Parametric Model Research Articles

Nonparametric Testing of the Covariate Significance for Spatial Point Patterns under the Presence of Nuisance Covariates

Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model of interactions between points is wrong. Therefore, we propose a fully nonparametric approach to testing significance of a covariate, taking into account the possible effects of nuisance covariates. Our tests match the nominal significance level, and their powers are comparable with the powers of parametric tests in cases where both the model for intensity function and the model for interactions are correct. When the parametric model for the intensity function is wrong, our tests achieve higher powers. The proposed methods rely on Monte Carlo testing and take advantage of the newly introduced concepts: the covariate-weighted residual measure and nonparametric residuals. We also define a correlation coefficient between a point process and a covariate and a partial correlation coefficient quantifying the dependence between a point process and a covariate of interest while removing the influence of nuisance covariates. Supplementary materials for this article are available online.

Prostate cancer diagnosis rates among insured men with and without HIV in South Africa: a cohort study.

Several studies have found lower prostate cancer diagnosis rates among men with HIV (MWH) than men without HIV, but reasons for this finding remain unclear. We used claims data from a South African private medical insurance scheme (07/2017-07/2020) to assess prostate cancer diagnosis rates among men aged ≥18 years with and without HIV. Using flexible parametric survival models, we estimated hazard ratios (HR) for the association between HIV and incident prostate cancer diagnoses. We accounted for potential confounding by age, population group, and sexually transmitted infections (confounder-adjusted model), and additionally for potential mediation by prostatitis diagnoses, prostate-specific antigen (PSA) testing, and prostate biopsies (fully adjusted model). We included 288 194 men, of whom 20 074 (7%) were living with HIV. Prostate cancer was diagnosed in 1 614 men without HIV (median age at diagnosis: 67 years) and in 82 MWH (median age at diagnosis: 60 years). In the unadjusted analysis, prostate cancer diagnosis rates were 35% lower among MWH than men without HIV (HR 0.65, 95% confidence interval [CI] 0.52-0-82). However, this association was no longer evident in the confounder-adjusted model (HR 1.03, 95% CI 0.82-1.30) or in the fully adjusted model (HR 1.14, 95% CI 0.91-1.44). When accounting for potential confounders and mediators, our analysis found no evidence of lower prostate cancer diagnosis rates among men with HIV than men without HIV in South Africa. Our results do not support the hypothesis that HIV decreases the risk of prostate cancer.

Distribution-Free Prediction Intervals Under Covariate Shift, With an Application to Causal Inference

Owing to its appealing distribution-free feature, conformal inference has become a popular tool for constructing prediction intervals with a desired coverage rate. In scenarios involving covariate shift, where the shift function needs to be estimated from data, many existing methods resort to data-splitting techniques. However, these approaches often lead to wider intervals and less reliable coverage rates, especially when dealing with finite sample sizes. To address these challenges, we propose methods based on a pivotal quantity derived under a parametric working model and employ a resampling-based framework to approximate its distribution. The resampling-based approach can produce prediction intervals with a desired coverage rate without splitting the data and can be easily applied to causal inference settings where a shift in the covariate distribution can occur between treatment and control arms. Additionally, the proposed approaches enjoy a double robustness property and are adaptable to different prediction tasks. Our extensive numerical experiments demonstrate that, compared to existing methods, the proposed novel approaches can produce substantially shorter conformal prediction intervals with lower variability in the interval lengths while maintaining promising coverage rates and advantages in versatile usage. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Low-frequency Raman active modes of twisted bilayer MoS2

We study the low-frequency Raman active modes of twisted bilayer MoS2 for several twist angles using a force-field approach and a parametrized bond polarizability model. We show that twist angles near high-symmetry stacking configurations exhibit stacking frustration that leads to significant buckling of the moiré superlattice. We find that atomic relaxation due to the twist is of prime importance. The periodic displacement of the Mo atoms shows the realization of a soliton network, and in turn, leads to the emergence of a number of frequency modes not seen in the high-symmetry stacking systems. Some of the modes are only seen in the XZ Raman polarization setup while others are seen in the XY setup. The symmetry of the normal modes, and how this affects the Raman tensors is examined in detail.

