Research Progress on Functional Components and Pharmacological Effects of Red Jujube ( Ziziphus jujuba Mill.)

Red jujube(Ziziphus jujuba Mill.),as a traditional Chinese medicine and edible plant,has a long history and extensive uses.Red jujubes are not only widely used in the food industry and traditional Chinese medicine preparations,but also have attracted widespread research interest due to their rich functional components and various pharmacological effects.This paper reviewed the main functional components of jujubes,including polysaccharides,triterpenoid saponins,organic acids and alkaloids,and discussed in detail the pharmacological effects of these components such as blood sugar-regulating,antioxidant,anti-inflammatory and anti-tumor effects.Furthermore,the paper also summarized the safety issues such as drug interactions and toxic side effects of jujubes,and prospected the future development direction of jujube research.

Research Progress of Tea Functional Components in the Prevention and Treatment of Lung Cancer

The incidence and mortality of lung cancer rank first among all malignant tumors,so the prevention and treatment of lung cancer has become a worldwide concern.Tea is one of the most popular healthy drinks in the world.A large number of studies have shown that tea polyphenols,theophylline,tea pigments,theanine,tea polysaccharides and other functional components in tea can promote apoptosis of cancer cells,inhibit proliferation and division of cancer cells,and regulate the expression of tumor-related gene protein through strong antioxidant activity,inhibiting the activity of key enzymes and blocking signal transduction to prevent and treat the occurrence,development and metastasis of lung cancer.In this study,the application of tea in the prevention and treatment of lung cancer was elaborated through the traceability,basic components,basic theory of traditional Chinese medicine,anti-lung cancer mechanism,expert opinions and so on,in order to provide direction for the prevention and treatment of tea and lung cancer.

DIRECT AND ADAPTIVE SLICING ON CAD MODEL OF IDEAL FUNCTIONAL MATERIAL COMPONENTS (IFMC)

A brand new direct and adaptive slicing approach is proposed, which canapparently improve the part accuracy and reduce the building time. At least two stages are includedin this operation: getting the crossing contour of the cutting plane with the solid part anddetermining the layer thickness. Apart from usual SPI algorithm, slicing of the solid model has itsspecial requirements. Enabling the contour line segments of the cross-section as long as possible isone of them, which is for improving manufacturing efficiency and is reached by adaptively adjustingthe step direction and the step size at every crossing point to obtain optimized secant height. Thelayer thickness determination can be divided into two phases: the geometry-based thicknessestimation and the material-based thickness verifying. During the former phase, the geometrytolerance is divided into two parts: a variety of curves are approximated by a circular arc, whichintroduces the first part, and the deviation error between the contour line in LM process and thecircular arc generates the second part. The latter phase is mainly verifying the layer thicknessestimated in the former stage and determining a new one if necessary. In addition, an example usingthis slicing algorithm is also illustrated.

