预学:小学数学课堂学习的前奏曲

预学是学生课堂学习之前的一种准备学习,有效的预学可以激发学生学习兴趣和主动性,提高教师课堂教学有效性。本文凸显了预学的价值性,并结合自己的教学思考,提出具有普适性的小学数学预学案设计思路,希望能帮助学生获得更好的学习方法,帮助教师打造更有效的课堂教学。

目标导向的高中英语课前预学

课前预学是课堂教学的起点,是提高课堂教学有效性和培养学生自主学习能力的重要途径。本文从高中英语课前预学现状出发,提出从预学目标(aim)、预学任务(assignment)、预学方法(approach)、预学评价(assessment)四个维度构建4A高中英语课前预学模式,以期提高课前预学的有效性。

培养“预学”习惯 落实先学后教

本文探讨了“以学定教”理念中“先学后教”的教学理念在小学英语教学中的实践与运用。笔者认为结合小学英语学科教学的特点，“预学”应纳入成为小学英语课堂教学中的重要环节。教师要重视培养良好的“预学”习惯，细化预习内容，明确预习要求，提供预习方法，促进学生有效地自学和思考，帮助学生提高课前预习质量，并切实地根据学生的预习反馈，以学定教，调整教案，提高课堂教学实效。

New methods for the management of esophageal varices

Bleeding from esophageal varices (EVs) is a catastrophic complication of chronic liver disease. Many years ago, surgical procedures such as esophageal transection or distal splenorenal shunting were the only treatments for EVs. In the 1970s, interventional radiology procedures such as transportal obliteration, left gastric artery embolization, and partial splenic artery embolization were introduced, improving the survival of patients with bleeding EVs. In the 1980s, endoscopic treatment, endoscopic injection sclerotherapy (EIS), and endoscopic variceal ligation (EVL), further contributed to improved survival. We combined IVR with endoscopic treatment or EIS with EVL. Most patients with EVs treated endoscopically required follow- up treatment for recurrent varices. Proper management of recurrent EVs can significantly improve patients’ quality of life. Recently, we have performed EVL at 2-mo (bimonthly) intervals for the management of EVs. Longer intervals between treatment sessions resulted in a higher rate of total eradication and lower rates of recurrence and additional treatment.

The Curl of Q Vector: A New Diagnostic Parameter Associated with Heavy Rainfall

As a powerful tool to diagnose vertical motion, frontogenesis, and secondary circulation, the Q vector and its divergence are widely used. However, little attention has been given to the curl of Q vector. In this paper, a new set of analyses combining the divergence of the Q vector （DQ） with the vertical component of the curl of the Q vector （VQ） is applied to a Northeastern cold vortex rainfall case. From the derivation, it was found that the expressions of the Q vectors and their divergences in saturated moist flow （DQm） differ from those of dry and unsaturated moist atmosphere （DQ）, while the VQs of various background flows are exactly the same, which largely simplified the analyses. This case study showed that, compared with the DQ, not only can the DQm diagnose precipitation more effectively, but the VQ may also be indicative of precipitation （especially for heavy rainfall and strong convection） because of its direct, close relationship with ageostrophic motion. Thus, the VQ may be computed and analyzed with ease, and may serve as a useful tool for analyses of precipitation and strong convective svstems.

An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using Neur-On-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment,and a new modified Broyden, Fletcher, Goldfarb, and Shanno （BFGS） quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation （BP） neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quicklv track the time-varving and nonlinear behavior of the bioreactor.

Prediction for asphalt pavement water film thickness based on artificial neural network

In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.

Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques

Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.

Value of COX-2 and HER-2 in Judging Condition and Prognosis in Non-Small Cell Lung Cancer Patients

OBJECTIVE To investigate the expressions of cyclooxygenase 2 （COX-2） and human epidermal growth factor receptor-2 （HER-2） in non-small cell lung cancer （NSCLC） and their clinical significance in identifying the progression and prognosis of the NSCLC patients. METHODS Immunohistochemical indirect method was used to detect the expressions of the COX-2 and HER-2 protein in 54 NSCLC specimens, 16 paraneoplastic specimens, and 10 normal tissue specimens. RESULTS The positive rates of COX-2 and HER-2 protein expressions were respectively 75.9% and 40.7% in the NSCLC specimens, 25% and 12.5% in the paraneoplastic specimens, and 0 in the normal tissue. The COX-2 protein expression in lung cancer （LC） was not only related to the smoking habit of the patients and histological grades of LC, but also to the TNM stages, and lymphatic metastasis （P 〈 0.05）. HER-2 protein expression closely correlated to the pathologic types, histological grades, TNM stages, and lymphatic metastasis （P 〈 0.05）. The result of univariate analysis showed that all the histological grades, TNM stages, lymphatic metastasis, and expressions of COX-2/HER-2 correlated to the prognosis of NSCLC patients （mean of P value 〈 0.01）. The multivariate survival analysis indicated that there were signi.cant di.erences in comparison of the survival time between the COX-2 （＋＋/＋＋＋） /HER-2 （＋＋/＋＋＋） and the COX-2 （-/＋）/HER-2 （-/＋） groups （P〈 0.001）, suggesting the COX-2/HER-2 was a negative prognostic factor. CONCLUSION COX-2 and HER-2 are valuable in identifying the progression of NSCLC and predicting the prognosis of NSCLC patients. COX-2 and HER-2 are useful for judging the NSCLC patient＇s condition, and are of great value to the decision of NSCLC prognosis.

