Search Results (1,399)

Exploring the spatial correlation network (SCN) characteristics and influencing factors of the eco-efficiency of cultivated land use (ECLU) in Xinjiang’s counties is crucial for clarifying inter-regional spatial mechanisms and supporting cross-regional collaborative governance. Using a panel dataset covering 85 counties across Xinjiang, this study adopts the super-efficiency SBM model, revised gravity model, social network analysis and QAP model to quantify ECLU, and further investigate its spatial network features as well as driving mechanisms. The results showed that: (1) ECLU exhibited a fluctuating upward trend with significant regional differentiation, and northern Xinjiang performed notably better than southern Xinjiang. (2) SCN remained connected overall, but network density was low, average path length was long, and spatial transmission efficiency was relatively low. (3) Regional differences in economic levels, labor productivity, and industrial structure all had positive effects on the formation of the SCN throughout the study period. Regional differences in fiscal support for agriculture had positive effects only in 2014 and 2017, while differences in the soil and water coordination ratio had a negative effect in 2021. Future policies for sustainable cultivated land use should be differentiated and zone-specific, based on each county’s role within the correlation network, to promote coordinated improvement of ECLU across counties. Full article

Endometrial senescence and the endometrial microbiota have both been implicated in the regulation of endometrial receptivity, yet their relationship remains poorly understood. The aim of this study was to investigate associations between p16-positive endometrial cells and microbiota composition during the implantation window and to determine whether these relationships differ according to implantation outcome. Endometrial senescence was assessed by p16 immunohistochemistry and digital image analysis, whereas microbial composition was characterized by 16S rRNA gene sequencing in endometrial biopsies collected from 68 women prior to undergoing transfer of a single euploid embryo, which was performed within six months of biopsy under the same hormonal preparation protocol. No significant differences in luminal epithelial or stromal p16 abundance were observed according to subsequent implantation outcome. Although senescence was not directly associated with implantation success, stromal p16 expression demonstrated multiple associations with the endometrial microbiota. Increased stromal p16-positivity was associated with lower relative abundance of Lactobacillus and higher abundance of Delftia. Notably, in exploratory subgroup analyses more pronounced associations were observed in women who achieved pregnancy, including a negative correlation between stromal p16 expression and Lactobacillus abundance (ρ = −0.48, p = 0.005) and positive correlation with Delftia abundance (ρ = 0.42, p = 0.016). Species-level analyses revealed implantation outcome-specific associations involving Lactobacillus iners and Limosilactobacillus vaginalis. In addition, stromal p16 expression was associated with differences in microbial co-occurrence networks, indicating broader effects on microbial community organization. Together, these findings suggest that stromal, but not luminal, senescence is closely linked to endometrial microbiota composition and microbial community organization during the window of implantation. The observation that the strongest senescence–microbiota associations occurred in women who subsequently achieved successful implantation supports the hypothesis that coordinated senescence–microbiota relationships may represent a previously underrecognized feature of the receptive endometrial microenvironment. Full article

LiDAR-based 3D object detection for autonomous driving must balance localization accuracy with real-time inference, while sparse point measurements make small-scale objects such as pedestrians and cyclists particularly challenging to represent at long range. This paper presents PES-PointPillars, an enhanced PointPillars detector with three coordinated design changes. First, pinwheel-shaped convolution (PConv) replaces selected backbone convolutions to expand horizontal and vertical receptive fields for sparse structural patterns. Second, an Improved Inter-Layer Feature Correlation (I-EFC) module uses soft gating and adaptive thresholding to fuse multi-level features through continuous, input-dependent weights. Third, a Smooth L1-NWD (SNWD) loss applies normalized Wasserstein distance to planar position and scale while retaining Smooth L1 regression for vertical position, height, and orientation. Using the parameter settings and configuration of the original PointPillars implementation, the locally executed PES-PointPillars experiment achieves Moderate 3D average precision values of 77.1% for cars, 46.7% for pedestrians, and 62.9% for cyclists at 68.3 FPS on the KITTI validation split. Relative to the source-reported PointPillars reference, the corresponding numerical differences are 2.1, 3.2, and 3.8 percentage points. The reported component-wise and staged ablations show category-dependent gains, with the complete model providing the strongest aggregate performance among the evaluated configurations. Full article

