The ability to split applications across different locations in the continuum (edge/cloud) creates needs for application break down into smaller and more distributed chunks. In this realm the Function as a Service approach appears as a significant enabler in this process. The paper presents a visual function and workflow development environment for complex FaaS (Apache OpenwhisK) applications. The environment offers a library of pattern based and reusable nodes and flows while mitigating function orchestration limitations in the domain. Generation of the deployable artefacts, i.e. the functions, is performed through embedded DevOps pipelines. A range of annotations are available for dictating diverse options including QoS needs, function or data locality requirements, function affinity considerations etc. These are propagated to the deployment and operation stacks for supporting the cloud/edge interplay. The mechanism is evaluated functionally through creating, registering and executing functions and orchestrating workflows, adapting typical parallelization patterns and an edge data collection process.

Cybersecurity is increasingly recognized as a key enabler of Sustainable Development Goals (SDGs) and especially SDG 3 (Good Health and Well-being) as healthcare systems become more digitized. This study prioritizes cybersecurity control families from the NIST 800-53r5 framework using a structured framework combining the Analytic Hierarchy Process (AHP) and the Impact–Feasibility Matrix. From the impact–feasibility perspective, expert judgment reveals that while impact is the primary driver in selecting controls, feasibility—particularly budget and cost constraints—plays a decisive role in real-world implementation. A group of fifteen experts, including cybersecurity officers, health IT professionals, and public health advisors, has participated in structured surveys as per the methodological framework of this paper. Financial and budgetary limitations emerged as the top feasibility barrier, often determining whether high-impact controls are deployed or delayed. This underscores the need for strategic investments and phased implementation approaches, particularly in resource-constrained health systems. The results provide a practical roadmap for policymakers and healthcare administrators to allocate cybersecurity resources effectively, balancing technical necessity with economic feasibility to support resilient digital health infrastructures.

Breast cancer is the most common cancer in the world. With a 5-year survival rate of over 90% for patients at the early disease stages, the management of side-effects of breast cancer treatment has become a pressing issue. Observational, real-world data such as electronic health records, insurance claims, or data from wearable devices have the potential to support research on the quality of life (QoL) of breast cancer patients (BCPs), but care must be taken to avoid errors introduced due to data quality and bias. This paper proposes a causal inference methodology for using observational data to support research on the QoL of BCPs, focusing on the osteopenia of patients undergoing treatment with aromatase inhibitors (AIs). We propose a machine learning-based pipeline to estimate the average and conditional average treatment effects (ATE and CATE). For evaluation, we develop a Structural Causal Model for the osteopenia of BCPs and rely on synthetically generated data to study the effectiveness of the proposed methodology under various data challenges. A set of studies were designed to estimate the effect of high-intensity exercise on bone mineral density loss using synthetic datasets of BCPs under AI treatment. Four observational study scenarios were evaluated, corresponding to synthetically generated data of 1000 BCPs with (a) no bias, (b) sampling bias, (c) hidden confounder bias, and (d) bias due to unobserved mediator. In all cases, evaluations were performed under both complete and missing data scenarios. In particular, machine learning-based models based on tree ensembles and neural networks achieved a lower estimation error by 23.8–51.3% and 32.4–89.3% for ATE and CATE, respectively, compared to direct estimation using sample averages. The proposed approach shows improved effectiveness in treatment effect estimation in the presence of missing values and sampling bias, compared to a “traditional” statistical analysis workflow. This suggests that the application of causal effect estimation methods for the study of BCPs’ quality of life using real-world data is promising and worth pursuing further.

The achievement of organizational interoperability is a crucial factor for the successful digital transformation and a catalyst for Digital Decade's 2030 main targets. The aim of this article is to develop and test a new conceptual model of organizational interoperability assessment. A new model to assess in an effective, holistic way the organizational interoperability of digital services has been developed, using a mixed qualitative and quantitative approach, based on literature research and feedback from experts in the field, composing a valid and effective model for evaluating the achievement of organizational interoperability. The developed model consists of 9 factors and 34 variables and is evaluated in terms of statistical reliability, relevance, and coherence of factors and variables. The evaluation of the model in terms of reliability and internal consistency is achieved through data collected from a sample survey responded to by 112 domain experts. The collected data analyzed with the aid of descriptive and inductive statistical methods such as Cronbach's Alpha coefficient and Factor analysis, from which resulted alpha= 0.869 for the total interoperability scale, which is satisfactory enough, as well as the total variance explained from the Factor Analysis found to be 60% or higher, resulted in a well-established model in statistical terms.

Nowadays Automated Teller Machines (ATMs) are one of the main cash circulation sources in societies. Banks and their clients depend heavily on their availability and their ability to consistently serve client requests. In order to achieve that high availability, banks are trying to optimize their ATM resupply planning in order to ensure client satisfaction while minimizing their own costs and the amount of capital that is idle inside the ATMs. The solution proposed in this paper is trying to find the most fitting algorithms in order to predict the optimal time that an ATM should be resupplied using machine learning techniques. The algorithms take into consideration historical data and external data that may affect the resupply process or the client activity per ATM, such as holidays or street events near the ATM. The results are being used by a route optimization algorithm in order to find the optimal supply planning, minimizing costs and energy consumption.

Greek Politicians and the Use of Online Technologies for Citizen Engagement: 10.4018/978-1-4666-6182-0.ch007: This chapter examines how politicians utilise Internet technologies to create an online presence, the motivating and hindering factors for doing so, and the