鲁锦的手工织造技术

在对鲁锦深入调查的基础上,对鲁锦的织造技术进行探究,并收集、整理大量的鲁锦图案,用工程技术学的方法探讨了几种典型图案的织造技术,重点分析经线显花技术,认为日本博多织与鲁锦之间在经浮显花技术存在一定历史渊源。通过鲁锦"包袱带"加工,分析独特的"挂经"织造技术。基于鲁锦"七个字"织纹图案,剖析源于古代的一经穿多综的省综穿综方式,并分析鲁锦的挑花技术。认为鲁锦的部分织造技术具有科学性和创造性,是用现代织机无法实现的。

新教材、新点、教学采用新方法——理论联系实际的交汇点

新教材、新特点、要求与之相适应的新方法,在今天改革开放的年代,随着中学生年龄增长,特别是日益增加的信息传播渠道,使各种价值观,政治、文化思潮及生活方式,传统观念交织在一起向他们渗透,商品经济的冲击波不断向他们幅射,此时中学生独立意识增强,他们不满足老师在课堂上“喂”、他们喜欢评价周围的人和事,评价国内外大事。他们认为靠自己觅来的食格外香甜。这要求我们老师跳出照本宣科的旧框框。我采取了新方法:

经济学家吴敬琏谈下步改革的重点

他指出,20年来,特别是十四大和十四届三中全会以来,我国第一步市场取向的改革取得了长足进展,市场体制的初步轮廓已经浮现在人们面前。但在一些重要环节上,例如国企改革和国有经济改组等方面,改革尚未到位;市场秩序混乱,竞争缺乏规则。可以说,市场经济作为一个完整的系统还没有建立起来。为实现本世纪末初步建立社会主义市场经济体系的目标,第二步改革的重点应放在4个方面,

Vertical structure and evolution of the Luzon Warm Eddy

Eddies are frequently observed in the northeastern South China Sea （SCS）. However, there have been few studies on vertical structure and temporal-spatial evolution of these eddies. We analyzed the seasonal Luzon Warm Eddy （LWE） based on Argo float data and the merged data products of satellite altimeters of Topex/Poseidon, Jason-1 and European Research Satellites. The analysis shows that the LWE extends vertically to more than 500 m water depth, with a higher temperature anomaly of 5℃ and lower salinity anomaly of 0.5 near the thermocline. The current speeds of the LWE are stronger in its uppermost 200 m, with a maximum speed of 0.6 m/s. Sometimes the LWE incorporates mixed waters from the Kuroshio Current and the SCS, and thus has higher thermohaline characteristics than local marine waters. Time series of eddy kinematic parameters show that the radii and shape of the LWE vary during propagation, and its eddy kinetic energy follows a normal distribution. In addition, we used the empirical orthogonal function （EOF） here to analyze seasonal characteristics of the LWE. The results suggest that the LWE generally forms in July, intensifies in August and September, separates from the coast of Luzon in October and propagates westward, and weakens in December and disappears in February. The LWE＇s westward migration is approximately along 19°N latitude from northwest of Luzon to southeast of Hainan, with a mean speed of 6.6 cm/s.

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.

Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.000 1, while the optimum chosen 4-8-1 BP needs over 10 000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be (adopted) to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.

Enhancement of fine particle filtration with efficient humidification

Filtration is one of the most effective methods to remove suspended fine particles from air. In filtration processes,pressure drop of compact dust cake causes problems in efficiency and economy, which has received increasing attention and still remains challenging. In this study, we developed a novel technique to intensify the filtration of fine particles with efficient humidification. Two strategies for humidification, including ultrasonic atomization and steam humidification(controlling of ambient humidity), were employed and proved to be both effective. The regeneration frequency of the filter could be reduced by 55% with ultrasonic atomization, while steam humidification could lead to a 78% reduction in regeneration frequency. The effect of operating conditions on pressure drop and the mass loading during filtration were investigated. The dust cake showed a loose and porous structure with an optimized droplet-to-particle ratio. With the ratio of 1.53 and 0.0282, the maximum mass loading was 552 g·m-2upon the ultrasonic atomization and 720 g·m-2upon the steam humidification. The results show that humidification could slow down the increase of pressure drop during filtration and improve the efficiency of process.

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.

Analysis on financing, investing and operating activities of Target Corporation

The paper analyzes the financing activity, investing activity and operating activity of Target Corporation by comparing with other companies in the same industry. The analysis shows that Target overall is better at meeting it in short-term obligations as well as earning money for its investors. Target is a safe and consistent investment. The company should pay attention to interest rate changes because the company＇s majority of financing charges are based on floating-rate debt obligations.

