Search Results (189)

In light of the growing prominence of environmental issues, the frequent occurrence of unexpected incidents, and the dynamic challenges of a changing market environment, suppliers must possess comprehensive capabilities that encompass both green and sustainable development as well as resilience to risks. Consequently, green supplier selection has emerged as a critical research topic. By integrating virtual and physical systems, digital twin technology enhances supply chain transparency and efficiency—a capability that plays a significant role in advancing sustainable supply chain development. In view of this, this study incorporates risk factors into the green supplier evaluation system, introduces indicators related to digital twin technology, and proposes a stochastic slack-based measure data envelopment analysis method, namely SSBM, for evaluating green suppliers. This approach expands and refines the existing evaluation criteria and the decision-making model. Finally, a numerical case study is conducted to validate the feasibility of the proposed method. This research provides more systematic and scientific decision support for green supplier selection, enriching the theoretical and practical applications in the fields of green supply chain and multi-criteria decision-making. Full article

Sensor bias faults in closed-loop HVAC systems pose a detection challenge that is both subtle and costly. Because the control loop compensates for biased readings by driving the affected sensor back toward its setpoint, the fault becomes invisible to conventional threshold monitors. The anomaly does not vanish, however; it is redistributed across correlated sensors, disrupting their mutual consistency. We propose a framework that automatically derives virtual temperature sensor models from BRICK schema metadata. LightGBM regressors, trained on fault-free inter-sensor relationships, produce z-scored prediction residuals that serve as detection signals. Fault isolation is achieved by ranking sensors by their median daily anomaly scores; fault-type discrimination relies on analysis of actuator command-position discrepancies. On the Lawrence Berkeley National Laboratory (LBNL) fault detection and diagnosis (FDD) benchmark, the method achieves an area under the receiver operating characteristic curve (AUC) of 0.9992 for the mildest sensor bias (SA +2 °C), an AUC of 1.0 for all other single-duct air handling unit (SD-AHU) scenarios, and an AUC of 1.0 for all fan coil unit (FCU) sensor bias scenarios. In all four SD-AHU sensor bias scenarios, the biased sensor (SA_TEMP) ranks first or second; for the larger biases (±4 °C), SA_TEMP consistently ranks first. A robustness analysis over 10 random seeds confirms that detection AUC remains above 0.997 in all cases. Sensor and mechanical faults fall into non-overlapping clusters in the command-position discrepancy space. On the FCU system, the proposed method substantially outperforms principal component analysis (PCA) (AUC = 1.0 versus 0.63–0.90) and provides diagnostic capabilities not available with PCA. Notably, a single pipeline function handles both system types without modification, confirming cross-system scalability through the BRICK metadata layer. The results confirm that BRICK-automated virtual sensor construction is a viable approach for scalable, deployment-ready sensor validation in smart-building HVAC systems. Full article

History museum exhibition spaces convey historical and cultural information through static artifacts, graphic–text narratives, and spatial atmosphere. The light environment affects not only exhibit visibility but also visitors’ visual comfort and perceptual preference. However, existing studies mainly focus on single lighting parameters, and perceptual differences across multiple lighting conditions remain insufficiently understood. This study investigated static exhibition spaces in history museums through a comparison of 12 virtual lighting conditions generated from different combinations of ambient illuminance, exhibit illuminance, and correlated color temperature. Visitors’ visual behavior and subjective perception were evaluated through eye-tracking experiments, heatmap analysis, and Likert-scale ratings. Different lighting combinations significantly affected visual attention allocation and subjective evaluation. Total duration of fixation, number of fixations, and average pupil diameter showed significant differences across conditions, whereas average fixation time did not. Overall, moderate ambient illuminance and higher exhibit illuminance were associated with more stable visual responses and more positive perceptual evaluations, while correlated color temperature showed a regulatory effect within the tested range of 3000–4000 K. These findings provide preliminary evidence for understanding perceptual responses to lighting combinations in static exhibition spaces and may inform future field-based validation of museum lighting design. Full article

