In Traditional Chinese Medicine (TCM), the functions of the brain are dispersed to five zang organs, and are maintained by comprehensive functional interactions among the five zang organs. Therefore, brain diseases are regarded as systematic diseases in TCM, and their treatments are aimed to normalize not only the activity of the organs, but also the balance of functional interaction. In addition, interestingly, the functional interaction between the five zang organs in TCM resembles a biological model based on chaos theory. These features of TCM derive from its theoretical basis in Yin-Yang and the five elements. In conclusion, TCM had co-opted the basic idea of a complex system for the diagnosis and treatment of human diseases thousands years ago. Research into TCM should not only evaluate the effects of herbal medicine or acupuncture, but should take into consideration the view of human beings in TCM.

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Decision support systems are nowadays used to disentangle all kinds of intricate situations and perform sophisticated analysis. Moreover, they are applied in areas where the knowledge can be heterogeneous, partially un-formalized, implicit, or diffuse. The representation and management of this knowledge becomes the key point to ensure the proper functioning of the system and to keep an intuitive view upon its expected behavior. This paper presents a generic architecture for implementing knowledge-base systems used in collaborative businesses, where the knowledge is organized into different databases, according to the usage, persistence, and quality of the information. This approach is illustrated with Cadral, a customizable automated tool built on this architecture and used for processing family benefits applications at the National Family Benefits Fund of the Grand-Duchy of Luxembourg.

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The Grid today plays the role of a universal infrastructure for data processing with a great number of services that allow not only solving the concrete applied tasks but also helping to search for necessary resources to collect information about their state and to save and to deliver data. The grid-infrastructure in hand is able to provide the Ukrainian universities, research centres, and virtual laboratories with needed information and required computational resources. As an example, the system of ecological monitoring and telemedicine support for the Chernobyl Nuclear Power Plant area is considered.

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Information dissemination platforms for supporting voluntary collaboration among researchers should assure that controllable and verified information is being disseminated. However, previous related studies on this field narrowed their research scopes into information type and information specification. This paper focuses on the verification and the tracing of information using an information dissemination platform and other Semantic Web-based services. Services on our platform include information dissemination services to support reliable information exchange among researchers and knowledge service to provide unrevealed information. The latter is also divided into the two: knowledgization using ontology and inference using a Semantic Web-based inference engine. This paper discusses how this platform supports instant knowledge addition and inference. We demonstrate our approach by constructing an ontology for national R&D reference information using 37,656 RDF triples from about 2,300 KISTI (Korea Institute of Science and Technology Information) outcomes. Three knowledge services including 'Communities of Practice', 'Researcher Tracing,' and 'Research Map' were implemented on our platform using a Jena framework. Our study shows that information dissemination platforms will make a meaningful contribution to the possibility of realizing a practical Semantic Web-based information dissemination platform.

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China's geosciences data sharing has progressed significantly but is still facing some key questions that need to be solved Because of the data's insufficiency, it has limited use in modern scientific research. The documents and metadata for the data are insufficient. The scientific data service is dated. The data application procedure is troublesome, etc. All this has become a bottleneck affecting the progress of China's scientific data sharing. Considering the reality of China, some potential solution have been proposed, which include changing the scheme of data integration, perfecting metadata and documentation, emphasizing data service, simplifying the application procedure, enhancing the shared consciousness in science and technology fields, establishing national data centers, and realizing sustainable data sharing.

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Patents contain much novel technological information. In this paper, the searching methods of the file index (FI) and F-term classification system developed by the Japan Patent Office (JPO) were employed to find patents containing information on carbon nanotube technology. All related patent data were searched for in the Intellectual Property Digital Library (IPDL). Moreover, using theme codes and term codes in the two-dimensional structure of the F-term list, we investigated and analyzed the technical features expressed by carbon nanotubes in related documents in Boolean operations.

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Defence, security, public works, and many other branches of government require small-scale, current geographical databases. With this aim, small-scale geographical databases in Turkey were produced in the past and are still in use today. There are, however, problems in using the available geographical databases. These problems in forming, sharing, updating, and meeting requirements necessitate the reconfiguration of the present system. Classical approaches are preferred when updating the present geographical databases. Many errors and difficulties in recording new objects have occurred in the field, in scanning and collecting data from related institutions, and in recording and controlling nonstandard completion data. Thus it is difficult to achieve the desired high quality data with the present method. In this study, we introduce and discuss updating and completing small-scale maps of geographical bases. The Geographical Information System formation studies in Turkey are summarized, and a model is proposed for the formation, updating, and completion of systems of small-scale maps of significant databases.

