Parametric Modeling Techniques Research Articles

Abstract P115: Hypothetical Blood Pressure Lowering Interventions and Risk of Mortality in Middle-Aged and Older Chinese Adults: A Population-Based Modelling Study

Background: Improving hypertension control is an important global health priority. We aimed to study the effects of hypothetical interventions on systolic blood pressure (SBP) on mortality risk at the population level. Methods: We estimated the relationship between SBP and all-cause mortality in adults aged 45 years and older from the China Health and Retirement Longitudinal Study and compared the mortality reductions associated with lowering SBP to different hypothetical targets (120, 130, 140, 150 mm Hg). Using the parametric g-computation modeling technique, we estimated the mean blood pressure reduction required to achieve each target, the share of the population in need of management, and the number needed to treat (NNT) to avert one death under different hypothetical population-wide scale-up scenarios. Results: Among 12355 participants, 1203 died over 7-year follow-up. We found a weak, non-linear relationship between SBP and mortality, with larger incremental mortality benefits at higher SBP values: reducing SBP from 160 mm Hg to 120 mm Hg was associated with a relative risk of mortality of 0.75 (95% CI 0.65 to 0.86). At the population level, reducing SBP to 120 mm Hg among all those with a starting SBP of more than 120 mm Hg (120 mm Hg scenario) was associated with 13.7 deaths averted per 1000 population (95% CI -19.3 to -8.1), compared with 3.8 deaths averted per 1000 population (95% CI -6.0 to -1.6) associated with reducing SBP to 150 mm Hg among all those with a starting SBP of more than 150 mm Hg (150 mm Hg scenario). However, there were large differences between scenarios in the proportion of the population that would require treatment, ranging from 64.3% (95% CI 63.4 to 65.1) in the 120 mm Hg scenario to 16.7% (6.0 to 17.3) in the 150 mm Hg scenario. To achieve the target, the 120 mm Hg scenario required a mean shift of -14.4 mm Hg in SBP (95% CI -14.7 to -14.1) compared with a -2.9 mm Hg mean change (-3.0 to -2.7) for the 150 mm Hg scenario. Based on the ratio of number of deaths averted to the proportion requiring treatment, the 150 mm Hg scenario had the smallest NNT (n=44), followed by the 120 mm Hg scenario (n=47), the 140 mm Hg scenario (n=49), and the 130 mm Hg scenario (n=51). Conclusions: These findings highlight the possible substantial benefits of reducing blood pressure. We find that scaling up management based on a 150 mm Hg target is more efficient in terms of the NNT compared with strategies to reduce SBP to lower levels.

Research on parametric analysis and simulation application of spatial texture in traditional villages: a case study of Cantonese traditional villages

ABSTRACT Traditional Chinese villages, as rural settlements shaped over extensive historical evolution in agrarian societies, have lacked a systematic method for quantitatively analysing and understanding the historical spatial forms. This study takes a morphological research perspective, using Lohong Village, Su’er Village, and Shangyue Village in Guangdong Province as case studies of typical Cantonese traditional villages. It focuses on three key spatial texture elements: roads, plots, and architecture, employing parametric modelling techniques to investigate their combination patterns and quantitative characteristics. The findings indicate that: (1) Parametric analysis techniques can comprehensively, quantitatively, and accurately reveal the patterns of village spatial texture; (2) Comparing optimized parametric simulation results with original villages shows high similarity in spatial texture, demonstrating the method’s high fidelity and practical value; (3) The study explores a research approach suitable for the computer era – “data collection – parametric feature extraction and optimization – simulation and data calibration” – to study the spatial texture of traditional villages. Furthermore, it establishes a parametric-based platform for rural planning and construction management, providing a digital design pathway and management basis to preserve and inherit the historical village aesthetics.

Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm

PurposeThis paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.Design/methodology/approachAn efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.FindingsMathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.Originality/valueThese findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Development of Automatic Assessment Framework for Spine Deformity Using Freehand 3-D Ultrasound Imaging System.