This paper presents the application of a structural finite element model (FEM) of a light patrol aircraft for numerical flutter analysis. The thin-walled structure was developed using 2D shells and additional 1D beam elements. The virtual structure was supplemented with additional point elements imitating lumped masses of non-structural on-board components. The model was subjected to validation for qualities such as the mass distribution, its CG location, the structural stiffness of its airframe units, and the similarity of natural modes. The comparative analyses showed satisfactory consistency of the mass and stiffness properties of the FEM with the actual aircraft. Numerical flutter analysis was then performed with the MD Nastran for an integrated aeroelastic model consisting of the FEM and the simplified aerodynamic model. The critical velocities of basic flutter modes were determined. Using simplified kinematic models of flight control systems built into the FEM, an analysis of the sensitivity of control surface flutter due to the pilot’s influence was carried out. The stick grip and the support of control pedals with the pilot’s legs cause specific conditions related to the imposition of additional stiffness and mass on the control manipulators. These conditions directly affect the natural frequencies of control surface modes, which translates into a change in the critical flutter speed of the tail. For the established range of changes in stiffness and mass added to the stick and pedals, a series of analyses of natural vibrations and flutter were carried out. The influence of the change in the support conditions of control manipulators was illustrated in graphs. Full article

3D digitization of museum artifacts is essential for both their virtual presentation and re-exhibition in the event of damage or loss. Given the number of artifacts that can be exhibited in a museum, the effectiveness of single-digitization practices under designed conditions is limited in terms of realism. In this study, a highly detailed sine quadrant object was digitized in a museum environment using photogrammetry and structured-light scanning (SLS) techniques. 3D models were generated from point clouds derived in photogrammetry and directly obtained from SLS. In the qualitative assessment based on the distinguishability of linear and edge details, the photogrammetric technique was found to be better; in the quantitative assessment based on the reference length values on the artifact, SLS was better, while photogrammetry was also found to be adequate. The maximum difference values for photogrammetry and SLS were 0.40 and 0.27 cm, respectively, while the average difference values were 0.24 cm and 0.10 cm. Additionally, cloud-to-cloud distance analysis revealed that two-point clouds overlapped quite well geometrically. Point clouds were also compared in terms of homogeneity using outlier detection analysis. This analysis showed that noise in the photogrammetric point cloud had a wider distribution over the artifact. In terms of data acquisition and processing time, SLS was found to be better, while the cost was comparable. After evaluating the techniques from various perspectives, photogrammetry was found to be preferable for modeling in a museum environment due to the priority need for high texture quality from the end-user’s perspective. In this respect, SLS is highly dependent on hardware capability for both data acquisition and processing. Full article

The collection of patient-reported outcome measures (PROMs) is a key measurement tool for patient-centred care. At the same time, collecting these measures poses obstacles for many patients, leading to these groups being underrepresented in the data. We have therefore developed a multimodal, AI-driven assistance system to support patients in collecting these data. The interface of the system comprised a digital tablet containing the PROM questionnaire items and the assistant in three forms of embodiment: A virtual avatar, a physical avatar, and a voice-only agent. To evaluate the users’ experience and ratings of the system, two separate studies were implemented in two rehabilitation centers with 195 patients. A mixed within–between RCT was conducted at an outpatient clinic, where patients completed PROMs both with and without an assistant, and a between-subject design at an inpatient clinic comparing routine PC-based care with avatar- and robot-assisted PROM administration. Our results suggest a preference for the non-assisted tablet-only condition in Clinic A, whereas, in Clinic B, both agent conditions were preferred over routine care. We have further analyzed aspects such as trust and social presence in this study to gain a more thorough understanding of the users’ experience. Our analysis shows a higher trust rating for the voice-only assistant, whereas the robot, virtual avatar, and the voice-only conditions were perceived as more socially present. The impact of demographic factors and affinity for technology on the user ratings was also thoroughly studied. Our findings shed light on the role of agent embodiment in PROM assistance and contribute to the future design and evaluation of effective, engaging, and trustworthy systems for data collection in healthcare settings. Full article