Zn–Sn–O (ZTO) thin-film transistors (TFTs) are promising indium-free oxide semiconductor devices, but their electrical stability is limited by oxygen-related defect states. In this study, Ga and Si incorporated ZTO TFTs were systematically compared using an identical bottom-gate top-contact device architecture to investigate dopant-dependent defect modulation and bias-temperature stability. Both Ga and Si incorporation induced a positive threshold-voltage shift and reduced the relative contribution of oxygen-deficient bonding components, suggesting modification of oxygen-related defect environments in the ZTO channel. Optical analysis further showed reduced Urbach energies after dopant incorporation, suggesting a decrease in localized band tail states and reduced structural disorder. Under negative bias temperature stress (NBTS), SZTO exhibited the smallest threshold-voltage shift, demonstrating the most effective stability enhancement. These results indicate that Ga incorporation preserves high field-effect mobility while improving stability, whereas Si incorporation more effectively reduces oxygen-related defect features and provides enhanced NBTS stability. This study provides insight into the dopant-dependent defect engineering for the improved reliability of indium free oxide TFTS. Full article

Medical treatment decision making is a complex process that involves integrating multivariate time-oriented data from multiple sources and is often influenced by factors such as patient load. In this study, we propose the Recurring Target Prediction (RTP) Pipeline to support treatment decision making by predicting the next medical action most likely to be administered, based on the historical data from patients in similar contexts. The method transforms raw time-stamped data into symbolic time intervals, incorporating domain knowledge. Each of the patient’s data are segmented by pre-defined trigger conditions (e.g., hypoglycemia), with each segment containing a feature window (historical data as symbolic time intervals); a prediction window (e.g., treatment dosage); and an optional prediction gap between the feature and prediction windows, enabling a future treatment alert. A frequent pattern-mining method is applied to the feature windows, and features generated from the mined patterns (e.g., count within each record and mean duration) are used as input to a Two-Step prediction model. First, a binary classifier predicts whether treatment is necessary, followed by a regression model to predict dosage. Finally, SHapley Additive exPlanations (SHAP) provide insights into the model’s decision making. We have evaluated the pipeline on an Intensive Care Unit (ICU) dataset, across three domains: hypoglycemia, hypokalemia, and hypotension. Key contributions include leveraging the recurrence of medical conditions and events to enrich the dataset, reducing false positives through a Two-Step prediction model, allowing prediction gaps for advance treatment notice, and incorporating SHAP, and introducing a two-level SHAP-based method for aggregating the relative weights of temporal patterns and components, to enhance the model’s interpretability. Full article

To address the challenges of multi-scale target variation, complex background interference, and insufficient feature fusion quality in maritime remote sensing ship detection, this paper proposes MarShip-DET, a frequency-aware multi-scale fusion detection algorithm based on YOLO11n. Three core modules are introduced: Channel-Decoupled Progressive Feature Extraction Module (CDPFEM), which employs asymmetric channel decoupling with dual-statistic channel attention and image-relative-position-encoded multi-head self-attention to enhance discriminative feature extraction; Edge-Aware Region Context Fusion Module (EARCFusion), which integrates learnable Sobel edge sensing and cross-attention correction to achieve precise foreground refinement; and Wavelet-guided Prototype Attention Module (WavePAM), which combines Haar wavelet frequency decomposition with prototype-guided spatial compression attention to strengthen deep semantic representation. Experiments on HRSC2016 demonstrate that MarShip-DET achieves an mAP50 of 94.9% and an mAP50-95 of 84.4%, improving by 3.7% and 4.3% over the baseline, respectively. Zero-shot experiments on HRSID and SSDD, including comparisons with YOLO11n, D-FINE-N, and YOLOv13n, provide additional evidence of cross-domain transferability under the evaluated protocol. Full article

Deep exploration in mature gold districts requires subsurface alteration evidence that can be related quantitatively to three-dimensional (3D) geological architecture. This study develops a workflow for the Tudui–Shawang deposit in the Muping–Rushan metallogenic belt that integrates drill-core short-wave infrared (SWIR) spectroscopy, 3D alteration modeling, ore-controlling geological constraints, positive–unlabeled (PU) learning, and ensemble prospectivity prediction. A total of 2140 spectra from 10 drillholes were processed to identify mineral assemblages, extract spectral scalars and feature-shape attributes, classify alteration facies, and construct continuous 3D alteration evidence. Discrete smooth interpolation and indicator kriging were used for continuous and categorical attributes, respectively, and CatBoost, LightGBM, XGBoost, and Random Forest were evaluated within a spatially separated PU-bagging design. Quantitative analyses show that individual SWIR attributes have weak deposit-scale relationships with Au grade. Nevertheless, local IC minima, relatively lower pos2200 values near several mineralized intervals, alteration-facies transitions, and a broader shift toward longer pos2250 wavelengths characterize relevant parts of the mineralized system. FUSE performed best under 1 km × 1 km spatial holdout validation, with an ROC AUC of 0.8900 and a PRAUC of 0.8926. Prediction-area analysis and the 3D probability volume delineated three ranked exploration targets (T1–T3). The results show that drill-core SWIR-derived 3D alteration evidence, when integrated with ore-controlling geology and spatially validated machine learning, provides a practical basis for target prioritization in mature gold districts. Full article

