能源动力类专业基础课参与式教学设计与实践

以教学实践为基础,结合能源动力类专业基础课课程的教学特点,深入探讨创新型人才培养背景下参与式教学在《热工理论基础》系列课程中应用的设计理念、实施策略、实践过程及其结果,结果表明参与式教学对于提高学生学习兴趣、自主能力和创新能力起着积极地作用,探索出了行之有效的工科专业基础课参与式教学模式。     

The Irritant Effects of Pharmaceutically Applied Surfactants

Dermal or transdermal medication may lead to irritant contact dermatitis. However, little information is available on the irritant effect of surfactants which are applied in topical formulations. Our aim was to examine the irritant effect of the most frequent compounds in topical products. A murine model was applied. The following compounds were examined: sodium lauryl sulphate (SLS), polyethoxylated (40EO) hydrogenated castor oil and sucrose laurate. SLS led to severe erythema, increase in transepidermal water loss (TEWL) and induced necrosis and accumulation of neutrophylic granulocytes and lymphocytes. Exposure to sucrose laurate resulted in an elevation of TEWL, but histology did not reveal impairment of the skin structure. Application of polyethoxylated (40EO) hydrogenated castor oil was not accompanied by tissue damage. Special attention should be paid to the irritant effect of SLS. Polyethoxylated (40EO) hydrogenated castor oil seems to be a non-irritant agent and sucrose laurate is also a promising candidate for application in topical preparations.     

正交试验优化超临界CO_2萃取贺兰山丁香挥发油工艺及其成分分析

以天然及人工种植贺兰山丁香(别名山沉香,以下简称山沉香)为原料,以其挥发油出油率为考察指标,研究了超临界CO2萃取山沉香挥发油的分离工艺。通过正交试验,优选了最佳萃取工艺条件:萃取温度55℃、萃取压力21 MPa、萃取时间120 min、CO2流量9~12 L/h。在此条件下天然及人工种植山沉香挥发油的出油率分别为1.22%及0.79%。并用气相色谱-质谱法(GC-MS)分析了贺兰山丁香挥发油中各组分的相对质量分数和结构,从而比较了天然及人工种植山沉香的主要有效成分及其相对含量,探讨了人工种植山沉香替代天然山沉香的趋势。     

Classification of EEG signals using neural network and logistic regression

Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.     

The human role in the age of information

Age of automation entails freedom from most of the common working roles. Signs are changes in employment, unemployment, professional structures, relevance of services, entertainment industry, working hours, and the nature of social relations. Warnings are suggested against voluntaristic interventions, neglect of social, historical relations. New approaches are required in the fields of lifelong education and in the education of socially disadvantaged people. The changes in evolutionary inherited motivations and life styles are critical challenges to mankind. Open society and lessons of system control are recommended.     

Face recognition with radial basis function (RBF) neural networks

A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting features are further processed by the Fisher's linear discriminant (FLD) technique to acquire lower-dimensional discriminant patterns. A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place. A hybrid learning algorithm is used to train the RBF neural networks so that the dimension of the search space is drastically reduced in the gradient paradigm. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.     

砂姜黑土小麦分蘖成穗及其调控研究

采用盆栽试验和小区试验相结合的方法 ,对砂姜黑土上不同小麦品种的分蘖成穗特性及其调控进行了研究 .结果表明 ,兰考 90 6分蘖发生快、多且衰亡也最快 ,分蘖成穗率较低 ,主要是由于二、三级分蘖发生较多 ,越冬期具有 4张以上叶片的分蘖才能成穗 .温麦 6号分蘖发生平稳 ,成穗率最高 ;百农 64分蘖力中等 ,增加一定的施肥量能明显提高分蘖成穗率 .不同生长调节剂对兰考 90 6分蘖成穗有明显的调控作用 ,以 6 BAt和 2 ,4 D控制分蘖成穗效果最为明显     

Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.