Abstract
This paper proposes a stochastic optimization model for industrial hubs to enable their participation in energy markets. The model aims to leverage the resources of multi-energy systems to minimize energy costs in the day-ahead market. It accounts for uncertainties in photovoltaic generation, electrical and heat demand, and outdoor temperatures. A comparison is made with a deterministic approach, along with an analysis of the impact of thermal storage and reserve market participation on costs and bidding strategies. The results show that the stochastic approach is more conservative than the deterministic, while the integration of thermal storage and reserve services help decrease costs.

Abstract
The main challenges hindering the adoption of deep learning-based systems in clinical settings are the scarcity of annotated data and the lack of interpretability and trust in these systems. Concept Bottleneck Models (CBMs) offer inherent interpretability by constraining the final disease prediction on a set of human-understandable concepts. However, this inherent interpretability comes at the cost of greater annotation burden. Additionally, adding new concepts requires retraining the entire system. In this work, we introduce a novel two-step methodology that addresses both of these challenges. By simulating the two stages of a CBM, we utilize a pretrained Vision Language Model (VLM) to automatically predict clinical concepts, and an off-the-shelf Large Language Model (LLM) to generate disease diagnoses grounded on the predicted concepts. Furthermore, our approach supports test-time human intervention, enabling corrections to predicted concepts, which improves final diagnoses and enhances transparency in decision-making. We validate our approach on three skin lesion datasets, demonstrating that it outperforms traditional CBMs and state-of-the-art explainable methods, all without requiring any training and utilizing only a few annotated examples. The code is available at https://github.com/CristianoPatricio/2step-concept-based-skin-diagnosis.

Abstract
The ever-changing landscape of distance learning AI and learning analytics transforms engagement and efficiency in education. AI systems analyze behavior and performance data to provide real-time feedback for improved outcomes. Learning analytics further help educators to identify at-risk students while fostering better teaching strategies. By integrating AI with learning analytics, distance education becomes more inclusive, ensuring learners receive the support necessary to thrive in an increasingly digital and knowledge-driven world. AI and Learning Analytics in Distance Learning explores the development of distance learning. It examines the challenges of using these systems and integrating them with distance learning. The book covers topics such as AI, distance learning technology, and management systems, and is an excellent resource for academicians, educators, researchers, computer engineers, and data scientists. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
Hypertension is the primary risk factor for cerebral small vessel disease (CSVD). However, its mechanistic links are yet to be completely understood. Advancements in diffusion-weighted magnetic resonance imaging (dMRI) increased sensitivity in detecting subtle white matter (WM) structural integrity changes. 44 hypertension patients without symptomatic CSVD underwent multi-modal evaluation of cerebral structure and function, including dMRI, neuropsychological tests and transcranial Doppler monitoring of the right middle cerebral artery (MCA) and left posterior cerebral artery (PCA) to assess neurovascular coupling (NVC). In the PCA, the modeled NVC curve was studied. We examined the cross-sectional relationship of WM integrity with NVC and cognitive performance, using correlational tractography. Diffusion measures from two dMRI models were used: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity from diffusion tensor imaging, and quantitative anisotropy (QA) and isotropy from q-space diffeomorphic reconstruction. Regarding the NVC in the PCA, vascular elastic properties and initial response speed markers indicated better functional hyperemia with better WM integrity. However, the amplitude suggested increased NVC with worse WM integrity. In the MCA, increased NVC was associated with lower WM integrity. Better cognitive performance associated with preserved WM integrity. Increased functional hyperemia despite worse WM integrity may reflect less efficient NVC in hypertensive patients, potentially arising from (mal)adaptive mechanisms and brain network reorganization in response to CSVD. This observational study highlights the potential of transcranial Doppler and QA as susceptibility markers of pre-symptomatic CSVD.

Abstract
Digital Twins (DTs) for the ocean are rapidly emerging as essential tools for understanding, forecasting, and managing environmental phenomena. However, most existing DT visualization solutions are tightly coupled to specific platforms and lack semantic coherence and interoperability-challenges that are particularly critical in federated and distributed DT systems. Furthermore, visualizing dynamic and spatio-temporal behaviors, such as oil spills, across multiple rendering environments remains a complex, platform-dependent task. In this paper, we present VChor, a domain-agnostic virtual choreography framework designed to address these limitations. Our approach integrates model-driven engineering, semantic web technologies, and platform-independent representations to support the declarative specification of behaviors and visual mappings. A single VChor instance describes spatio-temporal dynamics and associated actions, and can be interpreted by multiple visualization engines (e.g., Unity3D and CesiumJS) without the need for code recompilation or platform-specific programming. We demonstrate our approach through a real-world oil spill monitoring use case, developed in the context of the ILIAD H2020 project, and encapsulated within a modular Application Package. This package automates the generation, validation, and transformation of virtual choreographies from raw data to platform-specific outputs. The framework promotes interoperability, reusability, and scalability, while supporting FAIR principles in environmental Digital Twin workflows. The findings highlight VChor's potential to streamline scenario modeling, enable cross-platform visualization, and support decision-makers with accurate, flexible, and reusable visual representations of ocean dynamics.

Abstract
Interferometric observations of gravitational microlensing events offer an opportunity for precise, efficient, and direct mass and distance measurements of lensing objects, especially those of isolated neutron stars and black holes. However, such observations have previously been possible for only a handful of extremely bright events. The recent development of a dual-field interferometer, GRAVITY Wide, has made it possible to reach out to significantly fainter objects and increase the pool of microlensing events amenable to interferometric observations by 2 orders of magnitude. Here, we present the first successful observation of a microlensing event with GRAVITY Wide and the resolution of microlensed images in the event OGLE-2023-BLG-0061/KMT-2023-BLG-0496. We measure the angular Einstein radius of the lens with subpercent precision, theta E = 1.280 +/- 0.009 mas. Combined with the microlensing parallax detected from the event light curve, the mass and distance to the lens are found to be 0.472 +/- 0.012 M circle dot and 1.81 +/- 0.05 kpc, respectively. We present the procedure for the selection of targets for interferometric observations and discuss possible systematic effects affecting GRAVITY Wide data. This detection demonstrates the capabilities of the new instrument, and it opens up completely new possibilities for the follow-up of microlensing events and future routine discoveries of isolated neutron stars and black holes.