从神选萨满到法力萨满:美国苗人萨满当代传承机制探微

20世纪70年代中期以来,部分老挝难民开始流散到美国。其中,被美国政府单列为Hmong的老挝难民,即本文所称的美国苗人,大多保持着浓厚的萨满信仰,形成了独特的萨满当代传承机制。由萨满神灵选中的新萨满,需要跟从一位有经验的萨满师傅学习萨满知识,而之后只有通过入行仪式获得了萨满祖师亲授的各种法力,才能最终成为美国苗人社区接受的萨满,即法力萨满。美国苗人萨满的传承,强调新萨满的神选正统性,与萨满祖师崇拜有关,与美国苗人长期流散、饱受战争之苦有关,他们需要具有强大法力的法力萨满帮助修复心灵创伤。

《猎神杜朝选》传说探微

在我国少数民族民间文学中,有着非常丰富的文化资源有待我们去发掘。结合结构主义理论与文化人类学的研究方法,对白族神奇故事《猎神杜朝选》进行多层次的解读,以求获得新的认识。

方寸之下 另有天地——PDA选购指南

1992年美国苹果公司的斯考利等人，最早提出PDA(Personal Digital Assistant，个人数字助理)的概念。12年来，PDA的技术、产品和市场经历了前所未有的变化，其蓬勃发展的势头，带动着一个产业的前进，无数厂家、商家纷纷投入到这一领域。随着产品和用户的增加，如何选购也成了难题，也就是本文的主旨所在。

火神派名家用药规律分析

目的:分析火神派名家的用药规律,为临床研究火神派特色及传承其学术思想提供依据。方法:收集《中医火神派医案新选》中所载火神派名家处方,运用古今医案云平台(V2.2.2)软件数据挖掘分析模块,对处方数据进行频次统计、关联分析、中药属性分析、聚类分析及核心网络分析。结果:筛选得到329味药物,用药频数60次以上药物14味;药物关联分析显示,置信度较高的药物组合包括"干姜—附子""炙甘草—附子""肉桂—附子""茯苓—附子"等;药物属性、功效统计显示,药物性味以温性、平性、热性、辛味、甘味为主,归经以脾经、胃经为主,功效以补火助阳为主;聚类分析显示,高频药物具体可分为5种不同功用的组合;核心网络分析显示,核心处方为四逆汤。结论:温热药物占大多数,体现了火神派重用附子及其他温热药物的用药特点及重视阳气的核心思想。

Application of BP neural network model with fuzzy optimization in retrieval of biomass parameters

The retrieval of the biomass parameters from active/passive microwave remote sensing data （10.2 GHz） is performed based on an iterative inversion of BP neural network model with fuzzy optimization. The BP neural network is trained by a set of the measurements of active and passive remote sensing and the ground truth data versus Day of Year during growth. Once the network training is complete, the model can be used to retrieve the temporal variations of the biomass parameters from another set of observation data. The model was used in weights and microware observation data of wheat growth in 1989 to retrieve biomass parameters change of wheat growth this year. The retrieved biomass parameters correspond well with the real data of the growth, which shows that the BP model is scientific and sound.

Prediction of pre-oxidation efficiency of refractory gold concentrate by ozone in ferric sulfate solution using artificial neural networks

An artificial neural network model was developed to predict the oxidation of refractory gold concentrate （RGC） by ozone and ferric ions. The concentration of ozone and ferric ions, pulp density, oxygen amount, leaching time and temperature were employed as inputs to the network; the output of the network was the percentage of the ferric extraction iron from RGC. The multilayered feed-forward networks were trained by 33 sets of input-output patterns using a back propagation algorithm; a three-layer network with 8 neurons in the hidden layer gave optimal results. The model gave good predictions of high correlation coefficient （R2=0.966）. The predictions by ANN are more accurate when compared with conventional multivariate regression analysis （MVRA）. In addition, calculation with ANN model indicates that temperature is the predominant parameter and ozone concentration is the lesser influential parameter in the pre-oxidation process of refractory gold ore. The ANN neural network model accurately estimates the ferric extraction during pretreatment process of RGC in gold smelter plants and can be used to optimize the process parameters.

