kwasau, Henang, Hyman, Gabriella, Elghazaly, Shrouk Mahmoud, Al-Slaibi, Ibrahim, Hisham, Intisar, Franco, Helena, Arbab, Hana, Samad, Lubna, Soomro, Aqil, Chaudhry, Muhammad Amjad, Karim, Safina, Khattak, Muhammad Adnan Khan, Nah, Shireen Anne, Dimatatac, Doris Mae, Choo, Candy SC, Maistry, Niveshni, Mitul, Ashrarur Rahman, Hasan, Samiul, Karim, Sabbir, Yousuf, Hina, Qureshi, Taimur, Nour, Ibrahim Rabi, Al-Taher, Raed Nael, Sarhan, Osama Abdul Kareem, Garcia-Aparicio, Luis, Prat, Jordi, Blazquez-Gomez, Eva, Tarrado, Xavier, Iriondo, Martí, Bragagnini, Paolo, Rite, Segundo, Hagander, Lars, Svensson, Emma, Owusu, Sheila, Abdul-Mumin, Alhassan, Bagbio, Dominic, Ismavel, Vijay Anand, Miriam, Ann, T, Shajin, Anaya Dominguez, Marlene, Ivanov, Monica, Serban, Andreea Madalina, Derbew, Miliard, Elfiky, Mahmoud, Olivos Perez, Maricarmen, Abrunhosa Matias, Marcia, Arnaud, Alexis P, Negida, Ahmed, King, Sebastian, Fazli, Mohamad Rafi, Hamidi, Nadia, Touabti, Souhem, Chipalavela, Rossana Francisco, Lobos, Pablo, Jones, Brendan, Ljuhar, Damir, Singer, Georg, Cordonnier, Annelien, Jáuregui, Lorena, Zvizdic, Zlatan, Wong, Janice, St-Louis, Etienne, Shu, Qiang, Lui, Yang, Correa, Catalina, Pos, Lucie, Alcántara, Elvyn, Féliz, Erick, Zea-Salazar, Luis Enrique, Ali, Liza, Peycelon, Matthieu, Anatole, Nzanzu Kipata, Jallow, Cherno S., Lindert, Judith, Ghosh, Dhruv, Adhiwidjaja, Cathline Freya, Tabari, Ahmad Khaleghnejad, Lotfollahzadeh, Saran, Mussein, Haidar Mohammad, Vatta, Fabrizio, Pasqua, Noemi, Kihiko, David, Gohil, Hetal, Nour, Ibrahim R., Elhadi, Muhammed, Almada, Suad Ahmed, Verkauskas, Gilvydas, Risteski, Toni, Peñarrieta Daher, Alejandro, Outani, Oumaima, Hamill, James, Lawal, Taiwo, Mulu, Jack, Yapo, Benjamin, Saldaña, Lily, Espineda, Beda, Toczewski, Krystian, Tuyishime, Eugene, Ndayishimiye, Isaac, Raboe, Enaam, Hammond, Philip, Walker, Gregor, Djordjevic, Ivona, Chitnis, Milind, Son, Joonhyuk, Lee, Sanghoon, Hussien, Muaad, Malik, Sawazen, Ismail, Enas Musa, Boonthai, Ampaipan, Dahman, Nesrine Ben Hadj, Hall, Nigel, Castedo Camacho, Fabiola Ruth, Sobrero, Helena, Butler, Marilyn, Makhmud, Aliev, Novotny, Nathan, Hammouri, Ahmad G., Al-Rayyes, Maisara, Bvulani, Bruce, Muraveji, Qais, Murzaie, Muhammad Yousuf, Sherzad, Ajmal, Haidari, Sayed Aman, Monawar, Abdul Baqi, Samadi, Dr. Ahmad Zia, Thiessen, Jesh, Venant, Ntakarutimana, Hospital, Sonia Inamuco, Jérémie, Niyonkuru, Mbonicura, Jean Claude, Vianney, Butoyi Jean Marie, Tadesse, Amezene, Negash, Samuel, Roberts, Charles A., Jabang, John N., Bah, Abdoulie, Camamra, Kajali, Correa, Armandou, Sowe, Babucarr, Gai, A., Jaiteh, Musa, Raymond, Kwizera Jean, Mvukiyehe, Jean Paul, Itangishaka, Innocent, Kayibanda, Emmanuel, Manirambona, Emery, Lule, Joseph, Costas-Chavarri, Ainhoa, Shyaka Gashugi, Ian, Ndata, Albert, Gasana, Georges, Nezerwa, Yves Castar, Simeon, Turatsinze, Muragijimana, Jean De Dieu, Rashid, Sakina, Msuya, David, Elisante, Joseph, Solanki, Meghna, Manjira, Emmanuel, Lodhia, Jay, Jusabani, Mubashir, Tarmohamed, Murad, Koipapi, Sengua, Souhem, Touabti, Sara, Nabti, Sihem, Brahimi, Dania, Bouguermouh, Toufik, Iaiche