Abstract
Agile methodologies, initially designed for the project level, face challenges when applied at enterprise levels where complex projects and diverse stakeholders are involved. To meet this challenge, several large-scale agile methodologies have been proposed. However, these approaches are not flexible enough or tailored to the needs of organizations, projects, and their teams. It is in this context that hybrid methodologies have emerged. This study aims to conduct a systematic literature review to trace the evolution of hybrid scaling of agile and characterize different approaches to implement it. This study starts by assessing 1509 studies through the use of the PRISMA 2020 framework and identifies 38 relevant studies in this field. The findings indicate that the majority of studies are from 2021 onwards and that qualitative methodologies supported by case studies predominate, making it possible to characterize tailoring processes in these organizations. Moreover, the implementation of hybrid scaling of agile is supported by the paradigm of ambidextrous strategy, a combination of agile with traditional project management methodologies, and continuous improvements. This study contributes insights into navigating the complexities of agile scaling, offering practical guidance for organizations seeking to optimize their project management practices.

Abstract
Extensive computational research has been dedicated to detecting keys and modes in tonal Western music within the major and minor modes. Little research has been dedicated to other modes and musical expressions, such as folk or non-Western music. This paper tackles this limitation by comparing traditional template-based with unsupervised machine-learning methods for diatonic mode detection within folk music. Template-based methods are grounded in music theory and cognition and use predefined profiles from which we compare a musical piece. Unsupervised machine learning autonomously discovers patterns embedded in the data. As a case study, the authors apply the methods to a dataset of Irish folk music called The Session on four diatonic modes: Ionian, Dorian, Mixolydian, and Aeolian. Our evaluation assesses the performance of template-based and unsupervised methods, reaching an average accuracy of about 80%. We discuss the applicability of the methods, namely the potential of unsupervised learning to process unknown musical sources beyond modes with predefined templates.

Abstract

Abstract
Early diagnosis of cardiovascular diseases is essential for an effective treatment, potentially preventing severe health complications and improving clinical outcomes. Electrocardiogram (ECG) and phonocardiogram (PCG) are costeffective, noninvasive diagnostic tools providing crucial and complementary information about the heart's electrical and mechanical activities. This paper presents a novel approach to the assessment of cardiovascular health through the multimodal analysis of simultaneously recorded ECG and PCG signals. Combining multimodal analysis and transfer learning on publicly available data, the most successful multimodal approach achieved an accuracy of 82.79%, a ROC AUC score of 91.26%, and a recall of 93.10% demonstrating the potential of these techniques. This study provides a foundation for future research aimed at enhancing the performance of multimodal cardiac abnormality detection systems.

Abstract
The expected development and massification of Local Energy Markets (LEM), in particular the ones associated with Renewable Energy Communities, poses new challenges, and requires new operations strategies to their promoters, aggregators, and end-consumers. One of the mechanisms that can be used to speed up the spreading of this kind of market is the use of Demand Response (DR) programs since they can be designed to increase the community's savings and profits. In this framework, the end customers are induced to change their normal consumption patterns by temporarily reducing and/or shifting their electricity consumption away from periods with low local generation in response to a signal from a service provider, i.e., aggregator. To this purpose, this paper presents an Agent Based Model (ABM) using the Q-Learning mechanism to implement and to simulate a LEM and its interaction with the Wholesale Market (WSM), using also and incentive-based DR program. The overall objective of this design is to decrease average energy costs by moving the demand to periods of large availability of wind or solar resources or to store energy for future use. The developed model was tested considering real data regarding energy consumption and PV generation. The proposed paper describes and discusses the obtained market strategy and the profits that can be obtained with this approach.

Abstract
Existe uma ampla variedade de ferramentas e ambientes disponíveis para aplicações de realidade virtual imersiva, passíveis de utilização em contexto educativo. Para proporcionar uma perceção panorâmica das potencialidades disponíveis, este estudo efetuou um levantamento e categorização dessas ferramentas educativas, classificando-as por áreas de aplicação: exploração geográfica, entretenimento, ciência, arte e outras. Recorreu-se metodologicamente ao protocolo de levantamento (scoping review) proposto por Morgado & Beck. Com base neste protocolo efetuaram-se os processos de definição e desenvolvimento das buscas, da seleção e análise de elementos e extração das conclusões. As ferramentas foram também analisadas face à tipologia de usos de ambientes imersivos dos mesmos autores, segundo a qual constatámos que o tipo de ferramentas mais prevalente é o referente a “Manipulação Interativa e Exploração”, seguido pelas de “Interação Multimodal” e “Treino de Competências”. São também comuns as ferramentas de Colaboração. Algumas categorias menos prevalentes, como “Ver o Invisível”, “Envolvimento”, “Simulação do Mundo Físico” e outras, permitem ainda assim ter uma perceção de como se concretizam essas tipologias de usos enquanto experiências de aprendizagem possíveis em ambientes virtuais imersivos.