潮流能水平轴水轮机在剪切来流下的水动力分析

采用基于计算流体力学方法的全尺度数值模拟计算,建立了适用于潮流能水轮机模拟的剪切来流环境,通过数值计算结果,对潮流能水轮机随时间变化的宏观力和流场特征进行了系统分析,探索水轮机在复杂工况下的水动力特性规律。结果表明,水轮机整个叶轮的总体平均受力在剪切流工况中未受到明显影响; 单个叶片的瞬态轴向力系数和瞬态轴向扭矩系数在旋转周期内出现了明显的周期性变化,且周期性变化幅值随转速增大而增大; 非定常的瞬时轴向扭矩和力的周期性振荡容易引起单个叶片的结构性振动,可能对水轮机的机械结构造成影响。     

甘草黄酮的提取工艺优化、含量测定及体外抗氧化活性研究

以95%乙醇为提取溶剂提取甘草黄酮,以提取率为评价指标,采用响应面法优化提取工艺;以芦丁为对照品,采用亚硝酸钠-硝酸铝-氢氧化钠比色法测定不同产地甘草黄酮含量;通过DPPH自由基清除反应,比较不同产地甘草黄酮的体外抗氧化活性。结果表明,醇提法提取甘草黄酮的最优工艺条件为:提取温度86.7℃、提取时间4.0 h、液料比80.0∶1.0(mL∶g);在此工艺条件下,甘肃、新疆、内蒙古的甘草黄酮含量分别为18.22 mg·g~(-1)、17.75 mg·g~(-1)、16.82 mg·g~(-1),其对DPPH自由基的清除率分别为63.82%、62.74%、51.41%;3个产地的甘草黄酮的含量及体外抗氧化活性大小顺序均为:甘肃新疆内蒙古。该结论可作为甘草原产地鉴别及质量评价的参考依据之一,为甘草药材的开发利用提供帮助。     

高密度聚乙烯/铅硼复合材料屏蔽性能和力学性能研究

研究了高密度聚乙烯/铅硼复合材料的屏蔽性能和力学性能，通过屏蔽仿真比较了密度及碳化硼（B4C）含量对屏蔽性能的影响，通过试验比较了B4C含量对屏蔽性能、弯曲强度及冲击强度的影响。仿真结果表明,随聚乙烯/铅硼复合材料密度升高，快中子屏蔽性能下降，热中子屏蔽性能和γ屏蔽性能提高；保持聚乙烯/铅硼复合材料密度不变，随B4C含量的提高，中子屏蔽性能提高而γ屏蔽性能下降;实验数据表明,随B4C含量的升高，高密度聚乙烯/铅硼材料的快中子屏蔽性能、热中子屏蔽性能升高，γ屏蔽系数下降，冲击强度和弯曲强度下降明显，屏蔽性能测试结果和仿真结果规律性相符；综合仿真结果和实验数据表明，含B4C 2 %左右的高密度聚乙烯/铅硼复合材料同时具有较好的屏蔽性能和力学性能。     

某含锌锡多金属硫化矿石锌浮选试验研究

某含锌锡多金属硫化矿石Zn、Sn、Fe、S含量分别为6.04%、1.05%、29.33%、19.08%，锌主要以铁闪锌矿、闪锌矿的形式存在，锡主要以锡石的形式存在，铁主要以黄铁矿、磁黄铁矿等形式存在，其中的金属矿物共生关系密切，相互包裹现象普遍。为确保不影响后续选锡，对锌浮选流程进行了试验研究。结果表明：矿石在磨矿细度为-0.074 mm占80%的情况下，采用预先脱硫—锌硫混浮再分离流程处理，在选择硫酸铜为锌矿物活化剂、丁基黄药为捕收剂、松醇油为起泡剂、石灰为黄铁矿抑制剂的情况下，经1段脱硫、2粗1扫锌硫混浮、1粗2精锌硫分离，锌硫分离精选尾矿与锌硫混浮扫选精矿2次精选后锌硫分离，最终获得锌精矿Zn品位47.06%、回收率90.76%，试验指标良好。     

山东栖霞—大柳行地区金矿找矿靶区地质评价及优选

栖霞—大柳行地区位于胶东中部栖霞—蓬莱金矿成矿带内，是胶东重要的金成矿区，成矿条件优越。为了对圈定靶区进行优选分类，通过对该区地质背景、找矿靶区评价、找矿靶区优选方面展开综合研究，并结合勘查工作程度和矿业权设置情况，划分出3类预测区，选定了5个靶区为今后优先部署勘查工作的方向。     

Influence of Si-Doping on the Performance of InGaN/GaN Multiple Quantum Well Solar Cells

Semiconductors - The performance of InGaN/GaN multiple quantum well (MQW) solar cells with five different Si-doping concentrations, namely 0, 4 × 1017 cm–3, 1 × 1018 cm–3, 3...     

具有光照鲁棒的图像匹配方法

针对现有的基于局部特征的图像匹配算法对光照变化敏感、匹配正确率低等问题，提出一种具有光照鲁棒性的图像匹配算法。首先使用实时对比保留去色（RTCP）算法灰度化图像，然后利用对比拉伸函数模拟不同光照变换对图像的影响从而提取抗光照变换特征点，最后采用局部强度顺序模式建立特征点描述符，根据待匹配图像局部特征点描述符的欧氏距离判断是否为成对匹配点。在公开数据集上，所提算法与尺度不变特征变换（SIFT）算法、加速鲁棒特征（SURF）算法、"风"（KAZE）算法和ORB算法在匹配速度和匹配正确率上进行了对比实验。实验结果表明：随着图像亮度差异的增加，SIFT算法、SURF算法、"风"（KAZE）算法和ORB算法匹配正确率下降迅速，所提算法下降缓慢并且正确率均高于80%；所提算法特征点检测较慢和描述符维数较高，平均耗时为23.47 s，匹配速度不及另外四种算法，但匹配质量却远超过它们。对实时性要求不高的系统中，所提算法可以克服光照变化对图像匹配造成的影响。     

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.