Search Results (120)

Stratospheric solar-powered unmanned aerial vehicles (UAVs), commonly operated as High-Altitude Pseudo-Satellites (HAPS), promise satellite-like persistence for Earth observation, communications and remote sensing, but their feasibility is governed by a tight coupling between solar energy availability and onboard energy demand. This study presents an energy-balance simulation framework that predicts the diurnal charge–discharge behaviour and endurance of solar-powered UAVs. The framework couples a physics-based environmental irradiance model—astronomical solar position, an air-mass and pressure-scaled broadband atmospheric transmission and an eccentricity-corrected extraterrestrial irradiance—with a wing-geometry photovoltaic collection model that reduces the airfoil camber, planform, dihedral and cell layout of a real wing to three scalar coefficients, replacing the flat-plate assumption common in solar-UAV sizing. The closed-form collection coefficient captures the full dependence of collected power on sun position and aircraft heading and admits an exact orbit-averaging result for circular loiter. The model is implemented as a reproducible, modular tool with single-day, annual and global analysis modes. It is validated against a ground-based photovoltaic charging campaign conducted on the as-built Aurora solar UAV demonstrator (5.6 m span, 8 kg) over three clear-sky days spanning a 90-day seasonal range: predicted and measured wing-collected power agree with a Pearson correlation of 0.998, a coefficient of determination of 0.993, an RMS error of 6.0% and a daily-energy agreement within 3.5%. A structured residual identifies an unmodelled photovoltaic temperature effect bounded at the 6% level. The framework provides HAPS designers and operators with a transparent, validated tool for feasibility screening, component selection and mission planning across latitude and season. Full article

Fluidized solidified soil piles combine slurry-like constructability with post-hardening strength development and provide a potential approach for soft ground improvement. This study investigated the vertical bearing behavior and load-transfer mechanism of fluidized solidified soil piles in layered soft ground through field single-pile vertical static load tests, core drilling, and three-dimensional numerical simulation. The field tests and core drilling provided experimental evidence for evaluating load–settlement behavior, pile integrity, and material strength, while the internal load-transfer mechanism and geometric parameters were mainly interpreted using the numerical model. The field results showed that the Q-s curves exhibited staged deformation characteristics, with relatively stable settlement development during the main loading stage and more pronounced nonlinearity under high load levels. The ultimate vertical bearing capacities of the 10 m and 20 m test piles were 1050 kN and 950 kN, respectively. Core drilling indicated that the two pile groups had similar material strength, suggesting that the bearing capacity difference was mainly associated with the pile toe bearing stratum rather than pile material strength. After comparison with the measured Q-s curves, the numerical analysis showed that the 20 m pile mobilized a longer shaft resistance range and a higher shaft resistance contribution, but its pile toe extended into the lower mucky soil layer, resulting in reduced pile toe resistance. Parametric analysis indicated that increasing pile length does not necessarily improve bearing performance when the pile toe bearing stratum is unfavorable, whereas increasing pile diameter more directly reduces pile head settlement under the same pile toe bearing condition. These findings highlight the need to consider both shaft resistance mobilization and pile toe bearing stratum in the design of fluidized solidified soil piles in layered soft ground. Full article

Train-induced ground vibrations can propagate into pile foundations, potentially causing undesirable vibration in nearby buildings, laboratories housing vibration-sensitive equipment, and manufacturing facilities for high-precision processes. This paper presents an innovative method for predicting building vibration from free-field ground vibration measurements at locations away from the tracks during train pass-bys. The proposed method accounts for site-specific soil profiles and train-soil-pile-structure interactions and is implemented in four steps. In Step 1, train-induced vibration transmission into the ground is estimated using an axisymmetric finite element model that simulates wave propagation through layered soils from the tracks to free-field ground locations. Step 2 estimates free pile head vibration using a three-dimensional finite-element model that captures the ground-borne transmission of track inputs through soil layers to the pile. Step 3 estimates vibration at the junction of the pile head and depot column base using a finite-element model to estimate the pile head impedance and an analytical impedance model for the depot structures supported by the pile. In Step 4, estimates of column-base vibration that transmits into over-track buildings are compared to measured column-base vibration levels obtained during train pass-bys. The method was applied at a metro depot in China, where tracks were in close proximity to columns supporting over-track buildings. Ground and column base vibration levels were measured during multiple train pass-bys. The estimated vibration levels at the base of depot columns closely agreed with the measured vibration levels at the columns during six-car train pass-bys. It demonstrated the potential effectiveness of this hybrid method for assessing vibration transmission into structures atop existing railway tracks. By integrating field measurements, finite element simulations, and analytical impedance models, the proposed hybrid method provides a framework for evaluating the transmission of the train-induced vibration to nearby building structures. Full article

