Abstract
Medical imaging, mainly Magnetic Resonance Imaging (MRI), plays a predominant role in healthcare diagnosis. Nevertheless, the diagnostic process is prone to errors and is conditioned by available medical data, which might be insufficient. A novel solution is resorting to image generation algorithms to address these challenges. Thus, this paper presents a Deep Learning model based on a Deep Convolutional Generative Adversarial Network (DCGAN) architecture. Our model generates 2D MRI images of size 256x256, containing an axial view of the brain with a tumor. The model was implemented using ChainerMN, a scalable and flexible framework that enables faster and parallel training of Deep Learning networks. The images obtained provide an overall representation of the brain structure and the tumoral area and show considerable brain-tumor separation. For this purpose, and owing to their previous state-of-the-art results in general image-generation tasks, we conclude that GAN-based models are a promising approach for medical imaging.

Abstract

Abstract
Language is closely related to how we perceive ourselves and signify our reality. In this scope, we created Desiring Machines, an interactive media art project that allows the experience of affective virtual environments adopting speech emotion recognition as the leading input source. Participants can share their emotions by speaking, singing, reciting poetry, or making any vocal sounds to generate virtual environments on the run. Our contribution combines two machine learning models. We propose a long-short term memory and a convolutional neural network to predict four main emotional categories from high-level semantic and low-level paralinguistic acoustic features. Predicted emotions are mapped to audiovisual representations by an end-to-end process encoding emotion in virtual environments. We use a generative model of chord progressions to transfer speech emotion into music based on the tonal interval space. Also, we implement a generative adversarial network to synthesize an image from the transcribed speech-to-text. The generated visuals are used as the style image in the style-transfer process onto an equirectangular projection of a spherical panorama selected for each emotional category. The result is an immersive virtual space encapsulating emotions in spheres disposed into a 3D environment. Users can create new affective representations or interact with other previously encoded instances (This ArtsIT publication is an extended version of the earlier abstract presented at the ACM MM22 [1]). © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
The self-repair capacity of human tissue is limited, motivating the arising of tissue engineering (TE) in building temporary scaffolds that envisage the regeneration of human tissues, including articular cartilage. However, despite the large number of preclinical data available, current therapies are not yet capable of fully restoring the entire healthy structure and function on this tissue when significantly damaged. For this reason, new biomaterial approaches are needed, and the present work proposes the development and characterization of innovative polymeric membranes formed by blending marine origin polymers, in a chemical free cross-linking approach, as biomaterials for tissue regeneration. The results confirmed the production of polyelectrolyte complexes molded as membranes, with structural stability resulting from natural intermolecular interactions between the marine biopolymers collagen, chitosan and fucoidan. Furthermore, the polymeric membranes presented adequate swelling ability without compromising cohesiveness (between 300 and 600%), appropriate surface properties, revealing mechanical properties similar to native articular cartilage. From the different formulations studied, the ones performing better were the ones produced with 3 % shark collagen, 3% chitosan and 10% fucoidan, as well as with 5% jellyfish collagen, 3% shark collagen, 3% chitosan and 10% fucoidan. Overall, the novel marine polymeric membranes demonstrated to have promising chemical, and physical properties for tissue engineering approaches, namely as thin biomaterial that can be applied over the damaged articular cartilage aiming its regeneration.

Abstract
Renewable energy is an essential driver of the energy transition towards a more sustainable world. However, sustainability requires the coordination of the economic, environmental, and social dimensions, turning it into a complex objective. The aim of this study is to review the state of the art of the articles that analyze economic, environmental, and social metrics that can be used to evaluate the impact of renewable. In addition, this work also classifies metrics into two main approaches: macro-studies, corresponding to those that evaluate based on global and aggregated impacts, and micro-studies, corresponding to those that focus on regional and local impacts. A systematic literature review was used to identify and define these main metrics, based on common research databases. Seven metrics were found and described for the environmental impact, four for the economic impact and five for the social impact. The main finding revealed that micro-studies are more prevalent in comparison to macro-studies. Moreover, the systematic literature review allows achieving the objective and highlighting the proposed sustainability assessment framework as crucial for gauging and evaluating impact metrics across the economic, social, and environmental dimensions. The difficulty in isolating and measuring each metric may be attributed to the challenges involved in studying the corresponding impact, whether at the micro or macro level. More targeted studies can help in a more efficient energy transition. © 2023 The Authors, published by EDP Sciences.

Abstract
HEP-Frame is a new C++ package designed to efficiently perform analyses of datasets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high-performance servers and mini-clusters, and it was designed for natural science researchers with a user-friendly interface to access structured databases. HEP-Frame automatically evaluates the underlying computing resources and builds an adequate code skeleton when creating a data analysis application. At run-time, HEP-Frame analyses a sequence of datasets exploring the available parallelism in the code and hardware resources: it concurrently reads inputs from a user-defined data structure and processes them, following the user-specific sequence of requirements to select relevant data; it manages the efficient execution of that sequence; and it outputs results in userdefined objects (e.g., ROOT structures), stored together with the used input dataset. This paper shows how a domain expert software development can benefit from HEP-Frame, and how it significantly improved the performance of analyses of large datasets produced in proton-proton collisions at the LHC. Two case studies are discussed: the associated production of top quarks together with a Higgs boson (t (t) over barH) at the LHC, and a double- and single-top quark productions at the high-luminosity phase of the LHC (HL-LHC). Results show that the HEP-Frame awareness of the analysis code behaviour and structure, and the underlying hardware system, provides powerful and transparent parallelization mechanisms that largely improve the execution time of data analysis applications.