Mg gas infiltration for the fabrication of MgB2 pellets using nanosized and microsized B powders

MgB₂ superconductor pellets were synthesized through Mg gas infiltration method using nanosized- and microsized B powders. There was a marked difference in the superconducting properties of the two samples, particularly in the pinning force and dominant pinning mechanism. The microstructures of the samples were observed using HR-TEM and STEM-HAADF, and the results showed that the primary reason for the difference in the superconducting properties is the distribution of the nanosized second-phase particle MgO. Additionally, a feasible reaction model for the Mg gas infiltration method was established. Compared to the Mg liquid infiltration method, the gas infiltration showed better penetrability ability with a small amount of residual Mg. This study presents a novel synthesis process to fabricate an MgB₂ pellet with superior density and superconducting properties. This method can be used in multiple applications such as superconducting bearings, compact superconductor magnets, and magnetic shielding.     

基于视觉感知的智能扫地机器人的垃圾检测与分类

目的 为了提高扫地机器人的自主性和智能化程度，为扫地机器人配备视觉传感器，使其获得视觉感知能力，通过研究有效的垃圾检测分类模型与算法，实现对垃圾的定位与识别，引导扫地机器人对垃圾进行自动识别与按类处理，提高工作的目的性和效率，避免盲动和减少能耗。方法 选择检测速度较快的YOLOv2作为主网络模型，结合密集连接卷积网络，嵌入深层密集模块，对YOLOv2进行改进，提出一种YOLOv2-dense网络，该网络可以充分利用图像的高分辨率特征，实现图像浅层和深层特征的复用与融合。结果 测试结果表明，智能扫地机器人使用本文方法可以有效识别不同形态的常见垃圾类别，在真实场景中，测试识别准确率为84.98%，目标检测速度达到26帧/s。结论 实验结果表明，本文构建的YOLOv2-dense网络模型具有实时检测的速度，并且在处理具有不同背景、光照、视角与分辨率的图片时，表现出较强的适应和识别性能。在机器人移动过程中，可以保证以较高的准确率识别出垃圾的种类，整体性能优于原YOLOv2模型。     

不同煤阶煤孔隙特征及其吸附能力响应

我国是煤炭资源大国，煤层气储量规模相当可观，但煤储层又具有低孔、低渗的不足，照搬国外或常规石油天然气的开采程序和方法已被证实是走不通的。应结合实际，根据不同地质条件、不同煤阶的开采程序，进行孔隙特征研究。煤孔隙特征、连通性和吸附能力对煤层气开采影响尤为重要，为研究煤孔隙结构特征随煤变质程度的变化关系及其吸附能力的响应特点，采取不同地区不同变质程度煤样，进行压汞测试和等温吸附实验。实验结果表明，煤孔隙度和平均孔径均随变质程度增加呈现降低—升高—降低的趋势；煤中孔隙连通性随煤变质程度增加逐渐变差；随煤变质程度增加，其最大吸附能力也呈现降低—升高—降低的总体趋势。     

Environmental factors controlling phytoplankton dynamics in a large floodplain river with emphasis on cyanobacteria

Harmful algal blooms are occurring in large river ecosystems and at the mouth of large rivers with increasing frequency. In lentic systems, the chemical and physical conditions that promote harmful algal blooms are somewhat predictable but tracking prevalence and conditions that promote harmful algal blooms in lotic systems is much more difficult. We captured two of the most extreme discharge years within the last 20 years occurring in the Upper Mississippi River, allowing a natural experiment that evaluated how major shifts in discharge drive environmental variation and associated shifts in phytoplankton. Statistical models describing significant environmental covariates for phytoplankton assemblages and specific taxa were developed and used to identify management‐relevant numeric breakpoints at which environmental variables may promote the growth of specific phytoplankton and/or cyanobacteria. Our analyses supported that potentially toxin‐producing cyanobacteria dominate under high phosphorus concentration, low nitrogen concentration, low nitrogen‐to‐phosphorus ratio, low turbulence, low flushing, adequate light and warm temperatures. Cyanobacteria dominated in 2009 when low discharge and low flushing likely led to optimal growth environments for Dolichospermum, Aphanizomenon and Microcystis. Rarely will a single factor lead to the dominance, but multiple positive factors working in concert can lead to cyanobacteria proliferation in large rivers. Certain isolated backwaters with high phosphorus, low nitrogen, warm water temperatures and low potential for flushing could benefit from increased connection to channel inputs to reduce cyanobacterial dominance. Numerous examples of this type of habitat currently exist in the Upper Mississippi River and could benefit from reconnection to channel habitats.     

