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This article investigates a variable selection method in semi-functional partially linear regression (SFPLR) models for strong α-mixing functional time series data. We construct penalized least squares estimators for unknown parameters and unknown link functions in our models. Under some regularity assumptions, we establish the asymptotic convergence rate and asymptotic distribution for the proposed estimators. Furthermore, we make a comparison of our variable selection method with the oracle method without variable selection in simulation studies and an electricity consumption data analysis. Simulation experiments and real data analysis results indicate that the variable selection method performs well at extracting the primary information and reducing dimensionality.

Alkaline extraction is an important process in the integrated biorefining of leafy biomass to obtain protein, but the resulting alkaline protein extract (APE11 alkaline protein extract: APE.) may have poor emulsification properties for food applications. In this study, the components in the APE fractionations obtained by size exclusion chromatography were determined. The emulsification properties of APE were determined using oil/water with a ratio of 7:3. Whey protein and soybean protein isolate were used as controls while enzymes were used to improve APE's emulsification properties. The results showed that the APE could be divided into three fractions with protein content of 83, 56, and 34%. Carbohydrates mainly derived from homogalacturonan pectin were mostly in Fraction 2, while Fraction 3 consisted of peptides, oligosaccharides, and free polyphenols. The APE had similar emulsification capacity and emulsification stability as those of whey protein and soybean isolate. The emulsion made by the APE had a creaming index of 92% with emulsification activity index value of 44 m2 g−1, and these numbers could retain after storing at 25 °C for 15 days. The emulsification properties of the APE can be further improved by carbohydrate degradation. With the use of Viscozyme® L, the emulsification activity index value of treated APE was increased by 60%, and then still retained at 67 m2 g−1 after storing for 15 days. Treated by either pepsin or alkaline protease, the emulsification properties of APE were decreased, suggesting the key role of protein in APE for emulsification.

BackgroundNeuropathic pain is a debilitating disease caused by damage or diseases of the somatosensory nervous system. Previous research has indicated potential associations between neuropathic pain and aging. However, the mechanisms by which they are interconnected remain unclear. In this study, we aim to identify the common differentially expressed genes (co-DEGs) between neuropathic pain and aging through integrated bioinformatics methods and further explore the underlying molecular mechanisms.MethodsThe microarray datasets GSE24982, GSE63442, and GSE63651 were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and co-DEGs were first identified. Functional enrichment analyses, protein-protein Interaction (PPI) network, module construction and hub genes identification were performed. Immune infiltration analysis was conducted. Targeted transcription factors (TFs), microRNAs (miRNAs) and potential effective drug compounds for hub genes were also predicted.ResultsA total of 563 and 1,250 DEGs of neuropathic pain and aging were screened, respectively. 16 genes were further identified as co-DEGs. The functional analysis emphasizes the vital roles of the humoral immune response and complement and coagulation cascades in these two diseases. Cxcl14, Fblim1, RT1-Da, Serping1, Cfd, and Fcgr2b were identified as hub genes. Activated B cell, mast cell, activated dendritic cell, CD56 bright natural killer cell, effector memory CD8 + T cell, and type 2 T helper cell were significantly up-regulated in the pain and aging condition. Importantly, hub genes were found to correlate with the activated B cell, activated dendritic cell, Gamma delta T cell, central memory CD4 + T cell and mast cell in pain and aging diseases. Finally, Spic, miR-883-5p, and miR-363-5p et al. were predicted as the potential vital regulators for hub genes. Aldesleukin, Valziflocept, MGD-010, Cinryze, and Rhucin were the potential effective drugs in neuropathic pain and aging.ConclusionThis study identified co-DEGs, revealed molecular mechanisms, demonstrated the immune microenvironment, and predicted the possible TFs, miRNAs regulation networks and new drug targets for neuropathic pain and aging, providing novel insights into further research.

The X¯ control chart is usually used in statistical quality control (SQC) to observe the change in procedure mean. In this paper, X¯ control chart for Inverse Rayleigh Distribution (IRD) is designed under repetitive group sampling at lower record values. The width of the X¯ control limits and power is established by using the mean and standard deviation of the Inverse Rayleigh Distribution based upon lower record values. The designed control chart’s Average Run Length (ARL) is determined for different values of the shift, sample size, lower record, and specified ARL. The already existed X¯ control chart is used for the comparison and to propose a new X¯ control chart for Inverse Rayleigh Distribution.

