Search Results (163)

Post-rotation jacking is a critical construction stage for load-path reconstruction and alignment adjustment in rotation-constructed bridges, particularly for ultra-wide double-deck composite girder systems. Taking a two-span continuous steel truss–concrete composite girder bridge with spans of 2 × 85 m as the engineering background, this study investigates the mechanical behavior during post-rotation jacking through theoretical derivation, finite element simulation, and on-site monitoring. Based on the force method of structural mechanics, a linear relationship between vertical synchronous jacking force and displacement is derived, and an analytical formulation for bearing reaction redistribution under laterally asynchronous jacking is established by considering the coupling effects of vertical bending, torsion, and transverse multi-bearing support. A full-bridge spatial finite element model was developed in MIDAS Civil NX 2024 V1.1 to analyze the redistribution of bearing reactions and the stress response of the concrete crossbeam under different jacking conditions. The results show that, for the investigated bridge, the jacking force–displacement response remains highly linear during synchronous jacking. The B-axis middle bearing is more sensitive to jacking displacement than the two side bearings, with its fitted stiffness being approximately 2.19 times the average stiffness of the side bearings. Eccentric jacking causes reaction concentration at the jacked point and reaction reduction at adjacent supports, and the magnitude of reaction variation increases approximately linearly with jacking displacement. When the transverse non-uniform jacking magnitude reaches 20 mm, a tensile stress of 0.3 MPa appears at the bottom flange of the concrete crossbeam; therefore, a project-specific stroke-difference limit of 20 mm is recommended for this bridge, while the actual construction achieved a stroke control accuracy of ±0.5 mm and a transverse elevation difference within 1 mm. Field monitoring results validate the proposed analytical and numerical methods. The Pearson correlation coefficients of the measured jacking forces with the finite element and theoretical results are 0.9987 and 0.9988, respectively, and the corresponding mean relative errors are 3.84% and 4.23%. For stress responses, the measured and calculated values show a strong correlation, with a Pearson correlation coefficient of 0.9980 and a mean relative error of 12.77%; the critical mid-span monitoring point shows a relative error of only 0.65%. The final bridge alignment deviation is controlled within ±3 cm. The overall mean verification coefficient is 0.968, with a 95% empirical agreement range of [0.888, 1.048], indicating that the proposed mechanical analysis framework and combined force–displacement control strategy can provide a useful reference for refined construction control of similar ultra-wide double-deck composite girder bridges with comparable span arrangement and transverse bearing layout. Full article

Accurate and non-destructive diagnosis of leaf nitrogen content (LNC) is critical for improving nitrogen use efficiency in greenhouse cucumber production. However, strong physiological variation across growth stages limits the reliability of single-sensor approaches. In this study, leaf spectral reflectance and soil plant analysis development (SPAD) measurements were collected under four nitrogen levels (0, 135, 270, and 540 kg·ha⁻¹) at early, mid-, and late fruiting stages. Multiple machine learning models were developed using raw spectral bands (SP), vegetation indices (VIs), and SPAD data, and evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). Results showed that SPAD showed a significant positive correlation with LNC across all stages, with the strongest relationship observed at the mid-fruiting stage (R = 0.7975). Model performance exhibited clear stage dependence. Using single features, the best R² reached 0.800 (SP, early stage) and 0.794 (VI, early stage), but declined substantially at later stages. In contrast, integrating SPAD with spectral features significantly improved prediction accuracy, particularly at mid- and late stages. For example, the RF model based on SP + SPAD achieved R² values of 0.917 and 0.901 at the mid- and late fruiting stages, respectively, with low RMSE and RE. Similarly, the VI + SPAD combination achieved R² up to 0.893 at the late stage. Moreover, optimal algorithms varied across growth stages: SVR performed best at the early stage (R² = 0.819), RF at the mid stage (R² = 0.889), and XGBoost at the late stage (R² = 0.842) under full feature fusion. These results demonstrate that model accuracy is jointly regulated by growth stage, feature composition, and algorithm selection. Overall, this study highlights that a growth-stage-specific data fusion strategy integrating SPAD and spectral features is essential for robust LNC estimation, providing a practical framework for precision nitrogen management in greenhouse cucumber production. Full article

