经络相对模式的量子机理

我国古代的内丹家,曾经把人体比作“小宇宙”,“一壶天”。这虽然是对修炼的体会和总结,但是却反映了人体元气流的实质。即人身经络的运行模式跟宇宙天体的运行模式在机理上是一致的、都是场力的作用。正因为如此,所以找不到它的形态学解剖体系。为了搞清经络的实质,自然要借助相对论的理论,推出一个经络相对映式,并逐步探讨和总结它的机理,使之正确反映经络的客观事实,进而形成一套原理简单、有严格逻辑的经络理论。

Neural-Network-Based Charge Density Quantum Correction of Nanoscale MOSFETs

For the treatment of the quantum effect of charge distribution in nanoscale MOSFETs,a quantum correction model using Levenberg-Marquardt back-propagation neural networks is presented that can predict the quantum density from the classical density. The training speed and accuracy of neural networks with different hidden layers and numbers of neurons are studied. We conclude that high training speed and accuracy can be obtained using neural networks with two hidden layers,but the number of neurons in the hidden layers does not have a noticeable effect, For single and double-gate nanoscale MOSFETs, our model can easily predict the quantum charge density in the silicon layer,and it agrees closely with the Schrodinger-Poisson approach.

Solving a Model of Inversion Layer Quantization Effects in Deep Submicron MOSFETs with Artificial Neural Networks

Approximate the solution of a model for inversion layer quantization effects in deep submicron MOSFETs with feed-forward artificial neural networks (ANNs) is proposed.To realize this,the solution of eigenvalue problems actually need to be considered for differential and integrodifferential operators,using ANNs.To validate the method and verify its accuracy,it is applied to the Schr o ¨dinger equation for the Morse potential problem that has an analytically known solution.Then a model is proceeded with which approximates the Schr o ¨dinger equation and the Poisson equation problem called the triangular-potential approximation.In conclusion,the presented method is simple to implement,and have several verification applications.

Estimation of half-wave potential of anabolic androgenic steroids by means of QSER Approach

The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.

Estimation of Sunflower Seed Yield Using Partial Least Squares Regression and Artificial Neural Network Models

Statistical models can efficiently establish the relationships between crop growth and environmental conditions while explicitly quantifying uncertainties. This study aimed to test the efficiency of statistical models established using partial least squares regression(PLSR) and artificial neural network(ANN) in predicting seed yields of sunflower(Helianthus annuus). Two-year field trial data on sunflower growth under different salinity levels and nitrogen(N) application rates in the Yichang Experimental Station in Hetao Irrigation District, Inner Mongolia, China, were used to calibrate and validate the statistical models. The variable importance in projection score was calculated in order to select the sensitive crop indices for seed yield prediction. We found that when the most sensitive indices were used as inputs for seed yield estimation, the PLSR could attain a comparable accuracy(root mean square error(RMSE) = 0.93 t ha-1, coefficient of determination(R^2) = 0.69) to that when using all measured indices(RMSE = 0.81 t ha-1,R^2= 0.77). The ANN model outperformed the PLSR for yield prediction with different combinations of inputs of both microplots and field data. The results indicated that sunflower seed yield could be reasonably estimated by using a small number of crop characteristic indices under complex environmental conditions and management options(e.g., saline soils and N application). Since leaf area index and plant height were found to be the most sensitive crop indices for sunflower seed yield prediction, remotely sensed data and the ANN model may be joined for regional crop yield simulation.