Parametric Approach Research Articles

Fast Parameterization of Planar Domains for Isogeometric Analysis via Generalization of Deep Neural Network

One prominent step in isogeometric analysis (IGA) is known as domain parameterization, that is, finding a parametric spline representation for a computational domain. Typically, domain parameterization is divided into two separate steps: identifying an appropriate boundary correspondence and then parameterizing the interior region. However, this separation significantly degrades the quality of the parameterization. To attain high-quality parameterization, it is necessary to optimize both the boundary correspondence and the interior mapping simultaneously, referred to as integral parameterization. In a prior research, an integral parameterization approach for planar domains based on neural networks was introduced. One limitation of this approach is that the neural network has no ability of generalization, that is, a network has to be trained to obtain a parameterization for each specific computational domain. In this article, we propose an efficient enhancement over this work, and we train a network which has the capacity of generalization—once the network is trained, a parameterization can be immediately obtained for each specific computational via evaluating the network. The new network greatly speeds up the parameterization process by two orders of magnitudes. We evaluate the performance of the new network on the MPEG data set and a self-design data set, and experimental results demonstrate the superiority of our algorithm compared to state-of-the-art parameterization methods.

Testing the Multivariate Hüsler–Reiss Model as a Practical Parametric Approach for Multiple River Flood Risk Assessment Using d4PDF Data: A Case Study in Kyushu Island, Japan

Testing the Multivariate Hüsler–Reiss Model as a Practical Parametric Approach for Multiple River Flood Risk Assessment Using d4PDF Data: A Case Study in Kyushu Island, Japan

Probing the universe's expansion dynamics: The linear correction scenario perspective on dark energy

In this study, we investigate the cosmological evolution of the universe, focusing on the KMMS models in a model-independent approach, particularly the scenario involving linear correction, given by E2(z)=A(z)+β(1+γB(z)), where A(z)=Ωm0(1+z)3 and B(z)=z. By analyzing recent Cosmic Chronometers (CC) and the Pantheon+ samples, we determine the best-fit values of model parameters using a Markov chain Monte Carlo analysis. Our models exhibit unique dynamics, transitioning from super-exponential to exponential expansion, with a significant shift at redshift zt=0.83−0.10+0.11 and present deceleration parameter q0=−0.69−0.17+0.17. The jerk parameter exceeds 1, indicating a faster change in acceleration than predicted by ΛCDM. The energy density parameter is consistent with Planck observations, and the dark sector evolution follows the expected thermal history. The EoS parameter approaches ωde=−1 over time, with a current value of ωde0=−1.06±0.12, aligning with the phantom phase. Our model presents a new dark energy alternative, emphasizing the importance of considering models like KMMS in understanding cosmic evolution.

Numerical simulation of time-dependent viscous fluid flow with upward and downward fluctuation of spinning disk

The parametric approach towards time-dependent viscous fluid flow across a gyrating disk with upward and downward fluctuation. The major goal of this research is to assess fluid flow under the influence of magnetic fields and heat propagation processes. Because they provide a thorough description of electromagnetic interactions. Maxwell's equations are at the heart of all contemporary information and communication technologies. The governing equations comprising Navier Stokes equation, energy, concentration, and Maxwell equations have been represented appropriately for this purpose. The governing equations are turned down to the system of non-linear ODEs through a resemblance framework. The obtained system of differential equations has been resolved via numerical procedure Parametric Continuation Method (PCM). For the scale reliability purpose, the outcomes are compared to another numerical Matlab scheme boundary value solver. In the current analysis, the presence of convective boundary conditions correlated to mass and energy is of physical relevance. The numerical findings are provided in tabular and graphical forms. The consequences of suction and wall injection have been also highlighted. The upward motion of the spinning disc is thought to lead to comparable findings as in an injection scenario, whilst the downhill motion is thought to contribute to wall suction-like effects.

