Search Results (256)

This study introduced Softball Pitching Plane (SPP), a best-fit geometric plane designed to characterize the throwing arm spatial trajectory during the windmill softball pitch. The purpose was to evaluate the reliability of this planar representation and determine whether deviations from the SPP were associated with ball velocity. Forty-nine adolescent softball pitchers each performed 15 drop-ball pitches (735 total pitches). Kinematics were recorded using a 15-sensor electromagnetic tracking system. A weighted orthogonal least-squares algorithm was applied to compute the best-fit plane across three intervals (WU–BR, TOP–BR, and DS–BR). Reliability was assessed using within-subject variability, leave-one-trial-out error, and ICCs. Linear mixed-effects models were used to examine associations between SPP parameters and ball velocity. The downswing–ball release interval of the wrist trajectory showed the most stable planar pattern (RMS = 0.053 m). SPP parameters demonstrated high reliability (CV ≤ 4.2%; ICC = 0.81–0.90). RMS deviation negatively predicted ball velocity at both within-pitcher (−0.11 km·h⁻¹ per cm, p = 0.003) and between-pitcher levels (−0.40 km·h⁻¹ per cm, p = 0.03). These findings indicate that, in adolescent softball pitchers, the SPP provides a reliable geometric description of throwing-arm motion during the downswing–ball release phase, with reduced deviation associated with higher pitch velocity. Full article

This research article focuses on the method of vehicle crash velocity evaluation based on the tensor product approximation by B-splines. Weights associated with each observation are introduced in the least square method, which is based on probabilistic reasoning. The presented calculation algorithm is built based on the Compact vehicle class from the NHTSA database, which consists of 338 records of frontal crashes with stationary obstacles. The presented model is restricted to velocities in the range between 43 and 93.5 km/h. The relative error obtained for the presented calculation method is 5.2%. Hence, improvement has been observed in comparison with the linear model with the same probabilistic weights approach, for which relative error is equal to 5.47%. Full article

To address clock drift arising from the absence of GPS synchronization during ocean-bottom seismic observations, we propose a time-offset correction and quality-control scheme that uses the correlation of P-wave empirical Green’s functions (EGFs) as the metric, and we demonstrate its efficacy in mitigating cross-correlation asymmetry caused by azimuthal noise in shallow-water environments. The method unifies the time delays of the four components into a single objective function, estimates per-node offsets via sparse weighted least squares with component-specific weights, applies spatial second-difference smoothing to suppress high-frequency oscillations, and performs spatiotemporally constrained regularized iterative optimization initialized by the previous day’s inversion to achieve a robust solution. Tests on a real four-component ocean-bottom node (4C-OBN) hydrocarbon exploration dataset show that, after conventional linear clock-drift correction of the OBN system, the proposed method can effectively detect millisecond-scale time jumps on individual nodes; compared with traditional noise cross-correlation time-shift calibration based on surface-wave symmetry, our four-component fusion approach achieves superior robustness and accuracy. The results demonstrate a marked increase in the coherence of the four-component cross-correlations after correction, providing a reliable temporal reference for subsequent multicomponent seismic processing and quality control. Full article

Accurate, scalable, and outlier-robust state estimation (SE) is critical for large AC power systems with mixed SCADA and PMU measurements. This paper proposes D-BSE-L1, a distributed robust state estimator for the bilinear AC model. The method combines the bilinear state estimation framework with a convex weighted least absolute value (WLAV) loss so that all area subproblems become convex linear or quadratic programs coordinated by ADMM, and a cache-enabled Cholesky factorization is used to accelerate the third-stage linear solves. Simulations on the IEEE 14-, 118-, and 1062-bus systems show that D-BSE-L1 achieves estimation accuracy comparable to its centralized bilinear counterpart. Under severe bad-data conditions, its advantage over weighted least squares with the largest normalized residual test (WLS + LNRT) is pronounced: with 10% 1.5× bad data, the voltage magnitude and angle MAEs are about 62% and 54% of those of WLS + LNRT, and with 5% 5× bad data, they further drop to roughly 43% and 51%, while requiring only about one-tenth of the CPU time. On the 1062-bus system, D-BSE-L1 maintains the MAE of the centralized estimator but reduces runtime from 2.46 s to 0.72 s, providing a scalable, hyperparameter-free, and robust solution for partitioned state estimation in large-scale power grids. Full article

