细微之处见真章——例谈“微识”在笔记整理中的应用

做笔记是提高学习效果的一种常见方法,如何提升笔记对学习的促进作用是一个值得探讨的问题,作者结合当前流行的微博、微课等微形式,提出将笔记内容精炼,提取其中的精华,形成“微识”,助力高效学习。

南海西北部莺歌海盆地HBJM39-1气田储层岩相划分与微相识别

以HBJM39-1-1、HBJM39-1-2A、HBJM39-1-3和HBJM39-1-4等4口井的取心资料为基础,结合层序地层学解释,深入分析了莺歌海盆地HBJM39-1气田储层的岩相沉积特征。研究表明,HBJM39-1气田储层岩相具有颜色深、粒度细、水动力条件弱、生物扰动强且发育风暴沉积等特点。通过地震地质解释、盆地古地貌以及岩心描述等分析研究证实,HBJM39-1气田储层形成于相对较深的沉积水体环境。莺歌海盆地上新世主要发育滨浅海相沉积,但根据古水体环境、生物遗迹相、岩相以及测井相等资料揭示,上新世莺一段至第四系乐东三段尚发育滨外亚相,不发育前滨及以上亚相沉积。同时,为了进行储层非均质性研究,以"泥"、"砂"截然不同的渗流、渗流屏障作用为主线,亦对滨外亚相等进行了进一步细分和较深入研究。

《小学百变作文教学策略与效果实践的研究》之微作文

作文教学是小学语文教学中的一个非常重要的内容,但同时却是一个难点.通过微作文教学,有效提高学生习作的语言表达能力,让学生易于动笔,乐于表达.引导学生关注现实,热爱生活,表达真情实感,使学生的作文能力适应社会生活的发展,同时学会透视生活,培养热爱生活的情感.重视口头作文,鼓励自由表达和有创意的表达,丰富学生作文教学,构建开放的教学模式.

The Gene of Megalencephalic Leukoencephalopathy with Subcortical Cysts is Mapped on Chromosome 22q13.3 with 250 kb Interval

Objective: Vacuolating megalencephalic leukoencephalopathy with subcortical cysts (MLC) is a recently described syndrome with autosomal recessive mode of inheritance. Its possible gene was located on chromosomal 22q tel with 3-cM. The purpose of this study was to narrow down the genetical distance on chromosomal 22q tel with MLC. Methods: Thirty-nine MLC patients in 33 families were collected,and the linkage analysis and haplotype analysis of twelve informative families were done, using seven microsatellite markers and four SNP markers. Results: The maximum tow-point LOD score for marker 355c18 was 6.65 at recombination fraction 0.02. The haplotype analysis narrowed down the critical region of MLC to 250 kb on chromosomal 22q tel. Conclusion: One of the causing genes of MLC was located on chromosomal 22q tel with 250 kb. Four candidate genes were considered. The heterogeneity of one informative family indicated possible existence of a second locus for MLC.

Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass.The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology.An image identification model based on Convolutional Neural Network(CNN)is established in this paper for the seismic waveforms of microseismic events and blasts.Firstly,the training set,test set,and validation set are collected,which are composed of 5250,1500,and 750 seismic waveforms of microseismic events and blasts,respectively.The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training.Results show that the accuracies of microseismic events and blasts are 99.46%and 99.33%in the test set,respectively.The accuracies of microseismic events and blasts are 100%and 98.13%in the validation set,respectively.The proposed method gives superior performance when compared with existed methods.The accuracies of models using logistic regression and artificial neural network(ANN)based on the same data set are 54.43%and 67.9%in the test set,respectively.Then,the ROC curves of the three models are obtained and compared,which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model.It not only decreases the influence of individual differences in experience,but also removes the errors induced by source and waveform parameters.It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.

NECESSARY CONDITION OF IDENTIFICATION FOR A DISTRIBUTED PARAMETER SYSTEM

This paper considers the necessary condition of the parameter identification problem dudt=(A+B(q))u u(0)=x x∈X with the cost functional J(q)≡12∫ T 0‖Cu(t;q)-y(t)‖ 2 H d t It is proved that the optimal estimate q 0 is determined by the optimal system which consists of the sate equation,the adjoint equation and the optimal condition.