The search and discovery of underwater shipwreck sites represent the most arduous and critical phases of underwater archaeology. Wooden shipwrecks, in particular, are characterized by low acoustic impedance contrast and weak magnetic anomalies, coupled with their limited physical dimensions. Consequently, they predominantly exist as shallow-buried, discontinuous small targets scattered within confined areas, making their detection exceptionally challenging. Furthermore, the complexity of the submarine environment—including rugged topography, turbid water columns, and strong currents—poses formidable obstacles to the effective detection of these archaeological remains. Single geophysical methods are often limited by insufficient imaging resolution, interpretation ambiguity, and geological noise, making precise localization and characterization difficult. Focusing on the Nan’ao I Ming Dynasty shipwreck located in waters approximately 24 m deep off the coast of Nan’ao, Guangdong Province, China, this study proposes and validates an “acoustic-magnetic” multi-source data integration detection method. This approach systematically integrates high-resolution multibeam echo sounding (MBES), side-scan sonar (SSS), sub-bottom profiling (SBP), and marine magnetic data to establish a comprehensive framework for identification and integration analysis. The results indicate that the MBES bathymetric data reveal a regular, elongated structure oriented north–south (approximately 34 m × 12 m), closely matching the main hull and deck configuration. The SSS imagery exhibited high backscatter intensity and parallel linear textures, effectively delineating the hard shipwreck structure and the associated rigid protective frame employed for in situ preservation. SBP data confirmed the semi-buried state of the shipwreck (burial depth of approximately 0.6 m). Spatial variations in sediment thickness around the site suggested ongoing modification by strong hydrodynamic processes. Marine magnetic surveys identified localized negative anomalies (−210 nT relative to the ambient magnetic field), contrasting sharply with the positive anomalies of the surrounding natural reefs, thereby indicating an artificial ferromagnetic source. The spatial registration and feature superposition of multi-source data facilitated the characterization of the shipwreck, demonstrating its potential to mitigate environmental interference and enhance detection reliability in this complex environment. Using the Nan’ao I shipwreck site as a case study, this study provides a detailed characterization of the site’s 3D morphology, burial state, and physical properties. The proposed methodology offers a practical and robust technical solution for underwater shipwreck archaeology in complex nearshore environments, providing significant implications for proactive discovery, efficient investigation, and protection of underwater cultural heritage (UCH). Full article

4′-Hydroxymethoxychalcones represent structurally diverse chalcone derivatives that are attractive substrates for microbial functionalization; however, the impact of methoxy substitution position on their cascade biotransformation by fungi remains poorly understood. In this study, three regioisomeric 4′-hydroxymethoxychalcones, featuring ortho-, meta-, or para-methoxy groups on ring B, were transformed using eight entomopathogenic fungal strains belonging to the genera Beauveria, Isaria, and Metarhizium. Metabolic profiles were monitored over a 10-day period using ultra-high-performance liquid chromatography coupled with diode-array detection (UHPLC-DAD), and the structures of the major products were elucidated primarily by one- and two-dimensional nuclear magnetic resonance (NMR) spectroscopy and further supported by high-resolution electrospray ionization quadrupole time-of-flight mass spectrometry (HR-ESI-QTOF-MS). The investigated microorganisms catalyzed multistep transformations encompassing the reduction of the α,β-unsaturated carbonyl system, methylglucosylation, O-demethylation, and the formation of secondary polar metabolites. Although ene-reduction constituted the predominant initial reaction for all substrates, the relative distribution of subsequent metabolites varied depending on both the fungal strain and, to a lesser extent, the position of the methoxy group. The ortho-methoxy derivative exhibited the highest propensity for O-demethylation; the meta-substituted substrate generated the most heterogeneous secondary metabolite profiles, whereas the para-methoxy analogue showed the most consistent accumulation of methylglucosylated dihydrochalcones. These findings indicate that methoxy substitution position does not alter the common core biotransformation pathway, but can modulate the relative efficiency of individual steps and the extent of secondary metabolism. Full article

