A permeability model for the hydraulic fracture filled with proppant packs under combined effect of compaction and embedment

The authors acknowledge the financial support from Science Foundation of China University of Petroleum, Beijing (No. 2462014YJRC060 and No.2462014YJRC059)Peer reviewedPostprin

Utility-maximization Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks

Device-to-device(D2D) underlaying communication brings great benefits to the cellular networks from the improvement of coverage and spectral efficiency at the expense of complicated transceiver design. With frequency spectrum sharing mode, the D2D user generates interference to the existing cellular networks either in downlink or uplink. Thus the resource allocation for D2D pairs should be designed properly in order to reduce possible interference, in particular for uplink. In this paper, we introduce a novel bandwidth allocation scheme to maximize the utilities of both D2D users and cellular users. Since the allocation problem is strongly NP-hard, we apply a relaxation to the association indicators. We propose a low-complexity distributed algorithm and prove the convergence in a static environment. The numerical result shows that the proposed scheme can significant improve the performance in terms of utilities.The performance of D2D communications depends on D2D user locations, the number of D2D users and QoS(Quality of Service) parameters

Structure Identification in Panel Data Analysis

Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the groups in panel data with interactive effects induced by latent variables. It is assumed that the impact of a covariate is the same within each group, but different between the groups. An EM based algorithm is proposed to estimate the unknown parameters, and a binary segmentation based algorithm is proposed to detect the grouping. We then establish asymptotic theories to justify the proposed estimation, grouping method, and the modelling idea. Simulation studies are also conducted to compare the proposed method with the existing approaches, and the results obtained favour our method. Finally, the proposed method is applied to analyse a data set about income dynamics, which leads to some interesting findings

Study of Non-Local Chamber of Commerce: Retrospect, Review and Prospect

As one form of the Chamber of Commerce, non-local Chamber of Commerce plays an important role in social economy, and related research has made great achievements, while lacking systematic summary and comment at the same time. Based on the review of previous studies, this paper systematically reviews and comments on the results of non-local Chamber of Commerce, including its definition, foundations of its rising, the function positioning, the governance mechanism and the defects of management, recognizes the existing problems, makes up for the shortage, and puts forward suggestions for future research

ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution

Deep convolutional neural networks have significantly improved the peak signal-to-noise ratio of SuperResolution (SR). However, image viewer applications commonly allow users to zoom the images to arbitrary magnification scales, thus far imposing a large number of required training scales at a tremendous computational cost. To obtain a more computationally efficient model for arbitrary scale SR, this paper employs a Laplacian pyramid method to reconstruct any-scale high-resolution (HR) images using the high-frequency image details in a Laplacian Frequency Representation. For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels. SR of larger scales is computed by recursion from small scales, which significantly reduces the computational cost. For a full comparison, fixed- and any-scale experiments are conducted using various benchmarks. At fixed scales, ASDN outperforms predefined upsampling methods (e.g., SRCNN, VDSR, DRRN) by about 1 dB in PSNR. At any-scale, ASDN generally exceeds Meta-SR on many scales