Search Results (5,311)

Airborne LiDAR data acquired before and after the 2024 Noto Peninsula earthquake in Japan were used to estimate three-dimensional (3D) ground-surface displacements based on the Iterative Closest Point (ICP) algorithm. Digital elevation (terrain) models (DEMs) were generated from pre-earthquake point cloud data acquired by Ishikawa Prefecture and compared with post-earthquake DEMs developed by the Forestry Agency of Japan. Three-dimensional coseismic displacements were derived from the spatial correlations between pre- and post-event DEMs for 50 m × 50 m tiles. The results depend on the tile size and are influenced by ground movements within and surrounding each tile. Therefore, moving-average windows of 250 m and 550 m were applied to the 50 m tiles to obtain continuous 3D displacement fields across the ground surface. A comparison between GNSS-measured displacements and the corresponding moving-average estimates for tiles containing triangulation points and continuously operating reference stations (CORSs) showed that the accuracy of the estimated displacements in all three components was within 0.2 m in terms of the root mean square error (RMSE). Full article

The interaction between light and chiral plasmonic metasurfaces provides a powerful mechanism for controlling polarization states at the nanoscale. Utilizing displacement Talbot lithography for large-area fabrication, we characterized the chiroptical response by measuring the evolution of Stokes parameters to quantify phase retardation between orthogonal polarization components. To elucidate the underlying physical mechanism, we employ a hybrid finite element method and rigorous coupled-wave analysis approach to investigate the behavior of the far-field and local-field configurations. Our results reveal that the phase shift is highly sensitive to symmetry-breaking features, where the interplay between different modes dictates the overall circular dichroism signal. Furthermore, the analysis of local field plots suggests specific contributions of plasmonic modes to the chiroptical response. We conclude that the phase shift effects, characterized via Stokes parameters and modal analysis, provide a robust metric for engineering chiroptical properties in these systems. This work establishes a fundamental framework for developing compact polarization-control elements and enhances the understanding of phase-modulated light-matter interactions in chiral plasmonic metasurfaces. Full article

Peeling behavior of soft materials is important in a wide range of applications, e.g., electronics, healthcare, etc. When applied on soft substrates, soft adhesives demonstrate unique mechanical behaviors compared to adhesives applied on rigid substrates. Adhesive properties can be conveniently measured by “peel testing”. The focus of this work is characterization of commercial glues on fabric substrates using commonly used peel tests. We investigate energy dissipation on textile substrates. For practical applications, we aim to develop a systematic approach for selecting adhesives for soft, flexible substrates. Here, we developed a multi-criteria framework for evaluating adhesives using data from peel tests. The criteria used here consider the shape and stability of the T-peel trace. The results of the multi-criteria evaluation were compared to traditionally used peel strength and fracture energy. Although E6000 produced the highest peel force ($1.82\pm 0.27 \mathrm{N} \mathrm{m}{\mathrm{m}}^{-1}$) and the largest apparent fracture energy, ${\mathrm{G}}_{\mathrm{c}}$ ($8673\pm 1545 \mathrm{J} {\mathrm{m}}^{-2}$), it showed large force oscillation ($\mathrm{S}\mathrm{S}\mathrm{A}=4.05\pm 0.83 \mathrm{N}$). Fabri-Fuse was selected based on its low oscillation ($\mathrm{S}\mathrm{S}\mathrm{A}=0.69\pm 0.29 \mathrm{N}$), lowest $\mathrm{C}\mathrm{o}{\mathrm{V}}_{{\mathrm{F}}_{\mathrm{c}\mathrm{i}}}$(4.0%), high peel stability index (PSI), and high displacement at break. Functional evaluation showed that Fabri-Fuse increased strain-to-electrical-failure to $34.95\pm 2.43\mathrm{\%},$ higher than direct printing on fabric or printing on E6000 (highest peel strength). These results suggest that metrics that consider the shape of the peel trace and inter-sample repeatability provide a useful alternative for selecting adhesives other than highest peel strength. Full article

