Search Results (124)

Complex underwater environments induce difficult-to-quantify ranging errors, constraining the localization accuracy and robustness of heterogeneous networks. To address this, a node localization method based on a Geometric Topology Perception-Enhanced Multi-Stage Reinforcement Learning Strategy is proposed. First, an uncertainty quantification model under multi-source interference is established to characterize time-varying noise and accurately quantify the ranging errors of heterogeneous links. Subsequently, using the resulting ranging variance, an adaptive weight allocation mechanism based on Minimum Variance Unbiased Estimation is constructed to dynamically adjust link weights, achieving the robust fusion of multi-modal observation data. Finally, a Weighted Least Squares objective function is formulated, and the GP-AC strategy is developed. By utilizing Gaussian Process Regression and local Geometric Dilution of Precision, a multi-stage reward mechanism is constructed to circumvent topological traps and accurately estimate the single-epoch three-dimensional coordinates of static or quasi-static underwater sensor nodes. Simulation results demonstrate that system robustness is improved by 91.9%, average accuracy is enhanced by 54.9%, and the measured average localization time is 7.45 s. Full article

This paper examines whether and how analyst logical inconsistency affects the stock price crash risk in China’s A-share market. The sample includes 3784 listed non-financial companies. The final sample comprises 12,460 firm-year observations from 2016 to 2023. Specifically, we employ the open source Qwen1.5-14B-Chat model, an instruction-tuned generative large language model, to measure analyst logical inconsistency, classify the sentiment expressed in analyst reports, and determine the differences between earnings forecasts and textual tone. Using a panel fixed-effect model for basic regression, and applying two-stage least squares and propensity score matching to deal with endogenous problems, we find that analyst logical inconsistency significantly increases the stock price crash risk. The results remain robust to alternative variable definitions, additional control variables, alternative sample periods, and more stringent fixed-effects specifications. The mechanism test shows that the analyst logical inconsistency increases the stock price crash risk through three channels: increased financial risk, reduced investment efficiency, and degraded information disclosure quality. Heterogeneity analysis also shows that this positive impact is strongest in companies with high media coverage, good corporate reputation and low ESG performance. Our research results are helpful to the study of information intermediaries and stock price crash risk by introducing measures for the quality of analyst reports based on large language models, and provide useful suggestions for regulators and investors in emerging markets. Full article

Source identification in confined underground ventilation systems is essential for hazardous-gas monitoring, and uranium-mine radon provides a representative case in which release locations and strengths must be inferred from limited concentration measurements. This presents an underdetermined, ill-posed inverse problem whose solvability under different sparse-regression strategies and roadway configurations remains poorly understood. In this study, a computational fluid dynamics (CFD) forward model is coupled with sparse regression. The ventilation flow field and radon advection–diffusion process are solved in OpenFOAM to construct a source–sensor contribution matrix, and source recovery is formulated as a sparse linear inverse problem. Four methods—LASSO, LASSO with non-negative least-squares (NNLS) refitting, Elastic Net, and Elastic Net with NNLS refitting—are compared, and the contribution matrix is characterized by its mutual coherence, condition number, and singular-value spectrum. Numerical tests were conducted for single- and multiple-source scenarios in single-main and main–branch roadway models. The results indicate that inversion performance depends on the spatial information and local identifiability provided by the sensor configuration rather than on sensor number alone. LASSO and Elastic Net exhibited varying degrees of source-strength shrinkage or dispersion, whereas NNLS refitting reduced these effects when the first-stage support contained the dominant source candidates. In the prescribed three-source case, denser sensor coverage improved dominant-source localization and reduced the post hoc condition number of the prescribed-source submatrix, although the full-matrix condition number increased. This finding indicates improved local identifiability for the tested source combination rather than a general sensor-count effect. Because the synthetic observations and the inversion operator were derived from the same CFD response matrix, the results represent a controlled model-consistent proof of concept rather than an estimate of field-level performance. Full article