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Using satellite cloud images to simulate clouds is one of the new visual simulation technologies in Virtual Reality (VR). Taking the original data of satellite cloud images as the source, this paper depicts specifically the technology of 3D satellite cloud imaging through the transforming of coordinates and projection, creating a DEM (Digital Elevation Model) of cloud imaging and 3D simulation. A Mercator projection was introduced to create a cloud image DEM, while solutions for geodetic problems were introduced to calculate distances, and the outer-trajectory science of rockets was introduced to obtain the elevation of clouds. For demonstration, we report on a computer program to simulate the 3D satellite cloud images.

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Biological effects of low-dose radiation are studied by computational methods. Assessing the risks of low-dose radiation, i.e. radiation-induced cancer, is becoming important in the study of public health because of the many different types of exposures, medical exposures, and from radiation protection viewpoints. In general, radiation effects arise from damage done to DNA by ionizing radiation. Therefore, examining effects from the initial DNA damage to the risk assessment is a problem with a very wide spatiotemporal scale. We are studying this problem by dividing it into three parts: 1) the DNA strand is broken by ionizing radiation, 2) DNA lesion repair, and 3) the process of cell carcinogenesis and tumorigenesis. In this paper, we mainly focus on the third part, the study of modeling and simulation of cell carcinogenesis.

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In this paper, we present an evaluation of learning algorithms of a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to completely evaluate several thousands of rules from a large dataset with noise. To reduce the costs in such rule evaluation task, we have developed a rule evaluation support method with rule evaluation models that learn from a dataset. This dataset comprises objective indices for mined classification rules and evaluation by a human expert for each rule. To evaluate performances of learning algorithms for constructing the rule evaluation models, we have done a case study on the meningitis data mining as an actual problem. Furthermore, we have also evaluated our method with ten rule sets obtained from ten UCI datasets. With regard to these results, we show the availability of our rule evaluation support method for human experts.

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The sharing of scientific data is a problem that attracts worldwide attention. In answer, governments have started to establish many systems to provide the public with scientific data. However, sometimes the access does not consider the public's practical need but just stands as an "official" prototype. In this research, a questionnaire was devised to understand a Beijing citizen's practical need for scientific data and to explore the inconsistency between the needs of the public and the information published by the government. The research looks to find a more effective way to solve this problem.

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A global multi-resolution image data model and a feasible solution for its seamless management and archiving remain a challenging vision. The traditional methods of the raster pixel data structure based on the idea of map projections are effective to support local or small-scale areas. However, if this structure is applied to large-scale or whole global image archiving, some significant drawbacks are unavoidable, such as data discontinuity (or overlapping), geometric distortions, etc. To overcome these deficiencies, in this paper the Quaternary Triangular Mesh (QTM) (Dutton, 1989), as a continuous, hierarchal quadtree data structure with uniform grids on a sphere, is proposed for global-scale seamless image archiving. First, the mapping relation between raster image pixels and QTM pixels is approached based on the QTM subdivision and Quaternary coding scheme (Bartholdi & Goldsman, 2001), and a corresponding algorithm of QTM pixel grey level calculation is also developed. Then, the storage structure of global-scale image archiving based on QTM pixels is presented in detail. In the end, an experiment is described using the 1km resolution NOAA data for China, comparing the differences in pixel grey levels between original image pixels and QTM pixels. The result indicates that the QTM pixel data structure can keep global-scale images seamless, and the accuracy of transformation from the imaging pixel to the QTM pixel is a loss of less than 2 grey levels for 94.5% of all pixels, the loss from 2 to 4 is 1.9%, the loss from 4 to 10 is 2%, and the rest is 1.6%. The results are good and acceptable.

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The forecasting of software failure data series by Genetic Programming (GP) can be realized without any assumptions before modeling. This discovery has transformed traditional statistical modeling methods as well as improved consistency for model applicability. The individuals' different characteristics during the evolution of generations, which are randomly changeable, are treated as Markov random processes. This paper also proposes that a GP algorithm with "optimal individuals reserved strategy" is the best solution to this problem, and therefore the adaptive individuals finally will be evolved. This will allow practical applications in software reliability modeling analysis and forecasting for failure behaviors. Moreover it can verify the feasibility and availability of the GP algorithm, which is applied to software failure data series forecasting on a theoretical basis. The results show that the GP algorithm is the best solution for software failure behaviors in a variety of disciplines.

