Analysis of the Herbicides Chlorsulfuron, Sulfometuron Methyl and Metsulfuron Methyl in Soil and Water

Chlorsulfuron, sulfometuron methyl and metsulfuron methyl are new herbicides. They are effective in controlling weeds in different areas. Generally, they are applied at very low levels. Their decomposition rates vary with soil moisture, pH, etc. Since they may be used year after year and their decomposition rates are not very rapid in this climate, the persistence can be a problem with herbicide carryover harming crops in the following years. Therefore, there is a need for a method to measure very low levels of these herbicides in soil and water to ensure that the quantities which might be present are not sufficiently large to be injurious to agricultural crops. The objective of my thesis research was to develop a method to determine herbicides chlorsulfuron, sulfometuron methyl and metsulfuron methyl in soil and water and estimate the detection limits with the instruments available in this school. Much of my work involved efforts to improve detection levels, and this involved a study of a number of extractions, cleanup and chromatography methods

Stabilisation of highly non-linear continuous-time hybrid stochastic differential delay equations by discrete-time feedback control

In this study, the authors consider how to use discrete-time state feedback to stabilise hybrid stochastic differential delay equations. The coefficients of these stochastic differential delay equations do not satisfy the conventional linear growth conditions, but are highly non-linear. Using the Lyapunov functional method, they show that a discrete feedback controller, which depends on the states of the discrete-time observations, can be designed to make the solutions of such controlled hybrid stochastic differential delay equations asymptotically stable and exponentially stable. The upper bound of the discrete observation interval τ is also given in this study. Finally, a numerical example is given to illustrate the proposed theory

Boundedness and stability of highly nonlinear neutral stochastic systems with multiple delays

This paper reports the boundedness and stability of highly nonlinear hybrid neutral stochastic differential delay equations (NSDDEs) with multiple delays. Without imposing linear growth condition, the boundedness and exponential stability of the exact solution are investigated by Lyapunov functional method. In particular, using the M-matrix technique, the mean square exponential stability is obtained. Finally, three examples are presented to verify our results

Stability equivalence between the stochastic dierential delay equations driven by G-Brownian motion and the Euler-Maruyama method

Consider a stochastic differential delay equation driven by G-Brownian motion (G-SDDE) dx(t) = f(x(t), x(t − τ))dt + g(x(t), x(t − τ))dB(t) + h(x(t), x(t − τ))dhBi(t). Under the global Lipschitz condition for the G-SDDE, we show that the G-SDDE is exponentially stable in mean square if and only if for sufficiently small step size, the Euler-Maruyama (EM) method is exponentially stable in mean square. Thus, we can carry out careful numerical simulations to investigate the exponential stability of the underlying G-SDDE in practice, in the absence of an appropriate Lyapunov function. A numerical example is provided to illustrate our results

Attention Correctness in Neural Image Captioning

Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision. But despite their popularity, the "correctness" of the implicitly-learned attention maps has only been assessed qualitatively by visualization of several examples. In this paper we focus on evaluating and improving the correctness of attention in neural image captioning models. Specifically, we propose a quantitative evaluation metric for the consistency between the generated attention maps and human annotations, using recently released datasets with alignment between regions in images and entities in captions. We then propose novel models with different levels of explicit supervision for learning attention maps during training. The supervision can be strong when alignment between regions and caption entities are available, or weak when only object segments and categories are provided. We show on the popular Flickr30k and COCO datasets that introducing supervision of attention maps during training solidly improves both attention correctness and caption quality, showing the promise of making machine perception more human-like.Comment: To appear in AAAI-17. See http://www.cs.jhu.edu/~cxliu/ for supplementary materia

Will Decline in Foreign Trade Reshape Internal Economic Geography? Simulations in an Estimated Model of the Chinese Space-economy

This study evaluates the role of decline in foreign trade in shaping China’s internal economic Geography. In a first step, we develop a multi-region multi-industry economic geography model under Cournot competition, of which we estimate the parameter values from real regional and industry data to obtain a predictable model. Next, we set some scenarios reflecting a decline in export to simulate the evolution of industrial spatial pattern. The simulations indicate the evident impact of decreasing export on the spatial distribution of industries; besides, the degree of influence varies across different industries. Moreover, the decline in export of one industry not only influences its own location, but also the location of the forward or backward-linked industries. However, the general spatial pattern, observed after policy reforms and trade liberalization, will not be reversed due to export recession in China