Estimated Parameter Values Research Articles

RAINFALL MODELLING IN EAST JAVA USING A MODIFIED ORNSTEIN-UHLENBECK MODEL

One of the current global issues is climate change and weather variability. This phenomenon has real impacts on various regions, including East Java Province. East Java is experiencing increased rainfall intensity as one of the effects of climate change. High and continuous rainfall intensity can trigger disasters such as flooding, which has the potential to cause significant financial losses for the community. Therefore, effective risk management becomes crucial. One possible solution to address these risks is through the use of financial derivatives. The initial step in risk management involves modeling the behavior of rainfall. It is assumed that the rainfall pattern follows a mean-reverting process, specifically the Ornstein-Uhlenbeck process. The existing Ornstein-Uhlenbeck model is then modified to ensure that the resulting model accurately reflects the rainfall conditions in East Java. To validate the modified model, simulations of the Ornstein-Uhlenbeck process were conducted using estimated parameter values. The Ornstein-Uhlenbeck simulation achieved a minimum MSE score that approaches zero. This MSE score indicate that the proposed modified Ornstein-Uhlenbeck model is accurate in representing the rainfall patterns in East Java.

P-1236. Vancomycin Population Pharmacokinetics Using Volumetric Absorptive Microsampling in Critically Ill Children with Multiple Organ Dysfunction Syndrome

Abstract Background Vancomycin (VAN) is the most frequently used antibiotic for hospitalized patients in the United States. Wide use occurs as it is a first line treatment for severe gram-positive bacterial infections in both adults and pediatrics. Most VAN dosing in children with multiple organ dysfunction syndrome (MODS) is guided via population pharmacokinetic (PK) approaches based on creatinine clearance equations and frequent samplings. Volumetric absorptive microsampling (VAMS) offers sampling procedures that require as little as 20 mcL of blood. The primary objective of this study was to develop a population PK (popPK) model for VAN in children with MODS using VAMS.Table 1.Population parameter estimation values. Methods We conducted a multi-center prospective observational study of VAN PK in pediatric MODS. Subjects (< 18 years) participating in a larger observational study of children with MODS in the ICU were eligible. Up to 15 PK samples were collected over 3 consecutive days, along with pertinent covariate data. Parametric popPK modeling was performed using Monolix 2024R1. Covariate selection was based on improvements in objective function value and physiologic relevance. Children on CRRT were excluded from the present analyses. Subjects in our interim analysis were also excluded from analysis if they were missing demographic information or were pending data queries on VAN concentrations.Figure 1.Individual weighted residuals according to time and individual predictions. Results 52 subjects from 8 sites were included with a median age of 6.5 y (range: 2 m–17 y) and a median weight of 24.5 kg (range: 3-214). A two-compartment model with clearance adjusted for allometric scaling and estimated glomerular function best described the data. Population predictions were modest (R2=0.28) whereas individualized predictions were improved (R2=0.61). Between subject variation for clearance and volume of distribution was high even after correcting for U25 creatinine clearance (92.7% and 107.9%, CV% respectively).Figure 2.Observed vs. population (A) and individual (B) predictions for the model. Conclusion Children with MODS display high between subject variability of VAN clearance and volume of distribution. Frequent sampling is needed because “stead-state” does not exist in the early period of MODS for children. VAMS can facilitate small volume sampling and future Bayesian models based on data such as these can facilitate fewer sampling events to classify the patient’s dynamic clearance. Disclosures Nathaniel J. Rhodes, PharmD MS, Apothecademy, LLC: Advisor/Consultant Kevin J. Downes, MD, Paratek, Inc.: Grant/Research Support|Veloxis Pharmaceuticals, Inc.: Grant/Research Support Marc H. Scheetz, PharmD, MSc, Abbvie: Advisor/Consultant|Basilea: Advisor/Consultant|Cidara: Advisor/Consultant|DoseMe: Advisor/Consultant|Entasis: Advisor/Consultant|F2G: Advisor/Consultant|GSK: Advisor/Consultant|Lykos: Advisor/Consultant|Roche: Advisor/Consultant|Third Pole Therapeutics: Advisor/Consultant|Xelia: Advisor/Consultant

MULTINOMIAL LOGISTIC REGRESSION MODEL USING MAXIMUM LIKELIHOOD APPROACH AND BAYES METHOD ON INDONESIA'S ECONOMIC GROWTH PRE TO POST COVID-19 PANDEMIC

