经络环皮部挑治法联合中药治愈儿童抽动症病例报道2例

儿童抽动症是发生在儿童期的一种肌肉抽动性疾病。发病率较高,为1%~7%,多见于学龄前及学龄早期的儿童。男性明显占优势,男女之比为3:1~4:1。多呈良性经过,也有难治性患者。中医文献中没有与抽动–秽语综合征相对应的病名,但据其临床特点,应属“肝风内动”范畴,本病系风火痰湿为患,属本虚标实之证,以风火痰湿为标,肝、脾、肾三脏为本,阴虚阳亢是主要发病机理。其病情复杂,往往虚实并见,风火痰湿并存。采用经络环皮部挑治法治疗以厥阴少阳经脉为主的经络环治疗为主,配合太阴阳明经络环治疗,以百会穴为启动点,取风池穴、风府穴、肝腧、胆腧穴、脾腧穴、胃腧穴、足三里穴、丰隆穴、太冲穴、足窍阴穴等进行挑治。上肢以内关、外关、合谷、手三里、曲池等取穴为主,链接经络生理环及病理环,在基因程控的体系支配下开启自我修复系统,让更多的物质能量信息进行传输,从而对引起本病的风、痰、浊、湿等进行调配和运输,配合内调中药的作用,共同平肝熄风、豁痰开窍,益气健脾为主。经过一个疗程的综合治疗,诸证消失,达到临床治愈。

经络环皮部挑治法临床应用研究进展

皮部是指体表的皮肤按经络循行分布部位的分区,是十二经脉功能活动反映于体表的部位,也是络脉之气散布之所在。经络环皮部挑治法则是以不刺入皮肤、不破皮,以及不出血为原则,在皮肤表面,借助腕部转动的力量,按“经络环”运行规律进行表皮刺激的一种整体调整方法。本法是经络研究专家李定忠教授以经络感传现象研究为基础,结合创立的“经络环”理论,以表皮接力刺激作为方法学基础,最终创建的新型疗法。本法是在感传研究中经络环的发现和接力激发感传提高疗效的基础上完成的,疗法针对各种慢性病和疑难病均有较好的疗效。笔者通过总结既往本疗法治疗相关的发表文献,阐述本疗法诊治的优势病种和治疗手段,将其临床应用的相关研究总结如下,以供参考。

经络环皮部挑治法治疗小儿脑瘫的疗效观察

目的:观察经络环皮部挑治法治疗小儿脑瘫的临床疗效。方法:以60例脑瘫患儿为研究对象,将其随机分为两组。对照组采用针刺、穴位注射、康复训练等常规综合治疗,均为每日1次,10次为一疗程,共3个疗程。观察组在此基础上予经络环皮部挑治治疗,隔日1次,5次一疗程,共3个疗程。结果:观察组脱落1例,完成29例;对照组脱落2例,完成28例。观察组临床治愈7例,显效13例,有效7例,总有效率93.1%,其疗效与对照组比较有显著性差异(P<0.05)。结论:经络环皮部挑治法治疗小儿脑瘫疗效显著。

《中国针灸》杂志社临床针灸特技疗法研修班首期招生——2005年推出脐针诊疗法、经络环皮部挑治法

脐针诊疗法是齐永教授创立的集诊断与治疗为一体的新型诊疗法(详细内容见《中国针灸》杂志2002年第8期570页，2004年第10期732页)。脐针疗法的创立，打破了自古以来脐部禁针的禁忌，方法简单、疗效确切，其原理是来自于华夏文化源头的《洛书》和《易经》。脐诊法可根据脐部的外观变化诊断全身的各种疾病，

经络环皮部挑治法联合经穴贴磁疗法治疗癥瘕病案二则分析

经络环皮部挑治法是当代经络学研究的重大成果,并且在临床应用中得到了确切的疗效肯定。经络是物质能量信息的统一体,在基因程控下进行自我完善和调整,在治疗常见病疑难病等方面具有较高的临床推广价值。

经络环皮部挑治法结合归智丸治疗老年记忆障碍10例疗效分析

目的:探讨治疗记忆障碍的一种行之有效的中医治疗方法。方法:采用经络环皮部挑治法,配合归智丸联合治疗。结果:经络环皮部挑治法是当代经络学研究的重大成果,并且在临床应用中得到了确切的疗效肯定。采用此方法对记忆障碍群体进行干预治疗,得到了疗效确切的效果,深得此人群的称颂。结论:经络是物质能量信息的统一体,在基因程控下进行自我完善和调整,配合归智丸可以有效干预治疗老年记忆障碍,值得在社区推广。

