Mean Percentage Error Research Articles

Assessment of Reliability, Agreement, and Accuracy of Masseter Muscle Ultrasound Thickness Measurement Using a New Standardized Protocol.

There is no validated method of assessing masseter muscle thickness (MMT) by ultrasound imaging (US). However, this is important to ensure study and measurement quality of MMT by US in future studies, as MMT differs depending on the examined area. Thus, this study's aim was to present a new standardized method for assessing the MMT by US and to evaluate the reliability, consistency, and accuracy of its measurements. We also compared the results of MMT measurements obtained by US and computer tomography (CT). The study included nine healthy adults. The US and CT scans were collected in a supine rest position with the mandible in relaxed position. US measurements were determined according to a new standardized protocol (with precise probe location). The MMT measured by CT and US over a seven-day interval showed excellent intra-rater reliability. The mean MMT measured by CT was 12.1 mm (1.74) on the right side and 11.9 mm (1.61) on the left side. The mean MMT measured by US was 12.7 mm (2.00) on the right side and 11.5 mm (1.37) on the left side. The mean percent error in MMT measurement between CT and US was below 6%. A strong linear relationship was found between the CT and US measurements of the MMT on both body sides (p < 0.001, r ≥ 0.93). The proposed method of MMT measurement using US demonstrated excellent reliability, yielding results similar to those obtained from CT images. We recommend the use of this standardization protocol in further studies where precise assessment of MMT by US is expected.

PERAMALAN NILAI TUKAR PETANI PROVINSI ACEH: DITINJAU DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING DAN HOLT WINTER

This research aimed to compare the performance of the Double Exponential Smoothing and Holt-Winter methods in forecasting the Farmer Exchange Rate (NTP) for the Horticulture Farmer Subsector in Aceh Province. Secondary data on the NTP for the Aceh Horticulture Subsector from 2020-2023 was obtained from the Aceh Provincial Central Statistics Agency (BPS) website. The data processing was carried out using the R software. The results showed that the Double Exponential Smoothing method had a Root Mean Squared Error (RMSE) of 3.413201, a Mean Absolute Percentage Error (MAPE) of 2.85835, and a Mean Percentage Error (MPE) of -0.5980113. Meanwhile, the Holt-Winter's Additive method had an RMSE of 4.055652, a MAPE of 3.055992, and an MPE of 0.2534276. The best forecasting method was selected based on the smallest RMSE, MAPE, and MPE values, which was Double Exponential Smoothing. The 12-month forecast using the Double Exponential Smoothing method indicated that the lowest NTP would occur in January 2024 at 106.7195, while the highest NTP would be in December 2024 at 113.6443

Analytical approach to structural chemistry origins of mechanical, acoustical and thermal properties.

Crystalline matters with periodically arranged atoms found wide applications in modern science and technology. To facilitate the design of new materials and the advancement of existing ones, accurate and efficient models without relying too much on known inputs for predicting the functionalities are essential. Here, we propose an analytical approach for such a purpose, with only the knowledge of the structural chemistry of crystals. Based on the electrostatic interaction between periodically arranged atoms, the 1st, 2nd and 3rd derivatives of interatomic potential, respectively, enable a prediction of ten kinds in total of mechanical, acoustical and thermal properties. Over a thousand measurements are collected from ∼500 literatures, this results in the symmetric mean percentage error (SMPE) within ±25% and the symmetric mean absolute percentage error (SMAPE) ranging from 22%∼74% across all properties predicted, which further enables a revelation of bond characteristics as the most important but implicit origin for functionalities.

