Search Results (6,976)

Circular–circular regression models are widely used to investigate relationships between angular variables in various applied fields, including biostatistics. The classical von Mises (vM) circular–circular regression model, however, is known to be sensitive to outliers due to its light-tailed error structure. In this study, we investigate the wrapped Cauchy (WC) circular–circular regression model as a robust alternative to the vM-based approach for analyzing circular data contaminated by outliers. Parameter estimation is performed via maximum likelihood (ML) using a modern constrained gradient-based optimization algorithm, namely the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm with box constraints (L-BFGS-B), allowing for stable estimation under natural parameter bounds. Extensive simulation studies demonstrate that, under contaminated settings, the WC model provides substantially more stable parameter estimates than the vM model, yielding markedly lower mean squared error and variability, particularly for high concentration regimes and directional outliers. The robustness advantage of the WC model is further illustrated through a real biostatistical application involving the circular relationship between the months of diagnosis and surgical intervention in gastric cancer patients. Overall, the results highlight the practical benefits of WC-based circular–circular regression for robust inference in the presence of outliers. Full article

This study presents the experimental and model-based characterization of polydimethylsiloxane (PDMS) as a damping medium in a torsional vibration viscous damper. Particular emphasis is placed on the influence of the PDMS viscosity on the dynamic response of the damper under variable hydrodynamic loading generated by torsional vibrations of the system and the mass of the inertia ring. Investigations were conducted over a wide range of kinematic viscosities, enabling the identification of damper operating regimes and the assessment of lubricating film stability. The developed mathematical model, based on hydrodynamic lubrication theory, describes the relationships between the PDMS viscosity, the relative angular velocity, and the eccentricity of the inertia ring. Experimental results confirm the model’s ability to predict transitions between stable, unstable, and boundary operating modes of the damper. The proposed approach enables the functional, system-level characterization of PDMS under hydrodynamic loading conditions within a torsional vibration damper. In this framework, the rheological properties of PDMS are directly linked to the dynamic response and operational stability of the mechanical system. Full article

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

Traditional robust model predictive control (RMPC) strategies often face challenges of excessive conservatism and limited dynamic performance, which can hinder their effectiveness in trajectory tracking applications. To overcome these issues, this paper proposes a novel RMPC strategy based on crisscross optimization (CSO). The core innovation lies in a composite control framework that jointly designs a nominal controller and an additional optimized term. The nominal controller, derived from a min-max optimization problem, guarantees the closed-loop stability of the system. Building upon this stable foundation, the CSO algorithm is innovatively employed to search for a more effective control input within the feasible region, thereby actively enhancing the transient performance. The proposed method is validated through two trajectory tracking simulation cases on an angular positioning system in comparison with conventional RMPC. Results demonstrate that the new strategy not only maintains system stability but also significantly reduces the dynamic response time and improves overall control performance, confirming its superiority in mitigating conservatism while achieving better tracking responsiveness. Full article

Oriented object detection constitutes a fundamental yet challenging task in Artificial Intelligence of Things (AIoT)-enabled maritime surveillance, where real-time processing of dense visual streams is imperative. However, existing detectors suffer from three critical limitations: sequential attention mechanisms that fail to capture coupled spatial–channel dependencies, unconstrained deformable convolutions that yield unstable predictions for elongated vessels, and center-based distance metrics that ignore angular alignment in sample assignment. To address these challenges, we propose JAOSD (Joint Attention-based Oriented Ship Detection), an anchor-free framework incorporating three novel components: (1) a joint attention module that processes spatial and channel branches in parallel with coupled fusion, (2) an adaptive geometric convolution with two-stage offset refinement and spatial consistency regularization, and (3) an orientation-aware Adaptive Sample Selection strategy based on corner-aware distance metrics. Extensive experiments on three benchmarks demonstrate that JAOSD achieves state-of-the-art performance—94.74% mAP on HRSC2016, 92.43% AP₅₀ on FGSD2021, and 80.44% mAP on DOTA v1.0—while maintaining real-time inference at 42.6 FPS. Cross-domain evaluation on the Singapore Maritime Dataset further confirms robust generalization capability from aerial to shore-based surveillance scenarios without domain adaptation. Full article