A 3-D ultrasound (US) imaging technique has been studied to facilitate the diagnosis of spinal deformity without radiation. The objective of this article is to propose an assessment framework to automatically estimate spinal deformity in US spine images. The proposed framework comprises four major components, a US spine image generator, a novel transformer-based lightweight spine detector network, an angle evaluator, and a 3-D modeler. The principal component analysis (PCA) and discriminative scale space tracking (DSST) method are first adopted to generate the US spine images. The proposed detector is equipped with a redundancy queries removal (RQR) module and a regularization item to realize accurate and unique detection of spine images. Two clinical datasets, a total of 273 images from adolescents with idiopathic scoliosis, are used for the investigation of the proposed framework. The curvature is estimated by the angle evaluator, and the 3-D mesh model is established by the parametric modeling technique. The accuracy rate (AR) of the proposed detector can be achieved at 99.5%, with a minimal redundancy rate (RR) of 1.5%. The correlations between automatic curve measurements on US spine images from two datasets and manual measurements on radiographs are 0.91 and 0.88, respectively. The mean absolute difference (MAD) and standard deviation (SD) are 2.72° ± 2.14° and 2.91° ± 2.36° , respectively. The results demonstrate the effectiveness of the proposed framework to advance the application of the 3-D US imaging technique in clinical practice for scoliosis mass screening and monitoring.

Multi-Scale Analysis of Grain Size in the Component Structures of Sediments Accumulated along the Desert-Loess Transition Zone of the Tengger Desert and Implications for Sources and Aeolian Dust Transportation

Aeolian sediments accumulated along the desert-loess transition zone of the Tengger Desert include heterogeneous textures and complex component structures in their grain-size distributions (GSD). However, the sources of these aeolian sediments have not been resolved due to the lack of large reference GSD sample datasets from adjacent regions that contain various types of sediments; such datasets could be used for fingerprinting based on grain-size properties. This lack of knowledge hinders our understanding of the mechanism of aeolian dust releases in these regions and the effects of forcing of atmospheric circulations on the transportation and accumulation of sediments in this region. In this study, we employed a multi-scale grain-size analysis method, i.e., a combination of the single-sample unmixing (SSU) and the parametric end-member modelling (PEMM) techniques, to resolve the component structures of sediments that had accumulated along the desert-loess transition zone of the Tengger Desert. We have also analyzed the component structures of GSDs of various types of sediments, including mobile and fixed sand dunes, lake sediments, and loess sediments from surrounding regions. Our results demonstrate that the patterns observed in coarser fractions of sediments (i.e., sediments with a mode grain size of >100 μm) from the transition zone match well with the patterns of component structures of several types of sediments from the interior of the Tengger Desert, and the patterns seen in the finer fractions (i.e., fine, medium, and coarse silts with a modal size of <63 μm) were broadly consistent with those of loess sediments from the Qilian Mountains. The deflation/erosion of loess from the Qilian Mountains by wind was the most important mechanism underlying the production of these finer grain-size fractions. The East Asia winter monsoon (EAWM) played a key role in transportation of the aeolian dust from these source regions to the desert-loess transition zone of the desert.

Environmental aspect in traffic flow simulation modeling

The article deals with the problem of air pollution by automobile transportation. Such emissions are a problem in modern cities worldwide, and they harm numerous aspects of human life and the environment. Changing traffic flows and modernization of road infrastructure are ways to reduce the load on the ecological state of urban areas. Developing concepts and assessing their impact on the transportation system and the environment is possible through simulation models. The study establishes all the objects and their qualitative and quantitative characteristics necessary to create a digital twin of traffic flows. Using parametric modeling techniques allowed us to create a simulation model reflecting all aspects of the subject area. The developed model allows conducting experiments on changing the parameters of objects to predict changes in the system’s behavior in terms of its ecological state.