Calculating micro- and macro-scale bone quality parameters from computed tomography images using a parametric physical model and standard bone composition

Bone quality parameters (BQPs) calculated from computed tomography (CT) and micro-computed tomography (μCT) images have been applied in clinical to diagnose and prevent osteoporotic fractures. However, the BQPs calculated from CT and μCT images are at different scales and incomparable due to the differences in image resolutions and accounted bone compositions. In this study, we proposed a novel method to convert HU values of CT images into the BQPs that were originally calculated using different tools. By using the standard bone composition of human bones, the BQPs of bone volume fractions (BV/TV) and volumetric bone mineral densities (vBMD) of the bones were calculated using different accounted compositions of bones and mathematical relationships defined in this study. In addition, the HU values of the bones were estimated using a parametric physical model and spectrum characteristic parameters. Relationships for converting HU values into BQPs were determined by linear fitting the calculated BQPs and estimated HU values. Cadaveric and patient studies show that the micro-scale bone volumes and macro-scale vBMDs of vertebrae calculated from CT images using the conversion relationships determined by the proposed method were close to those calculated from the μCT images and quantitative CT, respectively, with mean percent errors lower than 5%. The proposed method could be applied in clinical to accurately compute micro- and macro-scale BQPs from CT images for assessing the bone quality of patients.

Development of a new equation and validation of earlier resting energy expenditure predicting equations in adults living in Tehran.

Predictive equations have been considered as a practical approach for estimating resting energy expenditure (REE) across multiple populations, but their accuracy for each community remains to be determined. Thus, the purposes of this study were to determine the validity of REE predictive equations and to develop a new REE predictive equation in adults living in Tehran. The study included 284 subjects (158 females) aged 18-60 years old from two cross-sectional studies conducted in Tehrani populations. Anthropometric measurements were assessed using standard protocols. REE was measured using indirect calorimetry (IC) and was estimated using preexisting equations. A new equation was also developed based on the REE from IC and variables such as age, sex, height, and weight. Measured REE was compared to new equation and preexisting predictive equations via correlation, linear regression, and Bland-Altman tests. The new equation and the equations by Mifflin-St. Jeor, Livingston, Frankenfield, Nichols, Müller, and Ganpule demonstrated the best predictive value at a group level (mean percentage error=-2.2 to 2.4 %). At an individual level, the new equation and the equations by Mara, Frankenfield, Roza, Nikooyeh, and Harris & Benedict showed the greatest accuracies compared to measured REE (accuracy prediction=50-53%). This study highlights the importance of considering race when predicting REE. It also demonstrates that the newly developed equation is more appropriate in a clinical setting at group but not individual level. Thus, further research is needed to examine the new equation in an independent sample.

Forecasting of global solar radiation: A statistical approach using simulated annealing algorithm

In recent years, the share of renewable energy sources in current energy production has been increasing due to the depletion of fossil fuel resources, increasing energy needs and environmental concerns. Solar energy, one of the most important renewable energy sources depending on being clean, sustainable and environmentally friendly energy source. Understanding the solar radiation value is crucial for maximizing the potential of solar energy and ensuring the efficient operation of solar energy systems. In this paper, it is aimed to develop a new global solar radiation (GSR) prediction model by using simulated annealing algorithm (SAA). Modeling solar radiation data with significant variability is a challenging task that necessitates the use of nonlinear approaches. Although the SAA is widely used in engineering and natural sciences, it has not previously been used to develop a GSR prediction model. While developing the SAA forecast model for the Adana region, long-term sunshine duration and solar radiation data obtained from the Turkish General Directorate of State Meteorology were used, as well as geographical features such as latitude and longitude of the selected region. The performance and applicability of the proposed model was examined by comparing it with different GSR estimation methods developed in the literature. The primary contribution of this study lies in the introduction of an inventive model, which has been constructed for the first time in the assessment of GSR, to the current collection of literature. The produced results were statistically compared with the observed data using six separate performance and error measures. The results of all years showed that the relative percentage error (RPE) less than 11.66 %, the mean percentage error (MPE) does not exceed 12.55 %, the correlation coefficient (R2) greater than 0.98, the mean absolute percentage error (MAPE) test results appear to vary close to the value 10, the sum of squared relative error (SSRE) test results seem to approaching 0 and the t-statistic test less than 4.06 for the annual GSR. It has been observed that the developed SAA GSR forecasting model has a successful performance when compared to different forecasting models popularly used in the literature. According to the statistical error results, the estimated GSR data from the novel SAA-GSR forecast model aligns well with the measured meteorological values. Given the efficacy of the established SAA model in GSR estimation, this study is anticipated to offer valuable insights and contributions to the application of the SAA approach in other energy applications.

