Abstract
Identifying the influential spreaders in complex networks has emerged as an important research challenge to control the spread of (mis)information or infectious diseases. Researchers have proposed many centrality measures to identify the influential nodes (spreaders) in the past few years. Still, most of them have not considered the importance of the edges in unweighted networks. To address this issue, we propose a novel centrality measure to identify the spreading ability of the Influential Spreaders using the Potential Edge Weight method (IS-PEW). Considering the connectivity structure, the ability of information exchange, and the importance of neighbouring nodes, we measure the potential edge weight. The ranking similarity of spreaders identified by IS-PEW and the baseline centrality methods are compared with the Susceptible-Infectious-Recovered (SIR) epidemic simulator using Kendall's rank correlation. The spreading ability of the top-ranking spreaders is also compared for five different percentages of top-ranking node sets using six different real networks.

Abstract
Industry 4.0 brought modernization to the productive system through the network integration of the constituent entities that, combined with the evolution of information technologies, allowed an increase in productivity, product quality, production cost optimization, and product customization to customer needs. In this paper a model was created using the open-source tool Knime that, based on a set of data provided by Bosch, parameterized the model with several pre-processing techniques, resource selection, and minimization of over-fitting, allowing the development of a final improved model for internal product failure prediction at Bosch production line. The study shows that model efficiency improved with the application of resource selection and reduction techniques, with Logistic Regression and PCA resource selection techniques standing out, obtaining a Recall of 100% and precision and accuracy, both with 99.43%.

Abstract
GLITCH is a versatile tool designed for detecting code smells in Infrastructure as Code (IaC) scripts across multiple technologies. Developed by researchers from INESC-ID (Lisbon), INESC TEC (Porto), Instituto Superior T & eacute;cnico / University of Lisbon, and the Faculty of Engineering / University of Porto, GLITCH automates the detection of both security and design flaws in scripts written in Ansible, Chef, Docker, Puppet, and Terraform. By using a technology-agnostic framework, GLITCH aims to improve the consistency and efficiency of code smell detection, making it valuable resource for DevOps engineers and researchers focused on software quality.

Abstract
Parkinson's Disease (PD) is a neurological disease that progresses over time and causes severe motor symptoms. Therefore, treating PD requires constant patient monitoring, which may turn clinical practice overwhelming, preventing its practical implementation, and raising the need for patient monitoring outside the clinical setting. The iHandU system described in this paper fulfils this need by providing an objective way to quantify motor symptoms of PD in non-clinical settings. It integrates an innovative real-time assessment of the severity of motor symptoms based on signal processing and Machine Learning models that mimic the clinical severity classification scales used in practice and allows for a more continuous and personalized therapy planning and management by doctors, through the use of a web dashboard user-friendly interface. This system, recently tested at 5 patients' homes, has shown promising results as a PD patient management digital platform, reaching a usability score of 83.9% (A grade) based on the System Usability Scale (SUS). Such a level shows a strong alignment between user needs, expectations and functionalities. This study highlights the potential of the used system as a Patient Management Tool showing a case study from an ongoing clinical study. By giving additional information to the doctors with features beyond the semi-quantitative rating scales currently used, allowing a more optimized and continuous PD symptom management, it will be possible to advance PD management further.

Abstract
This paper advocates for Digital Twin (DT) technology as a pivotal solution to address the complexities of Complex Operations Environments (COEs). Recognizing the need for a thorough understanding of COEs and their DTs, a methodology is introduced to bridge existing gaps. Given the lack of a universal definition, the approach leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Latent Dirichlet Allocation (LDA) to extract insights, facilitating the development of a comprehensive definition for COE and DT. The methodology integrates Ontology and Systems Modelling Language (SysML) to provide a semantic and conceptual model of COE and DT. Ontology enriches the semantic understanding, exploring existence and entity relationships, while SysML ensures clear and concise communication through standardized graphical representation. This paper aims to present a methodology to achieve a precise understanding of COEs and their corresponding DTs, providing a robust foundation for addressing operational complexities in dynamic environments.

Abstract
This article introduces an immersive Virtual Reality (VR) application designed to assess the interaction capabilities of users with physical and cognitive limitations, including older adults and individuals with disabilities, as well as ICU patients. The VR application encompasses six tasks varying in complexity, each designed to evaluate different aspects of VR interaction skills, such as movements of the head, arms, and fingers, alongside more intricate activities like pick-and-place, pointing, and painting.The paper details the VR application's specifications, including its system architecture, deployment framework, and data structure. The application's efficacy was tested through three pilot studies in a retirement home setting. The analysis focused on examining correlations among various factors, including age, cognitive abilities (evaluated using the Mini-Mental Status Examination), and previous VR experience. The findings reveal significant correlations, illuminating the effects of age, cognitive capacity, and past VR interactions on task performance. The results emphasize the importance of accounting for user-specific attributes, prior experiences, and cognitive abilities in the design of VR-based therapeutic interventions.