Search Results (1,361)

Copernicus, the Earth Observation component of the European Union Space Programme, plays a key role in monitoring planetary health and informing global sustainability agendas. Enhancing its uptake offers a strategic opportunity to translate climate information into actionable knowledge for sustainable institutional governance. This study examines how data visualization, translating complex climate information into context-relevant formats, can strengthen the uptake of Copernicus Climate Change and Atmosphere Monitoring Service by national institutions. Using the Italian initiative for the National Collaboration Programme of the Copernicus Climate Change Service as an empirical setting, we adopt a mixed-method design to bridge expert visualization practices with institutional stakeholders tasked with sustainability transitions. The findings show that users widely recognize the value of Copernicus. Nonetheless, uptake depends largely on how easily visual outputs can be integrated into workflows and decision procedures. By linking uptake to visualization practices, the study reveals a previously underexplored user–expert gap between production and use contexts. We introduce “adaptive translation” as a framework to align scientific integrity with usability through progressive disclosure, defensibility-oriented design, and iterative feedback loops. The results provide context-sensitive guidance for designing “workflow-ready” visual products in similar national institutional settings, enhancing the capacity of institutional actors to design the climate-resilient actions that are essential for a sustainable future. Full article

Human–computer interaction (HCI) is central to wearable technology; however, traditional interaction methods face constraints from environmental noise, privacy risks, and operational inconveniences. With the convergence of flexible electronics and artificial intelligence, smart wearable systems equipped with biomimetic biointerfaces are evolving into “external organs” that augment human capabilities, establishing a new paradigm for natural and intelligent interaction. This narrative review provides a comprehensive overview of the research progress in seamless HCI driven by wearable biointerfaces and intelligent systems. From the input perspective, we elucidate how high-fidelity physiological and motion signals are captured through biocompatible electronic skins, and subsequently decoded via intelligent algorithms capable of robust noise decoupling, cross-user generalization, and multimodal data fusion, while emphasizing algorithmic trustworthiness including privacy and interpretability. From the output perspective, we explore adaptive closed-loop feedback mechanisms, spanning both non-visual multi-sensory rendering and biomimetic actuation-based physical interventions. Finally, we discuss key engineering and algorithmic bottlenecks—such as material durability, internal latency, system integration, and trustworthiness—offering future perspectives for the development of next-generation personalized and immersive HCI systems. Full article

This is a narrative review that explores the development of non-implantable vestibular devices designed to address postural instability, particularly in aging populations and patients with vestibular hypofunction. It establishes that balance relies on complex sensory integration and that the functional decline of this system creates a significant medical need. Three principal technological strategies are examined: sensory substitution devices, galvanic vestibular stimulation (GVS), and immersive visual feedback systems. Sensory substitution devices, which convert balance data into auditory, tactile, or electrotactile cues, demonstrate significant promise. Examples like vibrotactile belts provide feedback that reduces postural sway, enhancing stability and patient confidence. Parallel to this, GVS—using electrical currents applied to the mastoids—emerges as a potent non-invasive method to modulate vestibular pathways, improving balance control and even inducing neuroplastic changes, especially with stochastic “noisy” signals. The most recently developed devices include augmented and virtual reality technologies that offer innovative visual feedback, creating enriched rehabilitation environments that accelerate recovery by promoting sensory reweighting and neural adaptation. This review concludes that while implantable prostheses are advancing, non-invasive devices offer versatile, affordable, and complementary solutions for balance restoration. The future success of non-invasive alternatives hinges on developing more sophisticated stimulation protocols that account for the complexity of natural movement and individual patient contexts, expanding therapeutic options for vestibular disorders. Full article

Visual object tracking finds extensive application in real-time video analysis on edge devices, yet faces dual challenges: decreased speed due to limited computational resources and weak anti-disturbance capability in complex scenarios. This paper proposes the Hybrid Closed-Loop Tracker (HCLT) to enhance both speed and robustness of embedded visual tracking. HCLT integrates high-precision and high-speed trackers to make real-time performance controllable, while a Kalman filter is employed for state observation and feedback. Within this closed-loop framework, we introduce motion and feature point information as supplementary states and further design mechanisms for adaptive search region adjustment and tracking recovery. Our methods effectively mitigate the impact of external disturbances. Experimental results demonstrate that HCLT further improves both speed and robustness on the basis of high-performance trackers, achieving high tracking accuracy across multiple public benchmark datasets. It demonstrates excellent anti-disturbance performance, particularly in challenging scenarios such as blur and occlusions, while maintaining frame rates exceeding 35 frames per second (FPS) at 720p resolution when deployed on an RK3588 embedded device, thus representing a significant improvement over deep neural network trackers. Full article