Optimization of Simulation Effect of Advanced Parametric Modeling Techniques on Foundation Structure in Revit Environment

Optimization of Simulation Effect of Advanced Parametric Modeling Techniques on Foundation Structure in Revit Environment

Parametric modeling and analysis of intake phases for side-ported Wankel rotary engines

This research presents a novel port parametric modeling technique using three-dimensional computational fluid dynamics for the design and optimization of intake and exhaust phases in side-ported Wankel rotary engines (WREs). Definitions for the port phases encompass parameters such as port start opening, port full opening, port start closing, and port full closing timings. The four port phase control arcs are obtained by translating and rotating the rotor flank to satisfy the high control accuracy. Further, the shape of the port is further smoothed and varied by four auxiliary circular arcs. Moreover, the influence of port full closing timing and the size of auxiliary circular arcs (R1, and R3) on the intake characteristics is studied.The results show that the novel method can flexibly and effectively control the phases and shapes. The early port full closing timing reduces fluid backflow and improves volumetric efficiency (VE) but increases intake loss (IL). The small size of R1 facilitates to increase the VE and reduce IL. A larger or smaller size of R3 is not conducive to reducing IL, and the smaller size of R3 improves the VE. The novel generation method proposed in this paper provides a theoretical basis to optimize the design of various sizes of side-ported WREs and guidance for practical manufacturing.

Earthquake source parameters in Zagros region (Iran) from the time-evolutive P-wave displacement

The rupture process of the recent moderate-to-large earthquakes in the Zagros area along the Iran plateau is investigated by analysing the strong motion data provided by the Iranian Building and Housing Research Centre (BHRC). The selected dataset includes the largest and deadliest 2017 Mw 7.3, Iran-Iraq (Ezgeleh) earthquake. The earthquake source parameters (moment magnitude, rupture duration and length, average slip, and static stress drop) are determined using a time-domain, parametric modelling technique based on the time evolution of the P-wave displacement signals. The earthquake source parameters are calculated from simulated triangular moment-rate functions assuming the circular source models for a constant rupture velocity. The anelastic attenuation effect is modelled through the independent frequency-Q parameter ranging from 50 to 200 and accounted for by a post-processing procedure that retrieves the attenuation-corrected, moment-rate triangular shape. Results show that the average static stress-drop with different {Q}_{P}, varies between <Δσ> = 0.9 (0.7–1.2) MPa and <Δσ> = 1.6 (1.2–2.0) MPa. Overall, in this research, the rupture radius/length empirically scales with the seismic moment with a self-similar, near-constant stress drop of about 1 MPa. Assuming a circular rupture model for the Ezgeleh earthquake, we estimate a moment magnitude of 6.9, rupture duration of 7 s, source radius of 16 km, average slip of about 2 m and static stress drop of 3.4 MPa.

Chebyshev polynimal dempster convolutional hearing sensitivity level detection with auditory evoked potentials

The EEG-based HTR utilizing AEP responses of both group of participants with normal hearing and abnormal hearing are managed with the objective of detecting hearing sensitivity level using Chebyshev Recurrence Polynomial and Dempster Convolutional Neural Network (CRP-DCNN) is designed. The CRP-DCNN method is split into three sections. They are preprocessing using Chebyshev Recurrence Polynomial Filter, feature extraction by employing Orthogonalized Singular Value and Median Skewed Wavelet. Here, both Orthogonalized Singular Value Decomposition-based parametric and Median Skewness-based non-parametric modeling techniques are employed for first obtaining the hearing threshold factors and then extracting statistical features for further processing. Finally Dempster Convolutional Neural Network-based Classification for detecting hearing sensitivity level is presented. Hence, the objective to determine the significant correlations between the brain dynamics and the auditory responses and detect the hearing sensitivity level of the group of participants with normal hearing and with the group of participants with hearing loss are designed on accordance with the features of EEG signals. Simulations are performed in MATLAB to validate the features of EEG signals.

