Parametric Model Research Articles

Design, optimization, simulation, and implementation of a 3D printed soft robotic peristaltic pump

Abstract This study presents an innovative approach to fluidic pumping using soft robotics, designed to circulate fluid through soft conduits for delicate environments like blood streams where traditional peristaltic pumps may not be feasible. A novel soft robotic peristaltic pump is optimized and implemented, featuring 3D printed ring-shaped actuators and a PDMS pipe housing a Newtonian fluid. The design includes a three-stage actuator ring structure, actuated sequentially for peristaltic motion. A parametric finite element model predicts the required pressure, and the Mooney-Rivlin 5 Parameters hyper-elastic material model ensures accurate material properties. Optimization uses response surface analysis in Minitab and MATLAB Simulink Simscape simulations to achieve maximum flow rate with minimal power and pressure. Experimental validation confirms the simulations, achieving an optimal flow rate of 0.27 ml s−1 at a 450 ms cycle, with minor discrepancies due to friction and measurement errors. This study demonstrates the scalability of linearly sequenced soft squeeze actuators into an effective pump, validated by both simulation and experiments. Future applications include medical devices addressing deep venous thrombosis, with further research exploring control theory for optimization and comparing performance with conventional pumps to enhance practical applicability.

Reverse design of load-bearing broadband metamaterial absorber assisted by deep learning

Abstract In response to the current challenges of narrow absorption bandwidth, weak load-bearing capacity, and low design efficiency in absorbing structures, this study focuses on the reverse design of load-bearing broadband metamaterial absorber. A parameterized model of load-bearing metamaterial absorber was developed by integrating the composite sandwich structure with the electromagnetic resonant layers. The resonant layer was constructed using the combination of Vicsek-fractal and circular rings, with resistive films employed to broaden the absorption bandwidth. A deep learning-based forward prediction model was established to accurately predict the absorbance of the metamaterial absorber. The SHapley Additive exPlanations (SHAP) framework was utilized to analyze the forward prediction network, revealing the influence of various design parameters on the absorbance at center frequencies across the L to K band spectrum. Additionally, the Group Teaching Optimization Algorithm (GTOA) was introduced into the design process, leading to the development of an automated reverse design method for metamaterial absorber that can achieve specific design objectives. Using the GTOA-based reverse design method, a metamaterial absorber capable of effectively absorbing vertically incident electromagnetic waves within the 3-20 GHz frequency range was designed. The designed absorbing structure was fabricated, and its absorption performance was measured using the arch method. The measurement results were found to be in good agreement with the simulation data. The absorbing mechanism of the designed metamaterial absorber was analyzed based on the calculation of equivalent electromagnetic parameters and the electromagnetic resonance observed at the resonant frequency. It was determined that the primary absorbing effect is induced by electric resonance triggered by electromagnetic waves. The proposed metamaterial absorber can be applied to radar stealth design for military targets such as naval vessels. The research methodology and approach demonstrate significant generalizability and engineering applicability.

Generation Mean Analysis in Physio-morphic Traits of Aerobic Rice in Assam, India

An experiment was carried out to study the genetic architecture of few physio-morphic quantitative traits of rice under aerobic condition. Generation mean analysis was carried out in three selected crosses viz.Banglami/Luit, Koimurali/Luit and Guni/Gopinath, involving six generations (P­1, P2, F1, F2, BC1 and BC2) at the experimental farm of SCS college of Agriculture, Assam Agricultural University, Dhubri, Assam, India during 2017 and 2018. The individual scaling tests were used to test the adequacy of additive dominance model. The gene effects were estimated using three parameter models (joint scaling test) suggested by Cavalli (1952) and six parameter model suggested by Haymen (1958). The analysis of variance among different generations of different crosses revealed significant variation for most of the characters indicating considerable variability in the material studied. All the characters except height growth rate in cross II, recorded significance for at least one of the four individual scaling tests in all the 3 crosses. Estimation of different gene effects and their interactions were done in OPSTAT software. Significant positive additive effects were recorded for plant height and height growth rate for in all the three crosses. Preponderance of dominance effect in the expression of height growth rate, spikelet fertility, grain yield, harvest index, root weight and root shoot ratio was evident from the significant positive dominance (h) effect in all the three crosses. With respect to interaction effects, all the characters except height growth rate in cross II, exhibited significant interaction effect for one or more epistatic interactions i.e.[i], [j] or [l] in all the three crosses studied. It is evident that for all the yield and other adaptive traits, additive, dominance and interaction effects were present indicating the complex inheritance of the traits under aerobic condition. Hence, focus should be on improving individual characters separately based on the nature of gene action.

