Search Results (871)

To address the nonlinear orbit determination problem under multi-satellite cooperative observation, this paper proposes an orbit determination method integrating a plane-constrained observation model with adaptive robust filtering. Based on angular measurements from multiple observation nodes, a linearized observation model is constructed using spatial geometric constraints. The Maximum Correntropy Criterion is then introduced to adaptively weight each measurement component, and a hybrid kernel function is employed to suppress the effects of non-Gaussian noise and outliers. Meanwhile, an adaptive factor based on the covariance matching principle is designed to adjust the process noise intensity online, thereby improving the robustness of the Cubature Kalman Filter in state prediction and update. Simulation results under severe non-Gaussian noise show that the proposed adaptive robust cubature Kalman filter (ARCKF) reduces the position RMSE from 95.3 m for CKF to 30.8 m, corresponding to an improvement of approximately 67.7%, while increasing the computation time from 6.52 s to 7.35 s. These results indicate that the proposed method can achieve improved accuracy and robustness under uncertain measurement statistics and dynamic disturbances, making it suitable for space-based angles-only orbit determination, although further computational optimization is still required for onboard applications. Full article

How older adults psychologically appraise their health while managing multiple chronic conditions is a behavioral-science question as much as a clinical one. This study estimated the weighted prevalence of favorable mental-health self-appraisal, identified its behavioral, social, and functional correlates, and compared the relative salience of diagnosed-condition burden and functional limitation among U.S. adults aged ≥ 50 years with multimorbidity. This retrospective cross-sectional secondary analysis used the 2023 Medical Expenditure Panel Survey (MEPS) Full Year Consolidated Data File (HC-251). Multimorbidity was defined as at least two diagnosed chronic priority conditions. The primary outcome represents favorable mental-health self-appraisal, derived from MNHLTH53 (excellent/very good/good vs. fair/poor). Covariates were organized using Andersen’s Behavioral Model and health-psychology concepts of adaptation, resources, and lived functional burden. Weighted prevalence estimates and survey-weighted logistic regression models were fitted using PERWT23F, VARSTR, and VARPSU. Robustness checks examined a stricter outcome threshold, proxy adjustment/non-proxy restriction, and a physical-health extension model. The analytic sample included 5523 respondents, representing approximately 77.9 million U.S. adults aged ≥ 50 years with multimorbidity. The weighted prevalence of favorable perceived mental-health self-appraisal was 86.6% (95% CI 85.4–87.7). In the fully adjusted core model (complete-case n = 5330), age 65–74 years (aOR 1.52, 95% CI 1.17–1.98) and age ≥ 75 years (aOR 1.79, 95% CI 1.36–2.36) were associated with higher odds of favorable appraisal. Lower odds were observed for Hispanic respondents, non-Hispanic Asian respondents, lower educational attainment, lower income, non-employment, ≥4 diagnosed conditions, and any functional limitation. The strongest inverse association was limitation status (aOR 0.32, 95% CI 0.27–0.39). Sensitivity analyses were directionally consistent. Favorable mental-health self-appraisal remained common in this medically complex older population, but it was socially and functionally patterned. Functional limitation appeared more behaviorally salient than diagnosis count alone. Because the analysis was cross-sectional and based on household-interview reported measures, these results should be interpreted as associations rather than causal effects. Full article