Strategies for multi-case physics-informed neural networks for tube flows: a study using 2D flow scenarios

Fluid dynamics computations for tube-like geometries are crucial in biomedical evaluations of vascular and airways fluid dynamics. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional computational fluid dynamics (CFD) methods. However, vanilla PINNs often demand longer training times than conventional CFD methods for each specific flow scenario, limiting their widespread use. To address this, multi-case PINN approach has been proposed, where varied geometry cases are parameterized and pre-trained on the PINN. This allows for quick generation of flow results in unseen geometries. In this study, we compare three network architectures to optimize the multi-case PINN through experiments on a series of idealized 2D stenotic tube flows. The evaluated architectures include the ‘Mixed Network’, treating case parameters as additional dimensions in the vanilla PINN architecture; the “Hypernetwork”, incorporating case parameters into a side network that computes weights in the main PINN network; and the “Modes” network, where case parameters input into a side network contribute to the final output via an inner product, similar to DeepONet. Results confirm the viability of the multi-case parametric PINN approach, with the Modes network exhibiting superior performance in terms of accuracy, convergence efficiency, and computational speed. To further enhance the multi-case PINN, we explored two strategies. First, incorporating coordinate parameters relevant to tube geometry, such as distance to wall and centerline distance, as inputs to PINN, significantly enhanced accuracy and reduced computational burden. Second, the addition of extra loss terms, enforcing zero derivatives of existing physics constraints in the PINN (similar to gPINN), improved the performance of the Mixed Network and Hypernetwork, but not that of the Modes network. In conclusion, our work identified strategies crucial for future scaling up to 3D, wider geometry ranges, and additional flow conditions, ultimately aiming towards clinical utility.

Fault-Tolerant Phototaxis of a Modular System Inspired by Gonium pectorale Using Phase-Based Control

In this study, we proposed a model for modular robots in which autonomous decentralized modules adaptively organize their behavior. The phototaxis of Gonium pectorale, a species of volvocine algae, was modeled as a modular system, and a fault-tolerant modular control method of phototaxis was proposed for it. The proposed method was based on the rotation phase of the colony and adaptively adjusted an internal response-related parameter to enhance the fault tolerance of the system. Compared to a constant parameter approach, the simulation results demonstrated a significant improvement in the phototaxis time for positive and negative phototaxis during module failures. This method contributes to achieving autonomous, decentralized, and purposeful mediation of the modules necessary for controlling modular robots.

Utilizing Artificial Neural Networks for Geometric Bone Model Reconstruction in Mandibular Prognathism Patients

Patient-specific 3D models of the human mandible are finding increasing utility in medical fields such as oral and maxillofacial surgery, orthodontics, dentistry, and forensic sciences. The efficient creation of personalized 3D bone models poses a key challenge in these applications. Existing solutions often rely on 3D statistical models of human bone, offering advantages in rapid bone geometry adaptation and flexibility by capturing a range of anatomical variations, but also a disadvantage in terms of reduced precision in representing specific shapes. Considering this, the proposed parametric model allows for precise manipulation using morphometric parameters acquired from medical images. This paper highlights the significance of employing the parametric model in the creation of a personalized bone model, exemplified through a case study targeting mandibular prognathism reconstruction. A personalized model is described as 3D point cloud determined through the utilization of series of parametric functions, determined by the application of geometrical morphometrics, morphology properties, and artificial neural networks in the input dataset of human mandible samples. With 95.05% of the personalized model’s surface area displaying deviations within −1.00–1.00 mm relative to the input polygonal model, and a maximum deviation of 2.52 mm, this research accentuates the benefits of the parametric approach, particularly in the preoperative planning of mandibular deformity surgeries.

A Systematic Formulation into Neutrosophic Z Methodologies for Symmetrical and Asymmetrical Transportation Problem Challenges

This study formulates a multi-objective, multi-item solid transportation issue with parameters that are neutrosophic Z-number fuzzy variables such as transportation costs, supplies, and demands. This work covers two scenarios where uncertainty in the problem can arise: the fuzzy solid transportation problem and the interval solid transportation problem. The first scenario arises when we represent data problems as intervals instead of exact values, while the second scenario arises when the information is not entirely clear. We address both models when the uncertainty alone impacts the constraint set. In order to find a solution for the interval case, we generate an additional problem. Since this auxiliary problem is typical of solid transportation, we can resolve it using the effective techniques currently in use. In the fuzzy scenario, a parametric method is used to discover a fuzzy solution to the earlier issue. Parametric analysis identifies that the best parameterized approaches to complementary problems are characterized by the application of parametric analysis. We present a suggested algorithm for determining the stability set. Finally, we provide a numerical example and sensitivity analysis for the transportation problem, which is both symmetrical and asymmetrical.

