Search Results (2,802)

Distributed maximum correntropy Kalman filters improve robustness to non-Gaussian noise, but existing variants generally introduce consensus through average or weighted fusion without explicitly separating the innovation residual from the state-disagreement residual in a dimensionally consistent objective. This paper proposes a Distributed Maximum Correntropy Kalman Filter with Innovation and Consensus Weighting Terms (DMCKF-IW-CWT). The innovation and consensus residuals are normalized separately and mapped by Gaussian kernels, after which the resulting information matrices are incorporated into a fixed-point local update. Posterior covariance intersection (CI) is then used to fuse neighboring estimates without requiring the unavailable cross-covariances. A sufficient contraction condition is given for the fixed-point iteration. In a five-node benchmark with 500 independent Monte Carlo runs and 1000 sampling steps, the proposed method obtains overall, transient, and steady-state MAEs of 0.172210, 0.188271, and 0.168195, respectively, corresponding to reductions of 0.254%, 0.526%, and 0.178% relative to DMCKF-W; the paired 95% confidence intervals of all three differences remain below zero. The consensus RMS is further reduced by 5.371%. Additional tests involving five noise families, packet loss and communication noise, a four-state nonlinear model, and systems with up to eight states and twenty nodes confirm the numerical convergence and extensibility of the framework. Full article

Ozone (O₃) is a prevalent environmental pollutant that can induce oxidative stress and respiratory epithelial injury. Dysregulation of pulmonary microbiota homeostasis plays an important role in the progression of lung damage and inflammatory responses. However, there remains a lack of systematic research on the effects of O₃ exposure on pulmonary microecology and its potential association with pulmonary inflammation. In this study, twenty-three SPF-grade C57BL/6N male mice (aged 6–7 weeks) were randomly divided into a filtered air control group (n = 11) and an O₃ exposure group (n = 12). Mice in the O₃ group underwent whole-body inhalation exposure to 1 ppm O₃ for 4 h daily over 8 consecutive weeks, while control mice were maintained under identical conditions with filtered air exposure. The 1 ppm concentration (equivalent to 0.2–0.3 ppm in humans) and 8-week daily 4 h exposure regimen was selected to model the 2-month summer high-O₃ season with typical afternoon O₃ peaks. The results showed that the O₃-exposed group exhibited marked inflammatory infiltrates in the lung, as well as significantly elevated macrophage inflammatory protein-2 (MIP-2) and tumor necrosis factor-alpha (TNF-α) in bronchoalveolar lavage fluids compared with controls. Pulmonary microbiota sequencing revealed that O₃ exposure significantly elevated the Shannon index of pulmonary microbiota in mice, and four differential genera including Deinococcus, Aerococcus, Enterococcus and Proteiniphilum were identified. Correlation analysis revealed that MIP-2 was significantly correlated with Aerococcus. In conclusion, the findings of this study suggest that exposure to O₃ induces inflammation and significant changes in the microbial composition of the lungs, which provides an experimental basis for further investigating mechanisms linking microbiota dysbiosis to lung injury triggered by ambient air pollutants. Full article

Microphthalmia, anophthalmia and ocular coloboma (MAC) are rare developmental eye disorders. Although over 100 causative genes have been identified, the molecular spectrum and genotype–phenotype correlations remain incompletely understood, particularly in genetically diverse populations. We set out to molecularly characterize MAC in the Israeli population. Forty-seven MAC-affected individuals from 43 unrelated families were enrolled. DNA of all probands was subjected to whole exome sequencing. The most common phenotype was microphthalmia (64% of patients). Definite or possible molecular diagnoses were achieved in 13/43 probands (30%) and involved 10 different genes (MFRP, SMO, GJA8, SOX2, RARB, TSPAN12, SHH, PTPN11, BEST1, and TP63). An in vitro splicing assay was used to explore the pathogenicity of a variant in the SMO gene. Following stringent filtering of exome data, 226 rare possibly pathogenic variants were identified in 218 genes not previously associated with MAC. The rate of molecular diagnosis achieved in this Israeli MAC cohort is similar to the reported range in other studies. The results further demonstrate the genetic heterogeneity of MAC, while supporting the involvement of complex inheritance and/or environmental factors in many of the cases. Further studies are required to reveal these underlying etiological factors, and to support the novel genotype–phenotype associations suggested here. Full article

