Abstract
Over the past two decades, Digital Transformation (DT) has been focused on improving businesses, industries, and the general public through significant breakthroughs. This paper examines the significant developments brought forth by DT and how they impact organizations. This analysis explores the impact of Virtual Reality (VR) and the Metaverse on global businesses, taking inspiration from successful case studies such as Netflix, Amazon, and Meta. This study emphasizes the potential of virtual reality and the Metaverse in facilitating remote meetings, training employees, engaging with consumers, and gathering data. Case studies and strategic recommendations are offered for overcoming barriers to the adoption of these digital technologies. The study finishes by addressing the future trajectory of DT and emphasizing the significance of devoting time, commitment, and resources to effectively utilize the range of potential offered by VR and the Metaverse. It highlights the importance for organizations to comprehend and handle this ever-changing environment to remain at the forefront of the digital frontier.

Abstract
Federated Learning (FL) is emerging in the medical field to address the need for diverse datasets while complying with data protection regulations. This decentralised learning paradigm allows hospitals (clients) to train machine learning models locally, ensuring that patient data remains within the confines of its originating institution. Nonetheless, FL by itself is not enough to guarantee privacy, as the central aggregation process may still be susceptible to identity-exposing attacks, potentially compromising data protection compliance. To strengthen privacy, differential privacy (DP) is often introduced. In this work, we conduct a comprehensive comparative analysis to evaluate the impact of DP in both traditional Centralised Learning (CL) frameworks and FL for polyp segmentation, a common medical image analysis task. Experiments are performed in PolypGen, a multi-centre publicly available dataset designed for polyp segmentation. The results show a clear drop in performance with the introduction of DP, exposing the trade-off between privacy and performance and highlighting the need to develop novel privacy-preserving techniques.

Abstract
This research idea paper proposes leveraging Large Language Models (LLMs) to enhance the productivity of Dafny developers. Although the use of verification-aware languages, such as Dafny, has increased considerably in the last decade, these are still not widely adopted. Often the cost of using such languages is too high, due to the level of expertise required from the developers and challenges that they often face when trying to prove a program correct. Even though Dafny automates a lot of the verification process, sometimes there are steps that are too complex for Dafny to perform on its own. One such case is that of missing lemmas, i.e. Dafny is unable to prove a result without being given further help in the form of a theorem that can assist it in the proof of the step. In this paper, we describe preliminary work on using LLMs to assist developers by generating suggestions for relevant lemmas that Dafny is unable to discover and use. Moreover, for the lemmas that cannot be proved automatically, we attempt to provide accompanying calculational proofs. We also discuss ideas for future work by describing a research agenda on using LLMs to increase the adoption of verification-aware languages in general, by increasing developers productivity and by reducing the level of expertise required for crafting formal specifications and proving program properties.

Abstract
At a time when European countries try to cope with escalating energy prices while decarbonizing their economies, waste heat recovery and reuse arises as part of the solution for sustainable energy transitions. The lack of appropriate assessment tools has been pointed out as one of the main barriers to the wider deployment of waste heat recovery projects and as a reason why its potential remains largely untapped. The EMB3Rs platform emerges as an online, open-source, comprehensive and novel tool that provides an integrated assessment of different types of waste heat recovery solutions, (e.g. internal or external) and comprises several analysis dimensions (e.g. physical, geographical, technical, market, and business models). It has been developed together with stakeholders, and tested in a number of representative contexts, covering both industrial and heat network applications. This has demonstrated the enormous potential of the tool in dealing with complex simulations, while delivering accurate results within a significantly lower time-frame than traditional analysis. The EMB3Rs tool removes important barriers such as analysis costs, time and complexity for the user, and aims at supporting a wider investment in waste heat recovery and reuse by providing an integrated estimation of the costs and benefits of such projects. This paper describes the tool and illustrates how it can be applied to help unlock the potential of waste heat recovery across European countries.

Abstract
Colorectal cancer is the second most common cancer and the second with the highest associated deaths in the world. Methods used in clinical practice for colon cancer diagnosis are fairly effective but quite unpleasant and not always applicable in situations where the patient has symptoms of colonic obstruction. This problem can be solved by the use of optical methods that can be applied less invasively. This study presents the results of classification of cancerous and healthy colon tissue absorption coefficient spectra. The absorption coefficient was measured using direct calculations from the total reflectance and total transmittance spectra obtained ex vivo. Classification was performed using support vector machine, multilayer perceptron and linear discriminant analysis.

Abstract
Encouraging pre-university students to pursue engineering courses at the university level is essential to meet the industry’s escalating demand for engineers. Each year, universities host hundreds of secondary students who tour their facilities to get a feel for the academic environment. This paper discusses an educational experiment designed as part of a semester-long undergraduate project in Informatics Engineering. The project involves tailoring a Dobot Magician robot, equipped with a standard webcam, to engage in a game of tic-tac-toe against a human user. The camera stream is continuously processed by a computer vision algorithm to detect the pieces placement in the game board. The paper outlines the project development stages, the elements involved, and presents preliminary test results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.