Search Results (1,209)

The Source Region of the Yangtze River is a high-altitude area with extensive permafrost on the Tibetan Plateau. While temperature, precipitation, and radiation significantly affect vegetation phenology, the influence of permafrost changes remains unclear. Using the daily Long-term Seamless NOAA AVHRR NDVI Dataset of China (2003–2022), we extracted the start (SOS) and end (EOS) of the growing season in the Source Region of the Yangtze River (SRYR). Soil thawing date (SOT) was obtained from freeze–thaw state products, while active layer thickness (ALT) was estimated using the Stefan model based on MODIS land surface temperature (LST). Partial least squares regression and mediation analysis quantified the direct and indirect effects of permafrost degradation. Results show: (1) The end of the growing season (EOS) became significantly earlier in 64.33% of the region, while the start of the growing season (SOS) showed little change. (2) The effect of SOT on SOS depends on moisture conditions. Earlier SOT leads to earlier SOS in wetter areas by supplying meltwater, but delays SOS in cold–dry areas by increasing soil water loss. (3) Thicker ALT strongly promotes earlier EOS, accounting for up to 42.61% of EOS variation in cold–dry zones, because a deeper active layer potentially promotes downward movement of water, which may further lead to the potential leaching of nutrients from the shallow root zone, limiting resources for shallow-rooted plants. (4) Alpine meadows respond more strongly to permafrost changes than alpine grasslands. Overall, water loss caused by permafrost degradation may reduce the potential lengthening of the growing season under climate warming, highlighting the key role of soil water in linking permafrost and vegetation dynamics. Full article

Processed coix seeds are widely consumed as both food and traditional medicinal materials, but their quality gradually deteriorates during storage due to lipid oxidation and rancid odor formation. In this study, volatile changes during storage were characterized using gas chromatography–ion mobility spectrometry (GC–IMS), and a rapid monitoring method based on near-infrared spectroscopy (NIRS) was developed. GC–IMS identified 74 volatile compounds, with aldehydes and ketones increasing significantly during storage, indicating progressive lipid oxidation. Key markers, including 2-furaldehyde, 1-pentanoic acid, and $\gamma$-caprolactone, were identified as indicators of quality deterioration. Based on these markers, composite flavor and storage deterioration indices were constructed and used as reference parameters for NIRS calibration. Partial least squares regression models developed in the 1300–2500 nm region showed strong predictive performance for these composite indices, with ${\mathrm{R}}^2$p > 0.93 and RPD > 4.0. The long-wave NIR region exhibited superior sensitivity to oxidation-related spectral changes. These results demonstrate that NIRS combined with GC–IMS analysis provides an effective, chemically interpretable approach for rapid, non-destructive monitoring of storage quality in processed coix seeds. Full article

This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

Background: Acute rejection remains a major complication following liver transplantation, yet reliable noninvasive biomarkers for its early prediction and diagnosis remain unidentified. This exploratory study characterized bile and serum metabolites associated with acute rejection in living donor liver transplantation using comprehensive metabolomic profiling combined with machine learning. Methods: Non-targeted metabolomics were performed on bile samples collected on post-operative day (POD) 1 (n = 38) and serum on POD 14 (n = 45) from liver transplant recipients. Partial least squares discriminant analysis-based variable selection was followed by logistic regression and least absolute shrinkage and selection operator models, which were evaluated via cross-validation in the discovery cohort to explore potential biomarkers for acute rejection. Results: A three-variable, bile-based model for predicting acute rejection achieved a mean cross-validated AUC of 0.872 (95% confidence interval: 0.814–0.930). Glycohyocholic acid and sulfolithocholylglycine were the main contributors. A nine-variable serum model for the Rejection Activity Index, including the change in γ-glutamyl transferase, showed a mean cross-validated R² of 0.728 (95% confidence interval: 0.609–0.846), with methionine, creatine, and oxidized fatty acids contributing prominently. Conclusions: These findings suggest that metabolomic profiling combined with machine learning may provide candidate biomarkers for acute rejection after liver transplantation. However, given the exploratory nature of the study and the lack of external validation, the clinical utility of these metabolite signatures remains to be determined. Therefore, external validation in larger, independent cohorts will be required. Full article