Non-alcoholic fatty liver disease (NAFLD) is a silent condition that can lead to fatal cirrhosis, with dietary factors playing a central role. The effect of various dietary interventions on male Wistar rats were evaluated in four diets: control, arsenic, sucrose, and arsenic–sucrose. SHG microscopy images from the right ventral lobe of the liver tissue were analyzed with a neural network trained to detect the presence or absence of collagen fibers, followed by the assessment of their orientation and angular distribution. Machine learning classification of SHG microscopy images revealed a marked increase in fibrosis risk with dietary interventions: <10% in controls, 24% with arsenic, 40% with sucrose, and 62% with combined arsenic–sucrose intake. Angular width distribution of collagen fibers narrowed dramatically across groups: 26° (control), 24° (arsenic), 15.7° (sucrose), and 2.8° (arsenic–sucrose). This analysis revealed four key statistical features for classifying the images according to the presence or absence of collagen fibers: (1) the percentage of pixels whose intensity is above the 15% noise threshold, (2) the Mean-to-Standard Deviation ratio (Mean/std), (3) the mode, and (4) the total intensity (sum). These results demonstrate that a diet rich in sucrose, particularly in combination with arsenic, constitutes a significant risk factor for liver collagen fiber remodeling. Full article

In this paper, composite structures were fabricated by incorporating silver nanowires (AgNWs) with various metal oxides via the sol–gel method. This approach enhanced the electrical performance of AgNW-based transparent electrodes while simultaneously improving their stability under damp heat conditions and modifying the local medium environment surrounding the AgNW meshes. The randomly distributed AgNW meshes fabricated via drop-coating were treated with plasma to remove surface organic residues and reduce the inter-nanowire contact resistance. Subsequently, a zinc oxide (ZnO) coating was applied to further decrease the sheet resistance (R_sheet) value. The pristine AgNW mesh exhibits an R_sheet of 17.4 ohm/sq and an optical transmittance of 93.06% at a wavelength of 550 nm. After treatment, the composite structure achieves a reduced R_sheet of 8.7 ohm/sq while maintaining a high optical transmittance of 92.20%. The use of AgNW meshes as window electrodes enhances electron injection efficiency and facilitates the coupling mechanism between localized surface plasmon resonances and excitons. Compared with conventional ITO transparent electrodes, the incorporation of the AgNW mesh leads to a 17-fold enhancement in ZnO emission intensity under identical injection current conditions. Moreover, the unique scattering characteristics of the AgNW and metal oxide composite structure effectively reduce photon reflection at the device interface, thereby broadening the angular distribution of emitted light in electroluminescent devices. Full article

We investigate the structure of correlation singularities for the Laguerre–Gauss beam of order $n=0$ and $m=2$ in the transverse plane during the propagation of the beam in the beam-wander model. We explicitly derive analytical expressions for the cross-spectral density of the corresponding beam order and the analytic expressions representing the singular behavior. We also verify that the singular points disappear at certain z values and reappear at other z values as shown in the previous numerical study. We investigate the dependence of the absolute value of the complex degree of coherence $\mu$ on the parameter $\delta$ of the beam-wander model during the propagation of the Laguerre–Gauss beam in the corresponding order. The complex degree of coherence depends not only on $\delta$ but also on the relative positions of two transverse observation points ${\mathit{\rho }}_1$ and ${\mathit{\rho }}_2$, as well as on the propagation variable z for the fixed values of the beam waist and the wavelength of the Laguerre–Gauss beam. Experiments on $\mu$ can demonstrate the range of the applicability of the beam-wander model in the turbulent atmosphere. Finally, we examine the orbital angular momentum flux density of the beam and confirm that the general behaviors of the previous studies also hold for $m=2$. Full article