Above-ground biomass (AGB) is a critical indicator for evaluating crop growth, with its large-scale monitoring being fundamental to precision agriculture. To improve the efficiency and reduce the cost of large-scale farmland monitoring, this study developed an unmanned aerial vehicle (UAV)–satellite collaborative inversion framework. The data, including rice AGB, UAV imagery, and satellite imagery, were collected in 2024. The proposed Distance-Correlation–Correlation-Feature-Selection (DC-CFS) algorithm was employed to select compact feature subsets for each growth stage. Subsequently, six machine learning models were compared to identify the optimal UAV-scale inversion model for each specific stage. Then, the AGB values simulated by the UAV-scale model were used to train the satellite-scale inversion model. A paddy field mask covering the entire district was generated using Segment Anything Model (SAM) and the temporal spectral variation pattern of rice, enabling county-scale AGB mapping. Research results indicate that the DC-CFS algorithm can effectively select a small number of low-redundancy features for each growth stage. The optimal UAV scale model type varies dynamically with growth stages, with ExtraTrees demonstrating overall superior performance. Except for the heading stage, the R² of the models remained above 0.69. Furthermore, the BayesianRidge algorithm also presents a viable and competitive alternative when computational efficiency is a consideration. At the satellite scale, eXtreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtraTrees) were identified as the optimal models for rice AGB estimation due to their stable performance across all stages, with R² values consistently above 0.74. Finally, rice growth classification maps and corresponding fertilization recommendations were generated based on the satellite-scale inversion results, providing technical support for precision agriculture practices. Full article

Tunnel seepage in heterogeneous ground can trigger hydrogeological hazards such as concentrated water inflow, groundwater depletion, deformation of surrounding structures, and subsequent eco-environmental degradation. However, these processes are still commonly evaluated using deterministic models that neglect the spatial variability of hydrogeological parameters. To address this limitation, this study develops a stochastic hydro–geo–mechanical–ecological framework that integrates random field theory with physics-informed neural networks (PINNs) for hazard evaluation and rapid prediction of tunnel seepage responses. The spatial variability of key parameters, including permeability and porosity, is characterized using the Karhunen–Loeve expansion and embedded into coupled governing equations for unsaturated–saturated seepage, seepage–stress interaction, and groundwater–soil–vegetation responses. A PINN surrogate model with random-field inputs is then constructed to predict hydraulic head, tunnel inflow, displacement, groundwater depth, vegetation coverage, and soil physicochemical indices, while simultaneously quantifying uncertainty. A karst tunnel case in Chongqing, China, is used to demonstrate the proposed framework. The results show that spatial heterogeneity promotes preferential flow paths and intensifies seepage-induced hazards compared with deterministic mean simulations, leading to larger groundwater drawdown, stronger ecological degradation, and greater overall response variability. The proposed PINN achieves high predictive accuracy (R² > 0.97) and reduces single-case computational time from hours to seconds, enabling efficient multi-scenario evaluation and uncertainty-aware risk assessment. This framework provides a physically consistent and computationally efficient tool for evaluating water-related hazards and long-term environmental impacts in underground engineering. Full article

