Search Results (189)

This paper for the first time derives some properties of the hydrogen atom inside a box with an impenetrable wall. Scaling of the Hamiltonian operator proves to be practical for the derivation of some general properties of the eigenvalues. The radial part of the Schrödinger equation is conditionally solvable and the exact polynomial solutions provide helpful information. There are accidental degeneracies that take place at particular values of the box radius, some of which can be determined from the conditionally-solvable condition. Some of the roots stemming from the conditionally-solvable condition appear to converge towards the critical values of the model parameter. This analysis is facilitated by the Rayleigh–Ritz method that provides accurate eigenvalues. Full article

As a core component of large marine engines, the compressor delivers robust and efficient power for propulsion. This study focuses on assessing and quantifying the uncertainty in the aerodynamic performance of a transonic rotor under various operating conditions, with the aim of investigating the impact of blade manufacturing variability on performance. Monte Carlo simulation (MCS) and sensitivity analysis were initially employed to identify parameters that significantly influence airfoil performance. Subsequently, a non-intrusive polynomial chaos (NIPC) uncertainty quantification model was developed to compare the effects of tip clearance deviation and surface geometry deviation on rotor performance. The study then analyzes how the geometric deviation at the different spanwise sections affects aerodynamic performance. The results reveal that geometric deviations have a more profound influence on aerodynamic performance than blade tip clearance. The impact of geometric deviations on average pressure ratio and efficiency of the transonic compressor rotor intensifies as the air mass flow rate approaches the near-stall point, while it decreases near the choking point. Interestingly, fluctuations in pressure ratio exhibit the opposite trend. Regarding spatial distribution, deviations in the upper half of the blade span (near the tip) exert a more dramatic influence on mass flow rate and pressure ratio fluctuation. A conceivable reason is that the inlet airflow velocity increases along the radial direction of the blade, and manufacturing variations in the same magnitude produce more notable relative geometric deviations in the upper half of the blade span. Centered on the machining tolerance guidelines for transonic compressor rotors, this work recommends stricter profile tolerance requirements for the upper half of the blade span. Full article

This study developed a natural rubber composite reinforced with selenol-functionalized carbon nanotubes, demonstrating significant mechanical enhancement. The composite exhibited remarkable improvements in elastic modulus, with 300% and 500% modulus increasing by 2.23 MPa and 2.68 MPa, respectively, along with a 1.22 MPa boost in tensile strength compared to conventional counterparts. Material characterization was successfully performed using a polynomial hyperelastic constitutive model. The optimized composite was applied to the tread of a Bridgestone 1270 × 455 R aircraft tire for performance evaluation. Finite element analysis in ABAQUS revealed that under 2.5 MPa inflation pressure, the tire achieved specified dimensional requirements with a cross-sectional width of 459.55 mm and a diameter of 1270.50 mm. Three-dimensional static load simulations showed characteristic elliptical contact patches that expanded with increasing load, while maintaining rectangular normal contact stress distribution. Critical performance evaluation demonstrated excellent radial stiffness stability of 22.9 kN/mm within the operational pressure range of 1.5–2.0 MPa under rated load conditions. These findings validate the composite’s potential for enhancing aircraft tire performance. Full article

Accurate spatial interpolation of environmental data requires utilizing flexible models that can capture complex spatial patterns. In this paper, we present two improved dual kriging (DK) models comprising a nonlinear trend function that combines Gaussian radial basis functions with a first-order polynomial. The proposed model, DK–RBFP, and its extension, DK–RBFPGA, which includes k-means clustering and a genetic algorithm for parameter optimization, respectively, exhibit enhanced performance in capturing spatial variation. The complete monotonicity of the covariance function and the strict positive definiteness of the coefficient matrix provide theoretical support for the uniqueness of the DK solution. When applied to datasets of PM_2.5 concentrations for northern Thailand, both models perform better than the conventional DK model using a second-order polynomial trend (DK–POLY), as evidenced by accuracy metrics including the mean absolute percentage error (MAPE), the mean squared error (MSE), and the root mean square error (RMSE). The outcomes indicate that integrating nonlinear trend components with data-driven optimization significantly enhances accuracy and flexibility in environmental spatial predictions. Full article