Achour, Mounira, Baghdadi Nour el islam, Habiba, Alouani, Aragão, Liliana, Gonçalves, Victor, Lino Urquizo, Marcelo Mauricio, Varela, Maria Florencia, Mercado, Pedro, Horacio, Bonavia, Damiani, Andrea, Mac, Carlos, Putruele, Daniel, Liljesthrom, Karen, Bernaus, Marianela, Jauri, Cesar, Cripovich, Alejandrina, Bianchin, Ezequiel, Puig, Maria Gabriela, Andreussi, Lorna, Iracelay, Susana, Marcos, Dolores, Herrera, Carina, Palacios, Nelly, Avile, Romina, Serezo, Belen, Montoya, Debora, Cepeda, Rodrigo, Vaquila, Justo, Veronica, Sofficci, Pardo, Liliana, Valeria, Pelussi, Julio, Lapalma, Martin, Aranda Diego, Lucio, Palazzi, Gabriel, Comba, Marianella, Depetrini, Calderón Arancibia, José Alfredo, Huespe, Enrique, Losa, Gabriela Natalia, Arancibia Gutiérrez, Elsa, Scherl, Humberto, Gonzalez, Daniel Emilio, Baistrocchi, Valentina, Silva, Yanina, Galdeano, Marcelo, Medard, Pablo, Sueiras, Ines, Romero Manteola, Enrique, Defago, Victor Hugo, Mieres, Carlos, Alberto, Carlos, Cornelli, Fabio, Molina, Marcelo, Ravetta, Pablo, Patiño Gonzalez, Celeste Carolina, Dallegre, Maria Belen, Szklarz, Maria Tatiana, Leyba, Marcos Federico, Rivarola, Nahuel Ignacio, Charras, Maria Delia, Morales, Adriana, Caseb, Paloma, Toselli, Luzia, Millán, Carolina, Junes, Maria del Carmen, Di Siervi, Oscar, Gilardi, Jose, Simon, Soledad, Contreras, Carla Sofia, Rojas, Nair, Arnoletto, Lucia Beatriz, Blain, Otilia Eva, Bravo, Mauro Nicolas, Sanchez, Nancy, Herrera Pesara, Luciana Martina, Moreno, Maria Eugenia, Sferco, Carlos Ariel, Huq, Umama, Ferdousi, Tamanna, Al-Mamun, Abdullah, Sultana, Sadia, Mahmud, Refoyez, Mahmud, Khalid, Sayeed, Fatema, Svirsky, Alexander, Sempertegui, Denisse, Negrete, Amalia, Teran, Araceli, Sadagurschi, Mariana, Popovic, Nusret, Karavdic, Kenan, Milisic, Emir, Jonuzi, Asmir, Mesic, Amira, Terzic, Sabina, Dendusic, Nejra, Biber, Elna, Sehic, Anesa, Zvizdic, Nada, Letic, Emina, Saracevic, Adna, Hamidovic, Ajla, Selak, Nejra, Horozic, Dzan, Hukic, Lamija, Muhic, Amila, Vanis, Nedim, Sokolovic, Emir, Sabic, Adnan, Becker, Karin, Novochadlo Klüppel, Elis, dos Santos Dias, André Iván Bradley, Agulham, Miguel Angelo, Bischoff, Cristiano, Sabbatini, Stella, Fernandes de Souza, Rachel, Souza Machado, Ana Beatriz, Werneck Raposo, Juliana, da Silva Augusto, Maria Lucia, Martins, Bianca M.R., de Souza Santos Ferreira, Mariana, Fernandes de Oliveira, Darli, Silva dos Santos, Carla, Ribeiro de Fernández y Alcázar, Fernanda, Alves Dutra da Silva, Érika, Furtado, Mariana, Tamada, Horácio, Silva Ferreira dos Santos, Marília, Lopes de Almeida, Thayná, Oliveira de Andrade, Susy, Gurgel do Amaral, Antonio Cipriano, Jr, Sartori Giovanoni, Lais, de Deus Passos Leles, Kamila, Corrêa Costa, Eduardo, Feldens, Leticia, Ferraz Schopf, Luciano, Soares de Fraga, José Carlos, Colombo de Holanda, Felipe, Brolin Santis Isolan, Paola Maria, Loyola Ferreira, Julia, Bruxel, Carla Luisa, Lopes Teixeira Ferdinando, Danielle, Zottis Barcelos, Fabricio, Baseggio, Natalia, Knorr Brenner, Nicole, Trindade