Search Results (653)

Cooperative Intelligent Transport Systems (C-ITS) have emerged as a key enabler of more efficient, safer, and environmentally sustainable road traffic by allowing vehicles and infrastructure to exchange information and coordinate behavior. To evaluate their benefits, impact assessment studies are essential. However, most existing studies focus on individual C-ITS services in isolation, overlooking how combined deployments influence outcomes. This study addresses this gap by presenting the first systematic evaluation of individual and joint deployments of Cooperative Adaptive Cruise Control (CACC) and Green Light Optimal Speed Advisory (GLOSA) under diverse conditions. A dual-model simulation framework is applied, combining controlled artificial networks with calibrated real-world corridors in Upper Austria. This allows both statistical testing and validation of plausibility in real-world contexts. Key performance indicators include travel time and CO₂ emissions, evaluated across varying lane configurations, numbers of traffic lights, demand levels, and equipment rates. The results demonstrate that C-ITS effectiveness is strongly context-dependent: while CACC generally provides larger efficiency gains, GLOSA yields consistent emission reductions, and the combined deployment offers conditional synergies but may also diminish benefits at high demand. The study contributes a guideline for selecting service configurations based on site conditions, thereby providing practical recommendations for future C-ITS rollouts. Full article

Background/Objectives: Decision-making under socially evaluative stress engages a dynamic interplay between cognitive control, emotional appraisal, and motivational systems. Contemporary models of multi-level co-regulation posit that these systems operate in reciprocal modulation, redistributing processing resources to prioritise either rapid socio-emotional alignment or deliberate evaluation depending on situational demands. Methods: Adopting a neurofunctional approach, a novel dual-task protocol combining the MetaCognition–Stress Convergence Paradigm (MSCP) and the Social Stress Test Neuro-Evaluation (SST-NeuroEval), a simulated social–evaluative speech task calibrated across progressive emotional intensities, was implemented. Twenty professionals from an HR consultancy firm participated in the study, with concurrent recording of frontal-temporoparietal electroencephalography (EEG) and bespoke psychometric indices: the MetaStress-Insight Index and the TimeSense Scale. Results: Findings revealed that decision contexts with higher socio-emotional salience elicited faster, emotionally guided choices (mean RT difference emotional vs. cognitive: −220 ms, p = 0.026), accompanied by oscillatory signatures (frontal delta: F(1,19) = 13.30, p = 0.002; gamma: F(3,57) = 14.93, p ≤ 0.001) consistent with intensified socio-emotional integration and contextual reconstruction. Under evaluative stress, oscillatory activity shifted across phases, reflecting the transition from anticipatory regulation to reactive engagement, in line with models of phase-dependent stress adaptation. Across paradigms, convergences emerged between decision orientation, subjective stress, and oscillatory patterns, supporting the view that cognitive–emotional regulation operates as a coordinated, multi-level system. Conclusions: These results underscore the importance of integrating behavioural, experiential, and neural indices to characterise how individuals adaptively regulate decision-making under socially evaluative stress and highlight the potential of dual-paradigm designs for advancing theory and application in cognitive–affective neuroscience. Full article

This paper presents the final implemented design and performance evaluation of the ground-based C-band Cheia radar system, developed to enhance Romania’s contribution to the EU Space Surveillance and Tracking (EU SST) network. All data used for performance analysis are real-time, real-life measurements of true spatial test objects orbiting Earth. The radar is based on two decommissioned 32 m satellite communication antennas already present at the Cheia Satellite Communication Center, that were retrofitted for radar operation in a quasi-monostatic architecture. A Linear Frequency Modulated Continuous Wave (LFMCW) Radar design was implemented, using low transmitted power (2.5 kW) and advanced software-defined signal processing for detection and tracking of Low Earth Orbit (LEO) targets. System validation involved dry-run acceptance tests and calibration campaigns with known reference satellites. The radar demonstrated accurate measurements of range, Doppler velocity, and angular coordinates, with the capability to detect objects with radar cross-sections as low as 0.03 m² at slant ranges up to 1200 km. Tracking of medium and large Radar Cross Section (RCS) targets remained robust under both fair and adverse weather conditions. This work highlights the feasibility of re-purposing legacy satellite infrastructure for SST applications. The Cheia radar provides a cost-effective, EUSST-compliant performance solution using primarily commercial off-the-shelf components. The system strengthens the EU SST network while demonstrating the advantages of LFMCW radar architectures in electromagnetically congested environments. Full article

