Parametric Model Research Articles

Model-implied simulation-based power estimation for correctly specified and distributionally misspecified models: Applications to nonlinear and linear structural equation models.

Closed-form (asymptotic) analytical power estimation is only available for limited classes of models, requiring correct model specification for most applications. Simulation-based power estimation can be applied in almost all scenarios where data following the model can be estimated. However, a general framework for calculating the required sample sizes for given power rates is still lacking. We propose a new model-implied simulation-based power estimation (MSPE) method for the z-test that makes use of the asymptotic normality property of estimates of a wide class of estimators, the M-estimators, and give theoretical justification for the approach. M-estimators include maximum-likelihood, least squares estimates and limited information estimators, but also estimators used for misspecified models, hence, the new simulation-based power modeling method is widely applicable. The MSPE employs a parametric model to describe the relationship between power and sample size, which can then be used to determine the required sample size for a specified power rate. We highlight its performance in linear and nonlinear structural equation models (SEM) for correctly specified models and models under distributional misspecification. Simulation results suggest that the new power modeling method is unbiased and shows good performance with regard to root mean squared error and type I error rates for the predicted required sample sizes and predicted power rates, outperforming alternative approaches, such as the naïve approach of selecting a discrete selection of sample sizes with linear interpolation of power or simple logistic regression approaches. The MSPE appears to be a valuable tool to estimate power for models without an (asymptotic) analytical power estimation.

Probabilistic analysis of tumor growth inhibition models to Support trial design

A large enough sample size of patients is required to statistically show that one treatment is better than another. However, too large a sample size is expensive and can also result in findings that are statistically significant, but not clinically relevant. How sample sizes should be chosen is a well-studied problem in classical statistics and analytical expressions can be derived from the appropriate test statistic. However, these expressions require information regarding the efficacy of the treatment, which may not be available, particularly for newly developed drugs. Tumor growth inhibition (TGI) models are frequently used to quantify the efficacy of newly developed anticancer drugs. In these models, the tumor growth dynamics are commonly described by a set of ordinary differential equations containing parameters that must be estimated using experimental data.One widely used endpoint in clinical trials is the proportion of patients in different response categories determined using the Response Evaluation Criteria In Solid Tumors (RECIST) framework. From the TGI model, we derive analytical expressions for the probability of patient response to combination therapy. The probabilistic expressions are used together with classical statistics to derive a parametric model for the sample size required to achieve a certain significance level and test power when comparing two treatments.Furthermore, the probabilistic expressions are used to generalize the Tumor Static Exposure concept to be more suitable for predicting clinical response. The derivatives of the probabilistic expressions are used to derive two additional expressions characterizing the exposure and its sensitivity. Finally, our results are illustrated using parameters obtained from calibrating the model to preclinical data.

Numerical study on the mechanical and deformation behavior of a shield tunnel under different geological conditions

Investigating the mechanical and deformation response of a shield tunnel to nearby cavities has a significant importance on the structural safety assessment and protection. Limited to the laborious modelling and analysis barriers, the refined structural model including lining segments and connecting bolts are proposed by the BIM-based parametric modelling technology, using which the geometric properties can be adjusted through one-click operation. The pretreatment algorithm has also been developed to impose springs on the numeric nodes effectively and accurately, forming an elaborate numerical simulation method. Numerical analysis has been exerted and verified considering the homogeneity of the excavation geological condition. Results show that for the shield tunnel excavated in the homogeneous stratum, the mechanical and deformation responses of the lining structure are symmetrical about the vertical central plane. The lining ring deforms from a circle to an elliptic shape. To inquire the effect of the geological cavity, three sceneries for cavity at various positions are studied. Numerical results reveal that the lining segments deform towards the cavity side significantly, leading to over 100 times the deformation increment at the vault and invert, even over 1000 times at the cavity side for the unilateral cavity. At the cavity(ies) side(s), the internal forces of the lining segments increase, and the stress states change from compression on the outer surface to tension. The stress of connecting bolts also indicates the stress concentration for the supporting structures at the cavity side. Reinforced supports are needed for the concrete linings near the geological defects.

