Search Results (177)

Enhancing spatial orientation and map-reading skills is a cornerstone of geography education, yet the comparative efficacy of physical versus virtual reality learning environments (VRLEs) remains a subject of ongoing debate. This study evaluates the development of navigational competencies through a counterbalanced crossover experimental design involving 20 geography and geography teacher major students. Participants performed standardized spatial tasks, including bearing calculation and distance estimation, in both the volcanic landscape of the Tapolca Basin, Hungary, and its smartphone-based 360-degree virtual reality (VR) counterpart. To assess longitudinal retention and cross-modal transfer, a three-month interval was maintained between the two learning phases, supported by a robust pre-test/post-test framework. Results indicate that while both environments are susceptible to spatial distortions driven by the visual dominance of physiographic landmarks, VR-based training effectively scaffolds the cognitive frameworks required for real-world navigation. The findings confirm that spatial mental models acquired in a virtual setting possess significant cognitive resilience, as navigational accuracy was maintained over the three-month interval. In conclusion, this research justifies a hybrid pedagogical approach, where immersive digital simulations serve as a preparatory tool for physical fieldwork. The synergy of both modalities is essential for cultivating the resilient spatial intelligence required for professional geographic practice. Full article

The next generation of naval and defense systems will strain current naval ship cooling systems. Throughout its life-cycle, this strain will alter the behavior of the physical system, and any virtual representation of the system will become outdated due to component aging. Digital twins are a trending tool that can assimilate real-time sensor data to tailor a virtual representation to its physical counterpart. The online faithful virtual representation of the physical system provided by digital twins can be used for real-time system optimizations and proactive fault detection, diagnostics, and control adjustments, alleviating the stress of component aging. To support these complex power systems throughout their lifecycles, data-driven solutions for digital twin tuning will become essential. This paper investigates the application of a parameter-tuning digital twin framework to enhance the performance of a multi-physics model. The digital twin framework comprises a digital twin tuning scheme, a physical testbed designed to emulate the cooling system of a ship, and a multi-physics representation of that system. The digital twin tuning scheme leverages a swarm of particles and online sensor data to evaluate permutations of parameters to update the digital representation periodically. The digital twin framework was applied to a physical system to provide experimental data results demonstrating the usefulness of the tuning system. The physical system was designed and constructed to emulate the heat generation and dissipation from 6 liquid-cooled power converters under loads ranging from 10–15 kW at 99% efficiency. Two scenarios were applied to evaluate the performance of the digital twin framework. Results demonstrate that the digital twin framework can adapt to system changes in real-time and improve the accuracy of the related virtual representation by more than 90% when measured at four points of the system under test. Full article

Despite its continued growth, cloud computing remains susceptible to significant security challenges, as shared virtualised environments pose threats at multiple levels. These vulnerabilities are caused by a lack of security coverage in the responsibility model between the provider and the tenant. In this work, we propose the multi-layered architecture VIRTUOSO (VIRTual Unified Operation Security Optimiser) to cover these security gaps through advanced automation and ML. VIRTUOSO has four layers. The Input Layer extracts key risk components from collected telemetry data. The Deep Automation Security Layer provides automated actions and continuous monitoring of security defences. Its counterpart, the Intelligent Security Layer, predicts threats using anomaly detection. The last layer, the Output Layer, returns an aggregated risk summary. The datasets we used were chosen for their relevance: the UNSW-NB15 dataset, a subset of the web-attack classification from CSE-CIC-IDS2018, and a sample of anonymised log events from AWS CloudTrail. Our ensemble classifiers achieve a best accuracy of 95.08% ± 0.13% on UNSW-NB15 (RF), with statistically significant differences among models confirmed by the Friedman test (p < 0.004) and Nemenyi post hoc analysis, and 99.25% ± 0.52% on web-attack (CatBoost), where ensemble differences are not statistically significant (p = 0.093), consistent with the high separability of this dataset. The training-test gap and DNN curves show no overfitting, whereas our adversarial tests show a maximum accuracy loss of 8.1% at ε = 0.02. With these promising results, we can assert that, pending verification in an actual cloud environment and potential integration with FL, our ensemble classifier model appears to be a good real-world prototype. Full article