A Fine-Grained Image Classification Method Built on MobileViT

With a rapid development of artificial intelligence technology, fine-grained image classification has gained widespread application. For mobile terminals, this paper introduces an image classification method built on MobileViT, and it can apply into fine-grained image classification. The original MobileViT model has been optimized in three ways. Initially, the h-swish activation function is used to enhance the network performance. Second, the cross-entropy loss function is used to further realize the parameter optimization and model accuracy improvement. Finally, a dropout layer is joined before the fully connected layer can effectively decrease the model recognition time and prevent over-fitting. Experimental data on public tomato disease datasets demonstrate that the improved fine-grained image classification method put forward in this paper exhibits higher classification accuracy, better stability and network generalization ability than other models.

Proof-of-principle of parametric stellarator neutronics modeling using Serpent2

This contribution presents neutron transport studies for the 5-period helical-axis advanced stellarator stellarator using the Serpent2 code. These studies utilize a parametric geometry model, enabling scans in neutronics modeling by varying the thickness of the reactor layers. For example, the tritium breeding ratio (TBR) can be determined by exploring various blanket material options and thicknesses to identify the threshold configuration that meets the TBR design criterion of 1.15. We found out that with the helium-cooled pebble ped candidate option, the TBR criterion is met with a breeding zone thickness of 26 cm, while with the dual-coolant lithium lead the threshold is exceeded at a thickness of 46 cm. Furthermore, the geometry includes non-planar field coils, allowing to study the fast neutron flux in these superconducting coils with a technological limit of 1×1091/cm2s . It is shown that the neutron fast flux is not constant at the coils, necessitating a neutron transport simulation to determine the distribution of the fast-flux at the coils. We show that the peak fast flux can be more than a factor of 2 higher than the average flux, and that the peak flux location rotates helically.

AI in evaluating ambulation of stroke patients: severity classification with video and functional ambulation category scale

ABSTRACT Background The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients’ walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor’s empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. Objective To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. Methods Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. Results The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. Conclusion This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.

Geometrical characterization of healthy red blood cells using digital holographic microscopy and parametric shape models for biophysical studies and diagnostic applications

Modeling of the red blood cell (RBC) shape is an integral part of the experimental and computer simulation investigations of light scattering by these cells for fundamental studies as well as diagnostic applications in techniques like cytometry and quantitative phase imaging. In the present work, a comprehensive study of the geometrical characterization of healthy human RBCs using digital holographic microscopy (DHM) and six frequently employed parametric shape models is reported. It is shown that the comparison of the optical phase profiles, and the thickness profiles given by the models with the DHM results gives a better judgment of the appropriateness of the parametric shape models. It is also shown that the RBC parametric models offer a simpler solution to the refractive index-thickness decoupling problem in QPI methods. Results of geometrical characterization of 500 healthy RBCs in terms of volume, surface area (SA), and sphericity index (SI) led to the classification of the parametric models in two categories based on the nature of variation of these quantities with the cell diameter. In light of the variability of the healthy RBC shapes, our findings suggest that the parametric models exhibiting a negative correlation between the SI and the cell diameter would provide more reliable estimates of the RBC parameters in diagnostic applications. Statistical distributions and descriptive statistics of the RBC volume, SA and SI serve as a guide for the assessment of the capability of the studied parametric models to give a reliable account of the variability of the healthy RBC shape and size.

A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.

A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery.

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.

