Abstract
Today, logistics activities are driven by the pressing need to simultaneously increase efficiency, reduce costs, and promote sustainability. In our research, we tackle this challenge by adapting a general vehicle routing problem with deliveries and pickups to accommodate different types of customers. Customers requiring both delivery and pickup services are mandatory, while those needing only a pickup service (backhaul customers) are optional and are only visited if profitable. A mixed-integer linear programming model is formulated to minimize fuel consumption. This model can address various scenarios, such as allowing mandatory customers to be served with combined or separate delivery or pickup visits, and visiting optional customers either during or only after mandatory customer visits. An adaptive large neighborhood search is developed to solve instances adapted from the literature as well as to solve a real-case study of a beverage distributor. The results show the effectiveness of our approach, demonstrating the potential to utilize the available capacity on vehicles returning to the depot to create profitable and environmentally friendly routes, and so enhancing efficient, cost-effective, and sustainable logistics activities.

Abstract

Abstract
Fabry disease (FD) is a rare genetic disorder associated with cardiac abnormalities and often overlooked brain white matter lesions (WMLs). Despite the importance of early WMLs detection, diagnosis is frequently delayed. The aim is to identify electrocardiographic biomarkers linked to WMLs in middle-aged FD patients using machine learning, assessing their potential as non-invasive diagnostic tools. This retrospective study analyzed electrocardiographic data from FD patients aged 40-59. A feature selection process based on variance inflation factor analysis identified nine relevant features, including heart rate variability and QT interval parameters. Machine learning classifiers-logistic regression, support vector machines, random forest, and k-nearest neighbors-were trained and evaluated using accuracy, sensitivity, specificity, and AUC. SHAP (SHapley Additive exPlanations) analysis was used to interpret model predictions. The random forest model achieved the highest accuracy (0.81) using all nine features. A subset consisting of SDANN 5 and QTc Min also performed well (accuracy 0.75) in other models. SHAP analysis highlighted SDANN 5 as a key predictor. Machine learning applied to ECG data shows promise for early WML detection in FD, supporting the integration of computational methods into diagnostics for complex genetic diseases.

Abstract
City councils play a crucial role in local governance, directly influencing citizens’ daily lives through decisions made during municipal meetings. These deliberations are formally documented in meeting minutes, which serve as official records of discussions, decisions, and voting outcomes. Despite their importance, municipal meeting records have received little attention in Information Retrieval (IR) and Natural Language Processing (NLP), largely due to the lack of annotated datasets, which ultimately limit the development of computational models. To address this gap, we introduce CitiLink-Minutes, a multilayer dataset of 120 European Portuguese municipal meeting minutes from six municipalities. Unlike prior annotated datasets of parliamentary or video records, CitiLink-Minutes provides multilayer annotations and structured linkage of official written minutes. The dataset contains over one million tokens, with all personal identifiers de-identified. Each minute was manually annotated by two trained annotators and curated by an experienced linguist across four complementary dimensions: (1) personal information, (2) metadata, (3) subjects of discussion, and (4) voting outcomes, totaling over 38,000 individual annotations. Released under FAIR principles and accompanied by baseline results on metadata extraction, topic classification, and vote labeling, CitiLink-Minutes demonstrates its potential for downstream NLP and IR tasks, while promoting transparent access to municipal decisions.

Abstract
This paper explores a Cycle-GAN architecture based on diffusion models for translating cardiac CT images with and without contrast, aiming to enhance the quality and accuracy of medical imaging. The combination of GANs and diffusion models has demonstrated promising results, particularly in generating high-quality, visually similar contrast-enhanced cardiac images. This effectiveness is evidenced by metrics such as a PSNR of 32.85, an SSIM of 0.766, and an FID of 42.348, highlighting the model's capability for accurate and detailed image generation. Although these results indicate substantial potential for improving diagnostic accuracy, challenges remain, particularly concerning the generation of image artefacts and brightness inconsistencies, which could affect the clinical validation of these images. These issues have important implications for the reliability of the images in real medical diagnoses. The results of this study suggest that future research should focus on optimizing these aspects, improving the handling of artefacts, and investigating alternative architectures further to enhance the quality and reliability of the generated images, ensuring their applicability in clinical settings

Abstract
The gold standard real-time core temperature (CT) monitoring methods are invasive and cost-inefficient. The application of the Kalman filter for an indirect estimation of CT has been explored in the literature for more than 10 years. This paper presents a comparative study between different state-of-the-art Extended Kalman Filter (EKF) approaches. Moreover, we proposed the addition of an extra layer to the pipeline that applies a pre-emptive mapping concept based on the physiological response of the heart rate (HR) signal, before using it as input to the EKF. The algorithm was trained and tested using two datasets (18 subjects). The best-performing approach with the novel pre-emptive mapping achieved an average Root Mean Squared Error (RMSE) of 0.34 ^?C, while without pre-emptive mapping, it resulted in an RMSE of 0.41 ^?C, leading to a performance improvement of 17%. Given these favorable outcomes, it is compelling to assess the efficacy of this method on a larger dataset in the future.