Cathepsin L (CatL) is a critical protease involved in cleaving the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), facilitating viral entry into host cells. Inhibition of CatL is essential for preventing SARS-CoV-2 cell entry, making it a potential therapeutic target for drug development. Six QSAR models were established to predict the inhibitory activity (expressed as IC₅₀ values) of candidate compounds against CatL. These models were developed using statistical method heuristic methods (HMs), the evolutionary algorithm gene expression programming (GEP), and the ensemble method random forest (RF), along with the kernel-based machine learning algorithm support vector regression (SVR) configured with various kernels: radial basis function (RBF), linear-RBF hybrid (LMIX2-SVR), and linear-RBF-polynomial hybrid (LMIX3-SVR). The particle swarm optimization algorithm was applied to optimize multi-parameter SVM models, ensuring low complexity and fast convergence. The properties of novel CatL inhibitors were explored through molecular docking analysis. The LMIX3-SVR model exhibited the best performance, with an $R^2$ of 0.9676 and 0.9632 for the training set and test set and RMSE values of 0.0834 and 0.0322. Five-fold cross-validation $R_{5-fold}^2$ = 0.9043 and leave-one-out cross-validation $R_{loo}^2$ = 0.9525 demonstrated the strong prediction ability and robustness of the model, which fully proved the correctness of the five selected descriptors. Based on these results, the IC₅₀ values of 578 newly designed compounds were predicted using the HM model, and the top five candidate compounds with the best physicochemical properties were further verified by Property Explorer Applet (PEA). The LMIX3-SVR model significantly advances QSAR modeling for drug discovery, providing a robust tool for designing and screening new drug molecules. This study contributes to the identification of novel CatL inhibitors, which aids in the development of effective therapeutics for SARS-CoV-2. Full article

This study aims to investigate the mechanical behavior of cement mortar and concrete through a hybrid approach that integrates artificial intelligence (AI) techniques with finite element modeling (FEM). Support Vector Machine (SVM) models with Radial Basis Function (RBF) and polynomial kernels, along with Multilayer Perceptron (MLP) neural networks, were employed to predict the compressive strength (Fc) and flexural strength (Fs) of cement mortar incorporating nano-silica (NS) and micro-silica (MS). The dataset comprises 89 samples characterized by six input parameters: water-to-cement ratio (W/C), sand-to-cement ratio (S/C), nano-silica-to-cement ratio (NS/C), micro-silica-to-cement ratio (MS/C), and curing age. Simultaneously, the axial compressive behavior of C20-grade concrete was numerically simulated using the Concrete Damage Plasticity (CDP) model in ABAQUS, with stress–strain responses benchmarked against the analytical models proposed by Mander, Hognestad, and Kent–Park. Due to the inherent limitations of the finite element software, it was not possible to define material models incorporating NS and MS; therefore, the simulations were conducted using the mechanical properties of conventional concrete. The SVM-RBF model demonstrated the highest predictive accuracy with RMSE values of 0.163 (R² = 0.993) for Fs and 0.422 (R² = 0.999) for Fc, while the Mander model showed the best agreement with experimental results among the FEM approaches. The study demonstrates that both the SVM-RBF and CDP-based modeling approaches serve as robust and complementary tools for accurately predicting the mechanical performance of cementitious composites. Furthermore, this research addresses the limitations of conventional FEM in capturing the effects of NS and MS, as well as the existing gap in integrated AI-FEM frameworks for blended cement mortars. Full article

In this paper, the Poisson’s ratio of black phosphorene nanotubes was examined through the molecular dynamics simulation method. Our research discovered that for the armchair black phosphorene nanotubes, the radial strain and the wall thickness strain are negatively linearly correlated with the axial strain, and both the radial Poisson’s ratio and the thickness Poisson’s ratio are positive. For the zigzag black phosphorene nanotubes, the wall thickness strain is negatively, linearly correlated with the axial strain, while the radial strain has a cubic polynomial function relationship with the axial strain. The thickness Poisson’s ratio is positive, while the radial Poisson’s ratio is a quantity related to the axial strain. As the axial strain increases, the radial Poisson’s ratio progressively diminishes from a positive value and becomes negative upon reaching a specific critical axial strain threshold. During the tensile deformation along the axial direction of the zigzag black phosphorene nanotubes, the radial strain initially decreases before subsequently increasing. Notably, the diameter of the nanotube may even surpass its initial value, demonstrating a radial expansion in response to axial tension. Full article

