Responses of root growth of Alhagi sparsifolia Shap. (Fabaceae) to different simulated groundwater depths in the southern fringe of the Taklimakan Desert, China

Alhagi sparsifolia Shap. （Fabaceae） is a spiny, perennial herb. The species grows in the salinized, arid regions in North China. This study investigated the response characteristics of the root growth and the dis- tribution of one-year-old A. sparsifolia seedlings to different groundwater depths in controlled plots. The eco- logical adaptability of the root systems of A. sparsifolia seedlings was examined using the artificial digging method. Results showed that： （1） A. sparsifolia seedlings adapted to an increase in groundwater depth mainly through increasing the penetration depth and growth rate of vertical roots. The vertical roots grew rapidly when soil moisture content reached 3%-9%, but slowly when soil moisture content was 13%-20%. The vertical roots stopped growing when soil moisture content reached 30% （the critical soil moisture point）. （2） The morphological plasticity of roots is an important strategy used by A. sparsifolia seedlings to obtain water and adapt to dry soil conditions. When the groundwater table was shallow, horizontal roots quickly expanded and tillering increased in order to compete for light resources, whereas when the groundwater table was deeper, vertical roots developed quickly to exploit space in the deeper soil layers. （3） The decrease in groundwater depth was probably respon- sible for the root distribution in the shallow soil layers. Root biomass and surface area both decreased with soil depth. One strategy of A. sparsifolia seedlings in dealing with the increase in groundwater depth is to increase root biomass in the deep soil layers. The relationship between the root growth/distribution of A. sparsifolia and the depth of groundwater table can be used as guidance for harvesting A. sparsifolia biomass and managing water resources for forage grasses. It is also of ecological significance as it reveals how desert plants adapt to arid environments.

Hopf Bifurcation Research of Hydraulic Turbine Governing System with a Saturation Nonlinearity

A nonlinear mathematical model for hydro turbine governing system with saturation nonlinearity in small perturbation has been proposed with all the essential components,i.e. turbine,PID type governor with saturation part and generator included in the model. Existence,stability and direction of Hopf bifurcation of an example HTGS are investigated in detail and presented in forms of bifurcation diagrams and time waveforms. The analysis show that a supercritical Hopf bifurcation may exist in hydraulic turbine systems in some certain conditions. Moreover,the dynamic behavior of system with different parameters such as Tw,Tab,Tyand K are studied extensively. An example with numerical simulations is presented to illustrate the theoretical results. The researches provide a reasonable explanation for the Hopf phenomenon happened in operation of hydroelectric generating unit.

Recommender Systems Based on Tensor Decomposition

Recommender system is an effective tool to solve the problems of information overload.The traditional recommender systems,especially the collaborative filtering ones,only consider the two factors of users and items.While social networks contain abundant social information,such as tags,places and times.Researches show that the social information has a great impact on recommendation results.Tags not only describe the characteristics of items,but also reflect the interests and characteristics of users.Since the traditional recommender systems cannot parse multi-dimensional information,in this paper,a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender systems.The original Singular Value Decomposition(SVD)model is optimized by mining the co-occurrence and mutual exclusion of tags,and their features are constrained by the relationship between tags.Experiments on real dataset show that the proposed algorithm achieves superior performance to existing algorithms.

Complex Time Series Analysis Based on Conditional Random Fields

A fundamental problem with complex time series analysis involves data prediction and repair.However,existing methods are not accurate enough for complex and multidimensional time series data.In this paper,we propose a novel approach,a complex time series predic-tion model,which is based on the conditional randomfield(CRF)and recurrent neural network(RNN).This model can be used as an upper-level predictor in the stacking process or be trained using deep learning methods.Our approach is more accurate than existing methods in some suitable scenarios,as shown in the experimental results.