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The research results of coal characterization using the XPS method are summarized. Microsoft Visual Studio.net is utilized to build a database functional group characterization for coal, which contains over 1000 records including the kind of functional group, binding energy value, coal specie, producing area, sample preparation, and literature information. The database can be used to search and analyze XPS data for coal conveniently and is also of significance to support further coal research using XPS.

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Using 64 ms count data of long gamma-ray bursts (LBs, T90 > 2.6 s), we analyze the quantity named relative spectral lag (RSL), τ₃₁/FWHM ₍₁₎ =τ_{rel, 31}. We investigate in detail the properties of the RSL for a sample of nine LBs, using the general cross-correlation technique that includes the lag between two different energy bands. We find that the distribution of RSLs is normal and has a mean value of 0.1. Our important discovery is that redshift (z) and peak luminosity (L_p) are strongly correlated with the RSL, which can be measured easily and directly, making the RSL a good redshift and peak luminosity indicator. In addition, we find that the redshift and luminosity estimator can also hold for short gamma-ray bursts (SBs, T90 < 2.6 s). With it, we estimate the median of redshift and peak luminosity of SBs to be about z≤0.06 and L_p ∼1.68×10⁴⁸ erg/s, which are in excellent agreement with the results suggested by some previous authors. We thus argue that the sources including SBs and LBs with positive spectral lags might be one united category with the same physical process.

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People believe what they can see. The Poles exist as a frozen dream to most people. The International Polar Year wants to break the ice (so to speak), open up the Poles to the general public, support current polar research, and encourage new research projects.
The IPY officially begins in March, 2007. As part of this effort, the U.S. Geological Survey (USGS) and the British Antarctic Survey (BAS), with funding from the National Science Foundation (NSF), are developing three Landsat mosaics of Antarctica and an Antarctic Web Portal with a Community site and an online map viewer. When scientists are able to view the entire scope of polar research, they will be better able to collaborate and locate the resources they need. When the general public more readily sees what is happening in the polar environments, they will understand how changes to the polar areas affect everyone.

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This paper analyzes the factors that influence the economic growth of the provinces of China by means of a Panel Data Model. Traditional analytical methods of economic growth are compared with a Panel Data Model. The results of empirical research indicate that the changes of fixed assets investment, gross domestic export, and macroeconomic policies will affect China's GDP. It is finally concluded that export is the driving force behind economic growth in China. This conclusion is quite different from traditional analysis.

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Based on data for the years 1995 to 2002, this paper has established a panel data model that reflects the relationship between China's foreign direct investment and China's exports, and regarding this, empirical analysis is made. The selected countries and regions include: Hong Kong of China, China's Taiwan, Japan, South Korea, the European Union, and the United States. We have found that the relationship between the accumulated FDI (FDE stock) of different countries and regions in China and Chinese exports to the target countries is quite strong.

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Many databases and platforms for human brain data have been established in China over the years, and metadata plays an important role in understanding and using them. The BrainBank Metadata Specification for the Human Brain Project and Neuroinformatics provides a structure for describing the context and content information of BrainBank databases and services. It includes six parts: identification, method, data schema, distribution of the database, metadata extension, and metadata reference The application of the BrainBank Metadata Specification will promote conservation and management of BrainBank databases and platforms. it will also greatly facilitate the retrieval, evaluation, acquisition, and application of the data.

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In order to use distributed and heterogeneous scientific databases effectively, semantic heterogeneities have to be detected and resolved. To solve this problem, we propose architecture for managing metadata and metadata schema using a metadata registry. A metadata registry is a place to keep facts about characteristics of data that are necessary for data sharing and exchange in a specific domain. This paper will explore the role of metadata registries and describe some of the experiences of implementing the registry.

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Based on the Small-World model of CAS e-Science and the power low of Internet, this paper presents a scalable CAS e-Science Grid framework based on virtual region called Virtual Region Grid Framework (VRGF). VRGF takes virtual region and layer as logic manage-unit. In VRGF, the mode of intra-virtual region is pure P2P, and the model of inter-virtual region is centralized. Therefore, VRGF is decentralized framework with some P2P properties. Further more, VRGF is able to achieve satisfactory performance on resource organizing and locating at a small cost, and is well adapted to the complicated and dynamic features of scientific collaborations. We have implemented a demonstration VRGF based Grid prototype—SDG.

