The Circular Economy (CE) aims to reduce value loss and avoid waste by extending the life of products, compo-nents, and materials, applying various CE strategies—R-strategies, e.g., reuse, recycling, etc. Our study targetsthe construction industry, and specifically flat glass, which is infinitely recyclable, but today virtually all glassends up as landfill. One key enabler of CE is digitalisation, allowing data to flow along with the products andmaterials themselves. However, scaling the CE digitalisation requires semantic interoperability, decentralizeddata sharing, collaboration, and automation of parts of the value chain interactions. In this in-use paper wedemonstrate a solution for supporting information flows of flat glass, based on the application of semantictechnologies. In particular, we focus on ontologies to describe the value chain data about flat glass, and designontologies to be used for building inventory, R-strategy selection, and potentially eventually automating parts ofthe value network setup and execution. The ontologies have so far been applied to generate a building inventoryuser interface for inventory applications, to annotate building inventory data, and to semi-automatically makeR-strategy decisions in a collaboration between a flat glass manufacturer and a recycling company, as well as totrain building inventory experts in national training events in Sweden.

The transition from a linear economic model to a circular model is crucial, due to the limited resources of our planet. The Circular Economy (CE) aims to maintain material continuity through the cycle of production, consumption and recycling. The Digital Product Passport (DPP) is recognised as a critical instrument for advancing CE, serving as a comprehensive digital repository for product lifecycle information. The DPP paradigm fosters transparency and traceability. However, so far there is no agreed-upon standard for technically representing and expressing DPPs. This paper aims to provide a comprehensive analysis of the requirements of a general (cross-sectoral) DPP, and to discuss a core DPP model. We propose to express this in the form of an ontology network, i.e., a formal model serving as a "translation layer" from raw data to interpreted information, along with SHACL shapes for ensuring increased data quality. Despite existing research on DPPs, a comprehensive tool for technically representing DPPs is missing, making this paper a pioneering exploration into the modelling of a DPP core ontology.

Background: Advancements in cancer treatments for children, adolescents, and young adults (CAYAs) over recent decades have significantly improved their prognosis. However, these advancements have also led to an increased risk of developing various cardiovascular diseases, such as arrhythmias. Atrial fibrillation (AF) the most common arrhythmia in the general population, becomes more prevalent with age (1). The risk of developing AF is elevated in patients with cancer compared to the general population (2). Nevertheless, symptomatic arrhythmias remain uncommon among long-term survivors of childhood cancer (3).

Purpose: The main objective of this study was to explore the rates and risk of arrhythmias in CAYAs with cancer compared to controls.

Method: The Rebuc study is a retrospective matched cohort study that encompasses all young cancer patients (<25 years) in Sweden from 1958 and 2021, with a total of 65,173 individuals matched with 312,935 controls. All individuals with an arrhythmia diagnosis were identified and compared for the following arrhythmias: AF and atrial flutter (ICD-10: I48), supraventricular tachycardia (I47.1), ventricular tachycardia (I47.2), ventricular fibrillation (I49.0), sick sinus syndrome (I49.5), second- and third-degree atrioventricular block (AV-block) (I44.1 and I44.2).

Results: A total of 12,415 individuals with arrhythmias were identified, 2,352 CAYAs (3.6%) and 10,063 controls (3.2%) (Odds Ratio (OR) 1.13 95% CI 1.08-1.18, p<0.0001).

CAYAs had a higher risk of ventricular fibrillation, (OR 1.66 95% CI 1.22-2.27, p= 0.001) and the risk of third-degree AV-block was also 66% higher (OR, 95% CI 1.32-2.07, p<0.0001) compared to controls. CAYAs had no elevated risk for AF but were 1 year younger when diagnosed (60 vs. 61 years, p= 0.03). No significant difference was found in risk for supraventricular and ventricular tachycardias, second-degree AV-block or sick sinus syndrome.

Conclusion: CAYAs have a higher risk for life-threatening arrhythmias such as ventricular fibrillation and third-degree AV-block.