Performance Comparison of Empirical Models for Estimating Global Solar Irradiation in the Soudano-Sahelian Zone of Cameroon: The Case of the City of Maroua and Garoua

The main objective of this study is to compare thirty-five (35) solar radiation models available in the open literature in order to predict monthly solar radiation in two main cities of Cameroon. This estimation and comparison are based on selected statistical comparison parameters named, root mean square error (RMSE), mean bias error (MBE), mean percentage error (MPE) and determination coefficient (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;). These different models are implemented using regression analysis tools named Exel and MATLAB. Estimated values were compared with measured values according to normalized values of statistical parameters, using measured meteorological data of more than 19 years, from 1984 to 2015. All the models have been classified with their associated ranking according to their statistical parameter accuracy. From this study it appears that the models of Ertekin and Yaldiz (MOD20), Togrul and Onat (MOD28), are more accurate than other models. Indeed, for the city of Maroua (MBE%=-2.82E-14; RMSE%=0.862; MPE=-0.00845; R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.985), while for Garoua (MBE%=-9.21E-15; RMSE%=0.806; MPE=-0.00631; R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.959). according to their accuracy these models can be therefore be used to predict monthly solar radiation for soudano-sahelian regions of Cameroon. Correlation equations found in this paper will help solar energy researcher to estimate data with trust because of its fine agreement with the observed one. hence the models presented in this study could be used to evaluate accurately the solar radiation at any locations with similar climate.

ChickenSense: A Low-Cost Deep Learning-Based Solution for Poultry Feed Consumption Monitoring Using Sound Technology

This research proposes a low-cost system consisting of a hardware setup and a deep learning-based model to estimate broiler chickens’ feed intake, utilizing audio signals captured by piezoelectric sensors. The signals were recorded 24/7 for 19 consecutive days. A subset of the raw data was chosen, and events were labeled in two classes, feed-pecking and non-pecking (including singing, anomaly, and silence samples). Next, the labeled data were preprocessed through a noise removal algorithm and a band-pass filter. Then, the spectrogram and the signal envelope were extracted from each signal and fed as inputs to a VGG-16-based convolutional neural network (CNN) with two branches for 1D and 2D feature extraction followed by a binary classification head to classify feed-pecking and non-pecking events. The model achieved 92% accuracy in feed-pecking vs. non-pecking events classification with an f1-score of 91%. Finally, the entire raw dataset was processed utilizing the developed model, and the resulting feed intake estimation was compared with the ground truth data from scale measures. The estimated feed consumption showed an 8 ± 7% mean percent error on daily feed intake estimation with a 71% R2 score and 85% Pearson product moment correlation coefficient (PPMCC) on hourly intake estimation. The results demonstrate that the proposed system estimates broiler feed intake at each feeder and has the potential to be implemented in commercial farms.

Statistical Analysis of Wind Resource Assessment for Different Locations in South-Western Thailand

Weibull parameters have been widely used to evaluate wind energy potential. In this work presents wind resource assessment by statistical analysis with a Weibull distribution model for Krabi, Phuket, and Ranong weather stations in south-western Thailand. Ten-minute intervals include wind speed and wind direction of 10m from four-year records obtained by the Thai meteorological department. Four numerical methods, namely empirical method, graphical method, energy pattern factor method and maximum likelihood method are examined to estimate the Weibull parameters. The Weibull distribution obtained from each method is compared with the observed wind speed distribution by the performance tests using root mean square error, mean percentage error, and chi-square error to select a suitable method for the station area. The results revealed that the maximum likelihood method was the most accurate for Krabi and Phuket stations, and the energy pattern factor method was the most accurate for Ranong station. At a hub height of 80m, the highest mean wind speed and mean wind power density found in Krabi station were 3.25 m/s and 44.84 W/m2. The most probable wind speed value in three stations had a range from 1.80 to 2.50 m/s. The maximum wind speed carrying maximum energy found in Krabi station was 5.53 m/s. The operating probability of a wind turbine in Krabi station was 49.61%, followed by Phuket station was 46.80%, and Ranong station was 37.84%, respectively. In conclusion, all three stations had wind power potential classified as wind class 1 and can be sorted as follows: Krabi, Phuket, and Ranong stations.