Against the dual background of restructuring global green trade rules and advancing China’s “dual-carbon” strategy, environmental information disclosure has increasingly become a crucial strategic tool for firms seeking to enhance international competitiveness. Existing studies have mainly examined the quantity, content, and quality of environmental information disclosure, while paying insufficient attention to its relative disclosure position, a strategic feature of information presentation that may affect signal salience and attention allocation. Drawing on signaling theory and the attention-allocation perspective, this study examines Chinese A-share listed exporters by matching environmental information disclosure positions observed during 2021–2023 with firms’ export product quality during 2022–2024. The final baseline sample contains 1472 firm-year observations. The findings show that earlier environmental information placement is significantly associated with higher subsequent export product quality. This relationship is mainly concentrated in the difference between Position_1 and Position_3, whereas Position_2 does not differ significantly from Position_1. The main result remains robust to alternative coding of disclosure position, additional controls for environmental disclosure volume, adjustment of the sample period, a panel-preserving placebo test, and an external peer-based instrumental-variable analysis. The three-step mechanism tests provide channel-consistent evidence for two potential transmission channels. More prominent environmental information placement is associated with a higher likelihood of ISO 14001 certification, reflecting stronger externally verifiable green credibility, and with greater realized green innovation output, reflected in granted green patents. Both mechanism variables are positively associated with subsequent export product quality. This study extends the literature on environmental information disclosure by shifting attention from static disclosure characteristics to the strategic prominence of information presentation, enriches the micro-level application of signaling theory in international trade, and provides practical insights for firms and regulators to improve the prominence, credibility, and substantive consistency of environmental disclosure while promoting export-quality upgrading. Full article

Background: In recent decades, the incidence of colorectal cancer (CRC) has been rising worldwide. CRC ranks second in cancer-related mortality. The identification of reliable biomarkers for early diagnosis and prognosis prediction, along with a deeper understanding of the underlying molecular events, holds substantial promise for improving patient outcomes. The tissue inhibitor of the metalloproteinase 1 (TIMP1) gene is overexpressed in various gastrointestinal malignancies and contributes to tumor progression. However, its role in regulating the CRC tumor immune microenvironment (TIME) and its potential as a clinically actionable prognostic biomarker remain unclear. Methods: To probe how TIMP1 acts as a prognosis-related candidate biomarker in colorectal carcinoma, TCGA-derived datasets were adopted to conduct Kaplan–Meier survival assessment. We also investigated the connection between the expression abundance of TIMP1 and the infiltration of immune populations and intratumoral lymphocytes; furthermore, immune checkpoint-related genes were systematically assessed across multiple tumor types via the TISIDB and TIMER2.0 platforms, with particular emphasis on CRC. We adopted the ESTIMATE scoring system to figure out how TIMP1 gene expression correlates with the phenotypic properties of the colorectal-cancer TIME. We relied on the limma toolkit for the screening of differential transcripts from high-TIMP1 and low-TIMP1 cohorts. Enrichment assessments covering Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes entries were then carried out to predict the potential biological pathways associated with TIMP1. We constructed the protein–protein interaction map for TIMP1-interacting partners via the STRING repository. To further explore TIMP1-correlated genes, we performed Venn diagram intersection analysis combined with Spearman’s correlation test. Finally, quantitative reverse-transcription PCR was then implemented to detect TIMP1 messenger-RNA abundance inside the RKO colorectal carcinoma cell line as well as normal colonic epithelial CCD-18Co cells, which offered in vitro experimental verification for our bioinformatic outcomes. Results: According to outcome data, TIMP1 transcripts were markedly up-regulated in CRC specimens and cell lines relative to normal samples. Elevated TIMP1 expression served as a poor-prognosis indicator for overall survival (hazard ratio [HR] = 0.43, 95% confidence interval [CI] = 0.29–0.64, p < 0.001) and disease-specific survival (HR = 0.39, 95% CI = 0.22–0.68, p = 0.001) among colorectal-carcinoma patients. TIMP1-high and TIMP1-low groups exhibited notable differences in immune cell infiltration (CD8⁺ T, macrophage, mast, neutrophil, B, monocyte, dendritic, and CD4⁺ T cells). TIMP1 expression was also significantly correlated with tumor-infiltrating lymphocytes, key immune checkpoint genes (e.g., CD274 [PD-L1] and CTLA4), and immunomodulatory chemokines (e.g., CCL3 and CCL5). Twelve TIMP1-interacting DEGs were selected: COL5A1, FN1, PRG4, and a cluster of nine MMPs (MMP1/2/3/7/8/9/11/13/14), all of which showed significant positive correlations with TIMP1 (r = 0.31–0.63, all p < 0.001). Conclusions: TIMP1 expression correlates with features of the tumor immune microenvironment and extracellular matrix remodeling in CRC, suggesting that TIMP1 shows potential as a candidate biomarker. However, its potential as a therapeutic target warrants further experimental investigation. Full article