Missing data remains a pervasive challenge in air quality data analysis, where inappropriate imputation techniques can introduce hidden biases and compromise the reliability of time-series models such as AutoRegressive Integrated Moving Average (ARIMA). This paper examines the impact of linear interpolation and mean/median imputation on the performance of the ARIMA model and biases in the prediction of fine particulate matter 2.5 (PM2.5) concentration, together with a detailed analysis of ARIMA generated error metrics and their implications for the accuracy and reliability of the prediction. The findings reveal that package-default imputation significantly influences ARIMA forecasts, while mean/median imputation consistently delivers superior predictive performance, highlighting its robustness for handling missing environmental data. Moreover, imputation during the data transformation stage exerts a greater influence on model outcomes than methods applied at later analysis stages. Full article

To investigate the effect of scribing wheel angle on the scribing behavior of LCD glass, an SPH-based numerical model was established in LS-DYNA and validated against experimental results for reaction force and median crack depth. The results show that the model can accurately capture the mechanical response and crack propagation during the scribing process. At a scribing depth of 10 μm, the maximum relative errors between simulation and experiment were 5.17% for reaction force and 2.36% for median crack depth. The results for the 110° scribing wheel indicate that median cracks mainly initiate and propagate rapidly during the penetration stage, while the median crack depth becomes nearly stable after the preset depth is reached, and the subsequent rolling stage has little influence on further crack growth. As the wheel angle increases from 90° to 140°, the experimental mean peak reaction force increases from 2.66 N to 9.97 N, the maximum effective stress increases from 374.4 MPa to 732.8 MPa, and the median crack depth increases from 68 μm to 97 μm. Experimental observations further show that small wheel angles tend to cause debris accumulation and edge chipping, whereas excessively large wheel angles are likely to induce lateral cracks. Overall, a wheel angle of about 110° provides better cross-sectional quality, surface quality, and crack controllability for 0.2 mm-thick LCD glass. Full article

Objective: To develop and validate a fully automated deep learning workflow that localizes key anatomical landmarks on standard canine hindlimb lateral radiographs, derives the tibial plateau angle (TPA), and recommends a saw blade size for tibial plateau leveling osteotomy (TPLO) preoperative planning. Study Design: Retrospective validation study. Animals: Two hundred annotated lateral radiographs obtained from 130 dogs representing 14 breeds, with body weights ranging from 2.4 to 38.0 kg. Methods: A customized four-stage U-Net was trained using three complementary grayscale representations (normalized, contrast-enhanced, and gamma-adjusted images) to detect five TPLO-related landmarks. A deterministic geometric module then calculated TPA and mapped the derived osteotomy geometry to the nearest clinically available saw blade class. Results: The mean absolute error for TPA prediction was 1.34 ± 1.73°, and the median absolute error was 0.75°. Overall, 164/200 cases (82.0%) were within 2° and 188/200 cases (94.0%) were within 4.8° of the surgeon reference. Mean bias was −0.39°, the 95% limits of agreement ranged from −4.62° to 3.85°, and Pearson’s correlation coefficient was 0.87. For saw blade size prediction, mean absolute error was 0.32 ± 0.85 mm, exact agreement was achieved in 175/200 cases (87.5%), and all predictions remained within one adjacent class. Conclusions: The proposed pipeline provided clinically useful automated estimates of TPA and saw blade size from routine lateral radiographs. However, occasional high-impact landmark failures remained, indicating that the system should be positioned as an interpretable decision-support tool that requires surgeon verification rather than as an unsupervised autonomous planning system. Full article

