气溶胶和氡浓度变化对氡子体影响的探讨

为了得到气溶胶变化后对氡子体产生的影响,实验通过气溶胶粒径谱仪SMPS测试氡室内不同浓度气溶胶,分析气溶胶的变化情况,根据AlphaGUARD测氡仪以及AlphaPM子体仪测试变化的氡浓度不同气溶胶下子体变化情况。实验结果表明：在两个NaCl浓度的气溶胶条件下,气溶胶的总量随着气溶胶发生器中的NaCl浓度的增大而变大,由于氡子体结合态随气溶胶浓度改变,氡子体浓度也随气溶胶浓度的增大而增大。并且在相同气溶胶浓度情况下,氡子体的浓度随着氡浓度的增大而增大。     

应用 BP 神经网络预测精锻斜齿轮损伤因子

通过利用DEFORM-3D软件对斜齿轮精密锻造过程进行有限元模拟,获得了损伤因子的分布特点。运用Matlab软件建立BP神经网络损伤因子预测模型,分析了不同锻造条件对损伤因子的影响,并对不同锻造条件下的损伤因子进行预测。利用检测样本对训练的神经网络进行验证,结果表明,BP神经网络预测损伤因子与模拟结果吻合较好,预测结果误差较小,预测精度满足实际应用要求。     

基于流固耦合的闸板防喷器建模及有限元分析

为了更加准确地分析流体对闸板剪切力的影响规律,需要对闸板防喷器剪切钻杆进行流固耦合分析。以钻杆窜动和扭转2种动态工况为例,利用SolidWorks建立闸板防喷器三维模型,并对防喷器内部流体域进行有限元建模,输出到ANSYS Fluent进行有限元分析。结果表明:在闸板剪切钻杆过程中,流体达到稳态后,在钻杆窜动工况下,流体力随钻杆受载荷力增大而减小; 在钻杆扭转工况下,流体力随钻杆扭转角度而变化,在扭转90°时流体力最大。研究结果可为防喷器剪切闸板的剪切能力分析及结构设计提供参考。     

Moire Patterns of θ''''Precipitates in Al-Cu Alloy

A θ＇precipitate in an Al-Cu alloy was imaged.Its Moire patterns shown by TEM were discussed.     

Dislocation Structures in a BulkPb－doped Bi－SrCa－Cu－O High Temperature Superconductor

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两种简化的侧凹抽芯机构

对于制品的内侧凹 ,在采用滑块、斜摆杆等一般抽芯机构较难实现时 ,通过改变抽芯机构的动作和复位形式 ,设计出两种简化的侧凹抽芯机构 ,并且制造方便,缩短了加工周期 ,实践证明该机构可有效地完成抽芯动作。     

Stress－rupture Property and Microstructure of Yttria Dispersion Strengthened Alloy MA956

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基于关键资源优先的三级装卸搬运分时协调策略

研究基于关键资源优先的单元化“装卸、搬运、装卸” 三级作业链的调度问题. 在已知关键资源调度方案的条件下, 将两个非关键各作业级的调度问题分别转化为最小单位流问题, 并建立相应的0/1 整数规划模型求解. 为求解大规模问题, 提出三级装卸搬运的分时协调策略. 参照宁波港码头数据, 设计面向集装箱码头的仿真算例, 实验结果验证了所提出模型和策略的有效性.

     

Who creates Trends in Online Social Media: The Crowd or Opinion Leaders?

Internet slang words can very quickly become ubiquitous because of social memes and viral online content. Weibo, a Twitter‐like service in China, demonstrates that the adoption of popular Internet slang undergoes 2 distinct peaks in its temporal evolution, in which the former is relatively much lower than the latter. An in‐depth comparison of the diffusion of these different peaks suggests that popular attention in the early stage of propagation results in large‐scale coverage, while the participation of opinion leaders at the early stage only leads to minor popularity. Our empirical results question the conventional influentials hypothesis and provide some insights for marketing practice and influence maximization in social networks.     

High-quality image restoration from partial mixed adaptive-random measurements

A novel framework to construct an efficient sensing (measurement) matrix, called mixed adaptive-random (MAR) matrix, is introduced for directly acquiring a compressed image representation. The mixed sampling (sensing) procedure hybridizes adaptive edge measurements extracted from a low-resolution image with uniform random measurements predefined for the high-resolution image to be recovered. The mixed sensing matrix seamlessly captures important information of an image, and meanwhile approximately satisfies the restricted isometry property. To recover the high-resolution image from MAR measurements, the total variation algorithm based on the compressive sensing theory is employed for solving the Lagrangian regularization problem. Both peak signal-to-noise ratio and structural similarity results demonstrate the MAR sensing framework shows much better recovery performance than the completely random sensing one. The work is particularly helpful for high-performance and lost-cost data acquisition.