Paper to parametric: Dreams and imagination in architecture

This study explores the transition from paper to parametric architecture, highlighting the role of dreams and imagination in shaping architectural design. It provides a comprehensive analysis of both concepts, examining their characteristics, methodologies, and impact on the field of architecture, beside with presenting a holistic, comparative, and interpretative study. Through detailed explanations and real-world examples, this study aims to shed light on the evolution of architectural practice and the transformative power of computational tools in realizing visionary designs. Through this comprehensive analysis and evaluation, the study tries to make evident that dreams and imagination have been pivotal in the evolution of architectural practice from paper architecture to parametric architecture. Using computational tools and parametric modeling techniques has empowered architects to push boundaries, optimize performance, and create built environments that were once considered impossible. By embracing dreams and imagination, it can be stated that architects can continue to shape the future of architecture, creating visionary and transformative designs that inspire and captivate.

Aircraft Fuel Mass Estimation Using Global Nonlinear Parametric Modeling

The accuracy of fuel measurement in aircraft tanks is directly related to flight safety and maneuverability. Capacitance gauging is widely used and is the industry accepted method for accurate measurement of the fuel quantity. In this paper, a global nonlinear parametric modeling technique is applied to estimate the fuel content using the capacitance probe data generated from 3-dimentional models of the aircraft fuel tanks. Automatic Net generation Tool for Structural Analysis (ANSA) software is used to create the fuel tank solid models. Using truth tables generated from ANSA software, several 3 rd order polynomial models are developed. The fuel estimation algorithm is implemented in real time using Microcontroller based electronic unit.

A machine learning-based characterization framework for parametric representation of liquid sloshing

There is a growing interest in developing a parametric representation of liquid sloshing inside a container stemming from its practical applications in modern engineering systems. The resonant excitation due to sloshing, on the other hand, can cause unstable and nonlinear water waves, resulting in chaotic motions and non-Gaussian wave profiles. This paper presents a novel machine learning-based framework for nonlinear liquid sloshing representation learning. The proposed method is a parametric modeling technique that is based on sequential learning and sparse regularization. The dynamics are categorized into two parts: linear evolution and nonlinear forcing. The former advances the dynamical system in time on an embedded manifold, while the latter causes divergent behaviors in temporal evolution, such as bursting and switching. The effectiveness of the proposed framework is demonstrated using an experimental dataset of liquid sloshing in a tank under horizontal excitation with a wide range of frequencies and vertical slat screen settings immersed in the tank perpendicular to the excitation.

An automated parametric ear model to improve frugal 3D scanning methods for the advanced manufacturing of high-quality prosthetic ears

Ear prostheses are commonly used for restoring aesthetics to those suffering missing or malformed external ears. Traditional fabrication of these prostheses is labour intensive and requires expert skill from a prosthetist. Advanced manufacturing including 3D scanning, modelling and 3D printing has the potential to improve this process, although more work is required before it is ready for routine clinical use. In this paper, we introduce a parametric modelling technique capable of producing high quality 3D models of the human ear from low-fidelity, frugal, patient scans; significantly reducing time, complexity and cost. Our ear model can be tuned to fit the frugal low-fidelity 3D scan through; (a) manual tuning, or (b) our automated particle filter approach. This potentially enables low-cost smartphone photogrammetry-based 3D scanning for high quality personalised 3D printed ear prosthesis. In comparison to standard photogrammetry, our parametric model improves completeness, from (81 ± 5)% to (87 ± 4)%, with only a modest reduction in accuracy, with root mean square error (RMSE) increasing from (1.0 ± 0.2) mm to (1.5 ± 0.2) mm (relative to metrology rated reference 3D scans, n = 14). Despite this reduction in the RMS accuracy, our parametric model improves the overall quality, realism, and smoothness. Our automated particle filter method differs only modestly compared to manual adjustments. Overall, our parametric ear model can significantly improve quality, smoothness and completeness of 3D models produced from 30-photograph photogrammetry. This enables frugal high-quality 3D ear models to be produced for use in the advanced manufacturing of ear prostheses.