Quadrupedal wheel-legged robots possess exceptional mobility in complex terrains, but their robust locomotion control is severely hindered by the difficulty of accurate state estimation without external sensors. Existing reinforcement learning methods relying on two-stage imitation often suffer from representation collapse and information loss during sim-to-real transfer. To address these challenges, this paper proposes a novel end-to-end reinforcement learning framework for implicit state estimation, incorporating terrain and external force features. Inspired by internal model control, the proposed method leverages a history of purely proprioceptive observations to extract explicit kinematic responses, as well as implicit environmental and external force representations via prototypical contrastive learning, completely circumventing explicit terrain regression and the need for physical force sensors. Furthermore, a tailored composite reward function and a progressive curriculum training strategy with large-scale domain randomization are integrated to ensure dynamic stability and hardware safety. Extensive cross-simulator validations and real-world deployments demonstrate that the approach achieves highly agile and robust locomotion, including adaptive traversal over diverse terrains. Experiments show that the method significantly enhances robustness under external disturbances, notably reducing the lateral linear velocity tracking error from 0.2421 m/s to 0.1319 m/s. The proposed method realizes zero-shot sim-to-real transfer with superior sample efficiency, providing a reliable and universal control paradigm for wheel-legged robots in unstructured environments. Full article

This study presents a two-stage deep learning framework for accurate and generalizable estimation of local earthquake magnitudes from three-component seismic waveforms, within the context of ground-based remote sensing systems. In the first stage, phase transition boundaries are identified at the sample level to enable consistent temporal alignment of the signals. In the second stage, earthquake magnitude estimation is performed using 30 s waveform segments aligned with the early portion of the signal and enriched with spectral and statistical features. The model was initially trained on the globally diverse dataset STEAD and later fine-tuned using a subset of KOERI waveforms, and its performance was evaluated on an independent KOERI test set. The results demonstrate high prediction accuracy, with a mean absolute error of approximately 0.09 and a coefficient of determination (R²) of about 0.95, indicating strong agreement between predicted and true magnitudes. The model maintains stable performance across varying signal characteristics and geographic regions, highlighting its strong transferability. These findings suggest that seismic sensor networks can be effectively utilized as remote sensing systems for rapid and reliable earthquake characterization. Full article

Triangular mesh is one of the most widely used representations for 3D surfaces. However, high-resolution mesh models often contain a large number of triangles, leading to significant burdens in storage, transmission, and real-time rendering. Mesh simplification aims to reduce model complexity while preserving geometric fidelity and structural features. Classical methods, such as quadric error metrics (QEM), rely solely on local geometric errors, making them difficult to distinguish between redundant regions and structurally important features, often resulting in feature loss and topological degradation. To address these limitations, this study proposes a structure-aware triangular mesh simplification framework based on graph neural networks (GNNs)-guided QEM. GNNs are employed as a structural importance estimator to predict geometric saliencies of mesh edges. The predicted importances are incorporated into the classical QEM edge collapse cost through a soft modulation mechanism. Furthermore, a geometry-saliency-driven dynamic cost modulation strategy is designed, enabling the simplification process to prioritize critical features in early stages and gradually transition to global error minimization in later stages, without compromising the geometric optimality of QEM. In terms of model design, hybrid structural representation GNNs are constructed by integrating spectral geometry and a dual-branch architecture. Laplacian positional encoding is introduced to capture global topological information, while 1-hop and 2-hop message passing branches enable multi-scale representation of complex geometric structures. In addition, a staged inference strategy is adopted to dynamically update graph structural features during simplification, effectively mitigating topological drift. Experimental results on the TOSCA dataset demonstrate that the proposed method achieves stable performance across various simplification ratios. It consistently outperforms FQMS and QEM in terms of geometric error ($P_{CD}$) and normal consistency ($P_{NE}$). For structural preservation ($P_{LE}$), the method shows advantages, with win-rates generally exceeding 90%. Moreover, it significantly improves the preservation of local geometric details at low to moderate simplification ratios. In summary, the proposed method effectively enhances local structural preservation while maintaining global geometric topology, providing an interpretable and practical solution for integrating learning-based structural awareness with classical geometric optimization in mesh simplification. Full article

