Abstract

Abstract

Abstract
Aesthetic outcomes in plastic and oncological surgery play a fundamental role in restoring patients' self-esteem, social engagement, and overall quality of life. Yet, managing pre-operative expectations and objectively assessing post-operative results remain as difficult challenges, compounded by the subjective nature of beauty and the scarcity of standardized evaluation tools. To address these challenges, we conduct a systematic review assessing computational methods for the prediction and evaluation of the aesthetic outcomes of plastic and oncological surgery, adhering to PRISMA guidelines. We propose a goal-oriented taxonomy that partitions computational approaches into two main categories: (1) prediction methods that pre-operatively predict the results of surgery through retrieval-based systems, generative artificial intelligence and advanced 3D modeling techniques, and (2) evaluation strategies that assess the post-operative outcomes through objective measurements, traditional machine learning, and deep learning models. Our synthesis indicates a potential paradigm shift from early work that relied on manual image annotation and manipulation to recent research that predominantly employs artificial intelligence. Nevertheless, over 90% of datasets remain private, and validation processes diverge among techniques with similar goals, limiting reproducibility and fair methodological comparisons. We conclude by advocating for the creation of larger publicly accessible datasets, integration of vision-language models to capture patient-reported outcomes, and rigorous clinical validation to ensure equitable, patient-centered care. By bridging computational innovation with clinical practice, this study contributes towards a more transparent, reliable, and personalized aesthetic outcome prediction and assessment.

Abstract
Abstract In the coming years, seaports will undergo significant electrification process, moving away from fossil fuels. In such new reality, obtaining accurate electricity load forecasting is critical for reducing costs, planning infrastructure improvements, and ensuring a stable energy supply. However, studies specifically addressing this need in ports are scarce. This paper presents several novel Long Short-Term memory (LSTM) models for forecasting the electricity demand of a highly electrified port, using the Port of Sines as a case study. These models incorporate operational data, such as vessel arrival schedules and quay crane usage, to enhance forecasting accuracy. Our results show that including these variables significantly improves forecast accuracy, reducing the Mean Absolute Percentage Error (MAPE) from 10.55% to 3.59% compared to models relying solely on historical data. This research provides a robust framework for ports to improve energy management and supports the broader goals of energy efficiency and sustainability in the maritime industry.

Abstract
The increasing adoption of the Digital Twin concept and technology for managing complex physical assets has led to the emergence of Digital Twin Ecosystems, where interconnected digital twins generate additional value. However, ensuring seamless data sharing and interoperability among diverse systems presents significant challenges. Although research on digital twin architectures has advanced, gaps remain in addressing data governance, security, and stakeholders' trust. This study performs a comprehensive literature review to investigate architectural solutions to overcome challenges in digital twin ecosystems. The findings identify key requirements such as interoperability, governance, and data management, emphasizing the role of Data Spaces as enablers of secure data sharing. By structuring the requirements for digital twin ecosystem architectures, this paper identifies gaps suggesting future research on scalable and sustainable digital twin ecosystem implementations. These insights are expected to contribute to the development of frameworks that integrate technical advances with organizational and regulatory considerations, ultimately fostering the adoption of digital twin ecosystems across industries.

Abstract
The notion of Union is strength is essential for preserving public goods and mitigating public bads such as air quality. International environmental agreements serve this role by forming stable coalitions, in which agents join or leave based on free-riding incentives. Building on the Baliga-Maskin model, we show that such coalitions can emerge from a simple Markov chain mechanism where agents enter or exit through utility-based bargaining. However, stable coalition formation is challenging, as members may receive substantially lower utility than free-riders. This asymmetry gives rise to Barrett's paradox of cooperation: even with large coalitions and strong preferences among free-riders, overall utility may remain far below that of the grand coalition. Encouragingly, the paradox of cooperation can be resolved when free-riders have sufficiently low preferences.