Abstract
Port wine stocks ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Varying climate conditions in the limited supply region lead to stochastic purchase prices for wine grapes. Decision makers must integrate recurring purchasing, production, and issuance decisions. Because stocks from different age classes can be blended to create final products, the solution space increases exponentially in the number of age classes. We model the problem of managing port wine inventory as a Markov decision process, considering decay as an additional source of uncertainty. For small problems, we derive general management strategies from the long-run behavior of the optimal policy. Our solution approach for otherwise intractable large problems, therefore, first aggregates age classes to create a tractable problem representation. We then use machine learning to train tree-based decision rules that reproduce the optimal aggregated policy and the enclosed management strategies. The derived rules are scaled back to solve the original problem. Learning from the aggregated optimum outperforms benchmark rules by 21.4% in annual profits (while leaving a 2.8%-gap to an upper bound). For an industry case, we obtain a 17.4%-improvement over current practices. Our research provides distinct strategies for how producers can mitigate climate risks. The purchasing policy dynamically adapts to climate-dependent price fluctuations. Uncertainties are met with lower production of younger products, whereas strategic surpluses of older stocks ensure high production of older products. Moreover, a wide spread in the age classes used for blending reduces decay risk exposure.

Abstract
Outlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset's distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

Abstract
In this letter, we propose a method for utilizing the internal cavities of optical circulator devices-commonly referred to as parasitic cavities-as optical reference cavities. The method involves using an optical circulator operating at 1550 nm, illuminated by a light source at 1330 nm, thereby enhancing the amplitude of the interferometric signals generated by the internal optical cavities. The system was characterized by using both an Optical Spectrum Analyzer (OSA) and the Low-Coherence Interferometry (LCI) technique. Experimental results indicate that the Optical Path Difference (OPD) remains constant with varying aperture sizes, thereby confirming the feasibility of employing the optical circulator as a reference sensor. Finally, its performance as a reference sensor is demonstrated through its integration with an external cavity that functions as a displacement sensor.

Abstract
In recent years, companies have faced increasing pressure from globalization, requiring them to adapt not only to survive but also to thrive in a highly competitive environment. This adaptation has been facilitated by the efficient integration of technology, achieved through digital processes and collaboration tools. Digital transformation has emerged as a critical element for maintaining competitiveness as economies become increasingly digital. To succeed in this everevolving environment, companies must balance leveraging existing strengths with seeking new organizational agility. Integrating advanced technologies like Artificial Intelligence (AI) and Web Technologies into education and professional training is a strategic response to the challenges posed by the current digital landscape. AI, with its adaptability and automation capabilities, offers benefits such as increased efficiency, personalized learning, and streamlined administrative processes. Continuous evaluation of teaching and learning, along with data extraction and predictive analysis, enhances e-learning quality and informs organizational decisions. This research aims to investigate how advanced technologies can predict and adapt organizational training needs to improve competency development and overall effectiveness. The research adopts a Design Science Research (DSR) methodology, focusing on the development and implementation of an AI-based framework for personalized training recommendations. Expected outcomes include integrating AI-driven predictive models with existing Human Resources Management Systems to identify and address training needs, fostering employee skill development, organizational agility, and competitiveness in a rapidly changing market. Additionally, addressing this issue promotes a more inclusive and empowering work environment, enabling employees to thrive in an increasingly digital world.

Abstract
This study investigates metropolitan-level initiatives in the Porto Metropolitan Area, located in northern Portugal, aimed at fostering healthy and sustainable communities. It maps 41 ongoing municipal projects and analyzes their key thematic areas. In addition to identifying focus areas, the study explores the governance models behind these initiatives, with particular attention to citizen participation and intersectoral coordination. It also examines how local authorities mobilize resources and implement policies, while assessing the integration of Sustainable Development Goals 3 and 11 (SDGs 3 and 11). Employing a qualitative approach through a thematic analysis, and using a comparative framework analysis, this study identifies strong thematic convergence around civic participation, social inclusion, well-being, and environmental sustainability. These elements emerge as key pillars of sustainable development, supporting systemic action across all levels. Citizen participation proves to be a strategic governance tool, requiring structured mechanisms like participatory budgeting. The study also highlights the role of stakeholders, especially schools and companies, in scaling good practices and stresses inclusive policies to reach vulnerable groups. It concludes by advocating for collaborative local governance capable of effectively coordinating diverse actors and promoting equitable, sustainable outcomes.

Abstract
The concept of data spaces has emerged as a structured, scalable solution to streamline and harmonize data sharing across established ecosystems. Simultaneously, the rise of AI services enhances the extraction of predictive insights, operational efficiency, and decision-making. Despite the potential of combining these two advancements, integration remains challenging: data spaces technology is still developing, and AI services require further refinement in areas like ML workflow orchestration and energy-efficient ML algorithms. In this paper, we introduce an integrated architectural framework, developed under the Green.Dat.AI project, that unifies the strengths of data spaces and AI to enable efficient, collaborative data sharing across sectors. A practical application is illustrated through a smart farming use case, showcasing how AI services within a data space can advance sustainable agricultural innovation. Integrating data spaces with AI services thus maximizes the value of decentralized data while enhancing efficiency through a powerful combination of data and AI capabilities.