Exciton spectra of SnO 2 nanocrystals with surficial dipole layer

Abstract Experimental results of absorption, luminescence and excitation spectra in both bare and coated SnO 2 nanocrystals are given. It is found that when the SnO 2 nanocrystals are coated by a layer of organic moleculae, the absorption edge shifts to the longer wavelength direction as the particle size decreases, which is inconsistent with that of bare SnO 2 nanocrystals. It is demonstrated that the size and surface situations of nanocrystals have great influence on their spectroscopic properties. The experimental data are discussed in terms of the quantum confinement effects and dielectric confinement effects

Selection of top-K influential users based on radius-neighborhood degree, multi-hops distance and selection threshold

Abstract Influence maximization in the social network becomes increasingly important due to its various benefit and application in diverse areas. In this paper, we propose DERND D-hops that adapt the radius-neighborhood degree to a directed graph which is an improvement of our previous algorithm RND d-hops. Then, we propose UERND D-hops algorithm for the undirected graph which is based on radius-neighborhood degree metric for selection of top-K influential users by improving the selection process of our previous algorithm RND d-hops. We set up in the two algorithms a selection threshold value that depends on structural properties of each graph data and thus improves significantly the selection process of seed set, and use a multi-hops distance to select most influential users with a distinct range of influence. We then, determine a multi-hops distance in which each consecutive seed set should be chosen. Thus, we measure the influence spread of selected seed set performed by our algorithms and existing approaches on two diffusion models. We, therefore, propose an analysis of time complexity of the proposed algorithms and show its worst time complexity. Experimental results on large scale data of our proposed algorithms demonstrate its performance against existing algorithms in term of influence spread within a less time compared with our previous algorithm RND d-hops thanks to a selection threshold value

Coal and Coalbed Methane Co-Extraction Technology Based on the Ground Movement in the Yangquan Coalfield, China

The Yangquan coalfield is one of the typical highly gassy mining areas in China. However, its coal seams are of lower permeability, which are not conductive to coalbed methane (CBM) drainage. In this study, based on the theory of the ground movement, we analyzed the principle of coal and CBM coextraction in the Yangquan coalfield, and established the technology system of coal and CBM coextraction which was further implemented in the coal and CBM coextraction in the Yangquan coalfield. The coal and CBM coextraction technologies based on the “pressure-relief and permeability-increase” effect caused by the mining overburden movement can optimally ensure the safe and efficient mining and improve the gas drainage rate. A series of developed coal and CBM coextraction technologies for the Yangquan coalfield were mainly characterized by the high-level gas drainage roadway. This reached a maximum gas drainage amount of 270,000 m3/day for single drainage roadway and a pressure-relief gas drainage rate of >90%. Those technologies significantly improved the gas drainage effect safely and efficiently achieving the coal and CBM coextraction

5G SLAM with Low-complexity Channel Estimation

5G millimeter-wave signals are beneficial for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment. Channel estimators can exploit received signals to estimate multipath components in terms of delays and angles, which can be used in localization and mapping. Thus, a good channel estimator is essential for 5G SLAM. This paper presents a novel low-complexity multidimensional ESPRIT-based channel estimator and applies it to a 5G SLAM framework. Simulation results demonstrate that the proposed channel estimator can accurately estimate channel information with low computational cost, with limited impact on mapping performance, compared to a tensor-ESPRIT benchmark

Jointly Modeling Aspect Information and Ratings for Review Rating Prediction

Although matrix model-based approaches to collaborative filtering (CF), such as latent factor models, achieve good accuracy in review rating prediction, they still face data sparsity problems. Many recent studies have exploited review text information to improve the performance of predictions. The review content that they use, however, is usually on the coarse-grained text level or sentence level. In this paper, we propose a joint model that incorporates review text information with matrix factorization for review rating prediction. First, we adopt an aspect extraction method and propose a simple and practical algorithm to represent the review by aspects and sentiments. Then, we propose two similarity measures: aspect-based user similarity and aspect-based product similarity. Finally, aspect-based user and product similarity measures are incorporated into a matrix factorization to build a joint model for rating prediction. To this end, our model can alleviate the data sparsity problem and obtain interpretability for the recommendation. We conducted experiments on two datasets. The experimental results demonstrate the effectiveness of the proposed model