This study presents a mathematical and kinematic modeling framework for analyzing trajectory behavior in a virtual reality (VR) welding simulator. Twenty novice participants performed repeated welding trials across three sessions, with torch trajectories recorded at 50 Hz in the task space. The proposed framework combines trial-level performance descriptors with derivative-based dynamic features, including spectral arc length (SPARC), log-normalized jerk (LNJ), and the number of velocity peaks (NVP), to characterize movement smoothness, intermittency, and longitudinal trajectory organization in a computer-simulated manual welding task. The results showed that spatial welding error decreased most clearly during the earliest stage of practice, with mean absolute lateral error declining from approximately 2.8 mm in the first trial to approximately 1.7 mm by the third trial. This early improvement was then broadly preserved across subsequent sessions. In contrast, smoothness- and fragmentation-related metrics exhibited more variable temporal patterns, indicating that improvements in task-space accuracy were not necessarily accompanied by uniform reorganization of movement dynamics. Associations between spatial error and kinematic features remained limited, suggesting that geometric task accuracy and dynamic trajectory organization represent complementary aspects of simulated manual performance. Overall, the findings show that high-frequency trajectory analysis in VR provides a useful basis for the mathematical modeling of dynamic behavior in simulated welding systems and supports the use of computer simulation for process-level investigation of manual task execution. Full article

Accurate prediction of lithium-ion battery state of health (SOH) is crucial for improving the safety, reliability, and operational efficiency of battery management systems (BMSs). However, many data-driven methods still struggle to maintain robust forecasting performance when degradation trajectories differ across cells, especially in later-stage aging. To address this issue, this study developed a robustness-oriented SOH prediction framework, termed Ada-TL, by integrating a Transformer encoder, an LSTM regressor, and adaptive hyperparameter tuning. Cycle-level health indicators were extracted from the publicly available CALCE dataset and transformed into a compact representation for supervised learning. The Transformer module captures non-local dependencies within each input window, whereas the LSTM summarizes sequential degradation dynamics. The number of attention heads, the initial learning rate, and the L2 regularization coefficient are adaptively optimized to reduce manual trial-and-error in model configuration. Experimental results on four CS2 cells show that Ada-TL consistently outperformed BP, CNN–LSTM, and the fixed-hyperparameter baseline in overall SOH prediction accuracy, achieving RMSE values of 0.0210–0.0310, MAE values of 0.0163–0.0262, and MAPE values of 4.17–9.30%. Additional late-stage and cumulative-drift analyses further indicate that Ada-TL provided more stable post-knee tracking and better control of long-horizon bias accumulation, with late-stage RMSE reduced to 0.0169–0.0217 across the four cells. An ablation study also showed that the KPCA-based three-dimensional representation improved the overall test-set accuracy on most cells while reducing input dimensionality. These results suggest that the main value of Ada-TL lies in robustness-oriented SOH forecasting under cell-to-cell variability. Full article

This study investigates the macro–meso shear characteristics of the geogrid–silty clay interface under cyclic loading through a combination of laboratory cyclic direct shear tests and numerical simulations. The effects of geogrid roughness, soil moisture content, shear displacement amplitude, and normal stress on the interface behavior are systematically analyzed. The results show that the interface shear strength and shear stiffness exhibit a three-stage evolution with increasing cycle numbers. This evolution is characterized by rapid attenuation in the early stage, gradual change in the middle stage, and stabilization in the later stage. The main degradation occurs within the first 1–10 cycles, while the interface response tends to stabilize after approximately 25 cycles. Increasing geogrid roughness and normal stress significantly enhances the interface shear strength and restrains cyclic degradation. In contrast, the shear strength reaches a maximum at the optimum moisture content level of 13%. The damping ratio shows an opposite trend to stiffness, increasing with cycle number and gradually approaching stability. Numerical simulation results are in good agreement with the experimental data, with relative errors within 5%. At the mesoscopic level, shear stress is mainly concentrated at the intersections of geogrid ribs, and the soil zone within 0–20 mm above the interface is identified as the primary region of shear deformation. Full article