This study aims to assess whether micro-, small-, and medium-sized enterprises in Ecuador’s Amazon region show evidence of sustained reorientation toward selected non-extractive activities under persistent extractive dependence. Guided by four empirical propositions concerning sectoral reorientation, differential firm viability, temporal discontinuity and limited reallocation, and territorial heterogeneity, the study uses a longitudinal administrative panel of 769,344 firm-year observations for 2006–2021, complemented by descriptive evidence for 2022–2024. The empirical strategy combines fixed-effects models, non-parametric trend and structural break tests, cohort analysis, survival analysis, and transition matrices. The results indicate an emerging but constrained diversification pattern. New-economy firms increased their relative participation after the 2015–2016 commodity downturn and showed higher survival rates and stronger formal employment generation than extractive firms. However, intersectoral mobility remained limited, and the evidence does not support the interpretation of a completed structural transformation. Provincial heterogeneity further shows that extractive expansion continues to influence local entrepreneurial dynamics, especially in mining-frontier territories. The main limitation is that the analysis captures formally registered firms and does not directly measure informal-sector activity, productivity upgrading, or full regional structural transformation. The study contributes to debates on regional development, sustainability, and firm-level transformation by showing that non-extractive reorientation may emerge in peripheral resource-dependent regions without fully displacing extractive dependence. Full article

Background/Objectives: The maxillary lateral incisor (U2) root has been proposed to influence the eruption pathway of the maxillary canine. This retrospective study aimed to evaluate the association between mesial root tipping of the U2 and the eruption of impacted maxillary canine (IPU3), and to identify radiographic predictors of eruptive movement. Methods: Orthopantomograms of 37 IPU3 from 29 patients aged 10–12 years were analyzed in this retrospective responder study; all included cases showed initiation of IPU3 eruption following U2 mesial root tipping, and this design was considered when interpreting potential selection bias and overestimation of effect. U2 and canine (U3) positions were measured at treatment initiation (T0) and at the 1-year follow-up (T1). Positional changes were analyzed using paired t-tests, Pearson’s correlation, and multiple linear regression. Results: Significant positional changes were observed for both U2 and U3 (all p < 0.001). The blockage point on the distal U2 root (2DBlock) shifted mesially by 2.0 mm, and U2 root angulation increased by 5.6° at the distal surface and 6.3° along its long axis. The U3 cusp tip (3Cusp) moved vertically by 3.7 mm, distally by 2.1 mm, and tipped distally by 7.5°. A strong correlation (r = 0.697) was observed between mesial root movement (2DHorz) and vertical cusp displacement (3Vert). Regression analysis identified 2DHorz as the only significant predictor of 3Vert (p < 0.001), explaining 51% of the variance; this indicates moderate explanatory power, while the remaining 49% suggests that additional biological, developmental, and three-dimensional spatial factors may also influence eruptive movement. Conclusions: Mesial root tipping of the U2 facilitates IPU3 eruption in early adolescents (10–12 years), specifically in cases with non-palpable IPU3 in sector II and fully developed U2 roots. Horizontal repositioning of the U2 root may serve as a clinically relevant radiographic indicator for guiding interceptive treatment; however, these findings should be interpreted as associations rather than evidence of causality. Full article

Background: Exercise plays a crucial role in the prevention and management of type 2 diabetes. During self-motion, optic flow provides visual information about heading direction and influences postural control. This study investigated postural responses and muscle activation in individuals with type 2 diabetes exposed to optic flow stimuli simulating self-motion, and examined whether these responses varied according to habitual physical activity over 12 months. Methods: Surface electromyographic (EMG) and stabilometric data were collected from 23 individuals during quiet standing under different visual motion conditions. Participants were classified as physically active or inactive based on standardized criteria. EMG activity was recorded bilaterally from the tibialis anterior and soleus muscles at baseline, 6, and 12 months. Center of pressure (COP) displacement was measured using two force platforms. Results: Stabilometric analysis revealed a significant effect of visual stimulus on COP displacement in both antero-posterior and medio-lateral directions, as well as on COP speed, indicating that optic flow modulates postural control. COP speed changes over time differed by sex, while medio-lateral sway showed time-dependent variations across sides and physical activity groups. EMG analysis showed a significant effect of visual stimulus on soleus activation, with no consistent effects for tibialis anterior. Conclusions: Optic flow significantly modulated postural control and lower-limb muscle activation in individuals with type 2 diabetes. Preliminary differences in response profiles associated with habitual physical activity level were observed, though these should be interpreted cautiously given the exploratory nature of the study. Larger, adequately powered studies are warranted to further investigate these associations. Full article

