"Recently the road networks are more and more exposed to an increased landslide risk. It is generally caused by heavy weather conditions and a severe deficit of maintenance. Usually, it is easy to locate the damage as it becomes visible whereas it is difficult to identify the causes. In order to diagnose the damage before it becomes visible and severe, to identify the main cause and to implement the most effective rehabilitation measure, we propose the use of Ground Penetrating Radar for geotechnical inspection of pavement and sub-pavement layers. Different equipments were used and a survey protocol was tested in several situations characterized by landslide movements with different degrees of severity. For the inspection, 600, 1000 and 1600 MHz antennae were used. The 1 GHz horn antenna was used for a first survey at traffic speed in order to reduce traffic interference and to locate any possible anomaly. The other dipole antennae were used for more detailed investigations. GPR data were post-processed in the time and frequency domain, using the “Surface reflection method” and the Rayleigh scattering. This approach provides an innovative method that can be applied for accurate and non-invasive monitoring of alert conditions near to road pavements.

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Among the technologies used to improve landmine\ud detection, Ground Penetrating Radar (GPR) techniques are\ud being developed and tested jointly by “Sapienza” and “Roma\ud Tre” Universities. Using three-dimensional Finite Difference\ud Time Domain (FDTD) simulations, the electromagnetic field\ud scattered by five different buried objects has been calculated and\ud the solutions have been compared to the measurements obtained\ud by a GPR system on a (1.3×3.5×0.5) m3 sandbox, located in the\ud Humanitarian Demining Laboratory at Cisterna di Latina, to\ud assess the reliability of the simulations. A combination of precalculated\ud FDTD solutions and GPR scans, may make the\ud detection process more accurate

Background Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates. Methods The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM. Expected results This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda. Discussion The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.