Reliable traffic speed forecasting depends on trustworthy historical road-sensor observations, yet deployed sensors may exhibit missing values, outages, noise, calibration drift, and stuck readings. Existing forecasting models are commonly evaluated on cleaned inputs, whereas conventional imputation optimizes historical reconstruction rather than downstream prediction. This study presents the Sensor-Reliability-Aware Framework with Identity-Preserved Design (SRAF-ID), a prediction-oriented speed-channel repair front-end trained end to end using only future forecasting loss. The final model requires no controlled fault-location labels during training or inference. SRAF-ID constructs same-sensor temporal and mask-aware graph-neighborhood candidates, combines them through learned two-way softmax fusion, and preserves node-identity and temporal-context features. On raw-time-disjoint 70%/10%/20% splits of the Metropolitan Los Angeles (METR-LA) and California Performance Measurement System Bay Area (PEMS-BAY) datasets, ten-seed matched stress tests cover six window-level controlled perturbations. SRAF-ID reduces faulty-average mean absolute error from 5.12 to 4.82 on METR-LA and from 1.99 to 1.94 on PEMS-BAY, corresponding to relative reductions of 5.7% and 2.4%, respectively. It achieves a lower mean MAE in all 12 dataset-fault comparisons and a lower faulty-average MAE in all ten seeds on both datasets; the clean-input MAE also decreases. Checkpoint-only tests retain positive all-sensor and affected-sensor mean gains in all eight localized dataset-condition pairs, whereas unseen 0.75-standard-deviation global drift produces small adverse means with paired intervals crossing zero. The evidence therefore supports fault-label-free robustness under the defined stress protocols while leaving field-recorded event continuity and fault frequency for external validation. Full article

Classical approaches often treat interaction as engineered product terms or as emergent patterns in flexible models, offering little control over how synergy or antagonism arises. We take a quantum-inspired view: following the Born rule (probability as squared amplitude), coherent aggregation sums complex amplitudes before squaring, creating an interference cross-term, whereas an incoherent proxy sums squared magnitudes and removes it. Representing input contributions as complex amplitudes, the relative phase between amplitudes modulates the sign and magnitude of this cross-term, providing a mechanism-level account of synergy versus antagonism. In a minimal amplitude-linear model over a $2\times 2$ design—the simplest setting for feature interaction—this cross-term equals the standard interaction contrast ${\Delta }_{\mathrm{INT}}$, which can be interpreted as the potential-outcome interaction measure under randomized assignment. We instantiate this idea in a lightweight Interference Kernel Classifier (IKC) and introduce two diagnostics: Coherent Gain (log-likelihood gain of coherent aggregation over the incoherent proxy) and Interference Information (the induced Kullback–Leibler gap). A controlled phase sweep recovers this identity. On a high-interaction synthetic task (XOR), IKC attains predictive performance closely matching that of the evaluated classical baselines under paired, budget-matched comparisons; on real tabular data, its competitiveness is dataset-dependent, trailing the best evaluated baseline on Adult while outperforming it on Bank Marketing. In coherent–incoherent ablations with learned parameters held fixed, removing the coherent cross-terms degrades negative log-likelihood, Brier score, and expected calibration error on both datasets, with positive Coherent Gain. This quantum-inspired approach offers an interpretable mechanism for modeling and diagnosing feature interactions in probabilistic classification. Full article

