Search Results (531)

Recursive least-squares (RLS) adaptive algorithms are capable of outperforming least-mean-square (LMS) methods for the identification of long-length impulse responses due to their ability to mitigate the high correlation properties of input signals, such as speech. Despite the encouraging results obtained in terms of tracking speed and accuracy, with respect to LMS methods, most RLS algorithms manifest numerical stability issues. Moreover, when an unknown system changes, the identification process needs to adapt to the new impulse response as soon as possible. The algorithm can require a significant amount of time to generate new accurate results in acoustic echo cancellation (AEC) scenarios. Due to the slow propagation speed of sound, acoustic echo paths are usually modeled using thousands of numerical coefficients, and adaptation energy remains relatively limited. A compromise is usually made between tracking capabilities and steady-state accuracy when choosing the forgetting factor (the most important parameter of the RLS algorithm). This paper analyzes a variable forgetting factor (VFF) RLS type of adaptive filter combined with the conjugate gradient (CG) line search method, which is designed to avoid the classical matrix inversion approach. This VFF-RLS-CG adaptive method is not susceptible to numerical stability issues and is designed to adapt its statistical estimates by determining whether a tracking situation occurs or whether the unknown system is not significantly different. Correspondingly, when necessary, the forgetting factor is decreased for faster adaptation to changes in the working environment. When the filter is estimated to work at steady-state, the above-mentioned parameter’s value is increased in order to boost the accuracy of the adaptive filter. The theoretical model is validated using simulations in AEC scenarios with tracking occurrences and relevant steady-state intervals. Full article

Soil respiration (Rs) is influenced by various factors, including soil temperature (ST), soil moisture (SM), and vegetation growth. Accurately and quantitatively estimating Rs from remote sensing data is essential for understanding the carbon cycle in desertification ecosystems. However, selecting appropriate vegetation representation factors poses a significant challenge during the remote sensing inversion. Sun-Induced Chlorophyll Fluorescence (SIF) is used extensively to monitor vegetation diseases and pests, assess drought conditions, and estimate Gross Primary Production (GPP). Nevertheless, the applicability of SIF for estimating Rs from remote sensing data and whether Rs modeling surpasses traditional vegetation indices requires further investigation. This study focuses on the Hongshaquan mining area, utilizing UAV hyperspectral, thermal infrared, and in situ monitoring data, combined with four machine learning methods: Random Forest (RF), Partial Least Squares (PLS), Support Vector Machine (SVM), and Back Propagation Neural Network Algorithm (BP) to establish a model for estimating Rs from remote sensing data. The determination coefficient (R²) and root mean square error (RMSE) were used to assess the performance of Rs inversion models characterized by SIF, Normalized Difference Vegetation Index (NDVI), and Near-Infrared Reflectance of Vegetation (NIRv) improved by radiance. The feasibility and modeling potential of estimating Rs from remote sensing data using SIF were explored. The results indicate that vegetation significantly impacts Rs in desertification mining area ecosystems, and the inversion accuracy of Rs improved by 26.8% after incorporating vegetation factors. The RF model displayed the best overall performance among the four machine learning methods. When the Salinity Index (SI) and Temperature Vegetation Dryness Index (TVDI) were treated as fixed components of the modeling independent variable, the modeling accuracy of the various vegetation representation factors ranked from highest to lowest as follows: SIF > NIRv > NDVI, with corresponding R² values of 0.63, 0.58, and 0.57, and RMSEs of 0.08 μmol·m⁻²·s⁻¹, 0.12 μmol·m⁻²·s⁻¹, and 0.13 μmol·m⁻²·s⁻¹, respectively. The research findings suggest that SIF holds significant promise for remote sensing estimation of Rs. The use of SIF can enhance the accuracy of Rs estimation. Full article

