Search Results (652)

Intraprocedural rupture (IPR) during stent-assisted coiling (SAC) after stent deployment can create a narrow and rapidly changing management problem: hemorrhage control, anticoagulation reversal, acute thrombotic occlusion, and postprocedural cerebrospinal fluid diversion may all become urgent within the same clinical sequence. We report a fatal IPR during SAC of an unruptured anterior communicating artery (AComA) aneurysm and use the case as an anchor for a targeted case-based narrative review. A 71-year-old woman underwent SAC for a 5.1-mm posteriorly directed AComA aneurysm with a bleb after treatment for vertebrobasilar ischemia. Fourth-coil insertion produced tactile resistance and contrast extravasation. Protamine reversal and temporary A1 flow control reduced the leak, but filling defects then developed from the internal carotid artery terminus to the A1 and M1 segments, requiring rescue thrombectomy. Computed tomography showed subarachnoid hemorrhage and intraventricular hemorrhage; same-day progression with hydrocephalus required bilateral external ventricular drainage. The patient died on postoperative day 7. This case highlights IPR during SAC as a time-dependent hemorrhagic–thrombotic escalation rather than a single technical event. We propose a staged assistant–operator communication model for risk mapping, rupture recognition, hemostatic-route preservation, thrombotic surveillance, and transition to computed tomography, external ventricular drainage, and intensive care. Full article

End-to-end autonomous driving remains fragile in long-tail scenarios, while incorporating vision-language models (VLMs) introduces substantial deliberation latency that cannot interfere with the real-time planning loop. We present ThinkDrive, a dual-process driving framework designed under explicit real-time queuing constraints. The framework contains four coordinated components. First, a Scene Complexity Estimator regulates System-2 activation through a trigger cool-down mechanism, allowing at most one asynchronous request every $\lceil L_2/\Delta t\rceil$ frames and thereby preventing queue saturation under a System-2 latency of $L_2=565$ ms. Second, a multi-modal System-1 planner generates $K_1=5$ candidate trajectories within 44 ms and is trained with winner-takes-all imitation learning together with explicit score supervision. Third, a two-stage Causal-CoT module uses the VLM to identify risk agents and predict a preferred spatial goal $G_{\mathrm{VLM}}$, after which a single batched scm_rollout selects the safest candidate and extracts its endpoint as a world-coordinate goal anchor ${\mathbf{g}}_{S2}$. Fourth, a Goal-Anchor Replanning module transforms ${\mathbf{g}}_{S2}$ into the current ego frame and selects the candidate whose waypoint at the remaining time horizon is closest to the transformed goal. This design avoids coordinate-space mixing, mitigates bias caused by mismatched temporal horizons, and prevents semantic instability across replanning cycles. On nuPlan test14-hard, ThinkDrive with InternVL2-8B and a 6.8% trigger rate achieves 74.9 PDMs, outperforming AdaThinkDrive at 73.1 while maintaining a nominal latency of 44 ms. Full article

Automatic detection of sensitive fields in structured data is a critical prerequisite for privacy compliance and data governance. However, existing approaches face severe cross-domain generalization challenges. Hand-crafted pattern rules often fail under highly heterogeneous naming conventions, while single statistical models tend to overfit and degrade sharply under distribution shifts between training and deployment domains. These limitations stem from the weak semantic signals and distributional heterogeneity of structured data, which make it difficult to simultaneously capture explicit rules and latent, variant-sensitive attributes. To address these challenges, we propose a detection framework based on multi-view complementary features and a Hierarchical Gated Residual Network (HGRN). The framework first constructs a full-spectrum feature system that integrates explicit rules and implicit statistical fingerprints (e.g., entropy and character texture) to fill the semantic gap. It then introduces a decision mechanism combining robust priors with dynamic residual calibration: a random forest provides a stable probabilistic anchor, which is further nonlinearly corrected by a learnable gating-and-expert network. This design explicitly resolves the cognitive conflict between rule-dominated regions and complex distributional regions. Experiments on multiple real-world datasets—including DeSSI, CMS Open Payments and Home Credit—show that the proposed method achieves a Macro-F1 of 0.9408 on DeSSI and exhibits strong in-domain performance. Under strict frozen-model cross-domain transfer, HGRN mitigates the catastrophic collapse observed in pure neural baselines and maintains moderate detection capability, offering interpretable trust allocation between rule-based priors and data-driven correction in both financial and healthcare scenarios. Full article

