Abstract
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning.

Abstract
The congress which ISPGAYA organized in Vila Nova de Gaia, Portugal, on 26 and 27 October 2023 is based on the theme of sustainability, meeting the current academic, social, and political agenda. The congress is based on the theme of digital sustainability, meeting the current academic, social, and political agenda. This event brings together the different scientific areas of ISPGAYA and seeks to highlight the set of interdisciplinary and multilevel efforts to change the existing reality, supported by the affinity of ideas and attitudes, knowledge, and practices. Contributions from different institutions, national and international, that make up the technological and scientific system, asserting the concertation of strategies at local, regional, and global levels, and the interconnection between synergistic contributions of the living forces to mobilize toward to consolidate planetary sustainability over the next few decades. The scope of this book comprehends manuscripts submitted under the various thematic: Industrial Automation; Energy Efficiency; Information Technology & Cybersecurity; Management & Administration; Marketing; Tourism & Leisure; Education. All these scientific areas are connected by the main issue sustainability. Researchers, professionals, and students from different areas participate in this sharing of knowledge, suggesting a reticular logic and partnerships in the approach to sustainability. The publication of this book of proceedings intends to increase and dynamize investigation linking theory and practice, always pointing to innovative approaches, and mainly, involve as many people and institutions as possible in the direction of a smart national and global development model, meeting the intentions of the 2030 agenda outlined by the United Nations. We lift the veil on responsible innovation including technology innovation and entrepreneurship toward innovation for sustainable development and make positive impact in the ongoing paradigm shift from a same development model to building a safe operating space for a SMART Earth3 model, comprising Local Sustainable Development Goals.

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AbstractThe correction of artifacts in Magnetic Resonance Imaging (MRI) is crucial due to physiological phenomena and technical issues affecting diagnostic quality. Reverting from corrupted to artifact-free images is a complex task. Deep Learning (DL) models have been employed to preserve data characteristics and to identify and correct those artifacts. We proposeMAC, a novel DL-based solution to correct artifacts in multi-contrast brain MRI scans.MACoffers two models: the simulation and the correction models. The simulation model introduces perturbations similar to those occurring in an exam while preserving the original image as ground truth; this is required as publicly available datasets rarely have motion-corrupted images. It allows the addition of three types of artifacts with different degrees of severity. The DL-based correction model adds a fourth contrast to state-of-the-art solutions while improving the overall performance of the models.MACachieved the highest results in the FLAIR contrast, with a Structural Similarity Index Measure (SSIM) of 0.9803 and a Normalized Mutual Information (NMI) of 0.8030. Moreover, the model reduced training time by 63% compared to its predecessor.MACmodel can correct large volumes of images faster and adapt to different levels of artifact severity than current state-ofthe-art models, allowing for better diagnosis.

Abstract
With technological advancement and the urgency to decarbonize energy consumption habits, smart grids have gained special prominence in recent years, highlighting the importance of the massive integration of endogenous renewable sources and decision-making tools, like forecasting tools. The relevance and accuracy of the forecast make it possible to add a contribution to energy management tools in residential communities, from the point of view of end-users and the distribution network operator. This work presents the development of a short-term hybrid forecasting model, combining Long-Short Term Memory (LSTM) model forecast with the Holt-Winters forecast model, where the ability of the LSTM stands out in capturing the complex temporal patterns of historical time series, while Holt-Winters deals with trends and seasonality of historical data. Combining these models results in an intelligent hybrid system capable of efficiently dealing with the complexity inherent to renewable energy. Then, the forecasted results from load and solar generation are introduced on the home energy management model considering a small residential community, showing the relevance of accurate forecasted results tools to assist in the making decisions processes.

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