音乐艺术与造型艺术

本文借助于视觉艺术的外视感应来阐述听觉艺术的内视感应 ,确定时间艺术具有更为广阔的空间想象力和创造力。举出音乐作品实例分析 ,进一步论述音乐艺术随时间流动所产生的心理造型和它的立体性效果。提出音乐魅力所带来的空间氛围、形象意境 ,在心灵感应下会取得长久性的感受与记忆。     

一维核废料污染问题的一个二阶格式

利用降价法研究了多孔介质中一维核废料污染问题，此模型由一非线性抛物－椭圆耦合偏微分方程组的初边值问题来描述，对此问题给出了一个关于时间和空间均具有二阶精度的差分格式，并进行了理论分析。     

东干语翻译再推敲

中亚东干人的语言“是百年前地道的陕甘方言”;有些语音及口音与今天陕西关中一带甚至宁夏回民的口音一模一样。在翻译东干语 (或研究东干文化 )时要注意遵循历史上约定俗成的、传承下来的思维规则 ;要把语音和语义结合起来。正确的翻译 (或研究 )对促进民族、地区间文化交流及扩大对外开放有重要意义     

New Membranes Based on Polybenzimidazole for Polymer Fuel Cells

1 Results Acid-doped polybenzimidazoles[1] are particularly appealing because of high proton conductivity with no or low humidification and promising fuel cells performances. PBI, in fact, contains basic functional groups which can easily interact with strong oxo-acids, such as H3PO4 and H2SO4. The acid partially protonates the polymer and partially is freely dispersed in the polymer backbone, so allowing proton migration via Grotthuss mechanism along the anionic chains[2]. Anyway, a technological limit...     

解读“节约型政府”

建设节约型政府是当前我国政府改革的必然要求。节约型政府模式的选择有其必然性。节约型政府应是廉价政府、廉洁政府、透明政府和效益政府。应通过加强节约型政府的制度建设、在行为方面加强政府自身建设、在各级政府中牢固树立节约理念和成本意识等措施全力，打造节约型政府。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.