The starch and moisture content of saffron corms are critical indicators of their flowering potential and yield. This study investigated the use of rapid, minimally invasive VNIR reflectance spectroscopy measurement to assess these parameters. The measurements were used to develop predictive models through four machine learning algorithms (PLSR, RF, SVR, and GPR). Spectral data were obtained from 130 fresh corm samples. Wavelength analysis identified key starch-sensitive intervals (~930–1000 nm and ~1150–1220 nm) and a broad moisture-sensitive region (~900–1350 nm). Among the evaluated models, the combination of the multiplicative scatter correction pre-processing method and Gaussian process regression (MSC-GPR) demonstrated the optimal predictive performance for water content (R² = 0.92, RMSE = 0.71%, RPD = 4.56, RPIQ = 5.37), and the combination of the MSC method and partial least squares regression (PLSR-MSC) demonstrated moderate performance for starch content (R² = 0.73, RMSE = 28.7 mg g⁻¹, RPD = 2.14, RPIQ = 2.81, dry weight). These results demonstrate the viability of VNIR spectroscopy as a minimally invasive tool for the pre-planting assessment of saffron corm quality under laboratory conditions. The method provides a laboratory-based framework for corm screening and selection, with potential for future adaptation to field settings using portable spectrometers following expanded calibrations and advanced modeling techniques. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO_2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics. Full article

As one of the six major traditional tea types in China, white tea’s quality formation is primarily influenced by the withering process. However, traditional methods for monitoring withering fail to achieve precise and stable control of moisture content. To address this issue, a total of 650 samples were collected at 13 withering time points (0–36 h), and the dataset was split into training and test sets at a 7:3 ratio. This study proposes a PRXBoost ensemble model for quantitative detection of withered white tea, which integrates data augmentation and intelligent algorithms. The ensemble model uses a Bagging-based weighted integration technique to combine Partial Least Squares Regression (PLSR), Ridge, and Extreme Gradient Boosting (XGBoost) models, and it conducts an in-depth analysis of the decision-making process within the PRXBoost model. First, the effectiveness of the data augmentation strategy and the superiority of the gradient descent algorithm are verified through pre-modeling based on the PLSR model and hyperparameter pre-search using the XGBoost model, respectively. Additionally, the Bayes algorithm is employed to optimize the weights of the sub-models, further enhancing the overall predictive performance. The results show that the PRXBoost model achieved the best performance among the compared models on the test set, with R² = 0.854 and RMSE = 0.080, exceeding the highest R² of a single model by 6%. These results indicate that PRXBoost provided improved predictive performance for moisture estimation within the current dataset. Finally, the SHapley Additive exPlanations (SHAP) algorithm is used to analyze the influence of each input feature on the prediction results, successfully identifying the 1916 nm and 1453 nm spectral bands as significant influencers of the prediction outcomes. These results suggest that the proposed model can support rapid, non-destructive monitoring of moisture evolution and provide actionable information for withering endpoint decision control. Full article

Estimation of the postmortem interval (PMI) remains a major challenge in forensic science. We used attenuated total reflection (ATR)–Fourier-transform infrared (FTIR) spectroscopy combined with chemometric modeling for PMI prediction using vitreous humor samples from 20 forensic cases with known PMI (24.8–97.6 h) and 10 with unknown PMI. The intensities of vibrational bands commonly associated with PMI were analyzed, and several peaks in the carbohydrate/phosphate region showed significant correlations with PMI. Principal component analysis revealed time-dependent spectral evolution, with PC1 (48.1%) associated mainly with carbohydrate/phosphate variations and PC2 (37.6%) with protein structural changes. Partial least squares regression with two latent variables achieved a cross-validated RMSE of 15.8 h (R² = 0.53) on all 20 known samples. Variable importance analysis identified glycoprotein degradation (1190 cm⁻¹) and phospholipid breakdown (736 cm⁻¹) as the dominant predictors, with traditional carbohydrate bands playing a secondary role. Predictions for unknown samples ranged from 27.1 to 80.1 h, with five of ten falling within the 90% prediction interval (±20 h) of the available estimates. This study presents a promising PMI estimation model that performed well on unseen samples, even if the sample size represents a methodological limitation that will be addressed in future investigations through larger, more diverse datasets. Full article