Distribution-free control charts can be useful in statistical process control (SPC) when only limited or no information about the distribution of the data of the process is available. In this paper, a linear prediction related double exponentially weighted moving average (DEWMA) sign control chart using a repetitive sampling scheme (RSNPDEBLP) has been considered for a binomially distributed process variable to improve the efficiency of detecting small drifts in its place of small changes. The proposed RSNPDEBLP control chart is assessed in average run length (ARL) for the various values of sample sizes. The efficiency of the proposed RSNPDEBLP control chart is compared with the existing EWMA and DEWMA sign control charts using single sampling and repetitive sampling schemes in terms of ARLs. When there are small changes in the process after the stabilization period, the proposed control chart is used to control small trends rather than small shifts.

Fault monitoring is often employed for the secure functioning of industrial systems. To assess performance and enhance product quality, statistical process control (SPC) charts such as Shewhart, CUSUM, and EWMA statistics have historically been utilized. When implemented to multivariate procedures, unfortunately, such univariate control charts demonstrate low fault sensing ability. Due to some limitations of univariate charts, numerous process monitoring techniques dependent on multivariate statistical approaches such as principal component analysis (PCA) and partial least squares (PLS) have been designed. Yet, in some challenging scenarios in industrial chemical and biological processes with notably nonlinear properties, PCA works poorly, according to its presumption that the dataset generally be linear. However, Kernel Principal Component Analysis (KPCA) is a reliable and precise nonlinear process control methodology, but the interaction mainly through upper control limits (UCLs) dependent on the Gaussian distribution may weaken its output. This article introduces time-varying statistical error tracking through Kernel Principal Component Analysis (KPCA) based on Generalized Likelihood Ratio statistics (GLR) using a sequential sampling scheme named KPCA-SSGLR for nonlinear fault detection. The main issue of employing just T2 and Q statistic in KPCA is that they cannot correctly give practitioners the change point of the system fault, preventing practitioners from diagnosing the issue. Based on this perspective, this study attempts to incorporate KPCA with sequential sampling Generalized Likelihood Ratio (SSGLR) for monitoring the nonlinear fault in multivariate systems. The KPCA is utilized for dimension reduction, while the SSGLR is employed as a tracking statistic. The kernel density estimation (KDE) was employed to approximate UCLs for variational system operation relying on KPCA. The testing efficiency of the corresponding KPCA-KDE-SSGLR technique was then analyzed and competed with KPCA and kernel locality preserving projection (KLPP), the UCLs of which were focused on the Gaussian distribution. The purpose of this analysis is to enhance the development of KPCA-KDE-SSGLR to accomplish future enhancements and to advance the practical use of the established model by implementing the sequential sampling GLR approach. The fault monitoring efficiency is demonstrated through different simulation scenarios, one utilizing synthetic data, the other from the Tennessee Eastman technique, and lastly through a hot strip mill. The findings indicate the applicability of the KPCA-KDE-based SSGLR system over the KLPP and KPCA-KDE methods by its two T2 and Q charts to recognize the faults.

In this article, a partially nonlinear model with random effects is proposed and its new estimation procession is provided. In order to estimate the link function, we propose generalised least square estimate and B-splines estimate methods. Further, we also use the Gauss–Newton method to construct the estimates of unknown parameters. Finally, we also consider the estimation for the variance components. The consistency and the asymptotic normality of the estimator will be proved. Simulated and real examples are given to illustrate our proposed methodology, which shows that our methods give effective estimation.

The control charts’ design is focused on system forecasting which is important in mathematics and statistics; these techniques are commonly employed in manufacturing industries. The need for a control chart that can conceptualize and identify the symmetric or asymmetric structure of the monitoring phase with more than one aspect of the standard attribute is a necessity of industries. The generalized likelihood ratio (GLR) chart is a well-known method to track both the decrease and increase in the mechanism effectively. A control chart, termed as a GLR control chart, is established in this article, focusing on a sequential sampling scheme (the SS GLR chart) to evaluate the geometrically distributed process parameter. The SS GLR chart statistic is examined on a window of past samples. In contexts of the steady-state average time to signal, the output of the SS GLR control chart is analyzed and compared with the non-sequential geometric GLR chart and the cumulative sum (CUSUM) charts. In this article, the optimum parameter options are presented, and regression equations are established to calculate the SS GLR chart limits.