Search Results (337)

Background: The presence of left atrial fibrosis is a marker of advanced remodeling and is associated with a worse outcome after pulmonary vein isolation (PVI). Conventional fluoroscopy-only cryoballoon ablation (CBA) lacks this prognostic information. The addition of electroanatomical mapping (EAM) using the inner lumen spiral catheter allows accurate voltage assessment of the left atrial posterior wall. However, the value of the finding of posterior wall low-voltage zones (pwLVZs) is unknown. Purpose: To study the value of left atrial voltage maps during CBA by comparing clinical and procedural characteristics and clinical outcome between patients with and without pwLVZs. Methods: A cohort of 250 consecutive patients who underwent index CBA for atrial fibrillation was analyzed. All patients underwent pre- and post-procedural EAM using the Achieve^TM catheter and EnSite^TM mapping system. The presence of LVZs was evaluated at the postprocedural voltage map of the posterior wall. Clinical success was defined as freedom from documented AF or atrial tachycardia (AT) >30 s after 1 year. Results: PwLVZs were found in 41/250 (16.4%) of patients. Patients with pwLVZs were older (69.3 ± 8.5 vs. 64.2 ± 10.4; p = 0.003), more frequently female (63.4% vs. 32.5%; p < 0.001) and had higher CHA₂DS₂-VASc scores (3.0 ± 1.6 vs. 2.0 ± 1.5; p < 0.001). The incidence of obesity (31.7% vs. 25.8%; p = 0.048), structural heart disease (35.5% vs. 17.4%; p = 0.021) and persistent AF (68.3% vs. 43.8%; p = 0.004) was higher in the pwLVZs group. Kaplan–Meier analysis of clinical outcome showed a higher recurrence rate in the pwLVZs group. The finding of pwLVZs was a predictor of atrial arrhythmia recurrence during follow-up (HR 2.583; 95%CI: 1.334–5.002; p = 0.005). Conclusions: In CBA facilitated by integrated EAM, pwLVZ was associated with older age, female sex, higher CHADS-VASc scores, obesity, structural heart disease and persistent AF. The finding of pwLVZs is predictive of a worse clinical outcome. Full article

Optimal coordination of directional overcurrent relays (DOCRs) in medium-to-large power systems constitutes a computationally demanding, mixed-integer, nonlinear optimisation problem whose complexity escalates rapidly with system size, making the simultaneous minimisation of relay operating time and computational cost a critical open challenge. This study presents a two-level hierarchical framework in which Type-2 Fuzzy C-Means (T2FCM) clustering partitions 226 fault scenarios into subproblems at the upper level, while the Hybrid Fractional Entropy Evolution (HFEE) algorithm independently optimises relay settings for each cluster at the lower level. HFEE integrates fractional-order velocity updates—derived from the Grünwald–Letnikov formulation—with a Shannon entropy diversity-control mechanism to prevent premature convergence. T2FCM captures inherent fault-current uncertainty through interval-valued type-2 fuzzy memberships, yielding more robust cluster assignments near protection-zone boundaries than crisp partitioning methods. The framework is validated on the extended IEEE 30-bus system. An ablation study demonstrates that standalone HFEE achieves a 29.19% improvement in $T_{\mathrm{op}}$ over the prior best-reported result; however, a comprehensive parameter sweep over cluster counts $K\in \{2,\dots ,8\}$ and fractional orders $\alpha \in \{0.1,\dots ,0.9\}$ across 50 independent runs per configuration shows that the proposed clustering-decomposed method achieves 3.68–66.67% lower wall-clock computation time while maintaining zero CTI violations across all active relay pairs. The communicationless, entirely offline framework demonstrates scalability for simultaneous sub-transmission and distribution protection coordination and offers a practically deployable strategy for modern power networks. Full article

