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
High-frequency radio networks, including those operating in the millimeter-wave bands, are sensible to Line-of-Sight (LoS) obstructions. Computer Vision (CV) algorithms can be leveraged to improve network performance by processing and interpreting visual data, enabling obstacle avoidance and ensuring LoS signal propagation. We propose a vision-aided Radio Access Network (RAN) based on the O-RAN architecture and capable of perceiving the surrounding environment. The vision-aided RAN consists of a gNodeB (gNB) equipped with a video camera that employs CV techniques to extract critical environmental information. An xApp is used to collect and process metrics from the RAN and receive data from a Vision Module (VM). This enhances the RAN's ability to perceive its surroundings, leading to better connectivity in challenging environments. © 2025 IFIP.

Abstract
A comprehensive understanding of the mechanisms underlying bone fatigue failure is crucial for advancing treatment strategies. In this regard, this study presents a novel approach to quantify crack propagation in cortical bone tissue through fatigue testing under mode I loading. To closely replicate real bone damage mechanisms, pre-cracked bone samples were subjected to cyclic loading. A compliance-based beam method and cubic B-spline interpolation method were employed to accurately extract fatigue coefficients and reduce experimental noise, yielding refined modified Paris law coefficients. A cohesive zone model for high-cycle fatigue was used to simulate crack propagation, capturing the nonlinear material response by means of the cohesive zone length, mimicking the non-negligible fracture process zone. The goal is to validate the followed experimental procedure. This study offers valuable insights into the fatigue and fracture mechanisms in cortical bone, providing a more accurate and realistic framework for characterizing fatigue life compared to previous methodologies. Coefficients produced from the cohesive model may be readily integrated into simulation tools commonly used in many areas of engineering, allowing biomechanical experts to create more robust designs that simulate actual world conditions for application in implants and orthopaedic structures.

Abstract
In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global inter-pretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.

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