Search Results (180)

Modeling time-to-event data without accounting for risk factors may lead to biased conclusions. Therefore, researchers sometimes assume the existence of competing risks to accurately model life data. Furthermore, since medical research can be time-consuming and costly, experimenters may consider optimizing their use of resources by employing a censoring plan, such as a progressive Type-II censoring scheme. This study investigates statistical inference for a competing-risks model based on Stacy’s generalized gamma distribution under the latter censoring scheme. A couple of frequentist estimation methods—namely, maximum likelihood estimation and maximum product-of-spacings estimation—are used to obtain point estimates of the model parameters. Using the point estimators, asymptotic confidence intervals are constructed alongside two bootstrapping confidence intervals—namely, the percentile and Studentized bootstrap confidence intervals. Monte Carlo simulations are used to numerically examine the performance of both point and interval estimation using appropriate criteria. Overall, the simulations indicate that maximum product-of-spacings estimation outperforms maximum likelihood estimation, particularly in estimating the shape parameter under heavier censoring. The practical illustration of Stacy’s competing-risks model is achieved by analyzing two real medical datasets concerning pneumonia in intensive care and leukemia stem cell transplantation. Overall, the information criteria confirm that Stacy’s competing-risks model provides a robust fit relative to its submodels, offering a highly flexible framework for complex survival data. Full article

This article presents a comprehensive inferential and applied investigation of the newly reparameterized Z-Weibull (ZW) distribution, a flexible Weibull-type lifetime model capable of accommodating both bounded and unbounded support regimes as well as a wide variety of hazard rate shapes. Unified frequentist and Bayesian inference procedures are developed for complete and censored samples using maximum likelihood, maximum product spacing, and Markov chain Monte Carlo methods. Theoretical properties of the estimators and their associated interval estimates are established, while extensive Monte Carlo simulations assess their finite-sample performance under diverse parameter configurations and censoring schemes. The results indicate that Bayesian spacing-based procedures generally provide more accurate estimation, lower bias, and improved interval performance than competing classical methods. Applications to biomedical survival and climatological datasets, together with comparisons against several Weibull-type and exponential-based competitors, demonstrate the superior flexibility and goodness-of-fit of the ZW model. These findings highlight the practical value of the reparameterized ZW distribution as a unified and effective tool for modeling complex lifetime and reliability data arising in survival, environmental, and engineering studies. Full article

The Huainan Mining Area features extensively developed, fragmented-soft and low-permeability coal seams, characterized by low porosity and permeability, complex geological structures, and significant difficulty in coalbed methane (CBM) drainage. Horizontal wells with staged fracturing in the coal seam roof have become a key method for regional gas control. To further enhance the volume fracturing stimulation effect and single-well gas production, this study targets the horizontal well group in the roof of the No. 8 coal seam in the Huainan Mining Area as the research object. A saturated volume fracturing technology for horizontal wells in the coal seam roof, centered on the concept of a high pump rate (18–20 m³/min) and a high proppant volume (>250 m³/stage), is proposed. This study investigates the fracture propagation mechanisms and fracturing parameter optimization of this technology, and conducts engineering application to verify its stimulation effect. Increasing the fracturing pump rate improves the proppant-carrying capacity of the fracturing fluid, successfully enabling high-rate and high-volume proppant placement. Optimization of the perforation parameters—12 holes per m per cluster and a cluster spacing of 15–25 m—utilizes high perforation friction and moderate stress interference to promote balanced initiation and propagation of multiple fractures within a stage. The optimized ‘saturated’ injection mode, with a single-stage fluid volume exceeding 2400 m³, a single-stage proppant volume exceeding 250 m³, and a maximum sand ratio exceeding 20%, combined with a multi-size proppant mixture, enables full propping of both main and branch fractures. Microseismic monitoring shows that the hydraulic fracture extension length increased by approximately 50% compared to conventional wells, significantly enlarging the stimulated reservoir volume (SRV). Saturated fracturing achieved stable gas production of 2000 to 3000 m³/d, with average production ramp-up rates of 21.47–26.40 m³/d (five times higher than the 5.34 m³/d of the conventional well), and the stable plateau period was notably extended from 36 days to over 150 days. The saturated volume fracturing technology proposed in this study provides an important reference for efficient CBM extraction and surface gas control in mining areas with similar geological conditions. Full article

In this paper, the maximum product spacing method of estimation has been investigated under progressive Type-I censoring scheme. This estimation method has not previously been considered in the life-testing literature, particularly under this censoring scheme. The optimality of the sampling plans under progressive Type-I censoring was studied using different criteria and proposed censoring plans. The applicability of the distribution was examined using the Chen distribution, which is capable of modeling various reliability behaviors. A Monte Carlo simulation was conducted to assess the efficiency of the maximum product spacing method and the optimality of the sampling plans. Finally, an engineering application was analyzed considering progressive Type-I censoring. Full article

