对流参数主分量旋转法在西昌地区雷暴预报中的应用

为研究对流参数主分量旋转方案对西昌地区雷暴预报的影响,利用2006～～ 2008年雨季（6～～9月）T213模式输出产品,采用F-分值法和主分量旋转法,分别初选和精选因子,并结合二元Logistic回归分析,建立了西昌北郊某站的雷暴释用预报的模型,并对该站2010年雨季雷暴进行预报试验.试验结果表明：精选因子的KMO检验值在0.65～～0.85,且Bartlett球形度检验显著,说明该站的雷暴对流参数适用于因子分析;极大方差旋转能够较好地分离因子载荷,对雷暴信息的提取能力较强;试报的TS评分在0.3左右,24小时预报的Hedike评分可达0.337,模型的预报准确率较高,持续性较好.综上所述,采用主分量旋转法对雷暴的MOS预报有一定的改善效果.

夏季中高纬500 hPa高度和海表温度异常特征及其相关分析

用旋转主分量（ＲＰＣ）方法，对１９４９～１９８８年４０个夏季的中高纬５００ｈＰａ高度场以及北太平洋和北大西洋海表温度异常（ＳＳＴＡ）的主要时空分布特征作分析，然后通过交叉相关讨论夏季海气相互作用的特征。结果表明，夏季中高纬５００ｈＰａ高度场最明显的异常型为副热带型（ＳＴ），极地北美型（ＰＡ），４波型（ＦＷ），以及３波型（ＴＷ）。夏季北太平洋ＳＳＴ的主要异常型为赤道东太平洋型（ＥＥＰ），阿拉斯加湾型（ＢＡＬ），热带中太平洋（ＣＴＰ），以及北部北太平洋型（ＮＮＰ）。而夏季北大西洋ＳＳＴ的主要异常型为赤道大西洋型（ＥＡＬ），加勒比海型（ＣＡＲ），东部北大西洋型（ＥＮＡ）和中部北大西洋型（ＣＮＡ）。夏季中高纬海气之间的最强相关出现在海气异常对应的空间位置上。这种区域性或邻域性的海气相关呈正相关的特征。但海气之间的相关耦合不及冬季紧密，赤道ＳＳＴＡ与中高纬５００ｈＰａ高度异常的相关不显著。太平洋ＳＳＴＡ所对应的５００ｈＰａ高度场的相关中心多为局地性，而大西洋ＳＳＴＡ所对应的５００ｈＰａ高度场的相关中心呈现更有组织性的，甚至是波状的形态。

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

基于双投影旋转图的烟叶样品香型聚类分析

【目的】基于感官评价指标对烟叶样品进行聚类分析及类别特征分析,为烟叶原料的综合利用提供参考。【方法】对322个不同烟叶产区的样品进行分析,采用12个香韵特征指标的评吸结果,建立基于主成分分析的得分—载荷双投影旋转图,分析烟叶产区样品的聚类情况及各类别样品的主要特征。【结果】在双投影旋转图中烟叶样品可清晰地分为清香型、中间香型和浓香型三大类;12个香韵指标中,清甜香、蜜甜香和焦甜香的模长均大于0.8000,表达能力较强,是决定烟叶香型的重要指标;且香型聚类结果与产区聚类结果具有较高的一致性。【结论】双投影旋转图能够对烟叶感官评价指标及其相互关系进行定量描述,可用于烟叶样品的特征分析。

RF-PSSM:A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets.An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases,facilitating studies based on computational methods.In this study,we proposed a new computational approach,rotation forest position-specific scoring matrix(RF-PSSM),to predict the interactions among HCV and human proteins.In particular,PSSM was used to characterize each protein,two-dimensional principal component analysis(2DPCA)was then adopted for feature extraction of PSSM.Finally,rotation forest(RF)was used to implement classification.The results of various ablation experiments show that on independent datasets,the accuracy and area under curve(AUC)value of RF-PSSM can reach 93.74% and 94.29%,respectively,outperforming almost all cutting-edge research.In addition,we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1,which can provide theoretical guidance for future experimental studies.