Research on the Coordinated Development of Digital Economy, Green Technology Innovation, and Ecological Environment Quality—A Case Study of China

Based on panel data from 285 prefecture-level cities in China from 2019 to 2023, the synergistic effects of the digital economy, green technology innovation, and ecological environment quality were analyzed. First, using the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Second, employing a neural network model with these measurements as input variables, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was explored. Finally, based on the calculation results of the neural network model, the importance and impact of each input parameter on ecological environment quality were determined using weight analysis methods. The research findings indicate: (1) Utilizing the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Analysis of each indicator measurement reveals that environmental pressure has a significant impact on ecological environment quality, with significant differences in environmental pressure among different regions. Industrial digitization emerges as the core factor influencing the digital economy, being the most significant driving effect, followed by digital industrialization. Green technology innovation is crucial for promoting environmental protection and achieving high-quality green economic development. (2) Based on the neural network model, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was revealed. The results indicate that the digital economy has a direct impact on improving ecological environment quality. The relationship between the digital economy and the ecological environment exhibits nonlinear effects, with the rate of change in environmental pressure and environmental status measurements initially increasing significantly and then gradually slowing down as the measurement levels of digital industrialization and industrial digitization increase. Improvement in digital governance and data value measurement levels will contribute to enhancing environmental status and environmental governance levels. (3) Through weight analysis, it was found that in terms of direct effects, industrial digitization, and digital industrialization have the most significant impact on environmental pressure, with importance coefficients of 0.45 and 0.3, respectively, while data valorization has the least impact. Regarding intermediary effects, industrial digitization and green technology innovation have the most significant impact on environmental pressure, while digital governance and green technology innovation have a relatively clear impact on environmental status and environmental governance. These results lay the foundation for promoting the coordinated cooperation between the digital economy and green technology innovation and for advancing the establishment of a win–win situation between economic development and environmental protection.

A new dimension in the variability of AGB stars: convection patterns size changes with pulsation

Stellar convection plays an important role in atmospheric dynamics, wind formation, and the mass-loss processes in asymptotic giant branch stars. However, a direct characterization of convective surface structures in terms of size, contrast, and lifespan is quite challenging, as spatially resolving these features requires the highest angular resolution. We aim to characterize the size of convective structures on the surface of the O-rich AGB star R Car to test different theoretical predictions based on mixing-length theory from solar models. We used infrared low-spectral resolution (Rsim 35) interferometric data in the $H$-band ( sim 1.76 mu m) obtained by the instrument PIONIER at the Very Large Telescope Interferometer (VLTI) to image the star's surface at two epochs separated by approximately six years. Using a power spectrum analysis, we estimated the horizontal size of the structures on the surface of R Car. The sizes of the stellar disk at different phases of a pulsation cycle were obtained using parametric model fitting in the Fourier domain. Our analysis supports that the sizes of the structures in R Car are correlated with variations in the pressure scale height in the atmosphere of the target, as predicted by theoretical models based on solar convective processes. We observed that these structures grow in size when the star expands within a pulsation cycle. While the information is still scarce, this observational finding highlights the role of convection in the dynamics of those objects. New interferometric imaging campaigns with the renewed capabilities of the VLTI are envisioned to expand our analysis to a larger sample of objects.

A Parametric Cox Proportional Hazard Model with Application

Survival analysis has become integral to clinical studies, especially in emerging diseases and terminal ailments. This study focused on improving the popular Cox PH model. The new method developed is a parametric type, incorporating the hazard rate of the exponential distribution. It was noted that though the functional form of the Cox PH model was altered, the assumptions were upheld. Additionally, the new model parameters were estimated using the same maximum partial likelihood as the Cox model. Data on the survival times of 137 patients who underwent bone marrow transplants were deployed, and the proposed parametric Cox PH model proved superior to the Cox PH model.

Research on Key Technologies of Jewelry Design and Manufacturing Based on 3D Printing Technology

In conjunction with the swift enhancement of China’s economic prowess, the demand for jewelry among the populace is gradually evolving towards personalized, customized, and intricate designs. Traditional manufacturing approaches are increasingly inadequate to meet these evolving demands. However, the advent of 3D printing technology presents a viable solution for the direct fabrication of such sophisticated jewelry. To this end, the conceptualization of personalized jewelry inspiration is initiated, followed by the implementation of parametric design using SolidWorks 2018 software. Subsequently, 3D printing technology is employed to materialize the jewelry directly. Results indicate that the “Guardian” jewelry model, crafted through the parametric modeling method, exhibits a commendable design, and adheres to processing requirements following a comprehensive risk analysis. The strategic adjustment of the jewelry’s position effectively reduces the required support, circumventing the necessity of adding support to critical surfaces. The surface of the Selective Laser Melting (SLM)-manufactured “Guardian” jewelry boasts a lustrous finish, showcasing optimal overlap between pillars and excellent connectivity among pores. Minimal powder adherence on the surface is observed, enabling direct utilization post-sandblasting, polishing, and plating. This establishes a solid foundation for the direct application of SLM-manufactured personalized jewelry.

