Search Results (770)

In order to provide a theoretical framework for the synergistic optimization of the “rigid backbone-flexible network” in the molecular design of polyimides with high Young’s modulus, high toughness, and excellent creep resistance for wearable electronics applications, the effects of sequence structure on the mechanical properties of siloxane-containing polyimides were investigated by molecular dynamics simulations. A series of poly(siloxane-imide) block copolymer models with distinct sequence structures were constructed via molecular dynamics (MD) simulations based on 4,4′-(hexafluoroisopropylidene)diphthalic anhydride (6FDA) and 2,2′-bis(trifluoromethyl)benzidine (TFDB) as hard segment A, and 6FDA and 1,3-bis(3-aminopropyl)tetramethyldisiloxane (SiDA) as soft segment B. The results indicate that extending hard segment length enhances Young’s modulus and suppresses creep because of the enhancement of chain rigidity and formation of stable physical aggregates. Appropriately extending soft segment sequence length can improve the failure strain through rapid conformational adjustment, while excessively long soft segments lead to stress concentration, thereby reducing the failure strain. The (A₅B₅)₂ model structure exhibits superior comprehensive performance among all systems, with a relatively high Young’s modulus, failure strain, and creep recovery rate. This is attributed to the synergistic balance between the rigidity of the hard segment and the mobility of the soft segment. Full article

Hard roof strata in deep coal mines commonly cause rib failure and roof collapse, restricting extraction efficiency. This study employs theoretical analysis, numerical simulation, and field experiments to investigate the fractal evolution of damage in coal–rock mass under loading and blasting disturbances, with the aim of quantifying progressive failure and optimizing roof control. Key findings include: (1) The fractal dimension D of fragment size distribution increases monotonically with loading rate, with fine-particle proportion rising from 48.5% to 52.3%, indicating more thorough fragmentation at higher rates. (2) Load intensity, elastic modulus, and seam thickness govern coal bearing capacity and energy accumulation, with D serving as a quantitative damage indicator. (3) Pre-splitting blasting shifts the stress peak away from the working face, with shear fractures dominating the fracture network and tensile fractures playing a secondary role. (4) Field application at Zhangji Coal Mine (9 coal seam, 7–23.5 m sandstone roof) confirms the effectiveness of segmented fan-shaped borehole pre-splitting blasting in controlling roof behavior; fractal dimension derived from borehole images quantifies fracture propagation. Dynamic adjustment of blasting parameters based on geological core samples is recommended to enhance fracture network complexity and improve roof control efficiency under varying hard rock conditions. Full article

Campus waste-sorting mobile manipulators operate in cluttered environments. This paper presents a task-oriented framework decoupling mobile-base navigation from manipulator motion. Its contribution is the safety-verified integration of goal-biased sampling, adaptive step-size adjustment, artificial potential field (APF)-guided directional correction, collision-recovery direction selection, and collision-checked bidirectional tree connection within Improved-BiRRT. After obtaining a feasible path, visibility-based pruning and piecewise cubic Hermite interpolating polynomial (PCHIP) smoothing are applied, followed by segment-wise collision verification with fallback to the verified pruned path. MATLAB R2024b simulations cover simple campus, complex campus, and narrow-passage environments. RRT, GoalBias-RRT, BiRRT, and Improved-BiRRT are evaluated under identical settings and post-processing. In the narrow-passage environment, Improved-BiRRT achieves a mean raw feasible-path length of $32.155\pm 1.935\mathrm{m}$, a mean final collision-verified path length of $27.041\pm 0.327\mathrm{m}$, and $39.25\pm 12.64$ iterations. Holm-adjusted Wilcoxon rank-sum tests show significantly lower final path lengths and iteration counts than baselines in the complex campus and narrow-passage environments ($p_{\mathrm{adj}}<0.05$). A five-target validation yields a cumulative collision-verified path length of $136.171\mathrm{m}$ and a cumulative MATLAB online planning time of $0.1305\mathrm{s}$. The results demonstrate feasibility for static two-dimensional campus waste-sorting tasks with predefined target ordering. Full article

