Search Results (4,719)

The dynamics of aboveground biomass (AGB) in rubber plantations (RPs) provide an important basis for evaluating carbon stocks and environmental adaptability in tropical plantations. However, continuous monitoring of AGB of RPs at the regional scale is lacking, and its nonlinear responses to hydrothermal conditions remain insufficiently understood. This study focused on RPs in western Hainan Island (WHI), including Danzhou, Baisha, Lingao, and Chengmai, and integrated field plot data with multi-source remote sensing datasets. A framework for mapping RPs combining rule-based constraints and phenology-based random forest (RF) classification was developed. After key variable screening, extreme gradient boosting (XGBoost), Shapley additive explanations (SHAP), and generalized additive model (GAM) were used for AGB estimation and identification of climatic responses. The results showed that mapping of RPs achieved an overall accuracy of 92.89% and a Kappa coefficient of 0.854. The XGBoost-derived estimates showed that AGB of RPs in the study area increased by approximately 1.43 × 10⁶ Mg from 2017 to 2025, with growth areas mainly concentrated in the Danzhou–Baisha and western Chengmai. AGB exhibited significant nonlinear responses to climatic factors. Specifically, the effect of precipitation (PRE) shifted to negative after approximately 1945 mm yr⁻¹, whereas annual mean maximum temperature (TMAX) shifted to a positive effect after about 29.72 °C, although this effect gradually weakened as temperature continued to rise. Combinations such as PRE × annual mean temperature (PRE × TMP), PRE × TMAX, and PRE × potential evapotranspiration (PRE × PET) exhibited significant nonlinear interactions, indicating that the direction and magnitude of the effect of PRE shifted with changes in temperature and PET levels. These findings link the spatiotemporal changes in AGB of RPs in WHI with hydrothermal thresholds and their interacting effects, deepening our understanding of the climatic response characteristics of AGB in RPs in this region. They also provide a scientific basis for RP monitoring, carbon stock assessment, and climate-adaptive management in WHI. Full article

Weld tracking aims to provide real-time compensation for weld deviations caused by groove assembly inaccuracies and thermal shrinkage during Gas Metal Arc Welding (GMAW). To achieve precise tracking control in swing-arc narrow-gap GMAW based on passive visual sensing, a Self-Adaptive Coefficient-of-Variation Recognition (SCVR) algorithm is proposed for adaptively detecting weld deviation by filtering out welding interference. SCVR adaptively constructs a data window by discriminating the original variation coefficient to acquire the raw data distribution of the groove centerline. It then designs an in situ bandpass data filter to locally search the data segment with the minimal coefficient of variation for adaptive bandwidth determination. By applying the filter to the raw data, the disturbed data are removed, in situ retaining the data with the globally minimized variation coefficient. Finally, SCVR recognizes the real groove center from the filtered data, accurately detecting a weld deviation by comparing this center to the torch position. Additionally, an SCVR-based real-time tracking control system incorporating a PLC-based actuator with a PI controller for optimal stability is developed to correct the torch position in real time, achieving a high tracking precision of −0.161~+0.126 mm. Experimental results demonstrate the robust adaptability and effectiveness of the SCVR-based weld detection and tracking control system. Full article

In the domain of rotating machinery fault diagnosis, challenges such as multi-operating condition distribution heterogeneity and the difficulty of distinguishing fault features within multi-scale temporal signals persist. To address these issues, this paper introduces the Time-Gated Multi-Expert Generative Adversarial Network (TGME-GAN), a fault diagnosis approach that integrates a multi-expert gated conditional generative adversarial network with a clustering structure-aware feature enhancement. This method combines unsupervised K-means clustering with supervised discriminative learning. The optimal number of clusters is selected adaptively using the silhouette coefficient, and the distance vector from each sample to the cluster centers serves as a topological prior feature. A spatial–temporal joint representation matrix is then formed by concatenating PCA principal components, differential features, cumulative statistical features, and standardized change rates, which together capture both abrupt mutations and progressive degradation in fault signals. In the model, the discriminator incorporates a multi-expert gated network. Each expert learns a feature subspace corresponding to a distinct operating condition, and the gated network dynamically assigns fusion weights, allowing the discriminator to capture heterogeneous distributions across industrial conditions. The generator extracts multi-scale local patterns with a three-layer one-dimensional convolutional network and models sequential dependencies with a two-layer LSTM, producing high-quality fault samples that preserve intrinsic consistency. At the engineering level, TGME-GAN is deployed for gearbox fault diagnosis in uneven, small-sample industrial settings. In two gearbox fault experiments, this method substantially outperforms current mainstream models. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article

