Search Results (1,579)

In the field of subgrade compaction quality assessment, accurate volume measurement of excavated pits is hindered by non-uniform point cloud distribution, environmental noise interference, and complex irregular boundary features. To address these challenges, this paper proposes a robust volume detection framework that integrates adaptive point cloud refinement and morphological discrimination. First, a pose normalization method employing RANSAC plane fitting and rigid body transformation corrects the spatial orientation of the raw point clouds. To balance data redundancy removal with feature preservation, a gradient adaptive simplification strategy based on local density feedback and K-nearest neighbor estimation is developed. Subsequently, a cross-sectional area calculation model utilizing piecewise-cubic polynomial fitting is proposed to mitigate boundary noise and accurately reconstruct irregular contours. Furthermore, a dynamic outlier removal mechanism based on the Median Absolute Deviation (MAD) and sliding windows is introduced to eliminate non-physical geometric fluctuations. Finally, the total volume is aggregated using a hybrid strategy of Simpson’s rule and a frustum compensation operator. Experimental results on simulated pits with typical topological defects demonstrate that the proposed algorithm outperforms traditional methods, achieving an average relative volume error of less than 0.8%. This approach significantly improves the robustness and precision of sensor-based automated subgrade compaction quality measurement. Full article

The uniformity of local photoelectric properties in infrared detectors is critical for detection sensitivity. However, micro-nano-scale surface abnormalities introduced during mercury cadmium telluride (HgCdTe) fabrication systematically degrade in-plane photoelectric response consistency. To overcome the optical diffraction limits of standard far-field metrology, we utilized a cryogenic scattering-type scanning near-field optical microscopy (Cryo-SNOM) system to achieve the first super-resolution, in situ imaging of local near-field photocurrent in HgCdTe photoconductive detectors at 10 K. Device-level measurements reveal that sub-wavelength surface protrusions (~tens of nanometers high) act as strong recombination centers, suppressing local photocurrent and causing a consistent 10~20% relative signal attenuation compared to planar regions. Power and bias-dependent testing indicate these defects function as unsaturated linear recombination states. Increasing bias voltage amplifies the coupling between the external field and the defect’s built-in field, broadening the local depletion region and driving a non-linear escalation in the attenuation ratio. This study establishes quantitative engineering tolerances for morphological deviations at the nanoscale, providing critical criteria for the chip integration, structural optimization, and precision manufacturing of high-performance infrared sensing arrays. Full article

This study evaluated the test–retest reliability, standard error of measurement (SEM), and minimal detectable change (MDC) of frontal plane projection angles (FPPAs) across five single-leg tasks in individuals with patellofemoral pain (PFP). Two-dimensional video data was collected from ten individuals with predominantly unilateral PFP. Participants performed single-leg squat, single-leg landing, single-leg hop, forward step-down, and lateral step-down across two testing sessions. FPPAs were measured at peak knee flexion for each task, including trunk lean angle, knee FPPA, hip FPPA, and dynamic valgus index. Test–retest reliability was assessed using intraclass correlation coefficients (ICCs). Our findings indicate that test–retest reliability and measurement error for trunk and lower limb FPPA varied across tasks in individuals with PFP. The lowest ICC was observed for hip FPPA, particularly during single-leg squat and lateral step-down tasks. Among the five tasks tested, the single-leg squat appeared to be the most demanding task, demonstrating the lowest ICCs, and highest SEM and MDC values across all four outcome measures (trunk lean angle, knee and hip FPPAs, and dynamic valgus index). The dynamic valgus index consistently showed larger SEM and MDC values than isolated hip or knee FPPAs, likely reflecting compounded measurement errors across segments. These findings provide preliminary insights, though confirmation in larger samples in persons with PFP is warranted. Full article