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This paper discusses the principle of electronic data and retrieval methods for the Secret History of the Mongols, which is a great classical historical work written in the 13th century with Chinese characters transliterated from Mongol. This handwritten work contains rather rich text information, which should be the contents of forming an electronic database. There are in the original book multi-types of information, including layouts, volumes, chapters, characters, interlinear translation, segments, and Chinese translation, each format of which has been approached in detail and divided separately with markers. On the basis of analysis, our project builds up a complete electronic retrieval system for this great book, which resolves the return to the original shape of the archaic handwriting form with three lines representing one content. The sorting methods of the system are also designed according to the original text formats, namely concordance technology, which can print out retrieved objects with their contexts, retrieve with statistical data, and freely browse search.

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This paper presents in detail the project "Development and Service of World Data Center for Seismology, Beijing," including its background, construction tasks, main results, societal effects, and perspective for future development.

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The World Data Center (WDC) for Geophysics, Beijing, was founded in 1988. Supported by The Chinese Academy of Science and The Ministry of Science and Technology, our center has made much progress in recent years. The center has not only established the database to restore data which contain heat flow data, geomagnetic data, gravity data, etc. but also put them on the Internet (http://gp.wdc.cn) to provide free data service. The center has expended a great deal of effort to rescue the magnetograms observed 100 years ago by the Sheshan Observatory, the earliest geomagnetic observatory in China. The geophysics data of our center are abundant, and the way to get the data and information from the website is very simple and easily obtainable. In the future, the center will edit more data and construct a strong, convenient database in order to provide the better service to users.

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In this paper, the author analyzes the gap in knowledge-based economy development between China and the United States, explores its cause, and gives some constructive suggestions to promote Chinese knowledge-based economy development. The paper has three parts. The first is a brief literature review. The author concludes that at present the indicator model is more proper than the econometric model and statistical framework. In the second part, the author develops an indicator model with four dimensions: knowledge input, human capital, ICT application, and innovation performance. Each dimension has several different indicators. The Analytic Hierarchy Process (AHP) is used to give those indicators different weights and to compose them into a compound index in all hierarchies. On the basis of the above methodology, the third part calculates and compares the overall index and four dimension index differences of the development of Chinese and American. knowledge-based economies. There is a large gap between China and the United States. The dimension of innovation performance embodies this gap. The next dimensions are human capital, knowledge input, and ICT application in turn. The author then discusses reasons for such a great lag between China and the United States. The conclusion sums up the main challenges and puts forward some suggestions to promote the Chinese knowledge-based economy development.

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The thermodynamic descriptions of the perovskite phase SrZrO₃ and the TbBr₃-KBr molten salt system were carried out using the available experimental information. Special attention was paid to the structural behavior of SrZrO₃ and the decomposition of K₃TbBr₆ at low temperature, respectively, to illustrate how to select an appropriate thermodynamic model based on crystal structure and chemistry information, how to identify and resolve the inconsistency between various kinds of experimental data, and how to use thermodynamic modeling as a basic tool in the development and optimization of materials and process. In the present work, different structures of SrZrO₃ were explained by thermodynamic calculation and confirmed with experimental technologies. The decomposition of the compound K₃TbBr₆ at about 593 K was detected by the present thermodynamic calculation and the new complementary experimental measurements. Comparison between the calculated and measured phase diagrams as well as thermodynamic quantities provided the final test of the overall consistency between the reliable experimental information and the present modeling and thermodynamic computation.

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Data mining is concerned with the extraction of useful knowledge from various types of data. Medical data mining has been a popular data mining topic of late. Compared with other data mining areas, medical data mining has some unique characteristics. Because medical files are related to human subjects, privacy concerns are taken more seriously than other data mining tasks. This paper applied data separation-based techniques to preserve privacy in classification of medical data. We take two approaches to protect privacy: one approach is to vertically partition the medical data and mine these partitioned data at multiple sites; the other approach is to horizontally split data across multiple sites. In the vertical partition approach, each site uses a portion of the attributes to compute its results, and the distributed results are assembled at a central trusted party using a majority-vote ensemble method. In the horizontal partition approach, data are distributed among several sites. Each site computes its own data, and a central trusted party is responsible to integrate these results. We implement these two approaches using medical datasets from UCI KDD archive and report the experimental results.

