Search Results (3,888)

Under the background of highway ecological green construction and traffic-tourism integration, tourist highways in world natural heritage sites bear the dual responsibilities of heritage ecological protection and regional economic boosting, yet existing routes prioritize connectivity over ecological and economic values, damaging heritage integrity and failing to drive surrounding township development. This study aims to build a dual-coordinated route selection framework balancing ecological protection and economic development, taking Mount Fanjing as the case. Adopting literature research, field survey and spatial analysis, and grounding in road ecology, point-axis system and tourism space competition theories, it constructs a four-part framework covering township tourism potential evaluation, ecological suitability assessment, binary matrix model and route generation. Empirically, nine townships including Minxiao and Taiping are screened as core tourism service nodes, and the optimal layout of the ring Mount Fanjing tourist highway is determined via ecological suitability matching. The findings reveal the prominent contradiction between heritage protection and regional development in current heritage tourist highway construction, and the proposed dual coordination model effectively balances heritage conservation and local economic growth, providing a feasible planning reference for sustainable tourist highway layout in world natural heritage sites. Full article

Spatial intelligence in autonomous driving requires object-level 3D geometry, yet existing monocular mesh reconstruction methods usually operate with a fixed inference path and a single mesh parameterization, which limits their flexibility under heterogeneous resource constraints. To address this issue, we propose DyPRSI, a dynamic-parameterized framework for monocular vehicle 3D reconstruction that provides multiple predefined accuracy–latency operating points within a single model. DyPRSI inserts two early exits into a shared Res2Net–BiFPN trunk and associates each exit with an exit-specific mesh specification, forming a coarse-to-fine reconstruction hierarchy across network depth. To better match the efficiency requirements of shallow branches, DyPRSI adopts lightweight coordinate-classification keypoint decoding for EE1 and EE2, while retaining a heatmap-regression keypoint head in the Main branch to preserve the upper bound of reconstruction accuracy. Experiments on ApolloCar3D show that DyPRSI-Main achieves competitive reconstruction performance, whereas EE1 and EE2 substantially reduce end-to-end inference latency and provide useful alternatives under different resource requirements. Ablation studies further show that the speedup mainly comes from the lightweight branch-specific keypoint heads, while the exit-specific mesh settings help organize stable coarse-to-fine reconstruction behavior across branches. These results indicate that DyPRSI is a practical monocular vehicle reconstruction framework for resource-aware spatial intelligence. Full article

Background/Objectives: Critical limits define quantitative thresholds for life-threatening diagnostic test results that require immediate clinician notification and may prompt urgent intervention to prevent adverse outcomes. This study aims to (1) characterize point-of-care (POC) critical limits for adults and newborns using a comprehensive U.S. national database, (2) identify POC instruments associated with these limits, and (3) support harmonization of point-of-care testing (POCT) practices. Methods: We gathered critical limit notification lists from 417 hospitals across all 50 states and Washington D.C., comprising university hospitals, trauma and heart centers, centers of excellence, community hospitals, and network hospitals. We extracted POC and central laboratory critical limits (at hospitals with POC), adult international normalized ratio (INR) data, and instrument usage. Results: Low and high glucose critical limits were the most frequently listed POC thresholds, with median values of 50 and 450 mg/dL, respectively, reported by 73 hospitals (17.5%). Troponin was listed by ten hospitals, specified as troponin (n = 4), troponin I (n = 5), or “troponin TnI” (n = 1). A few hospitals assigned instrument-specific critical limits for the same analyte, and 55 hospitals did not specify instrument usage for any measurand. Median differences in matched pairs of laboratory versus POC critical limits differed significantly (Wilcoxon signed-rank, p < 0.05) for low and high ionized calcium (n = 21), low hemoglobin (n = 23) and high INR critical limits for adults (n = 27) and newborns (n = 10). In some cases, matched pair analytes demonstrated identical critical limits. Conclusions: Harmonizing critical limit notification thresholds across point-of-care testing and different devices may improve consistency in clinical decision-making and patient outcomes. Despite the potential of POCT to shorten time to urgent intervention, relatively few hospitals currently include POCT critical limits on notification lists. Establishing standards, annual updating, and enforcing risk mitigation could enhance adoption and reliability. Broader inclusion and transparent sharing of POCT critical values could harmonize practices across institutions, facilitate inter-institutional collaboration, and promote more timely and reliable responses to life-threatening diagnostic results. Full article

