Parametric Model Research Articles

Multi-objective optimization for generative morphological design using energy and comfort models with a practical design of new rural community in China

In recent years, the development of new rural communities has emerged as a pivotal new trend in china, aimed at enhancing energy efficiency and residential comfort in rural regions. This study introduces a multi-objective optimization design framework for the morphological design of residential area, with a focus on performance objectives such as building energy use intensity, summer thermal comfort, and winter thermal comfort. A case study of a new rural community design project in Hongnan Village, Yancheng City, was conducted. Utilizing the Rhino-Grasshopper parametric platform, this study undertook parametric modeling, performance simulation, and multi-objective optimization to receive the Pareto-optimal solutions. ParetoResults indicate that in the Pareto-optimal morphology scheme for the Hongnan Village community, open spaces are predominantly located on the upwind side with respect to the prevailing summer winds of the site. The number of duplex houses is often 28, and the building orientations are predominantly aligned at 23°, mirroring the direction of the surrounding structures. There is a significant correlation between residential unit configuration, building spacing, and energy use intensity, with correlation coefficients of 0.53 and −0.43, respectively. This study would like to offer valuable insights into the energy-efficient design of new rural communities, thereby contributing to the creation of a more sustainable and habitable rural living environment.

Hand Vibration Reduction Using Nonlinear Vibration Absorber for the Vibro-Impact Hammer Model

Abstract This work investigates the effectiveness of a passive vibration control device to minimize the amplitude of vibrations from a hand-held impact machine (HHIM) transmitted to a hand-arm system (HAS). For this purpose, we employ a simplified lumped parameter model of an HHIM and the hand-arm system consisting of lumped masses, springs, and viscous dampers. For a better understanding of the system dynamics, we use a vibro-impact model for a more accurate representation of HHIM–ground interactions. We then examine the effectiveness of a cubic nonlinear vibration absorber in minimizing these undesirable vibrations via a nonlinear bifurcation analysis of the system. A parametric study on the numerical bifurcation analysis of the system reveals the criticality of different design parameters of the proposed nonlinear vibration absorber on the system dynamics. Our findings demonstrate that an appropriate selection of absorber parameters could significantly change the amplitude and the periodicity of vibrations transmitted to the hand. Furthermore, we observe the existence of complex motions such as period-2, period-4, and chaotic attractors in the system for the given values of the absorber and operating parameters.

Modeling and experimental study of air-cooled finned-tube condenser using coupled distribution parameter method and air-side temperature distribution

The air-cooled finned tube condenser (ACFTC) plays a crucial role in refrigeration systems, profoundly influencing overall operational efficiency. Its intricate structure, connectivity, and phase change characteristics pose challenges for numerical simulation modeling. While traditional distribution parameter methods effectively capture flow and heat transfer within tube mediums, they overlook ACFTC’s air-side temperature distribution, resulting in discrepancies between simulated and experimental outcomes. This study enhances existing distribution parameter models by incorporating ACFTC’s air-side temperature distribution characteristics, yielding an improved predictive model for transient operational behavior of both Z-shaped and U-shaped configurations. Dynamic experiments and model analyses were performed on the Z-shaped configuration using a cold storage experimental setup. Results demonstrate the improved model’s accurate prediction of tubing medium outlet temperature and pressure, with maximum deviations of approximately 3 °C and 0.01 MPa, respectively. Notably, the improved model exhibits around a 30 % enhancement in predictive accuracy compared to the existing models for both Z-shaped and U-shaped ACFTCs.

Quantifying flow regime impacts on hydrodynamic heat transfer in rough-walled rock fractures

Hydrodynamic heat transfer in rock fractures is crucial for many geophysical processes and engineering activities, notably in harnessing geothermal energy. Despite extensive research, characterizing hydrodynamic heat transfer in rock fractures, complicated by their geometric heterogeneity, remains a formidable challenge. To address this challenge, we experimentally investigated the hydrodynamic heat transfer in rock fractures with diverse geometric features at steady-state elevated temperatures. We found that both heterogeneous fracture geometry and elevated temperatures promote the transition of fracture flow from a linear to nonlinear regime. This flow regime transition enhances the heterogeneity of heat transfer intensity within the fracture and exacerbates the increase in heat transfer efficiency with flow rate. At a fortyfold increase in flow rate, the heat transfer coefficient in the most heterogeneous fracture rises by approximately 72 times, compared to about 28 times in the least heterogeneous one. Leveraging experimental data, we developed parametric models linking heat transfer coefficients, outflow temperature, and heat recovery rate to flow rate and geometric features, yielding a theoretical formulation for the maximal heat recovery rate. By introducing the concept of an energy conversion rate, we quantitatively evaluated the effect of nonlinear flow resistance on heat extraction feasibility, revealing that energy conversion rates in highly heterogeneous fractures can be up to 452 times lower than in less heterogeneous ones. This highlights the pivotal role of flow resistance in heat extraction feasibility. Our findings are instrumental for understanding and predicting the hydrodynamic heat transfer within fractured geothermal reservoirs and analogous hydro-thermal systems.