Analyzing Healthcare Production Under Uncertainty in a State‐Contingent Framework

ABSTRACTIn health care, decision‐makers grapple with uncertainty, influencing production choices. This paper introduces a model exploring healthcare production under uncertainty using a state‐contingent framework. Employing a parametric model and a general utility function, it calculates decision‐makers' subjective probabilities for potential future states. Through numerical examples, it illustrates how optimal production choices are made. The model defines production technologies as state‐allocable, facilitating resource allocation among various states and allowing for substitution of state‐specific outputs. This approach offers more flexibility compared to traditional production analyses, empowering healthcare producers to better adapt and respond to uncertainties.

Time to Cure and Predictive Factors Among Patients with Multi-Drug Resistance Tuberculosis in Puntland, Somalia: A Retrospective Cohort Study

Background: Multi-drug-resistant tuberculosis, also known as MDR-TB, is caused by bacteria that are resistant to the most effective first-line anti-tuberculosis medications, which are rifampicin and isoniazid. MDR-TB is an increasing global concern, and its spread has varying cure times for patients affected. Thus, this study was intended to investigate the median time to cure and identify predictive factors for patients with MDR-TB in Puntland, Somalia. Methods: A retrospective cohort study was carried out at the MDR-TB center in Galkayo, Puntland, Somalia between the years of July 2017 and August 2023. The study utilized data that were collected from the inquiry form and monitoring cards of a randomly selected sample of 130 patients over a 6-year period. The data were entered into Epi-Data version 4.3 and analyzed using R Programming version 4.4.0. Non-parametric methods were used to estimate the median time to cure for patients with MDR-TB. Semi-parametric and parametric models were employed to determine predictive factors that influence the time to cure for MDR-TB patients. The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) of different survival models were compared to select the model that offers the best fit. Results: Out of the total of MDR-TB patients, 109(83.85%) were cured and 21(16.15%) were censored during the follow-up period. The median time to cure from MDR-TB was found to be 619 days which is approximately 20 months. In accordance with the AIC and BIC, the Log-logistic accelerated failure time model was the best fit for the data as compared to the other AFT models. The result of the Log-logistic AFT model revealed that age, sex, disease site, and comorbidities significantly affect the time to cure for MDR-TB. According to this study, older age with MDR-TB patients, male MDR-TB patients, MDR-TB patients with comorbid conditions, and extrapulmonary MDR-TB patients had a longer time to cure than their reference categories. Conclusion: Based on the findings of the study, the median time to cure for patients with MDR-TB in Puntland was found to be 20 months. Sex, disease site, comorbidities and age were predictive factors of time to cure from MDR-TB.

Expanding the parametric city model for the structural design of two-technology transit systems, with an application to Santiago

Expanding the parametric city model for the structural design of two-technology transit systems, with an application to Santiago

Modeling and Estimation of Adaptive Optics-Corrected long-exposure Point Spread Function Using Gaussian Sum Approximation

Abstract Adaptive optics (AO) enhances astronomical images by correcting perturbations in the optical system, resulting in complex point spread function (PSF) shapes, which provide critical information for optical design, calibration, and diagnostics. This paper presents a parametric model for an AO-corrected long-exposure PSF that adapts to complex shapes and various seeing conditions. A complementary estimation method is also introduced, addressing the inverse problem of estimating the atmospheric PSF or power spectral density (PSD) from observed PSF data. The model, based on Gaussian sum approximation (GSA), was tested using simulated PSF data from the OOMAO toolbox for Fried parameter (r0) values ranging from 0.10 to 0.20 m and on-sky data from the Very Large Telescope’s MUSE instrument. Two approaches were analyzed: (i) direct PSF estimation using GSA, and (ii) PSF estimation derived from atmospheric PSD estimation. Both methods were evaluated using root mean square error (RMSE). While approach (i) provided the best performance, approach (ii) produced more accurate atmospheric PSF estimates compared to the Moffat approximation and the Fetick method. Additionally, approach (ii) enabled the estimation of key physical parameters, such as the Fried parameter (r0) and residual AO variance ($\sigma _{AO}^2$). The proposed methods effectively estimate atmospheric PSF and PSD, even for complex PSF shapes influenced by spiders, central obstructions, or static aberrations.

