Defensive strategies of Norway spruce and Kurile larch heartwood elucidated on the micron-level

To decarbonize the building sector, the use of durable wood materials must be increased. Inspiration for environmentally benign wood protection systems is sought in durable tree species depositing phenolic extractives in their heartwood. Based on the hypothesis that the micro-distribution of extractives influences durability, we compared the natural impregnation patterns of non-durable, but readily available Norway spruce to more durable Kurile larch by mapping the distribution of heartwood extractives with Confocal Raman Imaging and multivariate data decomposition. Phenolics of both species were associated with hydrophobic oleoresin, likely facilitating diffusion through the tissue. They accumulated preferentially in lignin-rich sub-compartments of the cell wall. Yet, the distribution of extractives was found not to be the same. The middle lamellae contained flavonoids in larch and aromatic waxes in spruce, which was also found in rays and epithelial cells. Spruce-lignans were tentatively identified in all cell types, while larch-flavonoids were not present in resin channels, hinting at a different origin of synthesis. Larch-oleoresin without flavonoids was only found in lumina, indicating that the presence of phenolics in the mixture influences the final destination. Together our findings suggest, that spruce heartwood-defense focuses on water regulation, while the more efficient larch strategy is based on antioxidants.

Local and global existence in L^{p} for the inhomogeneous nonlinear Schrödinger equation

This paper investigates the local and global existence for the inhomogeneous nonlinear Schrödinger equation with the nonlinearity λ|x|^{-b}|u|^{β}u. It is show that a global solution exists in the mass-subcritical for large data in the spaces L^{p}, p < 2 under some suitable conditions on b,β and p. The solution is established using a data-decomposition argument, two kinds of generalized Strichartz estimates in Lorentz spaces and a interpolation theorem.

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction.

A Hybrid Model for Air Quality Prediction Based on Data Decomposition

Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R2) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R2 by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method.