Deyl, Rafael, Dure, Carolina, Nunes Kist, Iuri, Bueno Mazzuca, Rafael, Bueno Motter, Sarah, Ramos, Yna, Suzana Trein, Cristine, Rezende Rosa, Bianca, de Assis Silva, Murilo, Menin, Flavio Augusto, Semensato Carloni, Isabela Cristina, Norberto da Silva, Juliana Antinarelli, Gomes, Adriano Luis, Girão Tauffer, Mariana, Bassan Gonçalves, Paulo César, Nogueira Marques, Geraldo Magela, Moriya, Eliane, Labonia, Carla, Carrasco, Ana Lucia, Furtado Meyer, Karine, Farion-Aguiar, Luiz, Amado, Fernando, Antunes, Amanda, Silva, Elisângela, Telles, Leila, Almeida, Giovana, Belmino Gadelha, Aluísio Augusto, de Azevedo Belesa, Flavia, Gonçalves da Cunha, Jr, Acimar, Souza Barros, Beatriz, Zanellato, Josiane Bernartt, Guimarães, Patricia, Silva, Karina Ilheu da, Ribas, Bianca, Reuter, Cristina, Casado, Francis Tanise, Correa Leite, Mila Torii, Testoni, Daniela, Guinsburg, Ruth, de Campos Vieira Abib, Simone, Khodor Cury, Edson, Dornellas do Nascimento, Suely, Almeida Aguiar, Arthur, Melo Gallindo, Rodrigo, Gonçalves Borges, Carolina, Liu, Yang, Duote, Cai, Wang, Jinhu, Gao, Zhigang, Liang, Liang, Luo, Wenjuan, Zhao, Xiaoxia, Chen, Rui, Wang, Peng, Han, Yijiang, Huang, Ting, Donglai, Hu, Xiaodong, Guo, Junjie, Chen, Zhu, Libin, Wu, Guowei, Bao, Xiaozhou, Li, Haijing, Lv, Junying, Li, Zhongrong, Yong, Feng, Gao, Zhou Chong, Bai, Qiang, Tang, Weibing, Xie, Hua, Motee, Jethishka, Zhu, Jianming, Wen, Gang, Ruan, Weiwei, Li, Shungen, Chen, Lulu, Huang, Shungen, Lv, Zhibao, Lu, Jinjing, Huang, Liuming, Yu, Mengnan, Dajia, Wang, Bai, Yu Zuo, Rincon, Luis Carlos, Mancera, Juliana, Alzate Gallego, Edgar, Torres-Canchala, Laura, Silva Beltrán, Nathalia, Osorio Fory, Ghordana, Castaño Avila, Daniela, Forero Ladino, Angelica Maria, Gomez, Juanita, Jaramillo, Martha, Morales, Otto, Sanchez, Beatriz, Tinoco Guzmán, Nestor Julien, Castañeda Espinosa, Sergio, Prieto Vargas, Osbaldo, Pardo, Lina Maria, Toral, Eliana, Cáceres Aucatoma, Freud, Hinostroza, Daniel, Valencia, Santiago, Salinas, Vicente, Landivar Cino, Enrique, Ponce Fajardo, Gabriela Yulissa, Astudillo, Miguel, Garcia, Virginia, Muñoz, Guillermo, Verduga, Leonardo, Verduga, Ivan, Murillo, Ericka, Bucaram, Elena, Guayelema, Marisol, Marmol, Monica, Sanchez, Janina, Vergara, Carolina, Mena, Adriana, Velaña, Junior, Salazar, Karla, Lara, Sandra, Chiriboga, Elena, Silva, Julian, Gad, Dalia, Samy, Doaa, Elsadek, Menan Ahmed, Mohammed, Hanan Mahmoud, Abouheba, Mohamed, Ali, Karim Osamy, Rashwan, Hayssam, Fawzy, Omar Moustafa, Kamel, Tarek mohamed, Nemer, Rawan, Hassan, Mohamed Abada, Falah, Eyad Hassan, Abdelhady, Dina Sobhy, Zain, Mostafa, Ibrahim, Eman Abouzeid Abouzeid, Elsiraffy, Omar Ossama, Aboelela, Ahmed, Farag, Eman mohamed, Oshiba, Ahmed Mohamed, Emam, Omar Sameh, Attia, Alaa Mobarak, Laymouna, Moustafa A., Ghorab, Islam Abdelmonem, Mohammed, Mansour Mkayed, Soliman, Nourhan Akram, Ghaly, Khaled Abd elrahman, Sadek, Kareem, Elsherbiny, Mohamed, Saleh, Amr, Sheir, Hesham, Wafa, Tamer, Elmenam, Mohamed