Office openness comprises two physically distinct dimensions—spatial openness and visual openness—yet studies quantifying their independent contributions to cognitive efficiency at the individual level remain scarce. Prior research has predominantly reported group-mean effects, leaving bidirectional individual responses insufficiently examined. This study independently manipulated both dimensions and measured individual-level EEG responses in 24 adults using a 3 × 3 within-subject factorial design. The beta/alpha ratio change rate was computed as an index of cognitive efficiency. Substantial neurophysiological variation across conditions was confirmed in every participant. The absence of significant group-level effects was interpreted not as a lack of environmental influence but as the result of bidirectional individual responses canceling each other out in group averages. Spatial and visual openness induced response ranges of equivalent magnitude at the individual level, and individually optimal conditions were widely distributed across the nine experimental conditions. The correspondence rate between subjective preferences and EEG-identified optimal conditions did not exceed chance, and this bidirectional cancellation mechanism is proposed as an explanation for the contradictory findings that have long characterized open-office research. These results support design strategies that offer diverse combinations of spatial and visual openness within activity-based working environments, paired with feedback systems grounded in objective cognitive performance data. Full article

Sustainability education increasingly centers on systems and design thinking to address complex socio-environmental challenges. While these approaches enhance reflexivity, interdisciplinarity, and problem-solving capacity, this paper argues that they also translate complex problems into forms that institutions can recognize, act on, and bring to closure. Drawing on institutional theory and visual semiotics, this paper uses grammar in a structural sense to examine how sustainability education organizes perception, responsibility, and action. The analysis focuses on recurring pedagogical images—including the iceberg model, feedback loops, empathy maps, and the double diamond—and is informed by prior analyses of visual representations. Rather than treating these images as representations, this paper analyzes them as pedagogical infrastructures that stabilize recurring grammars of actionability in the sustainability field. These grammars translate disagreement, complexity, uncertainty, causality, and moral distance into forms that are legible, actionable, and provisionally closable within institutional contexts. While this alignment enables coordination and responsiveness, it also narrows the scope of responsibility by privileging synthesis, adaptation, and iteration over redistribution, obligation, and structural transformation. For educators, this framework offers a way to teach students not only to use systems and design tools but also to reflect on what it means to be an agent of change while institutionally embedded. Full article

Nowadays, Python is very popular as the first programming language for novices, including high school students, to learn due to its short code features with rich libraries. Thus, it is important to provide a learning environment supporting studies starting from the fundamentals, since students have no knowledge on how a program runs on a computer. Previously, we have developed a web-based programming learning assistant system (PLAS) to allow the self-study of major programming languages, including Python, by university students. It offers several types of exercise problems that have different learning goals and levels for step-by-step study. Any student answer is automatically marked at the answer interface for quick feedback. However, PLAS has not implemented functions to assist the learning needs of high school-level students. In this paper, we propose a novice-friendly answer interface for a Python programming learning assistant system (PyPLAS) that introduces a code behavior visualization and an AI assistant with learning logs. The visualization allows learners to observe the changes in variable states and the control flow. The assistant provides multi-level hints during learning and reflective feedback after it by analyzing the logs based on engagement, reasoning strategies, learning pace, and tool usage. For evaluation, we implemented the proposed interface using Python Flask for the web platform and Ollama as a locally deployed AI model. A pilot application was conducted with high school students solving introductory Python exercises in PyPLAS. The results showed high task completion, positive questionnaire responses toward embedded visualization and interface usability, and teacher-observed usefulness of the four-dimensional learning analytics for interpreting learner behaviors. These findings provide preliminary evidence for the feasibility and practical value of the proposed interface, while larger controlled studies are required to validate its instructional effectiveness. Full article

Despite the growing use of artificial intelligence (AI) in science education, little is known about the motivational value of AI-supported digital tools in upper-primary ecosystem science learning. This quasi-experimental study examined whether participation in a teacher-guided digital ecosystems unit integrating AI-supported elements and interactive non-AI tools was associated with sixth-grade students’ ecosystem achievement, interest in science, attitudes toward science, and science self-efficacy. Four sixth-grade classes in an Israeli elementary school (123 students) participated. The experimental group completed six 45-min lessons; the control group studied the same content without the AI-supported components and integrated digital sequence. Students completed pretest and posttest questionnaires and an ecosystem achievement test; the experimental group also completed a satisfaction questionnaire. Semi-structured interviews were conducted with 10 students from the experimental group. Baseline-adjusted analyses indicated higher post-intervention achievement and motivational outcomes in the experimental group. Boys reported higher interest and self-efficacy than girls, and mothers’ education was positively associated with interest and attitudes. Within the experimental group, satisfaction was positively related to all motivational outcomes and significantly predicted self-efficacy. Interview themes highlighted visualization, feedback, collaboration, and occasional cognitive and technical challenges. Overall, this teacher-guided instructional package was associated with more favorable outcomes under classroom conditions in schools. Full article