Accurately quantifying carbon dioxide (CO₂) emissions from heavy-duty diesel trucks (HDTs) is crucial for developing effective transportation emission reduction strategies. In this study, we adopted a bottom–up approach and, in conjunction with the “International Vehicle Emissions” (IVE) model, constructed a high-resolution 1 × 1 km CO₂ emission inventory for the urban area of Kunming, China. Using data from 1.24 million track points collected from 5996 heavy-duty diesel trucks, we implemented a map matching algorithm based on a simplified hidden Markov model (HMM) to efficiently process large-scale GPS data. Furthermore, we improved upon traditional spatial allocation methods by dynamically integrating track point density with static road network density. The results indicate that although higher driving speeds correspond to lower CO₂ emission rates, heavy-duty diesel trucks typically operate within an observed speed range of 40–60 km/h, with an average emission factor of approximately 500 g/km. Vehicles compliant with the “National III” emission standards remain the primary source of CO₂ emissions in this region. Correlation analysis reveals a significant positive relationship (p < 0.01) between emissions from heavy-duty diesel trucks and both traffic volume and mileage. Notably, daytime vehicle restriction policies led to a temporal redistribution of emissions rather than a net reduction in emissions; this resulted in increased activity levels of heavy-duty diesel trucks at night, leading to a surge in nighttime emissions. In terms of spatial distribution, the “dual-density” allocation method proposed in this study more accurately captured emission hotspots, revealing that CO₂ emissions are primarily concentrated in the southeastern part of the city—a distribution pattern largely influenced by the city’s industrial layout. Full article

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss–Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further. Full article

LiDAR point cloud semantic segmentation is essential for autonomous driving, yet LiDAR-only methods remain constrained by sparsity and limited texture cues. We propose Cross-Modal Collaborative Manifold Distillation (CMCMD), which transfers open-world semantic priors from the DINOv3 Vision Foundation Model to a LiDAR student network. The framework combines an Adaptive Relation Convolution (ARConv) backbone with geometry-conditioned aggregation, a Unified Bidirectional Mapping Module (UBMM) for explicit 2D–3D interaction, and Manifold-Aware Topological Distillation (MATD), which aligns inter-sample affinity structures in a shared latent manifold rather than enforcing pointwise feature matching. By preserving relational topology instead of absolute feature coordinates, CMCMD mitigates negative transfer across heterogeneous modalities. Experiments on SemanticKITTI and nuScenes yield mIoU values of 72.9% and 81.2%, respectively, surpassing the compared distillation baselines and approaching the performance of multimodal fusion methods at lower inference cost. Additional evaluation on real-world campus scenes further supports the cross-domain robustness of the proposed framework. Full article

Reservoir water level fluctuations alter the saturation line in earth-rock dams, thereby affecting the accuracy of electrical leakage detection. To quantitatively investigate this influence, a three-dimensional (3D) geoelectric model of a concentrated leakage pathway was constructed using COMSOL Multiphysics based on parameters from a reservoir in Zhejiang Province. Numerical simulations were performed under unsaturated, partially saturated, and fully saturated conditions with respect to the leakage zone, and a fixed-electrode monitoring system was deployed for in situ validation. The results show that 3D resistivity slices can approximately delineate the leakage hazard center. Under fully saturated conditions, the low-resistivity anomaly center shifts upward by 0.7 m. Under unsaturated conditions, the high-resistivity anomaly center shifts upward by 1.7 m. Under partially saturated conditions, the high-resistivity anomaly center exhibits the most pronounced upward shift (3.0 m). Notably, under partially saturated conditions, the boundary point between the high- and low-resistivity anomalies is located close to the central depth of the leakage pathway (deviation of approximately 0.7 m above the center), serving as a useful diagnostic indicator. In situ tests corroborate these findings, with identified anomaly zones matching the actual leakage points. This study provides a quantitative framework for interpreting geoelectrical data in earth-rock dams under fluctuating reservoir levels. Full article

Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SSC) measurements are costly and slow, hindering long-term online monitoring. Bidirectional flows in tidal reaches further exacerbate this challenge. We propose a physics-constrained support vector machine (SVM) inversion method to estimate vertical sediment transport rates from single-point measurements. Constrained by modified logarithmic velocity and Rouse suspended sediment concentration profiles, it quantitatively relates single-point hydraulic variables to key parameters governing vertical distributions. Lower Yangtze River tidal reach field data validate the hybrid model’s successful reconstruction of vertical distributions. It accurately captures transient sediment responses across maximum flood and ebb. Inverted transport rates match measurements closely (RMSE = 0.085, NSE = 0.969, PBIAS = 2.50%) and exhibit strong cross-site generalization. Sensitivity analysis identifies 0.4 times the water depth above the riverbed as the optimal single-point sensor position. Although currently validated only in the lower Yangtze River, this low-cost, reliable method supports local basin management, flood control, and disaster mitigation by enabling continuous sediment flux monitoring. However, applying it to other river or estuarine systems may require recalibration or retraining to adapt to different local conditions. Full article

This paper presents a novel data fusion framework that integrates Automatic Identification System (AIS) data with radar surveillance for real-time vessel monitoring in inland restricted waterways. The approach exploits the complementarity between heterogeneous sensors: AIS provides semantic information with temporal sparsity, while radar offers high-frequency observations without vessel identity. The proposed solution combines Kalman filtering and Newton interpolation (K-N) for high-resolution AIS resampling, followed by optimal data association using the Kuhn-Munkres (KM) algorithm. By formulating data association as a global optimization problem, the framework achieves globally optimal sensor fusion while effectively handling data imbalance through virtual point augmentation. Experimental validation using real-world data demonstrates a matching accuracy of 94.2% in low-density scenarios and 80.1% in high-traffic conditions, with computational efficiency suitable for real-time deployment. The system performs consistently across different waterway geometries, although performance varies slightly between curved and straight channels. By fusing the high temporal resolution of radar data with the rich identity information from AIS, this framework enables more accurate and reliable vessel tracking, providing waterway authorities with enhanced situational awareness for improved traffic management and scheduling in restricted waterways. Full article

Background: Renal allograft rejection remains a major challenge in transplantation. Current diagnostic approaches, including biopsies, are invasive and may fail to detect subclinical immune activation, potentially contributing to progressive graft dysfunction. Reliable, non-invasive biomarkers capable of monitoring immune activation and distinguishing rejection phenotypes are therefore needed. Methods: In this retrospective study, we evaluated serum neopterin as a biomarker of immune activation and graft status over 12 months following transplantation. Associations between neopterin levels and immune parameters, including natural killer (NK)-to-CD3⁺CD16/CD56⁺ T cell ratios, cytokines (IFN-γ and IL-10), and CD4⁺CD25⁺FoxP3⁺ T cell frequencies, were assessed. A total of 211 first renal allograft recipients were followed longitudinally, including patients with acute rejection (AR) and matched stable graft (SG) recipients. Serum neopterin was quantified by enzyme immunoassay, and immunophenotyping, mRNA expression, and cytokine profiling were performed on peripheral blood samples. Results: Serum neopterin levels were significantly elevated in AR compared to SG recipients, with a threshold of 57 nmol/L distinguishing AR with 81% sensitivity and 80% specificity. While IFN-γ demonstrated higher diagnostic performance in cross-sectional analysis, neopterin showed a more sustained elevation over time, remaining increased in AR recipients even at later post-transplant time points. Neopterin correlated positively with IFN-γ, but not IL-10, and inversely with CD4⁺CD25⁺FoxP3⁺ T cell frequency. NK cells were enriched during rejection, whereas CD3⁺CD16/CD56⁺ T cells were more prominent in graft stability. The NK-to-CD3⁺CD16/CD56⁺ T cell ratio was highest during acute cellular rejection. Conclusions: Neopterin reflects Th1-associated immune activation in renal allograft recipients and provides a temporally stable, non-invasive marker of immune status. Although it does not outperform IFN-γ levels at the time of rejection, its stability and sustained elevation suggest a complementary role in longitudinal monitoring. Integration of neopterin with immune parameters, including cytokine profiles and cellular subsets, may enhance the assessment of graft immunological status and support clinical decision-making. Full article

