Search Results (12,247)

The Hotan rose, a high-value specialty crop cultivated in Xinjiang, exhibits pronouncedly non-uniform flowering phenology, which has historically necessitated labor-intensive manual harvesting. To date, however, no integrated framework has been reported that concurrently addresses lightweight visual perception, precise picking-point localization, and three-dimensional path planning for this specific crop. In this study, we develop L-SOP, an end-to-end continuous picking methodology tailored to the unique requirements of Hotan rose harvesting. At the perception stage, we introduce YOLOv11n-LPD-Seg, a lightweight segmentation model that incorporates a joint compression strategy combining global pruning with channel-wise distillation (CWD). This design improves the mAP@0.5 values for flowers and buds by 0.1 and 0.7 percentage points, respectively. It also reduces the model size from 6.1 MB to 2.4 MB, representing a 60.7% reduction, and decreases the computational cost from 10.2 GFLOPs to 6.8 GFLOPs, representing a 33.3% reduction. For picking point localization, we develop the MGRO-Loc algorithm, which integrates multiple geometric constraints to achieve accurate spatial positioning. The algorithm achieves mean absolute errors of 2.33 mm under indoor conditions and 2.84 mm under outdoor conditions. For path planning, we propose the OG-LKH algorithm, which replaces the conventional orthogonal polyline distance metric with an oblique gate-shaped distance measure and incorporates a multi-start strategy and a gate-aware heuristic search. Compared with the standard LKH algorithm, OG-LKH reduces the average path length by 23.5% while maintaining a computation time of 0.0117 s. Even in high-density scenarios involving 36 waypoints, its computation time is only 0.0387 s on edge-computing devices. These three modules work synergistically to form a complete solution for selective continuous rose picking. The proposed framework can be deployed on edge devices, providing a viable technological pathway toward efficient autonomous harvesting of Hotan rose flower and bud. Full article

Accurate and smooth path tracking is important for autonomous tracked agricultural robots operating in greenhouse-like environments. Existing adaptive look-ahead pure-pursuit methods mainly adjust the look-ahead distance according to vehicle speed or path geometry, while the influence of time-varying localization reliability has not been sufficiently considered. This study proposes a noise-aware adaptive pure-pursuit controller that combines Extended Kalman Filter (EKF) estimation with causal Savitzky–Golay (SG) endpoint smoothing. A bounded look-ahead law is designed by jointly considering normalized vehicle speed, lateral error, path curvature, and an innovation-derived localization-noise indicator. Numerical simulations were conducted on straight, circular, S-shaped, and U-shaped reference paths under prescribed localization disturbances. Under the 0.5 m positional-noise condition, the proposed method achieved an root mean square error (RMSE) of 0.087 m and an angular-velocity root mean square (RMS) of 0.28 rad/s, compared with 0.112 m and 0.36 rad/s, respectively, for conventional fixed-look-ahead pure pursuit. Compared with proportional-integral-derivative (PID), Stanley, model predictive control (MPC), and conventional pure-pursuit controllers, the proposed method provides a favorable balance between tracking accuracy and control smoothness. It also has better computational efficiency than MPC while retaining the low-computational-burden advantage of geometric control. In the sensitivity analysis, the relative RMSE increase from 0.1 to 0.8 m was 36.5% for the proposed method and 103.4% for conventional pure pursuit. These results indicate that the proposed lightweight noise-aware control strategy can improve tracking accuracy, control smoothness, and tolerance to localization disturbances under the specified numerical conditions, providing a practical design reference for low-speed greenhouse agricultural robots. Full article

The identification and spatial positioning of welds are key links in welding process matching and automatic welding path planning. Therefore, achieving automatic recognition and spatial positioning of weld point cloud features under online scanning imaging conditions is of great significance for the promotion and application of teaching free automatic welding technology. This article is based on a point cloud neural network model and conducts in-depth research on the recognition and spatial positioning algorithm of weld point cloud features. Specifically, the PointNet++ network, which is a point cloud neural network model, is first used to perform feature recognition on the three-dimensional point cloud of the welded parts obtained by laser line scanning. PointNet++ can distinguish different types of welded joints based on point cloud features and further perform preliminary rough positioning of the spatial position of the weld seam. After retaining the coarse positioning point cloud containing weld seam features, different algorithms are used to accurately locate the spatial position of the weld seam based on different weld seam features. The experimental results show that based on the PointNet++model for rough positioning, the weld length error does not exceed 0.3 mm, and the recognition accuracy and efficiency are much higher than traditional algorithms. The research results of this article can provide important references for the further intelligent development of automatic welding robots. Full article

