Abstract

Abstract
With industry evolution and the development of Industry 4.0, manufacturers are trying to leverage it and find a way to increase productivity. Digital Twins (DT) technologies allow them to achieve this objective and revolutionize Product Life-cycle Management as they provide real-time information and insights for companies, allowing real-time product monitoring. Virtual Reality (VR) is a technology that permits users to interact with virtual objects in immersive environments; even under constant development, VR has proven efficient and effective in enhancing training. DT integration into immersive VR environments is constantly developing, with many challenges ahead. This study aims the development of an immersive virtual world for training integrated with DT technologies to handle all users’ input using the simulator. Those were subject to a performance evaluation to understand how the application handles different input types, which confirmed the viability and reliability of this integration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Training in a virtual environment can augment the current methods of professional’s training, preparing them better for possible situations in the field of work while taking advantage of Virtual Reality (VR) benefits. This paper proposes a cost-effective immersive VR platform designed in real-context usage, consisting of an authoring tool that permits the creation and manipulation of training courses and the execution of these courses in an immersive environment. Accomplishing a good training experience in an immersive simulation requires an equilibrium between the simulator performance and the virtual world aesthetics quality. Thus, in addition to presenting the development of the proposed training platform based on Unity technologies, this paper describes an objective performance evaluation of a virtual training scene using the different render pipelines and across immersive and non-immersive setups. Results confirmed the platform’s viability and revealed that the rendering pipeline should be defined according to the display device used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Performing experiments with Internet-of-Things edge devices is not always a trivial task, as large physical testbeds or complex simulators are often needed, leading to low reproducibility and several difficulties in crafting complex scenarios and tweaking parameters. Most available simulators try to simulate as close to reality as possible. While we agree that this kind of high-fidelity simulation might be necessary for some scenarios, we argue that a low-fidelity easy-to-change simulator may be a good solution when rapid prototyping orchestration strategies and algorithms. In this work, we introduce SIMoT, a low-fidelity orchestrator simulator created to achieve shorter feedback loops when testing different orchestration strategies for task allocation in edge devices. We then transferred the simulator-validated algorithms to both physical and virtual testbeds, where it was possible to assert that the simulator results correlate strongly with the observations on those testbeds.

Abstract
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1-5 ratings carried out over a period of 10 years (2012-2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.

Abstract
The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protege tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.