A recent generalized Bilal (G-Bilal) model demonstrates remarkable flexibility in capturing a wide spectrum of failure behaviors, including monotonic and non-monotonic (upside-down bathtub-shaped) hazard patterns, outperforming several existing models such as the Weibull, gamma, and exponential families. This paper develops several inferential frameworks for different G-Bilal parameters of life using samples gathered by improved Type-II adaptive progressive censoring. This enhanced design ensures optimal control of test duration while maintaining high inferential precision. Expressions for the model parameters, reliability, and hazard rate functions are derived, followed by the development of maximum likelihood (ML) and maximum product of spacing (MPS) estimators with their asymptotic confidence intervals using the observed Fisher information with the delta approach. Furthermore, Bayesian estimators and two associated credible intervals are proposed under independent gamma priors and computed through Markov iterations, with both ML and MPS posteriors considered. Extensive Monte Carlo experiments confirm the consistency, robustness, and precision of the proposed estimators, with Bayesian spacing-based methods exhibiting superior accuracy and coverage. The model’s practical potential is further verified through two real applications: one involving mechanical system lifetimes and another analyzing health insurance premium data, representing physical and actuarial domains, respectively. Using the introduced censoring, the proposed G-Bilal model outperforms all competing models in terms of goodness-of-fit and reliability estimates in both cases. The results underscore the G-Bilal model’s adaptability, computational stability, and empirical superiority, establishing it as a powerful tool for modern reliability and actuarial risk assessments. Full article

In reliability and survival studies, lifetime data are frequently subject to progressive Type-II censoring, leading to incomplete failure-time information and challenging statistical inference problems. In this study, statistical inference for the Rayleigh–Logarithmic (RL) distribution is developed under progressive Type-II censoring. The RL distribution provides a flexible lifetime model by combining a Rayleigh lifetime component with a logarithmically distributed number of latent failure causes. A competing-risk interpretation of the model is presented, and parameter estimation is carried out using both maximum likelihood estimation (MLE) and maximum product spacing (MPS) methods. The performance of the proposed inference procedures is investigated through extensive Monte Carlo simulations under different parameter settings and censoring schemes. The results indicate that both MLE and MPS provide reliable estimates, with estimation accuracy improving as the sample size increases. The methodology is further illustrated using simulated and real lifetime data sets and compared with classical lifetime distributions. The findings show that the RL distribution offers a flexible and effective framework for modeling progressively censored lifetime data, particularly in the presence of heterogeneous and latent failure mechanisms. Full article

Although interior-point methods (IPMs) have transformed mathematical programming since 1984, the original projective Karmarkar algorithm is rarely documented step by step on reproducible engineering examples that combine algorithmic transparency with real resource allocation constraints. This article therefore does not propose a new variant of Karmarkar’s algorithm; rather, its scientific contribution is the reproducible MATLAB implementation, canonical-form conversion, and comparative validation of the original projective method against the revised Simplex method and Barnes’ affine scaling variant in two engineering settings. The case studies are (i) the minimum-weight plastic design of a rigid frame with seven candidate plastic hinge locations and six collapse mechanisms and (ii) the optimal allocation of crop patterns in the Caplina Valley (Tacna, Southern Peru), an arid irrigated system with an irrigated command area of 1253 ha, monthly labor availability of 22,239 jornales, and water availability derived from Caplina River discharges at 75% persistence. For Case I, the algorithm reached F = 1.001 in the normalized dual space, which corresponds to F = 4.251 in the original structural objective after applying the scaling factor 17/4; relative to the analytical optimum F* = 4.25, this gives |4.251 − 4.25|/4.25 = 2.4 × 10⁻⁴ after 20 iterations. For Case II, the model yielded the maximum net production value of USD 703,135.92, allocating 948.47 ha among 12 crops while satisfying water, labor, market, and land constraints. The double validation confirms the algorithm’s strictly interior trajectory, polynomial-time rationale, and transparent internal parameters (α = 0.7968, ε = 10⁻⁸), making the implementation a reproducible benchmark for educational use and for future AI–operations research hybrid solvers in regions with limited access to commercial optimization software. Full article