利用卡尔曼滤波和人工神经网络相结合的油藏井间连通性研究

油藏连通性的认识对于制定合理的开发调整方案和提高水驱油藏采收率具有重要意义。基于注采井的生产动态数据,建立一种卡尔曼滤波和人工神经网络相结合的分析方法,对油藏井间动态连通性进行定量表征研究。考虑到注入数据的噪声污染和注入信号在地层传播过程中的时滞影响,分别利用卡尔曼滤波算法和非线性扩散滤波器对注采数据进行预处理,从而减少注采数据对机器学习模型的干扰,提高连通性分析的准确性。基于预处理后的历史注采数据,对以生产井产液量为响应,注水井的注水量为输入的人工神经网络进行训练和参数优化,模拟和挖掘注采系统中的井间连通关系。通过对训练好的模型进行参数敏感性分析,量化油藏井间连通程度。应用所建模型和方法分析了均质、各向异性、包含封闭断层、具有高渗透带的4种典型特征油藏和实际非均质油藏的井间连通性。计算结果与油藏地质特征高度吻合,验证了该方法的实用性,可作为量化注采系统连通状况的有效方法。     

Effect of Ca and B incorporation into silicon oxycarbide on its microstructure and phase composition

Ca- and/or B-modified silicon oxycarbides were synthesized via pyrolysis of suitable polysilsesquioxane-based single-source precursors. Their polymer-to-ceramic transformation was investigated with thermogravimetric analysis, coupled with in situ evolved gas analysis. The prepared silicon oxycarbides were investigated with respect to their crystallization behavior, network architecture, and chemical compositions. The network connectivity in silicon oxycarbides can be affected/tuned upon using two different “tools”: (a) first, the use of network modifiers, such as Ca in our study, leads to a slight depolymerization of the network via generation of a small amount of Q³ sites; (b) second, the modification of silicon oxycarbide with B/Ca leads to a decrease of the carbon content in the network and thus to a significant decrease of its connectivity. Using these two different effects, the network connectivity in silicon oxycarbides can be finely tuned.     

鄂尔多斯盆地Y66井区延9油层组砂体连通性评价

鄂尔多斯盆地Y66井区延9油层组属于低孔-低渗储层,注水见效差异显著。为实现该油层组高效开发,利用静态和动态连通性组合分析方法系统评价延9油层组的砂体连通性。基于单砂体叠置类型确定砂体静态连通性,并引入连通厚度与连通系数对砂体连通性进行定量表征。依据开发过程中产能变化和注水响应定量描述井间砂体的动态连通性。综合钻井岩心、分析测试资料,结果表明,整个Y66井区延9油层组中部的砂体连通性较好,相对而言,西南部和东北部的砂体连通性较差;从各小层来看,延911小层和延921小层砂体具有良好的垂向叠置及横向叠置关系,连通厚度大多在6～10 m内,连通系数大多在80%以上。综合动静态资料,对油层组的连通关系进行了判别,为油气田生产开发提供理论基础。     

基于连通概率感知的车联网资源优化技术研究

研究一种自由连通状态下基于车辆速度的多车道连通概率分析模型。该模型主要关注各个路段的连通概率,基于随机过程统计分析理论推导出多车道的道路连通概率。其次,针对单播或多播的端到端的消息分发场景,提出一种基于连通度概率感知的资源优化选择算法。该算法通过考虑车辆速度及方向特征,宏观层次上选择连通度概率高的路段作为数据包转发的路径;微观层次上针对每一路段进行基于邻居信息的多跳广播,快速地将数据包从路段入口传输到路段出口,提高数据分发效率。NS2仿真结果表明,提出的资源优化算法提高了分组转发的有效性,降低了车载网络的分组传输时延。     

Coverage‐aware connectivity‐constrained unattended sensor deployment in underwater acoustic sensor networks

Careful deployment of nodes in underwater acoustic sensor networks in a distributed manner with the goal of maximized coverage and guaranteed connectivity is a challenging problem because it is very difficult and costly to access the 3D underwater environment. This paper presents a novel algorithm for self‐deployment of nodes in underwater acoustic sensor networks assuming that the nodes are randomly dropped to the water surface and form a densely populated connected network at the water surface. The idea of the algorithm is based on calculating an optimized depth for each node in the network in such a way that the possible sensing coverage overlaps are minimized and the connectivity of final topology is guaranteed. The algorithm has three main phases. In the first phase, nodes are organized in a tree structure that is rooted at the surface station. In the second phase, the depths for all nodes are computed iteratively at surface station. In the final phase, the calculated depths are distributed to nodes so that the nodes start sinking. The performance of the proposed approach is validated through simulation. We observed that the proposed approach performs at least 10% better in terms of network coverage than contemporary schemes in the literature. Copyright © 2016 John Wiley & Sons, Ltd.