Aircraft taxiing speed control along predefined airport surface routes is a constrained single-aircraft longitudinal control problem involving heterogeneous route geometry, action smoothness, and terminal velocity feasibility. Existing learning-based taxiing controllers commonly use a unified policy for the whole route, which may be insufficient for handling straight-segment propulsion, curved-segment speed regulation, and action discontinuities near straight–curve transitions. This paper proposes SegCoord-Taxi, a geometry-aware segmented deep reinforcement learning framework for taxiing speed control. The route is decomposed into straight segments, curved segments, and transition boundary zones. A Straight-Segment Policy (SSP) and a Curved-Segment Policy (CSP) generate geometry-dependent base acceleration commands, a Switch Residual Adapter (SRA) provides local residual correction near transition regions, and a Route-Level Feasibility Projection (RFP) maps the coordinated action into an executable acceleration satisfying route-level feasibility constraints. Experiments on departure taxiing routes at Chengdu Tianfu International Airport (ZUTF) included baseline comparison, ablation analysis, projection diagnostics, sensitivity analysis, and a trajectory-level case study. On the evaluated ZUTF case-study routes, SegCoord-Taxi achieves the lowest final velocity on the test set, $0.336\pm 0.017 \mathrm{m}/\mathrm{s}$, compared with $0.732\pm 0.061 \mathrm{m}/\mathrm{s}$ for the unified Proximal Policy Optimization (PPO) controller and 0.586 m/s for the curvature-aware constrained optimizer. The complete framework also reduces switch action jump from $1.022\pm 0.017 \mathrm{m}/{\mathrm{s}}^2$ to $0.429\pm 0.004 \mathrm{m}/{\mathrm{s}}^2$ in the ablation study. These results indicate improved terminal feasibility and transition-region smoothness in the evaluated single-airport case-study setting under an explicit efficiency–smoothness–feasibility trade-off. Future work will extend the framework to multi-aircraft and multi-airport settings under operational uncertainty. Full article

Equipment fault prediction in maglev train systems poses substantial challenges: fault events are inherently rare, class distributions are severely imbalanced, and individual equipment units are subject to complex spatial and functional couplings that single-device statistical approaches fundamentally cannot capture. To address these challenges, this study proposes a Knowledge Graph-driven Graph Neural Network (KG-GNN) framework. A fault knowledge graph encompassing equipment, fault, temporal, and environmental entities is constructed to unify multi-source maintenance data. Graph connectivity is established via three spatial relation types (co-location, co-zone, and co-level), with edge weights derived from Laplacian-smoothed Lift scores quantifying fault co-occurrence strength. A two-layer GATv2Conv-based graph attention network is designed: the first layer employs four-head attention with explicit edge-weight integration to capture heterogeneous neighborhood influences, while the second layer produces compact node embeddings via single-head attention. A Top-20 sparsification strategy suppresses weak-association noise, and training under severe class imbalance is stabilized through Focal Loss and F2-Score-guided early stopping. On the test set, the proposed method achieves an F2-Score of 0.5703, Recall of 0.6825, and AUC-ROC of 0.9329 (single-run evaluation); multi-seed evaluation (5 seeds) yields F2 = 0.5645 ± 0.0035, Recall = 0.6789 ± 0.0095, and AUC-ROC = 0.9298 ± 0.0026, outperforming the MLP baseline by 18.3% in F2-Score and substantially exceeding GCN (F2 = 0.1476 ± 0.0176) and GATConv (F2 = 0.4284 ± 0.0097). Ablation studies confirm the individual contributions of authentic graph topology, precise edge weighting, and graph sparsification to overall performance. Full article