Graph-based spatial analysis methods are widely used to evaluate accessibility, visibility, spatial hierarchy, and movement-related properties of architectural floor plans. However, these analyses are often conducted using standalone tools and separate simplified models, which can delay design feedback and introduce additional data preparation steps. This paper presents a BIM-embedded computational workflow for configuring, computing, and visualising spatial graph analyses within Autodesk Revit using Dynamo, Python scripting, and the Accessibility and Visibility Analysis (AVA) package. The contribution is not the development of new graph algorithms, but the documentation of a reproducible workflow that sequences existing tools, graph construction settings, metric configuration, spatial measure computation, and 2D/3D visual feedback within a modelling environment. The workflow is demonstrated through a two-storey residential case study and supports accessibility, visibility, centrality measures, visual step depth, shortest path, isovist, object visibility, and activity-based origin–destination analysis. Particular attention is given to incorporating vertical circulation connections into level-based accessibility graphs for selected cross-level movement analysis. Building on prior AVA–DepthmapX verification by the authors, the paper focuses on workflow transparency, reproducibility, and multi-level accessibility representation. The findings indicate that BIM-embedded spatial graph analysis can support iterative, performance-informed design evaluation. Full article

Virtual reality (VR) is increasingly used in public speaking training, yet the distinct roles of environmental context and virtual audience design remain unclear. This study examines how avatar visual style (realistic vs. stylized) and scene type (natural vs. indoor) influence subjective experience and physiological stress. A total of 132 participants were assigned to a 2 × 2 between-subjects experiment. Subjective experience was assessed using standardized questionnaires, while physiological responses were measured via electrodermal activity and heart rate variability, complemented by post-experiment interviews. Results revealed a dissociation between subjective and physiological responses. Natural environments significantly enhanced user satisfaction and overall experience, whereas avatar style primarily influenced physiological stress. Specifically, stylized avatars elicited lower electrodermal activity than realistic avatars, indicating reduced sympathetic arousal. No significant interaction effects were observed. Mediation analyses showed no significant roles of perceived support or threat, suggesting that physiological responses may not rely on explicit cognitive appraisal. Qualitative findings further indicated that ambiguous audience feedback limited evaluative interpretation. These findings support a dual-pathway framework in which environmental context shapes cognitive–affective experience, whereas avatar realism modulates implicit physiological stress. This study provides theoretical insights and practical implications for designing VR systems that enhance user comfort and reduce stress. Full article

Cycle-to-cycle variability of switching parameters inherent to memristive devices introduces significant problems in the design of neuromorphic systems and non-volatile memory. This study investigates the dynamics of a second-order memristive system incorporating capacitive effects that model parasitic charge within individual memristors, addressing both the technical need for accurate analysis of complex regimes and the demand for exploratory environments. Simulations were performed using CUDAynamics, an interactive software suite developed by the authors, which utilizes parallel computing, primarily via NVIDIA Compute Unified Device Architecture (CUDA). It integrates multiple analysis tools for dynamical systems, including bifurcation diagrams, the largest Lyapunov exponent and periodicity mapping, and interactive navigation in multidimensional parameter spaces. The memristive system was discretized applying multiple integration methods with a fixed time step and various waveforms of the input signal. Analysis tools revealed well-defined regions of chaotic dynamics in the memristor resistance parameter space as functions of input signal properties. Sinusoidal and triangular waveforms produced topologically similar distributions of dynamical regimes, whereas the square waveform, mimicking digital inputs, generated distinct dynamical patterns while still preserving chaotic trajectories under specific conditions. Interactive visualization capabilities of CUDAynamics effectively demonstrate attractor evolution and hysteresis deformation, providing immediate visual feedback that significantly enhances conceptual comprehension of nonlinear feedback mechanisms. Beyond its practical implications for the design of analog and digital memristive devices, CUDAynamics offers a scalable, open-source toolkit to aid researchers and engineers in exploring complex dynamical phenomena. Full article