This paper introduces the Topp–Leone Heavy-Tailed Odd Burr X-G (TL-HT-OBX-G) family of distributions (FOD), designed to model diverse data patterns. The new distribution is an infinite linear combination of the established exponentiated-G distributions. We used the established properties of the exponentiated-G distribution to infer the properties of the new FOD. The properties considered include the quantile function, moments and moment generating functions, probability-weighted moments, order statistics, stochastic orderings, and Rényi entropy. Parameter estimation is performed using multiple techniques, such as maximum likelihood, least squares, weighted least squares, Anderson–Darling, Cramér–von Mises, and Right-Tail Anderson–Darling. The maximum likelihood estimation method produced superior results in the Monte Carlo simulation studies. A special case of the developed model was applied to three real-world datasets. The model parameters were estimated using the maximum likelihood method. The selected special model was compared to other competing models, and goodness-of-fit was evaluated by the use of several goodness-of-fit statistics. The developed model fit the selected real-world datasets better than all the selected competing models. The new FOD provides a new framework for data modeling in health sciences and reliability datasets. Full article

Underground gas storage (UGS) facilities may cause ground displacements as a result of the cavern convergence or regular gas injection (alternate ground uplift and subsidence). The occurrence and scale of displacements are strongly dependent on the storage time and cavern capacity. At an early stage of facility operation, displacements can be difficult to detect in the presence of wetlands. The main objective of this study was to describe the global and local relationships between vertical ground displacements observed over a small and relatively new Kosakowo UGS facility (Poland) from 2014 to 2024 (dependent variable) and selected topographic, hydrological, and mining factors (independent variables). The dependent variable was determined through SBAS-InSAR analysis of Sentinel-1 SAR data, while the independent variables were developed using passive Sentinel-2 imagery and open geospatial data. The global relationships between variables were described using Ordinary Least Squares (OLS) and Generalized Linear Regression (GLR) models, while the Geographically Weighted Regression (GWR) model was utilized to analyze local relations. The results obtained indicate that ground displacements were characterized by seasonal fluctuations between 4 mm and 10 mm. The factors that had, both globally and locally, the strongest influence were soil moisture, vegetation water content, and the flora condition, indicating that the environmental hydrogeology had the greatest impact on the phenomenon under study. None of the considered models identified underground gas storage as a significant contributing factor to the observed ground displacements. The results confirm that the presence of wetlands can be a significant obstacle to an accurate description of the impact of gas storage on the ground movements, especially in UGS areas at an early stage of operation. Full article

In mineral processing and metallurgy, total iron grade serves as a critical indicator guiding the entire production chain from crushing to smelting, directly influencing the quality and yield of steel products. To address the limitations of conventional matrix effect correction methods in X-ray fluorescence (XRF) analysis—such as low accuracy, high time consumption, and labor-intensive procedures—this study proposes a novel hybrid model (DSCN-LS) integrating least squares (LS) with dynamically regularized stochastic configuration networks (DSCNs) for total iron ore grade quantification. Through feature analysis, we decompose the grade modeling problem into a linear structural component and nonlinear residual terms. The linear component is resolved by means of LS, while the nonlinear terms are processed by the DSCN with a dynamic regularization strategy. This strategy implements node-specific weighted regularization: weak constraints preserve salient features in high-weight-norm nodes, while strong regularization suppresses redundant information in low-weight-norm nodes, collectively enhancing model generalizability and robustness. Notably, the model was trained and validated using datasets collected directly from industrial sites, ensuring that the results reflect real-world production scenarios. Industrial validation demonstrates that the proposed method achieves an average absolute error of 0.3092, a root mean square error of 0.5561, and a coefficient of determination (R²) of 99.91% in total iron grade estimation. All metrics surpass existing benchmarks, confirming significant improvements in accuracy and operational practicality for XRF detection under complex industrial conditions. Full article

