基于Web的3D模型浏览与交互系统的研究与实现

设计了基于Web的3D模型浏览与交互系统，主要采用基于WebGL的第三方库Three.js来实现。该系统的建立主要包括场景的构建、场景的交互控制和模型的云存储。模型是场景构建的前提，用户可从外部导入所需模型或利用系统自带的简单模型进行模型的构建。场景交互技术主要包括模型的平移、旋转、缩放、拾取、剖切、3D动画模型的播放以及信息的显示等。模型的云存储是指用户可以通过登录账户将3D模型存储到服务器，日后需要时直接从服务器中加载即可。     

PSO优化多核RVM的模拟电路故障预测

针对模拟电路健康管理的特点，提出了一种基于PSO优化多核RVM的模拟电路故障预测方法。利用参数分析得到电路的输出频域响应作为特征，计算其与电路无故障标准响应的欧氏距离来表征电路元件健康值，将多个核函数线性组合，并用PSO优化多核RVM参数后的模型实现对各个时间点元件的健康值变化轨迹进行预测。仿真结果表明，该方法在小样本情况下，预测效果优于单一核函数的RVM模型，适用于健康管理中实时预测，具有较好的实用性。     

应用膨胀算法提取目标的直接定位算法

为了解决被动雷达系统中的多发射源定位问题，提出了一种基于多重信号分类(MUSIC)算法和图像膨胀(IE)算法的直接定位方法。该方法结合了谱分析中的MUSIC思想，通过对接收量测协方差矩阵进行特征分析求解目标的位置。首先，在目标个数未知的前提下，利用Akaike信息准则(AIC)来确定模型阶数；然后，推导了基于MUSIC的定位代价函数；之后，利用图像膨胀算法处理得到的代价函数平面；最后，膨胀处理后的输出为目标个数及目标位置的估计值。提出的算法有效地解决了目标检测及提取的问题，能够确定多个目标的位置坐标，为后续的定位性能分析提供可能性，也保证了算法的完整性。进一步地分析了多个临近目标情况下影响目标提取性能的主要因素。     

结合优化支持向量机与K-means++的工控系统入侵检测方法

针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题，提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法（PCA）对原始数据集进行预处理，消除其相关性；其次在粒子群优化（PSO）算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解；然后利用自适应变异粒子群优化（AMPSO）算法优化支持向量机的核函数和惩罚参数；最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型，从而实现工业控制系统的异常检测。实验结果表明，所提方法在检测速度和对各类攻击的检测率上得到明显提升。     

A Dual Inhibitor of DYRK1A and GSK3β for β-Cell Proliferation: Aminopyrazine Derivative GNF4877

Loss of β-cell mass and function can lead to insufficient insulin levels and ultimately to hyperglycemia and diabetes mellitus. The mainstream treatment approach involves regulation of insulin levels; however, approaches intended to increase β-cell mass are less developed. Promoting β-cell proliferation with low-molecular-weight inhibitors of dual-specificity tyrosine-regulated kinase 1A (DYRK1A) offers the potential to treat diabetes with oral therapies by restoring β-cell mass, insulin content and glycemic control. GNF4877, a potent dual inhibitor of DYRK1A and glycogen synthase kinase 3β (GSK3β) was previously reported to induce primary human β-cell proliferation in vitro and in vivo. Herein, we describe the lead optimization that lead to the identification of GNF4877 from an aminopyrazine hit identified in a phenotypic high-throughput screening campaign measuring β-cell proliferation.     

快速灵敏的LC-MS/MS法测定人血浆中阿莫西林并应用于两种制剂的一致性评价

目的: 建立快速灵敏的LC-MS/MS法测定人血浆中阿莫西林浓度，并用于两种阿莫西林胶囊的一致性评价。方法: 采用岛津公司LCMS-8060型LC-MS/MS仪，以MRM模式测定阿莫西林（m/z 366.00/114.00）的浓度，d4-阿莫西林作内标（m/z 370.10/114.05），离子源为ESI源。色谱柱选用Waters ACQUITY BEH C18（2.1×50 mm，1.7 μm），梯度洗脱。血浆样本加入内标，经甲醇沉淀蛋白后取上清液进样检测。结果: 所建方法经验证，其线性、准确度、精密度、最低定量限、提取回收率、特异性、基质效应、稳定性等各项指标均符合CFDA的指导原则及最新核查标准要求，并较文献报道中的方法有处理简单、灵敏度高、色谱峰形好的优点。结论: 所建方法快速、灵敏，适用于人血浆中阿莫西林浓度的检测。用于一致性评价的样本实测，两种制剂生物等效。     

Regulation of sensory nerve conduction velocity of human bodies responding to annual temperature variations in natural environments

The extensive research interests in environmental temperature can be linked to human productivity / performance as well as comfort and health; while the mechanisms of physiological indices responding to temperature variations remain incompletely understood. This study adopted a physiological sensory nerve conduction velocity (SCV) as a temperature‐sensitive biomarker to explore the thermoregulatory mechanisms of human responding to annual temperatures. The measurements of subjects’ SCV (over 600 samples) were conducted in a naturally ventilated environment over all four seasons. The results showed a positive correlation between SCV and annual temperatures and a Boltzmann model was adopted to depict the S‐shaped trend of SCV with operative temperatures from 5°C to 40°C. The SCV increased linearly with operative temperatures from 14.28°C to 20.5°C and responded sensitively for 10.19°C‐24.59°C, while tended to be stable beyond that. The subjects’ thermal sensations were linearly related to SCV, elaborating the relation between human physiological regulations and subjective thermal perception variations. The findings reveal the body SCV regulatory characteristics in different operative temperature intervals, thereby giving a deeper insight into human autonomic thermoregulation and benefiting for built environment designs, meantime minimizing the temperature‐invoked risks to human health and well‐being.     

Stick-Breaking Dependent Beta Processes with Variational Inference

Neural Processing Letters - The beta processes (BP) is a powerful nonparametric tool in feature learning, which is often used as the prior of Bernoulli process for choosing features from a feature...