As engines trend toward miniaturization, lightweight design, and higher power density, noise issues have become increasingly prominent, necessitating precise radiated noise prediction for effective noise control. This study develops a machine learning model based on surface vibration test data, which enhances the efficiency of engine noise prediction and has the potential to serve as an alternative to traditional high-cost engine noise test methods. Experiments were conducted on a four-cylinder, four-stroke diesel engine, collecting surface vibration and radiated noise data under full-load conditions (1600–3000 r/min). Five prediction models were developed using support vector regression (SVR, including linear, polynomial, and radial basis function kernels), random forest regression, and multilayer perceptron, suitable for non-anechoic environments. The models were trained on time-domain and frequency-domain vibration data, with performance evaluated using the maximum absolute error, mean absolute error, and median absolute error. The results show that polynomial kernel SVR performs best in time domain modelling, with an average relative error of 0.10 and a prediction accuracy of up to 90%, which is 16% higher than that of MLP; the model does not require Fourier transform and principal component analysis, and the computational overhead is low, but it needs to collect data from multiple measurement points. The linear kernel SVR works best in frequency domain modelling, with an average relative error of 0.18 and a prediction accuracy of about 82%, which is suitable for single-point measurement scenarios with moderate accuracy requirements. Analysis of measurement points indicates optimal performance using data from the engine top between cylinders 3 and 4. This approach reduces reliance on costly anechoic facilities, providing practical value for noise control and design optimization. Full article

High-fidelity finite element analysis (FEA) plays a key role in structural engineering by enabling accurate simulation of displacement, stress, and internal forces under static loads. However, its high computational cost limits applicability in real-time control, iterative design, and large-scale uncertainty quantification. Surrogate modeling provides a computationally efficient alternative by learning input–output mappings from precomputed simulations. Yet, the performance of individual surrogates is often sensitive to data distribution and model assumptions. To enhance both accuracy and robustness, we propose a model averaging framework based on Jackknife Model Averaging (JMA) that integrates six surrogate models: polynomial response surfaces (PRSs), support vector regression (SVR), radial basis function (RBF) interpolation, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Three ensembles are formed: JMA1 (classical models), JMA2 (tree-based models), and JMA3 (all models). JMA assigns optimal convex weights using cross-validated out-of-fold errors without a meta-learner. We evaluate the framework on the Static Analysis Dataset with over 300,000 FEA simulations. Results show that JMA consistently outperforms individual models in root mean squared error, mean absolute error, and the coefficient of determination, while also producing tighter, better-calibrated conformal prediction intervals. These findings support JMA as an effective tool for surrogate-based structural analysis. Full article

This paper presents a theoretical model of a thin, penny-shaped piezoelectric actuator bonded to an isotropic thermo-elastic substrate under coupled electrical-thermal-diffusive loading. The problem is assumed to be axisymmetric, and the peeling stress of the film is neglected in accordance with membrane theory, yielding a simplified equilibrium equation for the piezoelectric film. By employing potential theory and the Hankel transform technique, the surface strain of the substrate is analytically derived. Under the assumption of perfect bonding, a governing integral equation is established in terms of interfacial shear stress. The solution to this integral equation is obtained numerically using orthotropic Chebyshev polynomials. The derived results include the interfacial shear stress, stress intensity factors, as well as the radial and hoop stresses within the system. Finite element analysis is conducted to validate the theoretical predictions. Furthermore, parametric studies elucidate the influence of material mismatch and actuator geometry on the mechanical response. The findings demonstrate that, the performance of the piezoelectric actuator can be optimized through judicious control of the applied electrical-thermal-diffusive loads and careful selection of material and geometric parameters. This work provides valuable insights for the design and optimization of piezoelectric actuator structures in practical engineering applications. Full article

Thunderstorms downbursts can be hazardous during aircraft landing and take-off. A warming climate increases low- to mid-level troposphere water vapor, typically transported from high sea-surface temperature regions. Consequently, the future occurrence and intensity of destructive wind gusts from wet microburst thunderstorms are expected to increase. Wet microbursts are downdrafts from heavily precipitating thunderstorms and are several kilometers in diameter, often producing near-surface extreme wind gusts. Brisbane airport recorded a wet microburst wind gust of 157 km/h in November 2016. Numerous locations in eastern Australia experience warm season (October to March) wet microbursts. Here, eight machine learning techniques comprising forward and backward linear regression, radial basis forward and backward support vector regression, polynomial-based forward and backward support vector regression, and forward and backward random forest selection were employed. They identified primary attributes for increased atmospheric instability by warm moist air influx from regions of high sea-surface temperatures. The climate drivers detected here are indicative of increased future eastern Australian warm season thunderstorm downbursts, occurring as wet microbursts. They suggest a greater frequency and intensity of impacts on aircraft safety and operations affecting major east coast airports, such as Sydney and Brisbane, and smaller aircraft at inland regional airports in southeastern Australia. Full article

