Abstract
Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non-curated nature of the crowd-originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state-of-art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream-based scenario, and identifies future research opportunities.

Abstract
The present work is inspired by the aftermarket companies of the automotive industry. The goal is to investigate how companies react to market change, by understanding the effect of a perturbation (such as a business cessation) on the rest of the companies that are interconnected through peer-to-peer relationships. An agent-based model has been developed that simulates a multilayer network involving different types of companies: suppliers, aftermarket companies; retailers and consumers. The effect of the cessation is measured by the resilience of the multilayer network after suffering the perturbation. The multilayer network is inspired in a business model of the automobile industry’s aftermarket and each type of company has some defined characteristics. The agent-based model produces the network dynamics due to the changes in its configuration throughout time. No learning mechanism is introduced in this work. We demonstrate that the number of links, the volume of sales and the total profit of a node in the network has an impact on its survival throughout time. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The integration of Distributed Energy Resources (DERs) into low-voltage (LV) distribution grids poses significant challenges for grid management, particularly regarding the need for accurate information on the connection phases of installations to ensure proper load balancing and to enhance hosting capacity. This paper presents a novel voltage-based phase mapping approach using the Mahalanobis-Wasserstein (MW) distance - a metric that exploits voltage time series data to accurately assign users to their corresponding phases without requiring additional hardware or prior knowledge of the grid's topology. The proposed method demonstrates strong resilience to missing data, a frequent issue in real-world deployments, and incorporates a confidence score to quantify the reliability of the phase assignments.

Abstract
BackgroundDialysis recovery time (DRT) refers to the period during which fatigue and weakness subside following hemodialysis treatment, allowing patients to resume their daily routines. This study aimed to identify the factors influencing DRT in hemodialysis patients in Turkey and Portugal, where the prevalence of chronic kidney disease is notably high.MethodsA cross-sectional observational study was conducted in a private dialysis center in Turkey and three dialysis centers in Portugal. The study included hemodialysis patients aged 18 years or older who had been undergoing four-hour hemodialysis sessions three times a week for at least six months. Participants had no communication barriers and voluntarily agreed to take part in the study. Data were collected using a semi-structured questionnaire to gather descriptive characteristics and the Hospital Anxiety and Depression Scale. Logistic regression analysis was employed to identify independent variables influencing DRT.ResultsA total of 294 patients participated in the study, including 187 from Turkey and 107 from Portugal. In Turkey, increased interdialytic weight gain (P = 0.043) was associated with prolonged recovery time, while the use of high-flux dialyzers (P = 0.026) was linked to shorter recovery times. In Portugal, older age (P = 0.020) was found to extend recovery time.ConclusionRecovery time after dialysis is influenced by varying factors across different countries. Further research with larger sample sizes is needed to deepen understanding of these factors and their implications.Clinical trial numberNCT04667741.

Abstract
Contemporary transformations in the world of work, together with the growing emotional and physical demands in nursing, have led to the emergence of new labor phenomena such as quiet quitting, which reflects changes in professional engagement and in the management of nurses' well-being. Objective: To translate, culturally adapt, and validate the Quiet Quitting Scale for European Portuguese, evaluating its psychometric properties among the nursing population. Methods: A cross-sectional validation study was conducted following COSMIN guidelines. The process included forward and back translation, expert panel review, and pretesting with 30 nurses. The psychometric evaluation was carried out with 347 nurses from Northern Portugal. Data were analyzed using descriptive and inferential statistics, internal consistency measures (Cronbach's alpha and McDonald's omega), and confirmatory factor analysis (CFA) with maximum likelihood estimation to assess construct validity. Results: The Portuguese version (QQS-PT) maintained the original three-factor structure (Detachment/Disinterest, Lack of Initiative, and Lack of Motivation). The model showed satisfactory fit indices (CFI = 0.936; GFI = 0.901; AGFI = 0.814; TLI = 0.905; RMSEA = 0.133). The overall internal consistency was excellent (alpha = 0.918; omega = 0.922), with subscale alpha ranging from 0.788 to 0.924. Composite reliability (CR) ranged from 0.815 to 0.924, and average variance extracted (AVE) from 0.606 to 0.859, confirming convergent and discriminant validity. Conclusions: The QQS-PT demonstrated a stable factorial structure, strong reliability, and solid validity evidence. It is a brief and psychometrically sound instrument for assessing quiet quitting among nurses, providing valuable insights for research and management of professional engagement and well-being in healthcare contexts.

Abstract
The increasing integration of Artificial Intelligence (AI) across various sectors of society raises complex ethical challenges requiring systematic and scalable oversight mechanisms. While tools such as AIF360 and Aequitas address specific dimensions, namely fairness, there remains a lack of comprehensive frameworks capable of auditing multiple ethical principles simultaneously. This paper introduces a multidimensional AI auditing tool designed to evaluate systems across key dimensions: fairness, explainability, robustness, transparency, bias, sustainability, and legal compliance. Unlike existing tools, our framework enables simultaneous assessment of these dimensions, supporting more holistic and accountable AI deployment. We demonstrate the tool's applicability through use cases and discuss its implications for building trust and aligning AI development with fundamental ethical standards.