The risk of AF and other supraventricular arrhythmias was similar, but CAYAs experienced AF at a younger age than controls.

Background: The advances in the management of cancer in children, adolescents, and young adults (CAYAs) over recent decades have greatly improved prognosis. However, the risk of long-term complications persists. (1,2) It is known that cancer treatment such as chemotherapy and radiotherapy as well as conventional cardiovascular risk factors increase the risk of ischemic heart disease (IHD). (3)

Purpose: The aim was to study IHD among CAYAs and investigate mortality and revascularization compared to controls.

Method: This study utilizes the Rebuc study, a retrospective matched cohort study that includes all young (<25 years) cancer patients in Sweden between 1958 and 2021, comprising a total of 65,173 CAYAs and 312,935 controls. From the Rebuc study population, 1,486 CAYAs and 6,258 controls with IHD (ICD-10: I20-I25) were identified and compared regarding all-cause mortality, cardiovascular mortality, and revascularization (percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG)).

Results: The median age at index was 22 years (IQR 19–23), and at the end of the study 66 years (IQR 59–72). 801 CAYAs (54%) and 3,265 controls (52%) had myocardial infarction (ICD-10: I21)

The risk for all-cause mortality after diagnosis of IHD was 1.26-fold higher for CAYAs (Hazard ratio (HR) 95% CI 1.14-1.40, p<0.0001) and 1.20-fold higher for cardiovascular mortality (HR 95% CI 1.06-1.37, p<0.001) compared to controls. Revascularisation of any kind was registered among 47% of CAYAs and 48% of controls (p = 0.8). PCI was registered among 39% of CAYAs and 42% of controls (p = 0.045), while CABG was registered among 14% of CAYAs and 11% of controls (p = 0.001).

Conclusion: CAYAs with IHD had a higher risk of all-cause and cardiovascular mortality compared to controls. There was no difference regarding revascularization between the groups, however, CABG was more common among CAYAs while PCI was more common among controls.

Background: In recent decades, numerous cohorts have been established to investigate cardiovascular disease (CVD) and mortality among young cancer patients.(1,2) However, these cohorts rarely include young females with cervical cancer, a disease often treated surgically and associated with human papillomavirus (HPV) infection. HPV infections, which can be prevented through vaccination, has also been associated to an elevated risk of CVD. (3)

Purpose: The aim was to study CVD and mortality in young cervical cancer patients.

Method: Using the Rebuc study, which includes all young (<25 years) cancer patients in Sweden from 1958 to 2021. 30,507 individuals aged 15-24 years with cervical cancer (ICD-10 C53) were identified and compared to 152,276 matched controls with regard to CVD (ICD-10 I00–I99 and G45), all-cause mortality, and CV mortality.

Results: The median age at cervical cancer diagnosis was 23 years (IQR 22-24) and the median age at the study’s end was 41 years in both groups (IQR 29-59 vs. 29-60).

CVDs were identified in 5,460 cervical cancer patients (17.9%) and 24,113 controls (15.8%), (Odds ratio (OR) 1.16 95% CI 1.12-1.20 p<0.0001). The risk for ischemic heart disease was 42% higher in cervical cancer patients (OR 95%CI 1.31-1.54, p<0.0001), 54% higher for myocardial infarction (OR 95%CI 1.38-1.72 p<0.0001), 35% higher for heart failure (OR 95% CI1.38-1.72 p<0.0001), and 38% higher risk for cerebrovascular diseases (OR 95%CI 1.26-1.50 p<0.0001) compared to controls.

The risk for all-cause mortality was 64% higher, and the risk for cardiovascular death was 49% higher among females with cervical cancer compared to controls. (Hazard Ratio 95%CI 1.52-1.76; and 1.29-1.70 p <0.0001). Myocardial infarction, sudden cardiac death, and heart failure was the most common cardiovascular causes of death in these young females.