Economic growth in Indonesia has become a major concern in the global context, especially before and after the Covid-19 pandemic. Key sectors such as tourism, manufacturing, trade and transportation have been seriously affected by restrictions on travel and economic activity imposed to control the spread of the virus. Therefore, it is considered necessary to carry out modeling to describe existing conditions. In this research, two approaches were used, namely the Maximum Likelihood approach and the Bayes approach. The use of methods in general as research material for researchers to study these two methods further. So far the algorithm used for the Bayes concept method is Markov Chain Monte Carlo with Hasting's Metropolis method. The parameter estimation results obtained from both methods are considered quite identical. However, it is necessary to pay attention to the iteration procedure that will be carried out. The selection of factors used in the iteration process is very determining in obtaining estimated parameter values. Furthermore, the results obtained so far do not contain any fundamental differences regarding economic growth in Indonesia. In general, Indonesia can be said to be stable in terms of economic growth.

Passenger vehicle behavior modeled by Power Flow using different mathematical tire models

Ground Vehicles can be approached as a set of integrated subsystems, with cause-effect relationships. Through the Power Flow Modeling approach, each of these subsystems can be interpreted as a black box, and interrelated with the others in a modular way, as long as causalities are respected. This work aims to take advantage of the modularity of the automobile interpreted as a set of integrated subsystems and evaluate the influence of mathematical tire models on its acceleration and braking behavior. Each of the subsystems is mathematically modeled in individual blocks that are then integrated, without loss of causality, in MATLAB/Simulink® software. The purpose is to provide a fully modular vehicle model, taking advantage of this Power Flow feature, in an open-source code. Furthermore, Inverse Problems are applied to estimate parameter values of the Burckhardt and Dugoff mathematical models in order to obtain similar behaviors of the corresponding tire model by the Magic Formula.

Stochastic Modeling with Poisson Hidden Markov in Hepatitis B Cases

Hepatitis B is transmitted through blood or body fluids contaminated with the virus from Hepatitis B sufferers (carriers). The factors that cause a person to contract Hepatitis B are sexual intercourse, blood contact, placental contact from the mother to the baby, and saliva. The incubation period for Hepatitis B Virus (HBV) ranges from 30 - 180 days with an average of 60 - 90 days. HBV can be detected 30 - 60 days after infection and persists for a certain period. Thus, the behaviour of infectious diseases can be observed and described using mathematical modelling. Mathematical modelling is a field of mathematics that represents and explains physical systems or problems that occur in the real world and are solved in mathematical statements. The mathematical model used to overcome uncertainty in variable values ​​is a stochastic model. These causal factors are not directly observed and form a Markov chain. The model that can be used for the uncertainty of an event is the Hidden Markov Model. The Hidden Markov Model (MHM) is a type of stochastic modelling that does not recognize the factors that trigger the problem being modelled. The Poisson Hidden Markov model is used to model Hepatitis B disease. Hepatitis B disease data is a series of observations that experience overdispersion and depend on the trigger of Hepatitis B disease, which is assumed not to be observed directly and forms a Markov chain. The aim to be achieved in this research is to model Hepatitis B disease at the Medan Haji Hospital using the Poisson Hidden Markov model and to find parameter estimates using the Expectation Maximization Algorithm. This type of research uses quantitative research methods. The conclusions that can be drawn based on the results and previous discussions are as follows: the method for determining the average number of patients in patient B can use the PHMM (Poisson Hidden Markov Model) method with the EiM (Expectation-Maximization Algorithm) algorithm, the best model for the number of Hepatitis B patients in Haji Hospital at this hospital is the model with three hidden cases with the parameter estimation value. The average number of Hepatitis B patients is 0.0324 in 1 month, and the average predicted results obtained by the hidden condition model 3 align with the original conditions in the previous months.

Dynamic contrast-enhanced and diffusion-weighted MR imaging for predicting tumor growth of sporadic vestibular schwannomas: a prospective study.