SMARTboost Learning for Tabular Data

We introduce SMARTboost (boosting of symmetric smooth additive regression trees), an extension of gradient boosting machines with improved accuracy when the underlying function is smooth or the sample small or noisy. In extensive simulations, we find that the combination of smooth symmetric trees and of carefully designed priors gives SMARTboost a large edge (in comparison with XGBoost and BART) on data generated by the most common parametric models in econometrics, and on a variety of other smooth functions. XGBoost outperforms SMARTboost only when the sample is large, and the underlying function is highly discontinuous. SMARTboost’s performance is illustrated in two applications to global equity returns and realized volatility prediction.

Estimation of Tensile Strength of Carbon Fibers Using Exponentiated Exponential Weibull-Dagum Distribution Model (EEWD) and Its Properties

The tensile strength of Carbon fibers was investigated. Tested EEWD distribution ability to fit with data and observed the skewness of data. The T-R{.} framework has been recently used to generalize various distributions, but the viability of using Dagum distribution has not been investigated. Three distributions are combined in the T-R{.}, through one serving as a baseline distribution. The combined potency of each distribution, which is a weighted hazard function of the baseline distribution, would have more parameters but would also be highly flexible in handling bimodality in datasets. Thus, this paper used the quantile function of the Weibull distribution to generalize the Dagum distribution. In this present research work a novel generalized 6P (six parameter) model called Exponentiated Exponential Weibull Dagum Distribution (EEWD) has been introduced. Appropriate distributions including PDF, CDF, moments, Moment Generating Function (MGF) of EEWD distribution, stochastic ordering, Cumulant Generating Function (CGF) of EEWD distribution, mean, mean deviations and their sub-models have been given. Further EEWD model has been applied in the real-time data admissible.

Timing of Neonatal Discharge and Unplanned Readmission to PICUs Among Infants Born Preterm.

Children born very preterm (<32 weeks) are at risk of ongoing morbidity and admission to pediatric intensive care units (PICUs) in childhood. However, the influence of the timing of neonatal discharge on unplanned PICU admission has not been established. To examine whether the timing of neonatal discharge (postmenstrual age and season) is associated with subsequent unplanned PICU admission. This retrospective cohort study used linked national data from the National Neonatal Research Database and Paediatric Intensive Care Audit Network (PICANet) for children born from January 2013 to December 2018 at 22 to 31 weeks' gestational age who were admitted to a neonatal unit in England and Wales and were discharged home at 34 weeks' postmenstrual age or later. All National Health Service (NHS) neonatal units and PICUs in England and Wales were included. Children were followed up until 2 years of chronological age. Data analysis was conducted from October 2023 to August 2024. Timing of discharge. The primary outcome was unplanned PICU admission between neonatal discharge and chronological age 2 years to any PICU within England and Wales. Survival analysis using a flexible parametric model was conducted with season of discharge (time-dependent factor), gestation, sex, birth weight less than the 10th centile, bronchopulmonary dysplasia, necrotizing enterocolitis, brain injury, and earlier neonatal discharge (lower quartile of postmenstrual age at discharge for gestation) as variables. Of 39 938 children discharged home (median [IQR] gestational age, 29 [27-31] weeks; 21 602 [54.1%] male), 1878 (4.7%) had unplanned PICU admission. More than half of admissions occurred within 50 days of neonatal discharge (1080 [57.5%]). Compared with summer, the risk of unplanned PICU admission following neonatal discharge was 2.58 times higher in winter and 2.35 times higher in autumn (winter: adjusted hazard ratio [aHR], 2.58; 95% CI, 1.68-3.95; autumn: aHR, 2.35; 95% CI, 1.84-2.99). Among children born at 28 to 31 weeks' gestational age, earlier neonatal discharge was associated with increased risk (aHR, 1.30; 95% CI, 1.13-1.49), but this was not true for children born younger than 28 weeks' gestational age. In this retrospective cohort study of preterm children, autumn and winter discharge were associated with the highest risk of unplanned PICU admission following neonatal discharge. For children born at 28 to 31 weeks' gestational age, discharge at lower postmenstrual age was also associated with increased risk. Further work is required to understand whether delaying neonatal discharge for some children born at 28 to 31 weeks' gestational age is beneficial and to consider the wider costs and implications of prolonging neonatal care.

