深层优质碎屑岩储层全生命周期分析方法论

油气勘探领域的深层是指沉积盆地中埋深在3 500~4 500 m及以深的地层。在含油气盆地的深层碎屑岩储层中，优质储层是重要的勘探目标。优质储层具有大量孔隙，其形成与沙或砂岩的沉积条件、埋藏方式、区域温压场、膏盐岩效应等多种因素有关。通过系统研究全球不同区域不同时代不同类型深层碎屑岩储层，提出优质深层碎屑岩储层的全生命周期研究方法，即从储层形成演变的全部时间出发，将深部储层的形成过程划分为早期剥蚀搬运沉积阶段、中期埋藏阶段、中期或/和晚期改造阶段，从全生命周期的尺度分阶段综合分析深层碎屑岩储层的形成及演化过程，推演在地质历史时期中的优质储层演化时序及发育阶段、分布位置。     

龙背湾水电站坝体主堆石料运输干道方案的设计与比选

以龙背湾水电站为例,介绍了大坝主堆石料的运输要求,提出了运输干道布置的设计方案,从方案的特性及工程投资等方面进行了比选,确定了最优方案。特别介绍了采用交通洞方案的优点及应用效果。     

Effect of increased linings on micro-pressure waves in a high-speed railway tunnel

A micro-pressure wave (MPW) is generated when a train enters a tunnel at high speed, which causes a strong impact on the environment around the tunnel. The increased lining used to repair damage in high-speed railway tunnels changes cross-sections and has a strong influence on the MPW at the tunnel exit. In this paper, the methods of full-scale measurement, numerical simulation and moving-model experiments are used to study the MPW generated in a tunnel whose lining is increased. The rules governing the effect of increased linings on MPWs are obtained, which can be used as a reference for the Tunnel Damage Regulation Project in China.     

盾构通用衬砌环楔形量计算及应用

本文结合长沙地铁通用衬砌环拼装实例，分析了单环衬砌环不同点位拼装对平曲线、竖曲线所提供的空间转角矢量及楔形量的影响值，希望对拼装时点位选择、盾构姿态调整有一定的意义。     

公路隧道水平岩层施工超欠挖控制技术

在公路隧道施工中，发生水平岩层的超欠挖是必不可免的问题。本文对超欠挖现象发生的原因进行了总结，提出了水平岩层施工时可用的超欠挖控制技术，能有效降低此类现象带来的不良影响。     

Deep‐black‐coloring effect of fabrics made of noncircular cross‐section polyester filaments

The effects of noncircular cross‐section (NCCS) poly ethylene terephthalate (PET) filaments and its shape factor on deep‐black‐coloring of dyed fabrics were investigated by comparing to that of the circular cross‐section PET ones. Indexes such as K/S, L* and Integ values were used for characterizing the deep‐black‐coloring effect on fabrics. The results indicated that fabrics made with NCCS PET filaments exhibited good deep‐black‐coloring effects. The calculated shape factor of the NCCS PET fiber had a significant correlation with the degree of deep‐black‐coloring exhibited by the fabric made from the fibers. A qualitative optical analysis of the NCCS PET fibers was carried out to explain the causes of the deep‐coloring of the NCCS fibers. This analysis implies that the contours of the NCCS fiber composed of surfaces with varied curvature increase the scattering of light by lowering specular reflection and increasing interior reflected and refracted light. This, in turn, strengthens the deep‐coloring effect. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 511–518, 2014     

苏州圆融星座工程深基坑施工平面转换技术

苏州圆融星座工程体量大，结构复杂，但施工场地局促，施工平面布置及垂直运输协调管理困难。在深基坑工程阶段，通过合理地进行施工平面布置转换，解决了场地紧张的问题。     

地面出入式盾构法隧道施工技术研究综述

地面出入式盾构(GPST)由日本株式会社大林组研发,作为一种新型盾构隧道施工方法,无需传统盾构的端头工作井,明显减小对周围环境的影响,同时缩短工期。该技术目前的实践工程较少,存在多方面工程技术难题。根据已有研究资料,阐述地面出入式盾构的相关技术难题,主要从隧道衬砌、盾构姿态、同步注浆等几个方面进行总结,分析现有研究不足之处,提出需要进一步研究的方向和研究思路。     

隧洞开挖典型掏槽设计方法

针对目前隧洞开挖爆破施工中缺少具体、详细的掏槽设计方法的现状,提出了最常用的2种典型掏槽方式,并详细论述了其设计方法。这些掏槽方式及设计方法均在实际施工中得到了推广应用,并且取得了良好的爆破效果。实际应用表明:这些典型掏槽方式及设计方法能有效提高硬质岩石开挖的钻孔利用率,能将钻孔利用率提高到98%～100%,实现掌子面基本无残孔,从而有效提高单循环进尺,加快开挖进度,降低开挖成本。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.