CO2-EVAR: An innovative approach to Automated Carbon Dioxide Angiography during Endovascular Abdominal Aortic Aneurysm repair

Objectives CO2-EVAR was proposed for treatment of AAA especially in patients with CKD. Issues regarding standardization, such as visualization of lowest renal artery (LoRA) and quality image in angiographies performed from pigtail or introducer-sheath, are still unsolved. Aim of the study was to analyze different steps of CO2-EVAR to create an operative protocol to standardize the procedure. Methods Patients undergoing CO2-EVAR were prospectively enrolled in 5 European centers (2018-2021). CO2-EVAR was performed using an automated injector. LoRA visualization and image quality (1-4) were analyzed and compared at different procedure steps: preoperative CO2-angiography from Pigtail/Introducer-sheath (1st Step), angiographies from Pigtail at 0%,50%,100% main body (MB) deployment (2nd Step), contralateral hypogastric artery (CHA) visualization with CO2 injection from femoral Introducer-sheath (3rd Step) and completion angiogram from Pigtail/Introducer-sheath (4th Step). Intra-/postoperative adverse events were evaluated. Results Sixty-five patients undergoing CO2-EVAR were enrolled, 55/65(84.5%) male, median age 75(11.5) years. Median ICM was 20(54)cc; 19/65(29.2%) procedures were performed with 0-iodine. 1st Step: median image quality was significantly higher with CO2 injected from femoral introducer [Pigtail2(3)vs.3(3)Introducer,p=.008]. 2nd Step: LoRA was more frequently detected at 50% (93%vs.73.2%, p=.002) and 100% (94.1%vs.78.4%, p=.01) of MB deployment compared with first angiography from Pigtail; image quality was significantly higher at 50% [3(3)vs.2(3),p=<.001] and 100% [4(3) vs.2(3),p=.001] of MB deployment. CHA was detected in 93% cases (3rd Step). Mean image quality was significantly higher when final angiogram (4th Step) was performed from introducer (Pigtail2.6±1.1vs.3.1±0.9Introducer,p=<.001). Rates of intra-/postoperative adverse events (pain,vomit,diarrhea) were 7.7% and 12.5%. Conclusions Preimplant CO2-angiography should be performed from Introducer-sheath. MB steric bulk during its deployment should be used to improve image quality and LoRA visualization with CO2. CHA can be satisfactorily visualized with CO2. Completion CO2-angiogram should be performed from femoral Introducer-sheath. This operative protocol allows to perform CO2-EVAR with minimal ICM and low rate of mild complications.

Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.

Untangle sustainable development goal 8 through data visualization and HCI methods

Following the approval of the 2030 Agenda for Sustainable Development in 2015, sustainability became a hotly debated topic. In order to build a better and more sustainable future by 2030, this agenda addressed several global issues, including inequality, climate change, peace, and justice, in the form of 17 Sustainable Development Goals (SDGs), that should be understood and pursued by nations, corporations, institutions, and individuals. In this thesis, we researched how to exploit and integrate Human-Computer Interaction (HCI) and Data Visualization to promote knowledge and awareness about SDG 8, which wants to encourage lasting, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. In particular, we focused on three targets: green economy, sustainable tourism, employment, decent work for all, and social protection. The primary goal of this research is to determine whether HCI approaches may be used to create and validate interactive data visualization that can serve as helpful decision-making aids for specific groups and raise their knowledge of public-interest issues. To accomplish this goal, we analyzed four case studies. In the first two, we wanted to promote knowledge and awareness about green economy issues: we investigated the Human-Building Interaction inside a Smart Campus and the dematerialization process inside a University. In the third, we focused on smart tourism, investigating the relationship between locals and tourists to create meaningful connections and promote more sustainable tourism. In the fourth, we explored the industry context to highlight sustainability policies inside well-known companies. This research focuses on the hypothesis that interactive data visualization tools can make communities aware of sustainability aspects related to SDG8 and its targets. The research questions addressed are two: "how to promote awareness about SDG8 and its targets through interactive data visualizations?" and "to what extent are these interactive data visualizations effective?".

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.