Model Parameters Research Articles

Simulation analysis of coupling mechanism between transient flow field characteristics of bubble collapse and metal deformation based on surface micromorphology

In the process of modifying titanium alloy oral implants using cavitation water jet, the collapse of bubbles releases significant energy. This phenomenon is accompanied by micro-jets and shock waves, which induce changes in the three-dimensional microscopic morphology of the implant surface. The loose and porous surface of the implant will increase the adhesion area of the cells, which is more conducive to the combination of the oral implant with the surrounding bone tissue. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, based on different flow field and solid field model parameters, the numerical analysis software Ansys and the fluid-structure coupling simulation method are used to establish the numerical simulation model of single bubble collapse on the near curved wall. In order to explore the coupling mechanism between the instantaneous energy of bubble collapse and the surface deformation of titanium metal, the bubble growth process is ignored. Based on different flow field and solid field model parameters, this paper adopts the numerical analysis software Ansys and the fluid-structure coupling simulation method to establish the numerical simulation model of single bubble collapse on the near curved wall. The effects of flow field parameters and wall morphology on the transient flow field of bubble collapse and the effect of metal surface modification are revealed. The results show that when the initial bubble diameter is 180 μm, the instantaneous collapse high pressure reaches 7.24 GPa, and the maximum stress on the titanium surface is 689 MPa, which is 1.57 times higher than that under the bubble diameter of 60 μm. When the bubble collapses away from the wall, due to the weakened constraint of the wall, more intense energy is released, but the energy decays rapidly in the propagation process, and the energy loss when it reaches the wall is more serious. In this paper, the surface micromorphology is simplified into a near-curved shape. After the modification, the flow obstruction on the near-curved concave wall inhibits bubble collapse, resulting in an increase in bubble collapse time. The stress and deformation caused by a single bubble collapse are concentrated within a radius of 1mm and a depth of 5 μm.

A framework for upscaling aboveground biomass from an individual tree to landscape level and qualifying the multiscale spatial uncertainties for secondary forests

ABSTRACT Secondary forests, a typical forest type in the sub-frigid zone of Northeast China, have significant potential for carbon sequestration. Accurate estimation of the Aboveground Biomass (AGB) of secondary forests and assessment of multiscale uncertainties are crucial for promoting Reduced Emissions from Deforestation and Degradation. This study developed a novel framework to upscale the AGB estimation from the tree to the landscape level and assessed multiscale uncertainties based on multi-platform laser scanning data and Unmanned Aerial Vehicle (UAV) hyperspectral images. The framework included two stages: (1) quantifying multiple uncertainties (uncertainties of individual tree crown delineation, individual tree parameters estimation, and tree species classification) in individual tree-based AGB estimation using Monte Carlo simulations; (2) upscaling the plot to the landscape level estimated AGB using the Nonlinear Simultaneous Equation (NSE) with error-in-variables and quantifying the uncertainties of model residuals, model parameters, and model independent variables. The findings revealed a high accuracy from tree to plot AGB estimation (R2: 0.75, Root Mean Square Error (RMSE): 6.65 Mg/ha, relative RMSE (rRMSE): 5.40%), with the total and relative uncertainties of 16.85 Mg/ha and 16.29%, respectively, with the highest uncertainty (9.73 Mg/ha) observed in tree species classification. The AGB estimation using NSE achieved an R2 of 0.69, with an RMSE of 9.91 Mg/ha and an rRMSE of 10.43% from the plot to landscape level; and the uncertainties caused by model parameters, independent variables, and residuals were 5.52 Mg/ha, 14.56 Mg/ha, and 25.25 Mg/ha, respectively, accounting for 3.46%, 24.09%, and 72.45% of the total uncertainty. This study develops a framework for large-scale AGB estimation of mixed forests based on the individual tree approach and uncertainty quantification of multiscale estimates and provides a foundation for precise forestry, sustainable forest management, and carbon neutrality.

Discovering non-associated pressure-sensitive plasticity models with EUCLID

We extend (EUCLID Efficient Unsupervised Constitutive Law Identification and Discovery)—a data-driven framework for automated material model discovery—to pressure-sensitive plasticity models, encompassing arbitrarily shaped yield surfaces with convexity constraints and non-associated flow rules. The method only requires full-field displacement and boundary force data from one single experiment and delivers constitutive laws as interpretable mathematical expressions. We construct a material model library for pressure-sensitive plasticity models with non-associated flow rules in four steps: (1) a Fourier series describes an arbitrary yield surface shape in the deviatoric stress plane; (2) a pressure-sensitive term in the yield function defines the shape of the shear failure surface and determines plastic deformation under tension; (3) a compression cap term determines plastic deformation under compression; (4) a non-associated flow rule may be adopted to avoid the excessive dilatancy induced by plastic deformations. In contrast to traditional parameter identification methods, EUCLID is equipped with a sparsity promoting regularization to restrain the number of model parameters (and thus modeling features) to the minimum needed to accurately interpret the data, thus achieving a compromise between model simplicity and accuracy. The convexity of the learned yield surface is guaranteed by a set of constraints in the inverse optimization problem. We demonstrate the proposed approach in multiple numerical experiments with noisy data, and show the ability of EUCLID to accurately select a suitable material model from the starting library.