Conclusion: Young females with cervical cancer are at a higher risk of severe CVD and mortality compared to controls. These findings highlight the importance of cardiovascular risk evaluation in cancer patients and contribute to the growing evidence supporting a broad implementation of HPV vaccination programs to reduce the burden of HPV-related diseases.

As we live in a world of limited resources, the transition from a linear economic model to a circular model is crucial. The Circular Economy (CE) paradigm aims to maintain material continuity through the cycle of production, consumption and recycling. The Digital Product Passport (DPP) is currently recognised as a critical instrument for advancing CE, serving as a comprehensive digital repository for product lifecycle information. The DPP paradigm fosters transparency and traceability. However, so far there is no agreed-upon standard for technically representing and expressing DPPs. This paper aims to provide a comprehensive analysis of the requirements of a general (cross-sectoral) DPP, and to discuss the representation of a core DPP model. We propose to express this in the form of an ontology network, i.e., a formal model serving as a “translation layer” from raw data to interpreted information, along with SHACL shapes for increased data quality and validation. Despite existing research on DPPs, a comprehensive tool enabling this transition into using DPPs is yet to be developed, making this paper a pioneering exploration into the modelling of a DPP core ontology.

Circular Economy (CE) aims to reduce value loss and avoid waste by extending the life of products,components, and materials. Circular value networks (CVN), i.e. networks of actors realising partsof the CE, are often complex and involving a multitude of actors, such as suppliers, manufacturers,recyclers, and end-users, from different industry sectors. In addition, the networks enable and managevarious flows of resources, energy, information and value. To set up and operate such networks, datasharing is essential, however, one of the main challenges is semantic interoperability, and as a resultdata are difficult to understand, integrate, and use. Ontologies support semantic interoperability, andcan represent domain knowledge and enable stakeholders to communicate. However, the knowledgedomains involved are many, including sustainability, materials, products, manufacturing, and logistics,where well-established ontologies already exist. In addition, these domains need to be connected torelevant industry sectors. In order to bridge these domains we propose a set of core ontology modules,allowing to express links between existing ontologies as well as filling gaps related to core CE concepts.

The Semantic Web provides a framework for representing, sharing, and integrating data on the Web using a set of specifications promoted by the World Wide Web Consortium (W3C). These specifications include RDF as the model for data inter-change on the Web and languages (e.g., RDFS and OWL) for defining schemas and ontologies. While the Semantic Web has traditionally focused on static or slowly changing data, information on the Web is becoming increasingly dynamic, with sources such as Internet-of-Things devices, sensor networks, smart cities, social me-dia, and more. RDF Stream Processing (RSP) extends Semantic Web technologies to support streaming data and continuous queries and has been suggested as a candidate for bridging the gap between Complex Event Processing (CEP), which focuses on identifying meaningful events and event patterns from streaming data, and the Semantic Web standards. Systems that operate on real-world data must often deal with uncertainty, which can arise from, for example, missing information, incomplete domain knowledge, sensor noise, or linguistic vagueness. Uncertainty has received attention in both Semantic Web and CEP research, but little is known about how it can be managed in RSP and how it might impact performance. The contributions of this thesis are threefold. First, the issue of supporting a general model of CEP in RSP is addressed. A set of requirements for CEP is identified and used to define an event ontology for use in RSP. An approach is then proposed for creating a CEP framework that can scale processing beyond the limitations of a single RSP instance. Second, an extension of the RSP-QL data model is defined for representation of statement-level annotations. The data model is then used as a basis for capturing different types of uncertainty in a use case inspired by a research project in electronic healthcare. Finally, the performance impact of explicitly managing different types of uncertainty is evaluated in a prototype implementation and a set of optimization strategies is introduced with a goal of reducing the impact of uncertainty on query execution performance. The results show that the proposed approach to representing statement-level metadata reduces required data transfer bandwidth and that it can improve query execution performance com-pared with using RDF reification. The optimization strategies produce improved query execution performance overall, but the impact of the heuristic depends on multiple factors, including the selectivity of filters, join cardinalities, and the cost of evaluating uncertainty functions.