Under out-of-plane loading, composite sandwich wall panels may develop relative slip between the wythes and end slip at the intermediate-layer interface, weakening composite action and flexural stiffness. Previous studies have mainly focused on bearing capacity and connector performance, while the complete slip-development process, parameter effects, and quantitative slip-warning indicators remain insufficiently investigated. To investigate the flexural slip mechanism and design control method, three one-way composite sandwich wall panels with different wythe thicknesses, reinforcements, and stiffness ratios were tested under four-point bending. The load–deflection response, crack development, relative slip, and end slip were recorded. The measured slip values were normalized by the midspan yield deflection to obtain the absolute slip ratio, relative slip ratio, and end slip ratio. The results show that both the relative slip between the inner and outer wythes and the end slip exhibit a three-stage evolution with increasing load: almost no slip before cracking, approximately linear development after cracking, and rapid increase after yielding. The stiffness matching of the inner and outer wythes and the thickness of the intermediate layer are important factors affecting slip development. Based on the test results and comparison with existing experimental data, the yielding stage is recommended as the slip-warning control point, with warning values of 0.03 for the relative slip ratio and 0.06 for the end slip ratio. Finally, a simplified model based on partial composite action theory was established using binary linear regression and the least-squares method. The model achieved a centered $R^2$ of 0.937, while the slip influence coefficient increased from 2.4–8.8% at yielding to 30.3–66.0% at the peak stage, quantitatively supporting yielding-stage warning control. Full article

UAV multispectral remote sensing provides an efficient approach for monitoring crop salt stress in saline farmland. This study investigated maize and soybean grown under different salinity-management practices in a coastal saline region. The experiment was conducted at a single site during one growing season, including 14 maize plots and 13 soybean plots with three sampling sites per plot. A Salt Stress Response Index (SSRI) was developed using partial least squares path modeling (PLS-PM) based on baseline soil salinity and four crop growth indicators: leaf area index, plant height, SPAD, and fractional vegetation cover. Boruta was used for vegetation-index selection, and four regression algorithms (PLSR, EN, RF, and GPR) were evaluated for UAV-based SSRI retrieval using leave-one-plot-out cross-validation. SSRI was significantly and negatively correlated with crop yield across growth stages (r = −0.670 to −0.764), supporting its relevance as an integrated indicator of crop stress response. Linear models generally outperformed nonlinear models. EN performed best for maize at the jointing stage (R² = 0.774, RMSE = 0.124, RPD = 2.103) and soybean at the branching stage (R² = 0.737, RMSE = 0.131, RPD = 1.952), whereas PLSR performed best for maize and soybean at flowering (R² = 0.819 and 0.803, respectively). UAV-derived SSRI maps captured within-field spatial heterogeneity, and baseline-salinity-adjusted SSRI enabled exploratory comparison among management practices within the same crop and growth stage. The proposed framework provides a proof-of-concept approach for UAV-based crop salt stress monitoring and exploratory assessment of management responses in saline agricultural systems. Full article

This study evaluated the potential of a miniaturized portable near-infrared spectroscopy (NIRS) device for the non-destructive prediction of the physicochemical and functional quality attributes of red berries. A total of 145 samples from three berry species (aronia, haskap and goji), representing different harvest years and ripening stages, were analyzed. Spectra were acquired over the 908–1676 nm range using a MicroNIR™ 1700 OnSite-W spectrophotometer, and partial least squares regression models were developed to predict total soluble solids, moisture content, pH, total phenolic content and antioxidant capacity. The calibration models achieved coefficients of determination in cross-validation (R²CV) ranging from 0.83 to 0.92, with Root Mean Square Error of Cross-Validation (RMSECV) between 0.281 for pH and 2.085 g Trolox kg⁻¹ FW for antioxidant capacity. External validation confirmed the robustness of the models, yielding R²EV values between 0.76 and 0.88 and Root Mean Square Error of Validation (RMSEV) ranging from 0.381 for pH to 2.095% for moisture content. The highest predictive performance was obtained for total soluble solids (R²EV = 0.88; RMSEV = 1.391), followed by moisture content, pH and antioxidant capacity, whereas total phenolic content showed the lowest predictive accuracy (R²EV = 0.76; RMSEV = 1.914 mg GAE g⁻¹ FW). The Residual Prediction Deviation (RPD) and Range Error Ratio (RER) values further supported the practical applicability of the models for approximate quantitative prediction. Overall, these results demonstrate that portable NIRS is a rapid, non-destructive and reliable tool for the integrated assessment of the physicochemical and functional quality attributes of emerging red berry species. Full article