Unsafe behaviors by construction workers are a primary cause of accidents in commercial building construction. While traditional studies focus on the frequency of violations, they often overlook the disorder and unpredictability of such behaviors. This study introduces “Unsafe Behavior Entropy” as a new index to measure the disorder of workers’ behaviors, complementing traditional violation frequency. Utilizing a dataset from a large-scale commercial building construction project in Wuhan, China, this research uses Partial Least Squares Regression (PLSR) and Gray Relational Analysis (GRA) to examine the influence of six key factors, including safety meeting coverage and supervision density. The PLSR results indicate that the number of workers supervised per safety officer is the most critical driver of both frequency and entropy, while the coverage rate of entry safety education significantly impacts behavioral stability. GRA findings further reveal a high degree of correlation between management interventions and reductions in behavioral disorder. The study concludes that optimizing safety resource allocation and standardizing educational processes are fundamental to controlling human-related risks. By integrating the dual perspectives of frequency and entropy, this research provides a more comprehensive framework for safety management in complex building projects. Full article

Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the demands of high-throughput rapid detection. Although hyperspectral imaging technology offers the potential for non-destructive and rapid detection, existing analytical methods are often limited by issues such as high spectral band redundancy, insufficient feature extraction, and inadequate model stability, which constrain prediction accuracy and practical application potential. To address this, this study proposes a multi-scale residual convolutional neural network (MS-RCNN) based on competitive adaptive reweighted sampling (CARS) for feature band selection, combined with near-infrared hyperspectral imaging technology, to construct a rapid and non-destructive prediction model for the polyphenol content of Vigna umbellata. The model employs a parallel multi-scale convolutional module to extract spectral features with different receptive fields, and incorporates residual connections and adaptive pooling mechanisms to enhance feature reuse and robustness. Experiments compared the performance of partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multi-scale convolutional neural network (MS-CNN), and MS-RCNN models. The results indicate that the MS-RCNN model based on CARS screening achieved the best prediction performance, with a coefficient of determination (R²) of 0.9467, a root mean square error of prediction (RMSEP) of 0.0448, and a residual predictive deviation (RPD) of 4.33. Compared with the optimal PLSR and LSSVM models, its R² values were improved by 0.2078 and 0.1119, respectively. In summary, the MS-RCNN model proposed in this study enables rapid, non-destructive, and accurate prediction of polyphenol content in Vigna umbellata, providing an efficient technical approach for quality detection of edible and medicinal crops. Full article

