Abstract
Presence is often used to evaluate Virtual Reality (VR) applications. However, the raw scores are hard to interpret and need to be compared to other data to be meaningful. This paper leverages a database of 1909 responses to the Igroup Presence Questionnaire (IPQ) in different contexts to put forward a scale that qualitatively interprets raw Presence scores for VR experiences. The qualitative grading encompasses the acceptability dimension and analogous academic grading scales ranging from A to F and the adjective of such scores in a scale from Excellent to Unacceptable. Furthermore, the qualitative grading system encompasses Presence and its subscales Spatial Presence, Involvement, and Experienced Realism as defined by the IPQ. Adopting this grading system, supported by a robust dataset of Presence scores, enables practitioners to evaluate and interpret individual IPQ scores, allowing them to gain insights regarding the evaluated applications' effectiveness.

Abstract
There is a growing complexity, volatility, and unpredictability in the electric sector that hardens the decision-making process. To this end, the use of proper decision support tools and simulation platforms becomes essential. This paper presents the Multi-Agent based Real-Time INfrastructure for Energy (MARTINE) platform that allows real-time simulation and emulation of loads, resources, and infrastructures. MARTINE uses multi-agent systems that connect to physical resources and can represent additional simulated players that are not physically present in the simulation and emulation environment, enabling the creation of complex scenarios for testing and validation. MARTINE provides the seamless integration of real-time emulation with simulated and physical resources simultaneously in a unique simulation environment, which is only possible by supporting multi-agent systems. This work presents MARTINE's integration in a semantically interoperable multi-agent systems society developed for the test, study, monitoring, and validation of the power system sector. The use of ontologies and semantic web technologies eases the interoperability between the heterogeneous systems. The case study scenario demonstrates the use of MARTINE in simulating a local community electricity market that combines real-time data from physical devices with simulated data and the use of semantic web techniques to make the system interoperable, configurable, and flexible.& COPY; 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Abstract
A wide range of methods from computer science are being applied to many modern engineering domains, such as synthetic biology. Most behaviors described in synthetic biology have a hybrid nature, in the sense that both discrete or continuous dynamics are observed. Differential Dynamic Logic (dL) is a well-known formalism used for the rigorous treatment of these systems by considering formalisms comprising both differential equations and discrete assignments. Since the many systems often consider a range of values rather than exact values, due to errors and perturbations of observed quantities, recent work within the team proposed an interval version of dL, where variables are interpreted as intervals. This paper presents the first steps in the development of computational support for this formalism by introducing a tool designed to models based on intervals, prepared to translate them into specifications ready to be processed by the KeYmaera X tool.

Abstract
Effective quality control is essential for efficient and successful manufacturing processes in the era of Industry 4.0. Artificial Intelligence solutions are increasingly employed to enhance the accuracy and efficiency of quality control methods. In Computer Numerical Control machining, challenges involve identifying and verifying specific patterns of interest or trends in a time-series dataset. However, this can be a challenge due to the extensive diversity. Therefore, this work aims to develop a methodology capable of verifying the presence of a specific pattern of interest in a given collection of time-series. This study mainly focuses on evaluating One-Class Classification techniques using Linear Frequency Cepstral Coefficients to describe the patterns on the time-series. A real-world dataset produced by turning machines was used, where a time-series with a certain pattern needed to be verified to monitor the wear offset. The initial findings reveal that the classifiers can accurately distinguish between the time-series' target pattern and the remaining data. Specifically, the One-Class Support Vector Machine achieves a classification accuracy of 95.6 % +/- 1.2 and an F1-score of 95.4 % +/- 1.3.

Abstract

Abstract
[No abstract available]