Litopenaeus vannamei, the most widely farmed crustacean, relies on family-based selection where accurate pedigree information is essential. Although SNP-based tools offer high-accuracy pedigree assignment, adoption in commercial breeding remains limited. In this study, we developed a commercially viable 1K SNP panel with 1125 markers. Markers were selected from a 55K SNP dataset comprising 2330 individuals. We established a practical pedigree reconstruction workflow and implemented the panel in a field breeding population. The population included a selection group where families were reared separately and a test group where individuals were communally reared. We introduced anchor individuals from the selection group to enable pedigree linkage. All 1818 individuals from 72 families were accurately assigned. Family reconstruction achieved 100% consistency with known records, even when parents were partially missing. Heritability estimates for harvest weight ranged from 0.32 to 0.36 using pedigree-based BLUP (PBLUP), genomic BLUP (GBLUP), and single-step genomic BLUP (ssGBLUP). The ssGBLUP model, using a 0.15 to 0.85 weighting of G and A, achieved 6.67% and 19.40% higher accuracy than PBLUP and GBLUP. The panel also supported population structure analysis and diversity monitoring, demonstrating its value for genomic evaluation in commercial L. vannamei breeding. Full article

Physicians routinely operate in environments that require the rapid processing of complex and dynamic visual information to diagnose patient conditions, communicate effectively, and make informed decisions. Despite the central role of visual attention in clinical practice, these processes are rarely conceptualized or systematically measured in medical education research. In other professional domains, such abilities are described as professional vision (PV)—the situated capacity to selectively attend to relevant cues and interpret them considering domain-specific knowledge. Although the term professional vision foregrounds visual attention, we use it here to cover the multimodal clinical perception in which visual cues are typically embedded—predominantly visual, but in many tasks also auditory and verbal—with visual attention as the analytic anchor. This paper introduces a cognitive process model of professional vision for medical education (PV-CP) that specifies the perceptual and cognitive subprocesses underlying how physicians perceive and interpret clinically relevant information. Building on this model, we propose a theory-driven framework for the measurement of professional vision using multimodal indicators. Central to our argument is the assumption that professional vision represents a latent, temporally unfolding construct that cannot be validly captured through single behavioral metrics or outcome measures. Instead, robust measurement requires the coordinated analysis of gaze-based indicators of visual attention and cognitive indicators of reasoning, each reflecting distinct subprocesses of professional vision. By systematically linking families of indicators to specific subprocesses and clarifying their respective inferential strengths and limitations, the PV-CP model advances a process-oriented approach to studying professional vision in medical education. The framework provides a conceptual basis for integrating multimodal data sources and supports more precise interpretations of gaze and reasoning data in expertise research. In doing so, the model contributes to the theoretical refinement of professional vision and offers a structured foundation for future empirical research and the design of learning environments aimed at fostering clinically relevant perceptual–cognitive skills. Full article

Individual tree crown (ITC) detection from remotely sensed data is fundamental to forest inventory and ecological monitoring, but deep learning approaches remain constrained by limited labelled training data. We systematically evaluate three transformer detectors (the Detection Transformer (DETR), Deformable DETR, and DETR with Improved DeNoising Anchor Boxes (DINO)) paired with two backbones, ImageNet-pretrained ResNet-50 and a Masked Autoencoder (MAE) pretrained on unlabelled Canopy Height Model (CHM) data. These are benchmarked against a classical local maximum and watershed pipeline and Faster R-CNN across four test sets spanning boreal, temperate mixed-wood, and diverse North American forest types at 0.25–1.0 m resolution. Spatially held-out test regions with a one-patch buffer band eliminate sliding-window leakage; headline configurations are reported as mean ± standard deviation across three random seeds. With multi-resolution MAE pretraining, the practical lower bound for non-degenerate single-dataset transformer detection lies between ∼200 and ∼1200 patches. Without MAE pretraining, DETR fails at every dataset size we tested. Multi-dataset joint training reaches $F_1=0.84\pm 0.01$ on the boreal test set and 0.45–0.68 across the temperate-mixed-wood and NEON test sets, while Faster R-CNN narrowly wins on the smallest training pool. Standard DETR with ResNet-50 collapses regardless of the length of training schedule, but the same architecture with an MAE backbone reaches $F_1=0.83\pm 0.01$ at that schedule, showing that DETR’s reported instability is conditional on the combination of backbone initialization and training budget rather than architectural. Resolution and backbone interact: ResNet-50 wins at 0.25 m, and MAE wins at 0.5–1.0 m, consistent with the eight-pixel MAE patch-matching crown scale only at coarser resolutions. Full article