Autonomous inspection of discrete obstacles (e.g., tree trunks in orchards and forests) requires UAVs to visit every target with proper observation distance and heading, while simultaneously exploring the unknown environment. Existing space-guided exploration methods focus on eliminating unknown space and are inherently agnostic to the inspection targets themselves, leading to incomplete coverage and redundant traversal. We observe that the obstacles themselves encode the spatial topology of the environment and can serve as natural planning anchors. Based on this insight, we propose ORACLE, an Object-centric Autonomous Coverage Exploration framework that shifts the planning paradigm from space-guided to target-guided exploration. ORACLE integrates: (1) an online target detection and persistent identification module via occupied-voxel connected component labelling, (2) a density-aware global coverage planner that modulates ATSP costs to prioritize target-dense regions, and (3) a target-guided local planner that replaces frontier viewpoints with direct obstacle observation points in a Sequential Ordering Problem formulation. Experiments in two point-cloud environments reconstructed from real-world forests with contrasting tree densities (Environment I: 50 trunks, $\overline{n}=1.56$; Environment II: 70 trunks, $\overline{n}=2.19$; both with non-uniform spacing) show that ORACLE achieves $98.8\%$ and $99.7\%$ target coverage compared to $22.7\%$ and $25.1\%$ for the space-guided baseline, while reducing the mission overhead ratio from $202.9\%$ to $129.2\%$ (Environment I) and from $176.8\%$ to $126.6\%$ (Environment II). Ablation studies confirm that zone reactivation is the decisive factor for coverage completeness ($-18.8$ and $-17.2$ percentage points when disabled in Environments I and II, respectively) and that density weighting improves path efficiency. Full article

This study presents a comprehensive computational evaluation of radiofrequency (RF) ablation efficacy and the spatial formation of thermal ablation zones within a 3D model of a liver tumor. By systematically comparing these configurations, the study aims to elucidate the physical mechanisms governing electromagnetic (EM) energy dissipation in hepatic tissue and to provide clear engineering guidelines for optimizing RF applicator selection and treatment planning in clinical practice. To reliably simulate the biophysical phenomena of the RF ablation procedure, a coupled electro-thermal model based on the finite element method and the Pennes bioheat equation was implemented. The research investigates six distinct applicator variants: conventional needle-type applicators and advanced expandable umbrella-type RF applicators equipped with four- and eight-tine electrodes, each evaluated in both monopolar and bipolar configurations. Numerical simulations were conducted for a standard 10 min ablation procedure at varying applied voltages to assess the specific absorption rate (SAR) distribution, transient heating dynamics, and the exact volumes of the resulting coagulation necrosis which were quantified using rigorous isotherms and the cumulative equivalent minutes at 43 °C (CEM₄₃) thermal dose index. Volumetric analysis of the ablation zones revealed that bipolar multi-tine electrodes induce highly localized heat concentration. Conversely, monopolar multi-tine setups strongly disperse EM energy. The results demonstrated that, for conventional needle applicators, the monopolar configuration generated significantly larger necrosis zones than the bipolar operating mode. The RF applicator geometry and its operating mode directly dictate the spatial extent of liver tissue necrosis. Moreover, advanced numerical treatment planning is essential for optimizing SAR and CEM₄₃ distributions and ensuring safe and complete hepatocellular carcinoma eradication. Full article

Introduction: Cryoablation is an established nephron-sparing option for small renal masses, particularly in patients unsuitable for surgery. However, definitive histopathological assessment post-ablation is limited due to the in situ nature of treatment. This report details a case of delayed partial nephrectomy after cryoablation, enabling comprehensive histopathological evaluation of long-term treatment effects. Case presentation: A 50-year-old man with uncontrolled hypertension, diabetes, and triple-vessel coronary disease presented with a 2.5 cm right renal mass. Cardiovascular instability deferred initial surgery. Following coronary intervention requiring anticoagulation, percutaneous cryoablation was performed using CT-guided 3D reconstruction for precise probe placement and ice-ball confirmation. After 388 days, laparoscopic partial nephrectomy was performed. Histopathology revealed a 1.9 cm clear cell renal cell carcinoma. Approximately one-third of tissue showed post-cryoablation changes. Three distinct zones were identified: viable carcinoma, coagulative necrosis with preserved glomerular outlines, and viable parenchyma. Serial follow-up over 2 years showed transient creatinine elevation normalizing by 3 months, with no recurrence or metastasis. Conclusions: This case provides rare whole-lesion histopathological assessment after renal cryoablation, illustrating heterogeneous long-term tissue response and supporting cryoablation as a disease-control or bridging strategy in medically high-risk patients. Full article