Mesoscopic Tensile Deformation of Steel Cord with Diverse Layered Nonuniformity Based on Microcomputed Tomography Optimizing Simulation Model

The finite‐element analysis plays a crucial role in enhancing the quality and predicting the safety of steel cord. The conventional parametric model for finite‐element analysis often overlooks the inhomogeneity of steel cord, making it difficult to achieve precise simulation. This article utilizes microcomputed tomography to track the mesoscopic deformation of real steel cord during stretching, reconstructing 3D digital images and quantitatively analyzing the corresponding rule for an optimized finite‐element modeling method. In comparison to the parametric model's experimental deviation of 28.37% in strain, the actual structural tensile model aligns with the tensile test with a deviation of only 2.88%, indicating high processing quality. In addition, when simulating the impact of layered nonuniformity on the fracture strain of cords, it is observed that a small degree of nonuniformity leads to an increase in compression between the cord filaments and subsequently enhances the fracture strain. This study provides a practical and dependable quantitative analysis on a mesoscopic scale to optimize the design and production of cords.

Machine learning in causal inference for epidemiology.

In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimates. Correct model specification is challenging, especially in high-dimensional settings. Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form of the relationship between variables. However, when ML predictions are directly plugged in a predefined formula of the effect of interest, there is the risk of introducing a "plug-in bias" in the effect measure. To overcome this problem and to achieve useful asymptotic properties, new estimators that combine the predictive potential of ML and the ability of traditional statistical methods to make inference about population parameters have been proposed. For epidemiologists interested in taking advantage of ML for causal inference investigations, we provide an overview of three estimators that represent the current state-of-art, namely Targeted Maximum Likelihood Estimation (TMLE), Augmented Inverse Probability Weighting (AIPW) and Double/Debiased Machine Learning (DML).

Solving the Price-Setting Newsvendor Problem with Parametric Operational Data Analytics (ODA)

We study the data-integrated, price-setting newsvendor problem in which the price–demand relationship is described by some parametric model with unknown parameters. We develop the operational data analytics (ODA) formulation of this problem that features a data-integration model and a validation model. The data-integration model consists of a class of functions called the operational statistics. Each operational statistic maps the available data to the ordering decision. The validation model finds, among the set of candidate operational statistics, the ordering decision that leads to the highest actual profit, which is unknown because of the unknown demand parameters. This ODA framework leads to a consistent estimate of the profit function with which we optimize the pricing decision. The derived quantity and price decisions demonstrate robust profit performance even when the sample size is very small in relation to the demand variability. Compared with the conventional approach with which the unknown parameters are estimated and then the decisions are optimized, the ODA framework produces significantly superior performance in the mean, standard deviation, and minimum of the profit, suggesting the robustness of the ODA solution especially in the small-sample regime. This paper was accepted by David Simchi-Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02227 .

Impact of integrating traditional care with the modern healthcare system in reducing tuberculosis diagnosis delays in Ethiopia: a clustered randomized controlled study.

