静灵和集神口服液对小鼠脑功能障碍的影响

静灵和集神口服液对正常小鼠行为、情绪及学习记忆无明显影响。仅在大剂量时表现镇静作用,静灵的镇静作用较集神强。静灵和集神对L-多巴诱发的小鼠刻板运动和阿朴吗啡诱发的定向运动,均呈现显著的抑制作用;氯丙嗪对两种动物模型则呈完全的抑制作用。集神和哌醋甲酯对利血平诱发的小鼠眼睑下垂表现明显的对抗作用;静灵仅在大剂量时产生短暂的对抗作用。静灵和氯丙嗪可使阿朴吗啡诱发的小鼠学习记忆障碍完全恢复正常;集神的这种作用较弱。静灵和集神对利血平诱发的小鼠学习记忆障碍,能显著提高其获得学习记忆的百分率;哌醋甲酯可使其完全恢复正常。

二阶导数光谱法测定集神口服液中橙皮甙的含量

集神口服液是本溪中药厂与中华医学会共同研制而生产的用于治疗儿童多动症的新药,处方由陈皮等八味中药组成,对其中橙皮甙的含量测定尚未见报道,本文对此进行探讨,现报告如下。

Rapid assessment of flood loss based on neural network ensemble

Considering the defects of low accuracy and slow speed existing in traditional flood loss assessment, firstly, the technical route of flood loss assessment was presented based on the neural network ensemble. Secondly, through the study of certain country of Poyang Lake district, the flood loss assessment indicators of the test area were analyzed and extracted by utilizing analytic hierarchy process (AHP), and the weights of the impact factors were assigned. Subsequently, the approaches to generate individuals and conclusions of neural network ensemble model were also investigated. In the platform of C# language and neural network library under AForge.NET open source, a flood loss assessment program which could rapidly build neural network ensemble models was developed. Finally, the proposed method was tested and verified. The comparison results between the assessment results of the proposed method and the actual statistical flood loss proved the feasibility of this method, thus a new approach for flood loss assessment was provided.

Document classification approach by rough-set-based corner classification neural network

A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding. In the Rough-CC4, the documents are described by the equivalent classes of the approximate words. By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words. In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded. By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced. The Rough-CC4 can be used in automatic classification of documents.

Spatial interpolation method based on integrated RBF neural networks for estimating heavy metals in soil of a mountain region

A novel spatial interpolation method based on integrated radial basis function artificial neural networks （IRBFANNs） is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.

太平天国以后徽州祭祀礼仪的重整——以抄本《祭神祀祖大例集记》为例

北岸"正月半""八月一"祭祀,是传统时代徽州歙县南乡最为盛大的迎神赛会。每年此时"里做祭,外做戏"的热闹场景,引起了县域之内民众的极大关注。在较长的历史发展过程中,北岸吴氏改造了元代以来江南流行的李王信仰,创造出具有地方特色的老、嫩李王,将之附会成南宋名将吴玠、吴璘两兄弟,并与自己的祖先建立了直接的联系,从而拉近了神明与家族的关系。围绕李王信仰,歙南形成了与之相关的求子及诞育习俗。歙县北岸的迎神赛会,从一个侧面反映了区域社会历史发展的进程。及至19世纪中叶,太平天国运动席卷全国,对徽州亦影响深远。兵燹战乱之余,人们纷纷殚思竭虑地采取各类措施借以恢复、善后。其中,素重慎终追远的徽州人,对于礼仪之重整亦倾注了全力。本文从文本考证入手,结合实地考察,详细论述了抄本《祭神祀祖大例集记》的内容,并以此为基础展开分析,以期揭示徽州地方社会发展的一些侧面。

BIDIRECTIONAL ASSOCIATIVE MEMORY ENSEMBLE

The multiple classifier system （MCS）, composed of multiple diverse classifiers or feed-forward neural networks, can significantly improve the classification or generalization ability of a single classifier. Enlightened by the fundamental idea of MCS, the ensemble is introduced into the quick learning for bidirectional associative memory （QLBAM） to construct a BAM ensemble, for improving the storage capacity and the error-correction capability without destroying the simple structure of the component BAM. Simulations show that, with an appropriate ＂overproduce and choose＂ strategy or ＂thinning＂ algorithm, the proposed BAM ensemble significantly outperforms the single QLBAM in both storage capacity and noise-tolerance capability.