Multiple myeloma (MM) develops through asymptomatic precursor stages characterized by progressive remodeling of the bone marrow (BM) immune microenvironment and disruption of bone homeostasis. To delineate changes in natural killer (NK) cell states during disease evolution, we investigated coordinated immune-tumor remodeling by integrating NK cell functional states with plasma cell-intrinsic susceptibility programs derived from CRISPR-based screens across healthy donors (HD), monoclonal gammopathy of undetermined significance (MGUS), smoldering MM (SMM), and newly diagnosed MM patients. The integration of NK cell state-associated gene signatures with plasma cell transcriptional programs revealed stage-specific co-variation between immune and tumor compartments. Public single-cell RNA sequencing datasets were interrogated to resolve NK cell heterogeneity, identifying cytotoxic CD56^dim and regulatory CD56^bright subsets. NK cell dynamics displayed stage-dependent changes, with early expansion followed by the contraction of CD56^dim cells in BM, whereas CD56^bright cells showed predominantly compositional remodeling. Within the CD56^bright subset, transcriptional changes included an increased expression of KLRC1 (encoding NKG2A), subsequently validated by multiparametric flow cytometry. In parallel, plasma cell programs associated with NK sensitivity progressively decreased along disease stages, supporting tumor adaptation to immune pressure. The NKG2A ligand HLA-E displayed selective expression within CD16⁺ monocytes and followed a distinct variable pattern across disease stages, highlighting a microenvironmental contribution to NK cell regulation. Collectively, these findings indicate a coordinated process of immune-tumor co-evolution, characterized by dynamic remodeling of NK cell states and plasma cell susceptibility, with the NKG2A–HLA-E axis emerging as a central interface during MM progression. Full article

Background/Objectives: Parenting stress and life satisfaction are important indicators of family well-being and parent mental health in families of children with disabilities. However, limited empirical attention has been given to how these outcomes differ among parents whose children receive adapted physical activity (APA) services within South Korea’s Developmental Rehabilitation Service system. This cross-sectional study examined subgroup differences in parenting stress and life satisfaction according to sociodemographic, disability-related, and service-utilization characteristics among parents of children receiving APA services. Methods: Data were collected from 295 parents of school-aged children with disabilities enrolled in APA services at child development centers. Welch-type tests, Welch’s ANOVA or one-way ANOVA, Pearson correlation analyses, Benjamini–Hochberg FDR adjustment, and supplementary analyses of covariance (ANCOVA) were used to examine group differences and the stability of selected associations after adjustment for prespecified covariates. Confirmatory factor analysis and gender-based measurement invariance testing were also conducted for the adapted parenting stress scale. Results: Parenting stress subdomains were positively correlated with one another (r = 0.19–0.53) and negatively correlated with life satisfaction (r = −0.28 to −0.40). Female parents reported higher social and psychological stress than male parents. Household income showed the largest association with economic stress, and significant differences were also observed according to parental age, education level, disability severity, and selected service-utilization characteristics. Some associations remained after ANCOVA adjustment, whereas others were attenuated or emerged only after adjustment. Conclusions: The findings indicate subgroup differences in parenting stress and life satisfaction among parents of children receiving APA services. Because the study used a cross-sectional, self-reported design with convenience sampling and an adapted instrument, the results should be interpreted as preliminary associative evidence rather than evidence of causal or service-specific effects. Future longitudinal, comparative, and service-level research is needed to clarify how APA service contexts relate to caregiver well-being over time. Full article

LiDAR SLAM is widely used in robotic navigation and autonomous driving, but many existing methods still handle frontend point cloud processing and backend pose optimization as two loosely connected stages with fixed settings. This can lead to unnecessary computation and also limits the localization performance when the environment or motion changes. To address this issue, we propose a LiDAR–inertial SLAM framework with bidirectional closed-loop coupling between adaptive point cloud processing and pose optimization. In the frontend, depth image resolution is adjusted online according to backend pose uncertainty and loop closure importance, and a comprehensive score integrating point density, depth stability, geometric complexity, and motion consistency is used to select high-quality sparse points. In the backend, the comprehensive score is further combined with depth image quantization error to construct per-point covariance matrices for uncertainty-weighted scan-to-map ICP and factor graph noise modeling. Experiments on the KITTI and M2DGR datasets show that the proposed method reduced the mean RMSE by 15.8% and 15.2%, respectively, compared with FAST-LIO2, while the real-world field test further shows a 26.3% RMSE reduction with respect to the constructed reference trajectory. These results show that the proposed framework improves both mapping quality and localization accuracy. Full article