Advances in hybrid format‐based neuro‐transfer function techniques for parametric modeling of microwave components

AbstractElectromagnetic (EM) parametric modeling has become significant for EM designs of microwave devices. This paper outlines recent advances in hybrid format‐based neuro‐transfer function (TF) techniques for EM parametric modeling of microwave devices. To solve the problem of high‐sensitivity, a novel decomposition approach is discussed to develop a rational‐based neuro‐TF model of EM behavior of microwave devices. To handle the issue of non‐smoothness and discontinuity, a parametric modeling technique incorporating pole‐residue/rational and neural network hybrid transfer function (short for rational/pole‐residue hybrid neuro‐TF) of EM behavior is reviewed. This technique effectively combines rational and residue‐pole formats of the transfer functions. Compared with the residue‐pole‐based neuro‐TF modeling approach and the rational‐based neuro‐TF modeling approach, the rational/pole‐residue hybrid neuro‐TF technique allows for better accuracy in large geometrical changes and high order applications. A parametric modeling method combining neural network and polynomial‐transfer function (neuro‐PTF) is further presented as an advanced version of the rational/pole‐residue hybrid neuro‐TF method. In this approach, the pole–residue‐based transfer function and the polynomial function are used together to describe the EM responses, and can produce more accurate models, especially with large geometrical variables. Following the modeling process, trained models can be used to provide fast and accurate EM response predictions and can subsequently be used for advanced circuit and system design.

Revealing the gap between modernism and parametricism in architecture

Examining the relationship between humans and the natural world and the effects of interaction between them has deep roots in our understanding of society and culture. Cities, therefore, are a direct reflection of their citizens, as expressions of their architecture directly influence the living conditions of their people. The gap between architectural design ideas and their interpretation in a real built environment can be addressed differently by the opposite process and effect. Parametric design strategies propose manageable and flexible solutions at an early-stage process that respond to given conditions and outcomes. A method in designing buildings and other architectural forms, parametric modelling techniques often result in certain distinctive formal expressions that attract attention to themselves. This article describes how formal representation of this sort affects how things appear to the eye. However, during the design process, this issue is not considered objectively. One may now explore several design choices quickly and easily with the help of parametric design. The research explores the potential of using this technology to parameterise a constructed space’s formal look, which would then affect how people perceive it and their decisions in the future, leading to a more comprehensive approach to build-space design.

A Novel Electromagnetic Centric Multiphysics Parametric Modeling Approach Using Neuro-Space Mapping for Microwave Passive Components

An advanced Neuro-space mapping (Neuro-SM) multiphysics parametric modeling approach for microwave passive components is proposed in this paper. The electromagnetic (EM) domain model, which represents the EM responses with respect to geometrical parameters, is regarded as a coarse model. The multiphysics domain model, which represents the multiphysics responses with respect to both geometrical parameters and multiphysics parameters, is regarded as a fine model. The proposed model is constructed by the input mapping, the output mapping and the coarse model. The input mapping is used to map multiphysics parameters to EM parameters. The output mapping is introduced to further narrow the gap between the output of the coarse model and the multiphysics data. In addition, a three-stage training method is proposed for efficiently developing the proposed multiphysics model. The proposed technique, which combines the efficiency of EM analysis and the accuracy of multiphysics analysis, can achieve better accuracy with less multiphysics data than existing modeling methods. The developed Neuro-SM multiphysics model provides accurate and fast predictions of multiphysics responses. Therefore, the design cycle of microwave passive components is shortened while the modeling cost is significantly reduced. Two microwave filter examples are utilized to demonstrate the accuracy of the proposed parametric modeling technique.

