浅析国产动画创作中的突破与传承

近年来,国产动画产业成长迅速,日益朝着产业规模化的方向发展,然而在动画艺术的创作上却始终找不到准确的定位,无法突破瓶颈。本文试从动画电影的内容与形式两方面进行分析,探索一条适合中国动画发展的道路,提出了结合贴近现代社会生活的题材与符合中国传统审美意识的艺术形式的创作理念。     

农业古籍断句标点模式研究

农业古籍的整理已经引起了众多学者和专家的注意,但是,对于农业古籍的自动断句、标点模式的研究仍付之阙如。本研究探索并总结出部分农业古籍断句、标点识别模式。首先采用句法特征词断句法、同义语标志词法进行初步断句;进而利用反义复合词、引书标志、时序、数量词、重叠字词、动名结构及比较句法进一步对子句进行断句、标点;最后使用农业用语和禁用模式表进一步提高断句、标点后农业古籍的可读性和准确性。经测试表明,断句、标点的平均准确率分别达到48%和35%,证明本方法具有一定的正确性和可行性。     

The use of mindmapping software for patent search and management

Modern information technologies and in particular the internet have revolutionized the patent information professionals' work in terms of speed of access and information comprehensiveness from both company internal and external digital sources. Here, I describe how the digital mindmapping technique can be used to complement existing intellectual property management software solutions to meet the challenge of optimizing and managing patent search workflows as well as to rapidly organise and access highly dynamic, heterogeneous and scattered patent information sources. Both eye catching and highly memorable and at the same time self-explanatory mindmapping examples are presented. These were designed to include basic and advanced level digital mindmapping features tailored to significantly speed up and maintain a high work quality level of patent search professionals. A special emphasis is put on the great benefit of organising and accessing the plethora of internet-based worldwide online patent registers through mindmapping, both in terms of managing the constantly changing deep links to the actual search options for e.g., legal status information, and keeping track of the offered level of content.     

对设计做为生产力要素的思考

中国在改革开放以后,由"科技生产力"带领"中国制造"走向了世界。如今,在后工业时代背景下,对传统生产力重新反思并提出"设计生产力"的构思愈来愈显重要。笔者对设计作为生产力要素的多重属性和运行机制进行探索,并对设计生产力的社会意义进行归纳。     

Analyzing Regional Inequality of Socioeconomic Development in China with Nighttime Light

Satellite\|derived nighttime light (NTL)data have been extensively used as an efficient proxy measure for monitoring urbanization dynamics and socioeconomic activity.This is because remotely sensed NTL signals can be quantitatively connected to demographic and socioeconomic variables.The recently composited cloud\|free NTL imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS)provides spatially detailed observations of human settlements.We quantitatively estimated socioeconomic development inequalities across 30 provinces andmunicipalities in mainland China using VIIRS NTL data associated with both regional gross domestic product (GDP)and population census data.We quantitatively investigated relations between NTL,GDP,and population using a linear regression model.Our results suggest that NTL have significant positive correlations with GDP and population at different levels.Several inequality coefficients were derived from VIIRS data and statistical data at multiple spatial scales.NTL\|derived inequality coefficients enabled us to elicit more detailed information on differences in regional development at multiple levels.Our study of provinces and municipalities revealed that county\|level inequality was more significant than city\|level.The results of population\|weighted NTL inequality indicate an obvious regional disparity with NTL distribution being more unequal in China’s undeveloped western regions compared with eastern regions.Our findings suggest that given the timely and spatially explicit advantages of VIIRS,NTL data are capable of providing comprehensive information regarding inequality at multiple levels,which is not possible through the use of traditional statistical sources.     

Remote sensing of phytoplankton community composition along the northeast coast of the United States

Satellite imagery has proven to be a powerful tool for measuring chlorophyll a in surface waters. While this provides an estimate of total phytoplankton biomass, it does not distinguish between phytoplankton groups, many of which have functional differences and therefore affect biogeochemical cycles differently. Phytoplankton pigment analysis has been used to quantify a wide range of photosynthetic and accessory pigments, and chemotaxonomic analysis (e.g. CHEMTAX) has been used to successfully quantify functional taxonomic groups in nature based on pigment distributions. Here, we combine CHEMTAX analysis with satellite-derived distributions of specific phytoplankton pigments to describe the distributions of particular components of the phytoplankton community in the northeast coast of the United States from space. The spatial and seasonal variations in phytoplankton community structure elucidated through satellite remote sensing methods generally agreed with observations of abundance estimates of cell counts. Diatoms were generally the most abundant phytoplankton in this region, especially during Winter-Spring and in the inner shelf, but phytoplankton populations shifted to increasing abundance of other taxa during Summer, especially offshore. While still preliminary, satellite-derived taxa-specific information with proper regional controls holds promise for providing information on phytoplankton abundance to a taxonomic group level which would greatly improve our understanding of the impacts of human activity and climate change on ecosystems.     

Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields

ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~ 1.5 million km²) to map four stem volume classes (0-20, 20-50, 50-80, and > 80 m³/ha).     

Simulating multiple class urban land-use/cover changes by RBFN-based CA model

Land use systems are complex adaptive systems, and they are characterized by emergence, nonlinearity, feedbacks, self organization, path dependence, adaptation, and multiple-scale characteristics. Land use/cover change has been recognized as one of the major drivers of global environmental change. This paper presents a coupled Cellular Automata (CA) and Radial Basis Function Neural (RBFN) Network model, which combines Geographic Information Systems (GIS) to contribute to the understanding of the complex land use/cover change process. In this model, GIS analysis is used to generate spatial drivers of land use/cover changes, and RBFN is trained to extract model parameters. Through the RBFN-CA model, the conversion probabilities of each cell from its initial land use state to the target type can be generated automatically. Future land use/cover scenarios are projected by using generated parameters in the model training process. This RBFN-CA model is tested based on the comparison of model output and the real data. A BPN-CA model is also built and compared with the RBFN-CA model by using a variety of calibration metrics, including confusion matrix, figure of merit, and landscape metrics. Both the location and landscape metrics based assessment for model simulation indicate that the RBFN-CA model performs better than the BPN-CA model for simulating land use changes in the study area. Therefore the RBFN-CA model is capable of simulating multiple classes of land use/cover changes and can be used as a useful communication environment for stakeholders involved in land use decision-making.     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

Development and application of a simulation model for changes in land-use patterns under drought scenarios

In arid and semiarid areas of northern China, one of the most vulnerable regional environments, water resources are a key constraint on socioeconomic development. We constructed a simulation model for land-use patterns under a drought transition (i.e., the increased frequency and duration of drought since the late 1970s in the Yongding River Basin study area) to account for the complexity of both the driving factors behind land-use change and the micro-level changes in land-use patterns. This model was a combination of the “top-down” system dynamics model, the “bottom-up” cellular automaton model, and the artificial neural network model. In this model, we considered the socioeconomic development and water resource restrictions, as well as the balance between the land-use requirements and the land supply. We then verified the model through a case study. The results demonstrated the value of constructing a simulation model driven by water resource constraints under the influence of drought. The spatial distribution of land uses in future scenarios will help support decision-making for sustainable regional development.