To address the main challenges in thickness estimation of SiC epitaxial layers from infrared reflectance spectra, including refractive index dispersion, multi-beam interference, and spectral uncertainty, this study develops a physics-constrained inversion framework for reflectance spectrum-based analysis. For the measured spectra, Savitzky–Golay filtering is first used to suppress spectral noise, and Gaussian fitting is then employed to improve the localization of interference extrema. The Sellmeier equation is introduced to characterize refractive index dispersion, and the layer thickness is obtained together with the dispersion parameters through nonlinear least squares fitting. To account for spectra affected by higher-order internal reflections, a multi-feature confidence-based identification strategy is further constructed, and an adaptive filtering procedure is introduced for multi-beam interference correction. A Monte Carlo perturbation analysis with ±0.1% peak perturbations and Gaussian noise is additionally performed to assess the robustness of the inversion results. Using SiC datasets measured at two incident angles, the proposed framework reduces the inter-angle deviation of the thickness estimates from 1.14% to 0.08% after multi-beam correction. The results support the effectiveness and robustness of the proposed workflow for the main SiC application scenario considered in this study. In addition, silicon wafer spectra are included as a supplementary transfer test to examine whether the multi-beam identification and correction strategy can be applied beyond the SiC example, rather than as a comprehensive cross-material validation of the framework. Full article

Sustainable Community-Based Tourism (SCBT) has been predominantly assessed from residents’ perspectives, leaving unexplored how tourists perceive and validate community sustainability. This study analyzes the influence of three SCBT dimensions, authenticity and community empowerment, integral sustainability, and ethical co-design, on tourist experience. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), we analyzed 341 responses from Mexican tourists with experience in indigenous community destinations in Oaxaca. Results show that integral sustainability is the strongest predictor of tourist experience, followed by ethical co-design. Notably, authenticity and community empowerment exhibit a significant inverse relationship, suggesting tensions between genuine local governance and visitor expectations. These findings position tourists as external validators of SCBT and challenge the linear authenticity–experience relationship assumed in classic literature, highlighting the need for heritage interpretation strategies that mediate this interaction. The study provides evidence from underrepresented Latin American indigenous contexts, addressing theoretical and geographical gaps in sustainable tourism research. Full article

Sparse regularization methods play an important role in inverse problems for extracting key features of underlying parameters and have attracted increasing attention in meteorological data assimilation. However, when the condition number of the background error covariance matrix is extremely large (e.g., 10¹²), the instability of the inverse problem makes accurate reconstruction difficult. To address this issue, a gradient operator is incorporated into the sparse regularization term of the cost function, and a Kalman filter (KF) algorithm is developed within a majorization–minimization (MM) framework to solve the resulting optimization problem. The problem is reformulated as a weighted least-squares problem via the MM strategy and further decomposed into two subproblems in the null space and its oblique complementary space through oblique projection, which are then solved using the KF method. This approach avoids the use of an adjoint model typically required in four-dimensional variational data assimilation (4D-Var). In addition, a modified f-slope strategy with a constrained search interval is introduced to adaptively select the regularization parameter during computation. Numerical experiments on the initial condition inversion of the Convection–Diffusion equation demonstrate that the proposed method achieves more accurate reconstruction of key features than the l₁-norm regularized 4D-Var method, particularly in capturing sharp gradients and sparse structures. The adaptive regularization strategy automatically balances sparsity and smoothness without manual tuning. The inversion errors remain low even when the condition number ranges from 10⁸ to 10¹⁴, with relative MSE and MAE below 0.01 and relative bias below 0.005, indicating improved robustness and reconstruction accuracy under severely ill-conditioned settings. Full article

Monitoring avocado (Persea americana Mill., cv. Semil-34) in tropical mountain landscapes of Cambita, San Cristóbal, Dominican Republic is inherently complex due to the pronounced topographical and climatic heterogeneity that modulates the crop’s ecophysiological responses, specifically vegetative vigor, carbon allocation, and the synchronization of reproductive flushes. This study integrates 5-year (2020–2025) Sentinel-2 time series, ERA5-Land climatic variables (air temperature, total precipitation, and radiation), and geomorphometric covariates to explain variability in yield and fruit quality. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Red Edge (NDRE), and Normalized Difference Moisture Index (NDMI), were analyzed using Partial Least Squares Regression (PLSR) to characterize phenological dynamics and rank dominant predictors. The results revealed coherent spectral phenological trajectories; however, a significant inverse relationship was detected between canopy vigor and yield during reproductive phases. High vegetation index values were significantly and negatively associated with lower production (r = −0.58, p < 0.0021), reflecting a potential source–sink imbalance. Topography functioned as a structural filter, regulating root drainage and productive stability across the landscape. While yield variability was partially explainable (R² = 0.38), internal fruit quality, measured as dry matter content, exhibited comparatively high environmental stability. A central contribution of this research lies in identifying the “vigor paradox” in cv. Semil-34 and the suggestion that topography may exert a stronger influence than direct spectral signals under tropical hillside conditions. These findings provide an exploratory framework for anticipating yield and fruit quality through satellite remote sensing or UAVs, supporting site-specific management decisions in mountain agricultural systems. Full article