Previous studies on velvet antler processing have mainly evaluated single techniques, and systematic comparisons of processing combinations are limited. This study investigated the effects of different processing combinations on the nutritional composition and physicochemical properties of velvet antler from red deer and sika deer. A 2 × 2 factorial design was applied: Blood-Retained vs. Blood-Removed and Boiled/Fried (zhuzha; no deep-frying) vs. Vacuum Freeze-Dried. In this study, Boiled/Fried was treated as a single processing method. The four processing combinations were analyzed as independent groups using one-way ANOVA. Additionally, two-way ANOVA was conducted to evaluate the main effects of pretreatment, dehydration method, and their interaction on the measured indices. To account for species background, a three-way ANOVA (species × pretreatment × dehydration) was further conducted for key indices. Moisture, crude protein, ash, and crude fat contents were determined. All composition-related indices were evaluated on both wet-weight and dry-weight bases to distinguish moisture-driven concentration or dilution effects from processing-related retention changes. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted for multivariate evaluation. Spearman’s rank correlation was used for association analysis, and Pearson’s correlation with linear regression was applied to quantify linear relationships (reported as r). Freeze-drying significantly reduced moisture content (p < 0.01) and increased crude protein content (p < 0.05). PCA and OPLS-DA demonstrated clear compositional separation among the four processing combinations, with moisture and crude protein as the main contributors (cumulative explained variance > 83%). The effects of Blood-Retained and Blood-Removed treatments differed between species. Three-way ANOVA indicated significant species-dependent effects (e.g., species × pretreatment and or species × dehydration interactions), while the pretreatment × dehydration interaction was significant for TAAs. In the Boiled/Fried groups, total amino acid content (TAA) decreased with increasing moisture. In the Freeze-Dried groups, moisture was significantly negatively correlated with TAAs in the Blood-Retained treatment (Pearson r = −0.886, p < 0.05), whereas no significant correlation was observed in the Blood-Removed treatment (r = 0.429, p > 0.05). Wet- versus dry-basis comparisons indicated that some between-treatment differences were attributable to moisture-related concentration or dilution effects, whereas differences persisting on a dry basis more directly reflected processing-related nutrient retention. Processing combinations produced species-dependent effects in velvet antler. The three-way ANOVA supported species-dependent pretreatment effects and confirmed that the influence of blood retention or removal on amino acid outcomes was contingent on the dehydration regime (pretreatment × dehydration for TAAs). From an application standpoint, no single processing route is universally optimal across all quality attributes; freeze-drying provides a robust baseline, whereas the choice of blood retention or removal should be made in a target-oriented manner (e.g., physicochemical stability versus protein and amino acid retention) while accounting for species background and interaction effects. Therefore, these findings provide a scientific basis for improving product quality, processing efficiency, and standardization in China’s velvet antler industry. Full article

This study aims to evaluate the controlling factors of tight sandstone reservoir sensitivity in the third member of the Yanchang Formation, Xunyi–Yijun area, southern Ordos Basin. Based on core samples from 12 wells, we established a partial least squares regression (PLS) model through thin section observation, SEM, XRD, high-pressure mercury injection, and sensitivity flow experiments, to quantitatively analyze the relationship between reservoir sensitivity and its controlling factors. The results show that the study area reservoirs are dominated by feldspathic sandstone with moderate compaction, characterized by low porosity (4.4–17.8%, avg. 10.93%), low permeability (0.104–2.33 mD, avg. 0.82 mD), and heterogeneous distribution of clay minerals (mainly chlorite, illite, kaolinite, and illite/smectite mixed layer). The reservoirs generally show weak to moderately weak sensitivity. The PLS model reveals that reservoir sensitivity is controlled by the coupled effects of multiple factors, with no single absolute dominant factor for any sensitivity type. Porosity is the most influential variable for overall reservoir sensitivity, followed by feldspar, illite, and illite/smectite mixed layer, and porosity exerts the strongest control on most sensitivity types via VIP score analysis. This study provides a theoretical basis for reservoir damage prevention in the study area and a technical reference for quantitative sensitivity evaluation of similar tight sandstone reservoirs. Full article

Protein content (PC) stability is crucial for wheat quality. This study utilized partial least squares regression and structural equation modeling to distinguish the physiological effects of “thermal intensity” versus “thermal accumulation” on spring wheat PC across Inner Mongolia. Environmental factors were the dominant drivers of variation. Notably, the Erguna region achieved the highest PC (18.53%) despite recording the lowest total growing degree days. Structural equation modeling analysis revealed that thermal intensity during heading-to-anthesis exerted a strong positive effect on PC (path coefficient = 0.965), likely by enhancing nitrogen remobilization kinetics. Conversely, excessive thermal accumulation and sunshine duration during grain filling negatively impacted PC via a carbohydrate-driven “dilution effect”. These findings suggest that superior PC formation requires a specific spatiotemporal coupling: high thermal intensity prior to anthesis to prime nitrogen transport, combined with low thermal accumulation post-anthesis to restrict carbon dilution. This study provides a physiological basis for optimizing wheat quality zoning by decoupling heat magnitude from duration under future climate scenarios. Full article