Nonlinear state estimation plays an important role in aerospace sensing applications, where estimation accuracy must be balanced against computational efficiency. In this paper, a sparse-grid Gaussian kernel quadrature Kalman filter (SGKQKF) is proposed for discrete-time nonlinear state estimation by combining Gaussian kernel quadrature (GKQ) weighting with a Smolyak sparse-grid construction. The univariate GKQ rule is constructed on scaled Gauss–Hermite nodes through a truncated Mercer eigendecomposition of the Gaussian kernel and is then extended to multivariate cases via the Smolyak construction to alleviate the curse of dimensionality associated with tensor-product rules. The proposed method is positioned within the established sparse-grid filtering framework, with the specific contribution of integrating kernel-adapted quadrature weights into sparse-grid structures for discrete-time nonlinear Gaussian filtering. For fixed nodes, the exact kernel-quadrature weights minimize the worst-case integration error in the reproducing kernel Hilbert space (RKHS) induced by the Gaussian kernel, whereas the closed-form weights used in the implementation are interpreted as a Mercer-based practical approximation to this exact rule, with the approximation error characterized through the Mercer spectral-tail expression of the Gaussian kernel. For sparse grids, where a closed-form RKHS optimality result is not available, numerical maximum mean discrepancy (MMD) evaluations are presented as empirical diagnostics in the tested configurations. Numerical experiments demonstrate that the proposed filter achieves a favorable accuracy–efficiency trade-off compared with conventional deterministic Gaussian filters. Full article

Rhyolites serve as unconventional hydrocarbon-water reservoirs and also as high-quality volcanic reservoirs. Well BZ8-3S-B exhibits maximum productivity in vertical wells. Drilling results reveal significant mega-scale heterogeneity among different wells within the same rhyolitic volcanic edifice, as well as between different intervals within single wells. To clarify the mega-scale heterogeneity characteristics of rhyolitic reservoirs, this study investigates Block BZ8-3S in the Bozhong Depression, Bohai Bay Basin, China. Based on sidewall cores, logging data and seismic datasets, comprehensive research methods including petrographic/mineralogical analysis, image processing, porosity–permeability testing, mercury capillary pressure measurements, logging facies interpretation and seismic facies analyses were employed. The study establishes correlations between volcanic edifice architecture, stratigraphic boundaries, depositional units and their relationships with reservoir space composition/permeability characteristics, aiming to identify principal controlling factors of mega-scale heterogeneity through systematic stratigraphic architecture analysis. The key findings are summarized as follows: (i) The volcanic edifices in Block BZ8-3S exhibit massive-pseudostratified structural characteristics. (ii) Wells A and B belong to the same volcanic edifice system but occupy distinct facies belts. Well A is located in the crater-near crater belt, while Well B lies in the proximal belt. (iii) Eruptive interval unconformity boundaries (EIUBs) are identified at 1 and 4 depths in Wells A and B, respectively. The EIUBs control the vertical heterogeneity of depositional-unit reservoirs. Reservoir porosity exhibits inverse correlation with burial depth below EIUBs, indicating stratigraphic boundary control on reservoir development. Mega-scale heterogeneity of rhyolitic reservoirs is primarily controlled by the number of stratigraphic boundaries and depositional unit types. From an exploration perspective, lava dome deposited units within crater-near crater belt should be avoided, while priority should be given to proximal belt targets featuring thick sequences with high proportions of lava flow units. This study provides critical insights for optimizing exploration strategies and enhancing development efficiency of rhyolite volcanic edifices. 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

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

Deformation monitoring of roadway surrounding rock is the key link to ensure the safety production of the coal mine. The traditional monitoring method can only obtain the displacement information of discrete measuring points, and it is difficult to fully reflect the spatial distribution characteristics and evolution law of surrounding rock deformation. Based on the engineering background of the extra-thick coal seam roadway in the Yushupo Coal Mine, Shanxi Province, China, this study proposes a set of full-space deformation monitoring methods for roadway surrounding rock based on explosion-proof mobile 3D laser scanning technology. Firstly, a hierarchical denoising method based on improved statistical filtering is established. The quality of point cloud data is effectively improved by region clipping, a connectivity analysis guided by multi-dimensional geometric features and adaptive density threshold three-level processing strategy. Secondly, a hierarchical point cloud registration method combining physical anchor geometric constraints and deep learning patch guided matching is proposed to reduce the registration error to millimeter level. Finally, the deformation evaluation of surrounding rock is carried out by combining the overall deformation identification with the quantitative analysis of local section slices. The engineering application results show that the deformation of the roadway floor is the most significant during the monitoring period, the maximum deformation is 90.0 mm, and the average deformation is 46.9 mm. The maximum deformation of the roof is 35.0 mm, and the convergence of both sides is asymmetric. Compared with the total station, the results show that the maximum displacement error in each direction does not exceed 5 mm, and the standard deviation is within 1.3 mm, which meets the engineering accuracy requirements of coal mine roadway deformation monitoring. This study provides a complete technical scheme for panoramic and high-precision monitoring of surrounding rock deformation in coal mine roadway. Full article