Giant coronary artery aneurysms, defined as those with a diameter exceeding 8 mm or a four-fold increase relative to the reference vessel segment, are incredibly rare, with an estimated prevalence of approximately 0.02% in the general population. We present computed tomography angiography images of a thrombotic occlusion of a giant right coronary artery (RCA) aneurysm. An 80-year-old Caucasian man with chronic coronary artery disease, who had undergone percutaneous coronary intervention of the middle segment of the left circumflex artery (LCx) with drug-eluting stent implantation, was referred to the computed tomography department for coronary computed tomography angiography (CCTA) due to difficulty visualizing RCA during invasive coronary angiography. In CCTA, a giant aneurysm in the proximal segment of the RCA, with a massive thrombus, communicating with the typical origin of the RCA from the right aortic bulb sinus, then extending into the occluded part of the proximal segment of the RCA, was visualised. The maximum long dimension of the RCA aneurysm was 5.3 cm, and the maximum short dimension of the RCA aneurysm was 4.4 cm. The maximum thrombus thickness in the RCA aneurysm was 2.2 cm. The middle and distal segments of the RCA, presumably filled with collateral circulation, have significantly weaker contrast, and contain numerous predominantly calcified atherosclerotic plaques. In summary, the presented CCTA images confirm the clinical importance of this modality in diagnosing coronary artery aneurysms, even in situations where the results of invasive coronary angiography remain equivocal. Due to higher spatial resolution, the ability to perform image reconstruction in multiple planes, the ability to detect thrombus, and the ability to assess the vessel wall and extracoronary structures, CCTA not only enables the detection of coronary artery aneurysms but also enables risk prediction, thus enabling the planning of a more optimal treatment strategy. Full article

Biplane single-molecule localization microscopy (SMLM) enables three-dimensional (3D) super-resolution imaging by extracting the axial position of fluorophores from a pair of emission patterns detected at two axially separated planes. The separation between these two imaging planes, termed the biplane distance, is a key parameter that determines axial localization precision, yet a systematic investigation of its optimal selection remains lacking. Here, we calculate the theoretical localization precision across a range of biplane distances and identify an optimal value, construct a tunable biplane detection module and experimentally evaluate axial localization precision at three different biplane distances using both fluorescent beads and biological specimens. Experimental results confirm the theoretical predictions and provide a practical framework for optimizing the biplane distance in 3D-SMLM systems. Full article

Inspection inside small-diameter pipelines is difficult because the narrow interior space limits the field of view of onboard cameras. Even when a crack is successfully detected, it may still appear near the image boundary rather than in a suitable position for observation. To address this issue, this study proposes a vision-guided active crack alignment framework for small-diameter pipe inspection robots. The proposed framework uses a YOLOv5s detector to identify the crack region and extract the center of the detected bounding box. The positional difference between the crack center and the image center is defined as the image-plane alignment error. After low-pass filtering, this error is converted into actuator-side reference input through a pixel-to-motor mapping, and a PID-based closed-loop controller is used to regulate a local camera adjustment mechanism so that the detected crack region moves toward the image center. The framework is evaluated mainly through simulation, including controller comparison, different initial offset conditions, parameter sensitivity analysis, robustness tests under visual fluctuation and mapping uncertainty, and an ablation study. The controller comparison shows that all tested PID-based controllers achieve stable convergence, while the fuzzy PID controller provides the best overall performance among the tested cases in terms of settling time, steady-state error, and RMS error. The framework also remains stable under different crack positions and moderate uncertainty conditions. In addition, a preliminary laboratory-scale physical consistency test is conducted to examine whether the convergence tendency observed in simulation can also be reproduced under real visual feedback and actuator response. The preliminary physical results show a convergence tendency consistent with the simulation trend, thereby providing initial support for the practical implementability of the proposed detection-driven alignment concept. Complete integration with an in-pipe robot platform and validation under realistic pipe environments remain future work. Full article