This study investigates a range of parameter estimation methods for the Half-Logistic Inverse Rayleigh Distribution (HLIRD) under two distinct sampling frameworks: ranked set sampling (RSS) and simple random sampling (SRS). The estimation techniques considered include maximum likelihood estimation, ordinary and weighted least squares, and the maximum and minimum product of spacings methods. Model adequacy is evaluated using five goodness-of-fit criteria: the Anderson–Darling (AD) statistic, its right- and left-tail variants, the second-order left-tail AD statistic, and the Cramér–von Mises statistic. An extensive simulation study is conducted to thoroughly evaluate and compare the performance of the proposed estimators while maintaining a fixed total number of observations across both sampling schemes. The practical relevance of the proposed methods is further illustrated through an application to a real dataset consisting of 69 carbon fiber specimens, with tensile strength measurements (in GPa) recorded at a gauge length of 20 mm. The numerical results demonstrate that estimators based on RSS consistently outperform their SRS counterparts across all considered performance measures, including mean squared error, bias, and mean absolute relative error. Overall, the findings highlight the advantages of employing RSS for parameter estimation of the HLIRD, particularly due to its superior efficiency in small-sample scenarios. Full article

Postseismic deformation observed by terrestrial Global Navigation Satellite System (GNSS) and seafloor GNSS-Acoustic techniques (GNSS-A) provides essential constraints on the depth-dependent viscoelastic structure of subduction zones. In this study, we collect and process decadal postseismic observations following the 2011 Tohoku-oki M_w9.0 earthquake, including 232 onshore GNSS stations and six offshore GNSS-A sites. After removing the interseismic velocity terms, we extract the postseismic deformation signals mainly driven by viscoelastic relaxation during the period from 3 to 9 years after the earthquake. The inversion is primarily constrained by horizontal displacements, which have higher accuracy than vertical observations. We adopt a radially layered viscoelastic Earth model with lateral heterogeneity between continental and oceanic domains based on the Burgers rheology and half-space dislocation theory. Using the least-squares principle, we invert for the optimal viscoelastic structure under the strong constraint of fixed mantle viscosity. The optimal continental and oceanic crustal elastic thicknesses are 24.4 km and 37 km, with minimum horizontal Root-Mean-Square errors (RMS) of 5.68 cm and 6.81 cm, respectively. The mantle viscosity shows significant depth-dependence and obvious land–ocean differences. These results verify the critical role of joint land and seafloor geodetic constraints and provide a refined viscoelastic structure model for subduction zones. Full article

Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) is widely used for geohazard and infrastructure health assessment because it enables high-precision deformation monitoring. However, long-term time series observations often contain phase discontinuities caused by localized deformation with large spatial gradients, which can severely compromise phase unwrapping reliability. To address this limitation, we propose an Adaptive Shortest-Path Network (ASPN) method for GB-InSAR phase unwrapping. A temporal sliding window strategy is used to partition the acquisition stream into processing units. Within each unit, arc quality is quantified by least squares inversion using the mean square error (MSE) and temporal coherence. The unreliable arcs are removed, and the network is then reconnected using Dijkstra’s shortest-path algorithm to improve unwrapping stability and accuracy. The method is evaluated on a corner reflector-controlled deformation dataset and a stope slope dataset. In the controlled experiment, ASPN reduces the root mean square error (RMSE) of cumulative deformation from 1.684 mm to 0.037 mm, representing a 97.8% reduction, while in the stope slope experiment, it reduces the mean phase residual by 30.3% relative to the Delaunay network and by 11.6% relative to APSP. Overall, ASPN provides an efficient dynamic update mechanism and a robust, high-accuracy solution for long-term GB-InSAR time series deformation monitoring. Full article