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In order to achieve the potential of existing power stations, increase the efficiency of waterpower, and change the running of power stations from an individual plant basis to centralization, many people have researched the automation of power stations over the years with excellent results. However, in reality there are no power stations that have been fully automated. This paper proposes a control system based on Multi-Agent Theory, including a management agent, communication agent, control agent, echo agent, and so on. These agents are able to harmonize automation and intelligence to provide effective control of the running of a hydropower station. The end goal is a real control system that can operate independently of human intervention.

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Rapid increases of user data from terabytes to petabytes have created new challenges in data archiving. Modern data archive systems require higher adaptivity, reliability, and performance than traditional data archive systems can provide. Recently Hierarchical Storage Management (HSM) has been applied to a data archive that stores data in a multi-level storage system according to access frequency. In this paper, we describe the design and implementation of a novel HSM-based data archive system called T-Archive, which can meet the above requirements for order-of-magnitude scaling of storage.

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Very high spatial resolution remote sensing images have applications in many fields. However, research on the intelligent interpretation of such images is insufficient partly because of their the complexity and large size. In this study, a high spatial resolution remote sensing image intelligent interpretation system (HSR-RSIIIs) was designed with image segmentation, a geographical information system, and a data-mining algorithm. Some key methods such as image segmentation, feature extraction, feature selection, and classification algorithm for interpreting high spatial resolution remote sensing image have been studied. A land cover classification experiment was performed in the Zhuzhou area using a Quickbird multi-spectral image. The classification results were consistent with the visual interpretation results. In additional, the proposed interpretation method was compared with the traditional pixel-based method. The results indicate that the method proposed in the literature is more effective and intelligent than that used previously.

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Supported by the World Bank, the Integrated Information System for Natural Disaster Mitigation (ISNDM), including the operational service system and network telecommunication system, has been in development for three years in the Center of Disaster Reduction, Chinese Academy of Sciences, based on the platform of the GIS software Arcview. It has five main modules: disaster background information, socio- economic information, disaster-induced factors database, disaster scenarios database, and disaster assessment. ISNDM has several significant functions, which include information collection, information processing, data storage, and information distribution. It is a simple but comprehensive demonstration system for our national center for natural disaster reduction.

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Coal is the dominant source of energy in China, but about 50% of the coal resource is left underground unmined. Because of this, the "long-tunnel, large section, two-stage" Underground Coal Gasification (UCG) technology has been put forward, and the UCG model platform has been built. Simulation tests are underway and some gasification parameters have been obtained. Five field trials have been completed, which have produced gas with a heating value of about 4.18MJ/m³. Gas containing more than 40% hydrogen and a heating value above 8.36MJ/m³ is produced at two-stage gasification.

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As carriers and circulators of information, materials databases have been more and more extensively applied in the production of, scientific research on, and circulation and application of modern materials. In this paper, the history of materials databases is presented, the present status of domestic materials databases is discussed, and the development trends of Internet use, standardization, intelligence, and commercialization of materials databases are also discussed.

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Photometric redshifts have been regarded as efficient and effective measures for studying the statistical properties of galaxies and their evolution. In this paper, we introduce SVM_Light, a freely available software package using support vector machines (SVM) for photometric redshift estimation. This technique shows its superiorities in accuracy and efficiency. It can be applied to huge volumes of datasets, and its efficiency is acceptable. When a large representative training set is available, the results of this method are superior to the best ones obtained from template fitting. The method is used on a sample of 73,899 galaxies from the Sloan Digital Sky Survey Data Release 5. When applied to processed data sets, the RMS error in estimating redshifts is less than 0.03. The performances of various kernel functions and different parameter sets have been compared. Parameter selection and uniform data have also been discussed. Finally the strengths and weaknesses of the approach are summarized.

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In recent years, Geographic Information Systems (GIS) have gradually changed from using the traditional 2D map expression to 3D visualization. The combination of visual techniques and GIS is a multi discipline, leading edge field, the development of which needs advancement in many fields. This paper introduces related theories and algorithms of Digital Elevation Model (DEM) visualization. Advantages of the Triangle Irregular Network (TIN) model and data structure are illustrated. The algorithms include the visualization process and methods to increase the realism of the DEM. Illumination models and a special technique to map remote sensing images onto DEM are also presented.

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An efficient method is proposed to diagnose a type of abnormal data. We first start with analyzing an example, carry through with development of the theory once more, and finally list the method steps and its application fields. Experiments show that we need more important and better ways to diagnose abnormal data and eliminate them along with the development of information technology and control technology. The quality of measured data is improved by the use of this technique.

