借鉴与试构:“统能学习”叙论

素质教育改革背景下＂统能学习＂的尝试性、创构性提出,基于对赫尔巴特＂统觉＂论与陶行知＂常能＂论等思想进行的多向、融合式借鉴。须明晰其内涵特点,谨严其因材实施,以助力学生统合、统整式地动性生成学习、生活、实践、创新诸项能力。

Comparative Study between Waffle and Solid Slab Systems in Terms of Economy and Seismic Performance of a Typical 14-Story RC Building

In order to maximize the return of investments and at the same time improve the quality in the construction industry of midrise buildings, it is very important to derive an optimal solution to the building structural system, which would facilitate faster and easier construction activities with minimal quantity of construction material, while maintaining the satisfactory level of building safety and performance. This paper makes a comparative study between a ＂solid＂ and a ＂waffle＂ slab system. A typical 14-story RC building structure is selected as an example for this study purpose. The first part of this study is focused in deriving an optimal solution for a solid and waffle slab system which are later on considered as constituents of all stories of the 14-story building. In the second part, it is elaborated the effect of both slab systems over the 14-story building model. This study aims to emphasize the advantages of mid-rise buildings constituted of waffle slab system over the buildings characterized with solid types of slabs, in terms of economy, structural safety and performance.

Opportunities and challenges for developing closed-loop bioelectronic medicines

The peripheral nervous system plays a major role in the maintenance of our physiology. Several peripheral nerves intimately regulate the state of the brain, spinal cord, and visceral systems. A new class of therapeutics, called bioelectronic medicines, are being developed to precisely regulate physiology and treat dysfunction using peripheral nerve stimulation. In this review, we first discuss new work using closed-loop bioelectronic medicine to treat upper limb paralysis. In contrast to open-loop bioelectronic medicines, closed-loop approaches trigger ‘on demand' peripheral nerve stimulation due to a change in function(e.g., during an upper limb movement or a change in cardiopulmonary state). We also outline our perspective on timing rules for closedloop bioelectronic stimulation, interface features for non-invasively stimulating peripheral nerves, and machine learning algorithms to recognize disease events for closed-loop stimulation control. Although there will be several challenges for this emerging field, we look forward to future bioelectronic medicines that can autonomously sense changes in the body, to provide closed-loop peripheral nerve stimulation and treat disease.

Systemic Functional Grammar and its pedagogical implications

This paper focuses on the application of systemic Functional Grammar （SFG） to language study. By providing a sample of text analysis from the systemic functional point of view, the paper illustrates how this approach can be helpful to language teaching.

A New Control Strategy for Modeling Wind Energy Systems Using Fuzzy Cognitive Maps

Wind energy is currently a fast-growing interdisciplinary field that encompasses many different branches of engineering and science. Modeling and controlling wind energy systems are difficult and challenging problems. The basic structure of wind turbines and some wind control system methods are briefly reviewed. The need for using advanced theories from fuzzy and intelligent systems in studying wind energy systems is identified and justified. FCMs （fuzzy cognitive maps） are used to model wind energy systems. Simulation studies are performed and obtained results are discussed. A new mathematical approach has been proposed to model dynamical complex systems, the DYFUKN （dynamic fuzzy knowledge networks）. Many open problems in the areas of modeling and controlling wind energy systems are outlined.

Theoretical and Experimental Study of a Cylindro-Parabolic Solar Collector

This article outlines the theoretical and experimental performance studies of a cylindro-parabolic solar collector. The theoretical study consists on the establishment, through mass and energy balances, of a mathematical model to control the exiting temperature of the heating fluid as well as the temperatures of the absorber and the glass. The experimental level investigates the influence of the solar absorber tube diameter on the performances of the driving device. Several experiments were made in order to know the possibility to reach temperatures being able to ensure for example the ammonia vaporization in the generator of a solar absorption refrigeration system. These experiments were carried out under various operating and climatic conditions. The results are presented and discussed.

High-Performance of Power System Based upon ANFIS （Adaptive Neuro-Fuzzy Inference System） Controller

The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.

Spatial learning deficits in amyloid precursor protein 770 transgenic mice

Objective To determine whether learning deficits could be seen in transgenic mice expressing human amyloid precursor protein 770 (APP 770 ) Methods Female heterozygous transgenic and nontransgenic mice aged 3, 6 and 9 months at the start of testing were used, with eight mice in each age group All mice were subjected to various behavioral tasks including the Y maze task and the Morris water maze After behavioral testing, the mice were sacrificed, and their brain tissues were used for measuring the choline acetyltransferase (ChAT) activity Results Nine month old transgenic mice exhibited spatial learning deficits in the Morris water maze and in spontaneous alternation in the Y maze, compared with those of the age matched non transgenic mice The behavioral changes accompanied a reduction of ChAT activity in the cortical and hippocampal regions of transgenic mice On the other hand, these behavioral deficits were not observed in transgenic mice either at 3 or at 6 months of age, in which ChAT activity remained unchanged Conclusions The present results show that the learning impairment observed in 9 month old APP 770 transgenic mice are accompanied by a decrease in cortical and hippocampal ChAT activities This suggests that cholinergic deficits may be involved in the learning impairment observed in these APP 770 mice This model will be a useful tool in advancing our understanding of the relationship between the cholinergic system and the cognitive deficits observed in Alzheimer's disease (AD)

A biologically inspired model for pattern recognition

In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.

An Approach to Active Learning for Classifier Systems

In this paper, the active learning mechanism is proposed to beused in classifier systems to cope with complex problems: an intelligent agent leavesits own signals in the environment and later collects and employs them to assistits learning process. Principles and components of the mechanism are outlined,followed by the introduction of its preliminary implementation in an actual system.An experiment with the system in a dynamic problem is then introduced, togetherwith discussions over its results. The paper is concluded by pointing out somepossible improvements that can be made to the proposed framework.