经络环皮部挑治法联合中药治疗肺结节临床疗效观察

肺结节(PN)是指肺内直径≤3 cm的类圆形或不规则形病灶,影像学表现为密度增高的阴影,可单发或者多发,边界清楚或者不清楚的病灶。多为慢性炎性肉芽肿、腺瘤样增生或肺癌等。近年来,随着空气污染的加重及低剂量螺旋CT扫描在全民体检中的普及,肺结节的发病率和检出率不断增高。但由于其病因复杂,缺乏特异性的临床表现,在诊断时存在着一定的难度,具有较高的漏诊及误诊率[1-2]。现代医学对肺结节的临床诊断措施不断更新发展,但缺乏相对应的治疗措施,患者对本病的治疗需求十分迫切。

针刺大鼠后三里与三阴交对热水甩尾痛阈的影响

目的:观察针刺大鼠“后三里”与“三阴交”对热水甩尾测痛模型的影响。方法:分别针刺大鼠胃经线上“后三里”和脾经脉线上“三阴交”腧穴,以热水甩尾测痛方法分别测定其针刺前后的痛阈,并对结果进行统计学分析。结果:“后三里”腧穴针刺前(5.90±0.38)后(9.28±0.32)配对t检验比较差异显著(p=0.0001);“三阴交”腧穴在针刺前(5.95±0.42)后(9.27±0.42)配对t检验比较差异显著(p=0.0001);“后三里”和“三阴交”针刺前后痛阈差值比较,未发现有统计学意义的改变(t=0.4550,p=0.6682)。结论:针刺胃经线上“后三里”和针刺脾经线上“三阴交”腧穴,对大鼠热水甩尾模型具有相似的作用影响。

综合疗法治疗痤疮35例疗效观察

目的:观察挑治配合刺络拔罐等综合疗法治疗痤疮的临床疗效。方法:35例病例均采用经络环皮部挑治法,并在背部的膀胱经与督脉放血拔罐,背部反应点挑刺,耳尖放血治疗。结果:35例病例治疗2 w^4 w后,显效33例,有效2例,总有效率为100%。结论:该法治疗痤疮疗效好,值得临床进一步推广应用。

Backlash Nonlinear Compensation of Servo Systems Using Backpropagation Neural Networks

Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks(BPNN) is presented. Methods Based on some weapon tracking servo system, a three layer BPNN was used to off line identify the backlash characteristics, then a nonlinear compensator was designed according to the identification results. Results The simulation results show that the method can effectively get rid of the sustained oscillation(limit cycle) of the system caused by the backlash characteristics, and can improve the system accuracy. Conclusion The method is effective on sloving the problems produced by the backlash characteristics in servo systems, and it can be easily accomplished in engineering.

A Multilayer Recurrent Fuzzy Neural Network for Accurate Dynamic System Modeling

A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.

Wavelet neural network based fault diagnosis in nonlinear analog circuits

The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studled. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation（BP） neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

Catalytic Cracking and PSO-RBF Neural Network Model of FCC Cycle Oil

Catalytic cracking experiments of FCC cycle oil were carried out in a fixed fluidized bed reactor. Effects of reac- tion conditions, such as temperature, catalyst to oil ratio and weight hourly space velocity, were investigated. Hydrocarbon composition of gasoline was analyzed by gas chromatograph. Experimental results showed that conversion of cycle oil was low on account of its poor crackability performance, and the effect of reaction conditions on gasoline yield was obvi- ous. The paraffin content was very high in gasoline. Based on the experimental yields under different reaction conditions, a model for prediction of gasoline and diesel yields was established by radial basis function neural network （RBFNN）. In the model, the product yield was viewed as function of reaction conditions. Particle swarm optimization （PSO） algorithm with global search capability was used to obtain optimal conditions for a highest yield of light oil. The results showed that the yield of gasoline and diesel predicted by RBF neural network agreed well with the experimental values. The optimized reac- tion conditions were obtained at a reaction temperature of around 520 ~C, a catalyst to oil ratio of 7.4 and a space velocity of 8 h~. The predicted total yield of gasoline and diesel reached 42.2% under optimized conditions.

Turnout fault prediction method based on gated recurrent units model

Turnout is one of the important signal infrastructure equipment,which will directly affect the safety and efficiency of driving.Base on analysis of the power curve of the turnout,we extract and select the time domain and Haar wavelet transform characteristics of the curve firstly.Then the correlation between the degradation state and the fault state is established by using the clustering algorithm and the Pearson correlation coefficient.Finally,the convolutional neural network(CNN)and the gated recurrent unit(GRU)are used to establish the state prediction model of the turnout to realize the failure prediction.The CNN can directly extract features from the original data of the turnout and reduce the dimension,which simplifies the prediction process.Due to its unique gate structure and time series processing features,GRU has certain advantages over the traditional forecasting methods in terms of prediction accuracy and time.The experimental results show that the accuracy of prediction can reach 94.2%when the feature matrix adopts 40-dimensional input and iterates 50 times.