Biological thin-sheet systems, including leaves, insect wings, and flowering organs, achieve adaptive deformation through distributed compliance, segmentation, curvature, and controlled opening. Kirigami offers a bio-inspired route for translating such deformation logics into programmable thin-sheet surfaces; however, the geometric parameters that most strongly influence elastic displacement remain insufficiently quantified, especially across different loading regimes. This study investigates Bio-Inspired Regime-Dependent Parameter Selection in Parametric Kirigami through twenty-five laser-cut specimens spanning five boundary shapes and three thermoplastic substrates. Specimens were tested under two contrasting regimes: quasi-static tensile loading and gravity-drape loading. Elastic displacement was measured under eight-point boundary fixation and analyzed using regime-separated Pearson correlations, Bonferroni-corrected significance testing (α/18 = 0.0028), and shape-controlled partial correlations. Under tensile loading, the Number of Offsets (r = 0.807), Segments per Offset (r = −0.603), and outer-boundary void perimeter (r = 0.621) showed the strongest Bonferroni-robust associations with displacement. Under gravity-drape loading, effects were weaker and more curvature-sensitive, indicating that parameter relevance is not universal but regime-dependent. Within the tested parametric design space, the study provides an experimentally grounded basis for selecting Kirigami geometric parameters in thin-sheet structures whose adaptive deformation logic is analogous to compliant systems found in nature. Full article

This paper documents a fundamental sectoral divergence in capital–employment relationships using UK quarterly data (2014Q1–2024Q4, N = 44). While manufacturing automation studies consistently find negative employment effects, we show that information-intensive service sectors (SIC J: Information and Communication; K: Financial and Insurance; M: Professional/Scientific/Technical) exhibit robust positive co-movement between capital formation and employment. Structural vector autoregression analysis reveals persistent positive employment responses following capital shocks, with effects peaking at 5–6 quarters and remaining significant through 10 quarters. This pattern holds across eight alternative specifications with varying lag structure, variable ordering, and subsample periods. Granger causality tests reveal bidirectional temporal relationships (capital → employment: F = 3.932, p = 0.028; employment → capital: F = 5.659, p = 0.007), indicating joint determination from anticipated demand growth rather than unidirectional technology-driven dynamics. This finding—while complicating causal interpretation—strengthens the contribution by providing honest empirical characterization of coordination mechanisms in information-intensive sectors. Our capital formation proxy measures all investment in AI-intensive sectors (buildings, equipment, conventional IT, emerging AI systems) rather than AI expenditure specifically, creating measurement ambiguity we acknowledge transparently. The sectoral focus (J+K+M sectors with 22–34% AI adoption rates exceeding the 15% economy-wide average) provides indicative evidence that patterns relate to advanced technology deployment, but measurement breadth prevents definitive AI-specific conclusions. The contribution lies not in establishing AI-specific causality—which aggregate time-series methods cannot achieve—but in documenting robust sectoral heterogeneity using methodology comparable to manufacturing displacement studies. The positive association in information-intensive services contrasts sharply with manufacturing’s negative relationship, suggesting technology–employment dynamics vary fundamentally across sectors with different task structures. Three limitations constrain interpretation: (i) recursive identification cannot definitively rule out common demand shocks, (ii) the 44-quarter sample provides limited statistical power for precise magnitude estimation, and (iii) external validity to other countries, time periods, or service sectors remains uncertain. The findings motivate sector-specific rather than economy-wide technology policy approaches, recognizing that extrapolating manufacturing evidence to service-dominated economies may systematically mischaracterize employment dynamics. Full article

Accurate nanoscale displacement readout is essential for optical inertial sensors, precision positioning, and micro-vibration characterization. In this work, we develop a free-space optical heterodyne interferometric readout system for low-frequency nanoscale displacement sensing and establish an SNR-guided adaptive demodulation framework. Two complementary demodulation strategies are integrated: Bessel-function-based frequency-domain sideband extraction for small-amplitude low-SNR motion and IQ quadrature phase tracking for larger-amplitude displacement. The experimentally demonstrated framework maps the applicability regimes of the two methods and enables wavelength-referenced displacement readout over a range from sub-nanometer narrowband detection to 250 nm under the present experimental conditions. The implemented system achieves a repeated-measurement repeatability of 0.40 nm under a 10 Hz excitation condition, and spectral SNR analysis is consistent with time-domain statistical evaluation. Finally, the readout system is applied to a quartz pendulum inertial structure, demonstrating its potential for photonic displacement sensing and optical inertial sensor characterization. Full article