This study explored Entities’ Performance as an explained function of Human Resource Costs (HRC) to further justify recognition of the Labour Costs proxies in the Statement of Financial Position (SOFP). This has been investigated to provide robust empirical evidence from the Nigerian Exchange Group (NGX) to spur the International Accounting Standard Board (IASB) to release an Exposure Draft (ED) for public discussion and have a standard to recognize proxies of HRC as assets in the SOFP. To provide grounds for inclusion of HRC in the SOFP by the IASB, unlike most other empirical studies reviewed, which deployed limited methods and years of time series data, this study expanded the scope and methods using Pooled Cross-Sectional (PCS) time series data of 27 quoted companies from 1992 to 2023 in the NGX. While most studies employed inefficient Ordinary Least Squares (OLS), this current study progressed from Descriptive Statistics to OLS, Pooled OLS, and Rodman’s Xtabond2 Generalized Method of Moments (GMM) to resolve the conundrums of endogeneity, reversed causality, and stationarity common to unbalanced PCS time series data. The results revealed from the GMM showed that LSW (18.40), positive, and LTD (−22.63), inverse, and Wald ^2 = 66.35 with p-value (0.002), obviously validated the strong joint significance of the regressors on ROA (performance) of 27 sampled firms in the NGX. It is recommended that IASB align with the momentum from the output of research from academia by issuing standards to recognize HRC as assets in the SOFP. Full article

This paper presents the development of two adaptive autopilots for the Cessna Citation X business jet aircraft. The two adaptive control strategies, including a dynamic inversion controller and a neural network controller, provide dual adaptation. The control objective consists of tracking the vertical speed, altitude, and heading commands. Dynamic inversion is applied on each output variable, and then the neural network (NN) controller is updated using adaptive law, derived from backpropagation. Dynamic inversion (DI) is achieved locally using a Recursive Least Squares (RLS) algorithm for state estimation. An inner control loop for the pitch, roll and yaw rates is integrated within the autopilots. The longitudinal states were separated from the lateral states in order to differentiate between longitudinal and lateral control. Robustness tests were conducted under turbulence and wind-gust conditions. The autopilot results were compared with flight simulation data from a Cessna Citation X research flight simulator. Results have shown that the autopilots accurately track the vertical speed, altitude and heading reference signals. The flight simulation comparison has shown that the proposed adaptive controllers were better than the one currently on board the Cessna Citation X. Full article

Suicide is a major public health problem worldwide, and its reported reasons and methods show marked variation by gender and age. Although suicide rates are generally higher among men, suicides among women demonstrate distinct sociodemographic and age-related patterns that remain insufficiently explored. In Türkiye, national suicide statistics are available; however, nationwide, age-stratified analyses focusing exclusively on women are limited. This study aimed to investigate long-term trends, age-related differences in reported reasons and methods of suicide among women in Türkiye, and to provide insights relevant to age- and gender-sensitive prevention strategies. This retrospective, nationwide descriptive study analysed female suicide data obtained from the Turkish Statistical Institute between 2009 and 2023. A total of 12,868 female suicide cases were included (mean age 36.5 ± 19.3 years). Data were evaluated according to year, age group, marital status, educational level, suicide cause, and suicide method. Causes and methods were classified based on official administrative categories. Descriptive statistics were calculated, and associations between age groups and suicide causes and methods were assessed using Pearson’s chi-square test. During the 15-year study period, 12,868 women died by suicide in Türkiye. The annual suicide rate ranged from 1.81 to 2.46 per 100,000 population, with the lowest rate observed in 2017 and the highest in 2022. Among all age groups, the most frequent cause of suicide was illness, especially in women aged 45 and older. The proportion of suicides due to illness was 13.9% in the 15–24 age group, 24.6% in 25–34, 41.0% in 45–54, and 42.3% in 55–64 (p < 0.001). Emotional and relationship-related causes were more prevalent among younger women, particularly in the 15–24 age group (4.8%), but declined significantly with age (p < 0.001). Economic hardship was the least cited cause overall, especially among women under 35 (p < 0.001). Regarding methods of suicide, hanging was the most common method in all age groups and increased with age—35.8% in 15–24, 55.1% in 45–54, and 63.5% in 75+ age group (p < 0.001). The use of chemical substances peaked in the 15–24 age group (12.4%) and declined in older women (5.8% in 75+). Firearm use showed a significant inverse relationship with age, from 24.6% in those under 15 to 0.8% in women aged 75 and over (p < 0.001). These age-related differences in both the causes and methods of suicide were statistically significant (p < 0.001). Female suicide in Türkiye exhibits pronounced age-dependent differences in both causes and methods. Illness-related suicides and hanging predominate in older age groups, while younger women show a more diverse pattern of reported reasons and methods. The high prevalence of nonspecific classifications highlights limitations in current suicide reporting systems. These findings underscore the need for improved suicide classification, enhanced surveillance, and age- and gender-sensitive prevention strategies tailored to women across the lifespan. Full article