Abd, Abdelmaksoud, Sherif, Reda, Ahmed, Mansour, Islam, Elzohiri, Mohamed, Waseem, Basma, Elewaily, Mohamed, El-Ghazaly, Mohammed, Elhattab, Ahmad, Shalaby, Amr, Elsaied, Adham, Adawy, Ahmad, Sadek, Mirna, Ahmed, Mahmoud Abdelfattah, Herdan, Mohamed Omar, Elassall, Gena Mohamed Hamed, Mohammed, Azhar Arabi, Takrouney, Mohammed Hamada, Essa, Tarek Mohamed, Mahmoud, Ahmed Mokhtar, Saad, Alshaimaa M., Fouly, Mariam Albatoul Nageh, Ibrahim, Mahmoud abdelshakour, Nageh, Mohammad, Saad, Mahmoud M., Badr, Helmy, Fouda, Mohamed Fayez, Nofal, Ahmed Hassan, Almohamady, Hisham, Arafa, Mohamed Ahmed, Amad, Mohamed, Mansour, Mohamed Awad, O'Connor, Jennifer, O'Connor, Zachary, Anatole, Nzanzu, Nkunzimana, Elysé, Machemedze, Solomon, Dieudonné, Lemfuka, Appeadu-Mensah, William, Anyomih, Theophilus Teddy Kojo, Alhassan, Priscilla, Abantanga, Francis A., Michael, Vishal, Mary Koshy, Roshine, Raj, Ankit, Kumar, Vijay, PT, Sundeep, Prabhu, P Santosh, Vosoughi, Armin, Al-Mayoof, Ali Farooq, Fadhle, Muhamed Jassim, Joda, Ali Egab, Algabri, Hayder Nadhim Obaid, Abdelhamid, Sultan S., Al-Momani, Hashem M., Amarin, Marzouq, Zaghlol, Louay Y., Alsaadi, Nijmeh Nasser, Qwaider, Yasmeen Z., Qutishat, Hibah, Aliwisat, Ahmad Hasan, Arabiat, Esraa, Bsisu, Isam, Murshidi, Raghad M., Jabaiti, Mohammad S., Bataineh, Ziad A., Abuhayyeh, Husam Aldean, Quran, Thekraiat M. Al, Za'nouneh, Faris J. Abu, Alebbini, Mohanad Mutasem, Qudah, Hamzah Abullah, Hussein, Omar Ghazi, Murad, Amir M.I., Amarin, Justin Z., Suradi, Haya H., Alzraikat, Sayel H., Omari, Rand Y., Matour, Bashar M., Al-Halbouni, Layana, Zurikat, Rajai O., Yanis, Ahmad H., Hussein, Sara Al, Shoubaki, Ali, Ghanem, Waleed H., David, Kuria, Chitiavi, Soita Wycliffe, Mose, Moraa, Mugo, Robert, Ndungu, James, Mwai, Timothy, Shahbal, Swaleh, Malik, Janan, Chauhan, Nirav, Syovata, Francisa, Ochieng, Kevin, Omendo Liyenzero, Polycarp, Hussain, Syeda Ra'ana, Mugambi, Stanley, Ochieng, Roseline, Elkhazmi, Ebtesam Othman Abdulsalam, Khaled, Ala, Albozidi, Aya, Enbaya, Manal Ben, Elgammudi, Mala, Soula, Enas, Khalel, Wegden ibrahim almabrouk, Elhajjaji, Yasmine Ali, Alwaggaa, Nouriyah Ali, Ghayth, Sumayyah, Zreeg, Dafer abdulhakim .S., Tantush, Sara Abobaker, Bibas, Fatma, Layas, Tesneem, Sharif, Randa Alamen M, Aljadidi, Wesal Omar F. Saied, Tarek, Ahmed, Ahmed, Hazem, Essamilghi, Kamila Almabrouk Mohammed, Alfoghi, Mabroka, Abuhlega, Ma'aly A., Arrmali, Saddam, Abduljawad, Fatima Mousa, Alosta, Hasan Mustafa, Abuajaila, Abdulsalam, Abdelmutalib, Fakereldeen, Bashir, Fatma, Almengar, Inas, Annajjar, Mohammad hasan, Deyab, Abdelaziz, Elzowawi, Fathi, Krayem, Yousef, Drah, Weam, Meftah, Asma, Mohammed, Abobaker, Arrmalli, Lina Ali, Aljaboo, Hajir, Elayeb, Abdallah, Altomi, Mohamed, Altaweel, Ahmed, Tumi, Mohamed, Bazozi, Hana Milad, Shaklawoon, Aisha, Alglaib, Mohammed Meftah, Elkaloush, Abdullahn Abdousalam, Trainba, Sara, Swessi, Hisham, Alnaeri, Ali, Shnishah, Aya Essam, Mustufa, Hamassat, Gargum, Sondas Ali, Tarniba, Sara Ali, Shalluf, Hawa Ahmed, Shokri, Hajer Ali, Sarkaz, Taher L., Tababa, Osama, Elhadi, Ahmed, Naunova, Vesna