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.

Abstract
Semantic segmentation consists of predicting a semantic label for each image pixel. While existing deep learning approaches achieve high accuracy, they often overlook the ordinal relationships between classes, which can provide critical domain knowledge (e.g., the pupil lies within the iris, and lane markings are part of the road). This paper introduces novel methods for spatial ordinal segmentation that explicitly incorporate these inter-class dependencies. By treating each pixel as part of a structured image space rather than as an independent observation, we propose two regularization terms and a new metric to enforce ordinal consistency between neighboring pixels. Two loss regularization terms and one metric are proposed for structural ordinal segmentation, which penalizes predictions of non-ordinal adjacent classes. Five biomedical datasets and multiple configurations of autonomous driving datasets demonstrate the efficacy of the proposed methods. Our approach achieves improvements in ordinal metrics and enhances generalization, with up to a 15.7% relative increase in the Dice coefficient. Importantly, these benefits come without additional inference time costs. This work highlights the significance of spatial ordinal relationships in semantic segmentation and provides a foundation for further exploration in structured image representations.

Abstract
Mutation testing maybe used to guide test case generation and as a technique to assess the quality of test suites. Despite being used frequently, mutation testing is not so commonly applied in the mobile world. One critical challenge in mutation testing is dealing with its computational cost. Generating mutants, running test cases over each mutant, and analyzing the results may require significant time and resources. This research aims to contribute to reducing Android mutation testing costs. It implements mutation testing operators (traditional and Android-specific) according to mutant schemata (implementing multiple mutants into a single code file). It also describes an Android mutation testing framework developed to execute test cases and determine mutation scores. Additional mutation operators can be implemented in JavaScript and easily integrated into the framework. The overall approach is validated through case studies showing that mutant schemata have advantages over the traditional mutation strategy (one file per mutant). The results show mutant schemata overcome traditional mutation in all evaluated aspects with no additional cost: it takes 8.50% less time for mutant generation, requires 99.78% less disk space, and runs, on average, 6.45% faster than traditional mutation. Moreover, considering sustainability metrics, mutant schemata have 8,18% less carbon footprint than traditional strategy.