Search Results (18,669)

Medical image registration integrates information from different types of medical images to support and improve clinical diagnosis. Existing image registration approaches are mainly classified into two categories: model-driven methods and data driven methods. Model-driven methods can achieve high registration accuracy but suffer from low computational efficiency and long processing time. In contrast, data-driven methods stand out for their high efficiency, which gives them great practical value. Taking this advantage as the core basis, this paper proposes a simple unsupervised deep learning framework embedded with a multi-scale strategy. The overall network consists of two core modules: an Affine Transformation Network (AT-Net) and a multi-scale Deformable Transformation Network (DT-Net). The multi-scale design adopted in the DT-Net enables image registration at different feature scales, which effectively improves the overall registration accuracy. In addition, a dual consistency constraint is introduced into the framework to further enhance the model robustness. The entire network realizes end-to-end medical image registration. We verified the performance of the proposed method on a public dataset, with mutual information (MI) adopted as the evaluation metric. The experimental results show that our registration algorithm outperforms several mainstream methods, including Symmetric Image Normalization (SyN), VoxelMorph (VM), the coarse-to-fine deformable transformation framework for unsupervised multi-contrast MR image registration with dual consistency constraint (C-F-I-R), TransMorph and DiffuseMorph. The comparative experiments fully demonstrate that combining the multi-scale strategy with deep learning techniques is an effective solution for medical image registration tasks. Full article

As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems. Full article

To improve the path-following performance of an underactuated autonomous underwater vehicle (AUV) under varying path geometries and desired velocities, this study proposes a direct X-rudder control method based on Task-Informed Inductive-Bias Conservative Soft Actor–Critic (TIB-CSAC). The proposed method directly learns the X-rudder control policy from the path-following information of the current and subsequent path segments in a data-driven way, thereby avoiding the complex design and manual tuning of guidance laws and attitude controllers for rudder command generation. To support such two-segment policy learning, a task-informed inductive-bias encoder is proposed to construct structured and conditioned state representations, thereby improving sample efficiency and overall training quality. In addition, given the long-tail characteristics of task difficulty in agent training, a multi-head conservative value evaluation mechanism is incorporated to mitigate return drawdowns induced by challenging tasks in the tail stage of training and to enhance tail-stage convergence stability. The path-following performance is validated in three representative scenarios with different path pitch, path heading variations, and desired surge velocity conditions. The results show that, compared with the baseline soft actor–critic (SAC) method, TIB-CSAC improves multiple vertical and horizontal error metrics, including maximum absolute error, mean absolute error, tail error, and error threshold exceedance ratio. These results indicate that TIB-CSAC not only improves overall adherence to the reference path, but also more effectively suppresses extreme errors and tail errors, thereby demonstrating stronger path-following robustness and reliability. Full article

In wireless sensor networks (WSNs), establishing a robust key agreement is essential for securing communications. Various performance metrics are typically employed to evaluate these schemes, including storage requirements, communication overhead, and computational costs. Group key establishment ensures that sensitive information remains confidential, as only authorized nodes can decrypt broadcast messages. This paper proposes a group rekeying scheme based on symmetric polynomial key pre-distribution. By leveraging multivariable symmetric polynomials, a secure group key is constructed. Furthermore, the scheme incorporates a dynamic rekeying mechanism to update the group key whenever a sensor node is compromised, ensuring continuous forward and backward secrecy. Performance analysis demonstrates that the proposed scheme significantly reduces both communication overhead and computational complexity compared to existing methods. Full article

Grounding connectors critically influence the safety and long-term reliability of earthing systems through coupled electro-thermal, mechanical, and corrosion behaviors, yet no standardized quantitative framework exists for jointly evaluating these performance dimensions across diverse deployment scenarios. This study introduces a unified multi-criteria evaluation framework applied to six grounding connector configurations spanning four alloy families and three joining technologies. Electro-thermal response was characterized by coupled finite element simulations (0–100 A), mechanical reliability by quasi-static tensile testing (n = 10 per configuration), and corrosion durability by accelerated salt-spray exposure with image-based corroded area fraction quantification. Performance metrics were normalized and aggregated using equal-weight, Analytic Hierarchy Process, and Shannon entropy weighting schemes, with the Technique for Order of Preference by Similarity to Ideal Solution applied for multi-scenario ranking. One-way analysis of variance confirmed statistically significant effects of connector type on tensile performance (F(5, 54) = 3154.90, p < 0.001). The exothermic welded joint achieved the highest mean ultimate tensile load (61.5 ± 1.5 kN), while copper mechanical connectors exhibited the lowest steady-state temperature rise (~2 K above ambient at 100 A). Compression-crimped connectors ranked first under both equal and Analytic Hierarchy Process weighting (closeness coefficients 0.737 and 0.807, respectively), while stainless steel connectors ranked first under corrosion-critical deployment scenarios. Scenario-weighted analyses demonstrate that the optimal material–process combination shifts with environmental severity, current duty, and mechanical demand, providing a reproducible, evidence-based basis for context-dependent connector specification. Full article

