Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification

We develop an efficient,adaptive locally weighted projection regression(ALWPR)framework for uncertainty quantification(UQ)of systems governed by ordinary and partial differential equations.The algorithm adaptively selects the new input points with the largest predictive variance and decides when and where to add new localmodels.It effectively learns the local features and accurately quantifies the uncertainty in the prediction of the statistics.The developed methodology provides predictions and confidence intervals at any query input and can dealwithmulti-output cases.Numerical examples are presented to show the accuracy and efficiency of the ALWPR framework including problems with non-smooth local features such as discontinuities in the stochastic space.

Climate change effects fruiting of the prize matsutake mushroom in China

Climate change affects various facets of life but there is little data on its effects on wild mushroom fruiting.Yunnan Province in China is a rich source of wild mushrooms and has experienced a temperature rise over recent decades.This has resulted in warmer temperatures but the impacts of these changes on mushroom production lack documentation.We collected data on the fruiting of the highly prized matsutake mushroom(Tricholoma matsutake)in West Yunnan,China over an 11 year period from 2000 to 2010.Fruiting phenology and productivity were compared against the driving meteorological variables using Projection to Latent Structure regression.The mushrooms appeared later in the season during the observation period,which is most likely explained by rising temperatures and reduced rain during May and June.High temperature and abundant rain in August resulted in good productivity.The climate response of matsutake production results from a sequence of processes that are possibly linked with regulatory signals and resource availability.To advance the knowledge of this complex system,a holistic research approach integrating biology,ecology,genetics,physiology,and phytochemistry is needed.Our results contribute to a general model of fungal ecology,which can be used to predict the responses of fungi to global climate change.

Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds

In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection （SPA） strategy, is used as the descriptor selection and model development method. Then, the support vector machine （SVM） and multiple linear regression （MLR） model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model.