直面百姓苦难的民众情怀

胡学文以塞外坝上农民生活为核心的底层写作,以直面现实的勇气与真诚,写出了现代化进程中被文明、进步所遮蔽的偏远乡村惊人的苦难与愚昧,显示了作者直面黑暗的精神和同情民众的情怀。     

CFD-ACE＋软件对PCR微芯片传热过程的数值模拟和分析

针对基于MEMS技术的PCR微芯片温度控制难的问题，通过对该芯片的传热过程进行简化并建立物理模型和数学模型，对芯片内部的热传导和温度变化过程进行了理论推导分析。利用CFD-ACE＋软件对芯片的热传导过程进行了数值模拟。模拟结果表明，该芯片具有较高的升、降温速度。芯片在自行构建的温度控制系统下进行了热循环反应，对GUS基因成功实现了扩增。     

微型圆热管挤压犁削／拔拉成型刀具和模具的设计

在分析微型热管的功能、技术要求及其加工工艺的基础上，设计了微型热管犁削-拔拉用成型刀具和系列模具．该刀具可使定位装夹方便，加工的热管直径尺寸稳定；模具保证了热管的外径尺寸并可方便地根据要求改变外径尺寸，热管外表面质量好，内部具有更加优良的毛细结构．刀具采用高速钢，模具采用45钢．     

一种新型空间电压矢量调制方式的研究

通过对传统的空间电压矢量调制方式的研究,提出了一种改进型的空间电压矢量调制方式,并给出了此改进型SVPWM调制方法下的各矢量作用时间的推导.相对传统的调制方式,此方法能够同时满足变频器输出在高频段和低频段的需要,适应了不同应用场合下的要求.实验结果证明本方法切实可行,具有一定的推广价值.     

反应气压对生长金刚石膜的影响

实验以CH3CH2OH和H2 为气源,用微波等离子体CVD法沉积金刚石膜,并用金相显微镜(×400)观察其沉积效果,X射线衍射仪表征其成膜质量,最后分析反应气压对金刚石膜的影响.     

电脑刺绣机针杆及挑线机构设计

以电脑刺绣机原始针杆机构和挑线机构为研究对象,在保持原有针杆运动规律和挑线杆运动规律的前提下,运用机构创新设计理论,分析电脑刺绣机针杆机构和挑线机构的可能选型.为研制新型电脑刺绣机针杆机构和挑线机构奠定了理论基础.     

识别混凝土板内部缺陷的人工神经网络算法

以一维有缺陷混凝土板为研究对象，分别采用Leverberg-Marcluardt和径向基神经网络算法，对缺陷的深度与厚度进行识别，从而实现对混凝土板内部缺陷的三维重构，称为红外CT模拟．两类神经网络算法的识别结果表明：Leverberg-Marcluardt神经网络较径向基神经网络具有更好的收敛精度与计算效率．     

基于时延脉冲耦合神经网络的AOE-网问题求解算法

 在时延脉冲耦合神经网络(DPCNN-Delay Pulse Coupled Neural Network)的基础上,提出了mDPC-NN(modified DPCNN)模型.mDPCNN能方便地用于有向网的最大代价路径和最小代价路径的求解,并将其成功地运用其于求解AOE-网(Activity On Edge)有关问题.该方法通过快速并行计算,一次正向计算即可求解AOE-网大部分问题,与传统方法相比体现了较好的优势.     

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.