Cvetanovska, Jovcheski, Laze, Kamilovski, Marjan, Gavrilovska-Brzanov, Aleksandra, Latiff, Zarina Abdul, Pauzi, Siti Farhan Moh, Osman, Marjmin, Lim, Felicia, Bakar, Ainal Huda Abu, Zaman, Azrina SK, Ishak, Shareena, Teo, Rufinah, Qi, Dr. Tammy Teoh Han, Othman, Mohd Yusran Bin, Zahari, Dato' Dr Zakaria bin, Hassan, Zulfitri bin Md, Shan, Cheah Hui, Lechmiannandan, Abhirrami, Tamaddun, Hafatin Fairos bt, Adanan, Mohd Fitri Shukri bin Mohamed, Abdullah, Mohd Yusof bin, Junyi, Wang, Nor, Mohd. Tarmizi Mohd, Noor, Wan Ruzaimie, Hassan, Mohd Razin bin, Dalek, Noor Fa'izatul Rahil Ambok, Hashim, Hidayah Hayati binti, Zarwawi, Ahmad Zulhisyam bin, Vellusamy, V Muthualhagi M, Yuen, Quah Soong, Kannessan, Hemasutha a/p, Ramli, Najua binti, Bujarimin, Ahmad Shafiee bin, Anntinea, Jessmine, Dass, Anthony, Khalid, Hazlina Mohd., Hanifah, Nur Atiqah binti Mohd, Jyun, Keily Wong Yue, Razak, Rahilah binti Abd, Naim, Nur Atifah binti Mohd, Hamzah, Siti Nur Aien binti Hamid, Vidal, Cristian R. 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Gbenga, Ajao, Emmanuel Akinlabi, Agelebe, Efeturi, Olori, Samson, Mshelbwala, Philip Mari, Osagie, Olabisi, Oyinloye, Adewale, Abubakar, Auwal M, Oyebanji, Lateef, Shehu, Ibrahim, Cletus, Cyril, Bamanga, Ahmed, Suleiman, Faruk, Adamu, Sani, C.Nwosu, David, S.Alkali, Yahya, Jalo, Iliya, Rasaki, Aliu, T.Sambo, Yusuf, A.Mohammed, Kalakwa, M.Ballah, Abubakar, Modekwe, Victor, Ekwunife, Okechukwu Hyginus, Ezidiegwu, Ugochukwu S, Osuigwe, Andrew N, Ugwu, Jideofor O, Ugwunne, Chuka A, Akhter, Nadeem, Gondal, Mudassir Fayaz, Raza, Rafee, Chaudary, Ali Raza, Ali, Hassan, Nisar, Muhammad Umar, Jamal, Muhammad Umer, Pandit, Ghuri Shankar, Mumtaz, Uzma, Amjad, Muhammad Bin, Talat, Nabila, Rehman, Wajeeh ur, Saleem, Muhammad, Mirza, Muhammad Bilal, Hashim, Imran, Haider, Naveed, Hameed, Soban, Saleem, Ayesha, Dogar, Sohail, Sharif, Muhammad, Bashir, Muhammad Kashif, Naumeri, Fatima, Rani, Zarqa, Baniowda, Muath A.M., Ba'baa', Basheer, Hassan, Majd Yousef Mohammed, Darwish, Ammar, Sehwiel, Abrar 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Alkareem, Raghad mohammad abdu, Lahlooh, Raghad abdullateef, Halabi, Yasmeen Adly, Baker, Wisam, Almusleh, Tasneem Fathi Hasan, Tahyneh, Abdulraheem Adnan Abdulraheem, Atatri, Yazid yousef mahmoud, Jamie, Najlaa Abu, Massry, Nasrallah Ashraf Al, Lubbad, Walaa, A.Nemer, Ayoub, Alser, Mohammed, Salha, Aya Azmi Shehda, Alnahhal, Khaled, Elmzyyen, Aya Mahmoud, Ghabayen, Amir Talat Sheda, Alamrain, Abdulwhhab Ayman Abu, Al-Shwaikh, Samar H., Elshaer, Omar Adly, Shaheen, Nureddin, Fares, Jehad, Dalloul, Hisham, Qawwash, Anas, Jayyab, Mustafa abu, Ashour, Dina Ayman, Shaheen, Ahmad Ashraf, Naim, Samy Rafat Ramadan, Shiha, Eman Abu, Dammagh, Nagham Mohammed Al, Almadhoun, Walaa, Al-Salhi, Ashraf Ayman, Hammato, Abdalkarim Yhya, Salim, Jamal Mohammed, Hasanain, Doaa Khalil, Alwadia, Soha Marwan Salem, Nassar, Ismail, Al-Attar, Hala M., Alshaikhkhalil, Haya Abdulnasser Ali, Jamie, Yasmin Mohammed Khalil Abu, Ashour, Yara shareef, Alijla, Sharif S., Tallaa, Mohamed Anwer El, Abuattaya, Adham Ashraf, Wishah, Bisan