Semiparametric and multiplicative bias correction techniques for second-order discrete kernels

In this article, we propose to estimate the probability mass function (pmf) of a discrete supported random variable by a semiparametric bias corrected method using discrete associated kernels. This method consists in applying a two-stage multiplicative bias correction (MBC) approach for the initial parametric model in order to improve the accuracy of the estimator measured in terms of the vanishing bias. Various properties of the resulting semiparametric MBC discrete associated kernel estimator are provided (bias, variance, and mean integrated squared error). The common cross-validation technique and the Kullback–Leibler divergence are adapted for bandwidth selection. Monte Carlo simulations and a real-data application for count data illustrate the performance of the semiparametric-MBC estimator.

High-Voltage Cable Buffer Layer Ablation Fault Identification Based on Artificial Intelligence and Frequency Domain Impedance Spectroscopy.

In recent years, the occurrence of high-voltage cable buffer layer ablation faults has become frequent, posing a serious threat to the safe and stable operation of cables. Failure to promptly detect and address such faults may lead to cable breakdowns, impacting the normal operation of the power system. To overcome the limitations of existing methods for identifying buffer layer ablation faults in high-voltage cables, a method for identifying buffer layer ablation faults based on frequency domain impedance spectroscopy and artificial intelligence is proposed. Firstly, based on the cable distributed parameter model and frequency domain impedance spectroscopy, a mathematical model of the input impedance of a cable containing buffer layer ablation faults is derived. Through a simulation, the input impedance spectroscopy at the first end of the cables under normal conditions, buffer layer ablation, local aging, and inductive faults is performed, enabling the identification of inductive and capacitive faults through a comparative analysis. Secondly, the frequency domain amplitude spectroscopy of the buffer layer ablation and local aging faults are used as datasets and are input into a neural network model for training and validation to identify buffer layer ablation and local aging faults. Finally, using multiple evaluation metrics to assess the neural network model validates the superiority of the MLP neural network in cable fault identification models and experimentally confirms the effectiveness of the proposed method.

Undrained cavity expansion-contraction analysis in CASM and its application for pressuremeter tests

Many geotechnical scenarios involve cavity unloading from a loaded state, particularly in pressuremeter tests, and the unloading data of pressuremeter tests has exceptional attraction as it is less disturbed by the insertion process. However, the analyses for continuous cavity loading and unloading (i.e., cavity initially experiences expansion and then contracts) in critical state soils are rarely studied. To this end, a novel semi-analytical solution based on the unified state parameter model for clay and sand (CASM) is proposed for the whole expansion-contraction of spherical and cylindrical cavities under undrained condition. The problem assumes that the cavity is unloaded after a monotonic loading stage, leading to plastic regions during both loading and unloading periods. The cavity response for the whole expansion-contraction process is investigated, with the total pressure and stress paths at the cavity wall presented and validated against numerical simulation. The developed solution is successfully implemented to interpret both loading and unloading data of pressuremeter tests. The undrained shear strength, in-situ effective horizontal stress and initial overconsolidation ratio are back analyzed by using a curve fitting method based on the proposed solution.

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.

Fragility assessment for process pipelines in flood events through physically-based hazard response analysis

Natech events triggered by floods have occurred frequently, resulting in physical damage to process equipment and subsequent releases of hazardous substances, threatening the operational safety of the process industry. Therefore, it is necessary to conduct flood fragility assessments on typical process equipment, such as storage tanks and process pipelines. Compared to storage tank, there are relatively few flood risk analysis methods applicable to process pipelines, and there is a lack of dedicated fragility models to quantify the probability of pipeline damage in flood hazard scenarios. Thus, this paper develops a process pipeline fragility model to support the quantitative risk assessment (QRA) of flood-induced Natech events more comprehensively. This model simultaneously considers the structural physical damage caused by internal pressure and external load in the pipeline and establishes the limit state equation (LSE) corresponding to the failure mode. On this basis, parametric fragility functions are trained using Monte Carlo simulations and logistic regression. A pipeline case is used to test the proposed fragility functions, and the results show that the fitted parameterized fragility model can sensitively capture changes in the failure probability curve caused by hazard intensity and pipeline characteristics changes. The proposed pipeline fragility model is applied to a composite area of pipelines and storage tanks, accurately assessing the failure probability of different types of pipelines in flood scenarios.