Background/Objectives: Pleural effusions are frequently encountered on pre-procedural computed tomography (CT) scans in patients undergoing transcatheter aortic valve implantation (TAVI). While often regarded as a marker of congestion and advanced cardiovascular disease, their clinical and prognostic significance in contemporary TAVI populations remains poorly understood. This study investigated the relationship between AI-derived pleural effusion volume, cardiovascular dysfunction, and long-term mortality after TAVI. Methods: Consecutive patients undergoing transfemoral TAVI between 2016 and 2022 who had available pre-procedural CT imaging were retrospectively included. Pleural effusion volume was quantified using an artificial intelligence-based segmentation workflow and analyzed as categorical, continuous, log-transformed, and threshold-based variables. Associations with clinical and echocardiographic characteristics were evaluated using Spearman correlation analyses. Long-term mortality was assessed using Kaplan–Meier analysis, Cox proportional hazards regression, and restricted cubic spline models. Results: A total of 470 patients were included (median age 82 years, 50.9% male). Pleural effusion was present in 124 patients (26.38%), including 73 (15.53%) with small, 26 (5.53%) with moderate, and 25 (5.32%) with larger effusions. Increasing pleural effusion volume was associated with atrial fibrillation (AF) (rho = 0.189, p < 0.001), higher systolic pulmonary artery pressure (rho = 0.224, p < 0.001), lower tricuspid annular plane systolic excursion (rho = −0.236, p < 0.001), impaired right ventricular–pulmonary arterial coupling (rho = −0.267, p < 0.001), lower stroke volume index (rho = −0.229, p < 0.001), and reduced left ventricular ejection fraction (rho = −0.221, p < 0.001). Despite these associations, pleural effusion volume was not associated with long-term mortality in univariable analyses, multivariable Cox regression models, or restricted cubic spline analyses. In the fully adjusted model, log-transformed pleural effusion volume was not independently associated with mortality (HR 1.00, 95% CI 0.93–1.08; p = 0.976). Conclusions: AI-derived pleural effusion volume is associated with markers of cardiovascular congestion and adverse hemodynamic remodeling in patients undergoing TAVI. However, pleural effusion burden does not independently predict long-term mortality, suggesting that its value lies primarily in phenotyping cardiovascular disease severity rather than risk stratification. Full article

High-precision urban vegetation mapping is essential for assessing carbon sink capacities, mitigating the urban heat island effect, and supporting sustainable development. Although deep learning and high-resolution remote sensing have advanced automated vegetation monitoring, existing models still face challenges when a common segmentation architecture is evaluated under different imaging geometries. In this study, Cityscapes and ISPRS Vaihingen are treated as two independent benchmarks representing perspective street-level imagery and orthographic aerial imagery, rather than as simultaneous cross-view inputs. “Background dominance” caused by perspective distortion and the “gridding artifacts” inherent in orthographic textures severely constrain segmentation accuracy across varying vegetation scales, particularly for small targets. To address these limitations, we propose a Scale-Aware Mixture of Experts (SA-MoE) architecture for fine-grained vegetation segmentation under two distinct imaging views, together with a scene-specific optimization strategy. The core SA-MoE framework consists of two main components. First, the spatial gating network uses a temperature polarization mechanism with τ = 0.5 to adjust the initial logit maps, sharpening expert-weight differences while preserving stable gradient propagation. Second, we use a heterogeneous expert group with five parallel branches: a pixel-level expert, three spatial experts with different dilation rates, and a global average-pooling expert. A dynamic pixel-level weighted fusion mechanism is then applied, decoupling feature extraction from receptive-field allocation. Furthermore, to address the heterogeneity of “hard samples” and “label noise” across the two benchmark settings, we introduce a scene-specific optimization strategy. Our findings show that the Focal-Dice (FD) loss is more suitable for perspective scenes with severe target imbalance and hard-to-classify vegetation targets, whereas the Cross-Entropy (CE) loss is more robust to boundary jitter in orthographic imagery. Comparative experiments on the Cityscapes (perspective view) and ISPRS Vaihingen (orthographic view) datasets reveal that SA-MoE achieves a highly competitive balance between computational efficiency and fine-grained segmentation, particularly in micro-target recall. Notably, the recall for extra-small (XS) scale targets in the aerial dataset improved by 3.21 percentage points compared to the second-best model. For the street-level dataset, our model achieved competitive global performance in terms of Overall Accuracy (OA), Precision, and F1-Score. However, we also observed a performance trade-off, where Transformer-based models maintained an advantage in preserving fine boundary details for these extra-small targets. In the routing analysis, we observed a pattern that we refer to as “receptive field inversion”, in which the model assigns lower weights to large-dilation experts for large canopy regions in orthophotos. We interpret this pattern as a plausible routing hypothesis. Overall, SA-MoE offers an efficient and adaptive solution for urban vegetation mapping under two imaging views. Full article