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment. Full article

This practice-led research examines how virtual production represents a circular return to scenographic practice, reactivating integrated modes of spatial authorship that have long underpinned screen storytelling but were obscured by industrial fragmentation. Drawing on a single-day intensive workshop at the Australian Film, Television and Radio School (AFTRS), the study analyses how spatial authorship emerged through embodied, collaborative engagement with an LED volume environment. Grounded in scenographic theory and concepts of distributed cognition and situated authorship, the article reframes virtual production as a condition that renders pre-digital, collaborative modes of making visible within contemporary screen production. The LED volume functions simultaneously as scenic environment, lighting instrument, and compositional partner, requiring participants to negotiate space, light, movement, and camera as a unified spatial event. Analysis identifies how scenographic understanding emerged through virtual scouting, world-responsive storytelling, physical-digital integration, and embodied realisation. The findings extend production design theory by challenging ocular-centric models of mise-en-scène and positioning scenographic integration as screen practice—an epistemic mode of enacting through collective, materially grounded spatial experimentation. While situated within an educational context, the study points to broader implications for how spatial authorship and collective practice are understood in contemporary screen production. Full article

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

Background: Automated visual field testing is fundamental in ophthalmology, but differences in stimulus scaling and luminance between devices hinder direct comparison of sensitivity values. Virtual reality (VR)-based perimetry has emerged as a portable alternative, yet its relationship with conventional perimetry requires clarification. Methods: This prospective cross-sectional study included 60 healthy participants stratified into younger (<50 years) and older (≥50 years) groups. Differential light sensitivity was assessed in the right eye using Humphrey Automated Perimetry (HFA) with the 30-2 test pattern and a VR-based perimeter (Dicopt-Pro) in randomized order. Pointwise sensitivity values were analyzed using linear regression and Bland–Altman analysis, and sensitivity profiles were examined as a function of visual field eccentricity. Results: A strong linear relationship was observed between HFA and Dicopt-Pro sensitivity values in both age groups (R ≥ 0.96). A systematic and approximately constant inter-device offset was identified, with mean differences of 15.7 ± 0.4 dB in younger subjects and 13.7 ± 0.5 dB in older subjects. Bland–Altman analysis showed consistent bias without proportional error. Dicopt-Pro sensitivity profiles demonstrated an eccentricity-dependent decline comparable to HFA while preserving age-related differences. Conclusions: VR-based perimetry using Dicopt-Pro shows sensitivity patterns closely aligned with conventional Humphrey perimetry when a systematic, age-specific inter-device offset is considered, enabling clinically meaningful interpretation of Dicopt-Pro results within an HFA-referenced framework. Full article

In the last decade, the interdisciplinary field of neuroarchitecture has grown significantly, revealing measurable links between architectural features and human neural processing. This review synthesizes current research at the nexus of neuroscience and architecture, with a focus on how emerging virtual reality (VR) and artificial intelligence (AI) technologies are being utilized to understand and enhance human spatial experience. We systematically reviewed literature from 2015 to 2025, identifying key empirical studies and categorizing advances into three themes: core components of neuroarchitectural research; the use of physiological sensors (e.g., EEG, heart rate variability) and virtual reality to gather data on occupant responses; and the integration of neuroscience with AI-driven analysis. Findings indicate that built environment elements (e.g., geometry, curvature, lighting) influence brain activity in regions governing emotion, stress, and cognition. VR-based experiments combined with neuroimaging and physiological measures enable ecologically valid, fine-grained analysis of these effects, while AI techniques facilitate real-time emotion recognition and large-scale pattern discovery, bridging design features with occupant emotional responses. However, the current evidence base remains nascent, limited by small, homogeneous samples and fragmented data. We propose a four-domain framework (somatic, psychological, emotional, cognitive-“SPEC”) to guide future research. By consolidating methodological advances in VR experimentation, physiological sensing, and AI-based analytics, this review provides an integrative roadmap for replicable and scalable neuroarchitectural studies. Intensified interdisciplinary efforts leveraging AI and VR are needed to build robust, diverse datasets and develop neuro-informed design tools. Such progress will pave the way for evidence-based design practices that promote human well-being and cognitive health in built environments. Full article