D.M., ALDIRAWI, MOHAMMED A.M., Darwish, Ahmed S, Alzerei, Sulaiman T., Wishah, Nidal, Alijla, Sharif, Garcia, Isidora, Diaz Echegaray, Marlene, Cañapataña Sahuanay, Veronica Raquel, Trigoso Mori, Fernando, Alvarado Zelada, Jackelyne, Salinas Barreto, Juan Jose, Rivera Altamirano, Porfirio, Torres Miranda, Cesar, Anicama Elias, Rocio, Rivera Alvarez, Julio, Vasquez Matos, Juan Pedro, Ayque Rosas, Fernando, Ledesma Peraza, Jesmarina, Gutarra Palomino, Andrea, Vega Centen, Stephany, Casquero, Victor, Ortiz Argomedo, María Rosa, Lapouble, Francisco, Llap Unchón, Genaro, Delgado Malaga, Florangel Patricia, Ortega Sotelo, Luis, Gamboa Kcomt, Segundo, Villalba Villalba, Araceli, Mendoza Leon, Nancy Rossana, Cardenas Alva, Loreley Raquel, Loo Neyra, Maria Susana, Alanguia Chipana, Cathy Lee, Torres Picón, Cintya Maria de Jesus, Huaytalla Quiroz, Natalia, Dominguez, Danny, Segura Calle, Carlos, Arauco, Jenny, Ormeño Calderón, Luis, Ghilardi Silva, Ximena, Fernandez Wilson, Miriam Daniela, Gutierrez Maldonado, Joan Elizabeth, Diaz Leon, Cesar, Berrocal Anaya, Waldo, Chavez Galvez, Patricia, Aguilar Gargurevich, Prince Pamela, Diaz Castañeda, Flor de Maria, Guisse, Carmen, Ramos Paredes, Erika, Apaza Leon, Jose Luis, Aguilar Aguilar, Faye, Ramirez De La Cruz, Raul, Flores Carbajal, Lenny, Mendoza Chiroque, Carlos, Sulca Cruzado, Gladys Johana, Tovar Gutierrez, Natalia, Sotelo Sanchez, Jennifer, Paz Soldan, Carolina, Hernández Córdova, Karina, Delgado Quinteros, Edgar Fernando, Brito Quevedo, Luz Mery, Mendoza Oviedo, Juan Jose, Samanez Obeso, Angel, Paredes Espinoza, Patricia, de Guzman, Johann, Yu, Raisa, Cosoreanu, Vlad, Ionescu, Sebastian, Mironescu, Aurel, Vida, Lucian, Papa, Adrian, Verdeata, Roxana, Gavrila, Bogdan, Muntean, Liviu, Lukac, Marija, Stojanovic, Miona, Toplicic, Djordje, Slavkovic, Milan, Slavkovi, Andjelka, Zivanovic, Dragoljub, Kostic, Ana, Raicevic, Maja, Nkuliza, Delphine, Sidler, Daniel, Vos, Corné de, Merwe, Elmarie vd, Tasker, David, Khamag, Omar, Rengura, Cecilia, Siyotula, Thozama, Jooma, Uzair, Delft, Dirk von, Arnold, Marion, Mangray, Hansraj, Harilal, Shamaman, Madziba, Sanele, Wijekoon, Naveen, Gamage, Tharanga, Bright, Benedict Paul, Abdulrahman, Alaa, Mohammed, Ola Ahmed Abdulmjeed, Salah, Mohammed, Ajwa, Ahmad Elian Abu, Morjan, Mohammed, Batal, Mohammad Mohannad, Faks, Vivian, Mouti, Mohamad Bassel, Assi, Ahmadfateh, Al-Mouakeh, Ahmad, Tarabishi, Ahmad Sankari, Aljarad, Ziad, Alhamid, Aos, Khorana, Jiraporn, Poocharoen, Wannisa, Liukitithara, Sirima, Sriniworn, Anan, Nuntasunti, Wasun, Ngerncham, Monawat, Phannua, Ratiyaporn, Thaiwatcharamas, Kanokrat, Tanming, Patchareeporn, Sahnoun, Lassaad, Kchiche, Nahla, Abdelmoumen, Roua, Eroğlu, Egemen, Ozen, Mehmet Ali, Cömert, Hatice Sonay Yalçın, İmamoğlu, Mustafa, Sarıhan, Haluk, Kader, Şebnem, Mutlu, Mehmet, Aslan, Yakup, Beşir, Ahmet, Geze, Şükran, Çekiç, Bahanur, Yalcinkaya, Ali, Sönmez, Kaan, Karabulut, Ramazan, Türkyılmaz, Zafer, Şeref, Kıvanç, Altın, Merve, Aykut, Merve, Akan, M.Eren, Erdem, Melisa, Ergenekon, Ebru, Türkyılmaz, Canan, Keleş, Elif, Canözer, Ali, Yeniay, Aslı