Search Results (3,385)

Background: The click-evoked Auditory Brainstem Response (ABR) is the gold standard electrophysiological tool for assessing auditory pathway integrity in infants and young children. As normative data are inherently equipment-specific, the absence of pediatric reference values for the NeuroAudio system (Neurosoft, Ivanovo, Russia) represents a significant gap in clinical practice, given that existing normative datasets for this system are restricted to adult populations. Objective: To establish normative data for click ABR recorded with the NeuroAudio system in children under three years of age, stratified by age group according to auditory maturation patterns. Methods: A prospective, cross-sectional study was conducted at the Electrophysiology Laboratory of the Department of Speech Therapy, Paulista School of Medicine, Federal University of São Paulo (UNIFESP/EPM), under the approval of the Research Ethics Committee (protocol 7.939.564). A total of 203 children (121 males, 82 females; age range: 2 weeks to 36 months) with confirmed normal peripheral auditory function were included. Click stimuli (0.1 ms, rarefaction polarity) were delivered monaurally via ER-3A insert earphones at 80 dB nHL and a repetition rate of 19.3/s. Two average runs of 2000 artifact-free sweeps were recorded per ear. Absolute latencies of waves I, III, and V, interpeak intervals I–III, III–V, and I–V, and amplitudes of waves I and V were analyzed. Results: Statistical modeling supported the consolidation of 12 initial age bins into three clinically and statistically validated categories: 0–3, 4–12, and 13–36 months. Wave I latency remained stable across age groups, whereas waves III and V and all interpeak intervals showed progressive shortening with increasing age. Wave V amplitude increased progressively with age, while wave I amplitude remained unchanged. Females presented shorter latencies than males for waves III and V and for all interpeak intervals. The right ear exhibited a shorter III–V interpeak interval than the left ear, with a significant ear × age interaction indicating that this asymmetry is modulated during early maturation. Age, sex, and ear-stratified normative values (two SD and three SD reference limits) are reported. Conclusion: This study provides the first pediatric normative dataset for click-evoked ABR acquired with the NeuroAudio system in children under three years of age. The proposed three age stratifications, together with sex- and ear-specific reference values for the III–V interpeak interval, offer a clinically actionable framework for the accurate interpretation of pediatric ABR recordings and for the early identification of auditory pathway abnormalities. Full article

Background/Objectives: Cardiovascular disease (CVD) burden decreased in the North Africa and Middle East (NAME) region between 1990 and 2019. This study used Global Burden of Disease (GBD) 2023 data to examine whether trends in mortality, disability-adjusted life years (DALYs), incidence, and prevalence continued through 2023 across all 21 NAME countries. Methods: We analysed age-standardised CVD mortality, incidence, prevalence, and DALY rates from 1990 to 2023. Joinpoint regression identified changes in temporal trends and calculated the annual percent change (APC) and average annual percent change (AAPC) with 95% confidence intervals (CIs). Results: Age-standardised CVD mortality decreased from 579.6 per 100,000 in 1990 to 358.2 in 2023 (AAPC: −1.42%; 95% CI: −1.48 to −1.35). However, no significant reduction occurred between 2019 and 2023 (APC: −0.33%; 95% CI: −1.37 to 1.75). DALY, incidence, and prevalence rates followed similar patterns, with no significant decline in the final years of this study. Egypt was the only country with a long-term increase in CVD mortality, which accelerated after 2020 (APC: +5.20%; 95% CI: 1.20 to 12.87). High systolic blood pressure, dietary risks, lead exposure, and air pollution were the leading modifiable risk factors. Conclusions: The earlier decline in CVD burden in the NAME region did not clearly continue after 2019. The region is currently off track to meet Sustainable Development Goal 3.4 by 2030. Future progress may depend on improved blood pressure control, lipid management, dietary habits, and environmental risk reduction. Full article