This paper focuses on the significant potential of specific optical non-invasive methods, such as digital holographic microscopy, digital speckle pattern interferometry, and digital holographic interferometry, as scientific and technological tools for retrieving physical and biomechanical parameters embedded in the optical phase of laser-illuminated biomedical samples. These techniques take advantage of the laser speckle phenomena observed when non-specular surfaces are illuminated, enabling whole-field measurements and reconstruction of 3D images. Their versatility in implementation and application has led to advances in various fields of research and has broadened our understanding in both the basic and applied sciences. In clinical environments, the aforementioned quantitative optical studies are particularly valuable for understanding the behavior of biological samples, as they allow precise characterization of deformations, displacements, stress, strain, refractive index, and morphological features. Applications presented span from soft to hard tissues at both micro- and macro-scales, with results obtained from vocal cords, skin tissues, melanoma cells, and teeth. Furthermore, this overview provides a general perspective of some current speckle-based approaches and their growing relevance in biomedical research. Full article

This study presents numerical and experimental investigations of a voltage-controlled active preload adjustment system for an ultrasonic traveling wave piezoelectric motor intended for potential use in space-related systems. The proposed preload system consists of two ring-shaped piezoceramic elements driven by a DC voltage of up to 300 V_DC. The passive conical spring provides the nominal rotor preload, while the piezoelectric ring stack enables open-loop remote fine adjustment of the stator–rotor contact force by modifying the axial compression of the spring. Finite element simulations were performed over a temperature range from −25 °C to 55 °C to evaluate the electromechanical response and thermal sensitivity of the preload system. The numerical results indicated that the active preload system can generate a simulated preload force variation of approximately 0.47 N at 300 V_DC, corresponding to approximately 21.4% of the nominal initial preload force of 2.2 N. Experimental tests were conducted in a thermal vacuum chamber at a pressure of 5.6 × 10⁻⁶ mbar. The measured displacement of the piezoceramic preload stack ranged from 0.33 µm to 2.36 µm and showed good agreement with the numerical displacement results. Motor speed measurements demonstrated that increasing the preload-control voltage from 0 to 300 V_DC resulted in an average angular speed increase of approximately 17–20 RPM, depending on temperature. The results demonstrate that the proposed system can provide compact open-loop preload fine adjustment under thermal vacuum conditions, with preload force variation supported by FEM estimation and experimentally validated displacement response. Full article

Self-drilling hollow bar micropiles (HBMPs), which integrate drilling, grouting, and reinforcement into a single process, have broad application prospects in mountainous transmission lines and offshore wind power projects. However, existing research has focused mainly on friction piles in soil layers, and there is a lack of systematic understanding of the load-transfer mechanism and bearing capacity calculation method for rock-socketed HBMPs. Based on field static load tests of rock-socketed HBMPs, this study systematically investigates the vertical bearing behavior and capacity calculation method of single rock-socketed HBMPs through a combination of test data analysis, finite element numerical simulation, and theoretical analysis. The field test results show that the load-settlement curves of rock-socketed HBMPs are of a slowly varying type, exhibiting mixed friction-end-bearing characteristics. After data screening, the average Q-s curve of Pile No. 1 and Pile No. 5 was taken as the benchmark, and the representative ultimate bearing capacity of a single pile determined by the 40 mm settlement criterion is 5860 kN. The test data of Pile No. 3 and Pile No. 4 were retained as independent validation data. A three-dimensional finite element model considering the cohesive contact behavior at the pile–rock/soil interface was established using ABAQUS. After calibration with the test results, the error between the simulated and measured bearing capacity is −3.4%, demonstrating good model reliability. Parametric analysis indicates that the bearing capacity increases linearly with the grouting volume increase rate $V_{\mathrm{inc}}$, with the expansion effect being the main enhancement mechanism; the improvement amplitude under hard rock conditions is significantly smaller than that in cohesive soils. The effect of uniaxial compressive strength $q_u$ of hard rock on bearing capacity is negligible because the capacity is controlled by the pile–rock interface shear strength. The bearing capacity increases approximately linearly with the rock-socketed depth $L_r$, and a minimum rock-socketed depth of 1.0 m is recommended. Analysis of the load-transfer mechanism shows that rock-socketed HBMPs rely mainly on shaft resistance (accounting for 90.6%), and the axial force decays significantly along the pile length. Elastic compression of the pile accounts for 78% of the pile head settlement, and the limited displacement at the pile tip leads to insufficient mobilization of end bearing. A modified bearing capacity formula considering the grouting expansion effect is established with shaft resistance as the core. A hierarchical validation strategy is adopted to test its predictive ability: for the finite element cases not participating in parameter calibration, the prediction error is within ±2%; for the field test piles, the prediction error is +7.9%; and for Pile No. 3 and Pile No. 4, the errors are +1.7% and −2.1%, respectively. These values are significantly better than those of existing methods (errors ranging from −72.1% to +54.5%). The research results can provide a theoretical basis for the design of single HBMP bearing capacity under rock-socketed conditions. Full article