Virtual reality (VR)-based rehabilitation is an established modality for upper extremity motor recovery; however, existing systems frequently rely on fixed, random, or therapist-tuned target placement that disregards patient-specific motor capacity and population-level priors. This study proposes a cross-patient collaborative swarm intelligence framework that derives zone-based patient profiles from real VR trajectories and augments them with a similarity-weighted cohort prior distilled from clinically similar patients’ successful trajectory clouds and zone-transition graphs. A hybrid Ant Colony Optimization (ACO)–Particle Swarm Optimization (PSO) algorithm optimizes 12 targets per session across a 27-zone ($3\times 3\times 3$) workspace using a five-component fitness function encompassing reachability, zone balance, movement efficiency, heatmap-guided challenge coverage, and swarm-flow consistency. The framework was evaluated retrospectively on a single-center cohort of 36 post-stroke patients and 6373 sessions under a leakage-safe simulation protocol with 70/30 chronological splits; outcomes are model-based proxy success rates derived from each patient’s profile rather than directly observed task success. The hybrid strategy achieved a mean simulated success rate of 85.5% ± 5.5%, a 36.4% relative improvement over random placement (Wilcoxon $p<{10}^{-7}$, Cohen’s $d=4.91$); the leakage-safe split yielded 80.1% on the held-out segment versus 61.1% for random, with no statistically significant train–test gap ($p=0.470$). Ablation confirmed both PSO and ACO are individually necessary ($\mathsf{\Delta }\ge 2.7$ pp, $p<0.001$). Total session-start computation is 78 ms on standard CPU hardware. These findings constitute a proof-of-concept that collaborative personalized swarm optimization can substantially outperform heuristic target placement under in silico evaluation; clinical efficacy in terms of standardized motor outcome measures remains to be established in a prospective randomized controlled trial, and the findings should be replicated across centers, task modes, and a larger cohort before generalization. Full article

This paper proposes a multi-sensor fusion autonomous navigation method integrating a nine-axis IMU, the Leishen C16 mechanical LiDAR, and the LakiBeam1L single-line LiDAR, aimed at addressing issues such as track slippage and positioning drift that commonly occur in tracked chassis operating under continuously changing conditions on hilly slopes and farmland. IMU-derived slope and attitude information is used as a terrain prior and incorporated into adaptive ground segmentation, slope-cross-slope path cost modeling, and velocity regulation. Leishen C16 LiDAR point clouds are used for NDT scan-to-map localization and spatial obstacle representation, while the LakiBeam1L LiDAR establishes a velocity-dependent near-field safety zone for dynamic obstacle triggering and local avoidance. Python simulations were conducted in simple, general, and complex environments under five slope conditions, forming 15 environment-slope combinations. Three representative scenarios were further validated in ROS/Gazebo. To strengthen statistical reliability, 10 repeated trials were performed for each environment-slope-algorithm combination, and additional stress tests included obstacle-position perturbation, sensor noise perturbation, initial-pose perturbation, dynamic obstacle speed perturbation, and variable slope/local undulation perturbation. An isolated no-LakiBeam1L ablation, significance tests, IMU perturbation tests, planning-weight sensitivity analysis, and stronger-baseline comparison were also added. In the repeated-trial dataset, the proposed method improved the arrival rate from 23.3% to 94.7%, reduced tracking RMSE by 61.46%, reduced localization RMSE by 60.62%, and increased obstacle recall by 26.32%. Under mixed perturbations, the arrival rate of the proposed method was 81.3%, compared with 29.3% for the baseline. These results indicate improved simulation-level stability and perception reliability, while the applicability to real hilly farmland still requires hardware and field validation. Full article

