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Truncated Modified Weighted Exponential Distribution with Different Estimation Methods and Applications
No full textThis study introduces the Truncated Modified Weighted Exponential (TrMWE) Distribution, developed by extending the traditional modified weighted exponential distribution with an additional parameter and truncating its support to a finite range, enhancing its adaptability for modeling lifetime and reliability data. The statistical properties of the TrMWE, including moments, the moment-generating function, the quantile function, and order statistics, are examined. Parameter estimation is performed via maximum likelihood estimation (MLE), least squares and weighted least squares methods, maximum product of spacing method, Cramer-Von-Mises method, Anderson-Darling method, and right and left tails Anderson-Darling methods, with analysis of the asymptotic behavior of the estimators. Rényi and Tsallis entropies are also derived to assess the distribution’s uncertainty. The practicality of the TrMWE is illustrated using three real datasets and compared with existing distributions based on goodness-of-fit criteria, such as the Akaike Information Criterion and Bayesian Information Criterion. The results highlight the distribution’s flexibility and superior performance in modeling complex datasets.OPEN ACCESS Received: 14/08/2025 Accepted: 05/11/2025 Published: 03/02/202
Tuning Curvature in Quadratic Regression via Caputo Fractional Derivatives: Theory and Applications
No full textClassical regression can only examine the relation between response and predictor variables based on integer order calculus theory. What happens when non integer order calculus is considered is a field where a vast spectrum of studies can be undertaken. The purpose of this study introduces a novel fractional-order quadratic regression model grounded in the Caputo derivative framework, addressing the limitation and the rigidity of classical polynomial regression in adapting to the intrinsic curvature of data. The core innovation is the use of the fractional order ν as a tunable parameter for curvature-sensitive optimization. Our main contributions are fourfold: First, we establish a fundamental theoretical pillar by proving that the second-order Caputo derivative preserves the curvature direction of quadratic functions, enabling a principled optimization framework. Second, we rigorously demonstrate the model’s robustness by proving the existence and uniqueness of solutions via Banach’s fixed point theorem and establishing stability bounds through a fractional Grönwall inequality. Third, we develop a practical methodology to identify an optimal fractional order ν that minimizes the error-to-explained-variation ratio (SSE/SSR). Finally, we validate the framework on four diverse real-world datasets from air quality, soil science, education, and meteorology. The proposed model consistently outperforms classical quadratic regression, achieving a reduction in the SSE/SSR ratio by up to 21% in specific cases. The proposed method yields more efficient models with either lower estimation error or higher correlation coefficients, positioning Caputo fractional quadratic regression as a powerful and theoretically sound alternative for modeling cases where quadratic regression is considered appropriate.OPEN ACCESS Received: 10/09/2025 Accepted: 05/11/2025 Published: 23/01/202
Investigation of Compensated Foundation SettlementMechanism Based on FEM-DEM Coupling Method
No full textThe application of compensated foundations is common in engineering, but significant settlements often occur during the application process. A foundation pit model supported by a rigid retaining wall was established based on the Finite Element Method-Discrete Element Method (FEMDEM), and a cantilevered layer-by-layer excavation process was simulated. A rolling resistance linear model was used to simulate the sand, while a variable particle size method was adopted to establish the foundation model. It is shown that during the excavation process of the foundation pit, the stress of the sand at the pit bottom gradually decreases, and the displacement of the soil changes gradually fromsettlement to uplift as one moves frombehind the retaining wall to the bottomof the foundation pit. Moreover, theporosityof the sandat thepit bottomgradually increases.As a result, the strength of the uplifted sand at the pit bottomdecreases due to over-excavation during the foundation pit excavation stage. The uplifted sand is finally excavated during the site leveling stage, resulting in a further decrease in the strength of the sand at the pit bottom. Finally, an inverted arch bottom plate structure is proposed to mitigate the over-excavation settlement of the compensated foundation
Evaluation and Optimization of High Collapse Resistance Casing Strings for Salt Cavern Gas Storage in Salt Formations
No full textSalt cavern gas storage imposes stringent requirements on casing performance due to salt rock creep and high in-situ stress, necessitating a balance between mechanical strength and economic viability. This study evaluates the collapse resistance of BG110V and BG140V casings under 50°C–85°C through full-scale experiments (ASTM E2948) and elastoplastic finite element simulations. Results show that BG140V achieves a collapse strength of 75.94 MPa at 85°C, surpassing BG110V (52.96 MPa) at the same temperature by 43.4%, attributed to its thick-walled design (17.50 mm vs. BG110V’s 15.88 mm) and material enhancements. Simulations reveal lower prediction errors for BG140V (5.9% in full collapse) compared to BG110V (20.6%). A multi-criteria model integrating collapse strength (0.5), temperature sensitivity (0.3), and life-cycle cost (LCC, 0.2) with Monte Carlo analysis demonstrates BG140V’s LCC advantage in deep reservoirs (creep rate > 1.2× 10−7s−1, 12% maintenance cost reduction), while BG110V suits shallow scenarios (18% lower procurement cost). Dynamic selection strategies with real-time monitoring, low-friction thread optimization, crystal plasticity simulations, and smart maintenance systems are proposed. This study provides a quantitative framework for balancing safety and economy, advancing the standardization of non-API casings.OPEN ACCESS Received: 30/06/2025 Accepted: 19/08/2025 Published: 23/01/202
Analysis of Airflow Velocity on Microdroplets Using Weibull StressStress Reliability Index under Unified Type-I Progressive Hybrid Data