In multivariate statistical analysis, accurate modeling of the covariance structure is critical for high-dimensional data analysis, variable selection, and regularization. In high-dimensional settings, strong inter-variable correlation and redundancy are key factors limiting the performance of classical sparsity-based methods. While LASSO and its variants provide effective tools for coefficient shrinkage and variable selection, they may select redundant variables and produce unnecessarily complex models in highly correlated settings. In this study, a Correlation-Sensitive Adaptive LASSO (CDA-LASSO) method is proposed to address these limitations. The proposed approach is based on a hybrid weighting mechanism that makes the penalty term sensitive not only to initial coefficient magnitudes but also to the correlation structure between variables. This structure incorporates correlation-based redundancy information and imposes stronger penalties on predictors with higher directed redundancy scores. Under fixed-dimensional regularity conditions, the bounded correlation multiplier is shown to preserve the selection consistency and oracle limiting distribution of Adaptive LASSO. The method was evaluated through 14 high-dimensional simulation scenarios covering different sample sizes, dimensionalities, sparsity levels, correlation strengths, support structures, and normal or heavy-tailed errors. The results indicate that the Max and kMean variants generally reduce the false discovery rate and model size relative to LASSO and Elastic Net while maintaining broadly comparable predictive performance. Numerical improvements over Adaptive LASSO were also observed in several scenarios, although these differences were not uniformly statistically significant. Under very high correlation, reductions in false discoveries were sometimes accompanied by modest decreases in the true positive rate. The real-world Riboflavin analysis further showed that the CDA-LASSO variants produced smaller models than LASSO and Elastic Net while retaining comparable prediction errors. Overall, CDA-LASSO directly incorporates the internal correlation structure of the data into the penalty weights without requiring a predefined graphical structure and provides a practical methodological extension for more controlled and parsimonious variable selection in high-dimensional correlated settings. Full article

As the primary carrier for oil and gas transportation, pipelines are critical for the entire industry. Pipelines are continuously subjected to corrosion and abrasion in the oil and gas delivery process, leading to gradual wall thickness reduction, shortened service life, and deteriorated operational safety. Ultrasonic testing has been widely adopted for monitoring pipeline wall thickness. Conventional ultrasonic transducers possess rigid configurations, which hinder large-area inspection and exhibit poor adaptability to complex curved components. In contrast, flexible ultrasonic sensors show prominent advantages, with their small size, light weight, and excellent conformal contact with curved surfaces. Flexible piezoelectric thin-film sensors have been used in a wide range of fields. As one of the most representative piezoelectric polymers, poly(vinylidene fluoride–trifluoroethylene) (P(VDF-TrFE)) combines favorable piezoelectric coefficients and intrinsic flexibility, making it popular. Some research groups have investigated the influences of modified filler particles, doping ratios, and fabrication process optimization on the performance of P(VDF-TrFE)-based piezoelectric composites, while others have concentrated on the practical applications of existing flexible piezoelectric sensors. This study emphasizes a rapid customized fabrication strategy for flexible sensors instead of single-specification standardized probes; hence, it does not share the same comparison benchmark as conventional fixed-dimension sensors. Systematic research on flexible piezoelectric thin-film sensors is presented, including piezoelectric material modification, substrate design, laminated structural design, fabrication workflows, establishment of the testing platform, and the development of matched circuit systems. The material preparation and manufacturing processes are optimized, and a scalable technical route for fabricating flexible piezoelectric sensors is proposed. Using this route, flexible piezoelectric thin-film sensors can be rapidly tailored for different application scenarios to satisfy diverse engineering demands. Multiple experiments were conducted on pipeline samples with varying wall thicknesses and curvatures. The results verify that the sensor reaches a measurement precision of 0.01 mm, meeting the demands of high-precision pipeline structural health monitoring. Full article