The ablation behavior of 2.5D C/SiC composites under continuous-wave laser irradiation involves not only surface material removal but also internal structural degradation. In this study, laser ablation tests were conducted at power densities of 400, 800, and 1600 W/cm², and the ablated specimens were analyzed by macroscopic observation, infrared thermography, X-ray micro-computed tomography (micro-CT), cross-sectional scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS), depth measurement, and homogeneous thermal-field simulation. The results show that the surface morphology evolved from a transition-zone-dominated response to a typical zoned morphology consisting of a central ablation zone, transition zone, and edge zone as the power density and irradiation time increased. Under the present temperature measurement conditions, the surface transition zone corresponded to an apparent temperature window of approximately 2300–2700 K. Cross-sectional characterization further revealed a distinguishable internal reaction front beneath the external ablation surface, above which microstructural damage and Si depletion were observed. Depth measurements showed that the external ablation depth underestimated the actual degradation depth along the thickness direction. The calibrated homogeneous thermal-field model indicated that the internal front position corresponded to a relatively stable temperature range, suggesting that its formation was mainly governed by local thermal history and matrix-related reactions. The proposed internal reaction front provides a supplementary parameter for evaluating laser-induced subsurface degradation in 2.5D C/SiC composites. Full article

Accurate cladding segmentation is essential for quantitative quality assessment of stainless-steel/carbon-steel clad wire rods used in bridge cables, yet remains challenging because of weak core–cladding contrast, narrow interfacial transition zones, local cladding-thickness fluctuations, and limited repeatability of manual inspection. This study proposes an improved U-Net framework that integrates residual feature extraction, multi-scale contextual perception, and attention-guided feature refinement for robust cladding identification. A cross-sectional image dataset comprising 18,566 samples was constructed through standardized specimen preparation, chemical color development, image acquisition, pixel-level annotation, and data augmentation. In the proposed model, the original U-Net encoder is replaced with ResNet50 to enhance deep semantic representation, while atrous spatial pyramid pooling and a convolutional block attention module are embedded into the feature-fusion stage to improve boundary discrimination and thin-cladding recognition. On the test set, the model achieved a mean pixel accuracy of 97.29%, cladding intersection over union of 88.82%, and mean intersection over union of 93.72%, outperforming the baseline U-Net by 1.38, 9.19, and 5.17 percentage points, respectively. Ablation and comparative experiments further demonstrate improved boundary continuity, local-detail preservation, and segmentation stability compared with representative CNN-based segmentation models. These findings suggest that the proposed framework provides a practical and reliable vision-based approach for cladding-thickness measurement, eccentricity evaluation, uniformity assessment, and batch quality inspection of clad wire rods. Full article

Accurate and timely wildfire hotspot detection is essential for ecological sustainability and supporting climate resilience strategies. Although sensors such as MODIS and VIIRS have been widely used for wildfire detection, the potential of ocean color satellites for terrestrial wildfire monitoring remains largely unexplored. In this study, a Spectral–Spatial Attention U-Net (SSA-UNet) framework is proposed for wildfire hotspot detection using multispectral observations from the HY-1E Coastal Zone Color Scanner II (COCTS2) over the Three-North region of China. The proposed framework integrates spectral attention to enhance fire-sensitive bands and spatial attention to capture contextual wildfire patterns under complex environmental conditions. Experimental results show that SSA-UNet achieves a Precision of 0.8913, Recall of 0.7961, and F1-score of 0.8680, outperforming conventional threshold-based approaches and baseline deep learning models. Ablation experiments further demonstrate the effectiveness of the spectral–spatial attention mechanism, while band analysis highlights the important contributions of near-infrared, shortwave infrared, and thermal infrared observations for wildfire hotspot detection. The real wildfire case analysis further confirms the practical applicability of the proposed framework. The results demonstrate that HY-1E COCTS2 data have considerable potential for large-scale terrestrial wildfire monitoring when combined with deep learning techniques. Full article

