塔里木盆地中秋1凝析气藏成藏条件及演化过程

2018年12月,塔里木盆地秋里塔格构造带天然气勘探取得重大突破,中秋1井在下白垩统巴什基奇克组测试获高产工业气流,发现了该构造带迄今为止最大的整装凝析气藏。为了给该构造带下一步油气勘探部署提供依据,在充分利用前人研究成果的基础上,基于岩心、薄片、测井及地球化学分析资料,明确了中秋1大型凝析气藏的油气藏特征和气藏类型,分析了该气藏天然气成藏条件并推演其演化过程。研究结果表明:①中秋1大型气藏属于低含凝析油凝析气藏,天然气为煤成气,主要来自区内侏罗系腐殖型煤系烃源岩;②该构造带能够形成大型油气藏的有利地质条件是三叠系和侏罗系发育的两套巨厚高演化烃源岩、白垩系发育的大型辫状河三角洲规模有效砂岩储层、喜马拉雅晚期以来的构造活动形成的大规模楔状叠瓦构造圈闭、古近系和新近系巨厚膏盐岩形成的优越保存条件,以及油气生成与圈闭形成的良好时空匹配;③该气藏具有先油后气、晚期快速气侵聚集成藏的成藏模式,总体上是一个"早期聚油、晚期气侵、改造调整、快速成藏"的成藏过程。结论认为,该构造带油气成藏条件优越,具有广阔的油气勘探前景。     

不同热解工艺下半焦的微观结构特点及其对燃烧性能的影响

摘要：对比研究了3种直立内热炉半焦(熄焦方式分别为水泡熄焦、喷水熄焦及烟气干熄焦)及一种流化床热解半焦的微观结构及燃烧性能。通过对半焦组成、孔隙结构、有机官能团、碳化学结构及半焦微晶结构分析，获得了不同低温热解工艺半焦的组成及微观结构特点，明确了组成及微观结构与燃烧性能之间的关系。结果表明，不同热解工艺半焦的组成和微观结构差异明显，其对半焦的燃烧性能影响显著。干熄焦碳化学结构缺陷比例大，快速热解半焦孔隙结构发0达、挥发分高，与水喷焦相比水泡焦石墨化程度低；水喷焦燃烧性能优于水泡焦，干法所得半焦的燃烧性优于湿法；半焦的挥发分含量决定其燃点，在快升温速率条件下孔隙结构对半焦的燃烧性能影响较大，而慢升温速率条件下有序化度对半焦的燃烧性能影响较大。     

废脱硫剂在铁矿烧结中的应用

钢厂煤气脱硫工序排放的废脱硫剂是一种难以利用的有害固体废弃物，含一定量的铁及较高的钙和硫等元素。对废脱硫剂配入铁矿粉中进行了烧结杯的试验研究，结果表明，当废脱硫剂配比超过01%后，随着废脱硫剂配比的升高，烧结矿转鼓强度下降，利用系数下降，烧结烟气中SO2与NOx浓度增加，烧结矿的还原度有所下降，但烧结矿的低温还原粉化性能有所改善。烧结矿的矿相研究发现，废脱硫剂配比偏大时，磁铁矿体积分数增大，铁酸钙体积分数减少，并由针状结构向柱状、板状演变，烧结矿的气孔率上升。主要原因在于其粒度偏粗，含硫高，不利于成矿。当废脱硫剂粒度为-63mm及其配比控制在01%时，烧结矿的产量和质量有所改善，烟气中SO2、NOx较基准试验变化不大。工业试验表明，在烧结混匀矿中配入废脱硫剂配比小于012%时，烧结矿产量和质量没有明显变化，烧结烟气脱硫脱硝效果也没有受到影响。废脱硫剂掺入铁矿中进行烧结是一种资源化利用的有效方法，不仅解决了有害固体废弃物对环境的影响，而且使二次资源得到综合利用。     

Compilation of Modern Technologies To Map Genome-Wide Cytosine Modifications in DNA

Over the past few decades, various DNA modification detection methods have been developed; many of the high-resolution methods are based on bisulfite treatment, which leads to DNA degradation, to a degree. Thus, novel bisulfite-free approaches have been developed in recent years and shown to be useful for epigenome analysis in otherwise difficult-to-handle, but important, DNA samples, such as hmC-seal and hmC-CATCH. Herein, an overview of advances in the development of epigenome sequencing methods for these important DNA modifications is provided.     

