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The port of Tanger Med stands as the first port in Africa and the Mediterranean, with a throughput capacity of up to 9 million TEUs annually, accommodating the globe's largest vessels, such as ULCVs and MGX-24 classes, which boast capacities nearing 24,000 TEUs. To advance the safety and efficiency of handling deep-drafted vessels, the Tanger Med Port Authority, in cooperation with other terminals partners, try to deploy a sophisticated IoT-based system. This system integrates a high-precision non-linear 3D sea-keeping engine with detailed metocean forecasts, encompassing data on waves, winds, and other hydrodynamic factors. This IoT solution has been instrumental as a decision-support tool, facilitating the Harbormaster and Pilot teams in safely managing vessel transits. It has enabled an increase in the maximum allowable draft to 17.4 meters without requiring dredging. This paper delves into a model validation study assessing the effectiveness of the IoT solution in enhancing dynamic under-keel clearance management at Port Tanger Med, underlining its crucial role in improving operational efficiencies and ensuring navigational safety. [ABSTRACT FROM AUTHOR]

A medical entity (hospital, nursing home, rest home, revalidation center, etc.) usually includes a multitude of information systems that allow for quick decision-making close to the medical sensors. The Internet of Medical Things (IoMT) is an area of IoT that generates a lot of data of different natures (radio, CT scan, medical reports, medical sensor data). However, these systems need to share and exchange medical information in a seamless, timely, and efficient manner with systems that are either within the same entity or other healthcare entities. The lack of inter- and intra-entity interoperability causes major problems in the analysis of patient records and leads to additional financial costs (e.g., redone examinations). To develop a medical data interoperability architecture model that will allow providers and different actors in the medical community to exchange patient summary information with other caregivers and partners to improve the quality of care, the level of data security, and the efficiency of care should take stock of the state of knowledge. This paper discusses the challenges faced by medical entities in sharing and exchanging medical information seamlessly and efficiently. It highlights the need for inter- and intra-entity interoperability to improve the analysis of patient records, reduce financial costs, and enhance the quality of care. The paper reviews existing solutions proposed by various researchers and identifies their limitations. The analysis of the literature has shown that the HL7 FHIR standard is particularly well adapted for exchanging and storing health data, while DICOM, CDA, and JSON can be converted in HL7 FHIR or HL7 FHIR to these formats for interoperability purposes. This approach covers almost all use cases. [ABSTRACT FROM AUTHOR]

The huge amount of data produced by the Internet of Things need to be validated and curated to be prepared for the selection of relevant data in order to prototype models, train them, and serve the model. On the other side, blockchains and open data are also important data sources that need to be integrated into the proposed integrative models. It is difficult to find a sufficiently versatile and agnostic architecture based on the main machine learning frameworks that facilitate model development and allow continuous training to continuously improve them from the data streams. The paper describes the conceptualization, implementation, and testing of a new architecture that proposes a use case agnostic processing chain. The proposed architecture is mainly built around the Apache Submarine, an unified Machine Learning platform that facilitates the training and deployment of algorithms. Here, Internet of Things data are collected and formatted at the edge level. They are then processed and validated at the fog level. On the other hand, open data and blockchain data via Blockchain Access Layer are directly processed at the cloud level. Finally, the data are preprocessed to feed scalable machine learning algorithms. [ABSTRACT FROM AUTHOR]