El Niño–Southern Oscillation (ENSO) modulates tropical South American rainfall, but its Colombian expression is filtered by terrain, rainfall regime, moisture pathways, and atmospheric state. We quantify ENSO-related sensitivity of standardized precipitation anomalies in the Colombian Andes and Orinoquia using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0; 1981–February 2026), station records from Colombia’s Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), ERA5 atmospheric fields, and 1981–2010 climatologies. CHIRPS reproduced station-derived standardized anomalies ($r=0.94$ in the Andes; $r=0.91$ in Orinoquia), supporting regional anomaly analysis while retaining cautious comparison framing. Lagged associations with the Oceanic Niño Index (ONI) were evaluated for lags 0–6 months using effective sample size, block-bootstrap confidence intervals, and maximum-lag tests. ENSO sensitivity was stronger and more coherent in the Andes: annual lag-1 ONI–precipitation correlation was $-0.374$, with marked December–February and June–August responses. El Niño minus La Niña composites of column water vapor, 850-hPa moisture-flux convergence, 500-hPa vertical velocity, and Convective Available Potential Energy (CAPE) revealed seasonally heterogeneous moisture and convergence responses, but coherent positive $\omega$ anomalies over the Andes in DJF and JJA, consistent with reduced ascent. CAPE was significantly higher in MAM–SON, whereas the positive DJF difference was not statistically significant, showing that thermodynamic instability alone did not determine rainfall. Orinoquia did not exhibit a comparably consistent four-variable atmospheric signature. An elevation-stratified analysis showed a modest lowland-to-upland strengthening that plateaued above approximately 1000 m. A strictly antecedent ONI-lag model retained modest fixed-split skill in the Andes ($R^2=0.138$) but negligible skill in Orinoquia ($R^2=0.003$). The results support regional diagnosis, not causal or operational claims. Full article

Feature selection is a critical step in regression problems where a large number of continuous explanatory variables explain the same target through different dependency structures. Classical filters may remain sensitive to a single form of dependence, a single scale, or a specific discretization scheme; generalized entropy measures, on the other hand, typically require the parameters to be fixed at a single point. This study proposes a framework that evaluates the Sharma–Mittal entropy volumetrically across a two-dimensional parameter region rather than for a single parameter pair. For the continuous target and explanatory variables, the marginal, joint, and conditional densities are obtained using a Gaussian kernel density estimation; the conditional entropy and information gain surfaces are integrated across the region Ω = [0.05, 0.95]² in the α-β plane to define three indices: PICSME, which measures the conditional uncertainty volume; PIGSME, which measures the gain volume; and NIGSME, which is the ratio of this gain to the total entropy volume of the target. The method is supported by bandwidth consistency and the renormalization of conditional densities; thus, the issue of negative gain that can occur in the continuous variables is resolved, yielding positive and interpretable scores across all six datasets. It is formally demonstrated that the fact that the three indices produce the same ranking is not an empirical observation but rather the result of a monotonicity relationship valid under a fixed target entropy volume. The method is compared with Pearson and Spearman correlations, the Shannon information gain, mutual information, and random forest variable importance across six regression datasets (Airfoil Self-Noise, AirQualityUCI, BodyFat, Meteorology, Concrete, and WineQualityWhite) that differ in their sample size, dimensions, and application domain. The evaluation is not limited to ranking consistency; the out-of-sample prediction performance is measured using least-squares models on the top-k subsets, with rankings calculated from the training partition. The findings show that NIGSME exhibits a performance comparable to that of built-in filters, outperforms them on the Concrete and Meteorology datasets, and never ranks as the weakest method on any dataset. The results demonstrate that volumetric entropy metrics defined across the entire parameter space provide a feature-ranking tool that is independent of parameter selection for continuous variables. Full article