Package Positioning Based on Point Registration Network DCDNet-Att

The application of robot technology in the automatic transportation process of packaging bags is becoming increasingly common. Point cloud registration is the key to applying industrial robots to automatic transportation systems. However, current point cloud registration models cannot effectively solve the registration of deformed targets like packaging bags. In this study, a new point cloud registration network, DCDNet-Att, is proposed, which uses a variable weight dynamic graph convolution module to extract point cloud features. A feature interaction module is used to extract common features between the source point cloud and the template point cloud. The same geometric features between the two pairs of point clouds are strengthened through a bottleneck module. A channel attention model is used to obtain the channel attention weights. The attention weight of each spatial position is calculated, and a rotation translation structure is used to sequentially obtain quaternions and translation vectors. A feature fitting loss function is used to constrain the parameters of the neural network model to have a larger receptive field. Compared with seven methods, including the ICP algorithm, GO-ICP algorithm, and FGR algorithm, the proposed method had rotation errors (MAE, RMSE, and Error of 1.458, 2.541, and 1.024 in the ModelNet40 dataset, respectively) and translation errors (MAE, RMSE, and Error of 0.0048, 0.0114, and 0.0174, respectively). When registering the ModelNet40 dataset with Gaussian noise, the rotation errors (MAE, RMSE, and Error) were 2.028, 3.437, and 2.478, respectively, and the translation errors (MAE, RMSE, and Error) were 0.0107, 0.0327, and 0.0285, respectively. The experimental results were superior to those of the other methods, and the model was effective at registering packaging bag point clouds.

Quadruple-Q Skyrmion Crystal in Centrosymmetric Body-Centered Tetragonal Magnets

We conduct a numerical investigation into the stability of a quadruple-Q skyrmion crystal, a structure generated by the superposition of four spin density waves traveling in distinct directions within three-dimensional space, hosted on a centrosymmetric body-centered tetragonal lattice. Using simulated annealing applied to an effective spin model that includes momentum-resolved bilinear and biquadratic interactions, we construct a magnetic phase diagram spanning a broad range of model parameters. Our study finds that a quadruple-Q skyrmion crystal does not emerge within the phase diagram when varying the biquadratic interaction and external magnetic field. Instead, three distinct quadruple-Q states with topologically trivial spin textures are stabilized. However, we demonstrate that the quadruple-Q skyrmion crystal can become the ground state when an additional high-harmonic wave–vector interaction is considered. Depending on the magnitude of this interaction, we obtain two types of quadruple-Q skyrmion crystals exhibiting the skyrmion numbers of one and two. These findings highlight the emergence of diverse three-dimensional multiple-Q spin states in centrosymmetric body-centered tetragonal magnets.

A Quasi-Poisson Item Response Theory Model for Heterogeneous Dispersion in Count Data

Item-level count data frequently arise in cognitive, educational, and psychological assessments. Correctly handling different dispersion levels in count data is crucial for accurate statistical inference. This research proposes a Quasi-Poisson item response theory model that accommodates overdispersion, underdispersion, and equidispersion in count data, aiming to explicitly model the connection between the mean and variance parameters, providing a method that is both computationally efficient and statistically robust. This semiparametric model specifies the first two conditional moments for the count variables and derives marginal moments to estimate model parameters. Simulation studies demonstrate the Quasi-Poisson model’s efficacy in parameter recovery across different dispersion scenarios and its negligible computation time. Empirical data analysis further underscores the model’s superior fit and computational efficiency in a real-world setting.