This exploratory mixed-methods study examines self-reported perceptions of marital fidelity, marital infidelity, and digital boundary violations among adults recruited through one private university in the United Arab Emirates. Nationality or citizenship and direct socioeconomic indicators were not collected; participants are therefore described as a UAE university-affiliated sample rather than as Emirati or nationally representative. A quantitatively led design combined 156 complete questionnaires with 11 semi-structured interviews. Marital fidelity received very high endorsement (M = 4.50, SD = 0.47), and its observed association with perceived seriousness of infidelity was positive (r = 0.685, p < 0.001). Primary interpretation is limited to the strongly reliable marital-fidelity indicator and the comparatively better-supported seriousness indicator. Findings involving the three-item digital-infidelity index (alpha = 0.481), the two-item forgiveness/restoration index (alpha = 0.534), and the single boundary-clarity item are reported only as secondary exploratory analyses and are not used to support the principal conclusions. Women showed sample-specific differences on fidelity and seriousness, but no result is interpreted as behavioral or moral superiority. The observed correlation between seriousness and digital-infidelity judgments (r = 0.864) indicates substantial overlap; no correction for attenuation is presented because the dimensionality and error structure of the short indices were not established. Interview findings are reported as counts out of 11 rather than percentages. The findings are sample-bound, attitudinal, and hypothesis-generating. Full article

Background: The uric acid-to-magnesium (UA/Mg) ratio and frontal QRS-T angle have been associated with coronary artery disease, but their relationships with angiographic coronary disease severity in acute coronary syndrome (ACS) remain incompletely characterized. We investigated their associations with the Gensini score in patients with ACS. Methods: This single-center, retrospective observational study included 146 patients with ACS undergoing coronary angiography, including 85 with ST-segment elevation myocardial infarction (STEMI) and 61 with non-ST-segment elevation myocardial infarction (NSTEMI). The primary adjusted analysis evaluated the continuous Gensini score after natural logarithmic transformation using multivariable linear regression with HC3 robust standard errors. Median quantile regression was performed as a sensitivity analysis. A Gensini score ≥ 60 was evaluated as a secondary exploratory binary endpoint using a parsimonious multivariable logistic regression model, with bootstrap internal validation. Results: The UA/Mg ratio (ρ = 0.332; p < 0.001), uric acid-to-HDL cholesterol ratio (UHR) (ρ = 0.232; p = 0.005), and frontal QRS-T angle (ρ = 0.481; p < 0.001) correlated positively with the Gensini score. In the primary adjusted analysis, both the UA/Mg ratio (β = 0.294 per 1-SD increase; p < 0.001) and frontal QRS-T angle (β = 0.225 per 1-SD increase; p < 0.001) were independently associated with higher log-transformed Gensini scores. These associations remained significant in median quantile regression and after additional adjustment for C-reactive protein. Modeling uric acid and magnesium separately provided better model fit than use of the UA/Mg ratio. In the secondary exploratory analysis of a Gensini score ≥ 60, the UA/Mg ratio (OR = 1.86 per 1-SD increase; 95% CI: 1.22–2.95; p = 0.005) and frontal QRS-T angle (OR = 1.54 per 1-SD increase; 95% CI: 1.05–2.30; p = 0.030) remained independently associated with the endpoint. The parsimonious model had an apparent AUC of 0.787 and an optimism-corrected AUC of 0.765 after bootstrap internal validation. Conclusions: Higher UA/Mg ratio and wider frontal QRS-T angle were independently associated with a higher log-transformed Gensini score in the primary continuous-outcome analysis in patients with ACS. However, the UA/Mg ratio did not demonstrate statistical superiority over its individual components, and the secondary binary model showed only moderate discrimination after internal validation. These findings should be considered exploratory and require prospective external validation. Full article