Protein molecularly imprinted polymers (MIPs), as artificial antibodies, are promising for protein separation due to their low cost, easy preparation, and high stability, but their performance is limited by poor mass transfer, imprecise imprinting, and single interaction modes. Herein, dendritic mesoporous silica nanoparticles (DMSNs) were used as the support, and a self-designed multifunctional poly(ionic liquid) macromonomer (p(VIMCD-co-VAIM-co-VSIM-co-VVIM)) served as the functional monomer to achieve directional anchoring of cytochrome C (Cyt-C). Surface-imprinted microspheres (DMSNs@MPS@PILs-MIPs) were prepared via free-radical copolymerization for Cyt-C recognition. The DMSNs possessed interconnected mesoporous channels, good dispersibility, an average particle size of ~80 nm, and a specific surface area of 267.97 m²/g. Ionic liquid monomers were synthesized via alkylation, and the macromonomer was constructed through a two-step method. Molecular dynamics simulations and spectroscopic characterization revealed the macromonomer-stabilized Cyt-C conformation, with interactions dominated by van der Waals forces. The DMSNs@MPS@PILs-MIPs featured a thin imprinted layer (~5 nm) to reduce mass-transfer resistance. Adsorption studies showed Cyt-C adsorption followed Langmuir and pseudo-second-order models, with a maximum capacity of 383.14 mg/g and an imprinting factor of 2.17. Only 12% capacity loss occurred after repeated cycles, indicating robust regeneration stability. This study provides a feasible strategy for constructing protein surface-imprinted polymers based on multifunctional synergistic interactions and conformational stabilization. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

In this paper, a supervised deep reinforcement learning (SDRL)-based positioning scheme is proposed for indoor wireless localization. The proposed scheme formulates the positioning problem as a Markov decision process and introduces a target-aware reward design based on the artificial potential field (APF) to alleviate the sparse reward problem commonly encountered in search-based reinforcement learning. In the proposed scheme, supervision is provided at the reward level by incorporating the target position into the reward design, rather than at the action level via expert demonstrations. A multi-scale action set with 49 candidates is further adopted to provide a favorable trade-off between estimation accuracy and search efficiency. An anchor-based environment construction strategy is developed by selecting the four strongest reference points (RPs) and transforming their coordinates with respect to the strongest RP. Simulation results show that the proposed scheme achieves a mean absolute error (MAE) below 0.8 m and success rates above 99.1% within 1 m and 99.2% within 2 m under the default Bluetooth Low Energy setting, while the convex-valid rate of the anchor-based environment exceeds 99.5%. Compared with existing methods, the proposed scheme reduces the MAE by approximately 92.3%. Ablation studies confirm that multi-scale actions reduce the average search steps by approximately 69.5% compared with a single-scale baseline. The proposed scheme also retains stable performance across BLE, Wi-Fi, and Zigbee infrastructures when trained under a representative path-loss setting without retraining and maintains sub-meter accuracy under mild shadow fading. These results confirm that the proposed scheme can improve positioning accuracy and search efficiency for indoor wireless localization. Full article