Advanced MR imaging, such as diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) imaging, may provide valuable non-invasive information on intrinsic tumor biology. This study aims to evaluate apparent diffusion coefficient (ADC) and DCE-MRI-derived microvascular parameter values (Ktrans, ve, and vp) as potential imaging predictors for future sporadic vestibular schwannoma (VS) growth. In this prospective cohort study, patients with newly diagnosed unilateral sporadic VS and an initial wait-and-scan strategy were enrolled between January 2021 and January 2023. Patients underwent a single timepoint comprehensive MRI protocol, including DWI and DCE-MRI sequences. The estimated values of ADC, Ktrans, ve, and vp were calculated using established pipelines on a voxelwise basis within the delineated tumor region of interest. Associations of the estimated parameter values with volumetric growth were evaluated in uni- and multivariable logistic regression and survival analyses. Of the 110 analyzed patients, 70 (64%) exhibited growth during follow-up. A significant correlation was primarily observed between the DCE-MRI-derived parameters and VS growth. The combination of mean Ktrans (P<0.001) and ve (P<0.001) tumor values provided an internally validated model with an AUC of 0.85 for growth, yielding a sensitivity of 89% and specificity of 73% at the optimized cutoff value. Only the mean ADC values were found to be significantly higher in shrinking tumors (P=0.04). The strongly significant correlation observed between VS growth and Ktrans and ve tumor values indicates great potential of the non-invasive DCE-MRI for individualized VS management in clinical practice. External validation is needed to further substantiate these findings.

Relationships between emotional labor, job burnout, and emotional intelligence: an analysis combining meta-analysis and structural equation modeling

The present study adopted a meta-analysis design that incorporated structural equation modeling to explore the relationships between emotional labor (EL), job burnout (JB), and emotional intelligence (EI), and enable model validation. The results revealed that EL and JB were significantly and positively correlated, that EI was significantly and positively correlated with EL, and that EI was significantly and negatively correlated with JB. The SEM parameter estimation values were all positive, reaching the level of significance and meeting the basic fit criteria. The total effect size of EL on JB was 0.289, which was equal to the sum of the direct and indirect effect sizes (0.371–0.082). This result indicated that EL affected JB through EI, validating the presence of a moderating effect. Finally, the results were discussed, and practical suggestions were proposed.

Integrated Subsurface Hydrologic Modeling for Agricultural Management Using HYDRUS and UZF Package Coupled with MODFLOW

The present work aims to compare two different subsurface hydrological models, namely HYDRUS and MODFLOW UZF package, in terms of groundwater recharge; thus, both models were coupled with MODFLOW. The study area is an experimental kiwifruit orchard located in the Arta plain in the Epirus region of Greece. A novel conceptual framework is introduced in order to (i) use in situ and laboratory measurements to estimate parameter values for both sub-surface flow models; (ii) couple the developed models with MODFLOW to estimate groundwater recharge; and (iii) compare and evaluate the performance of both approaches, with differences stemming from the distinctive equations describing the flow in the unsaturated zone. Detailed soil investigation was conducted in two soil horizons in the research field to identify soil texture zones, along with infiltration experiments implementing both double-ring and single-ring infiltrometers. The results of the field measurements indicate that fine-textured soils are predominant within the field, affecting several hydrological processes, such as infiltration, drainage, and root water uptake. Field measurements were incorporated in unsaturated zone flow modeling and the infiltration fluxes were simulated with the application of both the UZF package of MODFLOW and the HYDRUS code. The two codes presented acceptable agreement between the simulated and observed hydraulic head values with a similar performance in terms of statistics; however, they produced different results regarding recharge rates in the aquifer as simulated by MODFLOW. HYDRUS produced higher hydraulic head values in the aquifer throughout the simulation, related to higher recharge rates arising from the root water uptake and the capillary effects that are computed by HYDRUS but neglected by the UZF package of MODFLOW.

Generating spatially realistic environmental null models with the shift‐&amp;‐rotate approach helps evaluate false positives in species distribution modelling

Abstract To circumvent reporting spurious correlations, species distribution models often explicitly account for spatial autocorrelation, for example by including spatially structured random effects. The validity of statistical inference derived from such models has been tested by simulations using null environmental predictors that do not have any causal dependency with the response. Such null environmental predictors can be obtained by permutations of the original predictors or by simulating spatial structures resembling the original predictors. In such approaches, it is important that the permuted or simulated predictors reflect the nature of spatial variation present in the original predictors. Here we present a novel approach for generating realistic null predictors by a shift‐&amp;‐rotate (S&amp;R) approach: we extract environmental variables after randomly translating and rotating the sampling area within a window of defined environmental layers. In this way, the null environmental variables have fully realistic spatial variation and covariation, but no relationship to the response variable. We implement the S&amp;R approach to three main R‐functions and demonstrate with a simulation study how they can be used to untangle causal versus non‐causal relationships within species distribution modelling. These methods allow us to quantify the predictive power attributed within the models due to non‐causal correlations generated by the realistic structure of the environmental covariates. In our case study, we identify when a model incorrectly estimates parameter values, yet still has high predictive power due to the structured nature of the predictor variables. The use of null models is imperative in ecological modelling for testing the accuracy of statistical inference in complex ecological systems and the choice of these null models is far from trivial. Here we provide R functions for generating spatially realistic null models to use in species distribution modelling as well as other spatially explicit fields such as landscape genetics.