Path tracking is a fundamental function for autonomous vehicles, but its performance often degrades under parameter variations and controller fragility—an issue seldom addressed together in prior studies. This paper develops a robust non-fragile Linear Quadratic Regulator (LQR) using linear matrix inequality (LMI) optimization, explicitly considering uncertainties in vehicle speed, mass, and cornering stiffness as well as gain perturbations from implementation. A two-degrees-of-freedom bicycle model is employed for controller design, and a weighted least-squares allocation method integrates multiple actuators, including front steering, rear steering, four-wheel independent drive, and braking. A double lane-change maneuver in CarSim evaluates the proposed design. The robust and non-fragile LQR maintains lateral offset within 0.02 m and overshoot below 1% under ±20% parameter variation, offering improved stability margins compared with the baseline LQR. The results highlight context-dependent actuator effects and clarify the trade-off between control complexity, robustness, and real-world applicability. Full article

The quantification of volatile compounds in virgin olive oil poses several analytical challenges due to the existence of different concentrations, chemical families, and the possible matrix effect. Accurate quantification, using adequate methodological calibration and statistical procedures, is essential for obtaining reliable results. The aim of this work was to develop and validate an analytical–statistical approach for the quantification of volatile compounds in virgin olive oil. Therefore, several analytical parameters were determined for four calibrations. The ordinary least square (OLS) linear adjustment was selected over the weighted least square due to the homoscedasticity of the variable errors. Additionally, standard addition (AC) and AC with an internal standard (IS) exhibited greater variability, whereas external matrix-matched calibration (EC) was identified as the most reliable approach for quantifying volatile compounds in virgin olive oil. The employment of an IS did not improve the performance of the method in any case. Thus, based on the statistical results, the OLS linear adjustment with EC was selected as the best statistical–analytical approach for quantifying volatiles in olive oil matrices. The volatiles of nine virgin olive oil samples were quantified, applying different methodological calibrations, and no differences were detected, underscoring EC as a superior alternative. Full article