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD (Soil and Plant Analyzer Development) method, though easy to operate, is inefficient, destructive, and unsuitable for high-throughput field monitoring. UAV (Unmanned Aerial Vehicle) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and eight texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the growth period. The correlations among SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms, i.e., RF (Random Forest), PLSR (Partial Least Squares Regression), and SVR (Support Vector Regression), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R²) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during the entire maize growth stage based on UAV data. Full article

To address the insufficient characterization of vertical heterogeneity in potato canopy leaf nitrogen content (LNC), this study developed a layer-specific LNC estimation framework based on canopy hyperspectral reflectance, fractional-order derivative (FOD) transformation, and two-band and three-band optimized spectral indices. Partial least squares regression (PLSR) was then used to evaluate the predictive ability of the selected spectral indices for Top, Middle, and Bottom LNC. Field experiments were conducted from 2022 to 2023 in the semi-arid region of Yulin, Shaanxi Province, China. Canopy hyperspectral reflectance from 350 to 1830 nm and LNC measurements of upper (Top), middle (Middle), and lower (Bottom) leaves were synchronously acquired during the tuber formation stage. The results showed that potato canopy LNC exhibited a clear vertical gradient, following the order Top LNC > Middle LNC > Bottom LNC. Traditional vegetation indices were significantly correlated with LNC, but their correlations decreased with increasing canopy depth, with the highest correlation for Bottom LNC being only 0.524. Compared with traditional vegetation indices, FOD-based two-band indices showed stronger Pearson correlations with layer-specific LNC. Under FOD1.5, the maximum absolute Pearson correlation coefficients (|r|) between the selected two-band indices and LNC reached 0.855, 0.849, and 0.814 for Top, Middle, and Bottom LNC, respectively. The three-band optimized spectral indices further enhanced spectral information extraction, with maximum |r| values of 0.893, 0.885, and 0.852, respectively. However, cross-year validation produced substantially lower R² values, indicating limited temporal transferability of the selected indices and the need for further validation before broader application. Compared with the traditional vegetation index model, it increased the testing-set R² for Bottom LNC by 0.279 and reduced RMSE from 0.159 to 0.113. These results suggest that FOD1.5-integrated three-band optimized spectral indices can improve the indirect estimation of layer-specific LNC from canopy reflectance, particularly for Bottom LNC, where the reflectance–LNC association is affected by canopy signal attenuation and mixing. The findings provide a methodological reference for describing canopy vertical nitrogen status and functional heterogeneity in potato, while their broader applicability requires further validation across growth stages, cultivars, sites, and nitrogen management conditions. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Transport infrastructure is widely viewed as a key lever for integrating lagging rural regions into broader economic systems. Western China, marked by vast territory, complex topography, and historically severe spatial market frictions, offers a particularly informative setting for examining this question within the sustainable rural development agenda. Exploiting the staggered rollout of China’s National Highway Expansion Program across 276 prefectures from 2003 to 2018, we combine high-frequency wholesale prices for 93 agricultural commodities, geocoded expressway network data, and the China Family Panel Studies. A staggered difference-in-differences design is supplemented by a time-varying minimum spanning tree instrument capturing network-efficiency considerations, alongside event-study and recently developed robust estimators for staggered treatments. Two-stage least squares estimates indicate that expressway connection raises the agricultural price integration index by 0.071, reduces within-prefecture price volatility by approximately 0.040 (about 13% of baseline), raises agricultural household income per capita by roughly 16%, and improves the household food-security index by 0.571 points. Event-study results show no pre-trends, with effects materializing over three to four years post-connection. Mechanism analysis highlights expanded market linkages, and the gains are stronger in nationally designated poverty counties and prefectures with rugged terrain. Partial-equilibrium welfare accounting implies annual gains of roughly USD 4.92 billion, and unconditional quantile regressions reveal a progressive distribution across farm incomes. These findings underscore the role of transport infrastructure in alleviating spatial frictions, integrating lagging regions, and advancing sustainable rural development while warranting careful attention to the environmental externalities of large-scale infrastructure. Full article