Photosynthetically Active Radiation Temperature Dependent Models and Its Variability with Climatic Variables over Akure, Nigeria

Photosynthetically Active Radiation (PAR) is an essential input for applications pertaining to biomass production, plant physiology, and natural lighting in greenhouses. It is the amount of light energy necessary for photosynthesis to occur, and the wavelengths of this light are typically between 400 and 700 nm. In this study, the photosynthetically active radiation for Akure (latitude 7.28°N, longitude 5.30°E, and 375 m asl.), located in the coastal region of Nigeria, was estimated and investigated using data on the monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature from the National Aeronautics and Space Administration (NASA) archives over a thirty-eight-year period (1984–2021). In order to determine the accuracy of the models, eight (8) new temperature PAR-based models were developed for the location and statistical tests were conducted using the coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), t-test, and index of agreement (IA). The findings demonstrated that PAR for Akure is lowest in the month of August with 6.5320 MJm-2day-1 during the rainy season (April to October) and highest during the dry season (November to March), with 8.6670 MJm-2day-1 in March. The best model equation for calculating PAR for Akure was found to be the one that links the PAR and natural logarithm of temperature changes; its R2, MBE, RMSE, MPE, t-test, and IA values were 68.1%, 0.0032 MJm-2day-1, 0.5208 MJm-2day-1, -0.4479%, 0.0204, and 82.8013%, respectively.

Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties

Abstract. Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements, called VarPy-mix. This new approach extends an existing variational method developed for ice cloud retrieval using lidar, radar and passive radiometers. We assume that the lidar attenuated backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice particles. Consequently, in the mixed phase the supercooled droplets are retrieved with the lidar signal and the ice particles with the radar signal, meaning that the retrievals rely strongly on a priori and error values. This method retrieves simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles, and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot, total water content (TWC) and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud-phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) observations from the CALIPSO and CloudSat satellites, respectively, belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on 7 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign and collected under the track of CloudSat–CALIPSO are compared with the retrievals of the new algorithm to validate its performance. Visible extinctions, water contents, effective radii and number concentrations derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ measurements is 39 % for αliq, 398 % for αice, 49 % for LWC and 75 % for IWC. It is also important to note that temporal and spatial collocations are not perfect, with a maximum spatial shift of 1.68 km and a maximum temporal shift of about 10 min between the two platforms. In addition, the sensitivity of remote sensing and that of in situ measurements is not the same, and in situ measurement uncertainties are between 25 % and 60 %.

Impact Assessment of Systemic Geometric Distortion in 1.5T Magnetic Resonance Imaging Simulation through Three-dimensional Geometric Distortion Phantom on Dosimetric Accuracy for Magnetic Resonance Imaging-only Prostate Treatment Planning.

Magnetic resonance imaging (MRI)-only radiotherapy has emerged as a solution to address registration errors that can lead to missed dose delivery. However, the presence of systemic geometric distortion (SGD) stemming from gradient nonlinearity (GNL) and inhomogeneity of the main magnetic field (B0) necessitates consideration. This study aimed to quantitatively assess residual SGD in 1.5T MRI simulation using a three-dimensional (3D) geometric distortion phantom and evaluate its impact on dosimetric accuracy for retrospective prostate cancer patients. Ten retrospective cases of prostate cancer patients treated with volumetric modulated arc radiotherapy (VMAT) were randomly selected. A geometric distortion phantom was scanned on a 1.5T MRI simulation using a 3D T1 volumetric interpolated breath-hold examination sequence, varying bandwidth (BW), and two-phase-encoding directions. Distortion maps were generated and applied to the original computed tomography (oriCT) plan to create a distorted computed tomography plan (dCT), and a dice similarity coefficient (DSC) was observed. Dosimetric accuracy was evaluated by recalculating radiation dose for dCT plans using identical beam parameters as oriCT. The SGD increased with distance from the isocenter in all series. DSC exceeded 0.95 for all plans except the rectum. Regarding GNL's impact on dosimetric accuracy, most mean percentage errors for clinical target volume, planning target volume, and both femurs were under 2% in all plans, except for the bladder and rectum. SGD pre-evaluation is crucial and should be incorporated into a quality assurance program to ensure effective MRI-simulation performance before MRI-only treatment planning for prostate cancer.