Hyperspectral image (HSI) classification regarding vegetation is hampered by strong intra-class spectral variability and inter-class similarity, and commonly used random pixel splits can introduce spatial-context leakage that inflates test accuracy in patch-based models. To address these issues, we propose a classification framework that couples a leakage-free block partition (LFBP) strategy with class-aware multi-prototype contrastive loss (CAMP-CL). LFBP assigns non-overlapping spatial blocks to training/validation/test sets and reserves a buffer matched to the patch radius to prevent contextual overlap while keeping class distributions balanced. CAMP-CL represents each class with multiple learnable prototypes and performs supervised contrastive learning at the prototype level, encouraging compact yet multimodal intra-class embedding and improved inter-class separation. Experiments conducted on the Matiwan Village airborne HSI dataset under the LFBP protocol show that the proposed method can achieve 91.51% overall accuracy (OA) and 91.49% average accuracy (AA). Compared with the strongest baseline, supervised contrastive learning (SupCon), the proposed method yields consistent gains of 1.07 percentage points (pp) in both OA and AA while improving OA by 5.76 pp over the cross-entropy baseline. The results suggest that CAMP-CL is beneficial for addressing the challenges of HSI classification for fine-grained vegetation, while leakage-free evaluation protocols are important for obtaining more reliable performance estimates in practical settings. Full article

Background: Benign prostatic hyperplasia (BPH) can be associated with lower urinary tract symptoms (LUTS) and potential avlterations in autonomic nervous system function, as reflected by heart rate variability (HRV). This observational study was designed to generate hypotheses regarding the differential impacts of surgical de-obstruction versus uroselective pharmacological blockade on autonomic nervous system equilibrium, HRV restoration, and symptomatic outcomes in men with BPH and bladder outlet obstruction. Methods: Data from a prospective cohort of 242 men undergoing TURP and 210 men receiving tamsulosin were analyzed. HRV parameters (standard deviation of normal-to-normal intervals [SDNN], low-frequency/high-frequency [LF/HF] ratio, total power [TP], very low frequency [VLF]) and International Prostate Symptom Score (IPSS) was assessed at baseline and 12 weeks. Propensity score matching (PSM) was used to address baseline differences in age, prostate volume, IPSS, and baseline SDNN. Inter-group comparisons used ANCOVA with baseline as a covariate. Results: After TURP, SDNN increased by 14.70 ms (40%; 36.97 ± 22.80 to 51.67 ± 27.59 ms; p = 0.032; paired Cohen’s d = 0.58), LF/HF decreased by 0.90 (55%; 1.63 ± 1.60 to 0.73 ± 0.52; p = 0.028; d = −0.76), TP increased by 1303 ms² (95%; 1367 ± 820 to 2670 ± 1420 ms²; p = 0.025; d = 1.12), and VLF increased by 810 ms² (85%; 950 ± 560 to 1760 ± 980 ms²; p = 0.030; d = 1.01). For tamsulosin, SDNN increased by 6.73 ms (18%; 38.12 ± 12.50 to 44.85 ± 11.20 ms; p = 0.004; d = 0.57), LF/HF decreased by 0.16 (8%; 1.95 ± 0.65 to 1.79 ± 0.55; p = 0.012; d = −0.27), TP increased by 559 ms² (39%; 1453 ± 620 to 2012 ± 580 ms²; p = 0.006; d = 0.93), and VLF increased by 355 ms² (35%; 1020 ± 450 to 1375 ± 420 ms²; p = 0.010; d = 0.82). Secondary p-values (LF/HF, TP, VLF) were adjusted via the Benjamini–Hochberg method; adjusted p > 0.05 was used for some. Inter-group differences in changes were significant (ANCOVA p < 0.01; partial η² = 0.12–0.22 for group factor). TURP was associated with greater IPSS reduction (−10.2 points; 18.5 ± 6.2 to 8.3 ± 4.1; p < 0.001) compared to tamsulosin (−5.3 points; 15.8 ± 5.6 to 10.5 ± 4.8; p < 0.001; d = −1.02; inter-group p < 0.001). PSM confirmed these associations with p < 0.01 for HRV changes. Change in SDNN was associated with IPSS improvement in multivariate regression (standardized β = −0.42, p < 0.01). Conclusions: In this observational study, TURP was associated with greater changes in HRV parameters and symptomatic improvement compared to tamsulosin. These findings are hypothesis-generating and require confirmation in long-term randomized trials. Full article