CAE-integrated Fabrication of Functionally Graded Components

A typical contemporary computerized product develop me nt workflow is outlined in Fig.1. Product geometry information is first prep ared with computer-aided design (CAD) software. The CAD format can then be com municated to other downstream-computerized applications like, computer-aided e ngineering analysis (CAE), computer-aided manufacturing (CAM) and/or rapid prot otyping. Since design may need to be modified to incorporate new requirements, a loop back path is also depicted in Fig.1. The design engineers will check ac cording to their experience, result of physical test and CAE simulation to decid e whether redesign is needed or not. If the design passes all tests, its pr ototype or product can be produced. Otherwise, the current practice is to chang e its geometry and/or select a more appropriate material. The iteration repeat s until the latest version satisfies the engineering specification and customer requirements. Note that the material is homogeneous in the part to be designed. With the advent of functionally graded material (FGM) research, a new workflow will become possible. Components incorporating FGM’s can be designed to achieve levels of performance superior to that of homogeneous materials by combining the desirable properties of each constituent phase. Theoretically, the material composition can be tailo red within a component to achieve local control of properties; for example, form ability, corrosion resistance, hardness, toughness, and so on. By such local co ntrol, monolithic components can be created that integrate the function of multi ple discrete components, saving part count, space, weight, and enabling concepts that would otherwise be impractical. Controlling the spatial distribution of p roperties via composition will allow for control of the state of the entire comp onent (the state of residual stress in a component). There are various methods p roposed to produce FGM components. In particular, solid freeform fabrication ( SFF) methods are commonly used to directly fabricate an FGM part in an additive fashion directly from a computer controlled, layer-by-layer, additive process in which a standard CAD is sliced into a series of horizontal planes. Common SF F techniques being investigated include three-dimensional printing (3DP), Lamin ate Object Manufacturing (LOM), Extrusion Freeform Fabrication (EFF), Selective Laser Sintering (SLS) and even Stereolithography (SL). Fig.1 Current CAE design workflow Fig.2 Proposed CAE design workflow for FGM Albeit the feasibility to fabricate FGM components, one gap still needs to be fi lled for real life FGM product design; namely, where and how to grade the compon ent. This paper will, thus, address issues on incorporating FGM for design impr ovement. Rather than changing the geometry or reselecting a new material, a FGM approach can be employed in design enhancement as shown in Fig.2. The same geo metry and material is retained except that functional property in needed regions is selectively reinforced. As in conventional workflow, CAE simulation is perf ormed after CAD modelling. CAE simulation is preferred since physical test is v ery expensive and most of them are destructive. Moreover, the experience of the engineers may not be accurate. More importantly, the result of CAE simulation is used in this research to produce a stress intensity map for selective reinfor cement. The map will be converted to tool path control signals for generating FG component via SFF machine. On the implementation side, SolidWorks is used fo r CAD modeling, COSMOS/Works is used for CAE simulation. The model is then selec tively reinforced according to the simulation result to produce a FGM enriched p ath plan to drive the Z-corp machine. Case studies are performed to verify the approach. The preliminary result is positive. Future extension to material oth er than starch and plaster powders and enhancement other than stress distributio n may be explored. In conclusion, a CAE-based methodology for FGM product des ign

Unsupervised Functional Data Clustering Based on Adaptive Weights

In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.

Comparative Study on Functional Properties of Mulberry Black Tea Fermented by Phellinus igniarius and Eurotium cristatum

[Objectives]To compare functional properties of mulberry black tea fermented by Phellinus igniarius and Eurotium cristatum.[Methods]The functional components of mulberry black tea fermented by P.igniarius and E.cristatum,the deep processing and health care effects of two probiotic fungi were explored.[Results]The polysaccharide content in the tea fermented by P.igniarius was the highest at 0.410 mg/mL,and the hydroxyl radical scavenging rate was the strongest,reaching 60.31%;the content of flavonoids in the tea fermented by E.cristatum was the highest,reaching 60.65μg/mL,and the content of polysaccharides was 83.17%higher than that of mulberry black tea,reaching 0.279 mg/mL.[Conclusions]The mulberry black tea fermented by P.igniarius and E.cristatum has improved in different functional components,and has its own advantages in aroma and mouthfeel,so it has certain research value and market prospect.

Study on Influence of Functional Composition Distribution on Combustion Performance of a Modified Single Base Propellant

The modified single base propellant samples were prepared by impregnating blasting oil into single base grains and deactivating deterrent in water medium. The concentration distribution of functional compositions in this propellant was determined by using FTIR micro-spectroscopy. Its combustion performance was investigated by means of closed-bomb and interior ballistic tests. The results show that the concentration of NG distributes parabolically along the radius and the concentration of NA decreases from the surface to the centre exponentially. The deeper the NG impregnates, the slower the NA concentration decreases, the stronger the progressive combustion is and the better the interior ballistic performance is. When the depth corresponding to maximum NG concentration is about 1/2 of the web and the NA decreases slowly, the progressive combustion is the strongest and the interior ballistic performance is the best.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

Functional Fibers and Functional Fiber‑Based Components for High‑Power Lasers

The success of high-power fiber lasers is fueled by maturation of active and passive fibers,combined with the availability of high-power fiber-based components.In this contribution,we first overview the enormous potential of rare-earth doped fibers in spectral coverage and recent developments of key fiber-based components employed in high-power laser systems.Subsequently,the emerging functional active and passive fibers in recent years,which exhibit tremendous advantages in balancing or mitigating parasitic nonlinearities hindering high-power transmission,are outlined from the perspectives of geo-metric and material engineering.Finally,novel functional applications of conventional fiber-based components for nonlinear suppression or spatial mode selection,and correspondingly,the high-power progress of function fiber-based components in power handling are introduced,which suggest more flexible controllability on high-power laser operations.