Novel approach for sheet metal constitutive parameters identification based on shape index and multiple regression

To accurately predict the behavior and mechanical damage of sheet metal during hot forming process, the use of advanced thermomechanical modeling and precise identification of the constitutive parameter are required. This paper proposes a novel identification approach for constitutive parameters that relies on multiple regression and the shape index of experimental data curves. This approach is used to determine the material constants associated with the Johnson-Cook material and failure models for steel and aluminum sheets. Therefore, the obtained results approve the applicability and high accuracy of the identification procedure for the damage and the constitutive parameters of sheet metal, which allows for substantial time savings. Furthermore, to evaluate the formability of sheet metal, experimental forming tests at various temperatures were conducted. The identification process enhances the prediction of forming results, as shown by the fact that the numerical simulation of Erichsen and square die hydroforming tests using identified Johnson-Cook parameters agreed with the results of these tests carried out at different forming conditions. Moreover, the thickness measurement shows the ability of the developed model to properly predict the thickness of deformed shapes.

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning.

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity.

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity. Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using metrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset. In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m2 (range: 40-70.3 kg/m2). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to c clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients). Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Integrating Dendrogeomorphology into Stress–Strain Numerical Models: An Opportunity to Monitor Slope Dynamic

Monitoring systems are recognized worldwide as fundamental tools for landslide risk management. However, monitoring can be difficult when dealing with large slopes in forested areas. In these situations, dendrogeomorphology can offer a low-cost and low-impact alternative for providing distributed information with an annual temporal resolution. The present study is a first attempt to integrate dendrometric and dendrogeomorphic data into a numerical finite difference model, in order to simulate the stress–strain behavior of the tree-slope system. By using a parametrical approach, the capability of the numerical model to effectively reproduce the tree stem anomalies (i.e., tilting angle, J-shaped feature, and internal stresses causing tree-ring growth anomalies such as eccentric growth and reaction wood) was verified, and the target parameters for the model calibration were identified based on a sensitivity analysis, which emphasized the relevance of the wood deformability; moreover, the interpretation of results allowed to point out different peculiarities (in terms of type of deformation, falling direction, and distribution of internal stresses) for different slope conditions (kinematics and depth of the failure surface) and different zones of the landslide (head scarp, main body, and toe). Afterwards, the modeling approach was applied to the Val Roncaglia landslide (Northen Italy), which involves a complex roto-translational kinematics, characterized by multiple sliding surfaces. The simulated stem anomalies showed good agreement with the ones arising from onsite dendrometric surveys, and they confirmed the conceptual model of the landslide, enabling the planning of further specific investigations. Moreover, the capability of the model in reproducing the tilting angle of trees, if correlated to their eccentricity, could provide a quite long time series (over more than 50–60 years) of the landslide reactivation and allow the use of dendrochronological data for the model calibration, thereby enhancing slope dynamic monitoring and landslide risk management.

Remote sensing approaches to identify trees to species-level in the urban forest: A review

Most urban tree inventories depend on resource-intensive, field-based assessments, which are unevenly distributed in space and time. Recently, these inventories have been conducted using field inventories combined with airborne multispectral, hyperspectral, LiDAR, and spaceborne multispectral remote sensing. Significant advances have been made in urban tree GIS databases and remote sensing methods, which include delineating individual tree crowns, extracting tree species metrics, and employing classification techniques. Generally, remote sensing methods distinguish individual urban trees using either pixel-based or object-based methods, while image classification procedures are typically divided into parametric (e.g., regression-based classification, Bayesian, and principal component analysis) and non-parametric approaches such as machine learning (e.g., random forests support vector machines) and deep learning (e.g., convolutional neural networks). Our synthesis of the current state of science suggests sensors with the highest spatial (m), spectral (bands), and temporal (repeat time) resolutions result in the most accurate tree species identification. Combining airborne LiDAR/hyperspectral or airborne LiDAR/spaceborne high-resolution multispectral sensors yields the highest accuracy for the most diverse urban forests. An object-based non-parametric approach, like a fully convolutional neural network, scores higher in accuracy assessments than pixel-based parametric approaches. Future studies can leverage global/regional GIS field inventory databases to expand the scope of studies within and across multiple cities, utilizing LiDAR and spaceborne sensors.

