In macro investment, an investment decision model is established by using an improved back propagation (BP) artificial neural network (ANN). In this paper, the relations between elements of investment and output of products are determined, and then the optimal distribution of investment is determined by adjusting the distributions rationally. This model can reflect the highly nonlinear mapping relations among each element of investment by using nonlinear utility functions to improve the architecture of artificial neural network, which can be widely applied in investment problems.
Large accumulations of copper (Cu) ions in the human body may cause damage, including organ and brain damage. In recent years, studies have proven that a large accumulation of Cu ions can lead to Parkinson's disease and Alzheimer's disease, therefore it is great important to develop novel strategies for detecting trace Cu in environmental and biological samples. In this work, we designed two new coumarin-based colorimetric and fluorescent probes HQ1 and HQ2. These two probes could selectively respond to Cu2+ with obvious color and fluorescence changes, and the presence of other metal ions had no effect on these changes. The two probes also exhibited high sensitivity for Cu2+, with a detection limit as low as 1.81×10-8 M/ 1.57×10-8 M. Notably, the two probes showed potential practical applications and were successfully used for detecting Cu2+ in a test strip, A549 cells and living zebrafish larvae.
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