结合深度学习与支持向量机的金属零件识别

目的 在视觉引导的工业机器人自动拾取研究中，关键技术难点之一是机器人抓取目标区域的识别问题。特别是金属零件，其表面的反光、随意摆放时相互遮挡等非结构化因素都给抓取区域的识别带来巨大的挑战。因此，本文提出一种结合深度学习和支持向量机的抓取区域识别方法。方法 分别提取抓取区域的方向梯度直方图（HOG）和局部二进制模式（LBP）特征，利用主成分分析法（PCA）对融合后的特征进行降维，以此来训练支持向量机（SVM）分类器。通过训练Mask R-CNN（regions with convolutional neural network）神经网络完成抓取区域的初步分割。然后利用SVM对Mask R-CNN识别的抓取区域进行二次分类，完成对干扰区域的剔除。最后计算掩码完成实例分割，以此达到对抓取区域的精确识别。结果 对于随机摆放的铜质金属零件，本文算法与单一的Mask R-CNN及多特征融合的SVM算法就识别准确率、错检率、漏检率3个指标进行了比较，结果表明本文算法在识别准确率上较Mask R-CNN和SVM算法分别提高了7%和25%，同时有效降低了错检率与漏检率。结论 本文算法结合了Mask R-CNN与SVM两种方法，对于反光和遮挡情况具有一定的鲁棒性，同时有效地提升了目标识别的准确率。     

面向数据流结构的指令映射优化方法

在高性能计算领域,数据流是一类重要的计算结构,也在很多实际场景表现出很好的性能和适用性。在数据流计算模式中,程序是以数据流图来表示的,数据流计算中一个关键的问题是如何将数据流图映射到多个执行单元上。通过分析现有数据流结构的指令映射方法及其不足,提出了基于数据流结构的新型指令映射优化方法。主要是根据多地址共享数据包的特性对指令映射方法进行优化,延迟多地址共享数据路由包的拆分,减少网络拥堵。     

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

     

Enhanced visible light photocatalytic activity in AgI/BiOCOOH composite

AgI/BiOCOOH composite photocatalysts have been synthesized via a simple deposition-precipitation method. The crystal structure, microstructure, element valance, light and electrical properties of as prepared samples were characterized by XRD, SEM, TEM, XPS, UV–Vis DRS, PL, EIS and photocurrent response. The loading of AgI nanoparticles endowed BiOCOOH with good visible light absorption and photocatalytic activity for degrading rhodamine B. The composition with Ag:Bi?=?1:1 exhibited the best photocatalytic activity. The enhanced photocatalytic performance could be mainly attributed to the effective separation of the photogenerated carriers at the heterojunction. O^2? and h⁺ were suggested as the main reactive species in the photocatalytic reaction. In addition, the photocatalysts showed excellent stability over multiple reaction cycles.     

深圳抽水蓄能电站监控新技术应用研究与实践

通过对安防、工业电视和门禁等新技术的研究,设计了一套先进的监控系统,包括硬件平台、系统管理、应用软件,利用深圳抽水蓄能电站已形成的光纤网络通道把分布在方圆10 km范围内多个站端设备以环路的方式连接起来,把可靠成熟的新技术融合到整个平台。自首台机组成功投运,监控系统为电站正常运行保驾护航,高清摄像机、视觉识别、行为分析、边界防护等新技术的应用为工程项目增添了亮点,使电站现场管理更加智能化、规范化。     

MooseFS热备元数据节点设计与实现

针对MooseFS元数据节点的单点故障问题，对MooseFS源码进行改造，为其增加一个热备的备元数据节点，并通过主备之间同步元数据，备节点回放操作日志等技术使得备元数据节点内存中的元数据时刻跟主元数据节点保持一致。当主元数据节点发生故障切换到备元数据节点后，备元数据节点无需从本地加载元数据即可快速接替主元数据节点对外提供服务。测试结果表明，备元数据节点本地和内存中的元数据均与主元数据节点保持一致，且故障恢复时间小于1 s。