Numerical investigations to assess ground subsidence and fault reactivation during underground coal gasification

Abstract. This paper aims to assess potential environmental impacts associated with commercial-scale application of in situ coal conversion in a target area in Hungary. The site is an environmentally protected forested area. Parametric numerical modelling techniques were employed using a discrete element method (DEM) to evaluate the surface subsidence and fault activation during mining processes. The Mohr–Coulomb elastic–plastic material model adopted to simulate rock formations. Zero-thickness interfaces with friction and cohesive characteristics were employed to simulate geological faults. A sensitivity study on rock formations and fault properties was conducted to address the importance of geological parameters that have an impact on surface subsidence as well as fault activation, which could in turn result in pollutant migration from the deep coal seams to the surface. The analysis of the results demonstrated that surface subsidence is affected by the average Young's modulus of the geological strata, whereby the activation of the faults is influenced by the friction angle between faults. Also, it was found that shallower seams are more likely to produce surface subsidence; i.e. as excavation depth increases, the surface subsidence decreases. Finally, computational outputs from this work were used to develop the web-based and interactive Environmental Hazards and Risk Management Toolkit (EHRM) for planning and decision-making processes during in situ coal conversion.

Electromagnetic Parametric Modeling Using Combined Neural Networks and RLGC-Based Eigenfunctions for Two-Port Microstrip Structures

This article proposes a novel electromagnetic (EM) parametric modeling technique using combined neural networks and resistance, inductance, conductance, and capacitance (RLGC)-based eigenfunctions (neuro-EF) for microwave components with two-port microstrip structures. In the proposed technique, neural networks are used to learn the unknown relationship between the parameters of RLGC of the eigenfunction and geometrical parameters. The generation of training data depends on obtaining the correct eigenvalues of different modes. However, for different geometrical parameter samples, there is no uniform correspondence between the calculated eigenvalues and the modes. The incorrect correspondence may cause the two modes to be swapped, defined as the mode-swap issue. We propose a mode-matching method based on eigenvectors to solve this mode-swap issue. After the training data of eigenfunction parameters is obtained, a preliminary training of the neural networks and a two-step refinement training of the neuro-EF model are proposed to develop the overall EM parametric model. By the proposed modeling technique, the trained model can provide a fast and accurate prediction of EM responses for two-port microstrip structures as geometrical parameters change. For the parametric modeling of microwave components with microstrip structures, the proposed technique can obtain better accuracy in larger geometrical variations compared with the existing methods. Two examples of microstrip structures are used to illustrate the proposed technique.

Parametric Modeling Incorporating Joint Polynomial-Transfer Function With Neural Networks for Microwave Filters

This article proposes a novel parametric modeling technique incorporating a joint polynomial-transfer function with neural networks (short for neuro-PTF) for electromagnetic (EM) behaviors of microwave filters. In the proposed technique, the polynomial function is introduced together with the pole-residue-based transfer function to represent the EM responses. The pole-residue-based transfer function is used to represent the whole EM response at the beginning and is subsequently divided into multiple subtransfer functions where each subtransfer function contains one pair of pole/residue. A novel smoothness-discriminating algorithm is proposed to judge the smoothness of each subtransfer function response and separate the pole/residue pairs whose subtransfer function response is determined to be smooth. The proposed method introduces low nonlinear polynomial functions to re-fit the smooth parts of subresponse and remains the nonsmooth parts of subresponse for the highly nonlinear transfer functions to represent. By this way, the proposed method avoids the discontinuity problems of nonunique parameter extraction caused by fitting smooth curves using the highly nonlinear transfer function. Neural networks are proposed to learn the relationship between the polynomial coefficients/poles/residues and the geometrical parameters. Utilizing both advantages of the polynomial functions and transfer functions, the proposed method can produce more accurate models than the existing neuro-TF methods, especially with large geometrical variations. The accuracy and robustness of the proposed technique are demonstrated using three EM application examples of microwave filters.