Analysis of Texture of Froth Image in Coal Flotation

Froth image features of coal flotation have been extracted and studied by neighboring grey level dependence matrix, spatial grey level dependence matrix and grey level histogram. In this paper, a basic algorithm of unsupervised learning pattern classification is presented, and coal flotation froth images are classified by means of self organizing map (SOM). By extracting features from 51 flotation froth images with laboratory column, four types of froth images are classified. The correct rate of SOM cluster is satisfactory. And a good relationship of froth type with average ash content is also observed.

Fault detection in flotation processes based on deep learning and support vector machine

Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.

Essentials for the Acupuncture Treatment of Radicular Sciatica

Acupuncture treatment can often produce a very good analgesic action on radicular sciatica, with quick effect, low recurrence rate and long period without attack, thus worthy of wide application in clinic.Following are the essentials for the acupuncture treatment of this disease.

Treatment of 50 Cases of Sciatica by Needling Zanzhu and Fengchi

Sciatica belongs to the category of Bizheng (arthralgia syndrome) in traditional Chinese medicine (TCM). From March 1997 to September 2000, we treated 50 cases of sciatica by needling Zanzhu (BL 2) and Fengchi (GB 20) with satisfactory therapeutic results. A report follows.

Clustering-based selective neural network ensemble

An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technology is used to classify trained networks according to similarity and optimally select the most accurate individual network from each cluster to make up the ensemble. Empirical studies on regression of four typical datasets showed that this approach yields significantly smaller en- semble achieving better performance than other traditional ones such as Bagging and Boosting. The bias variance decomposition of the predictive error shows that the success of the proposed approach may lie in its properly tuning the bias/variance trade-off to reduce the prediction error (the sum of bias2 and variance).

Performance prediction of gravity concentrator by using artificial neural network-a case study

In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.

Electro-acupuncture Combined with Plum-blossom Needle Tapping for Treatment of Supraorbital Neuritis—A Clinical Observation of 59 Cases

Supraorbital neuritis is an inflammatory infection of the supraorbital nerve due to invasion of viruses. The authors have treated 59 such cases by means of electroacupuncture combined with plum-blossom needle tapping, with satisfactory therapeutic results reported as follows.

Application of FAHP and Artificial Neural Network on Clothing Plant Location

Clothing manufacturers＇ direct investment and joint ventures in developing regions have seen to grow rapidly in the past few decades. Non-optimized selection can contribute to adverse effects affecting the performance of the plants on aspects of productivity, manufacturing and logistics cost. Selection of proper plant location is thus crucial. The conventional approaches to sites location are based on the factors and their weights. However, determining the weight of each factor is very difficult and time consuming. While the situation is changed, all the work must be redone again. This study aims to develop a decision-making system on clothing plant location for Hoog Kong clothing manufacturer. The proposed system utilizes artificial neural network to study the relationship between the factors and the suitability index of candidate sites. Firstly, the factors are stratified using the fuzzy analytical hierarchy process （FAHP） by review the related references and interviewing the experts. Secondly, the corresponding data are collected from the experts by questionnaire and the related government publication. Finally, the feedforward neural network with error backpropagation（EBP） learning algorithm is trained and applied to make decision. The results show that the proposed system performs well and has the characteristic of adaptability and plasticity.