Purpose: To investigate the agreement on perfusion parameters derived from two different commercially available solutions for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with prostate cancer (PCa). Methods: A total of 50 patients (mean age, 71.6; range 56–86) who had undergone radical prostatectomy between December 2021 and September 2022 were included in this retrospective study. All patients had undergone DCE-MRI on a single 3T-MR scanner. Tumor segmentation on MR images was performed by two radiologists in consensus after radiologic-pathologic correlation using topographic maps as a reference standard. Subsequently, four perfusion parameters were calculated by dedicated commercially available solutions from two different vendors. Both solutions adopted a population-based arterial input function and an extended Tofts model as the pharmacokinetic model. The perfusion parameters were as follows; volume transfer constant (K^trans), rate constant (k_ep), volume fraction of extravascular extracellular space (v_e), and volume fraction of plasma (v_p). The differences between paired measurements were compared by Bland–Altman analyses and the reproducibility was evaluated using the intraclass correlation coefficient (ICC). Results: The study population consisted of Gleason score (GS) 6 (n = 12), GS 7 (n = 34), GS 8 (n = 1), and GS 9 (n = 3). Significant differences were found for all parameters (p < 0.0001). Mean differences were as follows: K^trans, −0.2102 (95% confidence interval; −0.2687 to −0.1518); k_ep, −0.7632 (−0.9005 to −0.6258); v_e, −0.1507 (−0.2422 to −0.05907); v_p, −0.02929 (−0.03383 to −0.02476). ICCs for average measures were as follows: K^trans, 0.2989 (−0.2355 to 0.6021); k_ep, 0.6883 (0.4507 to 0.8231); v_e, −0.1331 (−0.9967 to 0.3570); v_p, 0.2653 (−0.3106 to 0.5881). Conclusion: All perfusion parameters were significantly different between the two solutions. Therefore, comparison of perfusion parameters across different solutions is not recommended. Full article

This paper presents a non-learning-based, seed-dependent, semi-automatic pedestrian candidate generation pipeline for LiDAR point clouds. The proposed method is designed to support 3D annotation workflows by reducing irrelevant candidate clusters while improving the reliability of pedestrian candidate selection under distance-dependent point sparsity. The pipeline integrates distance-aware DBSCAN clustering, Single Template Matching (STM), and Centralized Point Augmentation (CPA). First, LiDAR points within the camera field of view are preprocessed, and pedestrian candidate clusters are generated using DBSCAN parameters configured according to distance intervals. Ground-snapping-based bounding-box refinement and height-based filtering are then applied to improve geometric consistency and reduce non-pedestrian candidates. In the second stage, STM compares PCA-aligned projected silhouettes of candidate clusters with a seed pedestrian template to suppress false positives. To address silhouette instability caused by sparse mid-range pedestrian points, CPA adds centroid-contracted points in the projection-relevant plane before template matching. Experiments on pedestrian-containing frames from the KITTI dataset show that STM improves precision from 27.6% to 60.5% and increases the F1-score from 36.8% to 51.4% compared with the initial DBSCAN-based candidate generation stage. The final CPA configuration improves recall from 44.7% to 46.7% and the overall F1-score from 51.4% to 52.1%, while revealing a precision–recall trade-off. Supplementary IoU analysis shows that the final DBSCAN–STM–CPA configuration maintains meaningful spatial overlap with pedestrian ground-truth boxes, achieving 88.9% at 3D IoU ≥ 0.10 and 81.6% at BEV IoU ≥ 0.25. Runtime analysis further shows that height-based filtering reduces the average per-frame processing time from 151.5 ms to 125.1 ms, while the final CPA configuration introduces only a small overhead, resulting in 126.2 ms per frame. These results demonstrate that the proposed DBSCAN–STM–CPA pipeline can provide reliable pedestrian candidates for semi-automatic 3D labeling without requiring class-specific detector training. Full article

Short-term water demand forecasting is a key issue for the management of smart water networks, particularly in the context of remote control and active regulation. This study analyses a real-world dataset of water demand coefficients, collected at 15 min intervals, from a municipality in Southern Italy serving approximately 73,000 inhabitants. The proposed model, based on Gaussian Process Regression (GPR) with a Rational Quadratic kernel (RQ), is compared with a statistical benchmark constructed using average patterns for each time slot by the application of the Gauss Distribution. The results show a reduction in RMSE and MAE and a better ability to track the daily dynamics of demand using the GPR approach. Full article

