Melding Fog Computing and IoT for Deploying Secure, Response-Capable Healthcare Services in 5G and Beyond.

The fifth generation (5G) of mobile networks is designed to mark the beginning of the hyper-connected society through a broad set of novel features and disruptive characteristics, delivering massive connectivity, coverage and availability paired with unprecedented speed, throughput and capacity. Such a highly capable networking paradigm, facilitated by its integrated segments and available subsystems, will propel numerous cutting-edge, innovative and versatile services, spanning every possible business vertical. Augmented, response-capable healthcare services have already been identified as one of the prime objectives of both vendors and customers; therefore, addressing controversies and shortcomings related to the specific field is considered a priority for all stakeholders. The scope of this paper is to present the architectural elements of 5G which enable efficient, remote healthcare services along with emergency health monitoring and response capability. In addition, we propose a holistic scheme based on technical enablers such as Internet-of-Things (IoT) and Fog Computing, for mitigating common issues and current limitations which may compromise the proclaimed service delivery.

Towards building a Self-Sovereign Identity Framework for Healthcare.

Drifted by the hype of new and efficient machine learning and artificial intelligence models aiming to unlock the information wealth hidden inside heterogeneous datasets across different markets and disciplines, healthcare data are in the center of novel technological advancements in predictive health diagnostics, remote healthcare, assistive leaving and wellbeing. Nevertheless, this emerging market has underlined the necessity of developing new methods and updating existing ones for preserving the privacy of the data and their owners, as well as, ensuring confidentiality and trust throughout the health care data processing pipelines. This paper presents one of the key innovations of a Horizon Europe funded project named "TRUSTEE", which focuses on building a trust and privacy framework for cross-European data exchange by employing a secure and private federated framework to empower companies, organizations, and individuals to securely access data across different disciplines, use and re-use data and metadata to extract knowledge with trust. In particular we present our work on implementing strong authentication and continuous authorization schemes based on the duality of eIDAS trust framework and Self Sovereign Identity (SSI) management to ensure security and trust over authentication, authorization and accounting processes for healthcare.

Robofurnace: a semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery.

Laboratory research and development on new materials, such as nanostructured thin films, often utilizes manual equipment such as tube furnaces due to its relatively low cost and ease of setup. However, these systems can be prone to inconsistent outcomes due to variations in standard operating procedures and limitations in performance such as heating and cooling rates restrict the parameter space that can be explored. Perhaps more importantly, maximization of research throughput and the successful and efficient translation of materials processing knowledge to production-scale systems, relies on the attainment of consistent outcomes. In response to this need, we present a semi-automated lab-scale chemical vapor deposition (CVD) furnace system, called "Robofurnace." Robofurnace is an automated CVD system built around a standard tube furnace, which automates sample insertion and removal and uses motion of the furnace to achieve rapid heating and cooling. The system has a 10-sample magazine and motorized transfer arm, which isolates the samples from the lab atmosphere and enables highly repeatable placement of the sample within the tube. The system is designed to enable continuous operation of the CVD reactor, with asynchronous loading∕unloading of samples. To demonstrate its performance, Robofurnace is used to develop a rapid CVD recipe for carbon nanotube (CNT) forest growth, achieving a 10-fold improvement in CNT forest mass density compared to a benchmark recipe using a manual tube furnace. In the long run, multiple systems like Robofurnace may be linked to share data among laboratories by methods such as Twitter. Our hope is Robofurnace and like automation will enable machine learning to optimize and discover relationships in complex material synthesis processes.