Clustering for Bivariate Functional Data

In this paper,we consider the clustering of bivariate functional data where each random surface consists of a set of curves recorded repeatedly for each subject.The k-centres surface clustering method based on marginal functional principal component analysis is proposed for the bivariate functional data,and a novel clustering criterion is presented where both the random surface and its partial derivative function in two directions are considered.In addition,we also consider two other clustering methods,k-centres surface clustering methods based on product functional principal component analysis or double functional principal component analysis.Simulation results indicate that the proposed methods have a nice performance in terms of both the correct classification rate and the adjusted rand index.The approaches are further illustrated through empirical analysis of human mortality data.

Healthy function and high valued utilization of edible fungi

Edible fungi are large fungi with high added value that can be utilized as resources.They are rich in high-quality protein,carbohydrate,various vitamins,mineral elements and other nutrients,and are characterized by high protein,low sugar,low fat and low cholesterol.In addition,edible fungi contain a variety of bioactive substances,such as polysaccharides,dietary fiber,steroids,polyphenols,and most of these compounds have antioxidant,anti-tumor and other physiological functions.This review comprehensively discusses the bioactive components and functional characteristics of edible fungi(such as antioxidant,anti-aging,hypolipidemic activities,etc.).Then the recent developments and prospect in the high-valued utilization of edible fungi are discussed and summarized.The objective of this review is to improve the understanding of health-promoting properties of edible fungi,and provide reference for the industrial production of edible fungi-based health products.

Convergence rate of principal component analysis with local-linear smoother for functional data under a unified weighing scheme

The unified weighing scheme for the local-linear smoother in analysing functional data can deal with data that are dense,sparse or of neither type.In this paper,we focus on the convergence rate of functional principal component analysis using this method.Almost sure asymptotic consistency and rates of convergence for the estimators of eigenvalues and eigenfunctions have been established.We also provide the convergence rate of the variance estimation of the measurement error.Based on the results,the number of observations within each curve can be of any rate relative to the sample size,which is consistent with the earlier conclusions about the asymptotic properties of the mean and covariance estimators.

Bootstrap inference of the skew-normal two-way classification random effects model with interaction

In this paper,we consider the statistical inference problems for the fixed effect and variance component functions in the two-way classification random effects model with skewnormal errors.Firstly,the exact test statistic for the fixed effect is constructed.Secondly,using the Bootstrap approach and generalized approach,the one-sided hypothesis testing and interval estimation problems for the single variance component,the sum and ratio of variance components are discussed respectively.Further,the Monte Carlo simulation results indicate that the exact test statistic performs well in the one-sided hypothesis testing problem for the fixed effect.And the Bootstrap approach is better than the generalized approach in the one-sided hypothesis testing problems for variance component functions in most cases.Finally,the above approaches are applied to the real data examples of the consumer price index and value-added index of three industries to verify their rationality and effectiveness.

Concepts of Safety Critical Systems Unification Approach &Security Assurance Process

The security assurance of computer-based systems that rely on safety and security assurance, such as consistency, durability, efficiency and accessibility, require or need resources. This targets the System-of-Systems (SoS) problems with the exception of difficulties and concerns that apply similarly to subsystem interactions on a single system and system-as-component interactions on a large information system. This research addresses security and information assurance for safety-critical systems, where security and safety are addressed before going to actual implementation/development phase for component-based systems. For this purpose, require a conceptual idea or strategy that deals with the application logic security assurance issues. This may explore the vulnerability in single component or a reuse of specification in existing logic in component-based system. Keeping in view this situation, we have defined seven concepts of security assurance and security assurance design strategy for safety-critical systems.