The Effect of Integrating Inductive Approach and Deductive Approach With Multimedia Assistance Into Acquisition of Subjunctive Mood

This study applied both inductive approach and deductive approach with multimedia assistance into an English grammar class on the acquisition of subjunctive mood. It aimed to investigate whether this kind of teaching approach, as a general grammar pedagogy, would improve the efficiency of students＇ acquisition of certain grammar points. This study results from comparison and contrast between one same class on the acquisition of subjunctive mood to 70 students in total, employing three different teaching methods： inductive approach with multimedia assistance （method 1）, deductive approach with multimedia assistance （method 2）, and both inductive approach and deductive approach with multimedia assistance （method 3）, based on self-reported reflection on the experiment, observation of students＇ learning process, students＇ pre-test and after-class evaluation test results, and surveys. Two identical experiments were conducted to two groups of students of different levels of language proficiency to increase the generalizability of the results. Findings revealed that the evaluation test score of the grammatical points taught with method 3 is much higher than those taught with the other two methods, and most students felt positive about method 3. Students nevertheless encounter obvious difficulties in inductive approach, indicating certain lack of self-learning skills in Chinese students

Determination of Effective Degree-Day for Supporting Chemical Control Against Cutworms （Lep. Noctuinae） in the Sugar Beet

The aim of the research was to connect two methods of the chemical control. The first chemical treatments were applied according to the signalling method. The second method was applied according to the phonological criterion i.e., on the basis of the values of effective temperatures sums or heat sums for cutworms. The studies on cutworms infesting sugar beet crops were carried out in the years 2005-2008. The observation performed during the moth flights from May to September included two species, turnip moth （Agrotis segetum Den. ＆ Schiff.） and heart-and-dart moth （A. exclamationis L.）. The dynamics of moth flights was recorded in reference to readings of climatic conditions registered with the field meteorological stations set up near the light traps. Observations on cutworm occurrence during the vegetation season were done every 5-7 days. Moreover, additional studies were conducted under control conditions in the growth chambers at three programmed temperatures （17°C, 20 °C, 24 °C） and relative humidity （50%-70%）. Based on the results the values for the heat sum of 501.1 °C and effective temperatures sum of 230.0 °C were determined for the developmental stages of cutworm. On the base of the results obtained it can be stated that the improved method of short-term forecasting can be an alternative solution in the integrated protection management against pest.

A TREND DISPLACEMENT ANALYSIS FOR SPACE AND TIME ON XINTAN LANDSLIDE

This paper, having made systematic trend analysis on the front and rear segments of Xintan landslide for space and time respectively by using matbematical statistical principles,discovered that there is obvious trend displacement Of the monitoring points in the rear margin area of the slope and the rates of trend displacement gradually increase with time whereas there is no trend displacement of the monitoring points in the front margin area. This result suggests that the rear margin area of segment is an area of overall sliding and is transforming towards destabilization whereas the front margin area is an area of relative stability. This analytical result well coincides with the conclusion of evaluation on dynamic stability. The analytical result mentioned above shows that the medium to short term forecast and prediction of slope stability can be made by using trend displacement analysis technique in order to achieve the goal of timely evaluation and prevention.

Influence of surface etching pretreatment on PEO process of eutectic Al–Si alloy

To solve the problems generally encountered during the plasma electrolytic oxidation(PEO) of Al alloys with high Si content, a pretreatment of chemical etching was applied before the process. The influence of such pretreatment was studied by SEM, EDS and XRD. The pretreatment presents a significant effect on positive voltage at the beginning stage of PEO, leading to higher voltage over the whole process. The difference between the positive voltages of non-etched and etched specimens decreases gradually with the increase of processing time. The pretreatment exhibits much less influence on the negative voltage. For the sample with surface pretreatment, the average growth rate of PEO coating is increased from 0.50 to 0.84 μm·min-1and the energy consumption is decreased from 6.30 to 4.36 k W·h·μm-1·m-2. At the same time, both mullite and amorphous Si O2 contents are decreased in the coating.

