现代科学技术理论对古老针灸经络理论的分析

文章以国际上新的理论，从能量医学、生物发育、量子力学等几方面，对针灸经络的实质进行了深入的剖析。

微波合用氦氖激光治疗周围性面神经麻痹42例

目的观察微波和氦氖激光合用治疗周围性面神经麻痹的疗效。方法取穴翳风,用微波与氦氖激光穴位辐射。结果总有效率97%。提示采用脉冲微波治疗,小剂量辐射,可使辐射部位的血管舒张,解除血管痉挛,减少面神经水肿。氦氖激光的作用在于通过穴位辐射穿透皮肤,直接作用于神经末稍感受器,通过对神经的影响调节气血运行,加快血管新生,促进变性神经恢复,治疗时间越早,致残率越少。

经络学说的研究进展

西北农林科技大学畜牧兽医学院赵慧英等,对经络学说进行了综述。1、神经脉管说:认为经络相当于神经、血管综合组成,把经络分为两大体系:一为营血循行体系,刺可出血的相当于血液循环系统;

浅谈足部按摩施术中的技巧

笔者多年来，运用足部按摩疗法治疗疾病过程中，体会到治疗的效果满意与否，除了按准反射区外，操作的手法技巧也是很关键的问题。

半刺法治疗儿童面瘫60例

目的:观察半刺法治疗儿童面瘫的疗效。方法:取攒竹、阳白、丝竹空等穴,针刺用半刺法深入0.5寸。结果:总有效率为96.6%。提示:本方法能调整各阳经气血,疏调经气。半刺法刺入极浅,不伤及肌肉,急性期不提插捻转,恢复期可左右捻转360°,皆为的是驱邪不伤正。

A REALIZATION OF FUZZY LOGIC BY A NEURAL NETWORK

This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model.

经络相关的脊髓前角运动神经元对交感节前神经元的树突投射

目的:探讨脊髓中与经络有关的运动神经元的树突在神经元之间和对交感节前神经元的投射特性。方法:用含有1.0%HRP的CB-HRP溶液分别注射于41只SD大鼠的足三阳经胸-腰段穴位肌肉,即胃经"承满""梁门"等,膀胱经"肝俞""胆俞"等,胆经"阳陵泉""阳关"等,以及腹腔神经节、肠系膜上神经节等部位。常规灌流后冰冻切片,HRP成色反应后,显微镜下观察运动神经元的树突联系和对交感节前神经元的投射特性。结果:CB-HRP逆行标记在脊髓前角显示每条经都有特定的运动神经元支配,同经的运动神经元通过相互之间的树突特异性投射,形成每条经所特有的神经柱,不同经的运动神经元柱中的树突不发生投射关系。在有脊髓交感节前神经元的胸-腰节段,标记的运动神经元发出树突束,向交感节前神经元区投射;而位于脊髓颈段无交感节前神经元结构的部位则没有此种运动神经元的树突投射。结论:每条足三阳经在脊髓前角都有它们特定的运动神经元支配,同经运动神经元的树突在神经元之间特异性相互投射而形成运动神经元柱状结构;在脊髓胸-腰段,运动神经元的树突对脊髓交感节前神经元发出定向投射。

电针加艾灸治疗周围性面瘫60例

目的对比电针加艾炷灸与单纯电针治疗周围性面瘫的疗效。方法90例随机分为治疗组60例,电针加艾炷灸,取穴为患侧攒竹、晴明、阳白等;对照组30例,单纯采用电针治疗。结果治疗组总有效效率96.2%,对照组82.4%,两组疗效比较P<0.05,疗程疗效比较P<0.01。提示针灸具有调整阴阳,疏风活络,活血化淤的功能,治疗重取面部手足阳明经穴,配以循经远取合谷,以起到疏通阳明少阳经脉,祛风散寒,调和气血的作用,使面部筋肉得以温煦、滋养而痊愈。

Study on Pests Forecasting Using the Method of Neural Network Based on Fuzzy Clustering

Aimed to the characters of pests forecast such as fuzziness, correlation, nonlinear and real-time as well as decline of generalization capacity of neural network in prediction with few observations, a method of pests forecasting using the method of neural network based on fuzzy clustering was proposed in this experiment. The simulation results demonstrated that the method was simple and practical and could forecast pests fast and accurately, particularly, the method could obtain good results with few samples and samples correlation.

ACUPOINT-LIKE STRUCTURE AND MERIDIAN-LIKE DISTRIBUTION ON THE BODY SURFACE OF PLATYFISH

Objective To probe into whether an acupoint-like and meridian-like structure was existed in platyfish. Methods Adult platyfish was put in 30 μM 4-Di-2-ASP water solution for 3 h, then, in pipe water for 20min, afterward, the fish was anesthetized in 10% aether water solution, and the fluorescent labeling was observed under fluorescent microscope with B-3A combination filter. Results The labels observed under the microscope were in round bright dot, a majority of dots were distributed separately and a part of them was in cluster （2- 5 dots/cluster） on various parts of the body in regular arrangement. The labels on the head were circularly distributed around the eye and two arches were formed posterior to the eye and in the inferior 3/4 quadrant. These two arches joined one arch in the anterior superior 1/4 quadrant. On the fish trunk, it was observed that the labels were distributed from the back to the abdomen along the longitudinal axis of the trunk, forming 6 lines, located on No. 2, 4, 6, 7, 8 and 9 scale rows successively on the dorsal part of fish. Each line was composed of 7 to 22 label clusters and 1 -5 labels were counted in each cluster. The labels were arranged as 3-4 lines on the tail. Conclusion 1） Acupoint-like and meridian-like structure was existed in platyfish. 2）The skin sensory organs of animal were not distributed evenly all over the body. Instead, a number of sensory organs were put together in cluster and a number of them were in linear distribution regularly along the long axis of the trunk, which was similar to the distribution of traditional meridians and acupoints.