Firefighter training requires accurate posture monitoring to reduce injuries and improve performance assessment, yet traditional tracking methods suffer from high occlusion rates and the uniform appearance of trainees. To address these challenges, we propose an improved multi-target tracking algorithm that integrates YOLOX for detection, BlazePose for posture estimation, and a pose-constrained extension of DeepSORT. First, posture features are introduced into the association metric through a posture-cosine distance, which enhances discrimination between visually similar firefighters. Second, a pose-guided bounding-box correction is applied to ensure complete coverage of the human body region, improving the quality of extracted posture information. Experiments were conducted on a custom firefighter training dataset comprising 6602 labeled images and five multi-target video sequences (FM-1 to FM-5). The proposed method achieved a mean Average Precision (mAP) of 97.8% for detection and improved tracking performance compared to baseline DeepSORT, with MOTA rising from 74.72% to 82.96% and IDF1 from 74.77% to 82.36%. These results demonstrate that the algorithm effectively handles severe occlusion and appearance similarity, providing a reliable tool for posture tracking and behavior perception in firefighter training environments. Full article

Bug severity classification is an important task in software maintenance because it supports bug triage and resource allocation. However, newly created or evolving projects often lack sufficient labeled data, making cross-project severity prediction challenging due to domain shift and class imbalance. In this paper, we investigate cross-project bug severity classification using lightweight transformer models under practical deployment constraints. Specifically, DistilBERT and TinyBERT are employed and evaluated within a unified cross-project learning framework. Experiments are conducted on large-scale Mozilla bug repositories under both single-source and multi-source transfer settings. Macro-averaged F1 is used as the primary evaluation metric to ensure balanced assessment across severity levels. The results indicate that cross-project performance is strongly influenced by source–target pairing, reflecting the impact of domain shift. Multi-source training generally improves performance across several transfer scenarios, particularly for minority severity classes, although the improvements remain moderate. DistilBERT achieves higher overall performance, whereas TinyBERT shows comparable trends with only a small reduction in Macro-F1, suggesting a favorable trade-off between predictive performance and model efficiency. These findings suggest that lightweight transformer models can support practical bug triage processes by providing relatively consistent and computationally efficient severity predictions across projects, particularly in environments with limited labeled data and computational resources. Full article

Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R² = 0.648 vs. 0.630; RMSE = 0.224 vs. 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, suggesting a structural source of error in carbon stock estimation in tropical dry forests that warrants empirical validation. Full article

Lattice-based heat sinks have attracted increasing attention as volumetric thermal management architectures for forced-convection cooling of high-power electronic systems. In contrast to conventional plate-fin, pin-fin, and straight-channel configurations, lattice geometries promote three-dimensional flow–solid interaction through interconnected ligament networks that modify boundary-layer development, wake formation, and internal heat-spreading pathways. This review synthesizes recent experimental and numerical studies to examine the thermo-fluid mechanisms governing lattice performance, with emphasis on the coupled influence of porosity, ligament dimensions, topology, orientation, and channel confinement on heat-transfer enhancement and hydraulic resistance. The analysis indicates that while lattice structures can increase average Nusselt number and improve temperature uniformity, these gains are intrinsically linked to pressure-drop penalties associated with flow tortuosity and form drag, resulting in regime-dependent thermal-hydraulic behavior. Apparent discrepancies reported across the literature are frequently attributable to differences in geometric definition, Reynolds-number normalization, and boundary-condition specification rather than to inconsistencies in physical mechanisms. By consolidating geometric scaling, performance metrics, manufacturing considerations, and system-level constraints, this review clarifies the conditions under which lattice heat sinks may provide net benefit relative to conventional cooling technologies and identifies key research directions required to support application-relevant design and evaluation. Full article