Offshore floating photovoltaic (OFPV) is an important renewable energy technology, and assessing the reliability of mooring systems is of great significance for promoting the large-scale commercial deployment of OFPV. However, owing to the complexity of the system structure, relevant reliability research has not been extensively carried out. With this in view, this work focuses on the systematic reliability analysis of a novel parallel mooring system composed of elastic ropes and anchor chains under the ultimate limit state (ULS), accidental limit state (ALS) and fatigue limit state (FLS), considering both long-term cyclic and extreme environmental conditions. The first-order second moment (FOSM), first-order reliability method (FORM) and Monte Carlo simulation have been employed to calculate the failure probabilities. By applying the series-parallel model to integrate multimode failures, it is confirmed that the failure probability of the entire mooring system is significantly greater than that under any single limit state. The results indicate that anchor chain is the main fatigue-critical component, and the Monte Carlo simulation based on extensive random sampling data is more conservative in reliability estimation than FOSM and FORM which cannot fully capture all distribution characteristics. This work could provide essential theoretical support for the safe design of subsequent OFPV mooring systems. Full article

This study employed the Ex-Gaussian distribution model to analyse eye-tracking data, to elucidate the cognitive mechanisms underlying predictive processing during Chinese reading. Using a single-factor, two-level within-subjects design (contextual predictability: high vs. low), data from 32 adult readers were analysed across the pre-target and target word regions. The results revealed that predictive reading follows a three-stage cognitive model. In the expectation generation stage (pre-target region), a significant negative τ effect indicated resource pre-allocation driven by strong contextual constraints, thereby facilitating the construction of predictive lexical representations. In the verification and integration stage (target word region), a significant negative μ effect in the later measurement window indicated that successful prediction–input matching accelerated lexical identification and enhanced integration efficiency; the σ parameter did not reach significance in either measurement window. In the conflict resolution stage (pre-target and target word regions), a significant positive τ effect indicated that verification failure triggered lexical activation competition at the target word, driving regressive fixations to the pre-target region for contextual reanalysis; conflict resolution costs were markedly higher under the low-predictability condition, owing to the absence of a dominant activation anchor. These findings suggest that contextual predictability influences reading through a dual mechanism: the μ parameter modulates the automatic processing speed of lexical identification, whereas the τ parameter regulates the cognitive control processes underlying expectation generation and conflict resolution. Together, these results provide empirical support for the integration of predictive coding theory and cognitive control frameworks. Full article

Conservation management of large, multi-species landscapes requires integrating heterogeneous data streams—such as satellite imagery, GPS telemetry, camera traps, bioacoustic sensors, weather stations, and field reports—into a unified model capable of simulating ecosystem dynamics and generating actionable recommendations. This paper proposes a tiered, energy-aware AI architecture for constructing ecosystem digital twins that enables prescriptive, rather than merely descriptive or predictive, landscape-scale conservation management. The framework classifies conservation tasks across three computational tiers: classical machine learning for continuous environmental monitoring and species distribution prediction, deep learning for perception-oriented tasks such as computer vision and bioacoustic analysis, and foundation models for cross-domain synthesis and stakeholder interaction. We apply this architecture to a comprehensive digital twin of the Greater Yellowstone Ecosystem, anchored in the ongoing conservation crisis of the Sublette Pronghorn Herd—a population that crashed from 43,000 to 24,000 animals in a single winter due to compounding severe weather and a Mycoplasma bovis outbreak. We formalize a coupled change model linking population dynamics, forage condition, corridor permeability, winter severity, and disease pressure, and demonstrate how a prescriptive recommendations engine can generate goal-conditioned management actions for the herd’s 165-mile “Path of the Pronghorn” migration corridor. A comparative energy footprint analysis, grounded in hardware-level energy measurements using Intel RAPL instrumentation and the CodeCarbon framework, estimates that the tiered architecture reduces computational energy consumption by approximately 34% relative to a deep-learning-everywhere baseline and by over three orders of magnitude relative to a foundation-model-centric baseline. The architecture provides a replicable blueprint for resource-constrained conservation organizations seeking to deploy AI-powered ecosystem management at landscape scale. Full article