Healthcare professionals must acquire and maintain both declarative knowledge and fine psychomotor skills across a wide range of clinical procedures. Human working memory is physiologically limited, and the high cognitive demands of clinical environments frequently contribute to medical errors and adverse events. Intra-individual performance variability—driven by fatigue, stress, and motivation—represents a further challenge that conventional medical safety education has not adequately addressed. According to the World Health Organization, patient harm ranks fourteenth in the global burden of disease, with approximately 10% of hospitalised patients in high-income countries experiencing harm within healthcare facilities. This study reports the design, theoretical rationale, and preliminary outcomes of an augmented reality (AR) glasses system for hands-free, self-directed medical procedural training, developed from a human factors and ergonomics (HFE) perspective. The system integrates a see-through head-mounted display (HMD; Epson Moverio BT-40S), bone-conduction earphones (Shokz OpenComm), and an industrial-grade voice recognition application (NEC Solution Innovators), achieving fully hands-free operation compatible with aseptic technique. Content design is grounded in cognitive load theory (CLT) and the cognitive theory of multimedia learning (CTML), extended by neuroscientific evidence on multisensory integration and memory consolidation. More than 40 procedure-specific modules have been developed in-house at Tokyo University of Technology, spanning airway management, vascular access, respiratory therapy, dialysis, and cardiac support. In a four-year longitudinal survey (virtual reality (VR) simulator; n = 286), major satisfaction items consistently exceeded the scale midpoint. In an AR endotracheal suctioning cohort (n = 38/22), procedural flow understanding was rated 3.95/5.0. A peer-reviewed randomised controlled trial (Clinical Simulation in Nursing, n = 36) demonstrated significantly superior skill improvement (p < 0.001) and learning motivation (p = 0.001) in the AR group versus textbook self-practice. Principal ergonomic limitations of current HMD hardware—excessive weight, narrow field of view, and absence of medical-grade certification—are documented, and AI-based real-time procedural assessment is identified as a priority for the next research phase. Full article

This paper proposes LA-PPO, an improved Proximal Policy Optimization algorithm for quadruped robot locomotion control on mixed terrain. To address partial observability, temporal dependence in contact states, and non-uniform importance of historical information in complex-terrain quadruped locomotion, LA-PPO integrates Long Short-Term Memory (LSTM) and Multi-Head Attention (MHA) within an Actor–Critic framework. The LSTM module models temporal dependencies in historical observations, while the MHA module adaptively emphasizes historical information most relevant to the current action decision. Based on IsaacGym, we construct a mixed-terrain environment consisting of flat regions, sloped regions, and random rough-terrain regions and conduct algorithmic comparisons, statistics over multiple random seeds, reward component ablation studies, and attention mechanism analyses for both walking and trotting gaits. Simulation results show that LA-PPO achieves the highest final reward and the longest mean episode length in both gaits. Compared with the PPO baseline, the final reward and mean episode length are improved by approximately 42.3% and 42.7%, respectively, in the walking task, and by approximately 39.8% and 25.7%, respectively, in the trotting task. Real-robot tests further show that the learned policy can perform walking and trotting on flat ground, sloped terrain, and random rough terrain, demonstrating preliminary sim-to-real transfer capability. Full article

Indirect treatment comparisons (ITCs) are essential in the context of joint clinical assessments (JCAs) under Regulation (European Union [EU]) 2021/2282, bridging evidence gaps where head-to-head data are lacking and enabling assessment across diverse national patient, intervention, comparator, and outcome (PICO) requirements. This paper critically reviews the EU Health Technology Assessment Coordination Group’s (HTACG) guidelines on direct and indirect comparisons, with particular focus on ITCs. While the guidelines promote transparency and rigorous evaluation of assumptions, they adopt a restrictive stance on assumption violations, the use of unanchored comparisons, and population-adjusted methods such as matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC). The guidance shows limited support for Bayesian methods and undervalues meta-regression in favor of subgroup analyses. Operational implications for health technology developers (HTDs) are substantial, including new requirements for dual systematic reviews, multiple network structures, and shifted null hypothesis testing. Moreover, the guidelines effectively dissuade the use of non-randomized comparisons in rare or rapidly evolving indications and may inadvertently hinder access to effective treatments. Emerging practices such as external control arms (ECA) or target trial emulation are underdeveloped. Notably, there is no indication that the guidelines are grounded in systematic methodological validation studies. As JCAs evolve, greater methodological flexibility, empirical grounding, and clear operational guidance will be essential. Refining the guidelines along these principles would enhance their practical utility, mitigate intrinsic assessment variability, support consistent assessments across Member States (MS), and ultimately improve patient access to innovative therapies. Full article