To address the lack of a comprehensive quality control system for Ganoderma lucidum, we developed an integrated evaluation strategy across four varieties and three growth stages. This system integrates the targeted screening of anti-benign prostatic hyperplasia (BPH) triterpenoids acting on 5α-reductase type 2 (SRD5A2) with a chemical consistency assessment utilizing systematic quantitative fingerprint method (SQFM) and chemometrics. UPLC-Q-TOF-MS/MS identified 85 triterpenoids in the samples. An orthogonal partial least squares (OPLS) regression was utilized to screen seven chromatographic peaks that positively correlated with SRD5A2 inhibitory activity. Three principal bioactives were structurally identified as ganoderic acids DM and B, and ganoderenic acid A, which demonstrated significant in vitro SRD5A2 inhibition rates of 61.16 ± 1.87%, 36.41 ± 1.10%, and 41.82 ± 2.09%, respectively. Molecular dynamics simulations and averaged weak interaction analysis revealed that these compounds exert potent enzyme inhibition via hydrogen bonds and hydrophobic interactions with distinct SRD5A2 amino acid residues. The SQFM-chemometrics quality system confirmed ten samples reached Grade 6 or above, identifying the H3 variety at the initial stage as possessing the highest active ingredient content and optimal overall quality. This integrated framework enables rapid bioactive discovery and robust standardization for G. lucidum-based functional foods, thereby facilitating their industrial development. Full article

Rapid and cost-effective estimation of soil total nitrogen (TN) is essential for soil fertility assessment and nutrient management. However, the performance of laboratory visible–near-infrared (Vis–NIR) models is shaped not only by preprocessing and modeling strategy but also by sample preparation and the soil’s compositional background. In this study, TN prediction was evaluated using 376 topsoil samples from two contrasting datasets: Mollisols from the black-soil region of Northeast China and Ultisols from Qiyang County, Hunan Province, southern China. Spectra acquired over 350–2500 nm for three particle-size fractions were preprocessed using Savitzky–Golay smoothing combined with standard normal variate (SNV), first-derivative, or second-derivative transformations, and modeled using partial least squares regression (PLSR), support vector regression (SVR), and extreme gradient boosting (XGBoost). Model development used a 5 × 5 nested cross-validation followed by evaluation on a sample-grouped held-out test set. Among all combinations, XGBoost with first-derivative preprocessing on the 0.25 mm fraction produced the best performance, with test R² values of 0.91 for Mollisol and 0.78 for Ultisol. Shapley additive explanations (SHAP) and principal component analysis (PCA) consistently identified informative spectral regions at 430–480 and 1330–1450 nm for Mollisol and at 585–635, 820–900, and 2180–2240 nm for Ultisol. Prediction errors were larger in the sampled Ultisol dataset and increased with DCB-extractable Fe and mineral backgrounds. A second-stage log-domain residual correction incorporating ancillary soil properties further reduced the Ultisol RMSE from 0.30 to 0.27 g kg⁻¹. These findings support the 0.25 mm, first-derivative, XGBoost workflow as a robust laboratory Vis–NIR approach for TN prediction and indicate that composition-aware residual correction can improve prediction in oxide- and mineral-rich soils. Full article