Accurate crack width measurement is essential for the condition assessment of reinforced concrete (RC) bridges, yet most unmanned aerial vehicle (UAV) inspections remain limited to pixel-level observations that cannot be converted into reliable metric units without an external scale reference. This paper presents a lightweight, drone-agnostic UAV-mounted metrology system that enables standards-aligned metric crack width measurement directly from inspection imagery. The payload integrates a focusable diffractive optical element (DOE) red laser that projects a cross pattern of known angular geometry, three TF-Luna time-of-flight (ToF) distance sensors, and an ESP-WROOM-32 microcontroller that provides dual-rate sampling, Bluetooth Low Energy (BLE) streaming, and on-board logging. A two-stage calibration links the synchronized distance measurements to the physical length of the projected cross, yielding an image-specific pixel-to-millimeter scale that is applied to pixel-level crack widths obtained from a vision-based segmentation pipeline. The system is field-deployed on the Puente Huamani Bridge in Pisco, Peru, where measurements of 39 cracks classified under AASHTO MBEI condition states are compared against independent manual measurements by six inspectors. The proposed system reduces measurement variability across all condition states (CS), lowering the average coefficient of variation from 0.36 to 0.10 for fine CS1 cracks, from 0.27 to 0.11 for CS2, and from 0.22 to 0.07 for CS3. Cross-platform adaptability is demonstrated through an additional deployment on a DJI Matrice 350 RTK at the Low Level Bridge in Edmonton, Canada. The results indicate that the system provides a practical, low-cost, and scalable solution for repeatable, standards-aligned UAV-based bridge crack assessment. Full article

Cervical range of motion (CROM) has been used in research and clinically for assessing cervical health. Gold-standard goniometers tend to be cumbersome. However, Inertial Measurement Units (IMUs) or vision-based alternatives demand frequent calibration and/or costly hardware; moreover, the subject is aware of being measured and there is a risk of breaching privacy. In response, we have developed a non-contact HPNet system for head pose estimation (HPE) that can use a rear-facing camera to quantify CROM accurately. A Re-parameterized Visual Geometry Group (RepVGG)-D2se model is employed as the backbone of the network, and a Spatial Feature Enhancement (SCFE) module is incorporated to improve feature extraction. HPNet was evaluated on the large-scale Carnegie Mellon University (CMU) Panoptic dataset, achieving a mean absolute error (MAE) of 3.48°, 3.22°and 3.34° for yaw, pitch and roll respectively. Inter-instrument reliability was excellent for all six cervical movements when compared with the research/clinical-grade CROM device, with intraclass correlation coefficients (ICCs) averaging 0.939. Bland–Altman plots confirmed close agreement between the two methods. Cervical movement trajectory curves further confirmed the concordance between the clinical device and our method. The system is fully automatic, requires only a rear-facing camera, effectively preserves patient privacy, and provides accurate cervical posture estimation. This technology may provide a basis for future applications in neck-disorder screening, remote health monitoring, and personalized musculoskeletal wellness management, although further task-specific clinical validation will be required. To date, HPNet has been validated primarily on a computer-based platform and has not yet been deployed on smartphones. Future work will focus on model lightweighting, mobile deployment, and cross-device adaptation to facilitate its practical implementation on mobile devices. Full article

Integrated sensing and communication (ISAC) systems are envisioned as a key enabler for next-generation wireless networks. To simultaneously achieve multi-function of communication and sensing for this system, a hybrid reconfigurable intelligent surface (RIS) comprising both active and passive reflecting elements is proposed. An index-wise importance score matrix and a factorized representation of complex reflection coefficients are optimized by introducing a communication-sensing controllable coefficient. A novel ISAC system is investigated, which exploits the low-power advantage of passive reflecting elements while retaining the signal amplification capability of active reflecting elements. In the multiple-input multiple-output (MIMO) communication networks, the RIS reflection coefficient matrix is adaptively optimized via a Riemannian Hessian-based method. At the same time, the transmit precoding matrix is obtained using an extended weighted minimum mean square error (WMMSE) and Lagrange multiplier methods. Compared with baseline schemes including active-only RIS, passive-only RIS, random-phase RIS, and non-RIS, the proposed scheme enables multi-mode adjustability of communication and sensing which can be easily transplanted in current ISAC system. Under the constraint of transmit power, it exhibits more robust performance on communication-sensing with varying number of RIS elements and different signal-to-noise ratios (SNRs). Full article

