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.

Abstract
Understanding the structural features of perceived musical emotions is crucial for various applications, including content generation and mood-driven playlists. This study performs a comparative statistical analysis to examine the association of a set of musical features with emotions, described using adjectives. The analysis uses two datasets containing rock and pop musical fragments, categorized as human-generated and AI-generated. Focusing on four emotional adjectives (happy, sad, angry, tender-gentle) representing each valence-arousal plane's quadrant, we analyzed semantic differential meanings reported as symmetric pairs for all possible combinations of quadrants through diagonals, vertical, and horizontal axes. The results obtained were discussed based on Livingstone's circular representation of emotional features in music. Our findings demonstrate that the human and AI-generated datasets could be considered equivalent for diagonal symmetries, while horizontal and vertical symmetries show discrepancies. Furthermore, we assessed significant separability for both happy-sad and angry-tender pairs in the human dataset. In contrast, the AI-generated music exhibits a strong differentiation mainly in the angry-gentle pair. © 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 advent of Industry 4.0 has created a need for more flexible and adaptable manufacturing systems. This paper proposes the integration of AAS (Asset Administration Shells), SBM (Skill-based manufacturing) and OPC UA (Open Platform Communications Unified Architecture), to enable more flexible manufacturing systems. The integration of these concepts provides a solution for achieving faster and easier dynamic reconfiguration in manufacturing systems, which is essential for fulfilling the demand of customization and flexibility in modern production systems. An Asset Administration Shell provides a standardized structure for describing assets and their administration, while Skill-based manufacturing enables the deployment of task-oriented machines that can self-configure, self-diagnose, and self-optimize their performance. The use of OPC UA as a communication protocol ensures that these systems can communicate with one another in a secure and reliable way. This paper presents a conceptual framework for the integration of these three open technologies. This framework contributes to having a single interface and source of information for every asset, which can lead to increased efficiency by reducing changeover times, thus reducing the overall cost in flexible manufacturing system scenarios. Future work will focus on the implementation and validation of this framework in a real-world manufacturing setting.

Abstract