北京市专业化众创空间发展模式及对策建议

专业化众创空间是在众创空间基础上发展起来的服务于产业细分领域、高度专业化、重点服务于实体经济的一种新型创新创业服务载体.北京市作为全国科技创新中心,创新创业高度活跃,专业化众创空间不断探索模式创新和服务创新,在服务高精尖产业发展、加快新旧动能转换、促进科技成果转化、盘活科技创新资源等方面发挥了重要作用.文章通过分析北京市专业化众创空间发展的情况和特点,总结提炼了几种典型发展模式,分析了专业化众创空间对首都经济高质量发展的重要意义,同时针对目前北京市专业化众创空间发展存在的主要问题,提出相关对策建议.     

Meet-Cloud for Secure and Accurate Distribution of Negative Messages in Vehicular Ad hoc Network

Keeping Vehicular Ad hoc Network(VANET) from attacks requires secure and efficient distribution of information about bad entities. Negative messages are pieces of information that define the negative attributes of vehicles. By formally defining the negative message, we observe that accuracy is essential for its efficient distribution. We formally define the coverage percentage and accurate coverage percentage to describe the availability and distribution efficiency of negative message. These two metrics can jointly evaluate the performance of a distribution method. To obtain both high coverage percentage and high accurate coverage percentage, we propose meet-cloud, a scheme based on meet-table and cloud computing to securely and accurately distribute negative messages in VANET. A meet-table in a Road Side Unit(RSU) records the vehicles it encounters. All meettables are sent to cloud service to aggregate a global meet-table. The algorithm for distributing and redistributing negative messages are designed. Security analysis shows that meet-cloud is secure against fake and holding on to negative message attacks. Simulations and analysis demonstrate that meet-cloud is secure under denial of service and fake meet-table attacks. The simulation results also justify that meet-cloud outperforms the RSU broadcast and epidemic model.     

土壤肥料学实验课程的教学改革与实践

为全面培育卓越农林人才，提高教学培养质量，本研究结合土壤肥料学的课程内容、教学特点及实验方法，对存在的问题进行了剖析，并结合实际针对存在问题做出了相应改革，适时调整《土壤肥料学》实验课程内容和形式。通过删减验证性实验内容，增设综合性实验，传统部分教学内容实现网络化等三方面对该课程的实验课部分进行了改革与实践，以期提高学生综合运用所学《土壤肥料学》的相关知识来解决生产实际问题的能力。     

时标轴上二阶Dirichlet边值问题的弱解（英文）

采用变分方法和临界点理论研究一个时标轴上二阶Dirichlet边值问题弱解的存在性.     

利用SRTM数据修测高山区地貌的探讨

在高海拔区域测制地形图,如青藏高原地区,所获取的遥感影像局部存在着大范围的高山阴影或云雪覆盖,造成立体观测地貌困难的现象,探讨应用SRTM数据对这类特殊区域地貌进行修测补绘。介绍了数据处理的作业过程,并对实验区SRTM与SPOT5影像立体测图成果进行实验对比分析,成果符合要求。     

Learning Multi Labels from Single Label——An Extreme Weak Label Learning Algorithm

This paper presents a novel algorithm for an extreme form of weak label learning, in which only one of all relevant labels is given for each training sample. Using genetic algorithm, all of the labels in the training set are optimally divided into several non-overlapping groups to maximize the label distinguishability in every group. Multiple classifiers are trained separately and ensembled for label predictions. Experimental results show significant improvement over previous weak label learning algorithms.     

江苏省县域经济增长的趋同与差异分析

运用统计分析方法对江苏省2000~2011年县域经济增长的趋同与差异问题进行实证分析.结果表明,在这11年内出现了σ趋同,但不明显,且该阶段存在绝对β趋同;进一步分析发现,江苏省县域经济差距经历了先扩大后缩小的过程,特别是2007年以来,差距缩小的趋势明显.     

三维打印器件后处理液研究

分析了不同的填充强化剂(后处理A液)、不同的后处理B液基料、后处理B液中水性环氧乳液的掺量及后处理液浸泡时间对石膏基打印器件耐水性、抗压强度、尺寸偏差、表面微观结构的影响.结果表明:经纳米硅超硬固化剂处理后的打印器件抗压强度大幅提高,纳米硅超硬固化剂最适宜作为后处理A液;水性聚氨酯乳液PU-202B最适宜作为后处理B液的基料;后处理B液中加入水性环氧乳液,处理后打印器件的抗压强度增加、吸水率降低,但水性环氧乳液含量增加到一定程度后会出现尺寸偏差;后处理A、B液最佳浸泡时间分别为130 s和35 s;先浸泡后处理A液、后浸泡后处理B液的打印器件具有抗压强度高、吸水率小、表面较光滑、不脱粉等优良性能.     

梯度网络平稳点集合的Hopfield稳定性与吸引域

给出梯度神经网络模型的电路实现 ,证明了网络平稳点集合的全局 Hopfield稳定性。对各个平稳点吸引域的几何估计得到了相关结果 ,这为网络初始输入的数量与方向判定提供了基本的量度参数准则。     

Abrupt climate change：Debate or action

Global abrupt climate changes have been documented by various climate records, including ice cores, ocean sediment cores, lake sediment cores, cave deposits, loess deposits and pollen records. The climate system prefers to be in one of two stable states, i.e. interstadial or stadial conditions, but not in between. The transition between two states has an abrupt character. Abrupt climate changes are, in general, synchronous in the northern hemisphere and tropical regions. The timescale for abrupt climate changes can be as short as a decade. As the impacts may be potentially serious, we need to take actions such as reducing CO2 emissions to the atmosphere.