Öztürk, Eren, Elif, Cesur, İlknur Banlı, Özçelik, Zerrin, Kurt, Gökmen, Mert, Mustafa Kurthan, Kaya, Hatice, Çelik, Müge, Karakus, Suleyman Cuneyt, Erturk, Nazile, Suzen, Alev, Hakan, Nilay, Akova, Fatih, Pasaoglu, Mehmet, Eshkabilov, Shukurali, Yuldashev, Rustam Z., Abdunomonovich, Dekhkonboev Avazjon, Muslimovich, Aliev Makhmudjan, Patel, Azad, Kapihya, Chisengo, Ensar, Nicholas, Nataraja, Ramesh M, Sivasubramaniam, Mithila, Jones, Matthew, Teague, Warwick, Tanny, Sharman Tan, Thomas, Gordon, Roberts, Kiera, Venkatraman, Soundappan Sannappa, Till, Holger, Pigeolet, Manon, Dassonville, Martine, Shikha, Anas, Win, Win Sabai Phyu, Ahmad, Zahidah Adlynee Haji, Meloche-Dumas, Léamarie, Caouette-Laberge, Louise, St-Vil, Dickens, Aspirot, Ann, Piché, Nelson, Joharifard, Shahrzad, Safa, Nadia, Laberge, Jean-Martin, Emil, Sherif, Puligandla, Pramod, Shaw, Kenneth, Wissanji, Hussein, Duggan, Eileen, Guadagno, Elena, Puentes, Maria Consuelo, Leal, Paola Osses, Mendez Benavente, Carolina, Rygl, Michal, Trojanová, Barbora, Berková, Klára, Racková, Tereza, Planka, Ladislav, Škvařil, Jan, Štichhauer, Radek, Sabti, Shahad, Macdonald, Alex, Bouhadiba, Nordeen, Kufeji, Dorothy, Pardy, Caroline, Mccluney, Simon, Keshtgar, Alireza, Roberts, Rebecca, Rhodes, Hannah, Burns, Kate, Garrett-Cox, Robin, Ford, Kat, Cornwall, Hannah, Ravi, Krithi, Arthur, Felicity, Losty, Paul, Lander, Tony, Jester, Ingo, Arul, Suren, Gee, Oliver, Soccorso, Giampiero, Singh, Michael, Pachl, Max, Martin, Benjamin, Alzubair, Afnan, Kelay, Arun, Sutcliffe, Jonathan, Middleton, Thomas, Thomas, Amy Hughes, Kurian, Merina, Cameron, Fraser, Sivaraj, Jayaram, Thomas, Mark C, Rex, Dean, Jones, Ceri, Bradshaw, Kate, Bonnard, Arnaud, Delforge, Xavier, Duchesne, Camille, Gall, Caroline Le, Defert, Coralie, Laraqui Hossini, Samia, Guerin, Florent, Hery, Géraldine, Fouquet-Languillat, Virginie, Kohaut, Jules, Broch, Aline, Blanc, Thomas, Harper, Luke, Delefortrie, Thomas, Ballouhey, Quentin, Fourcade, Laurent, Grosos, Céline, Parmentier, Benoit, Levard, Guillaume, Grella, Maria Giovanna, Renaux Petel, Mariette, Grynberg, Lucie, Abbo, Olivier, Mouttalib, Sofia, Juricic, Mélodie, Scalabre, Aurelien, Haraux, Elodie, Rissmann, Anke, Krause, Hardy, Goebel, Peter, Patzer, Ludwig, Rolle, Udo, Schmedding, Andrea, Antunez-Mora, Alexandra, Tillig, Bernd, Bismarck, Sylvester von, Barbosa, Patricia Reis, Knorr, Christian, Stark, Domitille, Brunero, Marco, Avolio, Luigi, Manni, Francesco, Molinelli, Matilde, Guazzotti, Marinella, Raffaele, Alessandro, Romano, Piero Giovanni, Cavaiuolo, Silvia, Parigi, Gian