Three-dimensional integrated circuit (3D IC) packaging and heterogeneous integration have emerged as central pillars of contemporary semiconductor scaling. Yet, the multi-physics coupling inherent to stacked architectures manifesting as thermal hot spots, warpage-induced stresses, and interconnect aging demands monitoring and control capabilities that surpass traditional offline metrology. Although Digital Twin (DT) technology provides a principled route to real-time reliability management, the existing literature remains fragmented and frequently blurs the distinction between static multi-physics simulation workflows and truly dynamic, closed-loop twins. This critical review addresses these deficiencies through three main contributions. First, we clarify the Digital Twin hierarchy to resolve terminological ambiguity between digital models, shadows, and twins. Second, we synthesize three foundational enabling technologies. We examine physics-based modeling, emphasizing the shift from finite-element analysis (FEA) to real-time surrogates. We analyze data-driven paradigms, highlighting virtual metrology (VM) for inferring latent metrics. Finally, we explore in situ sensing, which serves as the “nervous system” coupling the physical stack to its virtual counterpart. Third, beyond a descriptive survey, we outline a possible hybrid DT architecture that leverages physics-informed machine learning (e.g., PINNs) to help reconcile data scarcity with latency constraints. Finally, we outline a standards-aligned roadmap incorporating IEEE 1451 and UCIe protocols to support the transition from passive digital shadows toward more adaptive and fully coupled Digital Twin frameworks for 3D IC manufacturing and field operation. Full article

Accurate alignment of real-world object poses with their virtual counterparts using sensors, e.g. cameras, is essential for consistent interaction in mixed-reality systems. However, objects can undergo abrupt, untracked movements during periods when a tracking system is inactive, e.g., overnight, causing stored pose records to become inconsistent with the real scene and breaking user interaction in the virtual environment. Off-the-shelf 3D reconstruction networks such as MASt3R (Matching and Stereo 3D Reconstruction) method provide metrically scaled 3D point maps and pixel correspondences, but they are trained on static scenes and therefore fail to produce reliable object correspondences when the object has moved. We propose a robust pipeline that combines MASt3R’s metrically scaled 3D outputs with a background-based alignment strategy to recover and apply the true pose change of moved objects. Our method first segments foreground and background and extracts 3D background point sets for a reference day and a current day. An affine transformation between these background point sets is estimated via a standard registration technique and used to express the current-day object 3D coordinates in the reference coordinate frame. Within that unified frame we compute the object pose change and apply the resulting transform to the virtual object, restoring real–virtual consistency. Experiments on real scenes demonstrate that the proposed approach reliably corrects pose misalignments introduced during inactive periods and substantially improves over applying MASt3R alone, thereby enabling restored and consistent user interaction in the virtual environment. Full article

Industrial pouring processes operate under highly dynamic conditions where small deviations can lead to defects, scrap, and production losses. Although modern foundries are equipped with multiple sensors and visual inspection systems, most monitoring approaches remain fragmented, unimodal, and difficult to interpret. Furthermore, annotated anomalous samples in industrial settings are scarce, hindering the development of traditional methods. As a result, many critical pouring anomalies are detected too late or lack sufficient contextual information for effective decision making. In this work, we propose a multimodal framework for industrial scene characterisation that combines visual information and process signals through an explainable Mixture-of-Experts (MoE)-style expert-fusion strategy. First, we deploy an ensemble of specialised modules that collaborate to identify regions of interest, assess pouring quality, and contextualise events within the production process, thereby generating an interpretable description of pouring events. Second, we introduce a novel anomaly detection method for multimodal video data, combining a self-supervised transformer with an outlier-aware clustering algorithm. Our approach effectively identifies rare anomalies without requiring extensive manual labelling. The resulting information is structured into a digital twin-ready representation, supporting synchronisation between the physical system and its virtual counterpart. This solution provides a scalable, deployable pathway to transform heterogeneous industrial data into actionable knowledge, supporting advanced monitoring, anomaly detection, and quality control in real foundry environments. Full article