Introduction: Chromosomal study via karyotype is one of the historical gold-standard procedures used to provide a clearer view of chromosomal trisomy abnormalities. It has been used to correlate several phenotypic manifestations that require immediate medical intervention. However, the laboratory procedure persisted with various drawbacks. The recent machine learning model shed light on prediction capabilities in the medical field. In this study, we aimed to use a support vector machine model for predicting postnatal chromosomal trisomy cases. Methods: A dataset of 946 neonatal records from the Royal Hospital, Muscat, Oman, covering the period from 2013 to 2023, has been used in this model. The model is based on features such as thyroxine hormone levels and thyroid-stimulating hormone levels. With different R packages, we used a support vector machine model with leave-one-out cross-validation and ten iterations to test three kernel functions: linear, radial, and polynomial. Results: Among the obtained kernel performances, the linear kernel has optimal classification performance. The training accuracy was 81%, and the testing accuracy was 82%. Sensitivity ranged from 97 to 98%, and specificity ranged from 79 to 80%. The area under the curve in relation to the training dataset came to 0.89, and it came to 0.90 for the test dataset. We deployed the trained models in a website tool called ChromoCheck. Conclusions: Our study is an example of how machine learning can be instrumental in augmenting conventional methods of cytogenetics diagnosis and decision-making in a clinical setup. Full article

The method of particular solutions using polynomial basis functions (MPS-PBF) has been extensively used to solve various types of partial differential equations. Traditional methods employing radial basis functions (RBFs)—such as Gaussian, multiquadric, and Matérn functions—often suffer from accuracy issues due to their dependence on a shape parameter, which is very difficult to select optimally. In this study, we adopt the MPS-PBF to solve the time-dependent Schrödinger equation in two dimensions. By utilizing polynomial basis functions, our approach eliminates the need to determine a shape parameter, thereby simplifying the solution process. Spatial discretization is performed using the MPS-PBF, while temporal discretization is handled via the backward Euler and Crank–Nicolson methods. To address the ill conditioning of the resulting system matrix, we incorporate a multi-scale technique. To validate the efficacy of the proposed scheme, we present four numerical examples and compare the results with known analytical solutions, demonstrating the accuracy and robustness of the scheme. Full article

The integration of intermittent Distributed Generations (DGs) like solar photovoltaics into Radial Distribution Systems (RDSs) reduces system losses but causes voltage and power instability issues. It has also been observed that seasonal variations affect the performance of such DGs. These issues can be resolved by placing optimum-sized Battery Energy Storage (BES) Systems into RDSs. This work proposes a new approach to the placement of optimally sized BESSs considering multiple objectives, Active Power Losses, the Power Stability Index, and the Voltage Stability Index, which are prioritized using the Weighted Sum Method. The proposed multi-objectives are investigated using the probabilistic and Polynomial Multiple Regression (PMR) approaches to account for the randomness in solar irradiance and its effect on BESS sizing and placements. To analyze system behavior, simultaneous and sequential strategies considering aggregated and distributed BESS placement are executed on IEEE 33-bus and 94-bus Portuguese RDSs by applying the Improved Grey Wolf Optimization and TOPSIS techniques. Significant loss reduction is observed in distributed BESS placement compared to aggregated BESSs. Also, the sequentially distributed BESS stabilized the RDS to a greater extent than the simultaneously distributed BESS. In view of the uncertainty, the probabilistic and PMR approaches require a larger optimal BESS size than the deterministic approach, representing practical systems. Additionally, the results are validated using Improved Particle Swarm Optimization–TOPSIS techniques. Full article

Photoacoustic imaging (PAI) has rapidly developed in biomedical imaging. The point spread function (PSF) is critical for addressing image blurring in PAI. However, in circular integrating detection systems, the PSF exhibits spatial variations. This makes PSF extraction challenging. The existing studies typically assume that the PSF is known or obtained through experiments. This study proposes a method for extracting the PSF based on the polar coordinate system. By transforming the image from the Cartesian coordinate system to the polar coordinate system, the “spin blur” problem is decomposed into multiple independent subproblems. With the separation of the radial and angular directions, the blurring kernel remains invariant at each radius, thereby simplifying the estimation of the PSF. To estimate the blurring kernel, we use polynomial algebraic common factor extraction techniques. The numerical simulation results validate the effectiveness of the method, and the impact of sample size on computational efficiency and accuracy is discussed. Full article