Parametric Modeling Framework Research Articles

Effects of perforated creases on the mechanical behavior and fatigue life of thick origami structures

Perforating crease lines has been widely adopted as a practical method for achieving thickness accommodation in designing engineering origami structures. In a previous work, we proposed a perforated crease design with three rows of oblong holes that induces smooth folding behavior of thick origami sheets and improves their mechanical properties. Here we further investigate the effects of this configured crease design on the mechanical behavior and fatigue life of 3D origami tubular structures, including the Bellow, Yoshimura, horizontally-symmetric Kresling, and inclinedly-symmetric Kresling origami tubes. We utilize Rhino/Grasshopper to develop a parametric modeling framework for the studied origami structures based on their respective geometric relationships. An origami model with or without perforated creases can be efficiently established by adjusting input parameters in the modeling framework. Then, finite element analysis is adopted to assess folding behavior and mechanical properties of the structures, and to predict their fatigue life combined with FE-SAFE. The results show that the perforated crease can alleviate the issue of panel interference, resulting in smooth folding behavior of origami structures. Although the mechanical responses are weakened due to perforations, the fatigue life of the structures is significantly enhanced by an order of magnitude. We therefore expect that the perforated crease design with three rows of oblong holes can be applied to engineering origami structures.

A Universal Parametric Modeling Framework for Electric Machine Design

At present, the majority of electric machine design software employs its own unique machine data structure. However, when users need to transfer their designs between software, they are often faced with significant obstacles or cannot obtain a parametric model suitable for optimization. In order to solve this issue, a universal parametric modeling framework is proposed for electric machine design. The geometric structure is strictly constrained to ensure that the model will not interfere with each part because of the randomness of input parameters. A data structure consisting of points, lines, and surfaces is constructed, and a conversion interface for parametric modeling with different software is established. Consequently, this universal framework can automatically generate parametric models appropriate for different finite element analysis (FEA) software according to the input parameters. The framework is especially convenient for users who need to design or optimize an electric machine, particularly when FEA software is required for verification. Numerical verification is performed using different software based on interior permanent magnet (IPM) synchronous machines to demonstrate the effectiveness of the framework.

CBR-RBR fusion based parametric rapid construction method of bridge BIM model

The development of BIM (Building Information Modeling) parametric modeling technology has improved the reusability and modeling efficiency of building information models. Aiming at the problems of too many parameters and long time in the rapid parametric construction of the bridge BIM model, we propose a novel parametric rapid construction method of bridge BIM model based on the integration of CBR (Case Based Reasoning) and RBR (Rule Based Reasoning), which is used to realize the rapid construction of bridge BIM model. Firstly, the modeling characteristics of CBR and RBR are analyzed, and a parametric modeling framework of CBR-RBR fusion is proposed. Secondly, a MHPO (Modified Hunter-Prey Optimization) algorithm is proposed to assign the weight of feature parameters to improve the accuracy of the prediction model. Then, modeling parameter reasoning rules are designed to realize the construction of parameter reasoning model. Finally, the prototype system is developed to verify the proposed method based on Revit and C#. Compared with manual modeling and traditional parametric modeling, the fusion method of CBR and RBR greatly shortens the modeling time and improves modeling efficiency and flexibility.

Causal mediation and sensitivity analysis for mixed-scale data.

The goal of causal mediation analysis, often described within the potential outcomes framework, is to decompose the effect of an exposure on an outcome of interest along different causal pathways. Using the assumption of sequential ignorability to attain non-parametric identification, Imai et al. (2010) proposed a flexible approach to measuring mediation effects, focusing on parametric and semiparametric normal/Bernoulli models for the outcome and mediator. Less attention has been paid to the case where the outcome and/or mediator model are mixed-scale, ordinal, or otherwise fall outside the normal/Bernoulli setting. We develop a simple, but flexible, parametric modeling framework to accommodate the common situation where the responses are mixed continuous and binary, and, apply it to a zero-one inflated beta model for the outcome and mediator. Applying our proposed methods to the publicly-available JOBS II dataset, we (i) argue for the need for non-normal models, (ii) show how to estimate both average and quantile mediation effects for boundary-censored data, and (iii) show how to conduct a meaningful sensitivity analysis by introducing unidentified, scientifically meaningful, sensitivity parameters.

Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data

In the past few decades, model averaging has received extensive attention, and has been regarded as a feasible alternative to model selection. However, this work is mainly based on parametric model framework and complete dataset. This paper develops a frequentist model-averaging estimation for semiparametric partially linear models with censored responses. The nonparametric function is approximated by B-spline, and the weights in model-averaging estimator are picked up via minimizing a leave-one-out cross-validation criterion. The resulting model-averaging estimator is proved to be asymptotically optimal in the sense of achieving the lowest possible squared error. A simulation study demonstrates that the method in this paper is superior to traditional model-selection and model-averaging methods. Finally, as an illustration, the proposed procedure is further applied to analyze two real datasets.

Longitudinal and time-to-event modeling for prognostic implications of radical surgery in retroperitoneal sarcoma.

Retroperitoneal sarcoma (RPS) is a rare malignancy which can be difficult to manage due to the variety of clinical behaviors. In this study, we aimed to develop a parametric modeling framework to quantify the relationship between postoperative dynamics of several biomarkers and overall/progression‐free survival of RPS. One hundred seventy‐four patients with RPS who received surgical resection with curative intent at the Peking University Cancer Hospital Sarcoma Center were retrospectively included. Potential prognostic factors were preliminarily identified. Longitudinal analyses of body mass index (BMI), serum total protein (TP), and white blood cells (WBCs) were performed using nonlinear mixed effects models. The impacts of time‐varying and time‐invariant predictors on survival were investigated by parametric time‐to‐event (TTE) models. The majority of patients experienced decline in BMI, recovery of TP, as well as transient elevation in WBC counts after surgery, which significantly correlated with survival. An indirect‐response model incorporating surgery effect described the fluctuation in percentage BMI. The recovery of TP was captured by a modified Gompertz model, and a semimechanistic model was selected for WBCs. TTE models estimated that the daily cumulative average of predicted BMI and WBC, the seventh‐day TP, as well as certain baseline variables, were significant predictors of survival. Model‐based simulations were performed to examine the clinical significance of prognostic factors. The current work quantified the individual trajectories of prognostic biomarkers in response to surgery and predicted clinical outcomes, which would constitute an additional strategy for disease monitoring and intervention in postoperative RPS.

Accelerated failure time modeling via nonparametric mixtures.

An accelerated failure time (AFT) model assuming a log-linear relationship between failure time and a set of covariates can be either parametric or semiparametric, depending on the distributional assumption for the error term. Both classes of AFT models have been popular in the analysis of censored failure time data. The semiparametric AFT model is more flexible and robust to departures from the distributional assumption than its parametric counterpart. However, the semiparametric AFT model is subject to producing biased results for estimating any quantities involving an intercept. Estimating an intercept requires a separate procedure. Moreover, a consistent estimation of the intercept requires stringent conditions. Thus, essential quantities such as mean failure times might not be reliably estimated using semiparametric AFT models, which can be naturally done in the framework of parametric AFT models. Meanwhile, parametric AFT models can be severely impaired by misspecifications. To overcome this, we propose a new type of the AFT model using a nonparametric Gaussian-scale mixture distribution. We also provide feasible algorithms to estimate the parameters and mixing distribution. The finite sample properties of the proposed estimators are investigated via an extensive stimulation study. The proposed estimators are illustrated using a realdataset.

A framework for BIM-based disassembly models to support reuse of building components

Reuse of building components and materials is essential in order to reduce global building waste and resource depletion. Reuse can also improve residual utility for building components and materials. Selective disassembly enables such reuse. Implementation of selective disassembly planning for buildings requires accurate and correct analytical disassembly models for building archetypes, which can be provided using Building Information Modeling (BIM). However, appropriate level-of-detail (LoD) for the required BIM-based parametric disassembly models and a methodology for automatically extracting the corresponding parameters from a BIM are unknown. These knowledge gaps are addressed by developing such a methodology for the appropriate LoD. This study aims to describe and validate a methodology for determining parameters for BIM-based disassembly models to support the reuse of building components towards a circular economy in the construction industry. Taking building structures as an example, disassembly model principles for typical building parts are proposed. Then, a framework of disassembly parametric models is determined and applied for BIM. Based on this framework, a method for automatically determining the necessary parameters is developed, and the critical attributes are suggested. Finally, the disassembly parameters of BIM elements are defined and demonstrated for four case studies in order to validate the consistency and effectiveness of this method. The results indicate useful disassembly models are properly produced within a reasonable amount of time.