Diagnosis and treatment initiation delays for tuberculosis (TB) are significant challenges in resource-limited settings. These delays can result in poor treatment outcomes, disease transmission, and increased costs. This study aimed to assess the effect of integrating traditional care with modern healthcare systems on reducing TB diagnosis delay. A cluster randomized controlled trial was conducted among TB patients, with 510 participants, 255 individuals were assigned to the intervention group and 255 to the control group. Training in the intervention group was provided for both traditional and modern healthcare providers in three rounds to enhance their knowledge, attitudes, and skills in TB screening and referral. A non-parametric independent sample test was used to compare the baseline and end-line data. The effect size was determined using Cohen's d. To account for individual and cluster-level variations, a mixed-effect parametric survival model was employed. Furthermore, conditional (fixed only) and marginal (random effects) graphs were used to compare between the intervention and control groups. A total of 510 participants were included in the baseline study, with a similar number of participants included in the endline study. In the intervention group, the delay in diagnosis was 4.185 per 1000 person-days post-intervention, compared to 4.608 per 1000 person-days pre-intervention. In the control group, the delay for diagnosis was 4.759 per 1000 person-days pre-intervention and 5.031 per 1000 person-days post-intervention. The median time to diagnosis was 135days. The non-parametric comparison showed that the intervention significantly reduced patient delays in the intervention group compared to the control group (p = 0.006), with a Cohen's d effect size of 0.246. The intervention also significantly reduced diagnosis delay in the intervention group compared to the control group (p = 0.036), with a Cohen's d effect size of 0.187. The diagnosis of TB was accelerated by 1.076 times due to the integration of traditional care with the modern healthcare system in the intervention group compared to the control group (δ: 1.076; 95% CI 1.021, 1.134). The involvement of traditional care providers in TB control programs significantly reduced diagnosis delays in Ethiopia. These findings suggest the need for integrating traditional care with modern healthcare systems for the effective prevention of TB in high-burden countries. Clinical trial registration ClinicalTrials.gov ID: NCT05236452.

Atomic H over crystal surface: effective potential dependence on sample properties

Abstract We considered the behavior of the lowest electronic level of atomic H in a semi-infinite space bounded by a flat surface. &amp;#xD;We impose a third kind boundary condition on the electronic wave functions, where the boundary condition parameter models the adsorbent properties of the surface.&amp;#xD;For the crystal surface, the double periodic function as the boundary parameter seems reasonable; therefore, this case is considered.&amp;#xD;It is shown that there are two modes of atom adsorption on the sample surface depending on the parameters of the boundary condition. &amp;#xD;In the first case the effective atomic potential, considered as a function of the distance between H and the boundary plane, exhibits a well pronounced minimum at some finite distance and a relatively small effective range of interaction distances between the atoms and samples.&amp;#xD;The second case occurs under the condition of a large positive affinity of the atomic electron to the sample boundary and low initial H-concentration inside the sample. &amp;#xD;In such a situation, the minimum of the effective potential is close to the sample surface, and a significant amount of energy can be emitted throughout the adsorption process.

Quantitative modelling of the morphology of age plateaux in muscovite argon spectra and the implications for thermochronology

For muscovite in ancient Proterozoic and Archean terranes, quantitative modelling of age spectrum morphology shows that a significant plateau segment does not form in an age spectrum unless there was rapid cooling that either began before and continued through, or which initiated at, the time given by the plateau age. Further, the modelling shows that a well-developed plateau is not preserved unless such rapid cooling continues until ambient temperatures are reached that are <∼220–200 °C. It thus follows that the age of a well-developed plateau segment in a muscovite age spectrum is an accurate constraint on when the rock was first exhumed to (and/or last cooled at) shallow crustal levels. In a sample held at a higher ambient temperature, argon continues to be lost from the lattice at a significant rate, well past the time of so-called ‘isotopic closure’ predicted by application of Dodson’s formula. Parametric modelling shows that monotonic cooling paths do have a well-defined point, at which isotopic closure takes place, but to form an age plateau: (1) the rate of cooling must be rapid; and (2) cooling must continue at the same or similar rates, until temperatures are reached that are low enough to inhibit significant further loss of argon to the environment. Quantitative modelling shows how Dodson’s formula begins to fail once ambient temperatures rise above these numerically computed values for isotopic closure. Yet these numerically computed closure temperatures are far below the nominal closure temperature for muscovite, typically quoted as ∼ 350–400 °C. Therefore, these results have significant implications for models that utilise closure temperatures based on estimates using Dodson’s formula, when attempting to invert thermochronological datasets to obtain constraints on the temperature–time path.