Background: Optimal phase selection in coronary computed tomography angiography (CCTA) is crucial. While the mid-diastolic slow-filling (SF) phase is typically predicted using a conventional formula based on heart rate and atrioventricular conduction time, its validity in complete left bundle branch block (CLBBB)—where pronounced QRS prolongation induces severe mechanical dyssynchrony—remains unclear. We evaluated the impact of bundle branch block on cardiac-phase selection and validated a QRS-corrected predictive model. Methods: We retrospectively analyzed 94 patients (sinus rhythm, n = 40; complete right bundle branch block [CRBBB], n = 36; CLBBB, n = 18). Measured SF at the proximal right coronary artery was compared against predictions from the conventional formula (SF = −362 + 0.742 × [RR − PQ]) and a proposed QRS-corrected formula incorporating a “−(QRS − 100)” subtraction. To test the necessity of a novel model, regression analyses were reconstructed exclusively for the CLBBB cohort. Results: In CLBBB patients, the conventional formula critically overestimated SF by an average of 37.9 ms (RMSE 42.5 ms). Reconstructing simple and multivariate regression models exclusively for the CLBBB group yielded coefficients remarkably similar to the conventional formula, indicating that the fundamental physiological dependency on RR and PQ intervals remains intact despite the bundle branch block. Crucially, the simple proposed QRS-corrected formula successfully eliminated the overestimation bias (mean error −6.9 ms; p = 0.176) and demonstrated the highest predictive accuracy (RMSE 21.2 ms). Conclusions: A completely new predictive regression model is unnecessary for CLBBB patients. Simply incorporating a theoretical subtraction of pathological QRS prolongation optimally corrects the diastolic resting phase. Full article

Post-fire rehabilitation structures are widely used in Mediterranean burned landscapes to reduce runoff and sediment transfer, yet their ecological associations with early vegetation recovery remain insufficiently documented. This observational study assessed vascular plant composition, species richness, vegetation cover, plant density, aboveground biomass, and soil properties across log barriers, wattles, and log dams in the burned landscape of Ancient Olympia, western Greece. The study area belongs to the humid climatic class of the United Nations Environment Programme (UNEP) aridity framework based on the Thornthwaite aridity index, providing a comparatively wetter Mediterranean post-fire context. Paired depositional and eroded microsites in operationally restored post-fire areas were monitored in 2022 and 2023. The sampling design comprised nine plots and 18 microsites (n = 9 plots, 18 microsites). Generalized estimating equations (GEE), change-score models, principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA) were performed to examine associations of monitoring year, microsite condition and rehabilitation structure type with soil and vegetation patterns. A total of 27 vascular plant species belonging to 16 families were recorded. The average vegetation cover increased from 39.17 ± 21.44% in 2022 to 75.11 ± 12.90% in 2023. Model-based marginal estimates with 95% confidence intervals indicated a large positive increase in vegetation cover over this period. Further, rapid early recovery was indicated by large increases in species richness, plant density and biomass. Depositional microsites were associated with stronger recovery signals than eroded ones, characterized by a larger increase in vegetation cover, density, biomass and species richness. Among rehabilitation structures, log dams showed the highest cumulative floristic richness and a broader observed floristic spectrum, although the species-level contingency analysis provided only marginal evidence for structure-associated differences in floristic composition. Changes in selected soil properties including total nitrogen (total N), ammonium nitrogen (NH4-N), nitrate nitrogen (NO3-N), pH, electrical conductivity (EC), and exchangeable calcium (Ca), magnesium (Mg), and potassium (K), were detected between 2022 and 2023; the multivariate soil pattern was driven primarily by mineral nitrogen, pH, and EC. These findings suggest that, under operational post-fire restoration conditions, rehabilitation structures are associated not only with erosion-control functions but also with microsite differentiation that may shape early plant establishment and biodiversity recovery in Mediterranean burned landscapes. Full article

