FFLUX: Transferability of polarizable machine‐learned electrostatics in peptide chains

The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a kriging model. Such a knowledgeable atom needs to be informed about a sufficiently large environment around it. The resulting complexity can be tackled by collecting the 20 natural amino acids into a few groups. Using substituted deca‐alanines, we present the proof‐of‐concept that a given atom's charge can be modeled by a few kriging models only. © 2017 Wiley Periodicals, Inc.     

从电子工业“脊梁”到全面开花的镓元素

镓是第一个根据化学元素周期律预言并在自然界中证实的元素，是室温下电导率和热导率均为最大的液态物质，镓在电子工业中得到了广泛应用，被誉为电子工业“脊梁”。近十几年来，镓的更多应用潜力被发掘出来，在电子工业、散热、增材制造、柔性机器、生物医学等领域均有重要的应用前景。     

摩擦磨损试验机模拟活塞环-缸套摩擦行为的功能开发

参考活塞环-缸套的实际运行条件,结合SRV-4摩擦磨损试验机自身的性能特点和工作原理,通过开发设计,增加合理的硬件及软件测试条件,实现摩擦磨损试验机模拟活塞环-缸套摩擦行为的功能.系统测试结果表明：功能开发后的摩擦磨损试验机可在较短时间内获得活塞环-缸套摩擦副性能优劣的试验数据,满足快速、直观评价活塞环-缸套的摩擦磨损性能的需要,在活塞环-缸套摩擦副研究中具有重要的应用价值.     

杭州老虎洞窑古陶瓷成分的化学计量学研究

用支持向量机算法研究了与杭州老虎洞古陶瓷有关的两个断源、断代问题。作为化学计量学的～种新型分类算法,支持向量机在小样本问题上表现出良好的泛化能力,与特征选择方法结合,可以有效处理样本少,特征多的问题。本研究综合利用支持向量机、特征选择算法和其它化学计量学算法研究了杭州凤凰山麓万松岭附近的古窑遗址和“传世哥窑”的断源、断代问题,证明老虎洞窑与郊坛下窑产品截然不同,万松岭附近地面收集瓷片样本是老虎洞窑宋代地层的瓷片滑落所致,而“传世哥窑”样品可能是老虎洞窑元代时的产品。实验表明：支持向量机算法与化学分析相结合可以成为研究古陶瓷断源和断代问题的一种新方法。     

指向“证据推理与模型认知”的教学设计——以“原子结构模型的演变”为例

以“原子结构模型的演变”为例,以化学史实为情境,设计环环相扣的问题,引导学生在分析证据及建构并评价模型的过程中体会实验证据与模型建构及演变之间的关系,认识模型在物质结构理论建立过程中的重要作用及其发展性特征。该教学设计在学生对原子模型的理解上具有显著效果。     

Rapid Diagnosis of Type II Diabetes Using Fourier Transform Mid-Infrared Attenuated Total Reflection Spectroscopy Combined with Support Vector Machine

Type II diabetes was diagnosed by Fourier transform mid-infrared (FTMIR) attenuated total reflection (ATR) spectroscopy in combination with support vector machine (SVM). Spectra of serum samples from 65 patients with clinical confirmed type II diabetes mellitus and 55 healthy volunteers were acquired using ATR-FTMIR and were first pretreated by three pretreatments (Savitzky–Golay smoothing, multiple scattering correction, and wavelet transforms algorithms) to reduce the interfering information before establishing the SVM models. The parameters of SVM (penalty factor C and kernel function parameter gamma) were optimized to improve the generalization abilities of the models. A grid search method (GS), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm, were used to find out the optimal parameter values. The results showed that the maximum accuracies were 95.74, 97.87, and 89.36% for the optimized GS, GA, and PSO algorithms. The maximum sensitivities were 96, 100, and 92, and the maximum specificity were 95.45, 95.45, and 86.36%, respectively. The results indicated that the accuracy of type II diabetes was improved using the GS, GA, and PSO algorithms for optimizing the SVM parameters. The GA was found to be slightly better than the GS and PSO. The results of the experiment confirmed that the combination of the ATR-FTMIR spectroscopy and SVM was able to rapidly and accurately diagnose type II diabetes without reagents.