This study introduces a novel framework for defining screw trajectory that utilizes PointNet—a deep neural network trained on lumbar vertebrae point clouds—to improve the manual surgical planning procedures. The conventional architecture of PointNet was modified to accommodate various vertebral orientations and predict six values, which were reconstructed into two control points that define a linear trajectory. A custom loss function was designed to align the predicted trajectory with the ground-truth trajectory. The neural networks were trained on 4284 point clouds of vertebrae, and 28 unseen point clouds were used to evaluate the model’s performance based on translational error, angular error, and clinical accuracy. For the left pedicle, the mean translational errors were 1.5 ± 0.8 mm at the entry point and 2.3 ± 1.2 mm at the target point. For the right pedicle, the mean translational errors were 1.5 ± 0.7 mm at the entry point and 2.3 ± 1.0 mm at the target point. The mean angular error was 3.5 ± 2.3° for the left pedicle and 3.9 ± 1.7° for the right pedicle. Clinically, the network generated 52 out of 56 trajectories without medial-cortical violations of the spinal canal. The trained neural network demonstrated promising technical and clinical accuracy, generating feasible screw trajectories across various vertebral orientations. Integrating a spinal segmentation network with the proposed framework could enable fully automated surgical planning in the future. Full article

Reading speed is hypothesized to have distinct neural signatures across orthographically diverse languages, yet cross-linguistic evidence remains limited. We investigated this by classifying speed readers versus regular readers among Sinhalese and Japanese adults ($n=142$) using task-based fMRI and 35 supervised machine learning classifiers. Functional activation was extracted from 12 reading-related cortical regions. We introduced Fuzzy C-Means (FCM) clustering for data augmentation and Shapley additive explanations (SHAP) for model interpretability, enabling evaluation of region-wise contributions to reading speed classification. The best model, an FT-TABPFN network with FCM augmentation, achieved 81.1% test accuracy in the Combined cohort. In the Japanese-only cohort, Quadratic SVM and Subspace KNN each reached 85.7% accuracy. SHAP analysis revealed that the angular gyrus (AG) and inferior frontal gyrus (triangularis) were the strongest contributors across cohorts. Additionally, the anterior supra marginal gyrus (ASMG) appeared as a higher contributor in the Japanese-only cohort, while the posterior superior temporal gyrus (PSTG) contributed strongly to both cohorts separately. However, the posterior middle temporal gyrus (PMTG) showed less or no contribution to the model classification in each cohort. These findings demonstrate the effectiveness of interpretable machine learning for decoding reading speed, highlighting both universal neural predictors and language-specific differences. Our study provides a novel, generalizable framework for cross-linguistic neuroimaging analysis of reading proficiency. Full article

To address the urgent need for high-fidelity motion data for validating navigation algorithms for Unmanned Underwater Vehicles (UUVs), this paper proposes a data generation method based on a parametric motion model. First, based on the principles of rigid body dynamics and fluid mechanics, a decoupled six-degrees-of-freedom (6-DOF) Linear and Angular Acceleration Vector (LAAV) model is constructed, establishing a dynamic mapping relationship between the rudder angle and speed setting commands and motion acceleration. Second, a segmentation–identification framework is proposed for three-dimensional trajectory segmentation, integrating Gaussian Process Regression and Ordering Points To Identify the Clustering Structure (GPR-OPTICS), along with a Dynamic Immune Genetic Algorithm (DIGA). This framework utilizes real vessel data to achieve motion segment clustering and parameter identification, completing the construction of the LAAV model. On this basis, by introducing sensor error models, highly credible Inertial Measurement Unit (IMU) data are generated, and a complete attitude, velocity, and position (AVP) motion sequence is obtained through an inertial navigation solution. Experiments demonstrate that the AVP data generated by our method achieve over 88% reliability compared with the real vessel dataset. Furthermore, the proposed method outperforms the PSINS toolbox in both the reliability and accuracy of all motion parameters. These results validate the effectiveness and superiority of our proposed method, which provides a high-fidelity data benchmark for research on underwater navigation algorithms. Full article

We present a method for analyzing the symmetries of excited states previously calculated with the popular GW-BSE approach. These are expressed through the Tamm-Dancoff approximation using the so-called batches representation. The method allows to establish how an excited state is transformed by symmetry operators as plane-reflection, proper and improper axis-rotation, point-inversions. It can also report if an excited state is eigen-state of an angular momentum operator. This permits the assignment to an irreducible representation of the underlying symmetry group and a prompt labeling of the GW-BSE states. We show results for a significant set of small molecules. Our approach can be easily extended to TD-DFT and can be used to probe the local environment of localized excitations. Full article