Effects of highly selective vagotomy plus resection of antral mucosa or highly selective vagotomy alone on motility function of pyloric antrum

Objective: To study the effects of highly selective vagotomy plus resection of antral mucosa (HSV+RAM) or highly selective vagotomy (HSV) alone on the motility function of the pyloric antrum. Methods: 48 patients with duodenal ulcer were studied. 18 dogs were employed as experimental animals. 20 patients were operated on with HSV and 28 with HSV+RAM. The frequency of gastric evacuation and the amplitude of electrogastrography were determined 4 to 6 months after operation. 18 dogs were divided into the control group, HSV group and HSV+RAM group. The time of gastric evacuation, antral myoelectric activity and antral pressure were determined in the dogs 4 to 6 months after operation. The preoperative findings of the patients and the control dogs served as the control. Results: After operation, barium meal revealed that the shape of the stomach and duodenum was normal and the gastric peristalsis was clearly visible in human patients and experimental dogs. In the HSV+RAM group of dogs, the initial evacuation time was (5.0+0.06) min and the time of complete evacuation was (4.0+0.4) h after food-taking, which were similar to those of the control and the HSV group of dogs (P>0.05). The frequency of the antral myoelectric action potential was (3.11+0.65) cycles/min in the dog HAS+RAM group and the frequency of electrogastrography was (3.25+0.75) cycles/min in the human HSV+RAM group, which were significantly lower than those of the control and the dog and human HSV groups (P<0.05). Injection of pentagastrin in dogs and food-taking in human beings significantly increased the antral pressure and the amplitude and frequency of electrogastrography. Conclusion: The findings of this study suggest that the motility function of the reconstructed pyloric antrum in the HSV+RAM group of both the experimental dogs and human patients approaches to the normal even though there is a decrease of antral myoelectric frequency. It is suggested that HSV+RAM should be the first choice for the surgical management of duodenal ulcer.

Fluidized bed control system based on inverse system method

The invertible of the Large Air Dense Medium Fluidized Bed (ADMFB) were studied by introducing the concept of the inverse system theory of nonlinear systems. Then the ADMFB, which was a multivariable, nonlinear and coupled strongly system, was decoupled into independent SISO pseudo-linear subsystems. Linear controllers were designed for each of subsystems based on linear systems theory. The practice output proves that this method improves the stability of the ADMFB obviously.

Acupuncture Treatment for 157 Cases of Anxiety Neurosis

Anxiety neurosis, a mental disorder characterized mainly by extensive anxiety or paroxysmal panic, is often seen in females in their 40s.

Application and comparison of RNN, RBFNN and MNLR approaches on prediction of flotation column performance

Evaluation of grade and recovery plays an important role in process control and plant profitability in mineral processing operations, especially flotation. The accurate measurement or estimation of these two parameters, based on the secondary variables, is a critical issue. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative. In this paper, two types of artificial neural networks(ANNs),namely radial basis function neural network(RBFNN) and layer recurrent neural network(RNN), and also a multivariate nonlinear regression(MNLR) model were employed to predict metallurgical performance of the flotation column. The training capacity and the accuracy of these three above mentioned types of models were compared. In order to acquire data for the simulation, a case study was conducted at Sarcheshmeh copper complex pilot plant. Based on the root mean squared error and correlation coefficient values, at training and testing stages, the RNN forecasted the metallurgical performance of the flotation column better than RBF and MNLR models. The RNN could predict Cu grade and recovery with correlation coefficients of 0.92 and 0.9, respectively in testing process.

Variable Screening of Neural Network based on MIV

Screening variables with significant features as the input data of network, is an important step in application of neural network to predict and analysis problems. This paper proposed a method using MIV algorithm to screen variables of BP neural network.And experimental results show that, the proposed technique is practical and reliable.

The Contribution of Artificial Intelligence Tools in Screening for Cancer of the Cervix

Recently, research into pathological cytology were intended to put in places of artificial intelligence systems based on the development of new diagnostic technologies and the cell image segmentation. These technologies are not intended to substitute the human expert but to facilitate his task. The objective of this work is to develop a method for diagnosing cancer cervical smears using cervical-vaginal segmented to build the authors＇ database and a human supervisor and as an automatic tool manage and monitor the execution of the operation of diagnostic and proposing corrective actions if necessary. The Supervisor Smart is manufactured by the technique of neural networks with a success rate of 43.3% followed by the technique of fuzzy logic with a success rate equal to 56.7% and finally to improve this rate we used neuro-fuzzy approach which has a rate which reaches 94%.