Vapor Sensing Theoretical Study on Optical Microcavities

When the organic vapors absorbed to the surface of porous silicon(PS), capillary condensation takes place due to the porous structure of the PS layer, accordingly resulting in the effective refractive index changing. For PS multi-layer microcavities, the different resonant peaks shift in the reflectivity spectrum of porous silicon microcavities(PSMs). The optical sensing model is set up by applying Bruggeman effective medium approximation theory, capillary condensation process and transfer matrix theoretically analytical method of one-dimensional photonic crystals. At the same time, comprehensively researched on are the sensing characteristics of PSMs which are exposed to give concentration organic vapors. At last, made is the theoretical simulation for sensing model of the PSMs in case of saturation by using computer numerical calculation, and found is the linearity relation between the refractive index of organic solvent and the peak-shift. At the same time deduced is the peak-shift as a function of the concentration of ethanol vapors.

Trace elements spatial distribution characteristics,risk assessment and potential source identification in surface water from Honghu Lake,China

Five trace elements including Zn, Cu, Cd, Cr and As were investigated in surface water from ten typical sampling sites in Honghu Lake. The consequence indicated that all of the detected trace element levels were within the allowed standard of China’s safe water guideline. The hazard quotients （HQ） and the hazard index （HI） value levels of all the five heavy metals in all sampling sites did not exceed the acceptable risk limits of non-carcinogenic value through the selected assessment method. Pearson’s correlation analysis and principal component analysis （PCA） indicated that Zn and Cu mainly originated from the natural alluviation and non-point agricultural sources, whereas Cr and As were mainly derived from industrial effluents. Moreover, Cd mainly originated from both non-point agricultural and industrial pollution sources. In addition, cluster analysis （CA） implied that cluster 1 （including S3, S5, S6 and S10） was considered the set of high pollution sites and cluster 2 （including S4 and S9） was identified as the set of moderate pollution sites.

MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK

Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.

Face recognition by combining eigenface method with different wavelet subbands

A method combining eigenface with different wavelet subbands for face recognition is proposed.Each training image is decomposed into multi-subbands for extracting their eigenvector sets and projection vectors.In the recognition process,the inner product distance between the projection vectors of the test image and that of the training image are calculated.The training image,corresponding to the maximum distance under the given threshold condition,is considered as the final result.The experimental results on the ORL and YALE face database show that,compared with the eigenface method directly on the image domain or on a single wavelet subband,the recognition accuracy using the proposed method is improved by 5% without influencing the recognition speed.

Colors of Peace

In this age of modernization and mechanization, we seem to have lost sensitivity to a collective awareness regarding the welfare of our surroundings, people, animals, microbes, and plants. We seem to encounter systemic problems of corruption, moral bankruptcy, and spiritual decay. This leads to a rise in violence at various levels： individual, familial, societal, global, environmental, and biological. In order to combat these problems, it is necessary to examine the diverse semantics of the concept of peace： positive peace, negative peace, world peace, socio-cultural peace, and individual peace. The semantics of peace changes according to the context, for example, ＂May he rest in peace,＂ is quite different from： ＂His beautiful memories will give you peace.＂ Since society is constituted of a group of persons, an individual becomes society＇s substantive foundation with his/her conscious self, made of mind, body and spirit. The individual with creative power can become an agent of positive change. Using the interdisciplinary socio-linguistic/semantic and humanistic-philosophical approaches, this paper analyzes the ＂all-inclusive＂ and ＂interdependent＂ nature of life and the role and power of the individual （self） in the creation of peace at diverse levels： personal, community, the globe, and the cosmos. The paper draws heavily from India＇s spiritual worldview and humanistic techniques in resolving conflicts and attaining freedom from ill-health for peaceful living.

Sedimentary Micro-phase Automatic Recognition Based on BP Neural Network

In the process of geologic prospecting and development, it is important to forecast the distribution of gritstone, master the regulation of physical parameter in the reserves mass level. Especially, it is more important to recognize to rock phase and sedimentary circumstance. In the land level, the study of sedimentary phase and micro-phase is important to prospect and develop. In this paper, an automatic approach based on ANN (Artificial Neural Networks) is proposed to recognize sedimentary phase, the corresponding system is designed after the character of well general curves is considered. Different from the approach extracting feature parameters, the proposed approach can directly process the input curves. The proposed method consists of two steps: The first step is called learning. In this step, the system creates automatically sedimentary micro-phase features by learning from the standard sedimentary micro-phase patterns such as standard electric current phase curves of the well and standard resistance rate curves of the well. The second step is called recognition. In this step, based the results of the learning step, the system classifies automatically by comparing the standard pattern curves of the well to unknown pattern curves of the well. The experiment has demonstrated that the proposed approach is more effective than those approaches used previously.