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This paper presents a framework for distributed service discovery based on VIRGO P2P technologies. The services are classified as multi-layer, hierarchical catalogue domains according to their contents. The service providers, which have their own service registries such as UDDIs, register the services they provide and establish a virtual tree in a VIRGO network according to the domain of their service. The service location done by the proposed strategy is effective and guaranteed. This paper also discusses the primary implementation of service discovery based on Tomcat/Axis and jUDDI.

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This paper presents an ontology learning architecture that reflects the interaction between ontology learning and other applications such as ontology-engineering tools and information systems. Based on this architecture, we have developed a prototype system CHOL: a Chinese ontology learning tool. CHOL learns domain ontology from Chinese domain specific texts. On the one hand, it supports a semi-automatic domain ontology acquisition and dynamic maintenance, and on the other hand, it supports an auto-indexing and auto-classification of Chinese scholarly literature. CHOL has been applied in ethnology and anthropology for Chinese information organization and knowledge discovery.

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The first case of arsenicosis was reported in China in the 1950s. That incident was associated with the so-called "black foot disease." In the late 1970s and early 1980s, arsenic specific coetaneous changes were diagnosed in the Xinjiang Autonomous Rregion and subsequently in the Inner Mongolia Autonomous Region and Shanxi Province. Recently, endemic arsenicosis was also found in Jilin, Ningxia, Qinghai, and Anhui Provinces. The prevalence of arsenicosis in China is becoming more and more serious. In order to prevent and control it, many departments and institutes have begun to work in this field. They have made a great progress including also the sharing of arsenicosis data within a limited area. But the limited nature of this data sharing is a barrier for preventing and controlling arsenicosis. Only once data sharing is realized within the whole nation, can we discover the best way of eliminating arsenicosis. With this goal in mind, we have set up a rudimentary platform of asenicosis data sharing. This gradually needs to be improved and improved.

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The project of the Digital Science & Technology Museum of China is constructed by the China Association for Science and Technology, Ministry of Education and Chinese Academy of Sciences, of which the objective is to integrate the high quality digital science popularization resources in China to construct an Internet-based national science popularization resources sharing platform. This project complies with the objective requirement of Internet-based science popularization in China that has groundbreaking significance. The construction at Stage I includes the construction of Museum A (museum), Museum B (experiencing museum), Museum C (resources museum), and an information service platform. During construction the project has been faced with many problems, such as how to integrate numerous science popularization resources for sharing and how to realize the function of science experiencing of the digital science & technology museum to attract the public. These problems will be explored and resolved in practice. The units participated in this project will, by combining the requirement for science popularization at the grass roots level, aggressively launch the application and extended service of this project.

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The objective of this work is to investigate the cognitive development of semantic process and arithmetic calculation in childhood using event-related potential tools. Sixty children of three age groups (8-, 9- and 11-year-old groups) participated in the experiment. Each group included 10 girls and 10 boys. Stimuli were presented in two separate lists: semantic priming list and mental arithmetic list. Participants were instructed to decide whether the target word was a real Chinese character or not in semantic priming task and decide whether the production of the arithmetic operation (addition, subtraction, multiplication) was correct or false in mental arithmetic task. The main new observation was that the cognitive process reflected by major ERP components changes with the age growing up. In the lexical decision task, the amplitude of N400 elicited by semantic non-related target was significantly larger than that of a related target in all the children groups. The latency and amplitude of N400 component in 8-year-old group were larger than that of 11-year-old group. A similar RP component was elicited by either a Chinese single-character word or pseudo-word as reported by other authors. In mental arithmetic task, similar results were observed that the latencies of P2, N2, P3 in 8-year-group were longer than those of the 11-year-old group. These was no significant differences in amplitude across the three operation and age groups. These results suggest that semantic priming effects and mental arithmetic are developmental processes even in the early childhood. These two cognitive processes may be used to evaluate the development of language and arithmetic abilities.

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Because remote sensing data can offer information on the geographical landscape of the earth's surface, these data can be widely used in researching types of land and monitoring vegetation. In this paper, we discuss how to extract land types and vegetation from remote sensing data using the method of Non-Supervised Classification. These data provide the means to study types of soil and vegetation along a freeway.