Tracked vehicles operating in hilly and mountainous agricultural environments are frequently subjected to pitch, roll, and vibration, which can introduce time-varying errors into ultra-wideband phase-difference-of-arrival (UWB-PDOA) relative localization. Aiming to improve localization accuracy under such disturbances, this study proposes a relative localization error compensation method that integrates long short-term memory (LSTM) residual learning with a residual-adaptive extended Kalman filter (RAEKF), referred to as LSTM-RAEKF. The proposed method combines UWB-PDOA measurements with inertial measurement unit information to learn disturbance-related localization residuals and adaptively compensate for relative position and theta observations before filtering. A UWB/IMU relative localization test bench was developed, and experiments were performed under static, pitch, roll, and vibration conditions. Across different fixed-point tests, the proposed method reduced the planar position RMSE and theta RMSE by 35.0–62.9% and 54.7–70.8%, respectively. Considering all experimental conditions, the position RMSE decreased from 4.00 cm to 1.81 cm, while the theta RMSE decreased from 5.64° to 2.17°, corresponding to reductions of 54.8% and 61.5%, respectively. Furthermore, LSTM-RAEKF outperformed the standard extended Kalman filter and the innovation-based adaptive estimation extended Kalman filter. Overall, these results demonstrate that LSTM-RAEKF can effectively suppress localization errors induced by attitude disturbances and provide stable relative localization information for subsequent tracked vehicle following control. Full article

Herein, the distributed variational Bayesian-assisted unscented Kalman filter (VB-UKF) is used to enhance the accuracy of pedestrian positioning. For this method, a distributed filter is employed. First, the UKF under colored measurement noise (CMN) is derived. The VB-assisted method is then derived. Subsequently, the Mahalanobis distance is used to determine whether the current noise estimate conformed to the navigation environment; if the Mahalanobis distance is higher than the preset threshold, the VB-assisted method is employed to update the noise, which can improve the UKF accuracy under CMN. The effectiveness of the proposed method was subsequently validated through two practical tests. Experimental results demonstrate that this method plays a notable role in reducing localization errors in the experiments. This observation emphasizes the efficacy of the proposed method. Full article

Free trade agreements are widely used to promote trade liberalization, yet their implications for export quality in resource-based sectors remain poorly understood. This study examines whether the China–ASEAN Free Trade Area (CAFTA) changed the quality of China’s forest product exports. Using HS6 product-level bilateral trade data for 1995–2023, we estimate export quality through a demand-side residual approach and apply double machine learning (DML) to identify the causal effect while controlling for product, destination, and year heterogeneity. The results show that CAFTA significantly reduces the average quality of China’s forest product exports, a finding robust to outlier treatment, alternative algorithms and sample splits, and alternative substitution elasticities, and that survives dropping potentially endogenous controls, excluding COVID-19 and other crisis periods, clustering standard errors by importing country, and excluding each ASEAN partner. Mechanism tests indicate that the quality-downgrading effect operates mainly through the trade cost effect and the standard harmonization effect; both channels lower entry barriers and weaken incentives for quality differentiation. The negative effect is concentrated in the ASEAN-6, whereas the ASEAN-4 exhibits a small positive effect, and resource-intensive products experience the largest quality decline. Policy should combine CAFTA 3.0 cooperation with stronger quality systems, traceability, and technology-oriented upgrading in Chinese forestry firms. Full article

To address contact force estimation and compliant control for biomimetic robotic arms interacting with uncertain environments, an adaptive variable-damping impedance control method based on a fuzzy-controlled forgetting-factor strong tracking Kalman filter (FSKF) is proposed. The proposed method improves the conventional strong tracking Kalman filter (SKF) by introducing a fuzzy control strategy to adaptively adjust the forgetting factor, thereby enhancing the filtering performance and improving the accuracy of contact force estimation. The estimated contact force is subsequently incorporated into an adaptive variable-damping impedance controller to achieve simultaneous contact force estimation and compliant control of the biomimetic robotic arm. During biomimetic robotic arm motion, the proposed controller utilizes the estimated contact force to adaptively regulate the damping coefficient, compensating for force-tracking errors caused by environmental uncertainties and thereby improving both force and position tracking performance. The simulation and experimental results demonstrate that the proposed adaptive variable-damping impedance controller has better force and position tracking accuracy compared with the conventional impedance controller. Compared with traditional methods, the estimation accuracy based on FSKF has improved by about 7.3%. These results demonstrate the potential of the proposed method for prosthetic systems and other applications involving compliant robot–environment interaction. Full article