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article

Background: Accurate estimation of the postmortem interval (PMI), the time elapsed between death and body discovery, is a critical challenge in forensic science due to the complex interplay of factors affecting decomposition. Traditional methods based on macroscopic changes often lack precision, especially in later postmortem stages. Methods: This study aimed to develop a novel PMI estimation framework by integrating the dynamics of endogenous small non-coding RNAs (sncRNAs) and exogenous bacterial-derived small RNAs (sRNAs) using sRNA transcriptomics and machine learning. Results: Cardiac RNA degradation strongly correlated with PMI, with a random forest (RF) model achieving high accuracy (coefficient of determination (R²) = 0.939, mean absolute error (MAE) = 2.987 h). Employing PANDORA-seq, we profiled temporal changes in sncRNAs (miRNAs, tsRNAs and piRNAs) in postmortem cardiac tissue within 30 h in a mouse model, while simultaneously assessing RNA integrity (RIN) across eight organs. PANDORA-seq revealed stable sncRNA landscapes with specific dynamic shifts, leading to the identification of seven novel biomarkers (four tsRNAs, three piRNAs) for PMI prediction (R² = 0.760, MAE = 158.990 min). Bacterial-derived sRNAs, predominantly from Staphylococcus aureus, were upregulated at 30 h postmortem, suggesting complementary biomarker potential. Bioinformatics analysis indicated that host miRNAs may target bacterial mRNAs, hinting at cross-kingdom interactions. Conclusion: These findings highlight the potential of integrated endogenous and exogenous sRNA analysis in PMI estimation, providing a high-precision, rapid diagnostic tool and revealing complex postmortem molecular processes. Full article

Objectives: This study evaluated the accuracy of an artificial intelligence (AI)-based cephalometric software (WebCeph version 2.0.0.) compared with manual tracing and determined whether stratifying patients by chronological age or dentition stage provides a more clinically relevant assessment of AI accuracy. Methods: Three hundred lateral cephalometric radiographs of Vietnamese patients were traced manually by an orthodontist (reference standard) and analyzed automatically by WebCeph. Intra-observer reliability was validated using ICC and Dahlberg’s error. We analyzed the data using three stratification strategies: (1) Overall; (2) Chronological age (<18, 18–25, >25 years); and (3) Dentition stage (<9 primary-early mixed, 9–12 late mixed, >12 permanent). The primary outcome was the absolute measurement difference (∣Δ∣), analyzed using the Kruskal–Wallis test and effect size (${\eta }^2$). Results: Overall, WebCeph showed high concordance with manual tracing (ICC > 0.80 for most parameters). Chronological age stratification showed weak associations with measurement error; differences between groups were largely non-significant ($p>0.05$) with a small effect size (${\eta }^2\approx 0.015$). In contrast, the dentition stage revealed significant performance disparities ($p<0.05$). Notably, accuracy for the Mandibular Arc (ICC = 0.349) and Mandibular Plane Angle ($p=0.048$) degraded significantly in the primary-early mixed group, a vulnerability obscured by chronological age-based stratification. Conclusions: Dentition stage is a more sensitive and biologically relevant predictor of AI accuracy than chronological age. While WebCeph is reliable for permanent dentition, accuracy degrades significantly in the primary-early mixed phase. Clinicians should prioritize manual verification of mandibular and incisor landmarks in mixed-dentition children. Full article

Electrochemical discharge machining (ECDM) with low machining reaction forces is useful for machining hard and brittle materials, which are required in precision equipment. Lathe-type ECD machines have been proposed to machine axisymmetric shapes while reducing cracks caused by thermal expansion, and they are suitable for thin workpiece machining due to the small reaction force. This paper demonstrates the micromachining of non-circular cross-sections using a lathe-type ECD machine equipped with an improved force-controlled tool electrode holder. The tool electrode holder combining a voice coil motor (VCM) with leaf springs arranged in parallel was built. This holder achieves both flexibility in the longitudinal direction of the tool electrode and high rigidity in the lateral direction. The relationship between the VCM current, tool electrode shift within the tool electrode holder, and thrust force was approximated using a polynomial. Consequently, this device allows for the stable, small contact force required in micromachining. An on-machine shape measurement method was also carried out by combining the tool electrode shift with the motion of an XZ stage. As a demonstration for non-circular cross-section machining, a square cross-section was grooved from a cylindrical glass rod. The removal and measurement processes were alternately repeated to achieve precision. During ECDM, the on/off of the DC power supply for ECDM was synchronized with the rotation of the workpiece. The measurement results indicated some dimensional errors, including bulging at the middle of sides and excessive removal at corners. The bulging was mainly caused by drift due to thermal expansion of the stage, as well as tool electrode wear. Since the tool electrode comes into close proximity to with the machined surface, the discharge from the side surface of the tool electrode caused excessive removal at the corners. Full article