“认识角”教学设计

随着教学改革的发展,素质教育在中小学教学中逐渐深化,寓教于乐成为发展素质教育的有效途径。笔者根据多年教学经验,针对小学生数学教学的特点及小学生特殊的接受心理,设计了一系列数学教学方法以完成并深化孩子们对数学这门学科的理解与掌握。本文针对小学数学中"角"的识别与掌握,设计出易于孩子们接受的一套教学方案。

Intelligent identifi cation method for near-surface ground fi ssures based on seismic data

Taking a study area in Jinzhong Basin in Qixian County,Shanxi Province,as an example,this work performs an intelligent interpretation of ground fissures.On the basis of a complete analysis of the regional geological background in the study area,dip-steering cube operation and median filtering of seismic data were performed using fast Fourier transform to improve the continuity of seismic events and eliminate random noise.A total of 200 stratigraphic continuous sample training points and 500 discontinuous training points were obtained from the processed seismic data.Thereafter,a variety of attributes(coherence,curvature,amplitude,frequency,etc.)were extracted as the input for the multilayer perceptron neural network training.During the training period,the training results were traced by normalized root mean square error(RMSE)and misclassifi cation.The training results showed a downward trend during the training period.The misclassifi cation curve was stable at 0.3,and the normalized RMSE curve was stable at 0.68.When the value of the normalized RMSE curve reached the minimum,the training was terminated,and the training results were extended to the whole data volume to obtain the attribute cube of intelligent ground fi ssure detection.The characteristics of ground fi ssures were analyzed and identifi ed from the sections and slices.A total of 11 ground fissures were finally interpreted.The interpretation results showed that the dip angles were 60°-85°,the fault throws were 0-43 m,and the extension lengths were 300-1,100 m in the whole area.The strike of 73%of the ground fi ssures was consistent with the direction of the regional tectonic settings.Specifi cally,four ground fi ssures coincided with the surface disclosed,and the verifi cation rate reached 100%.In conclusion,the intelligent ground fi ssure detection attribute based on the dip-steering cube is eff ective in predicting the spatial distribution of ground fi ssures.

Robust Integrated Models for Chinese Predicate-Argument Structure Analysis

Predicate-Argument （PA） structure anal- ysis is often divided into three subtasks： predicate sense disambiguation, argument identification and argument classification mostly been modeled in To date, they have isolation. However, this approach neglects logical constraints between them. We therefore exploite integrating predicate sense disambiguation with the latter two subtasks respectively, which verifies that the automatic predicate sense disambiguation could help the se- mantic role labeling task. In addition, a dual de- composition algorithm is used to alleviate the er- ror propagation between argument identification subtask and argument classification subtask by benefitting the argument identification subtask greatly. Experiment results show that our ap- proach leads to a better performance with PA a- nalysis than other pipeline approaches.

Identification of the Epitopes of Monoclonal Antibodies against P74 of Helicoverpa armigera Nucleopolyhedrovirus

P74 is a per os infectivity factor of baculovirus.Here,we report the production of three monoclonal antibodies (mAbs),denoted as 20D9,20F9 and 21E1,raised against P74 of Helicoverpa armigera nucleopolyhedrovirus (HearNPV),and the identification of their recognition epitopes.The full-length P74,without the transmembrane domains at the C-terminus,was first divided into three segments (N,M and C,respectively),based on the proposed cleavage model for the protein,which were then expressed individually.Western blot analyses revealed specific cross-reactions with the N fragment,for both 20D9 and 21E1.Extensive truncation,followed by prokaryotic expression,of the P74 N fragment was then performed in order to screen for linear epitopes of P74.The recognition regions of 20D9 and 21E1 were revealed to be localized at R144-T153 and T199-C219,respectively.In addition,immunofluorescence microscopy indicated that 20D9 and 20F9 could recognize native P74 in HearNPV-infected cells.These findings will facilitate further investigations of the proteolytic processing of HearNPV P74,and of its involvement in virus-host interactions.

Recognition of the Head of Submarine Canyon at the Base of Mahaweli River Delta, Sri Lanka

The coastal region around Sri Lanka have been subjected to considerable changes since Pleistocene and one remarkable observation is the occurrence of submarine canyons in eight places of the Island. The literature says that the head of the largest canyon at Trincomalee is situated 200 m from the shore. The objective of this paper is to highlight the extension of the canyon structure by studying the recent geotechnical investigations around the Mahaweli delta. A number of boreholes were constructed for groundwater investigations around the Mahaweli river floodplains and the other boreholes were constructed to determine the depth to the bedrock for a bridge foundation at the river outfall. The depth to the bedrock at the river outfall is more than 75 m and decreases towards upstream. The shape of the bedrock below the thick fluvial sediments in the studied area indicates the head of canyon should be marked more than 35 km from the shore towards inland. It is obvious that the submarine canyon at Trincomalee is only a part of a very large canyon. The thick fluvial sedimentary deposit over this canyon within the land is a result of erosion of bedrock along a shear zone or fault and then the rise of sea level in recent times.

Grade classification of neuroepithelial tumors using high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy and pattern recognition

Clinical data have shown that survival rates vary considerably among brain tumor patients,according to the type and grade of the tumor.Metabolite profiles of intact tumor tissues measured with high-resolution magic-angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1H NMRS) can provide important information on tumor biology and metabolism.These metabolic fingerprints can then be used for tumor classification and grading,with great potential value for tumor diagnosis.We studied the metabolic characteristics of 30 neuroepithelial tumor biopsies,including two astrocytomas (grade I),12 astrocytomas (grade II),eight anaplastic astrocytomas (grade III),three glioblastomas (grade IV) and five medulloblastomas (grade IV) from 30 patients using HRMAS 1H NMRS.The results were correlated with pathological features using multivariate data analysis,including principal component analysis (PCA).There were significant differences in the levels of N-acetyl-aspartate (NAA),creatine,myo-inositol,glycine and lactate between tumors of different grades (P<0.05).There were also significant differences in the ratios of NAA/creatine,lactate/creatine,myo-inositol/creatine,glycine/creatine,scyllo-inositol/creatine and alanine/creatine (P<0.05).A soft independent modeling of class analogy model produced a predictive accuracy of 87% for high-grade (grade III-IV) brain tumors with a sensitivity of 87% and a specificity of 93%.HRMAS 1H NMR spectroscopy in conjunction with pattern recognition thus provides a potentially useful tool for the rapid and accurate classification of human brain tumor grades.

View-invariant human action recognition via robust locally adaptive multi-view learning

Human action recognition is currently one of the most active research areas in computer vision. It has been widely used in many applications, such as intelligent surveillance, perceptual interface, and content-based video retrieval. However, some extrinsic factors are barriers for the development of action recognition; e.g., human actions may be observed from arbitrary camera viewpoints in realistic scene. Thus, view-invariant analysis becomes important for action recognition algorithms, and a number of researchers have paid much attention to this issue. In this paper, we present a multi-view learning approach to recognize human actions from different views. As most existing multi-view learning algorithms often suffer from the problem of lacking data adaptiveness in the nearest neighborhood graph construction procedure, a robust locally adaptive multi-view learning algorithm based on learning multiple local L 1-graphs is proposed. Moreover, an efficient iterative optimization method is proposed to solve the proposed objective function. Experiments on three public view-invariant action recognition datasets, i.e., ViHASi, IXMAS, and WVU, demonstrate data adaptiveness, effectiveness, and efficiency of our algorithm. More importantly, when the feature dimension is correctly selected （i.e., 〉60）, the proposed algorithm stably outperforms state-of-the-art counterparts and obtains about 6% improvement in recognition accuracy on the three datasets.