Testing for Error Correlation in Semi-Functional Linear Models

Existing methods for analyzing semi-functional linear models usually assumed that random errors are not serially correlated or serially correlated with the known order.However,in some applications,these assumptions on random errors may be unreasonable or questionable.To this end,this paper aims at testing error correlation in a semi-functional linear model(SFLM).Based on the empirical likelihood approach,the authors construct an empirical likelihood ratio statistic to test the serial correlation of random errors and identify the order of autocorrelation if the serial correlation holds.The proposed test statistic does not need to estimate the variance as it is data adaptive and possesses the nonparametric version of Wilks'theorem.Simulation studies are conducted to investigate the performance of the proposed test procedure.Two real examples are illustrated by the proposed test method.

Multiple functional linear model for association analysis of RNA-seq with imaging

Emerging integrative analysis of genomic and anatomical imaging data which has not been well developed, provides invaluable information for the holistic discovery of the genomic structure of disease and has the potential to open a new avenue for discovering novel disease susceptibility genes which cannot be identified if they are analyzed separately. A key issue to the success of imaging and genomic data analysis is how to reduce their dimensions. Most previous methods for imaging information extraction and RNA-seq data reduction do not explore imaging spatial information and often ignore gene expression variation at the genomic positional level. To overcome these limitations, we extend functional principle component analysis from one dimension to two dimensions （2DFPCA） for representing imaging data and develop a multiple functional linear model （MFLM） in which functional principal scores of images are taken as multiple quantitative traits and RNA-seq profile across a gene is taken as a function predictor for assessing the association of gene expression with images. The developed method has been applied to image and RNA- seq data of ovarian cancer and kidney renal clear cell carcinoma （KIRC） studies. We identified 24 and 84 genes whose expressions were associated with imaging variations in ovarian cancer and KIRC studies, respectively. Our results showed that many significantly associated genes with images were not differentially expressed, but revealed their morphological and metabolic functions. The results also demonstrated that the peaks of the estimated regression coefficient function in the MFLM often allowed the discovery of splicing sites and multiple isoforms of gene expressions.

Functional Multiple-Outcome Model in Application to Multivariate Growth Curves of Infant Data

Motivated by a medical study that attempts to analyze the relationship between growth curves and other variables and to measure the association among multiple growth curves,the authors develop a functional multiple-outcome model to decompose the total variation of multiple functional outcomes into variation explained by independent variables with time-varying coefficient functions,by latent factors and by noise.The latent factors are the hidden common factors that influence the multiple outcomes and are found through the combined functional principal component analysis approach.Through the coefficients of the latent factors one may further explore the association of the multiple outcomes.This method is applied to the multivariate growth data of infants in a real medical study in Shanghai and produces interpretable results.Convergence rates for the proposed estimates of the varying coefficient and covariance functions of the model are derived under mild conditions.

Statistical inference in the partial functional linear expectile regression model

As extensions of means, expectiles embrace all the distribution information of a random variable.The expectile regression is computationally friendlier because the asymmetric least square loss function is differentiable everywhere. This regression also enables effective estimation of the expectiles of a response variable when potential explanatory variables are given. In this study, we propose the partial functional linear expectile regression model. The slope function and constant coefficients are estimated by using the functional principal component basis. The convergence rate of the slope function and the asymptotic normality of the parameter vector are established. To inspect the effect of the parametric component on the response variable, we develop Wald-type and expectile rank score tests and establish their asymptotic properties. The finite performance of the proposed estimators and test statistics are evaluated through simulation study. Results indicate that the proposed estimators are comparable to competing estimation methods and the newly proposed expectile rank score test is useful. The methodologies are illustrated by using two real data examples.

Estimation of partial derivative functionals with application to human mortality data analysis

To better describe and understand the time dynamics in functional data analysis,it is often desirable to recover the partial derivatives of the random surface.A novel approach is proposed based on marginal functional principal component analysis to derive the representation for partial derivatives.To obtain the Karhunen-Lo`eve expansion of the partial derivatives,an adaptive estimation is explored.Asymptotic results of the proposed estimates are established.Simulation studies show that the proposed methods perform well in finite samples.Application to the human mortality data reveals informative time dynamics in mortality rates.

Rank-Based Test for Partial Functional Linear Regression Models

This paper investigates the hypothesis test of the parametric component in partial functional linear regression models.Based on a rank score function,the authors develop a rank test using functional principal component analysis,and establish the asymptotic properties of the resulting test under null and local alternative hypotheses.A simulation study shows that the proposed test procedure has good size and power with finite sample sizes.The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.