Sculptural Hybridization: Combining Digital Parametric Modeling and Manufacturing with Traditional Handcrafting Techniques

Abstract Traditional handcrafting techniques used in the art of sculpting usually produce three-dimensional works of art, while modern reproducing techniques specific to copying and printing machines usually produce two-dimensional results. Mixing these techniques has had a major role in the development of recently emergent 3D digital fabrication techniques. Through a personal case study, this article discusses the similarities and differences between specific traditional additive manual processes used in the art of sculpting and additive three-dimensional modeling and 3D printing techniques. The case study showcases the hybridization of digital modeling and 3D printing processes with traditional physical handcrafting techniques to chart and inform a mixed media approach, with an array of positive outcomes in the art of sculpting and architecture.

Modelling and application of typical nonlinear torsional elements in vibro-impact analysis of pickup truck driveline system

The torsional element method for the dynamic modelling of torsional system was proposed by Crowther and Zhang, which provides a modular modeling approach with high modeling efficiency and has a good application prospect in vibro-impact analysis of vehicle driveline system. The main purpose of this paper is to further enrich the element types of the torsional element method and to establish a nonlinear dynamic model for a pickup truck driveline system by applying the torsional element method to analyze the vibro-impact phenomenon. A general nonlinear clearance element with different contact stiffness and damping, a nonlinear friction element with smooth Stribeck effect and a nonlinear multistage stiffness-damping element with different torque transfer characteristics are developed. A dynamic lumped parametric model with 10 degrees of freedom for a pickup truck driveline system is established by assembling the developed nonlinear torsional elements. The transient vibro-impact phenomenon caused by the clearance, the stick-slip phenomenon caused by the friction and the transient hysteresis cycle phenomenon caused by the nonlinear multistage damper during transient engagement of clutch are numerically investigated. Furthermore, a real-vehicle experimental study is conducted to confirm the transient vibro-impact phenomenon in pickup truck driveline system during transient clutch engagement and to verify the correctness of the dynamic lumped parameter model. On these bases, the influence of the key parameters of the nonlinear torsional element on the transient vibro-impacts are investigated, the effective engineering countermeasures to reduce the vibro-impact of the vehicle driveline system are put forward.

Quantitative assessment of the extent of damage to timber structures during the full course of a real fire using the energy equivalence method

In this study, an innovative method, the Energy Equivalence Method (EEM), is proposed in the context of the Performance-Based Fire Engineering (PBFE) framework for quantitatively assessing the degree of damage of timber structures during the full process of a real fire. To verify the validity of the method, glued laminated timber (GLT) specimens and GLT columns were selected as the subjects of fire action, and a series of equivalent fire experiments were conducted by using a parametric fire model (referred to as the DNI fire model), which is currently used in Germany, to simulate real fire scenarios. By comparing the equivalence of charring depth (CD), pyrolysis zone temperature, and residual load-carrying capacity of the test specimens after the action of the DNI fire and its equivalent standard fire, the validity of the EEM in quantifying the degree of damage to timber members after the DNI fire was fully verified. Subsequently, based on the fire experimental data, accurate thermal and structural models were developed, and these models were utilized to further validate the effectiveness of the EEM in quantifying the extent of damage to timber members under the action of a DNI fire over an arbitrary time period. The results of the study show that the proposed EEM not only can be used to assess the extent of structural damage during fire action and the residual load carrying capacity of the structure after fire action, but also promotes the transition from standard fire safety design to the PBFE design.

Association of immune checkpoint inhibitor introduction and changes in survival disparities by health insurance coverage among individuals newly diagnosed with advanced cancers in the US.

