Abstract
The emergence of Digital Twins (DT) in Industry 4.0 has enabled the decision support systems taking advantage of more effective recommendation systems (RS). Despite the RS's growing popularity and ability to support decision-makers, these face two significant challenges, cold-start and data sparsity, which limits the system's capability to provide effective and accurate decision support. This paper aims to address these issues by conducting a literature review, analysing the current research landscape, and identifying the main enabling methods, algorithms, and similarity measures to mitigate these challenges. The performed analysis enables the point out of future research directions for developing effective and accurate RS that empower decision-makers.

Abstract
One of the indicators of possible occurrences of cardiovascular diseases is the amount of coronary artery calcium. Recently, approaches using new technologies such as deep learning have been used to help identify these indicators. This work proposes a segmentation method for calcification of the coronary arteries that has three steps: (1) extraction of the ROI using U-Net with batch normalization after convolution layers, (2) segmentation of the calcifications and (3) removal of false positives using Modified U-Net with EfficientNet. The method uses histogram matching as preprocessing in order to increase the contrast between tissue and calcification and normalize the different types of exams. Multiple architectures were tested and the best achieved 96.9% F1-Score, 97.1% recall and 98.3% in the OrcaScore Dataset.

Abstract
Electrochemical impedance spectroscopy (EIS) is a reliable technique for gathering information about electrochemical process occurring at the electrode surface and investigating properties of materials. Furthermore, EIS technique can be a very versatile and valuable tool in analytical assays for detection and quantification of several chemically and biologically relevant (bio)molecules. The first part of this Review (Introduction) provides brief insights into (i) theoretical aspects of EIS, (ii) the instrumentation required to perform the EIS studies and (iii) the most relevant representations of impedance experimental data (such as Nyquist and Bode plots). In the end of this section, (iv) theoretical aspects regarding the fitting of the Randles circuit to experimental data are addressed, not only to obtain information about electrochemical processes but also to illustrate its utility for analytical purposes. The second part of the Review (Impedimetric Detection of Disease Biomarkers) focuses on the applications of EIS in the biomedical field, particularly as analytical technique in electrochemical sensors and biosensors for screening disease biomarkers. In the last section (Conclusions and Perspectives), we discuss main achievements of EIS technique in analytical assays and provide some perspectives, challenges and future applications in the biomedical field.

Abstract
Parkinson's Disease (PD) is a neurological disease that progresses over time and causes severe motor symptoms. Therefore, treating PD requires constant patient monitoring, which may turn clinical practice overwhelming, preventing its practical implementation, and raising the need for patient monitoring outside the clinical setting. The iHandU system described in this paper fulfils this need by providing an objective way to quantify motor symptoms of PD in non-clinical settings. It integrates an innovative real-time assessment of the severity of motor symptoms based on signal processing and Machine Learning models that mimic the clinical severity classification scales used in practice and allows for a more continuous and personalized therapy planning and management by doctors, through the use of a web dashboard user-friendly interface. This system, recently tested at 5 patients' homes, has shown promising results as a PD patient management digital platform, reaching a usability score of 83.9% (A grade) based on the System Usability Scale (SUS). Such a level shows a strong alignment between user needs, expectations and functionalities. This study highlights the potential of the used system as a Patient Management Tool showing a case study from an ongoing clinical study. By giving additional information to the doctors with features beyond the semi-quantitative rating scales currently used, allowing a more optimized and continuous PD symptom management, it will be possible to advance PD management further.

Abstract
This research investigates the use of artificial intelligence algorithms to identify behavioural patterns in computer use, with the aim of detecting trends that help to flag cases of depression by analysing the human-computer interaction records of these users, thereby increasing the quality of the data for early detection of these situations. Following design science methodology, a case study will be conducted using an existing mental health screening questionnaire, integrating an artificial intelligence layer to map mouse and keyboard interactions, followed by machine learning analysis of the records. The results of the machine learning assisted questionnaires will be compared with the results of the questionnaires without the mapping. If there is a significant difference, this model could be useful for making predictions about emotional states, contributing to the field of artificial intelligence and helping to prevent depression, which is the focus of the research, although the aim is to look at mental health in a global way. © 2025 Elsevier B.V., All rights reserved.

Abstract
PurposeIn a society that encourages consumption, attributes such as exclusivity and social recognition are important in what is intended to be restricted to a certain exclusive segment. Luxury is something that is more desirable than necessary. This study develops and tests a model that analyses the brand loyalty-risk relationship in the luxury watch market.Design/methodology/approachTo test the proposed research model, a sample of 306 international consumers and enthusiasts of luxury brand watches was collected. The data were analysed using structural equation modelling.FindingsThe results show that perceived quality has a negative indirect influence on brand risk and brand trust has a strong direct negative effect on brand risk. However, the findings also show that in the luxury market, the greater the affection for the brand, the greater the risk perceived by consumers.Research limitations/implicationsThe study was conducted in a single market, luxury watches and the sample includes both enthusiasts and consumers of the luxury brands.Practical implicationsManagers should be aware of the double-edged role of brand affect on brand risk. The quality of a brand and the trust in its promise decrease the risk to the consumer.Originality/valueThis pioneering study is one of the first to approach an underexplored topic as is the case of the risk associated with a brand in the context of the luxury goods market. Moreover, it relies on an international sample composed of consumers from several countries.