Background/Objectives: Multiple Traffic Light (MTL) front-of-pack (FOP) labels are being considered in Bahrain. We tested whether an adapted MTL label improves the nutritional quality of grocery purchases. Methods: In a two-arm randomized controlled intercept trial (January–May 2025), adults (≥21 years) responsible for household grocery shopping were recruited in high-footfall public venues and asked to complete a one-time shop on a tablet-based, purpose-built online grocery platform. The MTL label was adapted for Arabic reading direction and displayed per-serving nutrients and % recommended daily intake. Treatment effects were estimated using ordinary least squares regressions with robust standard errors and covariate adjustment. Results: Of 395 randomized participants, 360 were included in primary analyses (control n = 183; MTL n = 177). MTL exposure was not associated with a significant change in the primary outcome (basket weighted average MTL score per serving; β = 0.037; p = 0.64) or in per-serving calories and nutrients of concern (all p > 0.17). In the post-shop assessment, only 47.2% of participants correctly interpreted MTL labels, indicating modest objective label comprehension under the study conditions. Conclusions: These findings suggest that the impact of front-of-pack labels likely depends on both implementation features and consumer understanding, and that pairing labels with public communication and nutrition literacy initiatives may be necessary to maximize the effectiveness of labels in Bahrain and the wider Gulf region. Full article

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method. Full article

Control-barrier-function-based safety filters are promising for autonomous driving, but most existing formulations treat obstacle perception as deterministic or account only for bounded ego state-estimation errors. This becomes limiting when obstacle existence, position, motion, and sensing quality vary online. We present a sensor-health- and belief-aware risk-adaptive high-order control barrier function (HOCBF) safety filter for dynamic obstacle avoidance. The method uses obstacle belief from a perception/tracking module, inflates residual obstacle uncertainty according to an object-wise sensor-health score, and converts upper-tail risk into adaptive HOCBF tightening through conditional value-at-risk (CVaR). Sensor health enters the controller through both covariance inflation and online CVaR confidence scheduling. The resulting quadratic program combines deterministic ego-error robustness with probabilistic perception uncertainty while minimally modifying the nominal control input. The zero-slack solution guarantees forward invariance of the risk-tightened safe set under the stated assumptions, whereas the slack-activated mode provides a quantified least-violation fallback rather than a strict safety guarantee. Simulations on a nonlinear 3-DOF bicycle model evaluate critical cut-in, sudden perception degradation, merge-bottleneck, fixed-CVaR, sensitivity, runtime-scaling, heterogeneous multi-obstacle, and heavy-tailed uncertainty cases. Full article

Orbit determination in Earth–Moon space is challenged by dynamic-model mismatch and unstable GNSS observation conditions, especially under weak and intermittent signals. To address this issue, this paper proposes a GNSS single-point positioning (SPP)-aided orbit determination method based on adaptive sliding-window filtering. A tightly coupled framework is constructed by integrating orbital dynamics propagation with SPP pseudo-range observations, allowing propagation errors to be corrected in real time through measurement updates. To enhance adaptability under time-varying observation conditions, a dynamic sliding-window strategy is introduced, in which the observation-noise covariance is adjusted according to carrier-to-noise ratio ($C/N_0$) variations. Simulations for three representative Earth–Moon trajectories, including a near-rectilinear halo orbit (NRHO), a distant retrograde orbit (DRO), and a Halo orbit, show that the proposed method significantly outperforms the conventional tightly coupled solution. The three-dimensional RMS position error is reduced from 6.65 m to 1.27 m for NRHO, from 6.57 m to 1.27 m for DRO, and from 5.91 m to 1.44 m for Halo, corresponding to improvements of 80.9%, 80.4%, and 75.4%, respectively. Under a simulated 200-epoch GNSS interruption in the Halo case, the method also improves outage robustness and post-recovery performance, reducing the three-dimensional RMS error by 23.2% in the interruption-centered interval and by 26.1% over the full arc. Full article

Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while conventional methods focusing on global statistical matching usually neglect local structures, leading to confirmation bias under dynamic loads. To improve diagnostic reliability, we propose a Noise-Resilient Whitened Domain Adaptation (NRWDA) framework. To handle covariance fluctuations caused by changing working conditions, a Lipschitz-bounded Temporal Whitening (LTW) module is designed as a low-pass filter. An Entropy-guided Prototype Truncation (EPT) mechanism is adopted to discard ambiguous labels and better calibrate semantic centers. In addition, a Dispersion-Adaptive Contrastive Sharpening (DACS) strategy is introduced to dynamically adjust the contrastive temperature based on predictive dispersion, thus tightening decision boundaries. The proposed method is evaluated on CWRU, PU, and MFPT datasets. The PU dataset, featuring fluctuating loads and non-stationary signals, poses a strict test, yet our model maintains its stability even at a 0 dB SNR—a condition where standard approaches usually break down. During the $P0\to P3$ transfer task involving substantial radial force variations, NRWDA secures a 72.36% accuracy and surpasses established baselines. These findings confirm that our technique successfully isolates dependable diagnostic features from corrupted sensor measurements within actual industrial settings. Full article

International students in Saudi universities face academic and adaptation challenges shaped by emotional, cognitive, linguistic, and sociocultural factors. This study examined whether emotional intelligence and cognitive flexibility predicted academic success and adaptation outcomes among international students in Saudi public universities. A quantitative cross-sectional survey was conducted with 410 international students using structured measures of emotional intelligence, cognitive flexibility, academic success, adaptation outcomes, Arabic proficiency, and sociodemographic characteristics. Data were analysed using descriptive statistics, chi-square tests, Kendall’s tau-b correlations, hierarchical regression, and observed-variable path analysis. Duration of residence was significantly associated with Arabic proficiency, χ²(8) = 82.40, p < .001. Arabic proficiency was positively associated with GPA, τ = 0.62, p < .001, and adaptation outcomes, τ = 0.48, p < .001. In hierarchical regression, sociocultural covariates and psychological predictors explained substantial variance in academic success, R² = 0.53, and adaptation outcomes, R² = 0.53. Emotional intelligence and cognitive flexibility remained positive predictors of both outcomes after accounting for Arabic proficiency, duration of residence, region of origin, and language of instruction. Findings suggest that international student success in Saudi universities reflects an interaction of emotional, cognitive, linguistic, and contextual resources. Universities should strengthen integrated support for emotional regulation, adaptive thinking, Arabic-language development, and culturally responsive academic guidance. Full article

To address the severe performance degradation of radar weak target detection induced by dense cluster targets and sea-spike interference in nonhomogeneous sea clutter environments, this paper proposes an enhanced Adaptive Normalized Matched Filter algorithm based on iterative weighting and sample purification (IWP-ANMF). The proposed algorithm establishes a closed-loop iterative detection framework capable of highly sensitive discrimination of anomalous data within the reference window—particularly cluster targets and strong discrete sea spikes that severely distort covariance matrix features—identifying them as “contaminated samples.” During each iteration, target-likelihood statistics are calculated for all reference samples based on the current covariance matrix estimate. Subsequently, an adaptive deep-notch suppression strategy is applied to contaminated samples, such as cluster targets, according to their statistical characteristics, thereby progressively purifying the sample covariance matrix (SCM) estimation. Theoretically, this iterative procedure is rigorously proven to converge to the optimal solution of a robust weighted covariance matrix estimation problem. Comprehensive validations using both Monte Carlo simulations and measured K-distributed sea clutter data demonstrate that, compared to classical ANMF and Generalized Inner Product (GIP) approaches, the proposed algorithm exhibits outstanding robustness and detection performance when confronted with heterogeneous contamination scenarios, especially high-density cluster targets. This method effectively eliminates the blind-zone expansion and performance deterioration caused by the wideband masking of cluster targets, significantly enhancing weak target detection capabilities under complex maritime conditions. Full article