Comparison of Two Neural Networks in MC-CDMA Multiuser Detection

MC CDMA is a thriving topic in recent years. Multiuser interference is also very severe as in DS CDMA. ML method is the best multiuser detection, but it has a computational complexity exponentially increased with the number of users. Mean field annealing and chaotic neural network are two promising optimum techniques. This paper applies them into the ML detection, comparison of the two methods is made.

Science Letters:Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor

To screen and evaluate protein biomarkers for the detection of gliomas (Astrocytoma grade Ⅰ-Ⅳ) from healthy individuals and gliomas from brain benign tumors by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) coupled with an artificial neural network (ANN) algorithm. SELDI-TOF-MS protein fingerprinting of serum from 105 brain tumor patients and healthy individuals, included 28 patients with glioma (Astrocytoma Ⅰ-Ⅳ), 37 patients with brain benign tumor, and 40 age-matched healthy individuals. Two thirds of the total samples of every compared pair as training set were used to set up discriminating patterns, and one third of total samples of every compared pair as test set were used to cross-validate; simultaneously, discriminate-cluster analysis derived SPSS 10.0 software was used to compare Astrocytoma grade Ⅰ-Ⅱ with grade Ⅲ-Ⅳ ones. An accuracy of 95.7%, sensitivity of 88.9%, specificity of 100%, positive predictive value of 90% and negative predictive value of 100% were obtained in a blinded test set comparing gliomas patients with healthy individuals; an accuracy of 86.4%, sensitivity of 88.9%, specificity of 84.6%, positive predictive value of 90% and negative predictive value of 85.7% were obtained when patient's gliomas was compared with benign brain tumor. Total accuracy of 85.7%, accuracy of grade Ⅰ-Ⅱ Astrocytoma was 86.7%, accuracy ofⅢ-Ⅳ Astrocytoma was 84.6% were obtained when grade Ⅰ-Ⅱ Astrocytoma was compared with grade Ⅲ-Ⅳ ones (discriminant analysis). SELDI-TOF-MS combined with bioinformatics tools, could greatly facilitate the discovery of better biomarkers. The high sensitivity and specificity achieved by the use of selected biomarkers showed great potential application for the discrimination of gliomas patients from healthy individuals and glioma from brain benign tumors.

Improving Land Resource Evaluation Using Fuzzy Neural Network Ensembles

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.

Daily and Monthly Suspended Sediment Load Predictions Using Wavelet Based Artificial Intelligence Approaches

In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For this purpose, in the first step, SSL was predicted via ad hoc LSSVM and Artificial Neural Network （ANN） models; then, streamflow and SSL data were decomposed into sub- signals via wavelet, and these decomposed sub-time series were imposed to LSSVM and ANN to simulate discharge-SSL relationship. Finally, the ability of WLSSVM was compared with other models in multi- step-ahead SSL predictions. The results showed that in daily SSL prediction, LSSVM has better outcomes with Determination Coefficient （DC）=o.92 than ad hoc ANN with DC=o.88. However unlike daily SSL, in monthly modeling, ANN has a bit accurate upshot. WLSSVM and wavelet-based ANN （WANN） models showed same consequences in daily and different in monthly SSL predictions, and adding wavelet led to more accuracy of LSSVM and ANN. Furthermore, conjunction of wavelet to LSSVM and ANN evaluated via multi-step-ahead SSL predictions and, e.g., DCLssVM=0.4 was increased to the DCwLsSVM=0.71 in 7- day ahead SSL prediction. In addition, WLSSVM outperformed WANN by increment of time horizon prediction.

Artificial neural network modeling of gold dissolution in cyanide media

The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.

Prediction Method for Network Traffic Based on Maximum Correntropy Criterion

This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function.At the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training.According to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network traffic.The simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE.The overall performance is improved by about 0.0131.

Nonlinear Time Series Prediction Using Chaotic Neural Networks

A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm.

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.

Development of a spontaneous combustion TARPs system based on BP neural network

Spontaneous combustion of coal is a major cause of coal mine fires.It not only poses a severe hazard to the safe extraction of coal resources,but also jeopardizes the safety of mine workers.The development of a scientific management system of coal spontaneous combustion is of vital importance to the safe production of coal mine.This paper provides a comparative analysis of a range of worldwide prediction techniques and methods for coal spontaneous combustion,and systematically introduces the trigger action response plans(TARPs)system used in Australian coal mines for managing the spontaneous heating of coal.An artificial neural network model has been established on the basis of real coal mine operational conditions.Through studying and training the neural network model,prediction errors can be controlled within the allowable range.The trained model is then applied to the conditions of Nos.1 and 3 coal seams located in Weijiadi Coal Mine to demonstrate its feasibility for spontaneous combustion assessment.Based upon the TARPs system which is commonly used in Australian longwall mines,a TARPs system has been developed for Weijiadi Coal Mine to assist the management of spontaneous combustion hazard and ensure the safe operation of its mining activities.