Background: Adherence to Medical Nutrition Therapy (MNT) is a key determinant of therapy success, particularly in chronic diseases like chronic kidney disease (CKD). MNT in CKD requires significant changes in patient’s dietary habits, which can affect long-term adherence. This study aims to evaluate the adherence to MNT in stage 5 CKD patients undergoing conservative kidney management (CKM), identifying potential challenges and strengths of nutritional intervention. Methods: We enrolled in 94 stage 5 CKD patients undergoing CKM at the University Hospital of Modena, Italy. We collect clinical data from medical and nutrition records. The inclusion criteria comprised patients of all genders, ages, and ethnicity with stage 5 chronic kidney disease (CKD), in pre-dialysis, enrolled in the nephrology and dietetics program, who had access to 24-h urine tests, anthropometric measurements, and dietary history records. Exclusion criteria included patients with CKD stages lower than 5, those who had not undergone at least one nutritional assessment, or lacked accessible 24-h urine data. The study utilized medical and dietary records from September 2017 to March 2025. The primary outcome was the assessment of adherence to medical nutrition therapy (MNT), comparing prescribed protein intake with actual intake, estimated from dietary history (DH). Protein intake was compared with normalized protein nitrogen appearance (nPNA) as stated by recent guidelines. Additional factors influencing adherence, such as age, gender, comorbidities, physical activity, and prior dietary interventions, were also evaluated. Anthropometric measurements and biochemical tests were collected, and dietary intake was assessed using a seven-day DH. Results: Data were analyzed using descriptive statistics, linear correlation models, univariate logistic regression, t-tests, paired t-tests, and chi-square tests, with significance set at p < 0.05. Most of the patients follow suggested energy and protein intakes limits; however, substantial individual variability emerged Bland–Altman analysis indicated a moderate bias and wide limits of agreement for energy intake (+116 kcal; limits of agreement –518.8 to +751.3 kcal), revealing frequent overestimation in self-reports. Protein intake showed less systematic error, but discrepancies between dietary recall and biochemical markers persisted. Protein intake decreased significantly over time (p < 0.001), while correlation with nPNA did not reach statistical significance (ρ = 0.224, p = 0.051). No significant associations were identified between adherence and most clinical or lifestyle factors, although diabetes was significantly associated with lower adherence to protein intake (p = 0.042) and a predominantly sedentary lifestyle showed a borderline association with energy intake adherence (p = 0.076), warranting further investigation. Longitudinal analysis found stable BMI and body weight, alongside notable reductions in sodium (p = 0.018), potassium (p = 0.045), and phosphorus intake (p < 0.001) over time. Conclusions: Assessing dietary adherence in CKD remains complex due to inconsistencies between self-reported and biochemical estimates. These findings highlight the need for more objective dietary assessment tools and ongoing, tailored nutritional support. Multifaceted interventions—combining education, personalized planning, regular monitoring, and promotion of physical activity—are recommended to enhance adherence and improve clinical outcomes in this vulnerable population. Full article

Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability. These issues severely hinder manual transcription and render conventional OCR techniques inadequate, as they are typically trained on modern printed or handwritten text and lack interpretability. To tackle these challenges, we propose FAIR-Net, a hybrid architecture that combines the unsupervised feature learning capacity of a deep autoencoder with the semantic transparency of a fuzzy rule-based classifier. In FAIR-Net, the deep autoencoder first compresses high-resolution character images into low-dimensional, noise-robust embeddings. These embeddings are then passed into a Fuzzy Neural Network (FNN), whose hidden layer leverages Fuzzy C-Means (FCM) clustering to model soft membership degrees and generate human-readable fuzzy rules. The output layer uses Iteratively Reweighted Least Squares Estimation (IRLSE) combined with a Softmax function to produce probabilistic predictions, with all weights constrained as linear mappings to maintain model transparency. We evaluate FAIR-Net on CASIA-HWDB1.0, HWDB1.1, and ICDAR 2013 CompetitionDB, where it achieves a recognition accuracy of 97.91%, significantly outperforming baseline CNNs (p < 0.01, Cohen’s d > 0.8) while maintaining the tightest confidence interval (96.88–98.94%) and lowest standard deviation (±1.03%). Additionally, FAIR-Net reduces inference time to 25 s, improving processing efficiency by 41.9% over AlexNet and up to 98.9% over CNN-Fujitsu, while preserving >97.5% accuracy across evaluations. To further assess generalization to historical scripts, FAIR-Net was tested on the Ancient Chinese Character Dataset (9233 classes; 979,907 images), achieving 83.25% accuracy—slightly higher than ResNet101 but 2.49% lower than SwinT-v2-small—while reducing training time by over 5.5× compared to transformer-based baselines. Fuzzy rule visualization confirms enhanced robustness to glyph ambiguities and erosion. Overall, FAIR-Net provides a practical, interpretable, and highly efficient solution for the digitization and preservation of ancient Chinese character corpora. Full article