Modelling and operation characteristics of air-cooled PEMFC with metallic bipolar plate used in unmanned aerial vehicle

The air-cooled proton exchange membrane fuel cell stack composed of the metallic bipolar plate with simple system and lightweight characteristics, is regarded as an ideal power source for unmanned aerial vehicles. A zero-dimensional model is developed to analyze the operation characteristics at heights in the range of 0–3000 m. The temperature should not exceed 60 °C to ensure safe and effective operation. The peak power decreases by 20 % and the stack has good adaptability to altitudes. The performance degradation can be largely attributed to the lower oxygen pressure at high altitudes through normalization analysis. It is more significant at higher current densities owing to increased air starvation. The increase of the air stoichiometry is conducive to heat dissipation and reduces mass transfer loss. The voltage initially increases and then decreases as the air stoichiometry increases, and the optimal air stoichiometry corresponding to the maximum voltage is determined. Appropriately increasing the air stoichiometry can improve the consistency of the temperature and voltage distributions.

Sandwich boosting for accurate estimation in partially linear models for grouped data

Abstract We study partially linear models in settings where observations are arranged in independent groups but may exhibit within-group dependence. Existing approaches estimate linear model parameters through weighted least squares, with optimal weights (given by the inverse covariance of the response, conditional on the covariates) typically estimated by maximizing a (restricted) likelihood from random effects modelling or by using generalized estimating equations. We introduce a new ‘sandwich loss’ whose population minimizer coincides with the weights of these approaches when the parametric forms for the conditional covariance are well-specified, but can yield arbitrarily large improvements in linear parameter estimation accuracy when they are not. Under relatively mild conditions, our estimated coefficients are asymptotically Gaussian and enjoy minimal variance among estimators with weights restricted to a given class of functions, when user-chosen regression methods are used to estimate nuisance functions. We further expand the class of functional forms for the weights that may be fitted beyond parametric models by leveraging the flexibility of modern machine learning methods within a new gradient boosting scheme for minimizing the sandwich loss. We demonstrate the effectiveness of both the sandwich loss and what we call ‘sandwich boosting’ in a variety of settings with simulated and real-world data.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant etal. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n=3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n=80) and validation (n=40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n=236) was evaluated. The vertebral segmentation module achieved a Dice score of.984 as compared to manual outlining results as reference (n=100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being.980 and.989, respectively. The vertebral labelling module achieved 100% accuracy (n=100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of.995 and.999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n=236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

A Neural Network Approach for Software Reliability Prediction

The increasing reliance on computer software has raised significant concerns regarding software reliability evaluation. Over the past four decades, various software reliability models have been developed, encompassing both parametric and nonparametric approaches. However, no single model has demonstrated effectiveness in handling all types of datasets. In response to this challenge, the deep neural network (DNN), a powerful deep learning model, has emerged as a promising solution. By leveraging the flexibility and adaptability of artificial neural networks (ANN), the DNN model exhibits remarkable prediction performance by capturing training variables and exploring deeper layers. This study presents an approach that utilizes a DNN model based on an ANN architecture with multiple activation functions. This approach aims to estimate software reliability and predict the frequency of software flaws. Through extensive experimental analysis and validation, the proposed DNN model surpasses the predictive accuracy achieved by existing parametric and nonparametric models. These results highlight the potential of the DNN model in effectively addressing the complexities involved in software reliability evaluation. By combining the power of deep learning with the adaptability of ANN, the proposed model offers a promising and accurate solution for software reliability prediction.

Rate-dependent energy dissipation of graded viscoelastic structures fabricated by grayscale vat photopolymerization

A major benefit of additive manufacturing technologies is precise control over structural topologies and material properties, which allows to tailor, for instance, energy absorption and dissipation. While vat photopolymerization is generally restricted to a single material, grayscale masked stereolithography (gMSLA) allows to customize material behavior by grading the light intensity within a structure. This study investigates the impact and opportunities of grayscale grading strategies on the rate-dependent mechanical behavior of structures fabricated by gMSLA. Considering the viscoelastic nature of polymers, rate-dependent energy dissipation is explored, introducing a parametric linear viscoelastic constitutive model for varying grayscales. The investigation includes the comprehensive characterization of mechanical properties, numerical finite element simulation, validation through experimental procedures, and exploration of dissipation energy under different strain rates. In this way, a rational function successfully determines the critical strain rate at which the maximum dissipation occurs. Overall, the research offers a comprehensive investigation of the mechanical dissipation behavior of graded 3D printed structures, laying the foundation for further studies and advancements aimed at optimizing these structures for enhanced energy absorption capabilities.