Three types of graphene–single crystal titanium dioxide composite (GR–TiO₂SCs) were prepared using the hydrothermal method, employing TiF₄ and graphite as raw materials with hydrofluoric acid serving as the morphology-directing agent. The phase composition and morphological features of the resultant composites were systematically characterized by X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy and X-ray diffraction. These complementary characterization results clearly demonstrate that graphene and TiO₂ single crystals have been successfully hybridized to form a well-defined heterostructure, rather than a simple physical mixture. Photocatalytic performances were evaluated by monitoring the photodegradation behaviors of methylene blue, rhodamine B, and methyl orange solutions under simulated light irradiation, with real-time concentration variations recorded by UV–visible absorption spectroscopy. The composite sample in which TiO₂SCs were in situ grown and uniformly anchored onto graphene oxide substrates effectively suppressed the self-stacking and agglomeration of individual crystallites, thus delivering the best photocatalytic response. Increased exposure of the active catalytic interfaces of TiO₂SCs was found to play a key role in elevating the overall photocatalytic activity. The hierarchical assembly protocol developed in this work provides a feasible pathway for the rational design of functional composites with controllable microstructures and tailored properties, which can be further extended to the development of advanced sensing materials. Full article

Spatial transcriptomics (ST) has emerged as an essential technology for interpreting the molecular profiles underlying pathological tissue morphology. Most existing ST analyses are limited to 2D sections, which ignore the complex structural and molecular heterogeneity of biological tissues in 3D space and may cause diagnostic oversights. Since acquiring complete 3D ST volumes is resource-intensive, recent 3D imputation paradigms provide a cost-effective alternative by integrating 3D whole-slide images (WSIs) with sparse 2D ST references (e.g., a single slide). Despite this methodological advancement, effectively modeling complex cross-layer spatial dependencies remains challenging. Current mainstream solutions predominantly adopt standard Transformers for cross-scale feature aggregation, which may bring computational overhead and higher overfitting risk while having limited explicit mechanisms for hierarchical anatomical guidance. To address these limitations, we propose a Hierarchical MambaOut-based Spatial Imputation Graph Network (HM-ASIGN) for anatomy-aware 3D spatial transcriptomics imputation. Our architecture leverages MambaOut’s dynamic gated 1D convolutions as a parameter-efficient alternative to dense global self-attention. This design captures the depth-wise evolution of pathological features while reducing over-parameterization. Inspired by the macro-to-micro diagnostic reasoning of clinical pathologists, HM-ASIGN introduces a multi-scale recursive guidance mechanism. It constructs a top-down information flow by extracting global anatomical priors at macroscopic scales and injecting them as contextual anchors into regional and spot-level features in a cascaded manner. This helps ensure that fine-grained molecular predictions are properly constrained by global morphological structures. Evaluation experiments on multiple public breast cancer datasets demonstrate that HM-ASIGN achieves competitive reference-level performance against existing baselines, reaching a Pearson Correlation Coefficient (PCC) of 0.772. Specifically, when evaluated against the foundational ASIGN framework, it improves predictive accuracy while reducing the total parameter count by approximately 33.3% and improving inference throughput. Our results suggest that HM-ASIGN provides a computationally efficient approach for 3D spatial molecular mapping. Full article

Mapping directed spillover pathways in urban housing prices is essential for monitoring the contagion of housing prices across cities. However, existing studies typically rely on either spatial gravity models or time-series models in isolation to analyze intercity connections, thus failing to simultaneously capture the spatiotemporal integration characteristics of housing price contagion. To address this, we embed a finite-length sequence correlation analysis (Correlation-Dependent Balanced Estimation of Diffusion Transfer Entropy, $CBEDTE$) into the gravity model, yielding the $CBEDTE$-GM integrated model. Using housing price data from 296 Chinese cities, we construct a spatiotemporal correlation matrix and employ the directed minimum spanning tree algorithm to extract core directed spillover pathways. Results reveal that China’s urban housing price spillover network exhibits a hierarchical architecture with pronounced ripple effects, where eastern coastal cities and the national core city serve as dominant radiation hubs. The East China sub-network occupies a distinctive net spillover position. We identify heterogeneous structural evolution patterns across regional sub-networks: (1) North China evolved from a dispersed multi-centered configuration to a Beijing-dominated single-core structure; (2) East China developed a robust multi-centered architecture anchored by Shanghai; and (3) South China transitioned from a Guangzhou-centered single-core pattern to a tri-polar configuration co-driven by Guangzhou, Shenzhen, and Nanning. Full article