Sun-induced chlorophyll fluorescence (SIF) is an effective proxy for vegetation photosynthesis, but tower-based retrieval suffers from atmospheric path interference under humid and variable conditions. We present a DOAS-based SIF retrieval algorithm that operates in Fraunhofer lines (680–686 nm, 745–758 nm) and water vapour-sensitive bands (717–727 nm). It constructs an adaptive reference spectrum from SCOPE simulations and PCA and incorporates H₂O absorption cross-sections into the fitting process for active atmospheric correction. The algorithm is implemented in a dedicated tower-based system integrating a 1° scanning gimbal with a high-resolution spectrometer. Validation with simulated and field data demonstrates the following: (1) the algorithm retrieves SIF with high fidelity (correlation coefficients >0.9 across all windows); (2) it exhibits lower water-vapour sensitivity and greater cloudy-sky stability than FLD, 3FLD, and SFM, achieving the lowest coefficient of variation (CV = 0.356); (3) over a complete wheat–rice rotation, the retrieved SIF tracks crop growth and phenological stages. This work provides a reliable solution for automated, high-precision tower-based SIF observation under complex atmospheric conditions. Full article

Medical image resolution plays a crucial role in early disease detection and fine-structure observation. Super-resolution reconstruction technology can restore low-resolution images to high-resolution versions, thereby assisting physicians in making accurate diagnoses. To address challenges in medical image super-resolution reconstruction, including insufficient global information acquisition, excessive network complexity, and suboptimal loss function adaptation for medical imaging data, this paper proposes an image super-resolution reconstruction algorithm named IDCASR-MMF based on improved dilated convolution and multi-module fusion. First, multi-dilation-rate dilated convolution is introduced to expand the receptive field and integrated with a spatial attention mechanism to dynamically calibrate high-frequency features after feature extraction. Subsequently, the Squeeze-and-Excitation module is fused with dilated convolution as a channel attention mechanism to streamline the network architecture. Finally, a weighted fusion strategy combining adversarial loss and MSE loss is adopted, where the dynamic adjustment of weighting coefficients balances pixel-level structural accuracy and high-frequency detail authenticity, achieving synergistic optimization of objective precision and subjective quality for medical images. To validate the effectiveness of the proposed algorithm, IDCASR-MMF is compared with 11 state-of-the-art methods across five datasets (Set5, Set14, BSD100, Urban100, and Bone FD). Experimental results demonstrate that the proposed algorithm achieves superior PSNR and SSIM values on multiple datasets, confirming that IDCASR-MMF can effectively reconstruct high-resolution medical images from low-resolution inputs. Full article

Accelerometer-based tilt angle estimation is widely used in engineering monitoring, yet random noise and outliers degrade its accuracy. Integer-order Kalman filters suppress noise, but their Markovian model assumes that the current state alone is sufficient to predict the next, neglecting the influence of earlier states on slowly varying processes. Fractional-order Kalman filters incorporate historical states into the prediction. However, the conventional formulation shares a single transition matrix between the state and covariance predictions, underestimating the prediction uncertainty, while the memory mechanism propagates gross errors across iterations. To overcome these limitations, this paper proposes a decoupled fractional-order Kalman filter (DFKF). The method assigns independent transition matrices to the state and covariance predictions, where a scaling coefficient inflates the predicted covariance to lower the prediction weight and strengthen reliance on measurements. A front-end gross-error pre-elimination strategy combining second-order differencing with adaptive peak detection is further introduced to block outlier propagation at the source before it enters the memory mechanism. Simulations under varying noise levels and gross-error conditions show that DFKF achieves a mean RMSE (Root Mean Square Error) of 0.147°, representing reductions of 17.4% and 6.4% over KF (0.178°) and FKF (0.157°), respectively, and a mean MaxAE (Maximum Absolute Error) of 0.548°, outperforming KF and FKF by 24.5% and 10.3%. Under gross-error conditions, DFKF converges in 0.012 s on average, approximately 2.8 times faster than KF and FKF, and the pre-elimination strategy restores accuracy to near-error-free levels. Full article