Photovoltaic power stations and impervious surfaces are difficult to distinguish from spectrally similar arid-region backgrounds, and their large differences in scale and spatial form further complicate joint extraction. This study proposes DBKNet, a dual-encoder semantic segmentation network for six-band Landsat imagery. ResNetV1c and BiFormer Tiny are used to capture local details and long-range context, respectively. ConvSwinMerge integrates the two feature streams, while a KAN-based decoder and D2T TransformerBlock improve multi-scale representation and contextual recovery. A three-class dataset containing background, impervious surfaces, and photovoltaic power stations was constructed from the 2025 Landsat composite of Ordos. DBKNet achieved an mIoU of 83.40%, an mDice of 90.26%, an overall pixel accuracy of 98.96%, and a Target-mIoU of 75.64% on the test set, outperforming six comparison models. Fixed-site evaluation on 64 independently interpreted image–label pairs from 2014, 2018, 2021, and 2025 produced a pooled mIoU of 76.82% and a Target-mIoU of 70.06% without retraining or threshold adjustment. The ablation results confirmed the contributions of the dual encoder, cross-branch fusion, decoder-side contextual enhancement, and KAN nonlinear mapping. The results demonstrate the potential of DBKNet for regional multi-year mapping, while the reduced accuracy for earlier imagery indicates remaining temporal-transfer limitations. Full article

Background/Objectives: Radical nephroureterectomy (RNU) is the standard of care for high-risk upper tract urothelial carcinoma (UTUC) but causes permanent renal decline, often disqualifying patients from essential cisplatin-based adjuvant chemotherapy. Segmental ureteral resection (SUR) preserves renal function, but its safety in high-risk patients remains fiercely debated due to historical treatment selection biases and a lack of competing risk adjustments. We aimed to compare long-term oncological outcomes and postoperative renal function preservation between SUR and RNU for high-risk UTUC strictly localized to the ureter. Methods: Retrospective data from 859 patients (783 RNU, 76 SUR) with high-risk ureteral UTUC (high-grade or pathologic T2–T4) were analyzed from a 21-hospital nationwide database. Propensity score overlap weighting was implemented to achieve covariate balance. Overall survival (OS) was assessed via Cox proportional hazards regression, whereas cancer-specific survival (CSS), metastasis-free survival (MFS), and local recurrence-free survival (LRFS) were evaluated using multivariable Fine–Gray subdistribution hazard models to robustly account for the competing risk of non-cancer mortality. Results: Overlap weighting achieved excellent baseline comparability with an effective sample size of 429.5 patients per cohort. Weighted analyses demonstrated comparable long-term trajectories between SUR and RNU for OS (p = 0.62), CSS (hazard ratio [HR]: 0.94, p = 0.835), and MFS (HR: 0.88, p = 0.664). The Fine–Gray model confirmed that the surgical approach was not a significant independent predictor of local recurrence (HR: 0.74, p = 0.351). Crucially, the SUR group demonstrated a significantly lower renal function decline both at 1 month (−0.11 vs. −10.58 mL/min/1.73 m², p < 0.001) and through final clinical follow-up (−5.33 vs. −12.49 mL/min/1.73 m², p = 0.001). Conclusions: For meticulously selected patients with high-risk ureteral UTUC, SUR provides equivalent oncological control and survival outcomes to standard RNU. Crucially, this kidney-sparing approach significantly preserves postoperative renal function, safeguarding the physiological reserve required for patients to maintain eligibility for optimal subsequent systemic adjuvant therapies. Full article