Auto recognition of carbonate microfacies based on an improved back propagation neural network

Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.

Using Array Methods to Identify and Process Weak Signals Generated by the Brightlight(Ⅰ) 50t Explosion

Waveforms generated by the 50t explosion of project Brightlight （ I ） were recorded by HILR array. Using array techniques, the author performed identification, f-k analysis, velocity spectrum analysis, etc. of the weak signals. The analysis results show that the signal-to-noise ratio after beamforming was obviously enhanced, and the signal could be clearly shown. The energy from this explosion was mainly concentrated in the 1 -8Hz range from f-k analysis. The velocity spectrum gave clear positions of event phases, which could not be seen in the original weak signals. The maximum energy distribution obtained by the Beaman method is close to the theoretical value in the azimuth-slowness domain.

Mechanical response of bridge piles in high-steep slopes and sensitivity study

The bridge piles located in high-steep slopes not only endure the loads from superstructure, but also the residual sliding force as well as the resistance from the slope. By introducing the Winkler foundation theory, the mechanical model of piles-soils-slopes system was established, and the equilibrium differential equations of pile were derived. Moreover, an analytic solution for identifying the model parameters was provided by means of power series method. A project with field measurement was compared with the proposed method. It is indicated that the lateral loads have great influences on the pile, the steep slope effect is indispensable, and reasonable diameter of the pile could enhance the bending ability. The internal force and displacements of pile are largely based upon the horizontal loads applied on pile, especially in upper part.

Transition of super-hydrophobic states of droplet on rough surface

Twelve samples with periodic array square pillars microstructure were prepared on the silicon wafer by plasma etching techniques, on which space b of the square pillars increased from 5 to 60 μm. In order to study the effect ofb on the wettability of the rough surface, the effects of apparent contact angle （CA） and sliding angle （a） of the droplet on the rough surface were measured with the contact angle meter. The results show that the experimental values of CA well agree with the classical wetting theory and a decreases with the increase of b. Two drop shapes exist on the samples＇ surface, corresponding to the Cassie state and the Wenzel state respectively. The contact state in which a drop would settle depends typically on the size of b. On the role of gravitation, the irreversible transition of a drop from Cassie state to Wenzel state should occur at a certain space of the square pillars. Since the transition has implications on the application of super-hydrophobic rough surfaces, theoretically, the prediction of wetting state transition on square pillar array micro-structured surfaces provides an intuitionistic guidance for the design of steady superhydrophobic surfaces.

From Knowledge to Wisdom： The Case of a Micro-financing Network

As one of the most important and strategic actors of sustainable development, the micro-financing （MF） organizations are continuously adopting new strategic methods of collaboration for the success of their micro-financing missions. The paper presents the case of a newly established network of MF organizations created for the strategic objective of not only collaboration but for optimizing inter-organizational knowledge sharing systems for the micro-financing services. The purpose of the paper is to show the method used for the creation of this network, the mechanisms for its management, and the dynamisms and success factor,~ leading to its continuous development. The findings are derived from an interview with one of its most strategic founders, and material is provided by the network, and a questionnaire is administered to the members of the network. The results imply theoretical implications to innovation management and organizational development and change since the source of the creation of this network is innovative, and for the competencies of the founders and their awareness of the importance of change lead to the creation of this network. And the results also lead to managerial implications in the micro-financing sector through the provision of a unique excellent command example to follow.

Design of IP core based on AMBA bus

With the rapid development of integrated circuit(IC)technology,reusable intelligent property(IP)core design is widely valued by the industry.Based on the in-depth study of the functional characteristics of advanced microcontroller bus architecture(AMBA),a design scheme of IP core is presented,and it is divided into the functional modules,and the structural design of the IP core is completed.The relationship between the internal modules of the IP core is clarified,and the top-down design method is used to build the internal architecture of the IP core.The IP core interface module,register module,baud rate module,transmit module,receive module,and interrupt module are designed in detail by using Verilog language.The simulation results show that the designed IP core supports serial peripheral interface(SPI)protocol,the function coverage of IP core reaches 100%,the maximum working frequency reaches 200 MHz,and the resource occupancy rate is less than 15%.The reusable IP core can support multiple data formats,multiple timing transmission modes,and master/slave operation modes,reducing the resource consumption of hardware circuits and having stronger applicability.

Automatic recognition and quantitative analysis of Ω phases in Al-Cu-Mg-Ag alloy

The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using Image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.