Multi-objective optimization design method for the dimensions and control parameters of curling hexapod robot based on application performance

The curling hexapod robot is different from previous curling robots, which can mimic the throwing actions of curlers. To ensure its performance, the size and control parameters of the robot need to be optimized. However, there is still a lack of parameter design optimization methods for hexapod curling robots based on application performance and considering the coupling effects of size and control parameters. To meet this demand, this paper combines Simscape Multibody simulation modeling and genetic algorithm to propose a multi-objective optimization method for the dimensions and control parameters of curling hexapod robot based on application performance. This method uses Simscape Multibody to establish a parametric model of the curling robot, and proposes an optimization search method using genetic algorithms based on constraints and objective functions. Finally, the dimensions and control parameters of the curling robot that meet the application performance requirements are solved through the optimization method.

Informatization model of Guqin based on grasshopper and CNC machining

ABSTRACT The integration of Computer-Aided Design (CAD) and Computer Numerical Control (CNC) machining has brought modern manufacturing techniques into the craft of Guqin crafting, introducing the concept of Industrialized Standardized Guqin Crafting (ISGC). This study proposes the concept of Guqin Information Modeling (GIM), on the basis of which, the parametric modeling method of Guqin is proposed to realize the parameter control of the key position of Guqin; combined with the CNC machining technology, it verifies the production of Guqin of different materials and shapes, summarizes the method of selecting the tools for CNC machining of Guqin and the machining parameters of each process, and puts forward the Industrialized Standard of Guqin Carving (ISGC). The proposal of GIM and ISGC provides information technology support for the research of the sound quality of Guqin and the protection of Guqin.

Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms.

Maximum likelihood inference can often become computationally intensive when performing joint modeling of longitudinal and time-to-event data, due to the intractable integrals in the joint likelihood function. The computational challenges escalate further when modeling HIV-1 viral load data, owing to the nonlinear trajectories and the presence of left-censored data resulting from the assay's lower limit of quantification. In this paper, for a joint model comprising a nonlinear mixed-effect model and a Cox Proportional Hazards model, we develop a computationally efficient Stochastic EM (StEM) algorithm for parameter estimation. Furthermore, we propose a novel technique for fast standard error estimation, which directly estimates standard errors from the results of StEM iterations and is broadly applicable to various joint modeling settings, such as those containing generalized linear mixed-effect models, parametric survival models, or joint models with more than two submodels. We evaluate the performance of the proposed methods through simulation studies and apply them to HIV-1 viral load data from six AIDS Clinical Trials Group studies to characterize viral rebound trajectories following the interruption of antiretroviral therapy (ART), accounting for the informative duration of off-ART periods.

Fertility model evolution: A survey on mathematical models of age-specific fertility with application to Nepalese and Malaysian data

Fertility pattern analysis and modeling to smooth age-specific fertility rates (ASFRs) form a well-established research field that holds particular importance for Asian countries. In developed nations, ASFRs typically display a bimodal skewed fertility curve, whereas, in developing countries, they usually exhibit a unimodal skewed fertility curve that diverges from the normal one. For decades, demographic experts worldwide have been interested in creating models using deterministic and stochastic approaches to represent these fertility curves. In this regard, parametric and non-parametric models have been created, with the latter providing a better fit for ASFR data. This research investigates the evolution of fertility models aimed at smoothing ASFRs. It explores suitable alternative models for countries with fast-declining, unimodal, and skewed fertility curves of ASFRs, such as Nepal and Malaysia. Nepal&amp;rsquo;s fertility rate is transitioning from a high level toward the replacement rate (2.1) at the year 2021; meanwhile, Malaysia&amp;rsquo;s fertility rate (1.7) in the year 2021 has dropped below the replacement rate. Given the lack of a universally applicable model for ASFR pattern variation, this study proposes the Kumaraswamy log-logistic distribution as a promising model to represent the ASFRs of Nepal and Malaysia accurately. Various approaches, including the Akaike information criterion, and Bayesian information criterion, are employed to validate the fitting of the proposed model.