平面并联机构工作空间的三维螺旋扫描CAD求解

针对平面并联机构无奇异位置工作空间求解困难、过程繁琐、计算量大等问题，提出了基于CAD求解平面并联机构工作空间的三维螺旋扫描方法。将[n]自由度平面并联机构分解成[n]条支链进行独立分析，得到每条支链下末端执行器的可达区域，再将所有支链可达区域取交集即为平面并联机构工作空间。应用SolidWorks软件建立平面并联机构模型，进行几何特征处理，通过自动求解器求解，将求解过程图形化，快速得到同轴布局5R机构和平面3-RPR并联机构的无奇异位置工作空间。通过同轴布局5R机构的运动学实验，验证了该求解方法的可行性。     

径向不耦合装药爆破效果的数值研究

为了研究径向不耦合条件下爆破效果的影响因素,利用AUTODYN-2D对比不耦合系数、不耦合介质、炸药种类对爆破效果的影响规律。结果表明,与耦合装药相比,径向不耦合装药条件下炮孔压力明显降低。对比水介质和空气介质下的爆破效果可知,水介质不耦合条件下,炸药爆炸能量的传递效率更高。炮孔压力与炸药种类密切相关,TNT产生的炮孔压力最大,但作用时间较短,而ANFO和乳化炸药的作用时间较长。     

一种抗运动干扰的实时心率提取方法

针对基于容积脉搏波（PPG）提取运动心率时，传统心率提取算法由于运动噪声干扰使测量结果误差大、实时性不好的问题，提出一种抗运动干扰的实时心率提取方法。该方法通过实时小波去噪，同时结合三轴加速度信号（ACC）对运动进行分类训练，计算各运动状态心率增益，对实时心率值进行补偿。实验结果表明，通过与同时采集的ECG信号计算出的实时心率进行对比，绝对误差率仅为1.2%左右。相比传统心率提取算法，该算法具有抗干扰性强，实时准确的特点。     

2-Phenyloxazole-4-carboxamide as a Scaffold for Selective Inhibition of Human Monoamine Oxidase B

A series of 2-phenyloxazoles bearing an amide group at position 4 were designed and synthesized for evaluation as potential inhibitors of human recombinant monoamine oxidases (hrMAOs). Results of kinetics experiments demonstrated that all compounds behave as competitive MAO inhibitors, with good selectivity toward the MAO-B isoform. The most potent and selective derivatives are characterized by inhibition constant (K_i) values in the sub-micromolar range and a good selectivity index (K_{i MAO-A}/K_{i MAO-B}>50). Some derivatives were also found to be able to inhibit MAO activity in nerve growth factor (NGF)-differentiated PC12 cells, taken as a model of neuronal cells. In particular, 2-(2-hydroxyphenyl)-N-phenyloxazole-4-carboxamide (compound 4 a ) may be a promising new scaffold, exerting the highest selectivity and inhibitory effect toward MAOs in NGF-differentiated PC12 cell lysates, without compromising cell viability. Molecular docking analysis allowed a rationalization of the experimentally observed binding affinity and selectivity.     

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。     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

酸改性CoPd/TiO2催化CH4-CO2直接合成C2含氧化合物

采用酸处理方法对CoPd/TiO₂催化剂进行改性，并将酸改性催化剂用于温和条件下CH₄-CO₂梯阶转化直接合成C₂含氧化合物（乙酸和乙醇）的反应。在150～300℃考察了浸酸方式和不同种类酸处理对催化剂活性和选择性的影响。采用X射线衍射（XRD）、X射线光电子能谱（XPS）、NH₃程序升温脱附（NH₃-TPD）和N₂吸附对催化剂进行了表征。结果表明，酸改性明显提高了CoPd/TiO₂上C₂含氧化合物的生成速率和选择性。浸酸方式对催化剂性能和结构有显著影响，先用酸浸渍载体然后再浸渍活性金属所得催化剂具有更高的活性。在H₃PO₄、HNO₃和HCl中，H₃PO₄浸渍的催化剂活性最佳，在150℃时C₂含氧化合物（乙酸和乙醇）的生成速率为3365 μg/(g·h)，选择性达到91%。     

低温水等离子体活化和表面接枝DMAE聚氯乙烯中空纤维膜研究

采用低温水等离子体技术，在三通道聚氯乙烯(PVC)膜表面接枝了甲基丙烯氧基苄基二甲基氯化铵(DMAE)单体，增强了膜亲水和抗菌性能。通过红外分析，表明DMAE成功接枝到了PVC膜上，水通量提高两倍，PVC-ir-H₂O膜(通过水等离子体处理的膜)对牛血清蛋白(BSA)的吸附能力下降67%，对BSA溶液的通量从7.7提高至40 kg?m^-2?h^-1，并且对BSA的截留能力不变。通过静态及动态抗菌实验，接枝后的PVC膜（PVC-g-PMAE膜）抗菌率达到100%，膜组件运行中的抗菌率也达到82%以上。在保证细菌截留率100%的同时，其渗透通量提高三倍。该膜表面修饰工程技术能实现膜表面的均一化改性，且绿色环保、操作简便、成本低，改性膜在饮用水处理领域，尤其是家用净水器中展现了很好的应用前景。     

建筑用钎焊合金的热腐蚀行为研究

研究了建筑用钎焊合金BCo46、DZ468和BNi-2在热腐蚀条件下的腐蚀行为。结果表明,在热腐蚀条件下,合金BCo46的抗腐蚀能力强于合金DZ468和BNi-2。3种合金在热腐蚀环境中都出现了硫化物析出和氧化薄膜溶解现象。