Tracking knee joint movement during activities of daily living can have the potential to transform the rehabilitation and functional assessment of patients. The present study evaluated the validity of a low-cost, instrumented knee brace to determine whether it was appropriate for the monitoring and quantification of human knee function during five activity-of-daily-living (ADL) tasks including walking, inclined walking, stepping, sitting, and object manipulation. A sensor platform was designed to acquire sagittal plane knee data from 13 healthy participants across five different tasks and compared to gold-standard motion analysis. The brace showed good-to-excellent validity (RMSE: 4.97–8.65°), with differences in knee joint angles and angular velocities noted during various ADLs, specifically during early and late portions of a given movement. The results for instantaneous knee joint angles and angular velocities were very similar to those of the gold-standard system (mean bias: 0.59–9.52°·s⁻¹), which may be applicable to everyday movement tasks, but may preclude analyses at a clinical level. Although the low-cost sensor platform shows promise an effective monitoring tool, it is not ready yet for a clinical application. Full article

Radiative cooling (RC) offers a passive pathway to reduce surface and system temperatures by emitting thermal radiation through the atmospheric window, yet its daytime effectiveness is often constrained by geometry, angular solar exposure, and practical integration limits. This work experimentally investigates the use of passive non-imaging optics, specifically compound parabolic concentrators (CPCs), as enhancers of RC performance under realistic conditions. A three-tier experimental methodology is followed. First, controlled indoor screening using an infrared lamp quantifies the intrinsic heat gain suppression of a commercial RC film, showing a temperature reduction of nearly 88 °C relative to a black-painted reference. Second, outdoor rooftop experiments on aluminum plates assess partial RC coverage, with and without CPCs, under varying orientations and tilt angles, revealing peak daytime temperature reductions close to 8 °C when CPCs are integrated. Third, system-level validation is conducted using a modified GUNT ET-202 solar thermal unit to evaluate the transfer of RC effects to a water circuit absorber. While RC strips alone produce modest reductions in water temperature, the addition of CPC optics amplifies the effect by factors of approximately three for ambient water and nine for water at 70 °C. Across all configurations, statistical analysis confirms stable, repeatable measurements. These results demonstrate that coupling commercially available RC materials with non-imaging optics provides consistent and measurable performance gains, supporting CPC-assisted RC as a scalable and retrofit-friendly strategy for urban and building energy applications while calling for longer-term experiments, durability assessments, and techno-economic analysis before deriving definitive deployment guidelines. Full article

Millimeter-wave (mmWave) massive multiple-input, multiple-output (MIMO) systems are a cornerstone technology for integrated sensing and communication (ISAC) in sixth-generation (6G) mobile networks. These systems provide high-capacity backhaul while simultaneously enabling high-resolution environmental sensing. However, accurate channel estimation remains highly challenging due to intrinsic noise sensitivity and clustered sparse multipath structures. These challenges are particularly severe under limited pilot resources and low signal-to-noise ratio (SNR) conditions. To address these difficulties, this paper proposes HASwinNet, a deep learning (DL) framework designed for mmWave channel denoising. The framework integrates a hierarchical Swin Transformer encoder for structured representation learning. It further incorporates two complementary branches. The first branch performs sparse token extraction guided by angular-domain significance. The second branch focuses on angular-domain refinement by applying discrete Fourier transform (DFT), squeeze-and-excitation (SE), and inverse DFT (IDFT) operations. This generates a mask that highlights angularly coherent features. A decoder combines the outputs of both branches with a residual projection from the input to yield refined channel estimates. Additionally, we introduce an angular-domain perceptual loss during training. This enforces spectral consistency and preserves clustered multipath structures. Simulation results based on the Saleh–Valenzuela (S–V) channel model demonstrate that HASwinNet achieves significant improvements in normalized mean squared error (NMSE) and bit error rate (BER). It consistently outperforms convolutional neural network (CNN), long short-term memory (LSTM), and U-Net baselines. Furthermore, experiments with reduced pilot symbols confirm that HASwinNet effectively exploits angular sparsity. The model retains a consistent advantage over baselines even under pilot-limited conditions. These findings validate the scalability of HASwinNet for practical 6G mmWave backhaul applications. They also highlight its potential in ISAC scenarios where accurate channel recovery supports both communication and sensing. Full article