A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%.

We used brightfield and epifluorescence microscopy, as well as permeability tests, to investigate the apoplastic histochemical features of plant roots associated with ion hyperaccumulation, invasion, and tolerance of oligotrophic conditions. In hyperaccumulator species with a hypodermis (exodermis absent), ions penetrated the root apex, including the root cap. By contrast, in non-hyperaccumulator species possessing an exodermis, ions did not penetrate the root cap. In vivo, the lignified hypodermis blocked the entry of ions into the cortex, while root exodermis absorbed ions and restricted them to the cortex. The roots of the hyperaccumulators Pteris vittata and Cardamine hupingshanensis, as well as the aquatic invasives Alternanthera philoxeroides, Eichhornia crassipes, and Pistia stratiotes, contained lignin and pectins. These compounds may trap and store ions before hypodermis maturation, facilitating ion hyperaccumulation and retention in the apoplastic spaces of the roots. These apoplastic histochemical features were consistent with certain species-specific characters, including ion hyperaccumulation, invasive behaviors in aquatic environments, or tolerance of oligotrophic conditions. We suggest that apoplastic histochemical features of the root may act as invasion mechanisms, allowing these invasive aquatic plants to outcompete indigenous plants for ions.

In the past few decades, with the advent of climate change, population growth, agricultural irrigation, and industrial development, there have been increasing demands for water resources across the globe, especially in widely distributed arid areas or densely populated areas

Automatic extraction of the skeleton lines of river systems from high-resolution remote-sensing images has great significance for surveying and managing water resources. A large number of existing methods for the automatic extraction of skeleton lines from raster images are primarily used for simple graphs and images (e.g., fingerprint, text, and character recognition). These methods generally are memory intensive and have low computational efficiency. These shortcomings preclude their direct use in the extraction of skeleton lines from large volumes of high-resolution remote-sensing images. In this study, we developed a method to extract river skeleton lines based entirely on run-length encoding. This method attempts to replace direct raster encoding with run-length encoding for storing river data, which can considerably compress raster data. A run-length boundary tracing strategy is used instead of complete raster matrix traversal to quickly determine redundant pixels, thereby significantly improving the computational efficiency. An experiment was performed using a 0.5 m-resolution remote-sensing image of Yiwu city in the Chinese province of Zhejiang. Raster data for the rivers in Yiwu were obtained using both the DeepLabv3+ deep learning model and the conventional visual interpretation method. Subsequently, the proposed method was used to extract the skeleton lines of the rivers in Yiwu. To compare the proposed method with the classical raster-based skeleton line extraction algorithm developed by Zhang and Suen in terms of memory consumption and computational efficiency, the visually interpreted river data were used to generate skeleton lines at different raster resolutions. The results showed that the proposed method consumed less than 1% of the memory consumed by the classical method and was over 10 times more computationally efficient. This finding suggests that the proposed method has the potential for river skeleton line extraction from terabyte-scale remote-sensing image data on personal computers.