Abstract
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.

Abstract
An attempt has been made to address the difficulty of identifying and measuring the benefits derived from investment projects and capturing capital gains for an organization, focusing on developing and implementing a management model and realizing benefits for a leading company in its activity sector. Thus, the objective is to understand how it is possible to achieve the expected benefits of an investment project: A model characterized as generalist was developed (applied to all areas of the company), with the objective of optimizing the realization of benefits, measuring them and thus create value for the organization. Among the methods used, we highlight, in a first phase, the research of some existing Frameworks, which later enabled the development of a proposed framework, validated internally using the existing Business Intelligence platform. Subsequently, based on a satisfaction questionnaire about the framework proposed to users, data related to its development and implementation were collected, with the aim of understanding its acceptance among the users and employees of the company. With the data from this questionnaire, an artifact was developed: a PowerBI dashboard that reflects the benefits identified and captured. In summary, the artifact made it possible to identify, measure, and achieve the benefits generated by the project in question, but also to motivate its use in other existing investment projects, by adapting it to each of the other ones. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
SummaryThe muscle synergy concept suggests that the human motor system is organised into functional modules comprised of muscles‘working together’towards common task-goals. This study offers a nuanced computational perspective to muscle synergies, where muscles interacting across multiple scales have functionally-similar, - complementary and -independent roles. Making this viewpoint implicit to a methodological approach applying Partial Information Decomposition to large-scale muscle activations, we unveiled nested networks of functionally diverse inter- and intra-muscular interactions with distinct functional consequences on task performance. This approach’s effectiveness is demonstrated using simulations and by extracting generalisable muscle networks from benchmark datasets of muscle activity. Specific network components are shown to correlate with a) balance performance and b) differences in motor variability between young and older adults. By aligning muscle synergy analysis with leading theoretical insights on movement modularity, the mechanistic insights presented here suggest the proposed methodology offers enhanced research opportunities towards health and engineering applications.

Abstract
Repetitive controllers (RCs) are linear control structures based on the internal model principle. This control strategy is known for its ability to control periodic reference signals, even if these signals have many harmonic components. Despite being a solution that results in a good performance, several parameters of the repetitive controller need to be correctly tuned to guarantee its stability. Among these parameters, one that has high impact on the system performance and stability is the finite impulse response (FIR) filter, which is usually used to increase the stability domain of RC-based controllers. In this context, this paper presents a complete tutorial for designing the zero-phase FIR filter, which is often used to stabilize control systems that use RC-based controllers. In addition, this paper presents a Matlab® application developed for performing the stability analysis of RC systems and designing its FIR filter. Simulation and experimental results of a shunt active power filter are used to validate the algorithm and the Matlab® application.

Abstract
Transthyretin (TTR)-related familial amyloid polyneuropathy (ATTRv) is a life-threatening autosomal dominant disease and the age of onset represents the moment when first symptoms are felt. Accurately predicting the age of onset for a given patient is relevant for risk assessment and treatment management. In this work, we evaluate the impact of combining prediction models obtained from neighboring time windows on prediction error. We propose Symmetric (Sym) and Asymmetric (Asym) models which represent two different averaging approaches. These are incorporated with a weighting mechanism as to create Symmetric (Sym), Symmetric-weighted (Sym-w), Asymmetric (Asym), and Asymmetric-weighted (Asym-w). These four ensemble models are then compared to the original approach which is focused on individual regression base learners namely: Baseline (BL), Decision Tree (DT), Elastic Net (EN), Lasso (LA), Linear Regression (LR), Random Forest (RF), Ridge (RI), Support Vector Regressor (SV) and XGBoost (XG). Our results show that by aggregating predictions from neighbor models the average mean absolute error obtained by each base learner decreases. Overall, the best results are achieved by regression-based ensemble tree models as base learners.

Abstract
Deep Learning (DL) algorithms allow fast results with high accuracy in medical imaging analysis solutions. However, to achieve a desirable performance, they require large amounts of high quality data. Active Learning (AL) is a subfield of DL that aims for more efficient models requiring ideally fewer data, by selecting the most relevant information for training. CheXpert is a Chest X-Ray (CXR) dataset, containing labels for different pathologic findings, alongside a Support Devices (SD) label. The latter contains several misannotations, which may impact the performance of a pathology detection model. The aim of this work is the detection of SDs in CheXpert CXR images and the comparison of the resulting predictions with the original CheXpert SD annotations, using AL approaches. A subset of 10,220 images was selected, manually annotated for SDs and used in the experimentations. In the first experiment, an initial model was trained on the seed dataset (6,200 images from this subset). The second and third approaches consisted in AL random sampling and least confidence techniques. In both of these, the seed dataset was used initially, and more images were iteratively employed. Finally, in the fourth experiment, a model was trained on the full annotated set. The AL least confidence experiment outperformed the remaining approaches, presenting an AUC of 71.10% and showing that training a model with representative information is favorable over training with all labeled data. This model was used to obtain predictions, which can be useful to limit the use of SD mislabelled images in future models.