Abstract
Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones.

Abstract
Azobenzenes are a class of compounds which allow the writing and erasure of linear birefringence along any desired direction, through their ability to photoisomerize. This property enables applications requiring polarization control, which, despite extensive exploration in the visible spectrum, have yet to be fully capitalized in the infrared region. This study aims to systematically characterize the creation and relaxation of induced linear birefringence dynamics in azopolymers thin films for the 1550 nm region. Maximum birefringence values as high as 6.02 x 10(-2) were attained during the recording phase with a 445 nm pump laser, that stabilized at 5.40 x 10(-2) during the relaxation phase, achieved for a 2.4 mu m sample. In addition, a maximum phase shift of Delta Phi = 0.54 pi stabilizing at Delta Phi = 0.50 pi, was observed for a 9.7 mu m sample with a 532 nm writing laser. Accordingly, this shows the promising potential of azopolymers for many applications.

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In real-world scenarios, numerous phenomena generate a series of events that occur in continuous time. Point processes provide a natural mathematical framework for modeling these event sequences. In this comprehensive survey, we aim to explore probabilistic models that capture the dynamics of event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that underpin the existing literature on this topic. To structure our survey effectively, we introduce an ontology that categorizes the existing approaches considering three horizontal axes: modeling, inference and estimation, and application. We conduct a systematic review of the existing approaches, with a particular focus on those leveraging deep learning techniques. Finally, we delve into the practical applications where these proposed techniques can be harnessed to address real-world problems related to event modeling. Additionally, we provide a selection of benchmark datasets that can be employed to validate the approaches for point processes.

Abstract
The virtual reconstitution of Castelo de Vide, Portugal, within the FRONTOWNS project, highlights the challenges and successes of multidisciplinary collaboration in heritage preservation through 3D modeling. The goal was to reconstruct the town's urban evolution, focusing on its role as a border settlement from the 13th to 16th centuries. The project combined archaeological evidence, historical sources, and digital technologies like photogrammetry and 3D scanning. Co-creation workshops aligned diverse knowledge, leading to creative solutions that balanced historical accuracy and technical feasibility. Despite budget constraints, it produced a high-quality digital reconstitution with insights for future virtual heritage projects.

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