活在五行间

提起五行,大家都能说出金木水火土,可与之对应的生活之道,您了解吗?火-食、木-衣、土-住、金-行、水-性,让我们循五行寻健康,在五行间找到适合自己的生活方式。五行在天为春夏秋冬长夏;在地为金木水火土;在人为仁义礼智信;在生活为衣食住行性;在脏为心肝脾肺肾;在腑为胃小肠大肠膀胱胆。五行是构成万事万物的基本要素,我们无时无刻不生活在五行之间。

聊斋的朋友与冤家

苏联汉学奠基人阿列克谢耶夫院士翻译的《聊斋志异》自 192 2年出版第一个题材分类译本《狐妖集》开始 ,而后又于 192 3、192 8、1937年陆续出版了《神僧集》、《志怪故事集》、《异人集》。阿列克谢耶夫译本以其优美、生动而传神的语言和独到的翻译风格受到苏联广大读者的喜爱多次再版 ,印数庞大。 2 0 0 0年俄罗斯“圣彼得堡东方学”出版社又将阿列克谢耶夫翻译的所有聊斋小说和研究作品结集出版 ,全面展示了俄版《聊斋志异》风貌以及译者对这部作品的深刻理解。本文作者以阿列克谢耶夫翻译、研究、出版《聊斋志异》为中心 ,将与俄版《聊斋志异》有关的各色人等分为“朋友”和“冤家”两大类 ,详细介绍了《聊斋志异》这部伟大的作品在苏联和俄罗斯不同寻常的传播历程 ,以及在此过程中产生的恩恩怨怨 ,展示了阿列克谢耶夫为俄版《聊斋志异》所付出的心血以及《聊斋志异》对阿列克谢耶夫及其弟子的深刻影响。作者不断将译者的命运和人格与蒲松龄及其小说中的人物和思想进行比照 ,反映了译者对作品独特领悟 ,披露了诸如译者与高尔基以及出版社编辑在译文上的分歧、读者对译本的意见反馈以及肃反时期阿列克谢耶夫的那些同样对《聊斋志异》抱有深厚感情的学生们的遭遇。

Optimizing neural networks by genetic algorithms for predicting particulate matter concentration in summer in Beijing

We developed and tested an improved neural network to predict the average concentration of PM10(particulate matter with diameter smaller than 10 ?m) several hours in advance in summer in Beijing.A genetic algorithm optimization procedure for optimizing initial weights and thresholds of the neural network was also evaluated.This research was based upon the PM10 data from seven monitoring sites in Beijing urban region and meteorological observation data,which were recorded every 3 h during summer of 2002.Two neural network models were developed.Model I was built for predicting PM10 concentrations 3 h in advance while Model II for one day in advance.The predictions of both models were found to be consistent with observations.Percent errors in forecasting the numerical value were about 20.This brings us to the conclusion that short-term fluctuations of PM10 concentrations in Beijing urban region in summer are to a large extent driven by meteorological conditions.Moreover,the predicted results of Model II were compared with the ones provided by the Models-3 Community Multiscale Air Quality(CMAQ) modeling system.The mean relative errors of both models were 0.21 and 0.26,respectively.The performance of the neural network model was similar to numerical models,when applied to short-time prediction of PM10 concentration.

An enhanced hybrid ensemble deep learning approach for forecasting daily PM_(2.5)

PM_(2.5) forecasting technology can provide a scientific and effective way to assist environmental governance and protect public health.To forecast PM_(2.5),an enhanced hybrid ensemble deep learning model is proposed in this research.The whole framework of the proposed model can be generalized as follows:the original PM_(2.5) series is decomposed into 8 sub-series with different frequency characteristics by variational mode decomposition(VMD);the long short-term memory(LSTM)network,echo state network(ESN),and temporal convolutional network(TCN)are applied for parallel forecasting for 8 different frequency PM_(2.5) sub-series;the gradient boosting decision tree(GBDT)is applied to assemble and reconstruct the forecasting results of LSTM,ESN and TCN.By comparing the forecasting data of the models over 3 PM_(2.5) series collected from Shenyang,Changsha and Shenzhen,the conclusions can be drawn that GBDT is a more effective method to integrate the forecasting result than traditional heuristic algorithms;MAE values of the proposed model on 3 PM_(2.5) series are 1.587,1.718 and 1.327μg/m3,respectively and the proposed model achieves more accurate results for all experiments than sixteen alternative forecasting models which contain three state-of-the-art models.