To investigate the feasibility of reconstructing electrocardiogram (ECG) waveforms from non-contact millimeter-wave radar measurements, a radar-based ECG reconstruction method using a CNN-BiLSTM network is presented. A synchronous acquisition platform integrating a millimeter-wave radar and a BIOPAC physiological signal acquisition system was established to collect chest-wall vibration signals and reference ECG signals. A multi-channel cross-correlation-based channel selection and temporal alignment procedure was employed to construct paired radar–ECG samples. The radar chest-wall vibration signals were filtered using an 8–30 Hz band-pass filter and then fed into the CNN-BiLSTM model, while a joint time–frequency loss function was introduced to constrain ECG reconstruction. On the self-built vital sign dataset, the reconstructed ECG achieved a correlation coefficient of 0.5631 with the reference ECG, while the mean absolute errors of heart rate and R–R interval were 1.00 BPM and 10.02 ms, respectively. These results suggest that the reconstructed signals preserve basic heartbeat timing and overall rhythm-related information, although the waveform-level agreement varies among samples and does not yet demonstrate consistent recovery of fine-grained ECG morphology. Evaluation on a public dataset further showed condition-dependent reconstruction performance under Resting, Apnea, and Valsalva conditions. Published MultiRes-LinkNet values were included only as contextual numerical references because the baseline was not reimplemented within the same experimental pipeline. Overall, the results provide preliminary evidence for the feasibility of contactless ECG reconstruction from millimeter-wave radar signals and suggest its potential value for radar-based vital sign monitoring. Full article

This work presents the first complete system for automated six degrees of freedom (6DOF) satellite pose estimation from spatially resolved, ground-based, adaptive optics (AO)-corrected imagery, addressing a key challenge in Space Domain Awareness (SDA). The approach mitigates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply shadowed imagery. A multi-stage deep neural network pipeline localizes the satellite, predicts pose, and optionally applies temporal filtering. Networks are trained exclusively on fully synthetic imagery generated from a CAD model, yet generalize effectively to real data, bridging the Sim2Real domain gap. On 137 real, human-labeled test images of Seasat, the model achieved a mean rotation error of $5^{°}$ and a mean image-plane translation error of 21 cm. Slant range error was quantitatively evaluated on synthetic data due to unknown real-sensor parameters. Qualitative evaluation of additional real Seasat imagery rated 177 of 199 predicted poses as “ground truth equivalent” or “high-confidence match,” with zero catastrophic failures. The system was extended to seven degrees of freedom (7DOF) for satellites with articulating components and demonstrated on real Hubble Space Telescope (HST) imagery, achieving ${5.5}^{°}$ rotation error, 51 cm image-plane translation error, and $8^{°}$ symmetry-adjusted solar array error on a 249-frame pass with causal temporal filtering. Across 586 real test images from Seasat and HST (captured over multiple decades under diverse conditions) the system consistently performed well. Full 6DOF performance was quantified on a high-fidelity wave optics (HFWO) synthetic test set of Seasat, where the model achieved ${8.4}^{°}$ mean rotation error, 34 cm image-plane translation error, and 1.4% line-of-sight range error at $r_0=6$ cm and 1031 km range. In a limited 200-image benchmark, the model demonstrated 48% lower mean rotation error than a single human labeler while operating ∼800× faster. It required <40 h and a single A100 GPU to generate data and train. The approach was also demonstrated for ARGOS, a smaller satellite with highly symmetric geometry. An exploratory General Image-Quality Equation-based image quality metric (AO-IQ) was introduced as an empirical correlate for pose accuracy. General-purpose models like GPT-4o and Depth Anything V2 failed across most SDA tasks, but rapid gains in vision-language models warrant continued monitoring. These results establish a new operational baseline for practical, real-time satellite pose estimation from AO SDA imagery. Full article

Numerical modelling is a powerful tool used in geomechanics; however, its reliability depends on proper validation. This study demonstrates the use of intelligent rock bolt measurements to validate the numerical model of underground deformation, providing a practical and applicable approach even with all numerical modelling simplifications. A numerical model of a selected Case Study Location was developed using geomechanical data from laboratory tests (uniaxial compressive strength, triaxial test, and Brazilian test). The numerical model was validated using deformation data collected by intelligent rock bolts installed in the underground mine. Applying statistical data correction methods, the model data accuracy was further improved. Applying Kalman filtering improved the correlation between measured and modelled deformations from 0.90 to 0.98, demonstrating the effectiveness of statistical methods. The novelty of this work lies in the combined use of intelligent rock bolts, FEM simulations, and statistical data correction to achieve a practical and reproducible validation framework, even when simplified geological assumptions are used. Full article