Automated anthropometric measurements from 3D point clouds of scanned bodies

Anthropometry plays a critical role across numerous sectors, particularly within healthcare and fashion, by facilitating the analysis of the human body structure. The significance of anthropometric data cannot be overstated; it is crucial for assessing nutritional status among children and adults alike, enabling early detection of conditions such as malnutrition, obesity, and being overweight. Furthermore, it is instrumental in creating tailored dietary interventions. This study introduces a novel automated technique for extracting anthropometric measurements from any body part. The proposed method leverages a parametric model to accurately determine the measurement parameters from either an unstructured point cloud or a mesh. We conducted a comprehensive evaluation of our approach by comparing perimetral measurements from over 400 body scans with expert assessments and existing state-of-the-art methods. The results demonstrate that our approach significantly surpasses the current methods for measuring the waist, hip, thigh, chest, and wrist perimeters with exceptional accuracy. These findings indicate the potential of our method to automate anthropometric analysis and offer efficient and accurate measurements for various applications in healthcare and fashion industries.

Superphot+: Real-time Fitting and Classification of Supernova Light Curves

Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F 1-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future.

When the tide wanes: A study of post systemic collapse portfolio management

This study applies the Black–Litterman model to portfolio management in a post-crisis scenario, where standard parametric models often fail due to market irrationality and investor overreactions. The research focuses on the performance of surviving firms, identifying that over-performing stocks tend to outperform in the medium- to long-term, while under-performing stocks exhibit stronger short-term returns as the market corrects its initial overreaction. The study specifically adjusts views within the Black–Litterman framework using only the top and bottom quartiles of stocks, which experience the most significant shifts during a crisis. By controlling for firm size and book-to-market ratios, consistent with the Fama-French three-factor model, the research demonstrates that adjusting portfolio weights with the Black–Litterman model can achieve substantially higher returns with lower downside risk during recovery periods. These findings have significant implications for portfolio managers seeking to optimize returns in volatile, post-crisis markets.

Multi-objective parametric modelling during wire-cut electric discharge machining of Incoloy 800H

Multi-objective parametric modelling during wire-cut electric discharge machining of Incoloy 800H

Sherpa: An Open-source Python Fitting Package

We present an overview of Sherpa, an open-source Python project, and discuss its development history, broad design concepts, and capabilities. Sherpa contains powerful tools for combining parametric models into complex expressions that can be fit to data using a variety of statistics and optimization methods. It is easily extensible to include user-defined models, statistics, and optimization methods. It provides a high-level user interface for interactive data analysis, such as within a Jupyter notebook, and it can also be used as a library component, providing fitting and modeling capabilities to an application. We include a few examples of Sherpa applications to multiwavelength astronomical data.

Multi-objective parametric modelling during minimum quantity lubrication machining of Incoloy 800H

Multi-objective parametric modelling during minimum quantity lubrication machining of Incoloy 800H

Evaluation of Extended Decision Outcomes

ABSTRACTSome decision outcomes consist of sequences of single experiences. The aim is to propose a conceptualization of how such sequences are evaluated if affective evaluations of single experiences evoke transient emotional responses with lasting changes in current mood. The conceptualization implies three modes in which the sequences of single experiences are evaluated: (i) Aggregation of affective evaluations of the single experiences retrieved from memory; (ii) Aggregation of current moods associated with emotional responses to the single experiences retrieved or reconstructed from memory; and (iii) Updating of current mood. Simulations of parametrized models are used to compare the different evaluation modes to each other and to show to which extent the simulation results are consistent with some common findings in previous research. The previous research has primarily investigated different rules for aggregation of affective evaluations of single experiences. The simulation results motivate research comparing this mode to the other proposed modes.

Modelling PM dipoles with longitudinal field gradient for SILA facility

Computational models are described which were built for parametric simulations of permanent magnet (PM) dipoles with a longitudinal field gradient. The dipoles will be used as bending magnets in the synchrotron facility SILA, a new project of the National Research Center “Kurchatov Institute”. The dipoles employ a Sm-Co permanent magnet material and pure iron. The need for high magnetic performance and anticipated interference between neighbouring components is a challenge for design and construction of the magnet system. For this reason the magnet designs must rely on precision 3D simulations. The dipoles are described in detail with parameterized models. Parametric simulations are to be applied at all stages of the project till the commissioning to provide realistic prediction of the magnet behaviour.