Auditory pseudowords are widely used in psycholinguistics and cognitive neuroscience, but their construction requires control of sublexical familiarity and careful characterization of how acoustic cue manipulations may shift perceived lexical plausibility. Here we introduce the Minho Pseudoword Wordlikeness Ratings (MPWR), the first normative dataset of wordlikeness judgments for European Portuguese (EP) auditory trisyllabic CV pseudowords, and evaluate whether adding a localized F0-based prominence cue modulates wordlikeness beyond distributional familiarity. One hundred and twenty pseudowords were assembled from naturally produced syllables drawn from the Minho Spoken Syllable Pool (MSSP) and recorded under uniform conditions. Each item was implemented in three token types with constant segmental content: a flat baseline and two F0-enhanced versions (+15%) targeting either the penultimate or final syllable. Native EP listeners (N = 101) provided wordlikeness ratings on a 7-point scale. MSSP-derived indices quantified pseudoword syllable familiarity (SWI_All, SWI_N3) and stress-position propensity for the targeted syllable (SPP_marked). Ratings were intentionally low overall yet showed substantial item-to-item variability. F0 enhancement produced a small but reliable decrease in wordlikeness relative to flat tokens, with no reliable difference between penultimate and final targeting positions. SWI_All robustly predicted ratings, whereas SPP_marked added little explanatory value. MPWR provides a practical EP resource for selecting and matching auditory pseudowords using normative wordlikeness ratings and transparent corpus-based descriptors. Full article

Background: Monitoring health-related variables across a competitive season is essential to understand factors associated with performance in Paralympic athletes. However, evidence on the interplay between sleep, mood states, nutritional factors, injuries, and performance remains limited. Objective: To examine the associations between injuries, sleep parameters, nutritional factors, mood states, and performance in Para powerlifting athletes during a competitive cycle. Methods: Twenty-four athletes from the Brazilian National Para powerlifting team were assessed at three time points: baseline (~3 months pre-competition), pre-competition (upon arrival), and post-competition (day after the event). Data were collected using standardized instruments and analyzed in R. Descriptive statistics, Mann–Whitney U tests, Spearman’s correlations, Friedman tests, and individual delta values (Δ) were applied. Results: No significant between-group differences were observed in pre-competition cross-sectional analyses. Longitudinally, sleep duration was the only variable consistently differing between performance groups. Athletes who matched or improved performance showed greater sleep stability, whereas those who did not improve exhibited larger post-competition increases in sleep duration. Negative mood states decreased over time, and baseline vigor was higher in the higher-performing group. Sleep duration changes were negatively correlated with performance variation (ρ = −0.575, p = 0.003). Conclusions: Sleep duration was the variable most consistently associated with performance variation. Mood changes reflected reduced negative affect over time. Findings support longitudinal monitoring in Para powerlifting, although caution is warranted due to the observational design and small sample. Full article

This study examines a cost-shaping method that considers distance information to obstacles in a line-of-sight (LOS) any-angle path-planning approach on grid-based maps. In the proposed approach, the safety distance to obstacles is added to the cost in a controlled manner via a single adjustable and interpretable parameter; thus, the balance between safety and efficiency becomes practically adjustable. Node selection in the planning process is performed while maintaining the classical search rule; the additional penalty related to the safety distance is only included in the transit cost. This design strengthens consistency between method definition and implementation and eliminates the risk of the same safety term being considered multiple times. The experimental evaluation was conducted on a three-by-three scenario set encompassing map type and difficulty level dimensions. Starting and ending points were selected in a layered and matched manner as easy/medium/difficult; the safety parameter was scanned at different values, following a repeatable protocol under all conditions. Outputs were evaluated using efficiency metrics such as path length and number of turns, as well as minimum safety distance, safety distance violation rate, and a curvature indicator representing the smoothness of the path geometry. In addition, practical costs such as planning time, an expanded number of nodes, and memory footprint were reported. The results show that exposure to low safety distance zones decreases and the path geometry becomes more regular with increasing safety parameters. Furthermore, it was observed that the success rate increased in pooled analyses while memory usage remained constant; paired statistical tests and effect size measurements confirmed that the improvements were strong and consistent. These findings reveal that safety distance-sensitive cost-shaping offers a lean control mechanism that enhances safety and maintains practical applicability within line-of-sight-based any-angle planning. Full article