Parametric FEM modeling of multilayer castellated canted cosine theta (CCT) magnets

Canted cosine theta (CCT) magnets have received increased attention in the recent years due to their inherent characteristics of high field quality and low conductor stresses, as each turn is supported by the metallic former. CCT geometry is suitable for complex magnets—such as nested dipoles, which allow full 360° control of the magnetic field direction. Modeling and design of such magnets is the subject of this work. A parametric 3D modeling framework for multilayer CCT magnets has been developed and optimized allowing efficient model generation without a need for both: CAD software and time consuming meshing. The model has been created using the APDL scripting language and solved using the Ansys software. By direct generation of nodes and elements the time to generate a model with one million elements was of the order of 10 min. A framework for script generation was developed, as well as the relations between the mesh parameters and the total number of nodes and elements. Detailed mesh dependence study was carried-out with in-depth analysis of solver performance (solution time, amount of written data, RAM requirements). Global and local convergence criteria were introduced to find the optimal mesh size. The case of a four layer 2.5 Tm nested dipole was analyzed in details including the worst-case scenario with full debonding between the CCT layers. Thanks to the castellations the nested-dipole is safe to support 144 000 Nm of torque due to Lorentz forces even with full de-bonding between the layers.

Detecting time lag between a pair of time series using visibility graph algorithm

Estimating the time lag between a pair of time series is of significance in many practical applications. In this article, we introduce a method to quantify such lags by adapting the visibility graph algorithm, which converts time series into a mathematical graph. Currently widely used method to detect such lags is based on cross-correlations, which has certain limitations. We present simulated examples where the new method identifies the lag correctly and unambiguously while as the cross-correlation method does not. The article includes results from an extensive simulation study conducted to better understand the scenarios where the new method performed better or worse than the existing approach. We also present a likelihood based parametric modeling framework and consider frameworks for quantifying uncertainty and hypothesis testing for the new approach. We apply the current and new methods to two case studies, one from neuroscience and the other from environmental epidemiology, to illustrate the methods further.

A Flexible Parametric Modelling Framework for Survival Analysis

SummaryWe introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard functions (constant; increasing; decreasing; up then down; down then up) and various common survival distributions (log-logistic; Burr type XII; Weibull; Gompertz) and includes defective distributions (cure models). This generality is achieved by using four distributional parameters: two scale-type parameters—one of which relates to accelerated failure time (AFT) modelling; the other to proportional hazards (PH) modelling—and two shape parameters. Furthermore, we advocate ‘multiparameter regression’ whereby more than one distributional parameter depends on covariates—rather than the usual convention of having a single covariate-dependent (scale) parameter. This general formulation unifies the most popular survival models, enabling us to consider the practical value of possible modelling choices. In particular, we suggest introducing covariates through just one or other of the two scale parameters (covering AFT and PH models), and through a ‘power’ shape parameter (covering more complex non-AFT or non-PH effects); the other shape parameter remains covariate independent and handles automatic selection of the baseline distribution. We explore inferential issues and compare with alternative models through various simulation studies, with particular focus on evidence concerning the need, or otherwise, to include both AFT and PH parameters. We illustrate the efficacy of our modelling framework by using data from lung cancer, melanoma and kidney function studies. Censoring is accommodated throughout.

Impact of deviation from guideline recommended treatment on breast cancer survival in Asia

Breast cancer survival has improved with significant progress in treatment and disease management. However, compliance with treatment varies. Treatment guidelines for older patients are unclear. We aim to identify predictors of noncompliance with recommended therapy in a large breast cancer population and assess the impact of noncompliance on survival. Our study included 19,241 non-metastatic female breast cancer patients, of whom 3,158 (16%) died within 10 years post-diagnosis (median survival = 5.8 years). We studied the association between treatment noncompliance and factors with logistic regression, and the impact of treatment noncompliance on survival with a flexible parametric survival model framework. The highest proportion of noncompliance was observed for chemotherapy (18%). Predictors of noncompliance with chemotherapy, radiotherapy and endocrine therapy included age, tumor size, nodal involvement and subtype (except radiotherapy). Factors associated with not receiving surgery included age and subtype. Treatment noncompliance was associated with worse overall survival for surgery (HR: 2.26 [1.80–2.83]), chemotherapy (1.25 [1.11–1.41]), radiotherapy (2.28 [1.94–2.69]) and endocrine therapy (1.70 [1.41–2.04]). Worse survival was similarly observed in older patients for whom guidelines generally do not apply. Our results highlight the importance of following appropriate treatment as recommended by current guidelines. Older patients may benefit from similar recommendations.

