Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach

This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast performance of a covariance estimator can be assessed according to its improvement in return density forecasting. Empirical applications to equity data show that several RCOV estimators consistently perform better than others and emphasize the importance of RCOV selection in covariance modeling and forecasting.

A Novel PM2.5 Concentrations Probability Density Prediction Model Combines The Least Absolute Shrinkage And Selection Operator With Quantile Regression

PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. PM2.5 concentration is influenced by a combination of factors from both meteorological conditions and air quality. It is essential to identify the significant factors influencing PM2.5 concentrations in the prediction process. To address this issue, this paper proposes the quantile regression (QR) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations obtained using the QR model are imported into the KDE model to obtain the probability density curves of PM2.5 concentrations. In this paper, empirical analysis is performed with the data sets of Beijing, China, and Jinan, China, and the accuracy of the model is evaluated using the mean absolute percentage error(MAPE) and the relative mean square error (RMSE). The simulation results reveal that the LASSSO-QR-KDE model has a higher accuracy than the traditional prediction models and the currently used research models. The model provides a novel and excellent tool for policy makers to predict PM2.5 concentrations.

Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study

Objectives: This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA). Methods: We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms. Results: The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kV–kV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom. Conclusion: Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations. Advances in knowledge: The present study is based on a full simulation environment, which accommodates various situations such as SECT, kV–kV DECT, and even kV–MV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms.

Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning

Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = −0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.

MATHEMATICAL MODEL OF PHYSICAL PROPERTIES CHANGE OF COCONUT SAP IN THE VACUUM EVAPORATOR

The potential of brown sugar as a substitute for granulated sugar is enormous considering the abundant coconut sap production. However, the quantity of brown sugar production through the traditional method is one of the main obstacles. This study used a vacuum evaporator that emphasizes the hygienic and effective mass production of brown sugar. For this reason, it is necessary to approach changes in the physical properties of sap juice during the cooking process. This knowledge is indispensable in the cooking process, which involves the proper evaporation and crystallization of brown sugar. This research is devoted to determining the viscosity, density, and dissolved solids expressed in Brix and proposes a mathematical model to predict the physical properties during the evaporation process of brown sugar as a function of the initial concentration the solution before proceeding to the crystallization process. Results confirm that the prediction model for Brix is Cθ=(Co–Ce)·exp(0.0067·t)+Ce, the model for viscosity µθ=µo·exp(0.011·t), and ρө=(0.44996·t)+ρ0 for the density prediction model. The resulted mathematical model can accurately predict the rate of change in coconut sap's physical properties, indicated by the high coefficient of determination (R2). Keywords : brix, brown sugar, density, vacuum evaporator, viscosity