The driving scenario and information load jointly influence the cognitive efficiency of augmented reality head-up display (AR-HUD) interfaces. However, the moderating role of drivers’ working memory capacity (WMC) remains unclear. To investigate this mechanism, a simulated driving experiment with a mixed design was conducted in a low-immersivity desktop virtual reality (VR) environment. First, 40 volunteers were screened using an automated operation span task, yielding 16 high- and low-WMC participants. They then drove under three scenarios (urban intersection, expressway, construction zone) and six levels of AR-HUD visual information load. Generalized linear models were applied to the reaction time, fixation duration, and pupil diameter. The results revealed a significant three-way interaction among WMC, scenario, and information load. High-WMC drivers maintained faster responses and lower subjective loads up to Levels 4–6, adopting a deep processing strategy; low-WMC drivers already showed cognitive overload at Level 4 and above, requiring an optimal load range of Level 2–3. The construction zone induced the steepest increase in cognitive load, whereas the expressway markedly reduced sensitivity to additional visual information. Therefore, the optimal AR-HUD information load must be adapted to drivers’ WMC: high-WMC drivers can safely handle Levels 4–6 in low- or medium-demand scenarios, whereas low-WMC drivers require a minimalist presentation of Levels 2–3 in high-demand situations. This study provides quantitative, empirically grounded guidelines for designing cognitively adaptive AR-HUD interfaces. Full article

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

In size, weight, and power (SWaP)-constrained optical systems, such as spaceborne LiDAR, high-precision centroid localization often relies on focal-plane measurements without dedicated wavefront sensors. Under such conditions, the nonlinear coupling between optical aberrations and sensor noise introduces systematic bias that is difficult to mitigate using conventional centroiding methods. To address this issue, we propose a physics-conditioned feature correction framework based on an aberration-conditioned attention mechanism. A hybrid CNN–Transformer architecture is employed to predict and compensate for systematic centroid bias. Specifically, convolutional layers encode the degraded spot morphology, while a multi-head attention mechanism leverages Seidel aberration coefficients to adaptively modulate spatial features for precise regression. Given the unavailability of absolute ground-truth coordinates in empirical scenarios, a physics-consistent simulation framework based on scalar diffraction theory is constructed to generate synthetic data for supervised learning. Simulation results indicate that the proposed method objectively reduces anisotropic systematic bias, achieving a localization root-mean-square error (RMSE) of 0.011 to 0.021 pixels, and maintains stable sub-pixel accuracy even under a 10% empirical prior perturbation. To evaluate generalization performance and engineering reliability, a wedge-based double-spot platform is developed to verify physical consistency via geometric invariance. Experimental results demonstrate a measured spacing standard deviation (SD) of 0.015 to 0.039 pixels. This validates the framework’s transferability from theoretical simulation to controlled physical measurements, providing an algorithmic foundation for precision optical metrology in hardware-constrained environments. Full article

Squeezed branch piles, originally developed for building and bridge foundations, have been downsized and deployed at larger pile spacing for reinforcing embankments over soft soils. However, the working mechanism of squeezed branch pile-supported embankments remains unclear. In this study, a three-dimensional numerical model of this embankment was established based on field tests. The analyses consider different pile types (squeezed branch piles and straight piles) and pile-head structures (beam-type cap and plate-type cap). These concrete components were modeled utilizing an advanced concrete model, which captures the strain-softening/hardening and yielding behavior. Simulation results show that squeezed branch piles provide better settlement control in the subsoil beneath the embankment than straight piles for the studied cases. The beam-type cap with squeezed branch piles behaves as a pile-beam foundation that reduces maximum settlement by around 38% compared to that of the plate-type cap, while the plate-type cap system functions as a composite foundation that enhances surcharge capacity by about 35–40%. The instability of the embankment is driven by tensile failure in concrete: The beam-type cap leads to a localized failure along the ground beam, and the plate-type cap system induces a progressive failure centered on the squeezed branch piles. Within the plate-type cap, the dimensions of the pile-head plate significantly influence settlement control and the stability of the embankment in soft soil. Full article

Civil aviation VHF communication is safety-critical, yet operational links are routinely disturbed by atmospheric effects, aging hardware, and electromagnetic interference. The resulting anomalies are typically weak, intermittent, and extremely rare, which makes real-time detection difficult under strong temporal dependence and severe class imbalance. We propose an end-to-end framework that couples (i) a learnable kernel projection for adaptive nonlinear feature extraction, (ii) a differentiable relevance–redundancy objective for feature refinement, and (iii) conditional temporal generation to augment minority anomaly patterns. A lightweight CNN–LSTM head is used for streaming inference. Training uses a mixture of operational anomalies and simulated degradation scenarios, while evaluation is conducted using operational data only. Experiments on 1.2 million VHF frames collected from real flight operations and ground station monitoring achieve an F1-score of 0.947, ROC-AUC of 0.972, and PR-AUC of 0.968, with an average inference latency of 34.7 ms. Full article

As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design. Full article