Wafer map defect pattern recognition is a key task for ensuring yield in integrated circuit manufacturing. However, in real production lines it commonly suffers from scarce labeled data, long-tailed class distributions, and limited feature representations, which cause existing deep learning models to degrade in performance, particularly for minority defect classes and complex defect morphologies. To address these challenges, we propose a semi-supervised classification method for wafer maps based on a multimodal Wasserstein autoencoder (MM-WAE). The framework constructs three parallel feature branches in the spatial, frequency, and texture domains, using a multi-head attention mechanism and gating mechanism for adaptive multimodal fusion. This allows defect patterns to be comprehensively characterized by macroscopic geometric distributions, spectral periodic structures, and microscopic texture details. The Wasserstein autoencoder is introduced, with the latent space distribution regularized by a maximum mean discrepancy (MMD) loss using an inverse multiquadratic kernel. Additionally, an inverse class-frequency weighted cross-entropy loss and a modality consistency loss between the encoder and classifier jointly optimize the reconstruction and classification paths while leveraging large amounts of unlabeled wafer maps for semi-supervised learning. Experimental results show that MM-WAE mitigates performance limitations caused by insufficient labels and class imbalance, significantly improving the accuracy and robustness of wafer defect classification, with promising potential for industrial application and further development. Full article

This paper presents a new continuous data model, the Unit Arcsine–Exponential distribution (UASED), a flexible data model on the unit interval. It is built up by an exponential-based arcsine-type transformation to allow it to represent a very wide range of shapes that can be used to model proportions and rates. A number of basic properties are obtained, such as closed-form formulas of the quantile function, moments, and entropy measures. Maximum likelihood and maximum product of spacings methods are developed to estimate parameters, and their performance is determined by Monte Carlo simulation, which shows that these methods can reasonably estimate the parameters and be stable over a variety of different parameter settings. To demonstrate that a model is practically useful, an application to real-world data on the reliability of devices in terms of failure time is discussed. The findings indicate that the UASED is a good fit to the data, in the sense that it is effective in terms of skewness and tail behavior and compares well or competes favorably with current unit distributions. All in all, the suggested model is a sparse alternative to model bounded data with sound inferential characteristics and high practical utility. Full article

Dairy cow body temperature is a key physiological indicator that reflects metabolic level, immune status, and environmental stress responses, and it has been widely used for early disease recognition. Infrared thermography (IRT), as a non-contact imaging technique capable of remotely acquiring the surface radiation temperature distribution of animals, is regarded as a powerful alternative to traditional temperature measurement methods. Under practical cowshed conditions, IRT images of dairy cows are easily affected by complex background interference and generally suffer from low resolution, poor contrast, indistinct boundaries, weak structural perception, and insufficient texture information, which lead to significant degradation in target detection and temperature extraction performance. To address these issues, a lightweight detection model named YOLOv11n-CGSD is proposed for dairy cow IRT images, aiming to improve the accuracy and robustness of region of interest (ROI) detection and body temperature extraction under complex background conditions. At the architectural level, a C3Ghost lightweight module based on the Ghost concept is first constructed to reduce redundant feature extraction while lowering computational cost and enhancing the network capability for preserving fine-grained features during feature propagation. Subsequently, a space-to-depth convolution module is introduced to perform spatial rearrangement of feature maps and achieve channel compression via non-strided convolution, thereby improving the sensitivity of the model to local temperature variations and structural details. Finally, a dynamic sampling mechanism is embedded in the neck of the network, where the upsampling and scale alignment processes are adaptively driven by feature content, enhancing the model response to boundary temperature changes and weak-texture regions. Experimental results indicate that the YOLOv11n-CGSD model can effectively shift attention from irrelevant background regions to ROI contour boundaries and increase attention coverage within the ROI. Under complex IRT conditions, the model achieves P, R, and mAP50 values of 89.11%, 86.80%, and 91.94%, which represent improvements of 3.11%, 5.14%, and 4.08%, respectively, compared with the baseline model. Using T_max as the temperature extraction parameter, the maximum error (Max. Error) and mean error (MAE. Error) in the lower udder region are reduced by 33.3% and 25.7%, respectively, while in the around the anus region, the Max. Error and MAE. Error are reduced by 87.5% and 95.0%, respectively. These findings demonstrate that, under complex backgrounds and low-quality IRT imaging conditions, the proposed model achieves lightweight and high-performance detection for both lower udder (LU) and around the anus (AA) regions and provides a methodological reference and technical support for non-contact body temperature measurement of dairy cows in practical cowshed production environments. Full article