No full textThis work presents a novel and comprehensive inferential framework for analyzing the stress-strength reliability parameter,R= P(Y < X), where X and Y denote independent stress and strength variables, respectively, both modeled as Weibull-distributed with a shared shape parameter but distinct scale parameters. A key innovation of this study lies in its integration of the unified Type-I progressively hybrid censoring scheme, which simultaneously accommodates time constraints and partial failure information, conditions often encountered in real-world reliability testing. To estimate R, we propose and evaluate four distinct inferential strategies: two frequentist (maximum likelihood estimation and maximum spacings estimation) and two Bayesian, each tailored to either the likelihood or spacings-based posterior formulation. The Bayesian methods employ Monte Carlo sampling to compute both Bayes point estimates and credible intervals under informative priors, offering robustness in small-sample or heavily censored contexts. An extensive simulation study is conducted to systematically compare the estimators in terms of bias, efficiency, and interval coverage. To validate the practical applicability of our framework, we further analyze two real-world microdroplet datasets, revealing critical insights into stress-tolerance behavior under experimental constraints. This study not only advances methodological tools for reliability inference under hybrid censoring but also establishes a blueprint for combining classical and Bayesian paradigms in stress-strength modeling.OPEN ACCESS Received: 01/07/2025 Accepted: 02/09/2025 Published: 23/01/202
A Deep Learning Network based on Channel and Temporal Attentions for Decoding Motor Imagery EEG Signals
No full text(1) Background: Accurately decoding motor imagery (MI) tasks is a prerequisite for creating a MI-based brain-computer interface (BCI). However, low signal-to-noise ratio and non-stationarity of EEG signals present a huge challenge for the classification of MI-EEG signals, restricting the extensive development of the BCI industry.''' '''(2) Methods: In this paper, we propose a novel deep learning model CTANet that integrates both channel and temporal attention mechanisms into a convolutional neural network to improve the classification accuracy of the MI-BCI systems. The model is constituted first by three serially connected temporal, spatial, and temporal convolution layers to extract features from the brain signals, with an efficient channel attention module inserted between the second and the third convolutional layers to highlight useful feature channels. Subsequently, the time segment for task decoding is partitioned into several time windows, and a variance layer is employed for computing the logarithmic variance of each window. Next, a multi-head attention mechanism is adopted to extract temporal dependency of features from different windows. Finally, a fully connected layer is used for classifying MI-EEG signals. (3) Results: The performance of the proposed model was evaluated on two publicly available BCI datasets and compared with the state-of-the-art methods. The experimental results show that for dataset BCIC-IV2a, our network achieved classification accuracies of 81.17% and 84.33% in inter-session and intra-session scenarios respectively, whereas for dataset OpenBMI, our network achieved classification accuracy of 73.06% and 77.59% in inter-session and intra-session scenarios respectively. (4) Conclusions: These results outperform state-of-the-art networks, indicating significant potential of the proposed model CTANet in MI decoding
Buckling analysis of laminates subjected to biaxial loads using cruciform specimens
No full textThis study presents the compression-compression test with cruciform specimens (test CC) as a viable methodology to assess the geometric instability of a ∓45° symmetric laminate. The central region of the specimen, subjected to biaxial loading, exhibits a geometry similar to that of a square plate fixed along its entire perimeter. The bifurcation of the strains recorded at the top and bottom surfaces of the laminate is considered to be the threshold between the in-plane biaxial response and the response dominated by bending and torsional moments. The nonlinearities observed in the evolution of the stress-strain relationship in the region subjected to biaxial loading are confirmed to be independent of the response of the specimen arms. The bending-torsion coupling effects at the beginning of the bifurcation are observed experimentally in the deflection surface recorded by Digital Image Correlation. The results obtained suggest that the test CC is potentially suitable for the observation and measurement of buckling modes under various boundary conditions. However, more work is needed to reduce the quantitative dispersion. Specifically, the research should focus on minimizing geometric imperfections and load misalignments
Experimental characterisation of the translaminar fracture toughness of an additive manufacturing c-CFRP composite
No full textFibre breakeage in composite materials is usually a determining damage mechanism for its structural integrity due to the high energy associated, in comparison with matrix cracking. For this reason, the assessment of the translaminar fracture toughness is relevant for accurate numerical predictions of composite structures. However, there are scarce investigations related to this topic for additive manufactured composites reinforced with continuous fibres. In this investigation, the translaminar fracture toughness of 3D-printed continuous fibre reinforced polymer (c-CFRP) composites was characterised using double-tapered compact tension (2TCT) specimens. The 2TCT geometric dimensions were obtained through a parametric study to prevent undesired failure modes. The results show a translaminar fracture toughness of 17.4 N/mm for the tested 0/90 laminates. The fracture toughness corresponding to the tensile failure of the 0° ply was derived using a rule-of-mixtures approach. Post-mortem micrographic and X-ray analysis indicated the presence of fibre pull-outs in the crack surface and confirmed the absence of any additional damage, validating the use of 2TCT geometry for the determination of the translaminar fracture toughness in additively manufactured c-CFRP composites.