Abstract
In the field of engineering education, hands-on experiences in laboratories are essential for effective learning. However, traditional laboratories face challenges such as cost and infrastructure, hindering access. And virtual laboratories are believed to have significant potential to offer efficient and cost-effective solutions for practical lab work compared to traditional labs. Moreover, currently there is a growing implementation of these solutions in various educational and professional areas where practical knowledge is crucial, especially due to the COVID-19 pandemic. Therefore, the current research focuses on the design and development of a 3D virtual laboratory for computer hardware classes aiming to provide a solution to the problem of the lack of physical equipment for conducting practical IT laboratory activities. Additionally, to develop the virtual laboratory, tools such as Blender, Sculptris, and ZBrush were used for modelling, and the Unity Game Engine was used for programming and rendering. This simulated environment allows interactions with digital computer hardware components and circuits, promoting accessibility, scalability, and cost savings.

Abstract
Expanding upon the potential of generative machine learning to create atemporal latent space representations of musical-theoretical and cognitive interest, we delve into their explainability by formulating and testing hypotheses on their alignment with DFT phase spaces from {0, 1}(12) pitch classes and {0, 1}(128) pitch distributions - capturing common-tone tonal functional harmony and parsimonious voice-leading principles, respectively. We use 371 J.S. Bach chorales as a benchmark to train a Variational Autoencoder on a representative piano roll encoding. The Spearman rank correlation between the latent space and the two before-mentioned DFT phase spaces exhibits a robust rank association of approximately .65 +/- .05 for pitch classes and .61 +/- .05 for pitch distributions, denoting an effective preservation of harmonic functional clusters per region and parsimonious voice-leading. Furthermore, our analysis prompts essential inquiries about the stylistic characteristics inferred from the rank deviations to the DFT phase space and the balance between the two DFT phase spaces.

Abstract
Human-Robot Collaboration (HRC) in advanced industrial scenarios has emerged as a transformative force. Modern robots, infused with artificial intelligence (AI), can enhance human capabilities, offering a wide spectrum of opportunities in agriculture, forestry, construction and many other domains. However, the complex nature of HRC demands realistic simulators to bridge the gap between theory and practice. This paper introduces the FEROX Simulator, purpose-built for robot-assisted wild berry collection. We briefly delve into the simulator's capabilities to showcase its potential as a platform to develop HRC systems. Our research underscores the need for simulators designed for challenging HRC contexts and aims to inspire further advancements in this domain.

Abstract
Context: Mutation testing is a rigorous approach for assessing the quality of test suites by injecting faults (i.e., mutants) into software under test. Tools, such as CosmicRay and Mutpy, are examples of Mutation Testing tools for Python software programs. Problem: With different Python mutation testing tools, comparative analysis is lacking to evaluate their effectiveness in different usage scenarios. Furthermore, the evolution of these tools makes continuous evaluation of their functionalities and characteristics necessary. Method: In this work, we evaluate (statically and dynamically) four Python mutation testing tools, namely CosmicRay, MutPy, MutMut, and Mutatest. In static evaluation, we introduce a comparison framework, adapted from one previously applied to Java tools, and collected information from tool documentation and developer surveys. For dynamic evaluation, we use tests built based on those produced by Pynguin, which are improved through the application of Large Language Models (LLMs) and manual analyses. Then, the adequate test suites were cross-tested among different tools to evaluate their effectiveness in killing mutants each other. Results: Our findings reveal that CosmicRay offers superior functionalities and customization options for mutant generation compared to its counterparts. Although CosmicRay's performance was slightly lower than MutPy in the dynamic tests, its recent updates and active community support highlight its potential for future enhancements. Cross-examination of the test suites further shows that mutation scores varied narrowly among tools, with a slight emphasis on MutPy as the most effective mutant fault model.

Abstract
Aging involves complex biological processes leading to the decline of living organisms. As population lifespan increases worldwide, the importance of identifying factors underlying healthy aging has become critical. Integration of multi-modal datasets is a powerful approach for the analysis of complex biological systems, with the potential to uncover novel aging biomarkers. In this study, we leveraged publicly available epigenomic, transcriptomic and telomere length data along with histological images from the Genotype-Tissue Expression project to build tissue-specific regression models for age prediction. Using data from two tissues, lung and ovary, we aimed to compare model performance across data modalities, as well as to assess the improvement resulting from integrating multiple data types. Our results demostrate that methylation outperformed the other data modalities, with a mean absolute error of 3.36 and 4.36 in the test sets for lung and ovary, respectively. These models achieved lower error rates when compared with established state-of-the-art tissue-agnostic methylation models, emphasizing the importance of a tissue-specific approach. Additionally, this work has shown how the application of Hierarchical Image Pyramid Transformers for feature extraction significantly enhances age modeling using histological images. Finally, we evaluated the benefits of integrating multiple data modalities into a single model. Combining methylation data with other data modalities only marginally improved performance likely due to the limited number of available samples. Combining gene expression with histological features yielded more accurate age predictions compared with the individual performance of these data types. Given these results, this study shows how machine learning applications can be extended to/in multi-modal aging research. Code used is available at https://github.com/zroger49/multi_modal_age_prediction.

Abstract
In recent years, there has been a significant growth in the use of technologies that rely on natural resources (wind, solar, etc.) as primary sources of energy. The generation originating from renewable sources brings an increased need for adaptation in power electrical systems. Predicting the amount of energy produced by these technologies is a complex task due to the uncertainty associated with natural resources. This uncertainty hinders decision-making, both at the system level and for consumers themselves who are increasingly using this type of technology for self-consumption. This study focuses on classifying solar intensity using imbalanced data, which means that some of the data categories are more prevalent than others. Oversampling techniques are be employed to increase the amount of data, thereby allowing for balanced training data and improving the performance of prediction models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.