Comparative analysis of event runoff coefficients and curve numbers in contrasting urban environments based on observed rainfall-runoff data

Comparative analysis of event runoff coefficients and curve numbers in contrasting urban environments based on observed rainfall-runoff data

Facial basis of stereotypes: Judgements of warmth and competence based on cross-group typicality/distinctiveness of faces.

Human migration is an increasingly common phenomenon and migrants are at risk of disadvantageous treatment. We reasoned that migrants may receive differential treatment by locals based on the closeness of their facial features to the host average. Residents of Türkiye, the country with the largest number of refugees currently, served as participants. Because many of these refugees are of Arabic origin, we created target facial stimuli varying along the axis connecting Turkish and Arabic morphological prototypes (excluding skin colour) computed using geometric morphometrics and available databases. Participants made judgements of two universal dimensions of social perception-warmth and competence-on these faces. We predicted that participants judging faces manipulated towards the Turkish average would provide higher warmth and competence ratings compared to judging the same faces manipulated towards the Arabic average. Bayesian statistical tools were employed to estimate parameter values in multilevel models with intercorrelated varying effects. The findings did not support the prediction and revealed raters (as well as target faces) to be an important source of variation in social judgements. In the absence of simple cues (e.g. skin colour, group labels), the effect of facial morphology on social judgements may be much more complex than previously assumed.

Bayesian analyses of multiple random change points in survival models with applications to clinical trials

ABSTRACT Single and multiple random change points (RCPs) in survival analysis have arisen naturally in oncology trials, where the time to hazard rate change differs from one subject to another. Recently, Xu formulated and discovered important properties of these survival models using a frequentist approach, allowing us to estimate the hazard rates, rate parameters of the exponential distributions for the RCPs, expected survival and hazard functions. However, these methods did not provide an estimation of the uncertainty or the confidence intervals for the parameters and their differences or ratios. Therefore, statistical inferences were not able to be drawn on the parameters and their comparisons. To solve this issue, this article implements a Gibbs sampler method to estimate the above parameters and the differences or ratios alongside the 100(1 − α )% highest posterior density (HPD) intervals calculated from Chen–Shao’s algorithm. The estimated rate parameters from the methods in Xu serve as empirical values in the Gibbs sampler method. Thus, formal statistical inferences can now be readily drawn. Simulation studies demonstrate that the proposed methods yield robust estimates, with the samples from the marginal posterior distributions converging rapidly and exhibiting favorable behavior. The 95% HPD intervals also demonstrate excellent coverage probabilities. This proposed method has a multitude of applications in clinical trials such as efficient clinical trial design and sample size adjustment based on the estimated parameter values at interim analyses.

Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference

Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference

Estimating the Values of the PDE Model Parameters of Rechargeable Lithium-Metal Battery Cells using Linear EIS

Abstract We introduce a partial-differential-equation model for rechargeable lithium-metal battery (LMB) cells whose parameter values are fully identifiable from cell-level experiments. From this model, we formulate a computationally tractable transfer-function (TF) model for use within optimization loops. A strategy is proposed for regressing the TF model to cell electrochemical impedance spectroscopy (EIS) measurements to estimate parameter values. We validate the regression using a synthetic dataset before application to a single-layer LMB pouch cell. The voltage RMSE between the fully identified model's predictions and laboratory measurements is about 4 mV for a GITT profile. We provide MATLAB code to simulate the model in COMSOL, compute cell impedance from the TF model, and perform model regression.

Integrating the Finite Element Method with Python Scripting to Assess Mining Impacts on Surface Deformations

Mining operations disrupt the structure of rock layers, leading to surface deformations and potential mining damage. This issue has been extensively studied since the 19th century using various analytical, geometric-integral, and stochastic methods. Since the 1990s, numerical methods have been increasingly applied to determine changes in the stress and strain states of rock masses due to mining activities. These methods account for numerous additional factors influencing surface deformation, offering significant advantages over classical approaches. However, modelling rock masses presents challenges, particularly in calibrating the mechanical parameters of rock layers, an area extensively researched with numerous publications. In this study, we determined the mechanical parameter values of rock layers at the advancing mining front using a custom Python script and Finite Element Method (FEM) numerical models. We also introduced a modification to evaluate the error of the estimated parameter values. Numerical analyses were conducted for the Piast–Ziemowit mine region in Poland, utilizing mining, geological, and surveying data. Our results demonstrate that accurate calibration of mechanical parameters is crucial for reliable predictions of surface deformations. The proposed methodology enhances the precision of numerical models, providing a more robust framework for assessing the impact of mining activities on rock layers.