OSL dating of very young aeolian sediments of NW Spain to assess dune erosion and accretion periods

Optically stimulated luminescence (OSL) ages of young coastal dune ridges have allowed getting more knowledge on the deposition, erosion and evolution of coastal dune systems in Northern Europe during historical times. However, there is a lack of knowledge of the evolution of the same systems in SW Europe. The studies published in the last years about fossil dunes in NW Spain have allowed the reconstruction of the coastal evolution of this area from the Marine Isotopic Stage (MIS 8) until the Middle Holocene. However, there is still a poor knowledge for the Late Holocene and namely the last centuries. Dating young sediments is challenging due to the low signal to noise ratio of the natural OSL signal and because young samples are very sensitive to thermal transfer or partial bleaching of the luminescence signal during transport. In the last century, erosion and accretion events are better known because remote sensing methods allowed to model some coastal dune systems. However, OSL dating has also been particularly useful in NW Europe to get a better knowledge on the coastal dune evolution. In this work, seven samples from dune ridges and a climbing dune of a coastal system in Playa de Trece (NW Spain) were dated by OSL, as they record the last accretion events occurred in the last two centuries, and above all the 20th Century. For assessing the ages we have compared the results from both parametric central age models and Bayesian statistic using R package BayLum.

Automatic generation of pipelines from 2D CAD drawings to 3D IFC models using a parametric method

Creating 3D models of pipelines is essential for effective digital building management. However, generating 3D pipeline models from 2D CAD drawings still heavily depends on human-computer interaction at the design stage. This paper addresses this challenge by proposing a method to generate parametric 3D pipeline models from CAD drawings automatically. The framework includes three parts. Firstly, automatic layer recognition algorithms are used to extract data from CAD drawings, and algorithms are designed to match pipeline coordinates and diameters. Then, data on straight pipes is processed, including judging types of pipe fittings and calculating relevant parameters to realize the automatic generation of 3D BIM models. Finally, IFC is utilized to express the geometry and topology of models for BIM model generation. The process is implemented by writing a C++ program in Visio Studio 2019. Experimental results on a building water supply and drainage system demonstrate that the proposed method can significantly improve the generation efficiency and automation level of pipeline models.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/, date of registration: 30th June 2017.

Governing accelerating Universe via newly reconstructed Hubble parameter by employing empirical data simulations

A new parametrization of the Hubble parameter is proposed to explore the issue of the cosmological landscape. The constraints on model parameters are derived through the Markov Chain Monte Carlo (MCMC) method by employing a comprehensive union of datasets such as 34 data points from cosmic chronometers (CC), 42 points from baryonic acoustic oscillations (BAO), a recently updated set of 1701 Pantheon+ (P22) data points derived from Type Ia supernovae (SNeIa), and 162 data points from gamma-ray bursts (GRBs). Furthermore, the models are compared by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), so that a comparative assessment of model performance can be available. Additionally, we compare the Dainotti relation via Gaussian likelihood analysis versus new likelihoods and Calibration of the Dainotti relation through a model-independent method. The kinematic behavior of the models is also investigated by encompassing the transition from deceleration to acceleration and the evolution of the jerk parameter. From the analysis of the parametric models, it is strongly indicated that the Universe is currently undergoing an accelerated phase with diagnostics of the model validating the quintessence phase.

Income disparities in loss in life expectancy after colon and rectal cancers: a Swedish register-based study

BackgroundDifferences in the prognosis after colorectal cancer (CRC) by socioeconomic position (SEP) have been reported previously; however, most studies focused on survival differences at a particular time since diagnosis. We...

Transmissibility-based operational modal analysis: A unified scheme and uncertainty quantification

The transmissibility-based Operational Modal Analysis (OMA) technique has attracted substantial research interest over the past two decades due to its capability to extract structural modal parameters in the presence of non-white excitation. The transmissibility function used in OMA can be categorized as Response Transmissibility (RT) and Power Spectral Density Transmissibility (PSDT). Modal parameters can be identified by combining RTs from multiple load cases or PSDTs from multiple transfer outputs. Nevertheless, the mathematical formulations and implementation procedures of the various RT-based and PSDT-based methods exhibit significant differences. Furthermore, the uncertainty associated with modal parameter estimates is rarely assessed in these methods. This work aims to propose a scheme that unifies various transmissibility-based OMA algorithms through a consistent parametric formulation. Within this scheme, a universal parametric model and a standardized implementation procedure are developed to accommodate two types of transmissibility in both time and frequency domains and six OMA algorithms; additionally, a generalized framework for quantifying the uncertainties associated with modal parameter estimates is provided. The proposed scheme is finally validated using a planar truss structure.