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Turbulent Schmidt number as an important parameter in computational fluid dynamic (CFD) simulations is strongly dependent on height, whereas it is mostly considered to be constant in the literature. This paper presents a new variable turbulent Schmidt number formulation which can calculate the relative concentrations (RCs) in neutral atmospheric conditions more accurately. To achieve this aim, RCs from continuous releases are calculated in different distances by the analytical Gaussian plume mode. CFD simulations are carried out for single stack dispersion on a flat terrain surface and an inverse procedure is then applied so that different turbulent Schmidt numbers are used as inputs to determine the RCs to select the “best-fit” turbulent Schmidt number value. This process is continued for different heights to fit a curve to obtain the new formulation for turbulent Schmidt number varying with height. The values are compared with experimental results. The comparison indicates that the new formulation for turbulent Schmidt number is more accurate and reliable than previous research works.

Distinguishing the long-range bonds with the regular ones, the critical temperature of the spin-lattice Gaussian model built on two typical small-world networks is studied. The results show much difference from the classical case, and thus may induce some more accurate discussion on the critical properties of the spin-lattice systems combined with the small-world networks.

In this paper, we propose a novel automatic image annotation model by mining the web. In our approach, the terms or words appearing in the associated text are extracted and filtered as labels or annotations for the corresponding web images. Sure, much noise exists in those selected labels. In order to reduce the influence caused by the noisy labels, for each label or potential word, we improve web image-word relationships using Mixture Gaussian Distribution Model. By doing so, the relationships between words and images are re-weighted both in terms of sematic relevance and in terms of visual feature similarity. In fact, all the words associated to an image are not semantically independent. We use co-occurrences between two words to describe their semantic relevance. Thus, we further use a method, called Word Promotion, to co-enhance the weights of all the words associated to a given image based on their co-occurrences. Our experiments are conducted in several ways and the results show that our annotation method can achieve a satisfactory performance in respects of system scalability and sematic evolution.

Due to declining trading volume growth in e-commerce platforms, physical channels have attracted considerable investments from various large international companies (e.g. Alibaba, JD, Walmart, Wanda, and Wuzhou International). However, e-commerce platforms can track consumers’ behaviors (attraction to landing page design, clicks on certain products, consumer behavior trajectory tracking, clicks on advertisements, and internal link optimization of product pages), a feat unachievable in current physical channels. Consequently, this study attempted to apply the characteristics of online channels in a physical channel by using image object tracking and image detection techniques. Through this inclusion, physical channels are capable of providing consumers with more favorable experience and interaction, and brick-and-mortar store owners can obtain a more accurate understanding of consumer behaviors of store consumers. Information acquired through this system can be provided to store owners to serve as reference for merchandise placement, arrangement of display shelves, and consumer circulation path planning. This study used the technique of image processing to locate the Region of Interest and applied object tracking to get the consumer’s trajectory which successfully implemented the consumer-tracking characteristics of online platforms in a physical channel while retaining the unique experience of the physical channel. This results in a win–win scenario for businesses and consumers.