This exploratory proof-of-concept study aimed to develop and preliminarily evaluate a 3D-printed holder designed for reuse in cone beam computed tomography (CBCT) in dental research, particularly for comparative studies requiring pre- and post-intervention tooth assessment. The holder was designed using computer-aided design software and fabricated from polylactic acid using 3D printing. A conventional alginate-based holder was used for comparison. The comparison therefore concerned two complete positioning systems with different geometries and modes of adaptation to the CBCT headrest. The two positioning devices were independently placed by two operators with different levels of experience, and all CBCT scans were acquired by the same operator. The procedure was repeated after a 5-day interval. Root canal morphology was assessed using volumetric and surface measurements, as well as voxel counts. The independently segmented canal models were qualitatively displayed together in their native coordinate space to visualize positional consistency between acquisitions. Paired analyses showed substantially lower positional deviations with the 3D-printed holder than with the conventional holder in all four acquisition comparisons (Holm-adjusted p < 0.001). Mean deviations ranged from 0.126 to 0.172 mm for the 3D-printed holder and from 3.626 to 8.318 mm for the conventional holder. Morphometric parameters derived from 3D analysis remained identical across all acquisitions, regardless of operator, time point, or positioning method. Qualitative 3D analysis showed near-complete overlap for the 3D-printed holder and clear spatial discrepancies for the conventional holder. Under the evaluated conditions, the 3D-printed holder exhibited less positional variability than the conventional alginate-based holder. Full article

The evaluation of analytical methods has evolved beyond traditional validation parameters to encompass environmental sustainability, analytical reliability, and practical applicability. While numerous assessment tools exist for individual dimensions, integrated multicolor frameworks often impose rigid parameter selection, assign equal weights to all dimensions regardless of analytical context, or lack the granular diagnostic feedback necessary for method improvement. To address these limitations, we present the Triple-Color Assessment (TriCA) tool, a web-based platform that integrates three validated metrics into a unified scoring system with a hierarchical weighting scheme. TriCA combines the Analytical Green Star Area (AGSA) for greenness assessment, the Click Analytical Chemistry Index (CACI) for blueness evaluation, and the Analytical Method Reliability Index (AMRI) for redness scoring. While these metrics are provided as defaults, users may select alternative assessment tools within each dimension according to their specific preferences or requirements. TriCA features a user-customizable weighting system with recommended defaults (red = 3, blue = 2, green = 1), reflecting the logical priority that a method must first be valid, then practical, and finally green. Users can adjust these weights for specific contexts, while the underlying metric scoring protocols (AGSA, CACI, and AMRI) remain fixed to ensure reproducibility. Beyond composite scoring, TriCA generates three detailed pictograms, 12 segments for AGSA, eight segments for CACI, and 10 segments for AMRI, each color-coded to show performance at the individual criterion level. This diagnostic capability enables analysts to identify exactly which green chemistry principles, practicality criteria, or validation parameters are deficient, transforming assessment from an opaque scoring exercise into actionable guidance for method improvement. Seven case studies encompassing diverse analytical techniques demonstrate TriCA’s discriminative power and diagnostic utility. The tool is freely accessible at bit.ly/TriCA, offering the analytical community a transparent, reproducible, and diagnostically rich framework for method evaluation, development, and optimization. Full article

In conventional CNC machining, the workpiece clamping pose is registered with a preset CAD model under multiple geometric constraints to establish the machining reference frame. The tool path, generated from this model, is subsequently used to produce components of identical geometry. However, this paradigm proves inadequate when a final shape must accommodate morphological variations specific to each individual blank. Manual grinding, as an alternative, is not only inefficient and hazardous but also relies heavily on subjective quality assessment. To address these challenges, we propose an adaptive local-region milling strategy tailored for blanks with similar yet non-identical surface morphologies, enabling the finished geometry to adjust dynamically to each workpiece. Under conditions of under-constrained clamping, visual positioning is first employed to automatically locate the target regions. Line laser scanning is then conducted over the planned area to acquire high-density point clouds. Through segmentation, points lying outside the region to be machined are extracted, from which a theoretical post-machining surface is reconstructed. Milling toolpaths are subsequently planned based on this reconstructed model to compensate for surface variations across different blanks. Experimental validation on a three-axis CNC milling machine demonstrates that the proposed adaptive strategy effectively replaces manual grinding by removing the bulk of the machining allowance from locally variant surfaces. With the reconstructed model serving as the reference, 77.1 percent of the machining errors fall below 0.055 mm. These results confirm that the method yields a smooth and level surface finish, thereby meeting the fundamental requirements for such adaptive machining tasks. Full article