Concrete cracks are ubiquitous in practical engineering structures and continuously affect structural safety and durability. Crack images provide important visual evidence of damage evolution; however, crack images alone are insufficient to determine the residual load-bearing capacity of concrete members. Although the development of deep learning algorithms has significantly improved the automatic detection of concrete surface cracks, most existing methods remain limited to the extraction of crack geometric features and lack a direct connection with mechanical performance. To explore the relationship between image-based crack geometry and mechanical response, this study combines U-Net-based crack segmentation, OpenCV-based crack geometry extraction, and phase-field fracture simulation to establish a preliminary visual–mechanical framework for plain concrete beams. In this framework, surface crack images are first segmented using a U-Net model, and crack length, average width, and propagation path are extracted from the predicted binary masks. The extracted crack length is then used as the primary variable to match the observed crack state with the phase-field crack evolution sequence. Once the corresponding simulation stage is identified, the associated load level and residual load-bearing capacity can be obtained from the simulated load–crack mouth opening displacement (Load–CMOD) response. Through a mixed-mode I–II fracture test, the crack geometric features extracted by deep learning are compared with the phase-field simulation results. The results show that the error in crack length is within 2.5%. Meanwhile, the relative error between the simulated peak load and the experimental value was 1.57%, which preliminarily verified the correlation between image-based crack information and the load-bearing capacity of plain concrete beams. The method is further applied to a Mode I fracture test without recorded load-bearing capacity data. By mapping the crack length identified from the image, namely 36.89 mm, to the phase-field evolution sequence, the load-bearing capacity of the member at this stage is estimated to be 74.4% of the peak load. The results indicate that the crack geometry extracted from images can be correlated with phase-field crack evolution, thereby supporting preliminary residual load-bearing capacity assessment of plain concrete beams. However, the proposed framework should be regarded as a case-level feasibility study rather than a general structural assessment method. Before broader engineering application, further validation using synchronized crack image sequences, crack mouth opening displacement (CMOD) measurements, and load records is required. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. Full article

Laboratory-based certification cycles systematically underestimate real-world fuel consumption and CO₂ emissions. On-board diagnostics (OBD-II) telemetry offers a low-cost alternative, yet most published approaches rely on mass air flow (MAF) sensors absent from many modern vehicles. This study validates a speed-density air-mass estimation method on a naturally aspirated RON 95 gasoline passenger car (1368 cm³, Euro 6) across seven drive cycles recorded over three measurement days in northwestern Türkiye, covering 609.6 km of highway, urban, and mixed conditions. Instantaneous air mass flow was estimated from four standard OBD-II PIDs—manifold absolute pressure, engine speed, intake air temperature, and fuel trim corrections—using the ideal gas law applied to actual engine displacement. Results were validated against pump-measured fill-up volumes. The speed-density model achieved errors of −3.6% to +4.3% across individual segments (combined error: −0.5%), outperforming the vehicle’s onboard trip computer, which exhibited errors of −10.6% to +14.6%. Derived CO₂ intensities ranged from 125.0 to 166.4 g/km, with a combined average of 147.2 g/km (pump reference: 147.9 g/km). Urban driving produced approximately 15% higher specific emissions than highway driving. These results demonstrate that a physics-based speed-density model can achieve within ±5% trip-level accuracy across diverse real-world conditions without machine learning, bespoke calibration, or a physical MAF sensor. Full article