Distributed control of spacecraft formation under [formula omitted] perturbation in the port-Hamiltonian framework

Distributed control of spacecraft formation under [formula omitted] perturbation in the port-Hamiltonian framework

Validation and psychometric evaluation of the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) among Czech adolescents using Item Response Theory

BackgroundThe topic of adolescent mental health is currently a subject of much debate due to the increasing prevalence of mental health problems among this age group. Therefore, it is crucial to have high-quality and validated mental well-being measurement tools. While such tools do exist, they are often not tailored specifically to adolescents and are not available in Czech language. The aim of this study is to validate and test the Czech version of the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) on a large sample of Czech adolescents aged 15 to 18 years.MethodsThe analysis is based on data from the first wave of the Czech Education Panel Survey (CZEPS) and was mainly conducted using Item Response Theory (IRT), which is the most appropriate method for this type of analysis. Specifically, the Graded Response Model (GRM) was applied to the data. This comprehensive validation study also included reliability and three types of validity (construct, convergent and criterion) testing.ResultsThe study found that the Czech version of the SWEMWBS for adolescents aged 15 to 18 years (N = 22,498) has good quality and psychometric properties. The data was analysed using the GRM model as it met the assumptions for the use of IRT. The estimated parameter values by GRM demonstrated good discriminant and informative power for all items, except for item 7, which showed poorer results compared to the others. However, excluding it from the scale would not enhance the overall quality of the scale. The five-category response scale functions effectively. Additionally, the results demonstrated high reliability, and all types of validity tested were also confirmed.ConclusionsThe Czech version of the SWEMWBS for adolescents has been validated as a psychometrically sound, reliable and valid instrument for measuring mental well-being. It can therefore be used with confidence in future studies.

Heterogeneous Models for UV Induced Precursor-Mediated Growth of CdS Nanoparticles in PMMA Films: Fitting Experimental Curves and Core-Shell Growth.

Experimental data on the absorbance evolution of an UV irradiated PMMA film containing the TEDBCd CdS precursor published earlier are fitted by the authors' recently published model of precursor-mediated heterogeneous nucleation and growth of nanoparticles in polymer films. Two types of nucleation centers are considered. The centers of the first type are nanoparticles initially existing in the material. The centers of the second type belong to polymer chains. The study shows that the experimental data indicate a wide size distribution of the obtained CdS nanoparticles. For the estimated parameter values, the problem of core-shell nanoparticle growth where the nucleation centers of the first type are nanoparticles of a different nature is considered.

Dynamic modeling of lytic virus transmission among phytoplankton driven by nitrogen and phosphorus

Dynamic modeling of lytic virus transmission among phytoplankton driven by nitrogen and phosphorus

Modelling of Forecasting ASEAN-5 Stock Price Index Using GSTAR Model

This research aims to apply the Generalized Space-Time Autoregressive (GSTAR) model to predict stock price indices in ASEAN-5 countries. Generalized Space Time Autoregressive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. The GSTAR model produces a space-time model that adopts the stages of the Autoregressive Integrated Moving Average (ARIMA) model. This research uses parameter estimation using the Maximum Likelihood method, which is a method used to estimate parameter values by maximizing the probability function seen based on observations. This research uses secondary data in the form of Stock Price Index data from 5 countries in Asia, namely the Composite Stock Price Index (JCI), Philippine Stock Exchange (PSEi), Strait Time Index (STI), Kuala Lumpur Composite Index (KLCI), and Thailand Stock Exchange Index (SETI). Stock Price Index data was divided into in-sample data for Generalized Space-Time Autoregressive (GSTAR) modelling and out-sample data used to validate presumptive results. In-sample data was taken from January 4, 2021, to December 29, 2023, and then out-sample data for presumptive was as many as 5 from January 2, 2024, to January 8, 2024. From the modeling results, it was found that the mean MAPE value of the GSTAR model was smaller than that of the ARIMA model. Moreover, based on the presumptive results for the following 5 periods using the GSTAR (2.1) I(1) model, a Mean Absolute Percentage Error (MAPE) of less than 10% in each location. The values shows that GSTAR model is more accurate than the ARIMA model.