The leaf area index (LAI) is a key parameter reflecting vegetation canopy structure and growth status. This study systematically compares the performance of spectral and texture features derived from Sentinel-2A imagery for LAI retrieval in winter wheat and maize. Multiple vegetation indices and gray-level co-occurrence matrix (GLCM) texture features were extracted, and three types of texture indices—Normalized Difference Texture Index (NDTI), Ratio Texture Index (RTI), and Difference Texture Index (DTI)—were constructed. Modeling was performed using Partial Least Squares Regression (PLSR) and Gaussian Process Regression (GPR). Results show that red-edge vegetation indices and mean texture features (e.g., NDVI_M) are robust predictors for both crops, with correlation coefficients reaching 0.87 for winter wheat and 0.83 for maize. Texture indices further enhance the representation of canopy structural information; the optimal NDTI achieved |R| > 0.88 for both crops, though the specific feature pairs were crop-specific. Using the proposed two-stage feature optimization strategy combined with GPR, the LAI estimation accuracy for winter wheat reached R² = 0.87 with RMSE = 0.41 on an independent test set, while for maize the accuracy was R² = 0.75 with RMSE = 0.38. The strategy significantly improved accuracy for winter wheat (uniform canopy) but yielded limited gains for maize (heterogeneous canopy), largely due to differences in canopy architecture. This study demonstrates that integrating multi-dimensional features with nonlinear modeling enhances LAI estimation accuracy. By providing a side-by-side comparative evaluation across two contrasting crop canopies, this study underscores the necessity of crop-adaptive feature selection and modeling strategies. The findings offer practical guidance rather than a universal model for large-scale crop monitoring in agricultural remote sensing. Full article

To address the limited accuracy of power demand prediction for hybrid electric vehicles under complex and dynamic driving conditions, this paper proposes a hybrid prediction approach based on the cascade correction of Long Short-Term Memory networks and Least Squares Support Vector Machines (LSTM-LSSVM-CC). The proposed method adopts a stage-wise modeling framework that exploits the least-squares optimality of LSSVM for low-frequency steady-state signals and the dynamic compensation capability of LSTM for high-frequency non-stationary residuals, thereby achieving complementary feature representation in the frequency domain. Specifically, an LSSVM is first used to construct a baseline regression model that captures stationary components, followed by an LSTM network that performs deep temporal modeling of the residual sequence to correct nonlinear prediction errors. Extensive experiments conducted on three standard driving cycles—CLTC-P, WLTP, and UDDS—demonstrate that the proposed model consistently outperforms conventional methods including LSSVM, RNN, ELMAN, and Random Forest in multi-step predictions, achieving an average RMSE reduction of 28–52% and maintaining correlation coefficients (R²) between 0.87 and 0.99. Particularly under highly dynamic and abrupt load conditions, the model exhibits superior real-time performance and stability while significantly mitigating cumulative prediction errors. These results demonstrate that the proposed LSTM-LSSVM-CC model achieves robust modeling performance of non-stationary time series while balancing prediction accuracy and computational efficiency, providing an effective technical foundation for hybrid vehicle energy management optimization and offering a transferable theoretical framework for time-series prediction in complex systems. Full article

This article introduces the CoLiRa (Computational Literature Review & Analysis) framework, a novel integration of established computational algorithms designed to quantitatively analyze and map the evolution of scientific fields. Employing a human-in-the-loop epistemological approach, CoLiRa combines the scalability of automated algorithms with the semantic coherence of expert-driven qualitative research. The multi-stage pipeline incorporates Latent Dirichlet Allocation (LDA) for thematic discovery, cluster analysis (K-Means and Multidimensional Scaling) for conceptual mapping, and Ordinary Least Squares (OLS) regression to monitor temporal trends. Algorithmic outputs are structurally validated by domain experts using quantitative metrics. The framework’s end-to-end capabilities are demonstrated through a proof-of-concept case study on the semantic enrichment of tabular data, encompassing studies up to 2024 that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis identifies three core research topics and finds no statistically significant linear trends, suggesting thematic coexistence. This work provides a validated, hybrid computational approach for conducting robust literature reviews and mapping research trajectories. Full article