Metal oxides are widely used in the fields of chemistry, physics and materials. Oxygen vacancy formation energy is a key parameter to describe the chemical, mechanical, and thermodynamic properties of...
Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) have attracted considerable interest for their promising applications for solar cells and optoelectronics. However, the fast and accurate prediction of basic band structure of...
While the thermoelectric (TE) materials have attracted significant attention in recent years, the design and discovery of new TE materials with optimal carrier concentration and band gap remains a great challenge. Herein, we report the development of machine learning (ML) methods to predict TE materials with introducing physically meaningful simple descriptors. Specifically, we use the number of electrons, Pauling electronegativity and relative atomic mass as the basic physical variables and compute 242 descriptors in 64 categories to characterize the molecular information of a TE material. Multiple stepwise regression is employed to reduce the dimensionality in the developed ML models, and 5 and 4 important features for the band gap and carrier concentration is selected, respectively. The important features are used as input of a total number of 19 ML methods to select the optimal ML models for the prediction of band gap and carrier concentration, respectively. It is shown that the least square support vector machines method is the best model for the prediction of the band gap, while the back propagating artificial neutral network model exhibits the best performance in predicting the carrier concentration values. This work provides novel theoretical guidance for the rapid prediction properties of TE materials. The simple descriptors we defined can accurately predict the band gap and carrier concentration of quaternary TE materials.
With the rapid development of network technology, electronic commerce and e-marketing had been formed and developed gradually. The number of Internet users was increasing and wound soon overtake the United States as the world's second-largest national Internet users. however the Chinese Internet users who were rarely engaged in online shopping which made the online retail was far from attaining its rightful amount. This study based on the research result of influencing factors of consumer behavior made by domestic and foreign scholars , analyzed and comprised consumer behavior under the condition of tradition and Internet, then putted forward the influencing factors and restrictive factors of online shopping in china.
Abstract-In this paper, we introduce a variant of the density peaks clustering (DPC) approach for discovering demand hot spots from a low-frequency, low-quality taxi fleet operational dataset. From the literature, the DPC approach mainly uses density peaks as features to discover potential cluster centers, and this requires distances between all pairs of data points to be calculated. This implies that the DPC approach can only be applied to cases with relatively small numbers of data points. For the domain of urban taxi operations that we are interested in, we could have millions of demand points per day, and calculating all-pair distances between all demand points would be practically impossible, thus making DPC approach not applicable. To address this issue, we project all points to a density image and execute our variant of the DPC algorithm on the processed image. Experiment results show that our proposed DPC variant could get similar results as original DPC, yet with much shorter execution time and lower memory consumption. By running our DPC variant on a real-world dataset collected in Singapore, we show that there are indeed recurrent demand hot spots within the central business district that are not covered by the current taxi stand design. Our approach could be of use to both taxi fleet operator and traffic planners in guiding drivers and setting up taxi stands.
I. INTRODUCTIONIn many major cities (mostly Asian ones), taxis are considered an important and integral mode of public transport. A major challenge faced by operating taxis as a mode of public transport is how to more effectively balance supplies and demands, given that demands are inherently ad hoc and unpredictable, and taxi drivers are mostly self-interested and cannot be controlled centrally. Such structural reason is behind the inefficiency of taxi fleet operations in most cities. For example, in one study [1], it's shown that a typical taxi fleet can speed over 50% of time vacant. Such phenomenon is of great concern to city planners as vacant taxis don't just wait at fixed locations, instead, they usually drive around, burning fuels and congesting limited road space.An important reason behind such inefficiency of taxi fleet operation is the asymmetry of demand information. In most cases, taxi drivers make their service decisions based on their limited and biased observations of past demand situations. As drivers don't have access to the global information, their decisions will inevitably be myopic and far away from being optimal. One way to mitigate such inefficiency is to make recurring demand hot spots a public information. For example,
Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge...
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