Prediction of Time Series Data with Low Latitude Features

The main purpose of this paper is to study the key tech-nology for the prediction of time series data.It has a very wide range of applications,such as forecasting sales.Forecasting sales can be said to play an important role in company operations.Whether for saving costs or inventory scheduling,accurate prediction can save unnecessary waste.From this aspect,this paper uses a neural network to achieve the purpose of the prediction.The application of neural networks in prediction has been a long time.However,most of them have not performed much research on the struc-ture and input of neural networks,and it is not easy to process time series data.Usually,there will be many features.However,the features of data in some scenarios are small.In this paper,we determined how to predict through low-latitude features.Atfirst,among all the ways of preprocess-ing data,the paper selects a mathematical method.After that,this paper builds three models in two aspects:the input and the network structure.To improve the accuracy of the results,this paper proposes two means.One is based on the seasonal characteristics of commodities.The other is based on the prediction error,called exponential smoothing.Finally,according to the results of the experiment,we come to some conclusions.

Dimension Reduction Based on Sampling

Dimension reduction provides a powerful means of reducing the number of random variables under consideration.However,there were many similar tuples in large datasets,and before reducing the dimension of the dataset,we removed some similar tuples to retain the main information of the dataset while accelerating the dimension reduc-tion.Accordingly,we propose a dimension reduction technique based on biased sampling,a new procedure that incorporates features of both dimensional reduction and biased sampling to obtain a computationally efficient means of reducing the number of random variables under consid-eration.In this paper,we choose Principal Components Analysis(PCA)as the main dimensional reduction algorithm to study,and we show how this approach works.

Growth Enterprise Board Initial Public Offerings:Characteristics,Volatility and the Initial-day Performance

我们从 2009 年 10 月使用中国起始的公共出现数据到 2010 年 8 月检查最新建立的生长企业董事会(GEB ) 。结果显示 GEB 是成功的并且正在提供一条可行隧道让新小、中型的公司提起外部资本。四个变量，轻快变量，周转比率，赢的彩票比率和 priceearnings 比率，是在回归分析驾驶起始天的回来的重要因素。在 GEB 上的新贸易暂停政策的实现被发现在减轻过多的推测有效。我们的分析结果能被决策者使用在 GEB 的起始的公共礼物的 underpricing 上计量政策变化的效果。

Rice SPL10 positively regulates trichome development through expression of HL6 and auxin-related genes

Trichomes function in plant defenses against biotic and abiotic stresses;examination of glabrous lines,which lack trichomes,has revealed key aspects of trichome development and function.Tests of allelism in 51 glabrous rice(Oryza sativa)accessions collected worldwide identified OsSPL10 and OsWOX3B as regulators of trichome development in rice.Here,we report that OsSPL10 acts as a transcriptional regulator controlling trichome development.Haplotype and transient expression analyses revealed that variation in the approximately 700-bp OsSPL10 promoter region is the primary cause of the glabrous phenotype in the indica cultivar WD-17993.Disruption of OsSPL10 by genome editing decreased leaf trichome density and length in the NIL-HL6 background.Plants with genotype OsSPL10^(WD-17993)/HL6 generated by crossing WD-17993 with NIL-HL6 also had fewer trichomes in the glumes.HAIRY LEAF6(HL6)encodes another transcription factor that regulates trichome initiation and elongation,and OsSPL10 directly binds to the HL6 promoter to regulate its expression.Moreover,the transcript levels of auxin-related genes,such as OsYUCCA5 and OsPIN-FORMED1b,were altered in OsSPL10 overexpression and RNAi transgenic lines.Feeding tests using locusts(Locusta migratoria)demonstrated that non-glandular trichomes affect feeding by this herbivore.Our findings provide a molecular framework for trichome development and an ecological perspective on trichome functions.

The Impact of Policy on the Clustering of the Blockchain Industry Evidence from GMM Estimates

Although local governments in China are encouraging the development of blockchain technology,the regional clustering of the blockchain industry still shows obvious differentiation.We use blockchain industry-related data during the period 2012–2019 to calculate the blockchain industrial clustering of each province in China.We find that the clustering state of the blockchain industry is quite different from the state of other industries and the situation of economic development in the same region.In less-developed regions,the blockchain industry is more prominent,which may benefit from local government management.We conduct an empirical analysis on the relationship between blockchain industrial clustering and regional government management using the generalized method of moments(GMM)of a dynamic panel.The results show that government management has a positive promoting effect on local blockchain industrial clustering as a whole,among which the promotion from economy,technology,infrastructure and policy is more significant.