Adaptive feedback can destabilize a loop that would be stable under any fixed gain, so the speed at which the gain adapts is itself a design parameter. We study this effect in a minimal three-dimensional model motivated by the photocatalytic degradation of microplastics: a pollutant concentration is driven toward a setpoint by an ultraviolet (UV) actuator whose gain adapts online. The model has a single bilinear nonlinearity, so the local analysis can be carried out in closed form. Under an explicit feasibility condition, the system has a unique positive equilibrium. The Routh–Hurwitz criterion shows that this equilibrium is locally asymptotically stable below an explicit critical adaptation speed ${\kappa }_c$ and unstable above it. At $\kappa ={\kappa }_c$, a purely imaginary eigenvalue pair crosses the imaginary axis transversally, and a Hopf bifurcation occurs, with an explicit onset frequency. The first Lyapunov coefficient is computed in closed form; it separates a supercritical onset, for well-damped actuators, from a subcritical onset with hysteresis, for weakly damped actuators. Numerical experiments confirm the predicted limit cycle and the classification. All the stability results established here are local. Full article

Second- and third-order elastic constants (SOEs and TOEs) are essential parameters for characterizing the nonlinear elastic behavior of metallic materials. However, their determination in small-diameter slender rods remains challenging due to the stringent requirements of existing bulk-wave acoustoelastic and resonant ultrasound methods on the specimen dimensions and measurement conditions. This study proposes a two-stage guided-wave acoustoelastic inversion method for identifying the second- and third-order elastic constants of isotropic metallic rods. A high-accuracy forward model based on the wave finite element (WFE) method is developed to calculate the L(0,1) guided-wave dispersion and acoustoelastic responses under different combinations of elastic constants and uniaxial prestress. The sensitivity characteristics of group velocity dispersion and acoustoelastic coefficients are systematically investigated to provide a basis for objective function construction and test frequency selection. A surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is employed to solve the resulting ill-conditioned and non-separable inversion problem. Numerical validations demonstrate that the proposed method can accurately recover both SOEs and TOEs while substantially reducing the computational cost of iterative inversion. Full article

Background/Objectives: Adolescence is a dynamic neurodevelopmental period during which structured language performance may reflect ongoing biological reorganization processes. This study examined whether inflammatory, nutritional, and hormonal biomarkers were modestly associated with structured language task performance and cognitive performance in healthy pubertal children. Methods: A total of 156 healthy pubertal children (78 female, 78 male; mean age of 11.84 ± 2.98 years) meeting Tanner Stage ≥ 2 were included in this retrospective cross-sectional study. Language was assessed with the Test of Language Development-Primary: Fourth Edition (TOLD-P:4; Turkish adaptation) and cognition with the Wechsler Intelligence Scale for Children-Revised (WISC-R). Pan-Immune-Inflammation Value (PIV), Prognostic Nutritional Index (PNI), Neutrophil-to-Lymphocyte Ratio (NLR), vitamin B12, 25-OH vitamin D, ferritin, and TSH were included as biomarkers. Two-block hierarchical OLS regression and Spearman correlations with Benjamini–Hochberg FDR correction were used. Results: The biomarker block provided a statistically significant incremental contribution to the spoken language model (ΔR² = 0.066, p = 0.041; full model R² = 0.287). The primary test statistic was ΔR². In exploratory coefficient-level analyses that were not corrected for multiple comparisons, the PIV (β = +0.17, p = 0.033) and TSH (β = −0.17, p = 0.031) coefficients reached nominal significance. After FDR correction, PIV was positively associated with WISC-R Verbal score (ρ = +0.24, p_FDR = 0.019) and TSH negatively with TOLD-P:4 Speaking (ρ = −0.22, p_FDR = 0.029). Biomarker contribution to WISC-R Full-Scale IQ was non-significant (ΔR² = 0.049, p = 0.223). Conclusions: At the block level, the biomarker set contributed significantly to the structured spoken-language model but not to the Full-Scale IQ model; however, one of the two FDR-significant bivariate associations involved a verbal cognitive measure (PIV with WISC-R Verbal). Any language-versus-cognition contrast should therefore be interpreted cautiously. The modest effect sizes suggest these relationships are best interpreted within a developmental and neuroimmune framework. Longitudinal and multimodal studies are needed to replicate these findings. Because the TOLD-P:4/TODİL was applied beyond its validated age range (4; 0–8; 11) and its cross-age scoring could not be verified from the retrospective records, all language-based findings are exploratory and require confirmation with an age-validated instrument; the study’s confirmatory finding is the FDR-significant association of PIV with age-appropriate WISC-R Verbal performance. Because the TOLD-P:4/TODİL composite scores could not be verified as comparable across the sample’s age range, all TOLD-P:4-based results reported here are exploratory and hypothesis-generating; the confirmatory analyses rely on the age-appropriate WISC-R outcomes. Full article