The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks

AbstractThe paper investigates the issue of volatility of stock index returns on the Warsaw Stock Exchange (WIG20 index returns volatility). The purpose of this review is to compare how other stock market indexes as HANG SENG, NIKKEI 225, FTSE 250, DAX, S&amp;P 500 and NASDAQ 100 influance the volatility of WIG20 index returns. The innovation of this work is the usage of a new neural network with three different activation functions to predict future volatility of WIG20 index returns. The input for this network is the last 3 values of WIG20 index returns volatility and the last 3 values of one of the considered foreign index returns volatility. As measurements for the best forecasting performance of neural networks are taken common used forecast errors: ME (mean error), MPE (mean percentage error), MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root mean square error). The study shows that the Polish stock market is mainly influenced by the European and US markets.

Investigation of Seasonal and Annual Wind Speed Distribution of Tarnab Based on Weibull and Rayleigh Distribution Models

This study aims to statistically analyze wind speed data of Tarnab, Peshawar, for the period 2004-2023. The data was recorded at the Agriculture Research Institute, Tarnab, Peshawar. Two statistical models (two-parameter Weibull and Rayleigh distribution functions) were applied to find the distributions of wind speeds. For the estimation of shape and scale parameters of Weibull and Rayleigh, two methods were employed: the method of moments and the energy pattern factor. Three statistical tools (mean percentage error, mean absolute percentage error, and root mean square deviation) were applied to check the error percentage of both models. The results of the Weibull distribution were much closer to the observed data than those of the Rayleigh distribution. The average values of wind speeds tended to increase from winter to summer and vice versa. The highest recorded annual and seasonal wind speeds were 26.19 in/s and 41.57 in/s, respectively, while the lowest values were 7.11 in/s and 4.95 in/s, respectively. Thus, while ruling out the possibility of harnessing wind as a significant source of energy, the findings are still useful for the crops produced in the region.

SandSnap: Measuring and mapping beach grain size using crowd-sourced smartphone images

Sediment grain size is a critical parameter for sediment mobilization and transport, but often has the highest uncertainty of any coastal sediment transport model input parameter. SandSnap is an initiative to engage the public to amass a beach grain size database by taking photos of the beach sand with a coin in the image for scale and uploading the image to a web application. Images are analyzed with two deep learning convolutional neural networks one to detect the coin and the second to measure the grain size, which is trained on sediment samples within the sand regime. The results for nine gradation metrics are returned to the user within 2 min of image upload. Results from 263 test images have a mean percent error of −6.5% and median absolute error of 22.4% for the median grain size (d50) with a small fine bias of −0.042 mm. The use of the database is highlighted by applying SandSnap output as an input to the AeoLiS aeolian sediment transport model to predict coastal dune growth at a nearly national scale using the full eight grain size classes (d10 – d90) from the SandSnap database. These outputs are used to inform the potential value of having spatially comprehensive grain size distribution information as part of coastal engineering design and planning. Education and outreach techniques for the SandSnap initiative are described in the manuscript. Though some challenges remain, the spatially and temporally robust beach grain size database being developed by SandSnap will help to improve numerous coastal engineering analyses including coastal resilience and vulnerability quantification, beach nourishment life cycle and uncertainty analysis, beach compatibility for the beneficial use of dredged sediment, and large-scale coastal morphology modeling.

Unveiling reflectance spectral characteristics of floating plastics across varying coverages: insights and retrieval model.