Monocular 3D human pose estimation is sensitive to depth ambiguity and upstream 2D-detector error. We investigate a detector-conditioned virtual 2D pose as an auxiliary hypothesis rather than an independent physical measurement. Joint reliability combines training-subject-only monotonic confidence calibration with detector-, joint-, and confidence-specific residual risk before a compact pose lifter. A four-arm factorial design separates observed-only (O), observed plus virtual (OV), observed plus reliability (OR), and observed plus virtual plus reliability (OVR). Across three subject-disjoint splits, three seeds, two RGB detectors, and 48 subject–sequence–camera clusters, OVR improved mean per-joint position error (MPJPE) by 3.049 mm (95% cluster-aware confidence interval (CI): 0.583–4.976); reliability-only improvement was supported, whereas the conditional virtual increment and interaction were not. Validation-only sensitivity covered eight yaw/pitch hypotheses and five multi-view policies; no multi-view policy improved primary MPJPE over the locked $-{15}^{\circ }$ view. A component-level audit of two synchronized directed camera pairs favored the generator over the observed-pose baseline in all 24 stratified rows, with only two sequence clusters per row. The locked MPI-INF-3DHP Official TS1–TS6 result improved MPJPE by approximately 0.97%, below its prespecified 2% practical threshold, with a CI crossing zero. On an additional external 3DPW cohort (13,579 eligible frames, 24 sequences, YOLO11L-Pose only), OVR improved MPJPE by 1.004 mm (95% CI: −2.965–5.016), while its Procrustes-aligned MPJPE point estimate worsened by 0.640 mm; neither contrast was conclusive. Thus, calibrated reliability is the most consistently supported mechanism, whereas virtual-view dominance, official superiority, and cross-dataset superiority are not established. Full article

High-precision manufacturing in unstructured factories imposes stringent requirements on real-time scene perception and end-effector positioning accuracy. Traditional single-sensor solutions suffer from perception blind spots in human-robot mixed environments with complex lighting, while chassis cumulative error often leads to rigid collisions during end-effector operations. To address this, this paper proposes and evaluates a decision-level multi-sensor coordination mechanism for robust navigation and high-precision planar positioning of industrial mobile robots. The mechanism assigns explicit sensor roles, distance-dependent trigger conditions, and deterministic safety priorities. At the navigation and obstacle avoidance level, a sequential decision policy is constructed: macroscopically, a lightweight You Only Look Once version 5 small (YOLOv5s) is utilized for the early detection of dynamic objects, providing bounding-box coordinates to trigger preemptive deceleration, while LiDAR independently provides geometric ranging for ROS local-costmap updating and detour replanning; microscopically, a low-level hardware interrupt strategy triggered by ultrasonic sensors is proposed to mitigate near-field blind spots and reduce communication latency. At the end-effector positioning level, under illumination conditions ranging from 200 to 1000 lux, an adaptive alignment algorithm combining hue-saturation-value color-space morphological processing and Kalman filtering is proposed to suppress measurement noise caused by illumination variations and mechanical vibrations. Experiments in the tested dynamic human-robot mixed scenarios showed no rigid collisions for the proposed system and an emergency response time of approximately 50 ms against sudden blind-spot intrusions. Simultaneously, the system achieves a 95% reliability rate in controlling the end-effector 2D planar positioning error (X-Y plane) within a ±2 mm tolerance under complex illumination interference. These results demonstrate improved navigation safety and planar-positioning reliability under the tested flexible-manufacturing conditions. Full article