Battista, Juhasz, Laszlo, Rieth, Anna, Strumila, Arunas, Dagilytė, Rūta, Liubsys, Arunas, Gurskas, Pranas, Malcius, Dalius, Mikneviciute, Agne, Vinskaite, Asta, Barauskas, Vidmantas, Vierboom, Liam, Hall, Timothy, Beasley, Spencer, Goddard, Lucy, Stringer, Mark, Weeratunga, Naveen, Adams, Stephen, Cama, Jitoko, Wong, Marilyn, Jayaratnam, Sridharan, Kukkady, Askar, Samarakkody, Udaya, Gerus, Sylwester, Patkowski, Dariusz, Wolny, Agnieszka, Koszutski, Tomasz, Tobor, Szymon, Osowicka, Marta, Czauderna, Piotr, Wyrzykowski, Dariusz, Garnier, Hanna, Anzelewicz, Stefan, Marta, Osowicka, Knurowska, Agata, Weiszewsk, Alicja, Grabowski, Andrzej, Korlacki, Wojciech, Pasierbek, Michal, Wolak, Przemyslaw, Piotrowska, Aneta, Roszkiewicz, Anna, Kalicińsk, Piotr, Trypens, Agata, Kowalewsk, Grzegorz, Sigalet, David, Alsaied, Amer, Ali, Mansour, Alsaggaf, Ameen, Ghallab, Alaa, Owiwi, Yazeed, Zeinelabdeen, Ali, Fayez, Mohamed, Atta, Ahmed, Zidan, Mazen, Radwan, Asaad saleh, Shalaby, Hanin, Abdelbaqi, Reem, Alattas, Khalid, Kano, Yar, Sindi, Omar, Alshehri, Abdullah, Altokhais, Tariq, Alturki, Fahad, Almosaibli, Mohammad, Krisanova, Dasha, Abbas, Wisam, Yang, Hee-Beom, Kim, Hyun-Young, Youn, Joong Kee, Chung, Jae Hee, Cho, Seok Hyeon, Hwang, In ji, Lee, Ju yeon, Song, Eung song, Arboleda, Jenny, Ruiz de Temiño Bravo, Mercedes, Siles Hinojosa, Alexander, García, Miriam, Casal Beloy, Isabel, Oliu San Miguel, Detlef, Molina Vazquez, Maria Elena, Alonso, Verónica, Sanchez, Alberto, Gomez, Oscar, Carrillo, Isabel, Wester, Tomas, Mesas Burgos, Carmen, Salö, Martin, Omling, Erik, Rudolfson, Niclas, Granéli, Christina, Arnadóttir, Helena, Grottling, Emma, Abrahamsson, Kate, Gatzinsky, Vladimir, Dellenmark Blom, Michaela, Borbonet, Daniel, Puglia, Paul, Jimenez Morejon, Vinicio, Acuna, Gaston, Moraes, Mario, Chan, Jonathan, Brahmamdam, Pavan, Tom, Alan, Sherer, Karen, Gonzales, Brandy, Cunningham, Aaron, Krishnaswami, Sanjay, Baertschiger, Reto, Leech, Mary, Williams, Regan, Camp, Lauren, Gosain, Ankush, Mora, Maria, Lyttle, Bailey D., Chang, Jeremy, McColl Makepeace, Lydia, Fowler, Kathryn L, Mansfield, Sara, Hodgman, Erica, Amaechi, Chukwubinyelum, Beres, Alana, Pernik, Mark N., Dosselman, Luke J., Almasri, Murad, Jain, Sunil, Modi, Varun, Fernandez Ferrer, Marianelly, Coon, John, Gonzalez, Joann, Honhar, Medhavi, Ruzgar, Nensi, Coghill, Griffin, Ullrich, Sarah, Cheung, Maija, Løfberg, Katrine, Greenberg, Jodie, Davenport, Kate, Gadepalli, Samir, Fox, Sarah, Johnson, Stephanie, Pilkington, Mercedes, Hamilton, April, Lin, Nicole, Sola, Juan, Yao, Yang, Davis, Jenna Kylene, Langer, Monica, Vacek, Jonathan, Abdullah, Fizan, Khlevner, Julie, Middlesworth, William, Levitt, Marc, Ahmad, Hira, Siddiqui, Sabina M, Bowder, Alex, Derks, Terry, Amoabin, Afua Amoabin, Pinar, Brooke, Owusu-Sekyere, Frank, Saousen, Benmanseur, Naidoo, Rasika, Karamustafic, Azra, Oliveira, Danielle Paula de, Motter, Sarah Bueno, Andrade, Jerhy, Šafus, Antonín, Langley, Jason, Wilke, Alexandra, Deya, Corazone, Murtadi, Habib Mansour, Berzanskis, Mindaugas, Calistus, Nwachukwu, Ajiboye, Olalekan S., Felix, Michael, Olabisi, Osagie O, Erçin, Seçil, Muradi, Teymursha, Burks, Stephen S., Lerma, Sergio, Jacobson, Jillian, Calancea, Calin, Valerio-Vazquez, Rafael, Sikwete, Guigui, Sekyere, Owusu, Mbonisweni, Akhona, Syed, Shahnoor, Hyeon, Cho Seok, Pajouhandeh, Fatemeh, and Kunfah, Sheba Mary Pognaa

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2021
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