A tutorial on reproducing a predefined autocovariance function through AR models: application to stationary homogeneous isotropic turbulence

Sequential methods for synthetic realisation of random processes have a number of advantages compared with spectral methods. In this article, the determination of optimal autoregressive (AR) models for reproducing a predefined target autocovariance function of a random process is addressed. To this end, a novel formulation of the problem is developed. This formulation is linear and generalises the well-known Yule-Walker (Y-W) equations and a recent approach based on restricted AR models (Krenk-Møller approach, K-M). Two main features characterise the introduced formulation: (i) flexibility in the choice for the autocovariance equations employed in the model determination, and (ii) flexibility in the definition of the AR model scheme. Both features were exploited by a genetic algorithm to obtain optimal AR models for the particular case of synthetic generation of homogeneous stationary isotropic turbulence time series. The obtained models improved those obtained with the Y-W and K-M approaches for the same model parsimony in terms of the global fitting of the target autocovariance function. Implications for the reproduced spectra are also discussed. The formulation for the multivariate case is also presented, highlighting the causes behind some computational bottlenecks.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators—derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity—from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in “flat” or “down” directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during “up” trends.

D-Pinitol Attenuated Ovalbumin-induced Allergic Rhinitis in Experimental Mice Via Balancing Th1/Th2 Response

Allergic rhinitis (AR) is a complex, chronic immunoinflammatory disorder of the membrane lining of the nasal mucosa. D-Pinitol is considered a cyclic polyol with a potential effect against various allergies. In the present study, we evaluated the anti-allergic effect of pinitol on ovalbumin (OVA)-induced AR model in mice. BALB/c mice were initially sensitized with an intraperitoneal injection of OVA and divided into 5 groups (n=18, in each group) for a treating schedule of distilled water (DW), montelukast (10 mg/kg), and pinitol (5, 10, and 20 mg/kg) through the mouth. Two saline-injected groups were considered as controls by orally administrating DW and pinitol 20. Thereafter, test and control groups were intranasally challenged by OVA and saline, respectively. Our results showed that the OVA challenge caused a marked elevation in AR symptoms like nasal rubbing, sneezing, and discharge which were remarkably diminished using pinitol (10 and 20 mg/kg) and the results were comparable with montelukast. Additionally, increased levels of total and OVA-specific serum Immunoglobulin (Ig) E and IgG1 were significantly attenuated by pinitol as compared to the control group but not the montelukast group. In AR-induced mice, pinitol had significant modulatory effects on representative markers of Th2 (GATA binding protein 3), signal transducer and activator of transcription-6, Interleukins (IL)-4, IL-5, IL-13, suppressors of cytokine signaling 1, Toll-like receptor 4, and myeloid differentiation factor 88), and Type 1 T helper (Th1) immune responses (T-box protein expressed in T cells and Interferon-gamma) as well as the histopathological aberrations induced in the nasal mucosa. In conclusion, Pinitol had potential effects on OVA-induced AR mice through amelioration of nasal symptoms and balancing the Th1/Th2 immune responses during the allergic rhinitis condition.

A New Method for On-line Measurement of the Straightness Error of Machine Tools Using an Acceleration Sensor

In order to improve the poor efficiency in the measurement of the geometric error of machine tools’ linear axes, this paper has presented a method to measure and restructure the geometric error of linear axes that is based on accelerometers. This method takes advantage of the phenomenon that when acceleration is measured under different measuring speeds, different frequencies and amplitudes are produced. The measurement data of the high signal-to-noise ratio for various velocities was fused together and the straightness error of the measured axis was obtained by integrating the acceleration twice. In order to remove the trend terms error in the integration, a zero phase IIR Butterworth filter was designed, which guarantees the signal’s phase invariance after filtering. The data was continued with the AR model to eliminate the endpoints’ effect in the filtering. The proposed method was verified by numerical values and experiments. The results showed that the proposed method has better robustness, a wider bandwidth and a higher efficiency than the methods of measuring by laser interferometer. It is also able to measure the geometric error of linear axes with an accuracy that reaches the micron scale.