Research and Implementation of Decreasing the Acetic Acid Consumption in Purified Terephthalic Acid Solvent System

Decreasing the acetic acid consumption in purified terephthalic acid （PTA） solvent system has become a hot issue with common concern. In accordance with the technical features, the electrical conductivity is in direct proportion to the acetic acid content. General regression neural network （GRNN） is used to establish the model of electrical conductivity on the basis of mechanism analysis, and then particle swarm optimization （PSO） algorithm with the improvement of inertia weight and population diversity is proposed to regulate the operating conditions. Thus, the method of decreasing the acid loss is derived and applied to PTA solvent system in a chemical plant. Cases studies show that the precision of modeling and optimization are higher. The results also provide the optimal operating conditions, which decrease the cost and improve the profit.

“全身”、“全喉”与“全生”——老舍《马裤先生》的一处异文探讨

《马裤先生》是老舍幽默讽刺短篇小说中的名篇。解放后,老舍曾亲自将它改编成独幕话剧《火车上的威风》。可见这也是他本人满意的作品。这篇小说写“我”在火车上遇到的一个穿马裤的先生,他一上车便一连串地喊“茶房”,把茶房支得团团转。

LITERATURE RESEARCH ON SCREENING OF THE NUCLEUS ACUPOINTS FOR TREATMENT OF INTELLECTUAL DISTURBANCES

Intelligence-benefiting acupuncturerefers to the acupuncture-moxibustiontreatment of intellectual disturbances byremoving obstructions in channels andcollaterals,regulating yin and yang,andeliminating pathogenic factors to strengthenthe body resistance,with the effects ofstrengthening the brain,benefiting intelligence,

Neural Network Based Algorithm and Simulation of Information Fusion in the Coal Mine

The concepts of information fusion and the basic principles of neural networks are introduced. Neural net-works were introduced as a way of building an information fusion model in a coal mine monitoring system. This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems. The in-formation fusion mode was analyzed. An algorithm was designed based on this analysis and some simulation results were given. Finally,conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.

STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency for the network to transform to an issue of local solution, a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP, that is, the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights, trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

Wafer bin map inspection based on DenseNet

Wafer bin map(WBM)inspection is a critical approach for evaluating the semiconductor manufacturing process.An excellent inspection algorithm can improve the production efficiency and yield.This paper proposes a WBM defect pattern inspection strategy based on the DenseNet deep learning model,the structure and training loss function are improved according to the characteristics of the WBM.In addition,a constrained mean filtering algorithm is proposed to filter the noise grains.In model prediction,an entropy-based Monte Carlo dropout algorithm is employed to quantify the uncertainty of the model decision.The experimental results show that the recognition ability of the improved DenseNet is better than that of traditional algorithms in terms of typical WBM defect patterns.Analyzing the model uncertainty can not only effectively reduce the miss or false detection rate but also help to identify new patterns.

Simultaneous integrated boost IMRT in pediatric:evaluation for two commercial treatment planning systems

Objective： The aim of the work was to compare the dosimetric results that were obtained by using two treatment planning systems （TPS） Siemens KonRad version 2.2.23, Elekta XiO version 4.4 to perform a simultaneous integrated boost （SIB） for head and neck and central nervous system （CNS） cases in paediatric patients. Methods： The CT scan data for five paediatric patients, with head and neck and CNS tumors, were transferred into both of the TPSs. Clinical step-and-shoot intensity-modulated radiotherapy （IMRT） treatment plans were designed using 6 MV photon beam for delivery on a Siemens Oncor Accelerator with multileaf collimator MLC （82 leaf）. Plans were optimized to achieve the same clinical objectives using the same beam energy, number and direction of beams. The analysis was based on isodose distributions, the dose volume histogram （DVH） for planning target volume （PTV） and the relevant organs at risk （OARs） as well as volume receiving 2 Gy and 5 Gy, also total number of segments, MU/segment, and the number of MU/cGy had been investigated. Treatment delivery time and conformation number were two other parameters in this study. Results： The segmentation using KonRad was more efficient, resulting in fewer segments （reduction between 13.2% and 48.3%）, fewer M Us （reduction between 10.7% and 33%） and that reflected on treatment delivery times to be shorter by up to 8 rain or 46%. In most of the cases KonRad had the highest volume receiving in excess of 2 and 5 Gy, and XiO showed the lowest. Also KonRad achieved slightly better conformality （0.76 ± 0.054） than XiO （0.73 ± 0.05） while XiO presented a higher modulation factor value （3.3 MU/cGy） than KonRad （2.4 MU/cGy）. Conclusion： The KonRad treatment planning system was found to be superior to the XiO treatment planning system. This is true for the possible increase of radiation-induced secondary malignancies as well as for the local control.

A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process

Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.