A method of automatic extraction of parameters of multi-LoD BIM models for typical components in wooden architectural-heritage structures

A building-information-modeling (BIM) model, which is established based on high-fidelity point-cloud data, can be well used to preserve architectural heritage. Two related critical issues for this conservation are multiple-level-of-detail (multi-LoD) parametric models that emphasize different protection requirements for typical components, and a method for automatically extracting the corresponding parameters from a high-fidelity point cloud. Taking typical Chinese wooden architectural-heritage structures as an example, multi-LoD principles for typical components without damage are proposed according to the different requirements. Then, a framework of multi-LoD parametric models was developed and implemented in BIM. Based on this, a method for automatically extracting the abovementioned parameters is developed and the critical parameters of this method are recommended. To validate the reliability and efficiency of this method, the parameters of multi-LoD models of typical components are extracted. The results indicate that the relative and absolute errors of values of such parameters are mostly less than 2% and 0.5 mm, respectively. Moreover, this method is capable of extracting parameters from millions of point-cloud data within 7 min, thus validating the high efficiency and reliability of the proposed method.

Parametric Classification of Bingham Distributions Based on Grassmann Manifolds.

In this paper, we present a novel Bayesian classification framework of the matrix variate Bingham distributions with the inclusion of its normalizing constant and develop a consistent general parametric modeling framework based on the Grassmann manifolds. To calculate the normalizing constants of the Bingham model, this paper extends the method of saddle-point approximation (SPA) to a new setting. Furthermore, it employs the standard theory of maximum likelihood estimation (MLE) to evaluate the involved parameters in the used probability density functions. The validity and performance of the proposed approach are tested on 14 real-world visual classification databases. We have compared the classification performance of our proposed approach with the baselines from the previous related approaches. The comparison shows that on most of the databases, the performance of our approach is superior.

Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates

BackgroundAn excess of nonsynonymous substitutions, over neutrality, is considered evidence of positive Darwinian selection. Inference for proteins often relies on estimation of the nonsynonymous to synonymous ratio (ω = dN/dS) within a codon model. However, to ease computational difficulties, ω is typically estimated assuming an idealized substitution process where (i) all nonsynonymous substitutions have the same rate (regardless of impact on organism fitness) and (ii) instantaneous double and triple (DT) nucleotide mutations have zero probability (despite evidence that they can occur). It follows that estimates of ω represent an imperfect summary of the intensity of selection, and that tests based on the ω > 1 threshold could be negatively impacted.ResultsWe developed a general-purpose parametric (GPP) modelling framework for codons. This novel approach allows specification of all possible instantaneous codon substitutions, including multiple nonsynonymous rates (MNRs) and instantaneous DT nucleotide changes. Existing codon models are specified as special cases of the GPP model. We use GPP models to implement likelihood ratio tests for ω > 1 that accommodate MNRs and DT mutations. Through both simulation and real data analysis, we find that failure to model MNRs and DT mutations reduces power in some cases and inflates false positives in others. False positives under traditional M2a and M8 models were very sensitive to DT changes. This was exacerbated by the choice of frequency parameterization (GY vs. MG), with rates sometimes > 90% under MG. By including MNRs and DT mutations, accuracy and power was greatly improved under the GPP framework. However, we also find that over-parameterized models can perform less well, and this can contribute to degraded performance of LRTs.ConclusionsWe suggest GPP models should be used alongside traditional codon models. Further, all codon models should be deployed within an experimental design that includes (i) assessing robustness to model assumptions, and (ii) investigation of non-standard behaviour of MLEs. As the goal of every analysis is to avoid false conclusions, more work is needed on model selection methods that consider both the increase in fit engendered by a model parameter and the degree to which that parameter is affected by un-modelled evolutionary processes.

Modeling Hybrid Traits for Comorbidity and Genetic Studies of Alcohol and Nicotine Co-Dependence.

