论鲁迅《故事新编》的神话重写

鲁迅《故事新编》用两种不同笔墨重写了两类神话人物:以灿烂、瑰奇的风格描写“神格”的女娲;以更多掺杂油滑和审视语调的笔触讲述更具“人性”的后世“新出之神”。鲁迅对神话的重写体现了他的神话观和宗教观,其与德国浪漫派的“新神话”观念、日本明治时期学者姉崎正治致力于文明探究的神话学理念都有着深刻联系。

中华“神格人”传统的人文意蕴及当代启示

中华民族性可以追溯到中国的创世神话。与张扬个性的西方"人格神"传统迥异,中华"神格人"传统崇尚悲天悯人的道德情操和经世致用的献身精神,其差异根源于中、西神话赖以产生的不同经济模式。准确把握中华"神格人"传统蕴涵的人文价值,并加以现代转化可以弥补西方现代文明的不足,推进人类文明的健康发展。

Optimization of Plasma Etching Parameters and Mask for Silica Optical Waveguides

Optical waveguides in silica-on-silicon are one of the key elements in optical communications.The processes of deep etching silica waveguides using resist and metal masks in RIE plasma are investigated.The etching responses,including etching rate and selectivity as functions of variation of parameters,are modeled with a 3D neural network.A novel resist/metal combined mask that can overcome the single-layer masks’ limitations is developed for enhancing the waveguides deep etching and low-loss optical waveguides are fabricated at last.

A convolutional neural artistic stylization algorithm for suppressing image distortion

Aiming at the problems of image semantic content distortion and blurred foreground and background boundaries during the transfer process of convolutional neural image stylization,we propose a convolutional neural artistic stylization algorithm for suppressing image distortion.Firstly,the VGG-19 network model is used to extract the feature map from the input content image and style image and to reconstruct the content and style.Then the transfer of the input content image and style image to the output image is constrained in the local affine transformation of the color space.And the Laplacian matting matrix is constructed by combining the local affine of the input image RGB channel.For each output blocks,affine transformation maps the RGB value of the input image to the corresponding output and position,which realizes the constraint of semantic content and the control of spatial layout.Finally,the synthesized image is superimposed on the white noise image and updated iteratively with the back propagation algorithm to minimize the loss function to complete the image stylization.Experimental results show that the method can generate images with obvious foreground and background edges,clear texture,restrained semantic content-distortion,realized spatial constraint and color mapping of the transfer images,and made the stylized images visually satisfactory.

Building and Researching the Bidding Model Based on the Costof Power Plant

A bidding model of neural network was presented to pursue the largest benefit according to the policy of separating power plants from network and bidding transaction. This model bases on the cost of power plant and its research object is a power plant in the market. The market clearing price (MCP) can be predicted and an optimized load curve can be decided in this model. The model may provide technical support for the power plant.

A Corpus-Based Analysis of the Animal Images in The Wind Among the Reeds

The Wind Among the Reeds, written from 1889 to 1939, is regarded as one of the most remarkable poetry collections of William Butler Yeats. It altogether includes 80 poems touching upon several themes such as love, religion, dignity, and life. Yeats is one of the most distinguished Irish poets throughout the world, whose works perfectly embody the incorporation of romanticism, modernism, and occultism. It is noteworthy that in some of his poems, animals are portrayed frequently or even taken as the title of a poem, such as bird, fish, swan and so on. Therefore, this essay attempts to study the meaning of animal images of this poetry anthology in terms of different writing phases of Yeats. Firstly, the author builds the corpus of The Wind Among the Reeds and employs corpus search software Ant Cone to check the number and distribution of the animal image. Next, the author focuses on certain prominent images and investigates them further by analyzing the concordance lines of them. Thirdly, according to the result of distribution information, the author also attaches importance to the phenomenon of image combination in the poetry and then explores its function and effect. To conclude, by exploring the animal image in The WindAmong the Reeds, a deeper understanding of the poetry and the writing style of the poet will be gained on another level. What is more, a more direct and objective data is provided through the method of corpus and its relevant software, thereupon a new research approach is introduced.

Comparison of Artificial Neural Networks,Geographically Weighted Regression and Cokriging Methods for Predicting the Spatial Distribution of Soil Macronutrients(N,P,and K)

Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.