Effectively modeling software failure behavior is crucial for reliability assessment and planning of releases. However, many current software reliability growth models assume that failures are independent and fault detection mechanisms are simplified. However, these assumptions may not accurately represent real-world testing environments. This study introduces a novel Nonhomogeneous Poisson Process (NHPP)-based Software Reliability Growth Model (SRGM) that includes dependent failure behavior and exponentially decaying fault detection rates to better reflect the software debugging process. The proposed model was validated using real failure datasets and compared with 17 existing models. The performance of the model was assessed using various goodness-of-fit criteria, such as errors, prediction accuracy, and metrics based on information theory. To provide a more thorough evaluation, a multi-criteria decision-making approach was used to rank the competing models based on their overall performance. Furthermore, a one-at-a-time sensitivity analysis was conducted to examine how the initial values of the parameters affected the model’s behavior. These findings indicate that the sensitivity of the model to this parameter varies depending on the dataset used. The results indicate that the proposed model achieved superior performance across multiple evaluation criteria and consistently obtained the best overall ranking under the integrated multi-criteria framework. In Dataset 1, the proposed model achieved the best performance in most goodness-of-fit criteria, whereas in Dataset 2 it produced the best results across all twelve evaluation criteria. The results show that the proposed model offers improved or competitive performance compared to existing models and provides greater flexibility in capturing complex failure processes within software systems. Full article

Background/Objectives: The technique used for flap creation in laser in situ keratomileusis (LASIK) may influence postoperative optical quality and visual outcomes. This prospective randomized paired-eye study compared higher-order aberrations (HOAs) and visual acuity outcomes following femtosecond laser-assisted versus mechanical microkeratome-assisted LASIK. Materials and Methods: Forty-four patients (88 eyes) underwent bilateral LASIK. In each patient, one eye was randomly assigned to high-frequency femtosecond laser flap creation (Femto LDV), and the fellow eye to mechanical microkeratome flap creation (Amadeus II). Inclusion criteria were stable refraction, central corneal thickness ≥ 520 µm, and normal corneal topography. HOAs were measured using Hartmann–Shack wavefront aberrometry over a 6 mm pupil diameter. Uncorrected and corrected distance visual acuity (UDVA and CDVA) were evaluated preoperatively and postoperatively at 1 day, 1 week, and 1, 3, and 6 months. Results: Both techniques induced significant postoperative changes in specific Zernike coefficients and an increase in total HOA root mean square (RMS) values (p < 0.05). A reduction in spherical aberration (Z4,0) was observed in both groups, while technique-specific changes were noted in individual aberration components including an increase in horizontal trefoil (Z3,3) in the femtosecond and a decrease in horizontal coma (Z5,1) in the microkeratome group. However, paired-eye comparisons revealed no statistically significant differences in total HOA six months postoperative. Despite comparable aberrometric outcomes, femtosecond-treated eyes demonstrated significantly better UDVA and CDVA at all postoperative time points (p < 0.05). Conclusions: Femtosecond laser-assisted and microkeratome-assisted LASIK resulted in comparable changes in higher-order aberrations, despite differing pattern in individual aberration components. The observed differences in visual acuity outcomes were not reflected in wavefront metrics, suggesting that postoperative visual performance may be influenced by factors. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

This paper presents the design, structural optimization, and two-dimensional (2D) technology computer-aided design (TCAD) simulation of a gallium nitride (GaN) vertical Merged P-i-N/Schottky (MPS) diode incorporating a multi-drift-layer doping profile, composite SiO₂/Si₃N₄ passivation, and field-plate (FP) termination. The proposed device is constructed on an n⁺-GaN substrate with a three-sub-layer n-type drift region and a p-GaN/p⁺-GaN anode region. Systematic TCAD simulations are performed to investigate the dependences of key performance metrics—including knee voltage (V_knee), specific on-resistance (R_on), breakdown voltage (BV), reverse leakage current (J_leak), and Baliga’s figure of merit (BFOM)—on the Schottky metal work function, multi-drift-layer doping concentration, drift-layer thickness, Schottky-to-PN contact length ratio (γ_w), operating temperature, and reverse recovery switching transients. Results demonstrate that the MPS architecture effectively decouples forward conduction loss from reverse blocking capability, overcoming the conventional R_on–BV trade-off. The optimal doping profile (n_mm = 2 × 10¹⁵, n_m = 2 × 10¹⁵, n = 1 × 10¹⁶ cm⁻³) achieves a BFOM of ~31.97 GW·cm⁻² with BV ≈ 5.98 kV and R_on ≈ 1.12 mΩ·cm². Joint doping–thickness optimization further identifies a graded doping profile (n_mm = 2 × 10¹⁵, n_m = 5 × 10¹⁵, n = 1 × 10¹⁶ cm⁻³) combined with layer thicknesses (T_nmm, T_nm, T_n) = (4.49, 5, 20) μm as the overall optimum, achieving BFOM = 55.36 GW·cm⁻² (BV = 6.61 kV, R_on = 0.79 mΩ·cm²)—a +73% improvement, governed by the punch-through/field-stop design principle. The optimal contact ratio of γ_w = 1.33 yields a BFOM of 38.71 GW·cm⁻². Temperature analysis confirms a positive BV temperature coefficient due to drift-region-limited avalanche breakdown, and the BFOM improves monotonically from 33.31 to 37.82 GW·cm⁻² between 200 K and 450 K. Mixed-mode switching simulations show that increasing γ_w substantially reduces reverse recovery charge (Q_rr), demonstrating the strong potential of the proposed MPS diode for high-voltage, high-frequency, and high-temperature power electronic applications. Full article