The growing penetration of variable renewable energy sources transforms power grid dispatch into a high-dimensional, stochastic, and multi-agent decision-making problem that challenges both classical optimization and standard Reinforcement Learning (RL) methods. Traditional RL policies, typically parameterized as unimodal Gaussians, lack the expressiveness to capture the multimodal action distributions that arise when multiple feasible dispatch strategies coexist, while diffusion-based generative policies achieve expressiveness at the cost of prohibitively many iterative denoising steps during inference. We propose Multi-Agent Flow-based Q-Learning (MAFQL), a framework that addresses this expressiveness–efficiency tradeoff by integrating conditional flow matching with conservative Q-learning under a Centralized Training with Decentralized Execution (CTDE) architecture. The framework consists of a unified training pipeline that combines four learning objectives: behavior cloning, flow matching, conservative Q-learning, and distillation. This allows for expressive policy generation through only 1–5 ODE integration steps. Measured per-agent inference latencies below $8\mathrm{ms}$ (P99) are achieved on both GPU and CPU hardware, which is compatible with the response requirements of automatic generation control. We formulate the dispatch task as a Dec-POMDP over three physically grounded control zones derived from the RTE network topology and evaluate MAFQL on the IEEE 118-bus and 14-bus systems in the Grid2Op simulator. Empirical results show that MAFQL CTDE substantially outperforms all tested baseline methods on the 118-bus system under a composite multi-objective reward function and that it demonstrates initial cross-scale generalizability on the 14-bus system. The decentralized execution variant consistently outperforms centralized execution, consistent with the hypothesis that distillation facilitates effective knowledge transfer. At the end of the paper we discuss current limitations such as the absence of ablation studies, end-to-end latency measurements, and formal safety guarantees, then outline directions for addressing them. Full article

This paper addresses an integrated production–transportation scheduling problem in assembly workshops, encompassing the processes of part machining, material handling via handling resources, and final synchronous assembly. The finite buffer capacities of production resources can cause blocking, thereby reducing efficiency. Material handling resources are subject to different service area restrictions, and some share safety zones with production resources, preventing simultaneous processing. To address this, a mixed-integer programming model is formulated with makespan and total empty travel time as bi-objective optimization targets. Since the mixed-integer linear programming (MILP) model faces difficulties in solving medium- and large-scale instances, an improved memetic NSGA-II algorithm (IMNSGA-II) is proposed. The algorithm adopts a three-segment chromosome encoding and incorporates a VNS-SA local search mechanism within the global evolutionary framework of NSGA-II. Small-scale computational experiments using Gurobi are first used to verify the correctness of the model. Decoupling experiments further demonstrate the necessity of integrated optimization: compared with phased baseline methods, IMNSGA-II reduces makespan and empty travel time by approximately 10.16% and 12.33%, respectively. In ablation and comparative experiments, results based on hypervolume (HV) and inverted generational distance (IGD) show that the proposed method achieves better convergence, diversity, and overall Pareto front quality than multiple baseline algorithms. These experiments confirm the effectiveness of the proposed model and algorithm. Full article

Accurate multi-class segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI) into the peripheral zone (PZ), central gland (CG) and tumour is essential for targeted biopsy guidance and treatment planning. We present DSBANet, an encoder–decoder architecture that combines a pretrained ResNet-50 encoder, Atrous Spatial Pyramid Pooling, Multi-Scale Attention Fusion on skip connections, a Feature Fusion Module, deep supervision and boundary refinement. We evaluate eight architectures across three input dimensionalities (2D, 2.5D, 3D), yielding 24 models trained under identical conditions on the Prostate158 dataset. DSBANet achieves the best anatomy segmentation with PZ DSC of 0.8176 and CG DSC of 0.7888 among 2D models. To address the severe class imbalance of the tumour class, we further train DSBANet 2D with a class-weighted cross-entropy term and tumour-positive slice oversampling, raising per-case tumour DSC from 0.003 to 0.170 (a sixty-fold absolute improvement). A systematic eight-variant ablation study, evaluated under matched-pairs effect-size analysis, identifies the SE-Residual blocks and skip-connection attention as the largest contributors to tumour segmentation, while every architectural component contributes a directionally consistent gain. Full article