Outbreaks of the Crown-of-Thorns Starfish (COTS, Acanthaster spp.) are a leading biological driver of coral cover loss, making timely and accurate population monitoring essential for reef management. Conventional diver-based surveys are labor-intensive and prone to missed detections, motivating automated detection from underwater imagery. However, COTS detection in complex underwater scenes still faces three major challenges. First, COTS individuals are often very small and carry limited discriminative information, making them inherently difficult to detect. Second, low underwater contrast and complex coral textures blur target boundaries and cause targets to be easily confused with the background. Third, ecological monitoring values recall more highly than precision—missing a COTS individual is far more costly than a false alarm—yet the recall of existing detectors remains insufficient. To address these challenges, we propose HiFi-Det (High-resolution Frequency-integration Detector), a collaborative multi-scale frequency-domain feature optimization method built on YOLO11. HiFi-Det integrates three complementary enhancements: a high-resolution detection branch that strengthens feature representation for small targets; wavelet transform convolution (WTConv) modules in the backbone and neck that apply band-separated processing in the wavelet domain to improve discrimination of COTS targets from low-contrast, textured coral backgrounds; and a WIoUv3 bounding box regression loss that dynamically focuses on ordinary-quality samples to improve recall while maintaining precision. On the public Great Barrier Reef dataset, HiFi-Det attains 81.02% F2 and 87.54% mAP@50, surpassing the YOLO11 baseline by 3.00% and 2.57%, respectively, while keeping the parameter count essentially unchanged relative to the YOLO11s baseline (within 3%), so that the accuracy gains are obtained without inflating model size. Ablation studies confirm the synergy of the three components: the high-resolution branch preserves spatial details, WTConv suppresses background textures, and WIoUv3 further curbs false positives while sustaining high recall. Applying the same recipe to a larger YOLO11m backbone yields HiFi-Det-m, which likewise improves over that backbone in both F2 and recall, indicating that the approach is a transferable recipe rather than a single fixed architecture. These results show that task-specific architectural and training designs can effectively adapt generic detectors to the demands of underwater ecological monitoring. Full article

This study investigated how detection distance affects spectral models for soluble solids content (SSC) and firmness evaluation in Korla fragrant pears and provides a reference for calibrating standardized indoor non-destructive detection equipment. Two hundred visually intact fruit samples at the early-ripening stage were collected from the Korla production area in Xinjiang. An FS-640 multispectral camera system equipped with a VS-SWR fixed-focus industrial lens (16 mm focal length, F1.8 maximum aperture, 1/2-inch sensor format) was used to acquire fruit reflectance spectra at seven vertical lens-to-fruit-surface distances of 90, 100, 110, 120, 130, 140, and 150 cm. A 625-pixel region of interest (ROI) was selected using ENVI at an undamaged equatorial or near-equatorial position of each fruit, and the regional mean spectrum was used as the spectral feature of one fruit sample. The sample-set partitioning based on joint X–Y distances (SPXY) algorithm was used to divide the calibration and prediction sets at a 3:1 ratio after outlier removal via a residual-threshold method. Four preprocessing methods, namely LOESS smoothing, standardization, vector normalization, and Savitzky–Golay (SG) smoothing, were compared. Competitive adaptive reweighted sampling (CARS) was performed with 50 Monte-Carlo sampling runs, a maximum of 30 principal components, and 10-fold cross-validation, yielding 99 characteristic wavelengths. Partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and artificial neural network (ANN) models were then established using identical input variables and sample partitions. Model performance was evaluated using the coefficient of determination for calibration ($R_c^2$), coefficient of determination for prediction ($R_P^2$), root-mean-square error of calibration (RMSEC), root-mean-square error of prediction (RMSEP), relative prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). Under the static laboratory acquisition conditions in this work, the SSC model achieved the best prediction performance at 110 cm with SG smoothing ($R_P^2$) = 0.8949, RPD = 3.0633, RPIQ = 5.8661), whereas the firmness model obtained optimal prediction performance at 140 cm with standardization ($R_P^2$) = 0.7460, RPD = 1.9425, RPIQ = 3.2867). Changes in detection distance altered illumination uniformity, effective reflected signal, photon-scattering paths, and background-noise proportion. These effects may partially explain why the chemical-absorption-dominated SSC index and the tissue-scattering-dominated firmness index responded differently to detection distance. The results provide a reference for setting spectral detection parameters for Korla fragrant pears; however, samples were obtained from only a single producing region, harvest season, and maturity stage, and no independent external validation dataset was used. Therefore, the generalization ability of the developed models needs to be further verified using cross-season and cross-orchard sample sets. Full article