Adaptive Controller Design for Improving Helicopter Flying Qualities

A comprehensive flight control law design method based on adaptive control is presented in this paper. The proposed method consists of three basic modules—model decoupling, online system identification and adaptive pole placement. The model decoupling module decouples the helicopter flight dynamics model based on dynamic inversion technique. This procedure helps to reduce the difficulties in online system identification and adaptive controller design. In online system identification module, a recursive extended least squares algorithm is established to identify the augmented linear flight dynamics model which is composed of helicopter model and unideal noise model. The helicopter model parameters and the noise parameters are identified simultaneously which improves the identification accuracy as well as robustness. Pole placement is implemented in the last module, and an optimization method is developed to help selecting ideal poles. The adaptive rule in this step is designed based on eigenvalue analysis of the model to remove all unnecessary oscillations of the control parameters. An adaptive controller is designed according to the developed method for the UH-60A helicopter based on a nonlinear simulation program. Typical response types are also implemented. The simulation results show that the designed adaptive controller has high performance as well as robustness in both hover and forward flight.

The impact of mass uncertainties on r-process nucleosynthesis in neutron star mergers

Theoretically predicted yields of elements created by the rapid neutron capture (r-) process carry potentially large uncertainties associated with incomplete knowledge of nuclear properties and approximative hydrodynamical modeling of the matter ejection processes. One of the dominant uncertainties in determining the ejecta composition and radioactive decay heat stems from the still unknown nuclear masses of exotic neutron-rich nuclei produced during the neutron irradiation. We investigate both the model (systematic) and parameter (statistical) uncertainties affecting nuclear mass predictions and explore their impact on r-process production, and subsequently on the composition of neutron star merger ejecta. The impact of correlated model uncertainties on masses is estimated by considering five different nuclear mass models that are known to provide an accurate description of known masses. In addition, the uncorrelated uncertainties associated with local variation of model parameters are estimated using a variant of the backward-forward Monte Carlo method, to constrain the parameter changes to experimentally known masses before propagating them consistently to the unknown masses of neutron-rich nuclei. The impact of nuclear mass uncertainties is propagated to r-process nucleosynthesis in a 1.38-1.38 M_⊙ neutron star merger model considering a large and representative number of trajectories. We find that the uncorrelated parameter uncertainties lead to ejected abundance uncertainties of 20% up to A ≃ 130 and 40% between A=150 and 200, with peaks around A ≃ 140 and A ≃ 203 giving rise to deviations around 100 to 300%. The correlated model uncertainties remain larger than the parameter ones for most nuclei. However, both model and parameter uncertainties have an important impact on heavy nuclei production. Improvements to nuclear models are still crucial in reducing uncertainties in predictions related to r-process nucleosynthesis. Both correlated model uncertainties and coherently determining parameter uncertainties are key in the sensitivity analysis.

Grocery Shopping Under Simplified Marginal Value Theorem Predictions.

This study examined whether supermarkets can be considered patches in the marginal value theorem (MVT) sense despite their particular features and whether they are models of human food foraging in resource-dense conditions. On the basis of the MVT, the quantitative relationship between gains in the Euro and patch residence time was modeled as an exponential growth function toward an upper asymptote, allowing the choice of an optimal strategy under diminishing returns.N = 61 participants were interviewed about their current shopping trip and contextual variables at a German supermarket and provided data to estimate relevant model parameters. A nonlinear model of the patch residence time and resulting gain based on an exponential function was fitted via nonlinear orthogonal distance regression.The results generally revealed the relationships predicted by the model, with some uncertainty regarding the estimation of the upper asymptote due to a lack of data from participants with long residence times. Despite this limitation, the data support the applicability of the MVT-based model.The results show that approaches from optimal foraging theory, such as the MVT, can be used successfully to model human shopping behavior even when participants' verbal reports are used.

Comparison of the Performance of Nonlinear Time-Dependent Constitutive Models Calibrated with Minimal Test Data Applied to an Epoxy Resin

Epoxy resins are extensively employed as adhesives and matrices in fibre-reinforced composites. As polymers, they possess a viscoelastic nature and are prone to creep and stress relaxation even at room temperature. This phenomenon is also responsible for time-dependent failure or creep fracture due to cumulative strain. Several constitutive equations have been used to describe the mechanical time-dependent response of polymers. These models have been proposed over the past six decades, with minimal direct and practical confrontation. Each model is associated with a specific application or research group. This work assesses the predictive performance of four distinct time-dependent constitutive models based on experimental data. The models were deemed sufficiently straightforward to be readily integrated into practical engineering analyses. A range of loading cases, encompassing constant strain rate, creep, and relaxation tests, were conducted on a commercial epoxy resin. Model parameter calibration was conducted with a minimum data set. The extrapolative predictive capacity of the models was evaluated for creep loading by extending the tests to five decades. The selected rheological models comprise two viscoelastic models based on Volterra-type integrals, as originally proposed by Schapery and Rabotnov; one viscoplastic model, as originally proposed by Norton and Bailey; and the Burger model, in which two springs and two dashpots are combined in a serial and parallel configuration. The number of model parameters does not correlate positively to superior performance, even if it is high. Overall, the models exhibited satisfactory predictive performance, displaying similar outcomes with some relevant differences during the unloading phases.

