Abstract
Hospitalization has been identified as stress-inducing event that potentially contributes to depression and anxiety among children, particularly when the duration of hospital stay is prolonged. This scoping review seeks to identify the role of videogames and other interactive technology in reducing stress and promoting well-being, exploring the specific considerations for developing videogames for in- patient children and focusing on understanding various outcomes with different types of interactive technologies. The databases used in this research were ACM, PubMed, Wiley Library, yielding a total of 90 articles. Following the application of exclusion criteria 7 articles were selected for analysis. It is noteworthy that many of the included articles exhibit limitations, such as restricted study durations and a small number of participants. Addressing these limitations is crucial for establishing the long-term efficacy of interactive technology and videogames in promoting the well-being of in-patient children.

Abstract
In man-made marine infrastructures, elements such as pillars, cables or ducts are common, which provide distinctive landmarks for Simultaneous Localization and Mapping purposes. In this work, we concentrate on the application of template matching to acoustic imagery for landmark detection and tracking, building on the modeling of common elements in marine environments. The proposed algorithm extends on the original method by employing a density-based clustering technique for match candidate selection and leveraging vehicle inertial information to identify regions of interest in the acquired images, tackling performance deterioration resulting from motion-induced image deformation and overall acoustic feature ambiguity. Experimental results are provided based on datasets collected in a testing pool environment. © 2024 IEEE.

Abstract
Accurate identification of transportation mode distribution is essential for effective urban planning. Recent advancements in machine learning have spurred research on automated Transportation Mode Detection (TMD). While existing TMD methods predominantly employ standard flat classification methods, this paper introduces HiClass4MD, a novel hierarchical approach. By leveraging the misclassification errors from standard flat classifier, HiClass4MD learns the class hierarchy for transportation modes. Although hierarchical met-rics initially indicated performance improvements when applied to real-world GPS trajectories dataset, a subsequent evaluation using conventional metrics revealed inconsistent results. While decision trees benefited marginally, other classifiers exhibited no significant gains or even degraded. This study highlights the complexity of applying hierarchical classification to TMD and underscores the need for further investigation into the factors influencing its effectiveness. © 2024 IEEE.

Abstract
Arbitrary, inconsistent, or faulty decision-making raises serious concerns, and preventing unfair models is an increasingly important challenge in Machine Learning. Data often reflect past discriminatory behavior, and models trained on such data may reflect bias on sensitive attributes, such as gender, race, or age. One approach to developing fair models is to preprocess the training data to remove the underlying biases while preserving the relevant information, for example, by correcting biased labels. While multiple label noise correction methods are available, the information about their behavior in identifying discrimination is very limited. In this work, we develop an empirical methodology to systematically evaluate the effectiveness of label noise correction techniques in ensuring the fairness of models trained on biased datasets. Our methodology involves manipulating the amount of label noise and can be used with fairness benchmarks but also with standard ML datasets. We apply the methodology to analyze six label noise correction methods according to several fairness metrics on standard OpenML datasets. Our results suggest that the Hybrid Label Noise Correction [20] method achieves the best trade-off between predictive performance and fairness. Clustering-Based Correction [14] can reduce discrimination the most, however, at the cost of lower predictive performance.

Abstract
Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire containment and reduce its intensity and rate of spread. However, such a method requires vast amounts of biomass fuels to be removed, over large areas, which can only be achieved through mechanized means, such as through using forestry mulching machines. This dangerous job is also highly dependent on skilled workers, making it an ideal case for novel autonomous robotic systems. This article presents the development of a universal perception, control, and navigation system for forestry machines. The selection of hardware (sensors and controllers) and data-integration and -navigation algorithms are central components of this integrated system development. Sensor fusion methods, operating using ROS, allow the distributed interconnection of all sensors and actuators. The results highlight the system's robustness when applied to the mulching machine, ensuring navigational and operational accuracy in forestry operations. This novel technological solution enhances the efficiency of forest maintenance while reducing the risk exposure to forestry workers.

Abstract
Energy Communities (ECS) and Self- consumption structures are receiving significant attention in Europe due to their potential contribution to a sustainable energy transition and the decarbonization process of the energy system. They are considered a powerful instrument to involve end-consumers in active participation in the energy system by becoming self-producers of renewable electricity and increasing their awareness of their potential contribution by adapting their energy behavior to the global or local power system needs. An EC can also contribute to alleviating energy poverty, which occurs when low incomes and poorly efficient buildings and appliances place a high proportion of energy costs on households. The main driver would be the reduction in energy costs obtained if some members agree to share their surplus electricity at a lower price with vulnerable members. Similarly, a renewable EC (REC) can facilitate access to energy assets by sharing the investments among the community members and exploiting existing complementarities. For example, vulnerable members could share their roofs with others to install solar panels in exchange for low-cost electricity. RECs can also help vulnerable members by reducing the barriers to accessing subsidies for building efficiency investments thanks to collective community initiatives, easing information dissemination and helping with bureaucratic processes.