A versatile Cloud Computing environment to facilitate African-European partnership in research: EO AFRICA R&D Innovation Lab

The African Framework for Research, Innovation, Communities and Applications (EO AFRICA) is an ESA initiative in collaboration with the African Union Commission that aims to foster an African-European R&D partnership facilitating the sustainable adoption of Earth Observation and related space technologies in Africa. EO AFRICA R&D Facility is the flagship of EO AFRICA with the overarching goals of enabling an active research community and promoting creative and collaborative innovation processes by providing funding, advanced training, and computing resources. The Innovation Lab is a state-of-the-art Cloud Computing infrastructure provided by the Facility to 30 research projects of African-European research tandems and participants of the capacity development activities of the Space Academy. The Innovation Lab creates new opportunities for innovative research to develop EO algorithms and applications adapted to African challenges and needs, through interactive Virtual Research Environments (VREs) with ready-to-use research and EO analysis software, and facilitated access to a wide range of analysis-ready EO datasets by leveraging the host DIAS infrastructure. The Innovation Lab is a cloud-based, user-friendly, and versatile Platform as a service (PaaS) that allows the users to develop, test, run, and optimize their research code making full use of the Copernicus DIAS infrastructure and a tailor-made interactive computing environment for geospatial analysis. Co-located data and computing services enable fast data exploitation and analysis, which in turn facilitates the utilization of multi-spectral spatiotemporal big data and machine learning methods. Each user has direct access to all online EO data available on the host DIAS (CreoDIAS), especially for Africa, and if required, can also request archived data, which is automatically retrieved and made available within a short delay. The Innovation Lab also supports user-provided in-situ data and allows access to EO data on the Cloud (e.g., other DIASes, CNES PEPS, Copernicus Hub, etc.) through a unified and easy-to-use and open-source data access API (EODAG). Because all data access and analysis are performed on the server-side, the platform does not require a fast Internet connection, and it is adapted for low bandwidth access to enable active collaboration of African – European research tandems. As a minimum configuration, each user has access to computing units with four virtual CPUs, 32 GB RAM, 100 GB local SSD storage, and 1 TB network storage. To a limited extent and for specific needs (e.g., AI applications like Deep Learning), GPU-enabled computing units are also provided. The user interface of the Innovation Lab allows the use of interactive Jupyter notebooks through the JupyterLab environment, which is served by a JupyterHub deployment with improved security and scalability features. For advanced research code development purposes, the Innovation Lab features a web-based VS Code integrated development environment, which provides specialized tools for programming in different languages, such as Python and R. Code analytics tools are also available for benchmarking, code profiling, and memory/performance monitoring. For specific EO workflows that require exploiting desktop applications (e.g., ESA SNAP, QGIS) for pre-processing, analysis, or visualization purposes, the Innovation Lab provides a web-based remote desktop with ready-to-use EO desktop applications. The users can also customize their working environment by using standard package managers. As endorsed by the European Commission Open Science approach, data and code sharing and versioning are crucial to allow reuse and reproduction of the algorithms, workflows, and results. In this context, the Innovation Lab has tools integrated into its interactive development environment that provide direct access to code repositories and allow easy version control. Although public code repositories (e.g., Github) are advised for better visibility, the Innovation Lab also includes a dedicated code repository to support the users' particular needs (e.g., storage of sensitive information). The assets (e.g., files, folders) stored on the platform can be easily accessed and shared externally through the FileBrowser tool. Besides providing a state-of-the-art computing infrastructure, the Innovation Lab also includes other necessary services to ensure a comfortable virtual research experience. All research projects granted by the EO AFRICA R&D Facility receive dedicated technical support for the Innovation Lab facilities. Scientific support and advice from senior researchers and experts for developing geospatial computing workflows are also provided. Users are able to request support contacting a helpdesk via a dedicated ticketing and chat system. After a 6-month development and testing period, the Innovation Lab became operational in September 2021. The first field testing of the platform took place in November 2021 during a 3-day hackathon jointly organized by EO AFRICA R&D, GMES & Africa, and CURAT as part of the AfricaGIS 2021 conference. Forty participants utilized the platform to develop innovative solutions to food security and water resources challenges, such as the impact of the COVID-19 pandemic on agricultural production or linking the decrease in agricultural production to armed conflicts. The activity was successful and similar ones are expected to be organized during major GIS and EO conferences in Africa during the lifetime of the project. Thirty research projects of African-European research tandems granted by the Facility will utilize the Innovation Lab to develop innovative and open-source EO algorithms and applications, preferably as interactive notebooks, adapted to African solutions to African challenges in food security and water scarcity by leveraging cutting-edge cloud-based data access and computing infrastructure. The call for the first 15 research projects was published in November 2021, and the projects are expected to start using the Innovation Lab in February 2022. In parallel, the Innovation Lab provides the computing environment for the capacity development activities of the EO AFRICA R&D Facility, which are organized under the umbrella of EO AFRICA Space Academy. These capacity development activities include several MOOCs, webinars, online and face-to-face courses designed and tailored to improve the knowledge and skills of African researchers in the utilization of Cloud Computing technology to work with EO data. Selected participants of the capacity development activities will use the Innovation Lab during their training. Moreover, the instructors in the Facility use the Innovation Lab to develop the training materials for the Space Academy. Access to the Innovation Lab will also be granted to individual researchers and EO experts depending on the use case and resource availability. Application for access can be made easily through the EO AFRICA R&D web portal after becoming a member of the EO AFRICA Community.This study is funded by ESA Contract No. 4000133905/21/I-EF

Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

Twitter, as the most popular social media platform, has made a great revolution in producing real-time user-generated data. This research aims to propose a method to extract the latent spatial pattern from geotagged tweets. We take both spatial autocorrelation and spatial heterogeneity into account while revealing the underlying pattern from geotagged tweets. Moreover, the textual similarity is considered to extract spatial-textual clusters. The method was implemented and tested during hurricane Dorian on the east coast of the U.S. The results proved the superiority of the proposed method against Moran’s Index and VDBSCAN algorithms in extracting clusters with various densities

SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications

The Study of Etiologic Causes of Dermatophyte in the Location of Foot And Groin, and the Possibility of Association of Dermatophytoses of These Two Locations Together

Superficial mycosis of the skin is one of the most prevalent human infections. Within these infections, tinea pedis and tinea cruris have been studied. Different aetiologic causes play role in these infections which the most important of them are Trichophyton rubrum, Trichophyton Mentagrophyte and Epidermophyton floccosum. Prevalence arrangement of these causes are defferent in societies. This study is a case series study which in the course of this period 42 affected patients 0 tinea pedis and 40 affected patients to tinea cruris have been studied. From patients with doubtfull clinical lesion, whom have reffered to Razi Hospital within the first six months of the year 77, smear and culture were provided and in the meanwhile for consideration of possible association of Dermatophytoses in these two location in cases of clinical doubt to tinea pedis among the affected patients to tinea cruris, smear and culture wase made and it wase observed that 40 of affected patients to tinea cruris, 4 patients simultaneously have tinea pedis (10%). In this study also, risk factors of tinea pedis and tinea cruris have been studied. Etiologic causes in tinea pedis in this study with respect to arrangment are: T.Ment, T.rubrum and then Epid.floccosum and the causes of thinea cruris with respect to arrangment are: Epid.floccosum, T.rubrum and then T.Ment. In this study foot and groin Etiologic factors have been considered, it was observed that the pattern of their etiologic causes in Iran with respect to other countries are different

Three familial cases of Pasini variant of dominant dystrophic epidermolysis

Epidermolysis bullosa (EB) is the term applied to a group of disorders whose common primary feature is the formation of blisters following trivial trauma. Hereditary EB comprises 3 major classes: simplex, junctional and dystrophic, and includes more than 23 phenotypes. The albopapuloid pasini variant of dominant dystrophic EB is characterized by a distinctive clinical appearance. In this article, we report this disease in three members of a family (father and two sons)

HADOOP-BASED DISTRIBUTED SYSTEM FOR ONLINE PREDICTION OF AIR POLLUTION BASED ON SUPPORT VECTOR MACHINE

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems

LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction and monitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vector machine (SVM) or artificial neural networks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal system is designed using a LaSVM-based online algorithm. Pollutant concentration and meteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of the Air Quality Index (AQI) with those of a traditional SVM algorithm. Results show an outstanding increase of speed by the online algorithm while preserving the accuracy of the SVM classifier. Comparison of the hourly predictions for next coming 24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for real-time spatial and temporal prediction of the urban air quality

Spatiotemporally explicit earthquake prediction using deep neural network

Due to the complexity of predicting future earthquakes, machine learning algorithms have been used by several researchers to increase the Accuracy of the forecast. However, the concentration of previous studies has chiefly been on the temporal rather than spatial parameters. Additionally, the less correlated variables were typically eliminated in the feature analysis and did not enter the model. This study introduces and investigates the effect of spatial parameters on four ML algorithms' performance for predicting the magnitude of future earthquakes in Iran as one of the most earthquake-prone countries in the world. We compared the performances of conventional methods of Support Vector Machine (SVM), Decision Tree (DT), and a Shallow Neural Network (SNN) with the contemporary Deep Neural Network (DNN) method for predicting the magnitude of the biggest upcoming earthquake in the next week. Information Gain analysis, Accuracy, Sensitivity, Positive Predictive Value, Negative Predictive Value, and Specificity measures were exploited to investigate the outcome of using a new parameter, called Fault Density, calculated using Kernel Density Estimation and Bivariate Moran's I, on the performance of the earthquake prediction, in comparison to other commonly used parameters. We discussed the behavior of the four models while dealing with different combinations of parameters and different classes of earthquake magnitudes. The results showed promising performance of the proposed parameter for the earthquakes of high magnitudes, especially using SVM and DNN models