This paper presents a computationally feasible/time-effective Scan-to-BrIM workflow for generating a highly detailed digital model of a complex steel pedestrian bridge. The proposed methodology integrates rapid and accurate point cloud acquisition with advanced parametric modelling and structural information management. First, a high-resolution point cloud is produced using a fast survey strategy that ensures the geometric precision required for a faithful representation of the existing structure. Second, the point cloud is processed in Rhinoceros/Grasshopper, where a custom Python (version 3.13) algorithm automatically detects and generates reference planes containing the structural components, enabling the creation of a consistent and fully parametric BrIM model. The latter approach includes metric normalization, voxel-based downsampling, reliable under tested conditions ground and outlier removal, and PCA (Principal Component Analysis)-based reorientation, followed by guided slicing of the point cloud and projection of each slice onto its section plane. The proposed workflow achieved a geometric RMSE of 2.5 mm with a total processing time of 7.3 h. The resulting parametric model achieves geometric consistency with the source point cloud within an operational tolerance range of approximately 5–10 mm, in line with the requirements of structural applications. Finally, the model is organised and managed within the BrIM environment and then transferred to a downstream FEM environment for preliminary structural application. The workflow is tested on a case study of a 40-m steel pedestrian bridge located in central Italy. Results demonstrate that the integrated approach provides a reproducible and semi-automated solution that reduces manual intervention in Scan-to-BrIM processes for producing accurate parametric models of steel pedestrian bridges, supporting structural assessment, asset management, and future maintenance strategies. Full article

Although breast cancer has been associated with subclinical left ventricular dysfunction in humans, the cardiac effects of CMTs remain poorly defined. This pilot, exploratory (communication) study compared clinical and echocardiographic parameters between dogs with CMTs and healthy controls and assessed the feasibility of combining myocardial deformation imaging with exploratory data-driven analysis for preoperative cardiac assessment. All dogs underwent a standardized clinical and echocardiographic assessment, including two-dimensional speckle-tracking echocardiography (2D-STE). Given the limited sample size, analyses were designed to generate hypotheses rather than to provide definitive predictive conclusions. Exploratory machine learning modeling (XGBoost), receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis were performed as proof-of-concept approaches without external validation. Despite normal conventional systolic indices, dogs with CMTs exhibited reduced global longitudinal strain (GLS) and mitral annular plane systolic excursion (MAPSE) (p < 0.01), suggesting subclinical systolic dysfunction. Deformation-derived parameters appeared more sensitive for detecting subtle myocardial alterations within this cohort. The exploratory machine learning model demonstrated moderate discrimination (AUC-ROC = 0.75); however, these findings are preliminary and should not be interpreted as evidence of clinical predictive performance. Overall, these results suggest that conventional systolic indices may underestimate early myocardial changes in dogs with CMTs. This communication highlights the feasibility of integrating deformation imaging with exploratory analytical approaches and provides a basis for future large-scale, validated studies in veterinary cardio-oncology. Full article

Background/Objectives: Coronal Plane Alignment of the Knee (CPAK) classification enables individualized alignment assessment in total knee arthroplasty (TKA), yet manual evaluation is time-consuming and lacks preoperative-to-postoperative transition analysis. Methods: This retrospective, single-center study aimed to develop and validate a fully automated deep learning-based CPAK classification system using internal validation on a held-out test set (n = 92) and to investigate individual-level transition patterns and their association with short-term clinical outcomes using paired radiographic data from a large Chinese cohort. A total of 919 KOA patients undergoing TKA were analyzed. A keypoint detection model (HRNet-W32) was developed to automatically calculate the medial proximal tibial angle, lateral distal femoral angle, arithmetic hip-knee-ankle angle, and joint line obliquity, from which CPAK types were derived. Results: On the validation set (92 cases), the model achieved a Mean Radial Error of 1.22 ± 0.43 mm for keypoint detection; mean absolute errors for MPTA and LDFA were ≤0.74°, while for aHKA and JLO they were 0.91° and 1.12°, respectively, with intraclass correlation coefficients ≥0.96 compared to manual annotations. Automatic CPAK classification accuracy was 80.98% (kappa = 0.767). Transition matrix analysis showed that only 9.36% of all patients maintained their original type postoperatively, with most shifting to types IV, V, or VII. After inverse probability weighting, no significant differences in clinical outcomes were observed among transition groups (all adjusted p > 0.05). Conclusions: These results demonstrate that the proposed automated system enables efficient CPAK assessment, revealing substantial postoperative alignment transitions that were not associated with differential short-term outcomes, thereby supporting AI-assisted individualized alignment planning in TKA. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly sensitive for early-stage lesion detection, interpretation may vary depending on observer experience. Therefore, reliable and explainable automated decision support approaches are needed. Methods: In this study, a deep learning-based approach was proposed to classify ONFH into early and late stages according to the Ficat–Arlet staging system. Stage I–II cases were defined as early-stage, whereas Stage III–IV cases were defined as late-stage. Axial and coronal MR images were evaluated separately to investigate plane-dependent classification performance. The images were converted into a three-channel format, resized to a common spatial resolution, normalized, and augmented during training. Feature extraction was performed using transfer learning with modern convolutional neural network architectures. ConvNeXt Tiny was used as the main classification backbone. Weighted loss was applied to reduce the effect of class imbalance, and the decision threshold was optimized on validation data to reduce missed clinically critical late-stage cases. Results: A dataset collected from the Orthopedics and Traumatology Department of Firat University Hospital was used in the experimental evaluation. The dataset was divided into training and test sets using an 80:20 split, and 10-fold cross-validation was additionally performed to assess model stability. In the hold-out test, the axial plane model achieved 94.51% accuracy, 96.80% sensitivity, 93.49% specificity, 0.9162 F1-score, and 0.981 AUC. In the coronal plane model, 92.84% accuracy, 96.13% sensitivity, 90.96% specificity, 0.9072 F1-score, and 0.988 AUC were obtained. The 10-fold cross-validation results provided a more conservative estimate of generalization performance. Conclusions: The findings indicate that deep learning-based plane-wise analysis of MR images can distinguish early- and late-stage ONFH with high performance. Grad-CAM-based visual explanations showed that the model focused mainly on clinically relevant subchondral and weight-bearing regions of the femoral head. The proposed approach may serve as an explainable decision support tool for reducing observer-dependent variability in clinical staging. Future studies should validate the method using external, multicenter datasets and paired patient-level axial–coronal images. Full article