Marine floating debris, particularly chemically stable plastics, poses a significant global environmental concern. These materials, due to their prevalence and durability, linger on the ocean surface for extended durations, inflicting considerable harm on marine ecosystems, life, and the food chain. The traditional methodology for investigating marine floating debris mainly uses field observations, which are time-consuming, laborious, and constrained in observational scope. Consequently, there is an urgent need for more effective methodologies, such as remote sensing, to monitor marine floating debris, which will be of great significance for enhancing the management of their pollution. In this study, we employ controlled experiments and theoretical model simulations to investigate the spectral characteristics of remote sensing reflectance (Rrs(λ)) of two common types of floating plastic debris, specifically polyvinyl chloride (PVC) buoys and polypropylene (PP) bottles. Our analysis reveals distinct Rrs(λ) spectral characteristics for each type of plastic debris, differing significantly from that of the background water. Furthermore, both PVC buoys and PP bottles exhibit a similar absorption valley in the short-wave infrared region, with its depth increasing alongside the plastic coverage. Based on these findings, we develop a novel floating plastic index (FPI) and a corresponding retrieval model for estimating the coverage of floating plastic debris. Validation with simulated data and measurements from control experiments shows good performance of the retrieval model with high inversion accuracy, demonstrated by the values of the coefficient of determination, mean percentage error, mean absolute percentage error, and root mean square error of 0.97, -0.3%, 17.5%, and 3.98%, respectively, for the experimentally measured dataset. Our research provides a theoretical and methodological foundation for remote sensing retrieval of the coverages of floating PVC and PP plastics, as well as offers valuable insights for the analysis of other floating debris types in future studies.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

Density dependent constitutive model for Bi-2212 powder compression deformation

Bi-2212 HTS materials are fabricated into multi-filamentary wires via powder-in-tube (PIT) method followed by proper heat treatment to obtain superconductivity, but how to predict the large compression deformation behaviors of the Bi-2212 powder is critical to design the processing of the Bi-2212 HTS wire. Drucker Prager/Cap (DPC) model was the most commonly used model for powders including Bi-2212 with soil-like mechanical behavior to consider its shear failure as well as hydrostatic compression. However, the parameters for DPC Cap evolve with densities change and the original model is inadequate to precisely describe the densification process of Bi-2212 powder with large strain. In this study, the modified DPC model with density dependent parameters was introduced for Bi-2212 powder compressions by measuring the failure strength and hydrostatic compressive behavior under different density states. The DPC yield surface was plotted with an evolution trend of non-linear outward expansion with density increased. FEM model of uniaxial compression based on the as-introduced model was built with subroutine VUSDFLD applied. The distribution of Mises stress and relative density were analyzed. The axial stress-density curve for FEM and experimental results were normalized and quantitively evaluated by Mean Square Error (MSE). The introduced model shows good convergence and could match the experimental results well with normalized MSE of 0.000207 and Root Mean Square Error (RMSE) of 0.0144, indicating the mean error percentage of 1.44%. The model introduced in this article provides supports toward large strain deformation simulation of Bi-2212 powder.

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/m 2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using Pmetrics. 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/m 2 (range: 40-70.3 kg/m 2 ). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to 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.

Investigations on application of curve fitting in power system inertia estimation

ABSTRACT As the share of renewable energy sources in the power system keeps growing, a new issue with frequency stability – diminishing and changing inertia – has emerged. Estimating power system inertia has received a lot of attention lately in order to market design of synthetic inertia as an additional service and implement timely corrective steps for robust frequency control. Curve fitting technique has emerged as one of the promising techniques for estimation of power system inertia, however, many challenging aspects need to be addressed for its practical applications. This study aims to systematically assess the effectiveness of the curve fitting method under a range of operational parameters and conditions, such as the curve fitting order, the length of data to be used for curve fitting after an event begins, and the length of the moving average filter applied to the frequency data to remove oscillatory and noise from the frequency transient. The simulation studies have been performed on DIgSILENT and MATLAB platforms. The results show that very high and very low order curve fitting results are high errors and the 5th order model with mean error in estimated value of inertia less than 1% has outperformed others. The analysis on various window width of moving average filter found that increasing the window width results in more error. For IEEE-9 bus system, a filter up to 850 ms width has a mean percentage error below 1%. The analysis also revealed that the duration of the data used for curve fitting is not significant. For variation of the duration of the data used for the curve fitting from 50 ms to 500 ms, the estimated inertia values are found to be in a close range of 2.4881 s to 2.5262 s, for the single machine system with H = 2.5 s. The analysis presented in this study will be useful for effective application of curve fitting method in inertia estimation.