Electric-propulsion (EP) orbit raising from geosynchronous transfer orbit (GTO) to geostationary Earth orbit (GEO) requires long-duration, continuously steered low thrust, for which small pointing deviations may accumulate over time, and practical execution is constrained by spacecraft attitude and momentum management. This study develops a cross-layer command-to-execution framework that couples mission-level thrust-command generation with smooth trajectory planning of an EP vectoring arm. At the orbit layer, an engineering-oriented mission-level transfer model with dominant J₂ secular correction is used to construct a time-tagged sequence of thrust magnitude and direction commands for the GTO–GEO transfer. At the execution layer, a 4-DOF revolute arm is modeled using Denavit–Hartenberg kinematics, and the desired thrust directions are mapped to feasible joint trajectories through a direction-only inverse-kinematics formulation cast as a constrained nonlinear least-squares problem with cross/dot residuals, smoothness regularization, and warm-start propagation. In numerical simulation, the GTO–GEO transfer is completed in approximately 278 days with Δv ≈ 3665 m/s, corresponding to a propellant consumption of 175 kg (spacecraft mass from 1800 kg to 1625 kg). The planned joint trajectories remain smooth over the full horizon, with maximum inter-sample variations of 1.84° and 1.04° for the major and minor motion groups, respectively. The numerical geometric thrust-direction tracking error in the kinematic mapping remains at the millidegree level, with a mean of 7.39 × 10⁻⁴° and a P95 of 0.00101°. The results demonstrate that the proposed cross-layer interface can generate executable, low-bandwidth joint commands while preserving high geometric consistency with the desired thrust directions in the numerical kinematic mapping sense, thereby providing a practical basis for implementation-oriented studies of EP orbit transfer with vectoring manipulators. Full article

Accurate simulation of reinforced concrete (RC) members under cyclic loading requires hysteresis models that capture degradation and pinching, yet inverse identification of such models remains challenging because the internal-state evolution is strongly coupled and sensitive to incremental consistency. This study develops a physics-constrained, model-based framework to identify the full twelve-parameter Bouc–Wen–Baber–Noori (BWBN) model directly from cyclic force–displacement records and to deploy the calibrated parameters in OpenSees. Parameter estimation is posed as a bound-constrained nonlinear least-squares problem, where each objective evaluation advances the BWBN internal variables through a discrete incremental constitutive update and accumulates the energy-driven deterioration measure using a consistent trapezoidal work integration. Validation on nine RC column tests covering flexural, flexural–shear, and shear failures shows good agreement between simulated and experimental hysteresis loops, with R² ranging from 0.956 to 0.986 and RMSE ranging from 0.06 to 0.09 over the full records. Unlike simpler hysteresis models that omit degradation and pinching, the calibrated BWBN model reproduces mode-dependent deterioration and reloading pinching, and the identified parameters can be used directly in OpenSees for subsequent nonlinear simulations. Full article

This study explores classical and Bayesian estimation for the two-parameter Chen distribution with randomly censored data, where censoring times follow an independent two-parameter Chen distribution with separate shape and scale parameters. We first derive the maximum likelihood estimators of the unknown parameters, together with their asymptotic variances and credible intervals, and further adopt the method of moments, L-moments and least squares methods for classical estimation. Under the generalized entropy loss function and inverse gamma priors, Bayesian estimation is implemented via Gibbs sampling, with the highest posterior density credible intervals of parameters constructed accordingly. We also investigate the estimation of key reliability and lifetime characteristics of the distribution, and conduct Monte Carlo simulations to compare the performance of all aforementioned estimation methods. Finally, two real-world CMAPSS jet engine lifetime datasets from NASA are applied to validate the practical effectiveness of the proposed estimation approaches, demonstrating the enhanced flexibility of the Chen distribution compared to the exponential distribution in fitting aerospace-related censored data, given the marginal p-values in the K-S tests. Full article