1580 Background: First approved by the US Food and Drug Administration (FDA) in 2011, immune checkpoint inhibitors (ICIs) have transformed treatment options for many cancers. Unfortunately, due to their high cost, individuals without health insurance coverage may not be able to afford them. Therefore, the introduction of ICIs may lead to greater disparities in cancer survival between patients with and without health insurance. This study examined the association of health insurance with changes in survival disparities associated with the introduction of ICIs. Methods: We identified individuals newly diagnosed at age 18-64 years at stage IV with any one of the following cancers with FDA approval for ICI treatment, including melanoma (first approved 03/25/2011, n=9992); HR+ HER- female breast cancer (02/03/2015, n=13632); non-small cell lung cancer (NSCLC; 03/04/2015, n=113866); and renal cell carcinoma (RCC; 11/23/2015, n=15403) from the National Cancer Database. Individuals with private health insurance or who were uninsured at the time of the cancer diagnosis were included. For each cancer, we applied a difference-in-differences (DD) approach to examine the changes in two-year overall survival before and after the ICI FDA approval date among individuals without health insurance compared to those with private health insurance, using flexible parametric survival models and controlling for key sociodemographic factors. Results: As shown (Table), among patients diagnosed with melanoma, 2-year survival rate increased from 16.33% pre-ICI approval to 27.04% after ICI approval among patients who were uninsured and increased from 28.22% to 45.45% among patients with private health insurance, with the disparity by health insurance status widened by 4.58 (95% confidence interval [CI] = 0.02 to 9.14) percentage points (ppt) after adjusting for sociodemographic factors. For patients diagnosed with stage IV NSCLC, the disparity by health insurance widened by 1.58 (95% CI = 0.52 to 2.64) ppt. Conclusions: The introduction of ICIs increased disparities in survival by health insurance status. Policies to improve health insurance coverage options or make new treatments more affordable to uninsured patients are needed. [Table: see text]

Model Falsification from a Bayesian Viewpoint with Applications to Parameter Inference and Model Selection of Dynamical Systems

Model Falsification from a Bayesian Viewpoint with Applications to Parameter Inference and Model Selection of Dynamical Systems

On the resonance frequency of the irregular shaped visco-acoustic Helmholtz cavity problem

In this paper we develop a closed-form mathematical solution for the resonance frequency of the axisymmetric irregular shaped visco-acoustic Helmholtz cavity problem. The solution is based on a conformal mapping that transforms the governing visco-acoustic equations of motion for the irregular cavity geometry into a new domain described by a set of orthogonal curvilinear coordinates. These orthogonal curvilinear coordinates have an important characteristic relative to the transformation of the physical boundary: the mapped domain is constructed such that the locus of all points for the length-wise profile of the physical boundary coincides with a constant value for a (quasi radial) coordinate in the new mapped domain. In these orthogonal curvilinear coordinates we obtain an analytical solution for the visco-acoustic equations of motion and exactly satisfy the no-slip fluid boundary conditions over the irregular shaped cavity walls by matching the coefficients of an orthogonal Legendre polynomial series representation for the non-uniform velocity on the cavity walls. The mathematical solution is an approximation to the extent that the scale factors chosen for the mapping consist of strictly the mutual-coupling terms of the true scale factors. Thus we analyze some results to understand the correlation of the analytical solution frequency response spectra with numerical finite element analyses of the idealized boundary conditions with various irregular shaped cavity geometries. The results for fundamental resonance frequency were found to exhibit good correlation with the results of finite element analyses over a significant range of fluid properties with different irregular cavity geometries. As the primary motivation for the derivation stems from the lack of a rapid yet accurate alternative to computationally intensive MultiPhysics FEA in the formulation of acoustic trendlines for sensor development, the solution’s acoustic impedance was applied as dispersive loading in a simple lumped parameter model of a fluid identification sensor for comparison. The resulting acoustic trendlines of the reduced (5 DOF) model were found to exhibit good correlation with the results of the MultiPhysics FEA (>105 DOF). Thus the solution was found to enable a closed-form analytical method for rapid construction of parametric studies in the practical design of fluid analysis sensors based on complex resonant cavity geometries.