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

Spatially explicit water-quality information is critical for precision management in pond aquaculture but point sampling alone cannot capture pond-to-pond heterogeneity in multi-unit farms. This single-date, single-farm study re-evaluated the potential of UAV hyperspectral imagery for water-quality screening in inland aquaculture ponds in Shanghai, China, using site-matched extraction from a 138-band orthomosaic (450–998 nm, Cubert S185) acquired during a single UAV survey on 24 August 2023 and matched with 23 GPS-registered sampling sites. Eight water-quality parameters were analyzed: chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), ammonium (${\mathrm{NH}}_4^{+}$ ), nitrite (${\mathrm{NO}}_2^{-}$), nephelometric turbidity unit (NTU), chlorophyll-a (Chla), and total suspended solids (TSS). Raw single-band correlations were modest ($\left|\mathrm{r}\right|$= 0.236–0.417), but two-band difference spectral indices (DSI), normalized spectral indices (NSI), and ratio spectral indices (RSI) substantially improved sensitivity, with $\left|\mathrm{r}\right|$ reaching 0.558–0.928. Quadratic inversion models were calibrated on the full dataset and assessed using three validation layers: two-fold cross-validation, nested leave-one-pond-out (LOPO) validation with within-fold predictor reselection, and extraction-window sensitivity tests. Bootstrap 95% confidence intervals for calibration (Cal) R² characterize small-sample uncertainty (n = 23). Three parameters satisfied all three defensibility criteria (Cal R² > 0.5, CV R² > 0.2, and LOPO R² > 0.2): ${\mathrm{NH}}_4^{+}$ (Cal R² = 0.836 [0.61, 0.94]; LOPO R² = 0.420), COD (0.607 [0.34, 0.82]; 0.328), and NTU (0.862 [0.77, 0.96]; 0.204). TP, TN, ${\mathrm{NO}}_2^{-}$, TSS, and Chla showed overfit behavior under nested holdout and were demoted to exploratory products. A TreeSHAP analysis confirmed that band-to-band contrast carried more explanatory power than raw reflectance magnitude. Extraction-sensitivity tests further demonstrated that positional uncertainty (±2-pixel offset: ΔCV $R^2$= 0.23–0.41) exceeded averaging-window sensitivity (3 × 3→10 × 10: ΔCV $R^2$ ≤ 0.11), identifying geolocation control as the dominant robustness constraint. This single-date, single-farm reanalysis suggests that UAV hyperspectral imagery may support exploratory pond-scale screening of ${\mathrm{NH}}_4^{+}$, COD, and NTU. However, robust quantitative inversion and broader transferability remain unverified and will require denser sampling, improved geolocation control, pond-edge masking, multi-site observations, and multi-temporal calibration. Full article

Radiomics has the potential to improve risk stratification in head and neck squamous cell carcinoma (HNSCC), but clinical adoption is limited by inconsistent performance across institutions. A key source of variability is how radiomic features are generated, preprocessed, and selected prior to model development. This multicenter study evaluated how radiomics parameterization and feature selection strategies affect external model performance, feature stability, and time-to-event risk stratification. We studied pre-treatment CT scans from 752 patients with primary HNSCC from three hospitals. For each scan, 1648 radiomic features were computed using 20 different preparation methods that varied in scaling, outlier removal, and gray-level bin width. We compared five feature selection methods: Graph-FS with connected components, Boruta, Lasso, RFE-RF, and mRMR. The classification models used were Random Forest, XGBoost, CatBoost, and Logistic Regression. We measured performance using external ROC-AUC, bootstrap confidence intervals, Brier score, and RobustScore. Stability of feature selection was assessed using the Kuncheva and Jaccard indices. Cox proportional hazards models confirmed time-to-event results, and consensus SHAP analysis helped explain the models. Radiomics parameterization influenced model performance, and no single configuration was optimal across all analyses. Radiomics-only models outperformed clinical-only models, while clinical–radiomics models achieved the highest overall performance. mRMR and Lasso produced the highest average external AUCs, while Graph-FS showed the greatest stability. The best classification model achieved an external AUC of 0.817. In Cox validation, the best clinical–radiomics configuration achieved an external C-index of 0.662 and separated high- and low-risk patients in the external cohort. Full article