The convergence of artificial intelligence and robotic surgery is redefining the management of genitourinary cancers by enhancing diagnostic accuracy, surgical precision, and training efficiency. This narrative review explores recent advancements in artificial intelligence applications across the cancer care continuum, with a focus on prostate, kidney, and bladder malignancies. Artificial intelligence tools, particularly those based on machine learning and deep learning, have demonstrated strong performance in analyzing imaging data, segmenting tumors, predicting pathological features, and supporting clinical decision-making. Intraoperatively, artificial intelligence enables skill assessment, personalized feedback, and real-time navigation by processing data from surgical videos and robotic system sensors. Augmented reality and intraoperative modeling further enhance visualization and margin control during complex procedures. The review also discusses emerging technologies such as single-port robotic platforms, which offer advantages in confined anatomical spaces and support less invasive approaches. Additionally, the growing field of telesurgery is addressed, highlighting its feasibility for complex urologic operations across vast distances. While many of these innovations are still in early stages of clinical validation, their integration into practice has the potential to improve oncologic and functional outcomes, expand access to expert care, and foster the development of next-generation surgical strategies in urologic oncology. Full article

Effective laboratory training is essential in engineering education, yet conventional on-site instruction is often constrained by time, accessibility, and safety considerations. To address these challenges, this study presents the design, implementation, and evaluation of a web-based virtual reality (WebVR) representation of a large-scale engineering laboratory constructed from massive colorized point cloud data. This study proposes a novel WebVR approach that integrates Unity and Potree for high-fidelity point-cloud visualization combined with advanced interactive capabilities in a browser-based virtual laboratory. It supports immersive first-person exploration, guided navigation, interactive hotspots conveying equipment and safety information, and emergency evacuation simulations. The usability, usefulness, and acceptance of the virtual laboratory were evaluated through an anonymous questionnaire administered to students and laboratory staff. User evaluation results indicated consistently positive feedback, with 100% of respondents rating the interface/navigation and visual/interactive content as good or excellent, 88.6% identifying scene realism as the biggest system strength (the most frequently selected), 74.3% reporting significantly higher engagement compared with traditional online laboratory training, and 82.9% indicating they would definitely recommend the system as a learning resource. In addition, a thematic analysis of qualitative feedback was performed to inform future enhancements of the WebVR environment. Overall, the findings demonstrate that the WebVR-based virtual laboratory can effectively complement conventional on-site laboratory instruction, offering a scalable, accessible, and low-risk platform that enhances learning experiences in engineering education. Full article

This paper presents FMECA-VR-0.1 Prototype, a Maintenance 4.0-oriented immersive Virtual Reality (VR)-based training platform that integrates tools in a digital and virtual environment with Failure Modes, Effects, and Criticality Analysis (FMECA) and the Qualitative Risk Criticality Matrix (QRCM) to enhance reliability-oriented maintenance training in the wind energy sector. The methodological framework is aligned with the Maintenance Management Model (MMM) developed by INGEMAN. It is applied to a VESTAS V100–2.0 MW wind turbine operating at the Valle de los Vientos Wind Farm in northern Chile. The study includes the definition of the operational context, subsystem-level criticality assessment, and a detailed FMECA of the blade subsystem, which are integrated as analytical layers within the immersive VR environment. The proposed platform enables users to visualize critical components, analyze physical failure modes, understand associated consequences, and review preventive and corrective maintenance strategies in an interactive 3D scenario. Preliminary qualitative feedback suggests potential improvements in user engagement and conceptual understanding; however, no formal experimental validation has been conducted at this stage. The FMECA-VR-0.1 prototype demonstrates a feasible path for incorporating risk-based engineering logic into immersive training ecosystems. It establishes the foundation for future developments involving digital twins, real-time monitoring data, multi-subsystem modeling, and quantitative assessment of learning performance. Full article

Universal Accessibility in Astronomy requires a paradigm shift from visual-centric communication to multisensory data interaction. Because astronomy communication relies inherently on high-resolution imagery and visual metaphors, it creates significant accessibility barriers for blind and low-vision (BLV) audiences. To address this, multimodal encoding offers a feasible and meaningful solution by redistributing information across alternative sensory channels, ensuring that the absence of sight does not preclude the comprehension of spatial data. This article explores the development and evaluation of a low-cost, multimodal tool designed to represent complex astronomical concepts—specifically stellar magnitude and color—through tactile and auditory stimuli. Unlike traditional methods, our approach focuses on the haptic-cognitive link, allowing users to “feel” data through physical relief models. We present a structured impact study involving a heterogeneous group of blind, low-vision, and sighted participants. The methodology followed a mixed-methods approach, including a participatory workshop with 20 individuals and a detailed usability assessment with a core group (n= 6) of blind and low-vision participants. Preliminary results from this pilot phase demonstrate that multimodal integration effectively reduces the perceived mental effort for complex spatial data comprehension. Quantitative and qualitative feedback suggests that tactile-auditory sensory substitution not only improves accessibility but also enhances engagement and information retention across all user groups. These findings highlight the potential of multimodal models in transforming public scientific environments, such as museums and observatories, into inclusive, interactive spaces. Full article