Protein kinase inhibition can be achieved through various mechanisms, including blocking phosphorylation activity or disrupting regulatory interactions. While small molecule inhibitors have shown promise, their selectivity remains challenging due to the structural similarities among kinase catalytic sites. To design selective kinase inhibitors based on peptide terminal tail interactions with the activation segment, focusing on five kinases with different conformational states: GSK3, PAK4, TTN (OUT conformation) and PKB, FLT3 (IN conformation). Three-dimensional structures from RCSB PDB were optimized using MODELLER version 9.0. Peptide sequences were designed with PeptiDerive (Rosetta) and RosettaDesign version 3.5, followed by pharmacophore modeling based on key interaction residues. Virtual screening was then conducted with PyRx 0.8 and molecular docking with AutoDock Vina 1.1.2. Molecular dynamics simulations were performed using Desmond v6.6 (Schrödinger Suite 2016, Multisim v3.8.5.19) (100 ns, NPT ensemble, 300 K). Analysis of the five kinases revealed distinct interaction profiles with designed peptidomimetic compounds. Kinases displaying the IN conformation of the activation segment (PKB and FLT3) consistently showed superior stability and stronger interaction profiles compared to those in the OUT conformation. The designed compounds formed key hydrogen bonds and hydrophobic interactions with critical residues in the activation segment binding pocket. The most promising inhibitors demonstrated stability throughout the molecular dynamics simulations, with IN conformation kinases maintaining more consistent conformational profiles than their OUT conformation counterparts. Kinases with IN conformation of the activation segment demonstrated superior stability and interaction profiles compared to OUT conformations. These findings contribute to our understanding of selective kinase inhibition and provide a framework for developing novel inhibitors, particularly for PKB and FLT3. The implications of this study extend to rational drug design approaches that leverage natural regulatory mechanisms for therapeutic intervention, though further optimization is needed for GSK-3β, PAK4, and TTN to improve stability and binding affinity. Full article

Background: Given global population growth and aging, it is imperative to prioritize early eye disease detection and treatment. However, as patient volume increases, providers are facing a shortage of workforce capacity, particularly in areas where eye doctors are already scarce, making it important to consider alternative innovative solutions that could help increase eye screening capabilities. This study compared virtual reality (VR) platform of vision screening exams that are used to evaluate ocular health, such as 24-2 perimetry, Ishihara tiles, and the Amsler grid, against their in-clinic counterparts. Methods: A total of 86 subjects were recruited from Mount Sinai’s ophthalmology clinic (New York, USA) for a comparison trial that was internally controlled across healthy eyes and those with glaucoma and retinal diseases. VR and in-office tests were administered to the patients during their clinical visit, including 24-2 perimetry, Ishihara tiles, and the Amsler grid in a randomized order, and the results were compared for each test. Results: Perimetry results from Humphrey Visual Field Analyzer (HVFA) and VR suprathreshold testing demonstrated a good sensitivity both overall (80% OD, 84% OS) and across control (86% OD, 89% OS), glaucoma (69% OD, 78% OS), and retinal disease (76% OD, 80% OS) groups. A Garway-Heath anatomical map showed an overall 70–80% agreement. Ishihara plate tests did not show a significant difference between the two testing modalities (p = 0.12; Mann–Whitney U test), which remained true across all groups. Amsler grid testing differences were also non-significant within each subgroup (p = 0.81; Mann–Whitney U test). Patient time required to complete VR exams was significantly improved (p < 0.0001; Welch’s t-test) compared to the clinical standard tests. Conclusions: All VR-based exams tested in this study showed high sensitivity and percent agreement when compared to their in-office standards. Given the results of this study, VR has a promising potential in visual function screening, which, in addition to its portable design and easy use, could assist eye doctors in screening for prevalent diseases such as glaucoma and retinal conditions. Translational Relevance: VR-based vision exams that test vision fields, color vision and visual distortions provide comparable results in healthy patients, as well as those with glaucoma and retinal diseases, indicating its potential as a screening technology for different ocular pathologies. Given VR’s portable and low-profile features, it is important to consider leveraging VR to augment delivery of vision care. Full article