Dynamic Progressive Failure Analysis of 3D Five-directional Braided Composites under Transverse Compression Based on Macro-scale Heterogeneous Model

At present, there are few systematic researches on macro-scale heterogeneous modeling and numerical simulation of dynamic mechanical properties of 3-D braided composites. In this paper, the parametric virtual simulation model of 3D five-directional braided composites is realized in the way of “point-line-solid” based on the integrated design idea of process-structure-performance. And the impact compression numerical simulation of the material is carried out by using multi-scale analysis method. The effects of strain rate and braiding angle on transverse impact compression properties and fracture characteristics of composites is studies and verified by comparing the test results with the numerical simulation results systematically. The dynamic failure mechanism of the matrix and fiber bundles during the impact compression process is revealed. The results show that the macro-scale heterogeneous simulation model of 3D five-directional braided composites established is effective, and the numerical simulation results agree well with the test results. The matrix fracture and shear failure of fiber bundles are presented simultaneously under transverse impact compression. The failure of fiber bundles and matrix mainly concentrates on two main fracture shear planes. And the included angle between the fracture shear planes and the vertical direction is consistent with the corresponding internal braiding angle of specimens.

Refined semi-empirical models for soil moisture retrieval in spaceborne GNSS-Reflectometry: Evaluation across diverse land cover types

The satellite-based Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a promising technique for detecting surface soil moisture (SSM), which plays a pivotal role in various applications. Different approaches have been developed for SSM retrieval, including parametric semi-empirical models and non-parametric machine learning methods. This study, however, specifically focuses on constructing and evaluating semi-empirical fit models. To this end, the study compares the effectiveness of our enhanced pixel-by-pixel model, the land cover-based linear model, and the Reflectivity-Vegetation-Roughness (R-V-R) model across different land surface types, aiming to both evaluate their efficacy and identify potential limitations. In the assessment, various factors were taken into consideration, such as the correction for vegetation and roughness attenuation, fitting functions employed, and the utilization of a lookup table (LUT). The results of the evaluation showed variations in the performance of the retrieval models across different land cover types, highlighting the impact of the choice of fitting functions and attenuation correction strategies on the accuracy of soil moisture retrieval. The pixel-by-pixel model demonstrated the highest prediction accuracy, with an unbiased root mean square difference (ubRMSD) of 0.056 cm3/cm3 and a correlation coefficient of 0.896. By showcasing these outcomes, this research underscores the significance of accounting for surface conditions and integrating relevant data to enhance the accuracy of GNSS-R SSM retrieval, thereby contributing to the advancement of SSM monitoring methodologies.

Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data.

On the Cosmic Variance of the Merger Rate Density of Binary Neutron Stars

The cosmic variance on the star formation history may lead to bias in the merger rate density estimation of binary neutron star (BNS) mergers by compact binary population synthesis. In this paper, we take advantage of the large box size of the Millennium Simulation combined with the semianalytic galaxy formation model GABE and the parameterized population binary star evolution model to examine how much effect the cosmic variance will introduce on the estimation of the merger rate density of BNS mergers. We find that for subbox sizes of 100 and 200 Mpc, the variance of merger rate density σ R /R at different redshifts is about 23%–35% and 13%–20%, respectively. On the one hand, as for the variance of the detection rate on BNS mergers with the current LIGO–Virgo–KAGRA (LVK) detector network, this value is very small at ≲10%, which indicates ignoring the cosmic variance is reasonable for estimating the merger rate density from current LVK observations. On the other hand, with next-generation gravitational wave detectors, it is possible to localize BNS mergers within subboxes possessing a length of 40 Mpc for a source redshift z s < 0.2. In such a small box, the cosmic variance of the merger rate density is significant, i.e., the value of σ R /R is about ∼55%. This hints that estimating the merger rate density of BNS in different sky areas may provide useful information on the cosmic variance.

Development of operable modular louvers Sited in a single-sided ventilated room through 3D parametric optimization

The current design practices of buildings to respond to environmental inputs have led to operable envelopes with external louver blinds systems. The key aspect of any openable louver flap design is to optimize inlets that might induce ventilation for occupants with low indoor airflow. Computational Fluid Dynamics (CFD) in 3D parametric modeling investigated the influence of openable louver inlet geometries (right trapezoid) and positions in a ventilated room. A single-sided ventilated room designed with two mirrored split blocks of inlets attached to that wall perpendicular to the wind was employed for this study. Twenty-eight different inlet shape geometries with their cross-sectional changes were investigated. In addition, the inlets' rotation, orientation, and elevation differences were also studied. Wind tunnel experiments were conducted to measure airflows inside the model room to validate the CFD simulations. A discernible impact was observed when the inclined side of the right trapezoid was oriented upward or downward, where inlet flaps open to the sides of the window. The mean air velocity in the ventilated room becomes more than double when inlet flaps open to the sides of the window, where the elevation difference in the inlet blocks introduces the pressure difference compared to no elevation difference in inlets. This study guides into refining an operable modular louver design to enhance air circulation and natural ventilation in dwelling spaces.

Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests

The combination of machine learning models with satellite imagery is becoming a popular data-modeling tool for biomass prediction, supporting land cover management. This study aims to select the most suitable model to estimate tropical forest aboveground biomass in Vietnam, helping to manage and monitor changes in biomass at regional and local scales. The study identified the optimal model for estimating forest aboveground biomass and minimizing the number of input variables while achieving satisfactory model performance. A total of 59 input variables, including topography, texture features, and vegetation indices, from satellite data were used in four non-parametric algorithms and a conventional parametric model, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression (MLR) to predict biomass and evaluate changes aboveground biomass over 10 years in two tropical forests in Vietnam. The results indicated that all models had good estimation performance with R2 ranging from 0.615 to 0.754. For RF, MLR, and XGBoost, vegetation indices contributed the highest model weights, occupying 77.71% – 92.48%. For ANN and SVM, textural and topographic features were the majority of the model weights (73.74 – 96.36%). The RF model performed the best using 59 variables (R2 = 0.754, MAE = 78.5 Mg·ha−1, and %RMSE = 13.57%) and ten variables (R2 = 0.745, MAE = 85.8 Mg·ha−1, and %RMSE = 16.17%). The biomass map using the RF and ten variables achieved a good degree of fitting of 0.76, so it was suitable for managing and monitoring forest biomass in Vietnam. The results indicated a sharp decrease in the areas of dense and very dense forests from 2013 to 2021 and a gradual increase in 2023.

Need of retrieval augmented generation for conversational assistants for government social welfare scheme popularization

Governments allocate substantial resources to uplift under-privileged communities through various assistance schemes. Despite these efforts, the effectiveness of such initiatives often fails due to variety of reasons including cryptic nature of information available, lack of awareness to the needy people and or lack of transparency. In India, a vast and diverse developing democracy, these challenges are exacerbated by disparities in access to information. To address these issues, we explored the use of conversational AI technologies, including chatbots, which have seen successful application in many developed nations. Conversational AI agents often get challenged to a variety of factual inaccuracies and other types of hallucinations. Our study compares the accuracy of responses across different architectures: Our architectures integrate advancements in natural language processing, such as large language models (LLMs) and retrieval-augmented generation (RAG), along with automatic speech recognition (ASR), knowledge distillation, hybrid retrieval strategies, and text-to-speech systems. Overall this research experiments demonstrate that leveraging retrieval augmented generation using the same model embeddings substantially improves the quality of the output, its similarity to desired responses. This also reduces hallucinations. We also notice that larger models perform better than smaller parameter models.

A Crack Detection Method for Civil Engineering Bridges Based on Feature Extraction and Parametric Modeling of Point Cloud Data

Accurate detection and analysis of cracks is critical for ensuring the safety and reliability of concrete bridges. Point cloud data (PCD) obtained from 3D scanning provides a promising avenue for automated crack assessment. However, processing the massive and unstructured PCD poses significant challenges in feature extraction and crack modeling. This paper proposes a novel method for bridge crack analysis by combining PCD feature extraction with a hierarchical neural network and Rodriguez rotation. The method first extracts crack features from PCD using outlier removal, denoising, and 3D coordinate conversion. A crack analysis model is then constructed by integrating multi-scale feature extraction and Rodriguez rotation into a hierarchical neural network, enabling the capture of both local and global crack patterns. Experiments on a benchmark data set demonstrate the effectiveness of the proposed approach, achieving 92.83% feature extraction accuracy, 95.73% parameter analysis accuracy, 93.51% recognition accuracy, and 0.91 F1 score. The method also shows improved efficiency compared to existing techniques. These results highlight the potential of the proposed PCD-based approach for accurate and efficient crack analysis in concrete bridges.

Parametric Cost Prediction Models for Light General Aviation Aircraft in the Early Design Phase

Parametric Cost Prediction Models for Light General Aviation Aircraft in the Early Design Phase