Abstract
Music-based therapies have been yielding favorable clinical outcomes in children with Autism Spectrum Disorder (ASD). However, there is a lack of guidelines for content selection in music-based interventions. In this context, we propose a methodology for conducting experimental studies on musical preferences in children diagnosed with ASD. It consists of a generative music system with seven manipulable musical parameters where participants are encouraged to create music content according to their preferences. We conducted a preliminary transversal study with 24 children in the state of Pará, Brazil. The results suggest preferences for fast tempo, higher pitch, consonance, high event density, and timbres with smooth attacks. Intriguingly, the results revealed inconsistency in the identified preferences across therapy sessions. The critical need for personalized regulation in music-based interventions for children with ASD highlights the unique nature of individual responses, emphasizing the imperative of tailoring therapeutic approaches accordingly. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

Abstract
The assessment of deposited material in tunnel reinforcement operations can be performed using a 3D model generated from multiple scans. For this purpose, an accurate alignment of the scanned models is required. Aligning existing structure model with data scanned after surface deformations can be challenging, particularly if reference markers are not available or were displaced. For scenarios where the surrounding structure is largely changed, certain procedures can be adapted when processing the scanned data to achieve consistent alignment between scanned and reference structure models. This work proposes a methodology to cope with these situations, analysing the impact of different approaches. Experiments were performed in a realistic scenario related with shotcrete of railway tunnels wall surfaces, with the results showing the applicability of the developed work. The proposed procedure relies in highlighting the importance of specific points that describe the same feature in the reference and aligning PC. The proposed methodology achieved an RMS difference of 0.0173 m, which lead to a drastic improvement in the point cloud alignment compared to the use of standard ICP algorithm without data preprocessing, which achieved 0.0518 m in the studied use-case.

Abstract
This paper presents a study comparing different user interface modes (Controller-Based Selection, Object Interaction, and Voice Recognition) within immersive Virtual Reality (iVR) environments for foreign language learning. Given the rapid advancements and potential of iVR in education, there is a need for focused research on optimising user interfaces for effective learning experiences. This study aimed to identify optimal interfaces for integrating iVR applications as complementary educational tools while gauging student preferences. Participants engaged in interactive learning tasks across the three conditions, with assessments focused on System Usability, Presence, User Satisfaction, Cybersickness, Learning Outcomes, and Task Duration. Findings indicate high usability across all conditions, with a preference observed for Controller-Based Selection and Object Interaction. Object Interaction showed strong motivational appeal but required more time to complete tasks than Controller-Based. Therefore, for time-constrained educational settings, the Controller-Based Selection interface is practical due to its lower physical effort requirement. Despite recent advances, our study found Voice Recognition interaction to be the least preferred interaction method, indicating a need for further technological improvements to boost its acceptance and effectiveness in educational contexts.

Abstract
Breast cancer remains a significant global health concern. This study presents an image retrieval system to aid specialists in the analysis of mammogram images. The system employs individual classifiers for eight dimensions: laterality, view, breast density, BI-RADS classification, masses, calcifications, distortions, and asymmetries. Four pre-trained networks, ResNet50, VGG16, InceptionV3, and InceptionResNetV2, were used to train these classifiers. The retrieval model combines these classifiers through a weighted sum. Four weight assignment strategies were explored, ranging from equal weights to weights based on empirical, literature-based, and specialist-informed considerations. Results are illustrated using the INBreast database, which comprises 410 images. Besides the native annotations, ground truth to validate retrieval models had to be acquired. Classification accuracy is as high as 100% for some of the dimensions. Results also demonstrate the effectiveness of the proposed weighted-sum approach, with variations in weight assignments impacting model performance. © 2024 Owner/Author.

Abstract
Seasonal and daily variations of gaseous emissions from naturally ventilated dairy cattle barns are important figures for the establishment of effective and specific mitigation plans. The present study aimed to measure methane (CH4) and ammonia (NH3) emissions in three naturally ventilated dairy cattle barns covering the four seasons for two consecutive years. In each barn, air samples from five indoor locations were drawn by a multipoint sampler to a photoacoustic infrared multigas monitor, along with temperature and relative humidity. Milk production data were also recorded. Results showed seasonal differences for CH4 and NH3 emissions in the three barns with no clear trends within years. Globally, diel CH4 emissions increased in the daytime with high intra-hour variability. The average hourly CH4 emissions (g h-1 livestock unit- 1 (LU)) varied from 8.1 to 11.2 and 6.2 to 20.3 in the dairy barn 1, from 10.1 to 31.4 and 10.9 to 22.8 in the dairy barn 2, and from 1.5 to 8.2 and 13.1 to 22.1 in the dairy barn 3, respectively, in years 1 and 2. Diel NH3 emissions highly varied within hours and increased in the daytime. The average hourly NH3 emissions (g h-1 LU-1) varied from 0.78 to 1.56 and 0.50 to 1.38 in the dairy barn 1, from 1.04 to 3.40 and 0.93 to 1.98 in the dairy barn 2, and from 0.66 to 1.32 and 1.67 to 1.73 in the dairy barn 3, respectively, in years 1 and 2. Moreover, the emission factors of CH4 and NH3 were 309.5 and 30.6 (g day- 1 LU-1), respectively, for naturally ventilated dairy cattle barns. Overall, this study provided a detailed characterization of seasonal and daily gaseous emissions variations highlighting the need for future longitudinal emission studies and identifying an opportunity to better adequate the existing mitigation strategies according to season and daytime.

Abstract
Power-to-Hydrogen (P2H) clean systems have been increasingly adopted for Virtual Power Plant (VPP) to drive system decarbonization. However, current models for the joint operation of VPP and P2H often disregard the full impact on grid operation or hydrogen supply to multiple consumers. This paper contributes with a VPP operating model considering a full Alternating Current Optimal Power Flow (AC OPF) while integrating different paths for the use of green hydrogen, such as supplying hydrogen to a Combined Heat and Power (CHP), industry and local hydrogen consumers. The proposed framework is tested using a 37-bus distribution grid and the results illustrate the benefits that a P2H plant can bring to the VPP in economic, grid operation and environmental terms. An important conclusion is that depending on the prices of the different hydrogen services, the P2H plant can increase the levels of self-sufficiency and security of supply of the VPP, decrease the operating costs, and integrate more renewables.