We propose a novel multivariate model for analyzing hybrid traits and identifying genetic factors for comorbid conditions. Comorbidity is a common phenomenon in mental health in which an individual suffers from multiple disorders simultaneously. For example, in the Study of Addiction: Genetics and Environment (SAGE), alcohol and nicotine addiction were recorded through multiple assessments that we refer to as hybrid traits. Statistical inference for studying the genetic basis of hybrid traits has not been well-developed. Recent rank-based methods have been utilized for conducting association analyses of hybrid traits but do not inform the strength or direction of effects. To overcome this limitation, a parametric modeling framework is imperative. Although such parametric frameworks have been proposed in theory, they are neither well-developed nor extensively used in practice due to their reliance on complicated likelihood functions that have high computational complexity. Many existing parametric frameworks tend to instead use pseudo-likelihoods to reduce computational burdens. Here, we develop a model fitting algorithm for the full likelihood. Our extensive simulation studies demonstrate that inference based on the full likelihood can control the type-I error rate, and gains power and improves the effect size estimation when compared with several existing methods for hybrid models. These advantages remain even if the distribution of the latent variables is misspecified. After analyzing the SAGE data, we identify three genetic variants (rs7672861, rs958331, rs879330) that are significantly associated with the comorbidity of alcohol and nicotine addiction at the chromosome-wide level. Moreover, our approach has greater power in this analysis than several existing methods for hybrid traits.Although the analysis of the SAGE data motivated us to develop the model, it can be broadly applied to analyze any hybrid responses.

Time-Varying Nonlinear Causality Detection Using Regularized Orthogonal Least Squares and Multi-Wavelets With Applications to EEG

A new transient Granger causality detection method is proposed based on a time-varying parametric modeling framework, and is applied to the real EEG signals to reveal the causal information flow during motor imagery (MI) tasks. The time-varying parametric modeling approach employs a nonlinear autoregressive with external input model, whose parameters are approximated by a set of multi-wavelet basis functions. A regularized orthogonal least squares (ROLS) algorithm is then used to produce a parsimonious or sparse regression model and estimate the associated model parameters. The time-varying Granger causality between nonstationary signals can be detected accurately by making use of both the good approximation properties of multi-wavelets and the good generalization performance of the ROLS in the presence of high-level noise. Two simulation examples are presented to demonstrate the effectiveness of the proposed method for both linear and nonlinear causal detection respectively. The proposed method is then applied to real EEG signals of MI tasks. It follows that transient causal information flow over the time course between various sensorimotor related channels can be successfully revealed during the whole reaction processes. Experimental results from these case studies confirm the applicability of the proposed scheme and show its utility for the understanding of the associated neural mechanism and the potential significance for developing MI tasks based brain-computer interface systems.

Direct likelihood inference on the cause-specific cumulative incidence function: A flexible parametric regression modelling approach.

In a competing risks analysis, interest lies in the cause-specific cumulative incidence function (CIF) that can be calculated by either (1) transforming on the cause-specific hazard or (2) through its direct relationship with the subdistribution hazard. We expand on current competing risks methodology from within the flexible parametric survival modelling framework (FPM) and focus on approach (2). This models all cause-specific CIFs simultaneously and is more useful when we look to questions on prognosis. We also extend cure models using a similar approach described by Andersson et al for flexible parametric relative survival models. Using SEER public use colorectal data, we compare and contrast our approach with standard methods such as the Fine & Gray model and show that many useful out-of-sample predictions can be made after modelling the cause-specific CIFs using an FPM approach. Alternative link functions may also be incorporated such as the logit link. Models can also be easily extended for time-dependent effects.

Closed form analytic solutions to heat and mass transfer characteristics of wall jet flow of nanofluids

In the present work, an interesting and simple analytic solution to wall jet flow of nanofluids is presented. The concept of exponentially decaying wall jet flows (those the integral constraint proposed by Glauert is retained) is targeted. A general scheme for momentum similarity equation is considered in which the effects of suction together with moving wall are allowed. A parametric two-phase modeling framework is brought into account to study the wall jet flow of nanofluids. The only simplification is with respect to thermophoresis effect elimination which in particular reveals some facts regarding the effectiveness of boundary conditions for particle transport equation. In this regard, a brief discussion is initially provided as may be of help to a better understanding of the nanoparticles behavior at the wall. Finally, closed form analytic expressions are obtained for heat and mass transfer characteristics of wall jet flow of nanofluids. It is hopeful that the rate of heat and mass transfer as well as temperature and concentration distributions can be better understood by means of the presented formulae.

High-resolution time–frequency representation of EEG data using multi-scale wavelets

ABSTRACTAn efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time–frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.