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.

The role of duckweed in bio-liquid fuel production and environmental protection

With the world economic development, population growth and improvement of people＇ s living standard, the energy shortage has become the core issue of restricting the development of the world economy. China faces serious energy crisis and environmental problems, so the development of biofuels in China is vital. This paper introduces the advantages of duckweed for energy production, summarizes the research results of Chengdu Institute of Biology on duckweed, and provides the direction of its further studv.

Therapeutic observation on superficial needling with different frequencies for intractable facial paralysis

Objective:To obverse the therapeutic effect of superficial needling with different frequencies for intractable facial paralysis.Methods:A total of 120 patients with intractable peripheral facial paralysis were allocated into a superficial needling with high frequency group(150 times/min),a moderate frequency group(100 times/min)and a low frequency group(50 times/min)according to the random number table method.The Toronto facial grading system(TFGS)was used to evaluate facial nerve functions before treatment and after 2 weeks and 4 weeks of treatment respectively.The electromyography(EMG)test of the mandibular branch of facial nerve was used to compare the motor nerve conduction velocity(MCV),sen sory nerve con ducti on velocity(SCV)and mono phasic acti on pote ntial(MAP)among differe nt groups,and was done before treatment and after 4 weeks of treatment.The clinical efficacy was also compared.Results:After 2 weeks and 4 weeks of treatment,the changes of TFGS scores in the three groups all showed statistical significanee(all P<0.05)z and the TFGS score in the low frequency group was substantially higher than that in the other two groups.After treatment,the changes of the MCV and SCV in the three groups all showed statistical significanee(all P<0.05),and the results in the low frequency group were higher than those in the other two groups;the change of MAP in the three groups showed no statistical significance(P>0.05).The total effective rate was 65.0%,80.0%and 95.0%in the high frequency group,moderate frequency group and low frequency group respectively,and the betweervgroup differences showed statistical significanee(P<0.05).Conclusion:Compared with the superficial needling with high and moderate frequencies,superficial needling with low frequency can produce more significant clinical efficacy for intractable facial paralysis.

Simple colorimetric detection of dopamine using modified silver nanoparticles

Dopamine(DA) plays an important role in health and peripheral nervous systems. Colorimetric detection of DA has the advantage of color change and simplicity in operation and instrumentation. Herein, we report a highly sensitive and selective colorimetric detection of DA by using two specific ligands modified Ag nanoparticles, where the DA molecules can make dual recognition with high specificity. The colloidal suspension of modified Ag nanoparticles was agglomerated after interacting with DA, while the color of Ag nanoparticles suspension changed from yellow to brown, arising from the interparticle plasmon coupling during the aggregation of Ag nanoparticles. The modified Ag nanoparticles suspension and agglomeration were confirmed by transmission electron microscope images. The optical properties behind the color change were thoroughly investigated by using UV-Vis and Raman techniques. The changes in p H, zeta potential, particle size and surface charge density by adding DA were also determined by using dynamic light scattering measurements. The detection limits of modified Ag probes for DA was calculated to be 6.13′10^(-6) mol L^(-1)(S/N=2.04) and the correlation co-efficient was determined to be 0.9878. Because of the simplicity in operation and instrumentation of the colorimetric method, this work may afford a feasible, fast approach for detecting and monitoring the DA levels in physiological and pathological systems.

Task space control of free-floating space robots using constrained adaptive RBF-NTSM

Trajectory tracking control of space robots in task space is of great importance to space missions, which require on-orbit manipulations. This paper focuses on position and attitude tracking control of a tree-floating space robot in task space. Since nei- ther the nonlinear terms and parametric uncertainties of the dynamic model, nor the external disturbances are known, an adap- tive radial basis function network based nonsingular terminal sliding mode （RBF-NTSM） control method is presented. The proposed algorithm combines the nonlinear sliding manifold with the radial basis function to improve control performance. Moreover, in order to account for actuator physical constraints, a constrained adaptive RBF-NTSM, which employs a RBF network to compensate for the limited input is developed. The adaptive updating laws acquired by Lyapunov approach guar- antee the global stability of the control system and suppress chattering problems. Two examples are provided using a six-link free-floating space robot. Simulation results clearly demonstrate that the proposed constrained adaptive RBF-NTSM control method performs high precision task based on incomplete dynamic model of the space robots. In addition, the control errors converge faster and the chattering is eliminated comparing to traditional sliding mode control.