Correction Model of BDS-3 Satellite Pseudorange Multipath Delays and Its Impact on Single-Frequency Precise Point Positioning

Precise point positioning (PPP) is used in many fields. However, pseudorange multipath delay is an important error that restricts its accuracy. Pseudorange multipath delay can be considered as the combination of effective information and observation noise; it can be modeled after removing the observation noise. In this work, elastic nets (EN) regularization denoising method is proposed and compared with L2-norm regularization denoising method. Then, quadratic polynomial (QP) model plus autoregressive (AR) model (QP + AR) are used to model the denoised pseudorange multipath delays. Finally, the modeling results are corrected to the observations to verify the improvement of BDS-3 single-frequency PPP accuracy. Three single-frequency PPP schemes are designed to verify the effectiveness of denoising method and QP + AR model. The experimental results show that the accuracy improvement of B3I and B2a is more obvious than that of B1I and B1C when the modeling values are corrected to the pseudorange observations. The improvement of B3I and B2a in the east (E) and up (U) directions can reach 10.6%∼34.4% and 5.9%∼65.7%, and the improvement of the north (N) direction is mostly less than 10.0%. The accuracy of B1I and B1C in E and U directions can be improved by 0%∼30.7% and 0.4%∼28.6%, respectively, while the accuracy of N direction can be improved slightly or even decreased. Using EN regularization denoising and QP + AR model correction, single-frequency PPP performs better at B3I and B2a, while L2-norm regularization denoising and QP + AR model correction perform better at B1I and B1C. The accuracy improvement of B2a and B3I is more obvious than that of B1I and B1C. The convergence time after MP correction of each frequency is slightly shorter. Overall, the proposed pseudorange multipath delays processing strategy is beneficial in improving the single-frequency PPP of BDS-3 satellite.

Tanshinone IIA improves degranulation of mast cells and allergic rhinitis induced by ovalbumin by inhibiting the PLCγ1/PKC/IP3R pathway

Allergic rhinitis (AR) is a common allergic inflammatory and chronic reactive disease caused by allergen-induced immunoglobulin E (IgE). Tanshinone IIA (Tan IIA) is one of the active ingredients in Salvia miltiorrhiza Bunge (Danshen) and plays a vital role in inhibiting inflammation. Thus, we hypothesized that Tan IIA has anti-allergic effects and studied the function of Tan IIA in mast cells and an AR animal model. We induced RBL-2H3 cell sensitization with monoclonal anti-2,4,6-dinitrophenyl-immunoglobulin (Ig) E/human serum albumin (DNP-IgE/HSA) and constructed an ovalbumin (OVA)-induced AR model in mice. The role of Tan IIA in AR progression was studied using the MTT assay, ELISA, western blot, toluidine blue staining, HE staining, and Alcian blue and safranin O (A&S) staining. Tan IIA treatment significantly increased IgE/HSA-induced cell viability. However, Tan IIA treatment markedly downregulated the expression levels of β-hexosaminidase, histamine, tumor necrosis factor (TNF-α), interleukin 1β (IL-1β), IL-4, and IL-5 in IgE/HSA-induced cells. Furthermore, Tan IIA improved typical symptoms in the OVA-induced AR model mice by inhibiting the phospholipase Cγ1 (PLCγ1)/protein kinase C (PKC)/IP3R pathway. Additionally, Tan IIA effectively improved the degranulation of RBL-2H3 cells and OVA-induced AR in mice. Together, these results suggest that Tan IIA may be a potential drug for the treatment of AR in the future.

Research on AR-AKF Model Denoising of the EMG Signal

Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages.

Updated Moving Forecasting Model of Air Maximum Temperature

In the current study, a moving forecasting model is used for the purpose of forecasting maximum air temperature. A number of recordings are used for building the AR model and next, to forecasting some temperature values ahead. Then the AR model coefficients are updating due to shifting the training sample by adding new temperature values in order to involve the change in temperature time series behaviour. The current work shows a high performance all over the temperature time series, which considered in the analysis.

Prediction and Forecasting of Maximum Weather Temperature Using a Linear Autoregressive Model

This paper investigates the autoregressive (AR) model performance in prediction and forecasting the monthly maximum temperature. The temperature recordings are collected over 12 years (i.e., 144 monthly readings). All the data are stationaries, which is converted to be stationary, via obtaining the normal logarithm values. The recordings are then divided into 70% training and 30% testing sample. The training sample is used for determining the structure of the AR model while the testing sample is used for validating the obtained model in forecasting performance. A wide range of model order is selected and the most suitable order is selected in terms of the highest modelling accuracy. The study shows that the monthly maximum temperature can accurately be predicted and forecasted using the AR model.