Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

Detecting Cysteine in Bioimaging with a Near-Infrared Probe Based on a Novel Fluorescence Quenching Mechanism

Near-infrared (NIR) fluorescent probes are very significant for detecting cysteine in biological systems. Herein, we report a highly selective and sensitive NIR turn-on fluorescent probe (BDP-NIR) based on BODIPY with large Stokes shift (105 nm) for detecting Cys. We clarified the sensing mechanism based on the different thiol-induced S_NAr substitution/rearrangement reaction of the probe with cysteine and homocysteine/glutathione, which leads to the corresponding amino- and thiol-BODIPY dyes with distinct photophysical properties. Moreover, a novel mechanism of fluorescence quenching was demonstrated by density functional theory calculation. The reason for the fluorescence quenching of the probe might be intersystem crossing (from singlet to triplet excited state). Moreover, BDP-NIR had a high linear dynamic range of 0–500 μM, which was promising for detecting cysteine quantificationally. Significantly, BDP-NIR was capable of sensing endogenous cysteine in living cells and in vivo.     

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。     

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...     

不同制样方法对直流辉光放电质谱法测定氮化硼中27种杂质元素的影响

讨论了3种制样方法对直流辉光放电质谱法(dc-GDMS)检测氮化硼中的Na、Mg、Al、Si等27种杂质元素的影响。3种制样方法分别如下所示:方法1,直接把氮化硼压在铟薄片上;方法2,把氮化硼压在铟薄片上后,再盖上一层铟罩;方法3,把压碎后的氮化硼放在针状钽勺上。在优化的辉光放电参数下对比了3种不同制样方法对基体信号强度的影响。试验表明:在方法1中,当氮化硼尺寸约为3mm×3mm,厚度小于1mm时,基体¹¹B的信号可达1.8×10⁷ cps;在方法2中,选择铟孔大小合适的铟罩,基体¹¹B的信号可达1.0×10⁷ cps;方法3获得基体信号强度比方法1、方法2高一个数量级。大部分元素在中分辨率下可获得较好的结果,而对于在高分辨率下也较难分离的元素,可选择丰度较低的同位素在中分辨率下进行测定,如Ge选择⁷⁰Ge⁺,Se选择⁸²Se⁺,Cd选择¹¹¹Cd⁺,Sn选择¹¹⁹Sn⁺,Ag选择¹⁰⁹Ag⁺,Pt选择¹⁹⁴Pt⁺。氮化硼中的杂质元素含量可通过样品片中待测元素含量减去来自于铟薄片或钽勺中该元素贡献的含量来计算获得。将样品平行测定5次,相对标准偏差均在20%以内。对于Al、Si、Ti等元素的测定,3种制样方法的测定结果基本一致;方法1、方法2中检测到的In含量较大,使得铟中的Ni、Cu对氮化硼的测定值影响较大;方法3由于钽中Fe、Cu的贡献导致氮化硼中Fe、Cu的检测值较大,但方法3获得的基体信号强度大,可降低部分元素的检出限,如Cr、Mn、Ga、Ge等。综上所述,方法3为优选方法。     

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.