Some laser scanners utilize stepper motor-driven optomechanical assemblies to position the laser beam precisely during triangulation. In laser scanners such as the presented Technical Vision System (TVS), to enhance motion resolution, gear transmissions are implemented between the motor and the optical assembly. However, due to the customized nature of the mechanical design, errors in manufacturing or insufficient mechanical characterization can introduce deviations in the computed 3D coordinates. In this work, we present a novel method for estimating the degrees-per-step ratio at the output of the laser positioner’s transmission mechanism using a stereovision system. Experimental results demonstrate the effectiveness of the proposed method, which reduces the need for manual metrological instruments and simplifies the calibration procedure through vision-assisted measurements. The method yielded estimated angular resolutions of approximately 0.06° and 0.07° per motor step in the horizontal and vertical axes, respectively, key parameters that define the minimal resolvable displacement of the projected beam in dynamic triangulation. Full article

Traffic simulation is essential for evaluating transportation policies, infrastructure changes, and mixed traffic scenarios with human-driven and autonomous vehicles, yet its reliability critically depends on accurate calibration of origin–destination (OD) traffic demand. Traditional calibration workflows rely on trial-and-error adjustments and static numerical outputs, making it difficult for analysts to interpret error patterns, understand OD-to-link relationships, and efficiently refine traffic demand in complex urban networks. To address these challenges, we conducted a design study with transportation simulation experts to characterize the OD calibration workflow and derive key analytical tasks, which informed the development of ODCalibrator, an interactive visualization system that supports human-in-the-loop calibration through coordinated views, visual diagnostics, and iterative adjustment capabilities. We demonstrate its utility via a narrative usage scenario and domain expert feedback, showing that the system enables analysts to quickly identify error-prone regions, explore the effects of OD adjustments, and leverage domain expertise to produce more efficient and interpretable calibration outcomes for urban traffic simulations. Full article

Tiltrotor aircraft, due to their vertical takeoff and landing capability and efficient high-speed cruise performance, are increasingly valuable in both modern military and civilian applications. However, traditional calibration methods for blade pitch control often lack the precision required for large actuator strokes, which limits the control accuracy. This study aims to overcome these limitations by introducing an improved polynomial fitting algorithm to model the nonlinear relationship between the blade pitch control angles and actuator strokes. Using a specific rotor model, a coordinate system was established for the pitch control mechanism and spatial geometric relationships were derived. Experimental comparisons demonstrate that the proposed cubic polynomial fitting algorithm reduces the collective pitch error by approximately 57% and cyclic pitch error by 33%, markedly outperforming traditional linear fitting methods. These improvements significantly enhance the control precision and operational stability. The findings provide a reliable theoretical and practical basis for improving tiltrotor flight performance and safety. Full article

This paper presents ALMODES, a discrete-time modeling approach for social systems that uses matrix algebra and directed graphs. The method bridges the gap between static network analysis and continuous System Dynamics, offering a transparent framework that reduces data requirements. The method enables clear causal mapping, rapid simulation, straightforward sensitivity analysis, and natural hybridization with agent-based or discrete-event models. Two case studies illustrate its utility for sustainable-development strategy: in an urban public-health setting, modernization and sanitation policies drive sustained declines in disease despite growth, whereas reversing the population-to-modernization link triggers a morbidity rebound that can be prevented by strengthening the modernization-to-sanitation pathway; in a high-tech services Balanced Scorecard model, a baseline backlog spike depresses customer satisfaction, aggressive hiring shortens the spike but erodes income, and coordinated boosts to training and incentives (about twelve percent productivity gain) remove the backlog early, stabilize customers, and improve income, highlighting human-capital policy as a robust lever. ALMODES thus supports pragmatic policy design under limited, expert-elicited parameters. Future research will address uncertainty quantification, time-varying structures and shocks, automated calibration and empirical validation at scale, optimization and control design, richer integration with hybrid simulation, participatory interfaces for stakeholders, and standardized benchmarks across domains. Full article

Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We trained a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets spanning ten elastic moduli and capillary numbers (0.07–0.77). The network takes as input the wall shear stress, bond stiffness, time, and initial particle coordinates and returns the full three-dimensional deformation of the membrane. Mean-squared-error minimization with Adam and adaptive learning-rate decay yields a median displacement error below 1%, a 90th percentile below 3%, and a worst case below 4% over the entire calibrated range while accelerating computation by four to five orders of magnitude. Leave-extremes-out retraining shows acceptable extrapolation: the held-out stiffest and most compliant platelets retain sub-3% median error and an 8% maximum. Error peaks coincide with transient membrane self-contact, suggesting improvements via graph neural trunks and physics-informed torque regularization. These results represent a first demonstration of how the surrogate has the potential for coupling with continuum CFD, enabling future platelet-resolved hemodynamic simulations in patient-specific geometries and opening new avenues for predictive thrombosis modeling. Full article