A Finite Element Method for Modeling Diffusion and Drug Release from Nanocellulose/Nanoporous Silicon Composites

Background and Objective: A previous study investigated the in vitro release of methylene blue (MB), a widely used cationic dye in biomedical applications, from nanocellulose/nanoporous silicon (NC/nPSi) composites under conditions simulating body fluids. The results showed that MB release rates varied significantly with the nPSi concentration in the composite, highlighting its potential for controlled drug delivery. To further analyze the relationship between diffusion dynamics and the MB concentration, this study developed a finite element (FE) method to solve Fick’s equations governing the drug delivery system. Methods: Release profiles of MB from NC/nPSi composites with varying nPSi concentrations (0%, 0.1%, 0.5%, and 1.0%) were experimentally measured in triplicate using phosphate-buffered saline (PBS) at 37 °C, pH 7.4, and 100 rpm. Mathematical models incorporating linear and quadratic dependencies of the diffusion coefficient on the MB concentration were developed and tested using the FE method. Model parameters were refined by minimizing the error between simulated and experimental MB release profiles. Results: The proposed FE method closely matched experimental data, validating its accuracy and robustness in simulating the diffusion and release processes. Conclusions: This study emphasizes the significant impact of the nPSi concentration on enhancing release control and highlights the importance of material composition in designing drug delivery systems. The findings suggest that the FE method can be effectively applied to model other complex systems, paving the way for advancements in precision drug delivery and broader biomedical applications.

A Simulation Study to Assess Impacts of Habitat-Dependent Parameters of Surplus Production Model on Stock Assessment of Chilean Jack Mackerel in the Southeast Pacific Ocean

A Simulation Study to Assess Impacts of Habitat-Dependent Parameters of Surplus Production Model on Stock Assessment of Chilean Jack Mackerel in the Southeast Pacific Ocean

Infectious disease time series modelling using transformer self-attention based network

Abstract Researchers are focusing on improving time series forecasting methods to address real-world problems like COVID-19. Current methods show de-creased accuracy due to unpredictable seasonality, and enhancing models to handle long-term dependencies is crucial for better forecasting accuracy. This paper presents a Transformer self-attention based novel approach for infec-tious disease time series forecasting, specifically for COVID-19. The proposed method utilizes the Ensemble Empirical Mode Decomposition (EEMD) and Local Outlier Factor (LOF) methods for data pre-processing and to detect outliers. Next, a modified self-attention model based on Transformer neural network is introduced for predicting COVID-19 time series forecasting for the first time. The research specifically investigates the application of encod-er/decoder networks with an enhanced Positional Encoding approach. This involves using a novel time encoding technique on the input pattern to achieve more precise intended output. Consequently, the parameters in the Transformer model are adjusted using the Arithmetic Optimization Algorithm (AOA) to enhance the accuracy of the prediction. The model generates more accurate predictions over broader time intervals, with the lowest MAE of 371.92 and RMSE of 674.61, indicating superior predictive accuracy by ap-proximately 30% compared to other state-of-the-art methods. The proposed Transformer model has demonstrated significant improvements in robustness and forecasting accuracy compared to standard approaches such as LSTM, RNN, Exponential Smoothing, AutoARIMA, and TBATS for the COVID-19 time series of India, USA, and Brazil. The suggested model, due to its superior predictive accuracy, is applicable in diverse time series forecasting domains such as stock market trends, sales, and industrial consumption forecasting etc.

Impact of Charge on Strange Compact Stars in Rastall Theory

Within the framework of Rastall theory, we investigate the impact of charge on the structural development of different types of spherically symmetric anisotropic stars. To do so, we present modified field equations based upon the Finch–Skea metric potentials expressed in terms of three parameters (A,B,C). These constants are determined using suitable matching conditions and observational data for compact objects which include Her X-1, SAX J 1808.4-3658, PSR J038-0842, LMC X-4 and SMC X-1. The equation of state offered by the MIT bag model for quark–gluon plasma is used to investigate the inner structure and other characteristics of these compact objects. For a fixed bag constant, B=60MeV/fm3, and two sets of the Rastall and charge parameters, ζ=0.255,0.259 and Q˜=0.2,0.7, respectively, we analyze the consistency of the matter variables in the model and other physical parameters such as energy conditions, stellar mass, compactness, and surface redshift. In addition, we assess the stability of the constructed model through two different approaches. It is found that the obtained model is physically viable and stable.