Time-fractional generalizations of the Lindblad master equation describe open quantum systems whose coupling to the environment exhibits power-law memory. We develop the optimal-control theory of such systems and analyse the consistency of the adjoint calculus on which every gradient-based pulse-design method relies. Casting the density operator in a fractional Bochner–Sobolev space of Hilbert–Schmidt operator valued functions, we establish well-posedness through Mittag–Leffler resolvent families, prove that the completely positive trace-preserving (CPTP) structure is preserved for the controlled, time-dependent generator without recourse to subordination, and obtain existence and uniqueness of optimal controls by the direct method. The adjoint is governed by the right Riemann–Liouville derivative with a fractional-integral terminal condition, a structure established for Caputo dynamics with a Mayer cost by Bergounioux and Bourdin, who also showed that a pointwise terminal costate cannot exist. Our central result concerns the discrete counterpart of that fact, where existence is never lost: imposed on the right-Caputo adjoint of a convergent scheme, the pointwise condition yields a bounded costate and a well-defined reduced gradient carrying an error that is mesh-independent. We further establish a sharp rate in the classical limit: the defect vanishes exactly linearly, $\parallel \Delta \left(\beta \right)\parallel =C(1-\beta )+O\left({(1-\beta )}^2\right)$, with C given in closed form through a digamma series. Two consequences follow: monotonicity of the defect in the memory order is proved near $\beta =1$, and the memory order is locally identifiable from gradient data alone. A corrected adjoint restores consistency with proven convergence rates. Numerical experiments on two-level, three-level and two-qubit open systems (Liouville dimension up to 16) confirm the mesh-independence, reproduce C to three significant digits, and recover the full rate on graded meshes. Full article

Objectives: This study aimed to examine the relationships between refractive error and axial length (AL) and body composition parameters, including obesity-related indices, in 8-year-old children enrolled in the Japan Environment and Children’s Study (JECS). Methods: From 2019 to 2022, data from the right eyes of 1866 children aged 8 years enrolled in the JECS adjunct study were analyzed. AL, noncycloplegic spherical equivalent (SE), and uncorrected visual acuity were measured. Relationships of these ocular parameters with height, weight, muscle mass, body fat percentage, the Rohrer index, and sex were evaluated. Results: The mean AL was 23.09 mm, and the mean SE was −0.52 D in the right eye. Despite the correlations of the Rohrer index with SE (r = 0.05, p = 0.02), uncorrected visual acuity expressed as logMAR (r = −0.07, p = 0.005) and AL (r = −0.06, p = 0.02) reached nominal statistical significance, and all correlation coefficients were close to zero, indicating no meaningful linear correlations. SE and logMAR showed no meaningful correlations with height, weight, muscle mass, or body fat percentage. AL showed small positive correlations with height (r = 0.17, p < 0.001) and muscle mass (r = 0.14, p < 0.001); its correlation with weight was negligible (r = 0.07, p = 0.004), and no meaningful correlation was observed with body fat percentage. Conclusions: Among 8-year-old children, the Rohrer index showed no meaningful linear correlation with refractive error, uncorrected visual acuity, or AL despite having nominally significant p-values. AL showed small positive correlations with height and muscle mass, suggesting a limited cross-sectional relationship with overall somatic size; however, the small effect sizes preclude strong conclusions. Because refraction was measured without cycloplegia, the SE findings should be interpreted cautiously, as accommodation may have shifted measurements in the myopic direction. Full article

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of $R_{95}$ = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications. Full article

Data obtained from experiments and surveys in human social systems are inevitably influenced by systematic measurement errors, and network data are no exception. Despite the prevalence of error in social network data, current research often lacks rigorous estimation of its expected precision, which may lead to biased conclusions. Signed networks, which encode both positive and negative relationships, constitute an important component of network science, and conducting measurement error analysis on them can substantially enhance the accuracy of social network analysis. This paper proposes a set of error measurement tools based on the Expectation-Maximization (EM) algorithm, specifically designed to estimate errors in signed network data. We extend traditional experimental error estimation to the network domain, derive a general error estimation method for signed networks, and validate its scientific validity and practical utility through extensive simulation experiments on both synthetic and real-world networks. The experiments reveal that network density and the ratio of positive to negative edges significantly influence the posterior probability distribution of the adjacency matrix. Specifically, as density increases, edge estimation accuracy exhibits a U-shaped trend, and the proportion of negative edges shows a nonlinear relationship with accuracy. The proposed method is applicable to repeatedly measured signed networks and provides a reliable framework for reconstructing network structures as faithfully as possible. Full article