Droplet-based microlens fabrication using Ultra Violet (UV) curable polymers demands the precise measurement of three-dimensional geometries, especially for non-axisymmetric shapes influenced by electric field deformation. In this work, we present a polar coordinate-based contour fitting method combined with Hermite interpolation to reconstruct 3D droplet geometries from two orthogonal shadowgraphy images. The image segmentation process integrates superpixel clustering with active contours to extract the droplet boundary, which is then approximated using a spline-based polar fitting approach. The two resulting contours are merged using a polar Hermite interpolation algorithm, enabling the reconstruction of freeform droplet shapes. We validate the method against both synthetic Computer-Aided Design (CAD) data and precision-machined reference objects, achieving volume deviations below 1% for axisymmetric shapes and approximately 3.5% for non-axisymmetric cases. The influence of focus, calibration, and alignment errors is quantitatively assessed through Monte Carlo simulations and empirical tests. Finally, the method is applied to real electrically deformed droplets, with volume deviations remaining within the experimental uncertainty range. This demonstrates the method’s robustness and suitability for metrology tasks involving complex droplet geometries. Full article

This paper presents a non-intrusive method for estimating the distance between a camera and a human subject using a monocular vision system and statistically derived interpupillary distance (IPD) values. The proposed approach eliminates the need for individual calibration by utilizing average IPD values based on biological sex, enabling accurate, scalable distance estimation for diverse users. The algorithm, implemented in Python 3.12.11 using the MediaPipe Face Mesh framework, extracts pupil coordinates from facial images and calculates IPD in pixels. A sixth-degree polynomial calibration function, derived from controlled experiments using a uniaxial displacement system, maps pixel-based IPD to real-world distances across three intervals (20–80 cm, 80–160 cm, and 160–240 cm). Additionally, a geometric correction is applied to compensate for in-plane facial rotation. Experimental validation with 26 participants (15 males, 11 females) demonstrates the method’s robustness and accuracy, as confirmed by relative error analysis against ground truth measurements obtained with a Bosch GLM120C laser distance meter. Males exhibited lower relative errors across the intervals (3.87%, 4.75%, and 5.53%), while females recorded higher mean relative errors (6.0%, 6.7%, and 7.27%). The results confirm the feasibility of the proposed method for real-time applications in human–computer interaction, augmented reality, and camera-based proximity sensing. Full article

Metropolitan areas have become the primary spatial form for China’s new-era urbanization. However, these boundaries have traditionally been delineated based on administrative factors, resulting in a notable discrepancy with the actual functional connections. To tackle this challenge, this study aims to devise and implement an innovative ‘PET’ tri-dimensional coupling model, leveraging the principles of integrated urban subsystems to scientifically delineate functional metropolitan boundaries. The proposed method integrates Population flow (P), Economic density (E), and Transportation accessibility (T) on a fine-grained 1 km raster grid. To enhance accuracy, the crucial population flow component is simulated using a gravity model calibrated with real-world Baidu Migration data. Applying this model to 35 potential metropolitan areas, our findings reveal two key points. First, a comparative analysis with five officially approved plans reveals a significant spatial alignment in core functional zones, which corroborates the model’s accuracy. effectiveness. Secondly, these delineations clearly quantify the notable difference between the ‘functional space’ influenced by socioeconomic factors and the ‘administrative space’ delineated by jurisdictional boundaries. In summary, this research presents a replicable methodology for delineating functional metropolitan areas. It offers vital technical support and policy guidance for optimizing regional planning, enhancing inter-city coordination, and promoting China’s national strategy for regional development. Full article