Background: White Matter Hyperintensities (WMH) are common MRI markers of cerebral small-vessel disease and are associated with cognitive impairment and dementia. Deep Learning (DL) tools have improved WMH segmentation, enabling faster and more reproducible lesion quantification. However, the lack of normative reference frameworks limits the clinical and translational use of WMH volumes. Aims: to develop and validate normative reference curves for automated WMH quantification and to define clinically useful thresholds for rule-out and rule-in interpretation in memory-clinic settings. Methods: We developed age- and sex-adjusted WMH normative models for 2D (n = 788) and 3D (n = 895) FLAIR acquisitions in cognitively normal individuals aged 40–95 years. WMH volumes were segmented using LST-AI and normalized to total intracranial volume. Normative percentiles were derived using Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a Johnson’s SU distribution and externally validated in 458 individuals spanning cognitively normal (CN), mild cognitive impairment (MCI), and dementia groups from two validation cohorts. Normative distributions have been made available through neuGRID, an online platform providing AI-based tools for neuroimaging analysis. Results: WMH burden increased progressively with age in both normative datasets and showed a stepwise increase across the cognitive continuum from CN to MCI and dementia. The optimal balanced thresholds corresponded to the 92nd percentile for the 2D model and the 85th percentile for the 3D model, yielding areas under the receiver operating characteristic curve (ROC-AUCs) of 0.71 (95% CI: 0.63–0.78) and 0.68 (95% CI: 0.61–0.74), respectively. Secondary threshold analyses highlighted complementary operating characteristics, with the 2D model favoring sensitivity and the 3D model favoring specificity, supporting their potential use in sequential diagnostic workflows. Conclusion: This study provides an externally validated normative framework for interpreting LST-AI-derived WMH burden through the neuGRID single-case service. The proposed modality-specific norms enable standardized identification and contextualization of elevated WMH burden and may support clinical stratification, second-opinion assessment, and research applications in cognitive disorders. Full article

Accurate identification of urban functional zones is critically important for urban planning and sustainable development. However, existing deep learning methods exhibit limited cross-city generalization capabilities, rendering them inadequate for large-scale automated mapping applications. To address this challenge, this study proposes a cross-city identification framework based on SFA-Mask R-CNN (Scale-Aware Feature-enhanced Attention Mask R-CNN), which achieves precise segmentation and classification of functional zones by integrating three complementary feature enhancement mechanisms: a Convolutional Block Attention Module (CBAM) for discriminative feature recalibration, a novel Scale-Aware FPN (SA-FPN) that dynamically adjusts feature pyramid layer weights according to the scale distribution of input imagery to improve cross-city transferability, and an ASPP (Atrous Spatial Pyramid Pooling) module for multi-scale contextual feature extraction, combined with multi-source data fusion. Using three cities along the Yangtze River Economic Belt—Chengdu, Wuhan, and Shanghai—as study areas, we systematically evaluated the model’s cross-city generalization performance. The results show that the Wuhan model exhibits the strongest cross-city transferability, achieving 85.37% accuracy in Chengdu, suggesting that models trained on cities at transitional development stages possess superior generalizability. In contrast, the gradient reversal layer-based domain adaptation approach tested in this study failed to effectively enhance model performance. This study provides a practical technical pathway for “train once, apply to multiple cities” large-scale functional zone mapping and offers new perspectives for research on regional disparities in urban development. Full article