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach

BackgroundLockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east–west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March—11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC).MethodsData were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression.ResultsDuring the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly.ConclusionsThis study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

Analysis and optimization of the coupling effect for duct-rotor based on aerodynamic performance

The duct has the potential to provide additional thrust, which can enhance the overall aerodynamic performance of the system. Nonetheless, since the duct and the rotor interact with each other, it is crucial to consider their interaction while optimizing the system for improved performance. This paper presents a parameterization approach for the duct and rotor. Primarily, a rotor space characterization and parameter modeling method that utilizes Bezier curves, class functions/shape functions, and Bernstein polynomials is proposed. Next, the aerodynamic performance of different key factors of the duct-rotor system is calculated and analyzed using a numerical calculation method. The study found that the coupling effect of camber yf and chord length parameter Bc3 had a significant impact on the thrust coefficient, with a contribution rate of 7.3 %. Correspondingly, the coupling effect of camber yf and torsion angle parameter Ba3 had a great influence on the Figure of Merit (FM), with a contribution rate of 7.1 %. The thrust coefficient and FM of the rotor-duct system were optimized by taking the coupling effect into consideration, and the optimized ducted rotor was verified by experimental measurement. The test results shows that the high thrust case achieves a 25.60 % increase in thrust coefficient, while the high-FM case establishes an 8.89 % increase in the FM. This implies that the collaborative optimization based on the coupling effect of duct and rotor shape parameters can effectively improve the system's performance, such as thrust coefficient and Figure of Merit.

An assessment of cumulative CO2 efflux as proxy of organic matter turnover time in the soil priming effect: analysis using the lumped parameter approach

An assessment of cumulative CO2 efflux as proxy of organic matter turnover time in the soil priming effect: analysis using the lumped parameter approach

Pointwise Nonparametric Estimation of Odds Ratio Curves with R: Introducing the flexOR Package

The analysis of odds ratio curves is a valuable tool in understanding the relationship between continuous predictors and binary outcomes. Traditional parametric regression approaches often assume specific functional forms, limiting their flexibility and applicability to complex data. To address this limitation and introduce more flexibility, several smoothing methods may be applied, and approaches based on splines are the most frequently considered in this context. To better understand the effects that each continuous covariate has on the outcome, results can be expressed in terms of splines-based odds ratio (OR) curves, taking a specific covariate value as reference. In this paper, we introduce an R package, flexOR, which provides a comprehensive framework for pointwise nonparametric estimation of odds ratio curves for continuous predictors. The package can be used to estimate odds ratio curves without imposing rigid assumptions about their underlying functional form while considering a reference value for the continuous covariate. The package offers various options for automatically choosing the degrees of freedom in multivariable models. It also includes visualization functions to aid in the interpretation and presentation of the estimated odds ratio curves. flexOR offers a user-friendly interface, making it accessible to researchers and practitioners without extensive statistical backgrounds.

Accelerated failure time frailty model for modeling multiple systems subject to minimal repair

AbstractThis article presents accelerated failure time models with and without frailty for modeling multiple systems subject to minimal repair. The study considers the conventional accelerated failure time model, the accelerated failure time model with Gamma frailty, and proposes the accelerated failure time model with weighted Lindley frailty, which has attractive properties such as a closed‐form Laplace transform. The proposed model extends the accelerated failure time model with the intensity function of a power law process. It retains the direct physical interpretation of the original accelerated failure time model, in which the role of covariates is to accelerate or decelerate the time to each repair. This framework includes parametric approaches to model fitting, which we consider for estimating the vector of regression parameters under this model and the parameter in the baseline intensity functions. The methodology is illustrated with a simulation study and a toy example to demonstrate the applicability of these models in the industrial context.

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Structural, mechanical and biocompatible evaluation of nanocrystalline Ti-Nb-Ta-Cr-Co0.2 HEA for implant applications

High entropy alloys (HEAs) have attracted significant interest in the field of medical applications due to their unique functional properties and remarkable strength. Thus, several Ti-based HEAs have been proposed for bone implants. For the same, the current study proposes a Ti-Nb-Ta-Cr-Co0.2 based HEA, which exhibits better properties compared to commonly used implant materials (such as 316L, cp-Ti, Co-Cr-Mo, and Ti-6Al-4V alloys). The preliminary selection of the atomic % of individual elements for the aforementioned HEA is based on the parametric approach. The proposed combination after mechanical alloying for 50h suggested the coexistence of two BCC phases. This was confirmed by X-ray and electron diffractions. The obtained powder was then consolidated through spark plasma sintering at 1000 °C, resulting into two BCC and one HCP phases. The sintered sample has shown hardness, compressive strength, and Young’s modulus values as ~652 HV, ∼1418MPa, and ~82GPa respectively, which makes it mechanically suitable for the implant application. Additionally, the present HEA after SPS sintering also displayed suitable biocompatibility with MG-63 cells which was evaluated by MTT assay.