Despite recent advances, medical question answering systems still struggle with domain-specific reasoning and data efficiency. This paper presents Med-LLaMA3, a family of medical large language models developed by parameter-efficient fine-tuning of the LLaMA-3.1 (8 billion) and LLaMA-3.2 (1 and 3 billion) architectures using quantized low-rank adaptation (QLoRA) and low-rank adaptation (LoRA) with 4-bit quantization. Beyond model training, this work contributes the following: (1) a formalized dataset curation taxonomy (source type × clinical granularity × task format) with a source-category ablation confirming that the multi-source combination drives benchmark gains beyond any single category; (2) a systematic characterization of low-rank-adaptation rank-scaling behavior for the LLaMA-3 family in the medical domain (monotonic improvement up to rank 128, with no observed plateau); and (3) statistically validated comparisons using McNemar’s test and 95% bootstrap confidence intervals. We curated a medical instruction dataset of over 1.5 million samples spanning medical examinations, clinical dialogues, and biomedical literature. Our approach trains only ∼4% of the base model’s parameters and, consistent with prior studies of parameter-efficient methods in the medical domain, achieves performance comparable to full fine-tuning at a fraction of the memory footprint. Evaluated with five in-context examples per prompt, the 8-billion-parameter model attains a mean accuracy of 75.71% across the eight medical-domain subsets of the Massive Multitask Language Understanding benchmark; improvements over the unmodified LLaMA-3.1-8B-Instruct baseline are statistically significant on the medical multiple-choice benchmark MedMCQA and, after Bonferroni correction across the eight subsets, on three subsets (Clinical Knowledge, Medical Genetics, and Nutrition), with two further subsets being significant only before correction. A structured named-entity-recognition evaluation on 100 hospital discharge summaries (macro-averaged F1 0.94; dual-annotator agreement $\kappa =0.87$) provides complementary evidence of clinical-text utility. A safety mitigation pilot shows that context-disambiguation preprocessing reduces the highest-severity abbreviation-ambiguity error rate from 30% to 10% on a 30-case held-out set. These results show that parameter-efficient fine-tuning can deliver high-performance medical large language models while training only ∼4% of the model’s parameters and reducing memory use by roughly 75%, enabling development on low-cost consumer-grade hardware. Full article

Accurate short-term photovoltaic (PV) power forecasting is important for grid dispatch and PV integration. However, PV power under complex weather conditions has strong fluctuation, non-stationarity, and multi-frequency coupling. These features make accurate forecasting difficult. This paper proposes a short-term PV power forecasting model named WOA-MS-TFformer-BiTCN. The model first constructs similar-day samples through daily feature extraction, Gaussian mixture clustering, and physical consistency correction. Then, the whale optimization algorithm (WOA) optimizes the key parameters of variational mode decomposition (VMD) and the forecasting network. VMD decomposes the original power sequence into modes with different frequency features. The multi-scale frequency-domain perception (MS) module extracts multi-scale frequency-domain features from these modes. TFformer captures global temporal relationships, while BiTCN models local dynamic changes. Experiments are conducted using PV data from Gansu, China. The Alice Springs PV dataset is used for cross-regional validation. The results show that the proposed model achieves the lowest MAE, RMSE and the highest R² in all 16 season-weather cases, corresponding to four seasons and four weather types, for the 15 min-ahead task. Its average MAE, RMSE and the highest R² are 0.5439, 0.7910, and 0.99898, respectively. The model also performs best on rainy samples from the Alice Springs dataset. In addition, prediction intervals based on validation-set residual quantiles provide uncertainty information for point forecasts. The results show that the proposed method improves the accuracy and stability of short-term PV power forecasting under complex weather conditions. Full article

Additive manufacturing (AM) enables the fabrication of complex, customisable components from metals, composites and polymers such as polylactic acid (PLA); however, the process commonly produces poor surface finishes and inherent defects. Centrifugal disc finishing (CDF) is an established mass finishing technique in conventional manufacturing but remains insufficiently characterised for additively manufactured polymers. This exploratory study investigates the influence of CDF on fused deposition modelling (FDM)-fabricated PLA components with varying geometrical features, focusing on three-dimensional surface parameters including average areal surface roughness, skewness and kurtosis. Samples were processed up to 720 min with analysis at predetermined intervals to capture transient and steady-state-like behaviour. Surface characterisation was conducted using non-contact optical interferometry to obtain quantitative roughness data and three-dimensional topographical maps, supported by digital optical microscopy and gravimetric analysis to quantify material removal rates. Analysis of the experimental data indicated apparent relationships between processing time, geometry and surface response. Results indicate that material removal behaviour and roughness evolution may be geometry-dependent. Flat and convex surfaces appeared to follow expected transient-like and steady-state-like behaviour, whereas restricted geometries and intricate features exhibited distinct responses with characteristic transition times. Surface roughness reductions ranged from 36% to 89% depending on geometry. These findings provide preliminary quantitative insight into geometry-specific mass finishing behaviour, supporting improved process understanding and informing future optimisation of post-processing strategies for additively manufactured polymer components. Full article