Soft story irregularity poses a critical seismic risk to existing building stocks. While current seismic codes define stiffness irregularity factors to detect this vulnerability, they are typically evaluated based solely on initial elastic properties. This study investigates the evolution of these code-defined factors (ASCE/SEI-7, UBC, NBC, TBEC-2018, and BSL) within the post-elastic range to examine how structural damage affects soft story irregularity. The methodology comprises two phases: a low-strength RC plane frame (Case A) and a parametric study on a 3D RC building with incrementally increased ground story heights (Case B). Nonlinear pushover analyses were conducted to track the variation in irregularity factors at each pushover step and examined graphically. Results demonstrate that soft story behavior is not a static characteristic; irregularity factors deteriorate significantly as plastic hinges form. Crucially, several models that initially satisfied code limits in the elastic range eventually exceeded irregularity thresholds under inelastic behavior. This indicates that relying solely on initial stiffness may mask latent irregularities emerging during seismic actions. Consequently, to capture the true severity of soft story mechanisms, it is recommended that stiffness irregularity factors be evaluated at target displacement levels corresponding to the design earthquake. Full article

Precision alignment and target contact are critical tasks for mobile manipulators in industrial inspection and flexible manufacturing. However, achieving high accuracy after navigation remains challenging due to accumulated errors from mobile base localization, perception noise, and calibration uncertainty. This paper proposes a vision-guided precision alignment framework for mobile manipulators using a single front-facing RGB-D camera. The method integrates YOLO-based target detection, AR marker-assisted plane depth estimation, and depth-based 3D localization within a coarse-to-fine alignment strategy. After navigation, the manipulator first moves to a predefined pre-alignment pose, followed by visual localization and iterative refinement to compensate for residual errors before executing precise target contact. The proposed system is implemented and evaluated in a Gazebo-based simulation environment using a mobile manipulator platform model. In a static touch panel experiment with 50 trials, the system achieves a success rate of 98%, with positioning errors maintained within a millimeter-level range. Simulation results demonstrate that the proposed method provides stable alignment performance in the simulation environment without relying on external sensing devices such as force sensors or multi-camera systems. The proposed approach shows promising potential for precision contact tasks in mobile manipulation. Full article