Underwater vessel localization using passive magnetic anomaly sensing is a challenging problem due to the variability in vessel magnetic signatures and operational conditions. Data-based approaches may fail to generalize even to slightly different conditions. Thus, we propose an Attentive Neural Process (ANP) approach, in order to take advantage of its few-shot capabilities to generalize, for robust localization of underwater vessels based on magnetic anomaly measurements. Our ANP models the mapping from multi-sensor magnetic readings to position as a stochastic function: it cross-attends to a variable-size set of context points and fuses these with a global latent code that captures trajectory-level factors. The decoder outputs a Gaussian over coordinates, providing both point estimates and well-calibrated predictive variance. We validate our approach using a comprehensive dataset of magnetic disturbance fields, covering 64 distinct vessel configurations (combinations of varying hull sizes, submersion depths (water-column height over a seabed array), and total numbers of available sensors). Six magnetometer sensors in a fixed circular arrangement record the magnetic field perturbations as a vessel traverses sinusoidal trajectories. We compare the ANP against baseline multilayer perceptron (MLP) models: (1) base MLPs trained separately on each vessel configuration, and (2) a domain-randomized search (DRS) MLP trained on the aggregate of all configurations to evaluate generalization across domains. The results demonstrate that the ANP achieves superior generalization to new vessel conditions, matching the accuracy of configuration-specific MLPs while providing well-calibrated uncertainty quantification. This uncertainty-aware prediction capability is crucial for real-world deployments, as it can inform adaptive sensing and decision-making. Across various in-distribution scenarios, the ANP halves the mean absolute error versus a domain-randomized MLP (0.43 m vs. 0.84 m). The model is even able to generalize to out-of-distribution data, which means that our approach has the potential to facilitate transferability from offline training to real-world conditions. Full article

Control point detection is a critical initial step in camera calibration. For checkerboard corner points, detection is based on inferences about local gradients in the image. Infrared (IR) imaging, however, poses challenges due to its low resolution and low signal-to-noise ratio, hindering the identification of clear local features. This study proposes a physics-informed neural network (PINN) based on the YOLO target detection model to detect checkerboard corner points in infrared images, aiming to enhance the calibration accuracy of infrared thermal cameras. This method first optimizes the YOLO model used for corner detection based on the idea of enhancing image gradient information extraction and then incorporates camera physical information into the training process so that the model can learn the intrinsic constraints between corner coordinates. Camera physical information is applied to the loss calculation process during training, avoiding the impact of label errors on the model and further improving detection accuracy. Compared with the baselines, the proposed method reduces the root mean square error (RMSE) by at least 30% on average across five test sets, indicating that the PINN-based corner detection method can effectively handle low-quality infrared images and achieve more accurate camera calibration. Full article

Quantum computing poses an unprecedented threat to classical cryptography, requiring new risk assessment paradigms. This paper proposes a Quantum-Adjusted Risk Score (QARS) model, a theoretical and methodological innovation within the EU’s PAREK framework (Post-quantum asset and algorithm inventory, risk assessment, road mapping, execution, key governance). QARS extends Mosca’s inequality—which defines a quantum threat timeline threshold—into a multi-factor risk scoring formula. We formalise QARS with mathematical expressions incorporating timeline, sensitivity, and exposure dimensions, each calibrated by factor weights and scaling functions. The design motivations for including these dimensions are discussed in depth. We present method for model calibration (including sector-specific weight adjustments) and outline validation strategies combining quantitative analysis and expert judgement. The proposed QARS model is situated in the context of the EU’s coordinated roadmap for post-quantum cryptography and cybersecurity regulations, illustrating how QARS supports compliance and strategic migration prioritisation. A prototype tool implementing QARS model is also provided to demonstrate practical applicability. Our contributions provide a unified approach to quantum risk assessment, marrying theoretical rigour with policy-relevant risk management needs to help organizations proactively address the quantum threat. Full article

Purpose: To quantify real-time hand–foot coupling in tennis and test whether the coupling pattern differs by playing standard. Methods: Fifteen nationally certified second-level male athletes and fifteen recreational beginners performed multi-directional swings, alternating forehand–backhand groundstrokes and serve-and-volley sequences while tri-axial ankle acceleration and racket-grip force were synchronously recorded in wearable inertial measurement units (IMUs). Grip metrics (mean force, peak force, force duration) and acceleration magnitudes were analysed with MANOVA and Hedges’ g effect sizes, followed by the Benjamini–Hochberg correction (α = 0.025). Results: Across tasks, athletes showed higher mean ankle acceleration (standardised mean difference, Hedges’ g) but 45% lower mean grip force (Hedges’ g = −1.28; both p < 0.01). The association between acceleration and grip metrics was moderate-to-strong and negative in athletes (r = −0.62 with mean grip force; r = −0.69 with force duration), whereas beginners exhibited moderate-to-strong positive correlations (r = 0.48–0.73). Conclusion: We quantified hand–foot coordination in tennis by synchronising tri-axial ankle acceleration with calibrated racket-grip force across three match-realistic tasks. Relative to beginners, athletes demonstrated an inverse coupling between ankle acceleration and grip-force metrics, whereas beginners showed a direct coupling, consistent with our purpose of quantifying coordination via synchronised wearable sensors. Full article