The present study explores the relationship between the systemic approach, educational innovation, and the use of digital technologies in higher education, with an emphasis on military academies. The aim of the research is to shed light on how systemic thinking can support strategic planning, the quality of education, and the effective integration of innovative practices, such as artificial intelligence, information and communication technologies, and virtual reality. The methodology was based on quantitative research using a questionnaire, which was distributed to 452 members of the Hellenic Non-Commissioned Officers Academy educational community (teaching staff, cadets, and recent graduates). Data analysis showed that the adoption of a systemic approach is positively associated with the readiness of trainers, including both instructors and future professionals (cadets), to support and implement educational innovations. Furthermore, it was found that the clarity of educational objectives and the alignment of critical elements of the educational system (resources, technology, instructors, trainees, and processes) significantly reinforce the intention to adopt innovative practices. The findings also show that educators’ positive perceptions of artificial intelligence and virtual/augmented reality are associated with a higher appreciation of learning benefits, such as improved performance, trainee satisfaction, and collaboration. In contrast, demographic and professional factors have a limited effect on attitudes toward innovation. Overall, findings indicated that innovation in military academies is not limited to the technological dimension, but requires a holistic, systemic approach that integrates organizational, pedagogical, and strategic parameters. The study contributes both theoretically and practically, providing empirical evidence for the role of systemic thinking in the design and implementation of innovative educational policies in military and broader academic education. Full article

Nowadays, despite the advancements in several technological areas, the education process of various subjects shows minimal evolution from the approaches used in prior years. In light of these, some fields struggle to capture the student’s attention and motivation, in particular, when the subject addresses remote locations that students are unable to visit and relate to. Therefore, an opportunity exists to explore novel technologies for such scenarios. This work introduces an educational approach that integrates 3D Reconstruction, Virtual Reality (VR), and a Large Display to enrich Geoscience learning at the university level. In this teacher-centric approach, manipulation of virtual replicas of real-world geological sites can be performed, creating an immersive yet asymmetric collaborative environment for students in the classroom. The teacher’s VR interactions are mirrored on a large display, enabling clear demonstrations of complex concepts. This allows students, who cannot physically visit these locations, to explore and understand the sites more deeply. To evaluate the effectiveness of this approach, a user study was conducted with 20 participants from Geoscience and Computer Science disciplines, comparing the VR-based method with a conventional approach. Analysis of the collected data suggests that, across multiple relevant dimensions, participants generally favored the VR condition, highlighting its potential for enhancing engagement and comprehension. Full article

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

Augmented reality (AR) near-eye displays (NEDs) couple microdisplay image light to the human eye via integrated optical modules, enabling seamless virtual–real fusion. As core components that synergistically transmit and diffract light, diffractive waveguides are promising for next-generation AR NEDs but face two bottlenecks: compromised full-color performance in single-layer structures caused by grating dispersion and lack of scalable fabrication technologies. To address these, we first propose a mass-production-compatible workflow based on deep ultraviolet (DUV) lithography for large-area nanostructured optics. This workflow enables high-precision wafer-level production with 200 mm wafers and nine dies per wafer, overcomes scalability issues, and is fully compatible with straight-configuration nanostructures to ensure manufacturing feasibility. Leveraging this workflow, we develop a single-layer diffractive waveguide system for AR NEDs, which comprises a thin glass substrate, a broadband high-efficiency multi-layer dielectric in-coupler, and a 2D out-coupler that concurrently expands and out-couples light. Rigorous coupled wave analysis (RCWA) optimized coupler diffraction, while ray tracing refined guided light intensity and significantly improved exit pupil uniformity. This work establishes a foundation for full-color, high-efficiency AR waveguides and provides a scalable paradigm for large-area nanostructured optical systems such as telescopes and lithography equipment. Full article