4,828 research outputs found
Policy Recommendations for Promoting the Development of Cross-Border E-Commerce between China and Central Asian Countries
As the core area of the entire Belt and Road, Central Asian countries’ prosperity has a direct bearing on the smooth implementation of the China’s Belt and Road Initiative (BRI). The trade and economic relations between China and Central Asia are developing entirely within the worldwide economic globalization trends. In this research, we analysis the several problems that exist in the development of cross-border e-commerce between China and the Central Asian five countries at the first. And then we put forward four countermeasures for the Chinese government and enterprises investing abroad to promote the cross-border e-commerce transactions between China and the countries along the Belt and Road
Joint Distributed Precoding and Beamforming for RIS-aided Cell-Free Massive MIMO Systems
The amalgamation of cell-free networks and reconfigurable intelligent surface
(RIS) has become a prospective technique for future sixth-generation wireless
communication systems. In this paper, we focus on the precoding and beamforming
design for a downlink RIS-aided cell-free network. The design is formulated as
a non-convex optimization problem by jointly optimizing the combining vector,
active precoding, and passive RIS beamforming for minimizing the weighted sum
of users' mean square error. A novel joint distributed precoding and
beamforming framework is proposed to decentralize the alternating optimization
method for acquiring a suboptimal solution to the design problem. Finally,
numerical results validate the effectiveness of the proposed distributed
precoding and beamforming framework, showing its low-complexity and improved
scalability compared with the centralized method
Uplink Performance of Cell-Free Extremely Large-Scale MIMO Systems
In this paper, we investigate the uplink performance of cell-free (CF)
extremely large-scale multiple-input-multipleoutput (XL-MIMO) systems, which is
a promising technique for future wireless communications. More specifically, we
consider the practical scenario with multiple base stations (BSs) and multiple
user equipments (UEs). To this end, we derive exact achievable spectral
efficiency (SE) expressions for any combining scheme. It is worth noting that
we derive the closed-form SE expressions for the CF XL-MIMO with maximum ratio
(MR) combining. Numerical results show that the SE performance of the CF
XL-MIMO can be hugely improved compared with the small-cell XL-MIMO. It is
interesting that a smaller antenna spacing leads to a higher correlation level
among patch antennas. Finally, we prove that increasing the number of UE
antennas may decrease the SE performance with MR combining
Channel Estimation for XL-MIMO Systems with Polar-Domain Multi-Scale Residual Dense Network
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising
technique to enable versatile applications for future wireless
communications.To realize the huge potential performance gain, accurate channel
state information is a fundamental technical prerequisite. In conventional
massive MIMO, the channel is often modeled by the far-field planar-wavefront
with rich sparsity in the angular domain that facilitates the design of
low-complexity channel estimation. However, this sparsity is not conspicuous in
XL-MIMO systems due to the non-negligible near-field spherical-wavefront. To
address the inherent performance loss of the angular-domain channel estimation
schemes, we first propose the polar-domain multiple residual dense network
(P-MRDN) for XL-MIMO systems based on the polar-domain sparsity of the
near-field channel by improving the existing MRDN scheme. Furthermore, a
polar-domain multi-scale residual dense network (P-MSRDN) is designed to
improve the channel estimation accuracy. Finally, simulation results reveal the
superior performance of the proposed schemes compared with existing benchmark
schemes and the minimal influence of the channel sparsity on the proposed
schemes
TKRD : trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis
The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel rootkits. Memory forensic, which observes the malicious tracks from the memory aspect, is a useful way for malware detection. In this paper, we propose a novel TKRD method to automatically detect kernel rootkits in VMs from private cloud, by combining VM memory forensic analysis with bio-inspired machine learning technology. Malicious features are extracted from the memory dumps of the VM through memory forensic analysis method. Based on these features, various machine learning classifiers are trained including Decision tree, Rule based classifiers, Bayesian and Support vector machines (SVM). The experiment results show that the Random Forest classifier has the best performance which can effectively detect unknown kernel rootkits with an Accuracy of 0.986 and an AUC value (the area under the receiver operating characteristic curve) of 0.998
local fractional fourier series solutions for nonhomogeneous heat equations arising in fractal heat flow with local fractional derivative
The fractal heat flow within local fractional derivative is investigated. The nonhomogeneous heat equations arising in fractal heat flow are discussed. The local fractional Fourier series solutions for one-dimensional nonhomogeneous heat equations are obtained. The nondifferentiable series solutions are given to show the efficiency and implementation of the present method
Surface-Based Regional Homogeneity in First-Episode, Drug-Naive Major Depression: A Resting-State fMRI Study
Background. Previous volume-based regional homogeneity (ReHo) studies neglected the intersubject variability in cortical folding patterns. Recently, surface-based ReHo was developed to reduce the intersubject variability and to increase statistical power. The present study used this novel surface-based ReHo approach to explore the brain functional activity differences between first-episode, drug-naive MDD patients and healthy controls. Methods. Thirty-three first-episode, drug-naive MDD patients and 32 healthy controls participated in structural and resting-state fMRI scans. MDD patients were rated with a 17-item Hamilton Rating Scale for Depression prior to the scan. Results. In comparison with the healthy controls, MDD patients showed reduced surface-based ReHo in the left insula. There was no increase in surface-based ReHo in MDD patients. The surface-based ReHo value in the left insula was not significantly correlated with the clinical information or the depressive scores in the MDD group. Conclusions. The decreased surface-based ReHo in the left insula in MDD may lead to the abnormal top-down cortical-limbic regulation of emotional and cognitive information. The surface-based ReHo may be a useful index to explore the pathophysiological mechanism of MDD.</p
The Nondifferentiable Solution for Local Fractional Tricomi Equation Arising in Fractal Transonic Flow by Local Fractional Variational Iteration Method
We present the nondifferentiable approximate solution for local fractional Tricomi equation arising in fractal transonic flow by local fractional variational iteration method. Some illustrative examples are shown and graphs are also given
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