Chinese cross-border e-commerce (CBEC) platforms have rapidly reshaped contemporary digital retail, achieving substantial adoption. However, prior research on consumer heterogeneity has yet to sufficiently examine how this fast-growing consumer base is segmented across these platforms’ broad category spectrum. The present study accordingly applies latent class analysis to 16 product purchase indicators from 600 Korean AliExpress or Temu users, and conducts bias-adjusted three-step analyses that relate segment membership to demographic and platform covariates and to distal outcomes in shopping behavior and platform awareness channels. Five qualitatively distinct segments emerge: Minimal, Fashion, Household, Auto & Home, and Omnivore shoppers. Omnivore shoppers, the smallest segment, register the highest per-transaction expenditure, satisfaction, and active search. Auto & Home shoppers form a second active-search tier for product search and deal seeking. Household shoppers, the lowest-income tier, show greater receptiveness to offline broadcast advertising. Fashion shoppers have the highest share of consumers aged younger than 20 years and the lowest satisfaction. Minimal shoppers, the largest segment, have the lowest per-transaction expenditure and item count. Platform affiliation crosscuts these segments, with Minimal and Auto & Home shoppers favoring AliExpress and the remaining three Temu. Consumer heterogeneity on CBEC platforms thus forms a clearly differentiated subpopulation structure. Full article

Rotary tillage–seeding combined operations require stable power take-off (PTO) speed during rotary tillage and accurate tracking of the prescribed travel speed for seeding. Existing energy management strategies for hybrid electric tractors mainly focus on fuel economy and commonly use fixed objective weights, limiting their ability to adjust control priorities under changing operating conditions. To address this issue, an operation-quality-oriented energy management strategy based on model predictive control, termed OQ-EMS/MPC, is proposed. An equivalent combined-operation condition was constructed using the PTO-side rotary-tillage load, drive-side equivalent traction load, segmented travel-speed reference, and equivalent seeding-quality risk. A condition-severity index integrating the PTO-load coefficient of variation, PTO-load impact intensity, and equivalent seeding-quality risk was developed to distinguish steady, fluctuating, and impact-dominated conditions. Based on the identified condition, the weights assigned to PTO-speed regulation, equivalent seed synchronization, and energy economy were adjusted online. These weights were used in the MPC to optimize torque allocation among the engine, motor-generator 1 (MG1), and motor-generator 2 (MG2). The proposed strategy was validated on a dual-side loading bench and compared with a rule-based energy management strategy and a fixed-weight MPC strategy. The overall PTO-speed root-mean-square error (RMSE) was reduced to 1.76 r/min, representing reductions of 58.40% and 45.66% relative to the two comparative strategies, respectively. The equivalent seed-synchronization RMSE was reduced by 69.15% and 52.66%, respectively. Under the impact-dominated condition, the PTO-speed RMSE decreased to 1.65 r/min. The normalized composite cost decreased by 13.53% and 6.26%, while the equivalent fuel consumption increased by 3.40% and 3.76%, respectively. The results demonstrate that the proposed strategy improves PTO-speed stability and equivalent seed-synchronization performance as operating severity increases while accounting for energy economy. Full article

Sparse underground pressure monitoring limits the direct characterization of full-field pressure distributions during coalbed methane (CBM) extraction, while conventional numerical simulations often require repeated parameter adjustments. This study proposes an intelligent inversion framework that couples an enhanced attention-based long short-term memory (EA-LSTM) model with a COMSOL-based forward model. Coal Seam No. 3 at Xinjing Mine was selected as the engineering case study. The vertical stress correction coefficient, reservoir pressure, extraction pressure, and initial permeability were treated as inversion parameters, while the working-face advance distance was introduced to represent the mining stage. A multiparameter sample library was constructed using an orthogonal design and segmented advance conditions, and pressure responses from five extraction boreholes were used as inversion constraints. The proposed model achieved a mean squared error of 0.013 and a coefficient of determination of 0.987, outperforming nine benchmark machine-learning and deep-learning models. The relative errors between the inverted and measured borehole pressures ranged from 0.41% to 5.07%, with a value of 1.84%. The coupled framework improves the efficiency of boundary-parameter identification and enables rapid reconstruction of pressure fields in a coalbed methane reservoir. Full article