This review presents the covalent chemistry of carbon from the point of the spin-radical concept of electron interaction in the framework of the unrestricted molecular orbitals (UHF MO) theory. Using the language of valence bond trimodality, the regions of classical spinless spin-symmetric covalence and its spin-dependent asymmetric counterpart are defined. Carbon is the only element exhibiting spin covalent chemistry. Classical covalent chemistry of carbon of molecular substances whose valence bond structure includes segregate or chained single sp³$C-C$ bonds meet its spin counterpart only at these bonds breaking. Substances with double sp²$C=C$ and triple sp¹$C\equiv C$ bonds are the subject of spin covalent chemistry of carbon. The mathematical apparatus of the UHF MO allows forming algorithms controlling the chemical modification of carbon substances, polymerization processes, and catalysis involving them, making it possible to supplement the empirical spin covalent chemistry of carbon with its virtual analog. 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: Among many deleterious effects on the well-being of children and youth, the COVID-19 pandemic contributed to a surge in youth mental health distress. This, coupled with pre-existing prolonged wait times for mental health care, highlighted the need for accessible community-based mental health supports. The Healthy Living Project (HELP) is a virtual lifestyle change support program aimed at promoting positive lifestyle changes and improved mental well-being among youth with mental distress. A pilot feasibility study explored youth engagement with HELP e-resources, and preliminary mental health and lifestyle measures over a 3-month period. Methods: Youth were enrolled in a 3-month pilot of the HELP e-resource. Feasibility metrics (recruitment, retention, and platform engagement) were documented, while exploratory self-reported data on emotional and behavioral difficulties, youth quality of life, sedentary behavior (screen time), sleep hygiene, and physical activity were assessed at baseline and 3 months. Results: Twenty-three youth (mean age 15.7 years, SD 1.7) completed baseline assessments and started the intervention, with ten participants retained by the end of the study. Compared with non-completers (n = 13), study completers (n = 10) tended to report higher quality of life and healthier habits (lower screen time, improved sleep hygiene, and higher activity). Ongoing access to HELP over 3 months was associated with suggestive trends toward improvement in emotional and behavioral difficulties and sleep hygiene. Engaged participants who received screen time education tended to report lower screen times as compared to unengaged counterparts. Conclusions: This study provides early insights into the implementation and acceptability of HELP e-resources among youth experiencing mental distress, with suggestive trends toward potential benefit. Low recruitment and high attrition preclude definitive conclusions, and the findings should be interpreted as exploratory. Lessons from this pilot will inform the design of a subsequent trial to more rigorously evaluate feasibility and the potential impact of HELP on youth with mental distress. Full article

Trust (or distrust) in artificial intelligence (AI) is a critical research topic, given AI’s pervasive integration across societal domains. Despite its significance, scholarly attention to process-based learned trust in AI remains limited. To address this gap, this study designed a virtual non-fungible token (NFT) investment task, featuring seven rounds of risk decision-making scenarios, to simulate an investment/trust game to explore participants’ multifaceted trust under the influence of different chatbots’ social role. The findings suggested the chatbot’s social role had a significant impact on participants’ trust behaviors and perceptions over time. Trust in the two chatbot types diverged until the system-induced failures occurred. The friend-like chatbot elicited a higher level of behavioral trust than the servant-like counterpart. During those trust-damaging moments, the friend-like chatbot proved more effective in mitigating trust erosion and facilitating trust repair, as evidenced by relatively stable investment behaviors. The findings reinforce the notion that friendship with AI can function as a relational buffer, softening the impact of trust violations and facilitating smoother trust recovery. Full article