Improving growth while preserving breed-defining characteristics is a central challenge in indigenous livestock breeding. We addressed this issue in Jining Grey goats using low-depth whole-genome sequencing and a conservative multi-evidence marker-prioritization framework. The discovery cohort included 70 performance-graded goats at 3 months (n = 37) and 6 months (n = 33; mean depth, 2.07×), and 47 seven-day-old goats from the same project were used for exploratory early-life trend analysis (mean depth, 2.10×). After site-level filtering, phasing, and within-cohort imputation, masking-based assessment of 120,000 genotype cells yielded 94.38% exact genotype concordance and a hard-dosage correlation of 0.919. Performance grading captured consistent differences in body weight and body-size traits, but the small 3-month Conventional group required conservative interpretation. Selective-sweep analyses highlighted breed-background regions, including signals near KIT, whereas GWAS and superior-genotype comparisons prioritized candidate growth loci, including chr10:78518687 for six-month chest girth and literature-supported genes involved in muscle growth and developmental regulation. Overlap and permutation analyses indicated limited physical overlap between growth-prioritized loci and major selective-sweep regions. A 99-SNP candidate panel showed exploratory prediction potential (maximum cross-validated r ≈ 0.47), whereas seven-day analyses showed only nominal trends without FDR significance. These findings provide a preliminary marker resource for future Jining Grey goat breeding and conservation, pending external validation, denser genotyping, and functional confirmation. Full article

Background/Objectives: Accurate verification of single-isocenter multiple-target (SIMT) stereotactic radiosurgery is challenging owing to the complexity of multi-lesion delivery and the sensitivity of stereotactic dose gradients. We aimed to evaluate the efficacy of electronic portal imaging device (EPID)-based multileaf collimator (MLC) transmission and dosimetric leaf gap (DLG) correction for improving portal-dose prediction agreement in SIMT stereotactic radiosurgery (SRS) and to propose an exploratory target count-based institutional action level. Methods: This retrospective analysis included 112 consecutive patients treated with HyperArc™-based SRS (1–13 targets). Treatment plans were calculated using the Acuros XB algorithm for 6 MV flattening filter-free (FFF) beams. Two sets of MLC parameters were compared for portal-dose image prediction (PDIP): (1) standard parameters measured using an ion chamber; (2) EPID-derived corrected parameters. Portal-dose accuracy was evaluated using gamma index analysis with a 95% pass rate threshold. Results: The uncorrected method showed a strong negative correlation between target number and gamma passing rates, with complex plans (≥9 targets) dropping as low as 70%. EPID-based correction substantially improved dose agreement, yielding consistent passing rates above 98%, regardless of target number. Although uncorrected parameters remained within tolerance for plans with one to two targets, accuracy declined markedly starting at three targets. Conclusions: Our findings indicate that the EPID-based correction of MLC transmission and DLG mitigated cumulative modeling discrepancies in complex SIMT SRS. Thus, we propose the exploratory institutional observation of three targets, beyond which EPID-based correction may be considered to achieve optimal portal-dose prediction agreement in HyperArc-based stereotactic radiosurgery. Full article

We study the thermoelectric performance and stationary quantum correlations of a coherent double quantum dot heat engine driven solely by two conventional electronic reservoirs. The role of coherence is isolated by comparing the fully coherent dynamics with those under an energy-conserving pure dephasing channel that does not alter the system energy. Reducing the dephasing strength enhances the particle current, heat current, output power, and thermodynamic efficiency over a broad voltage range. After optimizing the electrochemical load and the dot energy levels, we find that coherence primarily amplifies the attainable power and efficiency without significantly relocating the optimal operating region. Although appreciable interdot coherence already exists at moderate Coulomb interaction, stationary entanglement emerges only when Coulomb blockade sufficiently suppresses the mixed-state contribution from the empty and doubly occupied states. We further construct a transport-based lower bound on the concurrence, providing an experimentally accessible entanglement witness that avoids full state tomography. These findings establish a clear hierarchy among energy filtering, quantum coherence, and Coulomb blockade in a minimal quantum thermoelectric device. Full article