Delay-Dependent Exponential Stability of Stochastic Delayed Recurrent Neural Networks with Markovian Switching

The exponential stability problem is investigated for a class of stochastic recurrent neural networks with time delay and Markovian switching. By using Ito＇s differential formula and the Lyapunov stability theory, sufficient condition for the solvability of this problem is derived in term of linear matrix inequalities, which can be easily checked by resorting to available software packages. A numerical example and the simulation are exploited to demonstrate the effectiveness of the proposed results.

Seismic-inversion method for nonlinear mapping multilevel well–seismic matching based on bidirectional long short-term memory networks

In this paper,the recurrent neural network structure of a bidirectional long shortterm memory network(Bi-LSTM)with special memory cells that store information is used to characterize the deep features of the variation pattern between logging and seismic data.A mapping relationship model between high-frequency logging data and low-frequency seismic data is established via nonlinear mapping.The seismic waveform is infinitely approximated using the logging curve in the low-frequency band to obtain a nonlinear mapping model of this scale,which then stepwise approach the logging curve in the high-frequency band.Finally,a seismic-inversion method of nonlinear mapping multilevel well–seismic matching based on the Bi-LSTM network is developed.The characteristic of this method is that by applying the multilevel well–seismic matching process,the seismic data are stepwise matched to the scale range that is consistent with the logging curve.Further,the matching operator at each level can be stably obtained to effectively overcome the problems that occur in the well–seismic matching process,such as the inconsistency in the scale of two types of data,accuracy in extracting the seismic wavelet of the well-side seismic traces,and multiplicity of solutions.Model test and practical application demonstrate that this method improves the vertical resolution of inversion results,and at the same time,the boundary and the lateral characteristics of the sand body are well maintained to improve the accuracy of thin-layer sand body prediction and achieve an improved practical application effect.

Study of CNG／diesel dual fuel engine＇s emissions by means of RBF neural network

Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resumé, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.

Application of a Neural Network Technique for Prediction of the Water Quality Index in the Dong Nai River, Vietnam

Recent trends in environmental management of water resource have enlarged the demand for predicting techniques that can provide reliable, efficient and accurate water quality. In this case study, the authors applied the Artificial Neural Networks （ANN） to estimate the water quality index on the Dong Nai River flowing through Dong Nai and Binh Duong provinces. The information and data including 10 water quality parameters of the Dong Nai River at 23 monitoring stations were collected during the recorded time period from 2010 to 2014 to build water quality prediction models. The results of the study demonstrated that the Water Quality Index （WQI） forecasted with GRNN was very significant and had high correlation coefficient （R2 = 0.974 and p = 0.0） compared to the real values of the WQI. Moreover, the ANN models provided better predicted values than the multiple regression models did.

Analysis of the thinking method of English teaching under network environment

How to make a good job of foreign language teaching and improve the quality of teaching is a topic which the general teaching workers are facing, in this paper, the author express ideas from many aspects, multi-angle, multi-level, this brings a valuable theory reference on methods of English teaching which is under network environment. As for the problems of how to solve the bottle neck of English teaching, how to train the talent who are more adaptive to the world economy globalization, how to get rid of the traditional teaching method in teaching and how to improve the teaching level more efficiently, this paper gave some good methods and suggestions.

Geographical patterns and anti-poverty targeting post-2020 in China

Poverty has been a focus of Chinese government for a long time. It is therefore of great significance to investigate both the mechanisms and spatial patterns of regional impoverishment in order to adequately target Chinese anti-poverty goals. Based on the human-environment relationship and multidimensional poverty theory, this study initially develops a three-dimensional model encompassing human, society, and environmental factors to investigate the mechanisms of rural impoverishment as well as to construct an indicator system to evaluate the comprehensive poverty level(CPL) in rural areas. A back propagation neural network model was then applied to measure CPL, and standard deviation classification was used to identify counties that still require national policy-support(CRNPSs) subsequent to 2020. The results of this study suggest that CPL values conform to a decreasing trend from the southeast coast towards the inland northwest of China. Data also show that 716 CRNPSs will be present after 2020, mainly distributed in high-arid areas of the Tibetan Plateau, the transitional zones of the three-gradient terrain, as well as karst areas of southwest China. Furthermore, CRNPSs can be divided into four types, that is, key aiding counties restricted by multidimensional factors, aiding counties restricted by human development ability, aiding counties restricted by both natural resource endowment and socioeconomic development level, and aiding counties restricted by both human development ability and socioeconomic development level. We therefore propose that China should develop and adopt scientific and targeted strategies to relieve the relative poverty that still exist subsequent to 2020.