The concept of a digital twin has become a key driver of industrial transformation, enabling a seamless connection between physical systems and their virtual counterparts. The growing need for adaptability has accelerated the use of advanced technologies and tools to maintain competitiveness. In this context, the article introduces the concept of a hierarchical digital twin and illustrates its operation through a practical example. Production resource structures and timing data were generated in the KbRS (Knowledge-based Rescheduling System), which will serve as the Level II digital twin in this article. The acquired data is transferred via Excel to the FlexSim simulation environment, which represents the Level I digital twin responsible for modeling the flow of production processes. Because a digital twin must accurately reflect a specific production system, the study begins by formulating a general mathematical model. Algorithms for product ordering and for constructing the digital twin of the production processes were developed. Furthermore, three implementation scenarios for the hierarchical digital twin were proposed using the KbRS and FlexSim tools. The implementation of the hierarchical digital twin concept facilitated the development of the more comprehensive virtual model. At the same time, the integration of data between the two software environments enabled the generation of more detailed and precise results. Traditionally, a digital twin created solely within a single simulation platform is unable to represent all the structural components of a production system—an issue addressed by the hierarchical approach presented in this study. Full article

Digital Twins (DTs) integrated with Artificial Intelligence (AI) are emerging as transformative tools in smart manufacturing. By bridging the physical and virtual domains, DTs enable real-time monitoring, predictive analytics, and autonomous decision-making. Originally conceived as advanced simulation models, DTs have evolved significantly with the incorporation of AI, which enhances their ability to acquire process knowledge, optimize scheduling, and autonomously control system variables. This evolution transforms DTs from passive representations into prescriptive, self-optimizing systems. AI-driven DTs support a wide range of applications, including predictive maintenance, process optimization, quality control, and dynamic scheduling, using techniques such as deep reinforcement learning and convolutional neural networks. These capabilities have been successfully deployed across industrial domains such as CNC machining, robotics, and industrial printing, yielding substantial improvements in efficiency, reliability, and responsiveness. Despite these advancements, the full realization of intelligent DTs relies heavily on the availability of high-fidelity, real-time data and a seamless alignment between physical systems and their digital counterparts. This literature survey provides a state-of-the-art review of AI-driven DTs in manufacturing, highlighting their key applications, challenges, and emerging research directions that will shape the future of intelligent and adaptive manufacturing systems. To present a structured perspective on the evolution and scalability of AI-driven DTs, the application case studies are organized according to four integration levels—machine, cell, shop floor, and enterprise—highlighting how these technologies scale from individual assets to fully interconnected manufacturing ecosystems. Full article

This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical assets with their virtual counterparts and management systems. The digital twin acts as a real-time virtual model of critical equipment—such as aeration motors, mixers, and reactors—enabling continuous monitoring, dynamic simulation, and predictive fault detection. Meanwhile, the ERP system provides an integrated environment for maintenance scheduling, data management, and resource allocation, ensuring that maintenance decisions are data-driven and synchronized with operational workflows. Machine learning algorithms, implemented using hybrid physical–data models, predict equipment degradation trends and optimize maintenance interventions. The proposed framework was validated in an industrial-scale composting facility, where results demonstrated a 40% increase in mean time to failure (MTTF), a 35% reduction in repair time, and a 30% decrease in maintenance costs, resulting in a return on investment of 42.5% within the first year. The system’s modular architecture and high adaptability to different machinery types confirm its potential applicability to broader machine design and automation contexts, supporting the transition toward intelligent, self-maintaining industrial systems. Full article