When the global navigation satellite systems (GNSSs) are unavailable, cellular signals of opportunity (SOPs) can be used to achieve positioning. However, in low observability environments where both GNSS signals and cellular SOPs are less than 2, the current research on cellular SOPs–GNSS signals fusion positioning faces challenges regarding the difficulty in precise position initialization and spatiotemporal uncertainty. To address these issues, a cellular SOPs and GNSS signals fusion positioning model by the pseudorange single difference at multi-epoch (PSDM) is proposed. The spatiotemporal uncertainty of fusion positioning is solved by differential pseudorange. Then, to solve the problem of difficult precise location initialization during the differential pseudorange positioning process, a pseudo-linearization closed-form method was derived, and its limitations were analyzed. Moreover, the pseudo-linearization equation was reconstructed. Based on this, a constrained multi-step weighted least squares (CMWLS) method is proposed that reduces the impact of noise on the PSDM positioning models and improves global convergence. According to the simulation and field test results, the proposed fusion positioning method shows good positioning performance in low-observability environments. For urban positioning in such environments, this study provides a new solution strategy and avoids the requirement of the prior information of the receiver’s initial position for positioning. Full article

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

We propose an efficient Nyström-based unitary subspace method for low-complexity two-dimensional (2D) direction-of-arrival (DOA) estimation in uniform rectangular array (URA) signal processing systems. The conventional high-resolution DOA estimation methods often suffer from excessive computational complexity, particularly when dealing with large-scale antenna arrays. The proposed method addresses this challenge by combining the Nyström approximation with a unitary transformation to reduce the computational burden while maintaining estimation accuracy. The signal subspace is approximated using a partitioned covariance matrix, and a real-valued transformation is applied to further simplify the eigenvalue decomposition (EVD) process. Furthermore, the linear prediction coefficients are estimated via a weighted least squares (WLS) approach, enabling robust extraction of the angular parameters. The 2D DOA estimates are then derived from these coefficients through a closed-form solution, eliminating the need for exhaustive spectral searches. Numerical simulations demonstrate that the proposed method achieves comparable estimation performance to state-of-the-art techniques while significantly reducing computational complexity. For a fixed array size of $M=N=20$, the proposed method demonstrates significant computational efficiency, requiring less than $50\%$ of the running time compared to conventional ESPRIT, and only $6\%$ of the time required by ML methods, while maintaining similar performance. This makes it particularly suitable for real-time applications where computational efficiency is critical. The novelty lies in the integration of Nyström approximation and unitary subspace techniques, which jointly enable efficient and accurate 2D DOA estimation without sacrificing robustness against noise. The method is applicable to a wide range of array processing scenarios, including radar, sonar, and wireless communications. Full article

A geographically and temporally weighted regression (GTWR) model is an effective tool for dealing with spatial heterogeneity and temporal non-stationarity simultaneously. As an important characteristic of spatiotemporal data, spatiotemporal autocorrelation should be considered when constructing spatiotemporally varying coefficient models. The proposed local autoregressive geographically and temporally weighted regression (GTWRLAR) model can simultaneously handle spatiotemporal autocorrelations among response variables and the spatiotemporal heterogeneity of regression relationships. The two-stage weighted least squares (2SLS) estimation can effectively reduce computational complexity. However, the weighted least squares estimation is essentially a Nadaraya–Watson kernel-smoothing approach for nonparametric regression models, and it suffers from a boundary effect. For spatiotemporally varying coefficient models, the three-dimensional spatiotemporal coefficients (longitude, latitude, and time) inherently exhibit larger boundaries than one-dimensional intervals. Therefore, the boundary effect of the 2SLS estimation of GTWRLAR will be more serious. A local–linear geographically and temporally weighted 2SLS (GTWRLAR-L) estimation is proposed to correct the boundary effect in both the spatial and temporal dimensions of GTWRLAR and simultaneously improve parameter estimation accuracy. The simulation experiment shows that the GTWRLAR-L method reduces the root mean square error (RMSE) of parameter estimates compared to the standard GTWRLAR approach. Empirical analyses of carbon emissions in China’s Yellow River Basin (2017–2021) show that GTWRLAR-L enhances the adjusted $R^2$ from 0.888 to 0.893. Full article