Times Series Prediction to Basis of a Neural Network Conceived by a Real Genetic Algorithm

Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.

Improved Crude Oil Price Forecasting With Statistical Learning Methods

Reliable forecasts of the price of oil are of interest for a wide range of applications. For example, central banks and private sector forecasters view the price of oil as one of the key variables in generating macroeconomic projections and in assessing macroeconomic risks. Of particular interest is the question of the extent to which the price of oil is helpful in predicting recessions. This paper presents a statistical learning method （SLM） based on combined fuzzy system （FS）, artificial neural network （ANN）, and support vector regression （SVR） to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. A number of quantitative factors were discovered from this model and used as the input. For verification and testing, the West Texas Intermediate （WT1） crude oil spot price is used to test the effectiveness of the proposed learning methodology. Empirical results reveal that the proposed SLM-based forecasting can model the nonlinear relationship between the input variables and price very well. Furthermore, in-sample and out-of-sample prediction performance also demonstrates that the proposed SLM model can produce more accurate prediction results than other nonlinear models.

FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID ECONOMETRIC-AI ENSEMBLE LEARNING APPROACH

Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.

Indicator and Multivariate Geostatistics for Spatial Prediction

There are various occasions where simple, ordinary, and universal kriging techniques may find themselves incapable of performing spatial prediction directly or efficiently. One type of application concerns quantification of cumulative distribution function (CDF) or probability of occurrences of categorical variables over space. The other is related to optimal use of co-variation inherent to multiple regionalized variables as well as spatial correlation in spatial prediction. This paper extends geostatistics from the realm of kriging with uni-variate and continuous regionalized variables to the territory of indicator and multivariate kriging, where it is of ultimate importance to perform non-parametric estimation of probability distributions and spatial prediction based on co-regionalization and multiple data sources, respectively.

From electronic excited state theory to the property predictions of organic optoelectronic materials

We introduce here a work package for a National Natural Science Foundation of China Major Project. We propose to develop computational methodology starting from the theory of electronic excitation processes to predicting the opto-electronic property for organic materials, in close collaborations with experiments. Through developing methods for the electron dynamics, considering superexchange electronic couplings, spin-orbit coupling elements between excited states, electron-phonon relaxation, intermolecular Coulomb and exchange terms we combine the statistical physics approaches including dynamic Monte Carlo, Boltzmann transport equation and Boltzmann statistics to predict the macroscopic properties of opto-electronic materials such as light-emitting efficiency, charge mobility, and exciton diffusion length. Experimental synthesis and characterization of D-A type ambipolar transport material as well as novel carbon based material will provide a test ground for the verification of theory.

Forecasting Helicoverpa populations in Australia： A comparison of regression based models and a bioclimatic based modelling approach

Long-term forecasts of pest pressure are central to the effective managementof many agricultural insect pests. In the eastern cropping regions of Australia, seriousinfestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hiibner)(Lepidoptera:Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches ofadult moths were used to describe the seasonal dynamics of both species. The size of the springgeneration in eastern cropping zones could be related to rainfall in putative source areas in inlandAustralia. Subsequent generations could be related to the abundance of various crops inagricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figuredprominently as a predictor variable, and can itself be predicted using the Southern OscillationIndex (SOI), trap catches were also related to this variable. The geographic distribution of eachspecies was modelled in relation to climate and CLIMEX was used to predict temporal variation inabundance at given putative source sites in inland Australia using historical meteorological data.These predictions were then correlated with subsequent pest abundance data in a major croppingregion. The regression-based and bio-climatic-based approaches to predicting pest abundance arecompared and their utility in predicting and interpreting pest dynamics are discussed.

Prediction of the effective thermal conductivity of packed bed with micro-particles for thermochemical heat storage

The heat transfer property of the powder bed greatly affects the performance of a thermochemical heat storage system. Therefore, an accurate evaluation of effective thermal conductivity （ETC） is a key for developing thermochemical heat storage systems. This paper focuses on the ETCs of commonly used por- ous thermochemical materials, such as MgOJMg（OH）2 and CaOJCa（OH）2 powders, as well as the corre- sponding composites with embedded metal foams. Random sphere-like particles packing （RSPP） method is proposed to reconstruct the microstructures of the powder and micro-scale generation method and computed tomography are adopted for the metal foams. Energy transport equations through porous media are solved by the lattice Boltzmann method （LBM） to obtain ETC. Results obtained using RSPP-LBM method agree with experimental data better than other existing methods. For thermochemical heat stor- age, the variation of ETC during chemical reactions is numerically predicted. Metal foam-embedded ther- rnochemical materials are also studied to evaluate the enhancing effects of the metal foams. Results show that ETC of the powders is dominated by the gas phase, whereas that of the metal foam composites is dominated by the metal Phase.