Abstract
The wireless capsule endoscopy is a non-invasive imaging method that allows observation of the inner lumen of the small intestine, but with the cost of a longer duration to process its resulting videos. Therefore, the scientific community has developed several machine learning strategies to help reduce that duration. Such strategies are typically trained and evaluated on small sets of images, ultimately not proving to be efficient when applied to full videos. Labelling full Capsule Endoscopy videos requires significant effort, leading to a lack of data on this medical area. Active learning strategies allow intelligent selection of datasets from a vast set of unlabelled data, maximizing learning and reducing annotation costs. In this experiment, we have explored active learning methods to reduce capsule endoscopy videos' annotation effort by compiling smaller datasets capable of representing their content.

Abstract
Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households' electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Abstract

Abstract
This paper describes the development of a complete controller for the FANUC S-420FD 6-axis industrial robot. The original controller of the robot presented failures that made it impossible to operate and that negatively impacted the academic and research activities. To solve this problem, it was proposed the development of a new open-technology controller and also the design of an intuitive and functional graphical interface, allowing the programming, control and monitoring of the robot parameters. The developed interface offers advanced features such as trajectory programming, custom parameter configuration, and real-time visualization of the robot's state. This work highlights the importance of efficient and affordable solutions for the maintenance of industrial robots in university environments, encouraging scientific and technological advancement in these areas of study.

Abstract
In this paper we present simulations for international kidney exchange programmes (KEPs). KEPs are organised in more than ten countries in Europe to facilitate the exchanges of immunologically incompatible donors. The matching runs are typically conducted in every three months for finding optimal exchanges using hierarchical optimisation with integer programming techniques. In recent years several European countries started to organise international exchanges using different collaboration policies. In this paper we conduct simulations for estimating the benefits of such collaborations with a simulator developed by the team of the ENCKEP COST Action. We conduct our simulations on generated datasets mimicking the practice of the three largest KEPs in Europe, the UK, Spanish and the Dutch programmes. Our main performance measure is the number of transplants compared to the number of registrations to the KEP pools over a 5-year period, however, as a novelty we also analyse how the optimisation criteria play a role in the lexicographic and weighted optimisation policies for these countries. Besides analysing the performances on a single instance, we also conduct large number of simulations to obtain robust findings on the performance of specific national programmes and on the possible benefits of international collaborations.

Abstract
Inherited retinal diseases such as Retinitis Pigmentosa and Stargardt's disease are genetic conditions that cause the photoreceptors in the retina to deteriorate over time. This can lead to vision symptoms such as tubular vision, loss of central vision, and nyctalopia (difficulty seeing in low light) or photophobia (high light). Timely healthcare intervention is critical, as most forms of these conditions are currently untreatable and usually focused on minimizing further vision loss. Machine learning (ML) algorithms can play a crucial role in the detection of retinal diseases, especially considering the recent advancements in retinal imaging devices and the limited availability of public datasets on these diseases. These algorithms have the potential to help researchers gain new insights into disease progression from previous classified eye scans and genetic profiles of patients. In this work, multi-class identification between the retinal diseases Retinitis Pigmentosa, Stargardt Disease, and Cone-Rod Dystrophy was performed using three pretrained models, ResNet101, ResNet50, and VGG19 as baseline models, after shown to be effective in our computer vision task. These models were trained and validated on two datasets of autofluorescent retinal images, the first containing raw data, and the second dataset was improved with cropping to obtain better results. The best results were achieved using the ResNet101 model on the improved dataset with an Accuracy (Acc) of 0.903, an Area under the ROC Curve (AUC) of 0.976, an F1-Score of 0.897, a Recall (REC) of 0.903, and a Precision (PRE) of 0.910. To further assess the reliability of these models for future data, an Explainable AI (XAI) analysis was conducted, employing Grad-Cam. Overall, the study showed promising capabilities of Deep Learning for the diagnosis of retinal diseases using medical imaging.