Wi-Fi Channel State Information (CSI)-based indoor localization enables high-precision positioning, but its deployment across multiple environments faces two major challenges: privacy concerns from centralizing CSI data, and severe statistical heterogeneity (non-IID) arising from the strong environment-dependency of CSI. This heterogeneity creates a stability–plasticity trade-off in federated learning—maintaining precision in known environments (stability) while adapting to unseen domains (plasticity). To address this trade-off, we propose AdaFed-LDR, which combines server-side Confidence-Weighted Adaptive Aggregation with client-side Layerwise Dynamics Regularization (LDR). The aggregation recalibrates client contributions based on feature covariance changes, while LDR imposes depth-dependent constraints—stronger constraints on shallow layers to preserve environment-agnostic features and weaker constraints on deeper layers to allow environment-specific adaptation. Evaluated across 8 indoor environments using Leave-One-Out Cross-Validation and 5 random seeds, AdaFed-LDR achieved a mean localization error (MLE) of 0.41 cm in known environments, corresponding to an 88.2% reduction compared with FedAvg. In domain generalization to unseen environments, AdaFed-LDR achieved an MLE of $218.2\pm 2.8$ cm, demonstrating an improvement over FedPos ($257.6\pm 14.04$ cm). With one adaptation sample per reference point, MLE improved to 21 cm. Ablation experiments confirmed that combining the two proposed components achieved the highest improvement (83.9%) compared with applying them individually, supporting AdaFed-LDR as a reproducible approach to the stability–plasticity trade-off in federated CSI-based localization. Full article

To address the uncertainty of the measurement noise covariance matrix in vehicle state estimation, this paper proposes a symmetry-aware extended Kalman filter optimized by a chaotic-gradient strategy. The symmetry-aware concept is introduced from the approximate mirror symmetry of vehicle lateral dynamics under left and right steering excitations. Under identical road adhesion and vehicle operating conditions, the yaw-rate and sideslip-angle responses should exhibit balanced statistical characteristics for positive and negative lateral motions. However, a fixed measurement noise covariance matrix may break this balance and lead to direction-dependent estimation bias or delayed convergence. To improve the statistical consistency of the estimation process, the proposed method adaptively tunes the measurement noise covariance matrix according to the innovation covariance mismatch. A chaotic search mechanism is first used to enhance global exploration, and a variable-step gradient method is then applied to refine the local optimal solution. Through the iterative combination of chaotic traversal and gradient-based refinement, the proposed observer improves the balance between model prediction and measurement correction under stochastic disturbances. The effectiveness of the proposed method is verified through CarSim and MATLAB/Simulink co-simulation. The results show that, compared with EKF, UKF, and AEKF benchmark observers, the proposed CG_EKF provides more accurate estimation of vehicle yaw rate and sideslip angle. Full article

This paper proposes an adaptive residual extended Kalman filter method optimized by a multi-strategy improved parrot optimization algorithm (MIPO-ARKEKF) to improve the kinematic parameter calibration accuracy and efficiency of robotic polishing systems. To address the limitations of the standard extended Kalman filter (EKF), such as truncation-error accumulation during repeated linearization and sensitivity to manually selected noise parameters, an integrated improvement framework is developed. Specifically, a gradient stabilizer based on state-estimation increments is introduced to alleviate estimation degradation caused by accumulated truncation errors, while the proposed MIPO algorithm is employed to adaptively optimize the process and measurement noise covariance matrices, thereby improving the robustness of parameter identification under practical measurement uncertainty. The calibration process is established on the basis of high-precision external measurement data obtained from the robotic polishing system. In benchmark-function tests, MIPO demonstrates superior convergence performance. In physical experiments based on a KUKA KR210 R2700 robot, the proposed MIPO-ARKEKF method reduces the root mean square positioning error from 0.8927 mm to 0.4858 mm, corresponding to a 45.58% improvement in accuracy. Compared with representative hybrid calibration methods, the proposed method achieves comparable compensation accuracy while reducing computation time by 34.88% to 65.08%. Practical polishing experiments on ultra-low-expansion glass lenses further verify that the proposed method effectively improves end-effector trajectory tracking accuracy and polishing quality, providing an efficient solution for high-precision robotic polishing. Full article