Self-collision detection has become the dominant computational bottleneck in GPU-accelerated cloth simulation, as modern parallel solvers such as XPBD have drastically reduced the cost of position updates while leaving collision resolution largely unoptimized. Existing spatial partitioning methods treat all cloth regions uniformly, saturating GPU memory bandwidth despite the fact that the vast majority of the mesh surface remains geometrically flat and collision-free at any given frame. We propose a hierarchical self-collision culling framework built upon a resolution-independent discrete curvature metric derived from the $h^2$-normalized Laplace-Beltrami operator, integrated with a discrete Kirchhoff–Love shell model combining distance and dihedral bending constraints within XPBD. Unlike prior cache-dependent acceleration strategies, our method tightly couples curvature-driven geometric pruning with a fused GPU kernel design and shows that this stateless formulation is both faster and physically more reliable. Evaluated on meshes of $512\times 512$ and $1024\times 1024$ particles, our method achieves a $5.5\%$ and $9.7\%$ FPS improvement alongside a $34.9\%$ and $28.4\%$ reduction in active collision pairs, respectively, with qualitative validation via high-fidelity rendering confirming artifact-free self-contact and strict ground-plane non-penetration. Ablation results further reveal that temporal coherence, conventionally regarded as an optimization standard, strictly degrades both performance (FPS decrease of $1.4\%p$ to $1.9\%p$) and physical accuracy (penetration depth increase of $36.1\%$ to $100.0\%$ relative to the curvature-only stage) on RTX 3070 GPU, advocating for stateless per-frame geometric evaluation as the preferred design paradigm. Full article

Software-defined networking (SDN) represents a paradigm shift in network management, but its centralized control plane introduces new and severe security vulnerabilities. Conventional intrusion detection systems, including signature- and rule-based methods, lack adaptability and interpretability in the face of evolving threats. This paper proposes a multi-hybrid deep learning fusion ensemble (MHDLFE) to enhance intrusion detection in SDN environments. The framework integrates Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models via feature fusion and a meta-classifier, thereby improving both detection performance and robustness. To address the critical need for transparency in security systems, the proposed approach incorporates Explainable AI techniques, specifically Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), providing interpretable insights into model decisions. The proposed model achieves strong performance on the NSL-KDD and CIC-IDS2017 datasets, attaining near-perfect binary classification scores of 97.91% and 93.30%, and multiclass accuracies of 98.61% and 97.91%, respectively. These results demonstrate that the proposed framework delivers an effective and trustworthy SDN intrusion detection system by combining deep learning, ensemble fusion, and explainable AI to support accurate, transparent, and reliable cybersecurity decision-making. Full article

Background/Objective: Smoking is known to be associated with reflux-related mucosal damage and deleterious esophageal outcomes, yet no non-invasive imaging biomarkers of smoking-induced esophageal remodeling have been identified. We aimed to compare cervical esophageal ultrasound morphology between habitual smokers and non-smokers, in terms of esophageal wall thickness, number of sonographically discernable wall layers, and esophageal diameter, and investigate whether smoking is an independent predictor of these findings. Methods: In this cross-sectional study, 60 participants (30 smokers, 30 non-smokers) underwent high-resolution B-mode ultrasound of the cervical esophagus. Examinations were performed in transverse and longitudinal planes. Outcomes included esophageal wall thickness (mm), number of discernible wall layers, and esophageal diameters in transverse and longitudinal planes. Group comparisons used independent t-tests and chi-square tests. Multiple linear regression assessed independent associations with smoking status (adjusting for age and weight). Within smokers, Pearson correlation evaluated relationships between smoking duration and ultrasound outcomes; exploratory subgroup analyses compared smoking modalities. Results: Smokers were older and had higher weight and BMI than non-smokers. Compared with non-smokers, smokers had greater wall thickness (3.06 vs 2.61 mm), more discernible wall layers (5.03 vs 3.60), and larger transverse (11.68 vs 7.87 mm) and longitudinal (12.90 vs 8.26 mm) diameters (all p < 0.001). In regression analysis, smoking status independently predicted wall thickness (B = 0.411 mm, 95% CI 0.243–0.578; p < 0.001). Smoking duration showed significant correlations with the number of visible layers (r = 0.82; p < 0.001) and wall thickness (r = 0.42; p = 0.021). Conclusions: High-frequency ultrasound detected significant differences in cervical esophageal morphology between smokers and non-smokers. Smoking was independently associated with differences in the diameter, thickness, and number of visible layers of the cervical esophagus. Further studies with larger sample sizes, improved exposure assessment, and use of reference standards are needed. Full article