Research on the credit classification of practicing qualification personnel in construction market based on self-organizing neural network

Combining with the characters of the practicing qualification personnel in construction market,evaluation method based on the self-organizing neural network is brought out to analyze the credit classification of the practicing qualification personnel. And the impact factors on the credit classification of the practicing qualification personnel,such as the number of neurons,the training steps,the dimension of neurons and the field of winning neurons are studied. Then a self-organizing competitive neural network is built. At last,a case study is conducted by taking practicing qualification personnel as an example. The research result reveals that the method can efficiently evaluate the credit of the practicing qualification personnel;thus,it could provide scientific advice to the construction enterprise to prevent relevant discreditable behaviors of some practicing qualification personnel.

Improved Crude Oil Price Forecasting With Statistical Learning Methods

Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method （SLM） based on combined fuzzy system （FS）, artificial neural network （ANN）, and support vector regression （SVR） to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate （WT1） crude oil spot price is used to test the effectiveness of the proposed learning methodology. Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.

Endovascular repair of traumatic arteriovenous fistula between axillary artery and vein

Traumatic arteriovenous fistula between the axillary artery and vein may present a difficult problem in treatment. There are few reports demonstrating the endovascular repair of this challenge. Herein, we present such a case of endovascular repair of traumatic arteriovenous fistula between the axillary artery and vein with false aneurysm formation. The patient was discharged 11 days after successful operation. Oral clopidogrel and aspirin were administerted for 18 months. At one year follow-up, the patient was in good condition and showed no evidence of neurological deficit in the left upper limb.

Case Report of Three Patients with Wei-flaccidity Syndrome Cured by Acupuncture-moxibustion Therapy

Acupuncture-moxibustion therapy has an advantage in treating Wei-flaccidity syndrome resulted from peripheral nerve injuries. We have adopted acupuncture and moxibustion to treat 3 patients with Wei-flaccidity syndrome in clinical practice and now reports as follows.

Spatial Estimation of Saturated Hydraulic Conductivity from Terrain Attributes Using Regression,Kriging,and Artificial Neural Networks

Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.

Wnt/β-catenin signaling in midbrain dopaminergic neuron specification and neurogenesis

Loss of midbrain dopaminergic （mDA） neurons underlies the motor symptoms of Parkinson＇s disease. Towards cell replacement, studies have focused on mechanisms underlying embryonic mDA production, as a rational basis for deriving mDA neurons from stem cells. We will review studies of [3-catenin, an obligate component of the Wnt cascade that is critical to mDA specification and neuro- genesis, mDA neurons have a unique origin--the midbrain fLoor plate （FP）. Unlike the hindbrain and spinal cord FP, the midbrain FP is highly neurogenic and Wnt/β-catenin signaling is critical to this difference in neurogenic potential. In β-catenin loss-of-function experiments, the midbrain FP resembles the hindbrain FP, and key mDA progenitor genes such as Otx2, Lmxlo, MsxJ, and Ngn2 are downregulated whereas Shh is maintained. Accordingly, the neurogenic capacity of the midbrain FP is diminished, resulting in fewer mDA neurons. Conversely, in β-catenin gain-of.function experiments, the hindbrain FP expresses key mDA progenitor genes, and is highly neurogenic. Interestingly, when excessive β-catenin is supplied to the midbrain FP, less mDA neurons are produced sug- gesting that the dosage ofWntJ β-catenin signaling is critical. These studies of β-catenin have facilitated new protocols to derive mDA neurons from stem cells.

Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks

This study explored the potential of using probabilistic neural networks (PNN) to predict shrinkage of thermal insulation mortar.Probabilistic results were obtained from the PNN model with the aid of Parzen non-parametric estimator of the probability density functions (PDF).Five variables,water-cementitious materials ratio,content of cement,fly ash,aggregate and plasticizer,were employed for input variables,while a category of 56-d shrinkage of mortar was used for the output variable.A total of 192 groups of experimental data from 64 mixtures designed using JMP7.0 software were collected,of which 120 groups of data were used for training the model and the other 72 groups of data for testing.The simulation results showed that the PNN model with an optimal smoothing parameter determined by the curves of the mean square error (MSE) and the number of unrecognized probability densities (UPDs) exhibited a promising capability of predicting shrinkage of mortar.