This study investigates the feasibility, accuracy, and data-sufficiency requirements of smartphone- and smartwatch-based crowdsensing for pedestrian bridge modal identification under real-world conditions. Full-scale experiments were conducted on a bridge across two crowdsensing scenarios with varying dynamic excitation intensities by six pedestrians performing walking, running, and bicycling activities while carrying smartphones and wearing smartwatches. Triaxial acceleration data were collected over 300 s and processed using a framework comprising preprocessing, modal estimation, growing-window convergence analysis, and block-bootstrap uncertainty quantification. Using the full dataset, both devices reliably identified the four consistently detectable bridge modes with average errors of approximately 3% across the scenarios relative to the benchmark. In the convergence analysis, smartwatches consistently produced narrower confidence intervals and more stable early-window estimates, which may be related to their more constrained wearing condition and reduced incidental motion compared to pocket-carried smartphones. Higher pedestrian excitation with additional pedestrians running accelerated the convergence, reducing the required data duration and number of pedestrian passes, albeit with increased uncertainty. The study established data-sufficiency thresholds, showing that reliable modal estimates require in the range of 5–17 walking or running passes, while bicycling passes range from 14 to 28, depending on bridge excitation level and device type. Results demonstrate that commodity smartphones and smartwatches are viable, scalable, and cost-effective platforms for crowdsensed bridge modal identification, provided that uncertainty ranges are properly accounted for and sufficient passes across different pedestrian activities are collected to achieve the desired accuracy. Full article

Tight sandstone reservoirs exhibit strong pore-throat heterogeneity, which exerts important controls on reservoir quality and fluid-flow behavior. To investigate the pore-throat structure characteristics and their influence on permeability, integrated analyses of thin sections, X-ray diffraction (XRD), scanning electron microscopy (SEM), cathodoluminescence (CL) and mercury intrusion porosimetry (MIP) were conducted on the Chang 8 tight sandstones in the southern Ordos Basin (China). Results show that the Chang 8 tight sandstones are characterized by low porosity and ultra-low permeability, with average porosity and permeability of 7.5% and 0.331 mD, respectively. The pore systems mainly include intergranular, intragranular pores, intercrystalline micropores and microfractures, reflecting strong pore-throat heterogeneity. Segmented MIP analysis reveals two distinct pore-throat response intervals. The fine pore-throat segment shows valid fractal scaling, whereas the large pore-throat segment is interpreted as an early-stage intrusion response. A dimensionless MIP-derived heterogeneity index (H_MIP) was therefore used to characterize connected pore-throat heterogeneity. H_MIP ranges from 2.446 to 2.973 and shows negative associations with permeability and pore-throat radius, indicating that finer and more heterogeneous connected pore-throat systems are generally associated with lower flow efficiency. H_MIP exhibits weak to moderate associations with mineral composition, particularly with carbonate and quartz contents, whereas feldspar and clay minerals show limited relationships. Sensitivity analysis of characteristic pore-throat radii demonstrates that r₁₀ shows the strongest association with permeability within the present MIP dataset, and model performance decreases monotonically from r₁₀ to r₅₀, suggesting that early mercury-accessible coarse pore-throats are more closely related to effective fluid flow than smaller pore-throat populations in the Chang 8 tight sandstone reservoirs. These findings suggest that permeability in the Chang 8 tight sandstones is closely associated with the development of connected large pore-throats, whereas H_MIP provides empirical information on connected pore-throat heterogeneity and flow-path complexity. Full article