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According to the fact that high order cumulants (HOC) retain the phase information of signals and the HOC of the Gaussian color noise is always equal to zero, a new method of wavelet reconstruction is provided in this paper, based on 4th-order cumulants of non-Gaussian seismic signals. The feasibility of this method is demonstrated by the simulation of wavelet estimation for synthetic seismic traces. Furthermore, the seismic wavelet of field data processed with this method can be reconstructed correctly.

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Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm called ABBM is proposed in this paper. This new algorithm adopts a Boolean vector "relational calculus" method to discovering frequent itemsets. Experimental results show that this algorithm can quickly discover frequent itemsets and effectively mine potential association rules.

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There are many large-size and difficult computational problems in mathematics and computer science. For many of these problems, traditional computers cannot handle the mass of data in acceptable timeframes, which we call an NP problem. DNA computing is a means of solving a class of intractable computational problems in which the computing time grows exponentially with problem size. This paper proposes a parallel algorithm model for the universal 3-SAT problem based on the Adleman-Lipton model and applies biological operations to handling the mass of data in solution space. In this manner, we can control the run time of the algorithm to be finite and approximately constant.

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Collaborative research is one of the most noteworthy trends in the development of scientific research, and co-authored papers are some of the most important results of this research. With the speed-up of globalization, wider adoption of computers and advanced communication technologies, and more frequent academic exchanges and co-operation, collaborative research across organizations, regions, and fields has provided greater access to Chinese researchers in the humanities and social sciences. Accordingly, co-authored papers have witnessed considerable growth in number and proportion. The Social Sciences Citation Index (SSCI) and the Arts & Humanities Citation Index (A&HCI), published by the Institute for Scientific Information (USA), enjoy a high reputation worldwide as large-scale and comprehensive retrieval systems for international large comprehensive papers and citations. This article aims to reveal the trends of Chinese collaborative research in the humanities and social sciences from the perspective of bibliometrics and offer advice for Chinese researchers and managers in these fields, by analyzing Chinese co-authored papers in the humanities and social sciences indexed in the SSCI and A&HCI in the last decade (1995-2004).

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Heavy rains and typhoons cause major disasters, including great economic losses and casualties, in China. Therefore, it is very important to research these torrential rains and typhoons in order to improve forecasting accuracy to mitigate their consequences. The Center of Disaster Reduction of the Chinese Academy of Sciences has cooperated with the Department of Atmospheric and Oceanic Science at the University of Maryland in the US in studying heavy rains and typhoons for over 7 years. Some of the findings from the study of heavy rains and typhoons are introduced in this paper.

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Within folded strands of a protein, amino acids (AAs) on every adjacent two strands form a pair of AAs. To explore the interactions between strands in a protein sheet structure, we have established an Internet-accessible relational database named SheetsPairs based on SQL Server 2000. The database has collected AAs pairs in proteins with detailed information. Furthermore, it utilizes a non-freetext database structure to store protein sequences and a specific database table with a unique number to store strands, which provides more searching options and rapid and accurate access to data queries. An IIS web server has been set up for data retrieval through a custom web interface, which enables complex data queries. Also searchable are parallel or anti-parallel folded strands and the list of strands in a specified protein.

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Monitoring the condition of rolling element bearings and defect diagnosis has received considerable attention for many years because the majority of problems in rotating machines are caused by defective bearings. In order to monitor conditions and diagnose defects in a rolling element bearing, a new approach is developed, based on the characteristic defect octave frequencies. The characteristic defect frequencies make it possible to detect the presence of a defect and diagnose in what part of the bearing the defect appears. However, because the characteristic defect frequencies vary with rotational speed, it is difficult to extract feature information from data at variable rotational speeds. In this paper, the characteristic defect octave frequencies, which do not vary with rotation speed, are introduced to replace the characteristic defect frequencies. Therefore feature information can be easily extracted. Moreover, based on characteristic defect octave frequencies, an envelope spectrum array, which associates 3-D visualization technology with extremum envelope spectrum technology, is established. This method has great advantages in acquiring the characteristics and trends of the data and achieves a straightforward and creditable result.

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Based on the theory of actuarial present value, a pension fund investment goal can be formulated as an objective function. The mean-variance model is extended by defining the objective loss function. Furthermore, using the theory of stochastic optimal control, an optimal investment model is established under the minimum expectation of loss function. In the light of the Hamilton-Jacobi-Bellman (HJB) equation, the analytic solution of the optimal investment strategy problem is derived.

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