Soil moisture is a key forecast variable of land surface models. Direct assimilation of microwave brightness temperature data to optimize soil moisture initial fields is an effective approach to improve the simulation accuracy of soil moisture. However, most existing direct assimilation methods adopt physical radiative transfer models as observation operators, and their complex parametric errors greatly restrict the improvement in assimilation performance. This study introduces a high-precision MLP (Multilayer Perceptron)-based surrogate radiative transfer model as the observation operator. Combined with the Simplified Extended Kalman Filter (SEKF), it develops a direct radiance data assimilation system for the Common Land Model (CoLM). Assimilation experiments are conducted using brightness temperature data from the Microwave Radiation Imager (MWRI) onboard the FY-3D satellite. Their performance over China’s land areas is systematically assessed through comparison with the assimilation scheme based on the Community Microwave Emission Model (CMEM). The results show that the MLP-based assimilation scheme can effectively improve soil moisture simulation accuracy, yet the improvement varies across vegetation types: grassland areas achieve the largest error reduction (10.2%), while semidesert areas present the most prominent increase in the correlation coefficient (53.9%). Compared with the CMEM scheme, the MLP scheme exhibits better error stability and produces generally improved assimilation effects; specifically, in semidesert areas, the error decreases by 9.4%, and the correlation coefficient increases by 62.8%. This study demonstrates that deep learning-based observation operators have strong application potential for land surface data assimilation under complex physical mechanisms. Full article

Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability. Full article

This study addresses target-to-ROV relative localization in an unmanned surface platform-assisted UAV–ROV search-and-recovery system. Because the submerged ROV is not assumed to be visible from the air, the UAV observes the floating target and a GNSS-equipped ROV-associated surface buoy in the same image. The buoy position and target-to-buoy image displacement are combined to construct a world-frame target-position measurement, whose covariance accounts for buoy GNSS uncertainty and correlated image-projection errors. An upward-looking ROV imaging sonar provides range–bearing measurements. A delay-aware extended Kalman filter fuses the asynchronous observations using sea-state- and confidence-dependent covariance adaptation and normalized-innovation gating. ROV acoustic/inertial navigation uncertainty is propagated into the sonar measurement covariance and the reported relative-state covariance, avoiding duplication of the same navigation error in the aerial channel. The method is evaluated using a JONSWAP-based temporal disturbance model, Monte Carlo simulations, and single-factor and joint sea-state–occlusion–delay sensitivity tests. Under the nominal sea-state-5 condition, the proposed method achieves a mean ROV-frame relative RMSE of 0.992 m, compared with 1.083 m for ROV-only localization and 1.054 m for fixed-covariance fusion, with no run exceeding the 5 m divergence threshold. The results demonstrate improved relative-localization robustness within the simulated environment. Full article

Background/Objectives: This study proposes Diffusion Inverse Filtering (DIF), a spatially informed transformation designed to counteract spatial smoothing in functional-connectivity representations (i.e., functional networks) and thereby enhance their discriminative power for pattern recognition. Spatial smoothing in electroencephalography (EEG) signals and derived features, such as functional connectivity, is largely attributed to volume conduction. Functional connectivity has been increasingly used in brain–computer interface (BCI) studies; however, this spatial smoothing can introduce spurious connections and distort functional-connectivity patterns. Methods: DIF approximates spatial smoothing in functional connectivity, which is potentially associated with volume conduction, as a diffusion-like process and applies a regularized inverse operation to transform the observed functional networks into networks with enhanced discriminative representations. The effectiveness of DIF in enhancing the discriminative power of functional-connectivity representations in pattern recognition was evaluated using emotion recognition as the paradigm task, which is a critical component of BCI systems. Experimental results show that DIF generally improves emotion-recognition performance relative to the originally observed functional networks under electrode-sparsification conditions, with the most consistent improvements observed for Pearson correlation coefficient (PCC) estimation. Both signal-level processing, which operates on EEG signals before functional-connectivity estimation, and function-al-connectivity-level transformations, including graph signal processing (GSP)-based filtering applied after functional-connectivity estimation, were included as comparators. Under this framework, DIF is also considered a functional-connectivity-level transformation, but does not rely on a GSP framework. Results: The performance of DIF demonstrates the potential of functional-connectivity-level transformation as a complement or alternative to signal-level processing for enhancing connectivity-based pattern recognition. Overall, DIF improves classification performance and offers strong compatibility with modern functional-connectivity-based BCI pipelines. Full article