Interval estimation for Topp-Leone distribution

. This article presents several point estimation and interval estimation methods for the Topp-Leone distribution and applies them to the stress-strength model. A new moment estimation and inverse estimation are derived. The algorithm of the maximum likelihood estimation is discussed. Using the generalized pivotal quantity method, the generalized confidence intervals for model parameters and some reliability metrics are also obtained. For the estimation on reliability of stress-strength model, the generalized confidence intervals and bootstrap-p confidence intervals are given. The performance of the proposed methods is assessed by Monte Carlo simulation. Finally, a real example is used to illustrate the proposed methods.

Estimation of Radon Flux Density Changes in Temporal Vicinity of the Shipunskoe Earthquake with Mw = 7.0, 17 August 2024 with the Use of the Hereditary Mathematical Model

Using the data of radon accumulation in a chamber with excess volume at one of the points of the Kamchatka subsurface gas-monitoring network, the change in radon flux density due to seismic waves and post-seismic relaxation of the medium is shown. A linear fractional equation is considered to be a model equation. The change of radon-transport intensity due to changes in the state of the geo-environment is described by a fractional Gerasimov–Caputo derivative of constant order. Presumably, the order of the fractional derivative is related to the radon-transport intensity in the geosphere. Using the Levenberg–Marquardt method, the optimal values of the model parameters were determined based on experimental data: air exchange coefficient and order of fractional derivative, which allowed the solving of the problems of radon flux density determination. Data in the temporal neighborhood of a strong earthquake with Mw=7.0, which occurred in the northern part of Avacha Bay on 17 August 2024, were used. As a result of the modeling, it is shown that the strong seismic impact and subsequent processes led to changes in the radon flux in the accumulation chamber. The obtained model curves agree well with the real data, and the obtained estimates of radon flux density agree with the theory.

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach’s accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.

Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach

A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. Experimental results based on reported data from China between January 1, and December 1, 2022, demonstrate that the proposed dynamics informed neural networks (DINNs) method can accurately learn the dynamics and predict future states. Our proposed hybrid SEIRV-DNNs model can also be applied to other infectious diseases such as influenza and dengue, with some modifications to the compartments and parameters in the model to accommodate the related control measures. This approach will facilitate improving predictive modeling and optimizing public health intervention strategies.

Temporal, Spatial Characteristics and Prediction of Precipitation in Guangzhou Based on the SARIMA Model

This article obtains nearly 18 years of precipitation data for Guangzhou from the National Centers for Environmental Information (NCEI) under the National Oceanic and Atmospheric Administration (NOAA) and conducts visualization analysis and modeling predictions using R software.Using the classic SARIMA model (the ARIMA model with seasonal components) as well as regression and residual ARIMA models to build and simulate the precipitation series.We also used Basic-bootstrap prediction, which involves resampling the residuals of the fitted model with replacement, using historical values to predict the future and obtain a model parameter, as well as Full-bootstrap prediction (which assumes that the residuals have uncertainty and that the estimated model coefficients themselves also have uncertainty).Therefore, not only are reasonable model residuals obtained through the bootstrap method, but multiple model parameters are also derived from this method to determine more reasonable model parameters, which are then used to forecast the next 12 periods for the fitted model. Comparing the results of the four prediction models, the Full-bootstrap model under the regression model showed the best predictive performance. The study found that there is no significant trend in precipitation changes over time, but short-term fluctuations still exist. Although some extreme values appeared, they remain within a predictable range.Therefore, the relevant city government departments prone to extreme rainfall can make reasonable adjustments to drainage facilities based on local precipitation forecasts

Optimization of Rendering Parameters of Cesium 3DTiles Model Based on Differential Evolution Algorithm

Cesium is an open-source 3D virtual earth engine based on WebGL and one of the most widely used 3D GIS engines. The 3DTiles rendering parameters in Cesium improve rendering efficiency but involve numerous data types and complex interdependencies, making simultaneous optimization challenging. In this paper, we proposed a multi-strategy probabilistic discrete differential evolution algorithm (MSPDDE) for finding the optimal values of the rendering parameters of Cesium 3DTiles model, which increases the search space and improves the convergence speed by introducing multiple mutation strategies. These strategies effectively reduce the probability of falling into local optimality due to too many parameters and deal with discrete variables in the rendering parameters by applying a probabilistic discretization strategy to the discrete variables so that MSPDDE is able to find all the rendering parameter optima of Cesium 3DTiles. Under three different sizes of 3DTiles model cases, the rendering model time using the optimal parameter configurations found by the algorithm is reduced by 28.84%, 27.89%, and 13.32%, respectively, compared with the default parameter configurations of Cesium, which shortens the time of rendering 3DTiles models by Cesium.