This paper presents a methodology for synthesizing static state feedback controllers utilizing a Fractional-Order Performance Index. Linear Quadratic Regulators are designed using integer-order integral weighting functions. In the proposed approach, fractional-order calculus is utilized to introduce an additional degree of freedom in controller synthesis, enabling enhanced shaping of the plant’s dynamic properties. The controller gains are obtained by solving a fractional Riccati-like equation, through which the temporal weighting properties inherent to fractional integration are embedded into a static feedback matrix. This formulation is a minimalist control structure suitable for implementation on resource-constrained hardware. The proposed method is validated via rapid control prototyping on an industrial NI PXIe platform and an analog third-order plant. Performance evaluation using Integral Absolute Error and Integral Absolute Control metrics demonstrates that the fractional order serves as a flexible tuning parameter, providing an alternative trade-off between settling time and control effort. Furthermore, frequency domain sensitivity analysis demonstrates the absence of resonant peaks and inherent attenuation of high-frequency measurement noise. As a result, the presented framework bridges fractional-order optimization techniques with industrial control platforms. Full article

Background: Intracranial pressure (ICP) monitoring remains the cornerstone of neurocritical care in severe traumatic brain injury (TBI), yet its prognostic value as a standalone metric is limited. The Pressure Reactivity Index (PRx), a continuous measure of cerebrovascular reactivity derived from ICP and arterial blood pressure, may offer additional or complementary prognostic information. This systematic review aimed to compare the prognostic performance of PRx-derived metrics versus standard ICP monitoring for mortality and functional outcome in patients with TBI. Methods: A systematic search of PubMed, Web of Science, and Scopus was conducted for studies published between January 2000 and December 2025. Studies were eligible if they included adult TBI patients with continuous multimodal monitoring and reported comparative prognostic data for PRx- and ICP-based metrics. Risk of bias within the studies was appraised via the QUIPS tool, and the GRADE system was used to rate the strength of the evidence. Due to methodological heterogeneity, findings were synthesized narratively. Results: Nine studies were included. Applying a maximum-cohort estimation to account for overlapping registries, the pooled sample comprised a minimum of 1240 unique patients. In the majority of included studies, direct within-cohort head-to-head comparisons demonstrated that specific PRx-derived metrics—such as the individualized ICP threshold (iICP), Longest Continuous Duration of Autoregulatory Impairment (LCAI), Lower Limit of Reactivity (LLR), and time-integrated burdens (%Time > Threshold)—yielded stronger prognostic discrimination compared to standard ICP thresholds for both mortality (PRx: AUC 0.747–0.648 and ICP: AUC 0.660–0.614) and functional outcome. When added to established predictive models, PRx-derived metrics provided clinically meaningful incremental improvements in prognostic accuracy, with descriptive incremental AUC gains ranging from +0.039 to +0.170 across the six studies reporting model augmentation. Due to heterogeneity in baseline models, PRx-derived metrics, and patient populations, these findings are presented strictly as a descriptive range. Conclusions: PRx and PRx-derived cerebrovascular reactivity metrics-namely iICP, LCAI, LLR, and time-integrated burdens of autoregulatory failure—show potential to offer additive prognostic value beyond standard ICP monitoring in severe TBI. However, because current evidence is strictly observational and likely influenced by institutional confounders, it cannot currently support definitive clinical recommendations. Further prospective, multicenter studies utilizing standardized thresholds are necessary to confirm these associative findings and isolate their true prognostic value. Full article