Integration of Signal and Artificial Noise in MIMO Wiretap Channel

In this paper, the integrated signal-to-artificial noise (ISAN) design is applied in MIMO wiretap channel to ensure wireless communication security. When the information of eavesdropper is unknown, the total power is divided into two parts: signal and artificial noise. The signal can secure certain quality at the legitimate receiver. The artificial noise which is in the null space of the receiver channel matrix can deteriorate eavesdropper channel by the method of beam forming. The artificial noise power is distributed evenly in other space, so that the eavesdropper channel is deteriorated in all directions. The signal to interface and noise ratio (SINR) is regarded as the efficient parameter on measuring reliability and security of information at the legitimate receiver. The simulations reveal that ISAN can deteriorate the eavesdropper channel and safeguard the information transmission on the premise of the given SINR of the legitimate receiver

Direct Growth of Copper Oxide Films on Ti Substrate for Nonenzymatic Glucose Sensors

Copper oxide (CuO) films directly grown on Ti substrate have been successfully prepared via a hydrothermal method and used to construct an amperometric nonenzymatic glucose sensor. XRD and SEM were used to characterize the samples. The electrochemical performances of the electrode for detection of glucose were investigated by cyclic voltammetry and chronoamperometry. The CuO films based glucose sensors exhibit enhanced electrocatalytic properties which show very high sensitivity (726.9 μA mM−1 cm−2), low detection limit (2 μM), and fast response (2 s). In addition, reproducibility and long-term stability have been observed. Low cost, convenience, and biocompatibility make the CuO films directly grown on Ti substrate electrodes a promising platform for amperometric nonenzymatic glucose sensor

Strong Convergence of Non-Implicit Iteration Process with Errors in Banach Spaces

The purpose of this paper is to study the strong convergence of a non-implicit iteration process with errors for asymptotically I-nonexpansive mappings in the intermediate sense in the framework of Banach spaces. The results presented in this paper extend and improve the corresponding results recently announced

Electrical transport across metal/two-dimensional carbon junctions: Edge versus side contacts

Metal/two-dimensional carbon junctions are characterized by using a nanoprobe in an ultrahigh vacuum environment. Significant differences were found in bias voltage (V) dependence of differential conductance (dI/dV) between edge- and side-contact; the former exhibits a clear linear relationship (i.e., dI/dV \propto V), whereas the latter is characterized by a nonlinear dependence, dI/dV \propto V3/2. Theoretical calculations confirm the experimental results, which are due to the robust two-dimensional nature of the carbon materials under study. Our work demonstrates the importance of contact geometry in graphene-based electronic devices

The evolution of microbialite forms during the Early Triassic transgression: A case study in Chongyang of Hubei Province, South China

The widespread development of microbialites in shallow areas of the Tethys Ocean at the start of the Early Triassic reflects the deterioration of marine ecosystems in the aftermath of the extinction that marked the demise of the majority of Palaeozoic marine faunas. Here we present a study of the evolving microbialite forms and associated biotic assemblages of this pioneering microbialite interval from exposures at Chongyang, Hubei Province, China. This research provides a perspective on the effects of eustatic transgression on marine ecosystems as water depths increased at the beginning of Mesozoic, through the study of the changing forms, microfacies and distribution of microbialites. Microbialite forms evolved from stratiform stromatolites to a sequence of tabular thrombolites (with an intercalated layer of columnar stromatolites), followed by domical thrombolites that were overlain, in turn, by oolites. The stratiform stromatolites contain poorly preserved remains of calcified cyanobacteria, but microfossils with chambered structure can also be seen. Metazoan fossils increased from the base of the overlying tabular thrombolite, reflecting increasing biodiversity with deepening of seawater. The occurrence of columnar stromatolites within the tabular thrombolite may indicate a temporary sea-level shallowing. Foraminiferans and other metazoans are absent within the columnar stromatolites, but spherical cyanobacterial remains are extremely abundant. Well-preserved calcified cyanobacteria may reflect an absence of metazoan predation and/or carbonate supersaturation of seawater. As water deepened, domical thrombolites developed and the more complex seafloor relief created varied niches between and within the domes that harboured more ecologically diverse communities. During the process of transgression within the microbialite interval, carbon isotopes exhibit a negative relationship with biodiversity, implying that upwelling of anoxic deep-ocean water, if associated with the negative excursion of carbon isotope values, did not inhibit the diversification of benthic organisms at least on shallow carbonate platforms in the period immediately after the end-Permian mass extinction.This study was jointly supported by the National Natural Science Foundationof China (Grants No. 41730320 and No. 41572001) and the 111 project(B08030

Predicting new molecular targets for rhein using network pharmacology

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.</p> <p>Methods</p> <p>In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.</p> <p>Results</p> <p>Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.</p> <p>Conclusions</p> <p>Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.</p

Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification

Multi-spectral vehicle re-identification aims to address the challenge of identifying vehicles in complex lighting conditions by incorporating complementary visible and infrared information. However, in harsh environments, the discriminative cues in RGB and NIR modalities are often lost due to strong flares from vehicle lamps or sunlight, and existing multi-modal fusion methods are limited in their ability to recover these important cues. To address this problem, we propose a Flare-Aware Cross-modal Enhancement Network that adaptively restores flare-corrupted RGB and NIR features with guidance from the flare-immunized thermal infrared spectrum. First, to reduce the influence of locally degraded appearance due to intense flare, we propose a Mutual Flare Mask Prediction module to jointly obtain flare-corrupted masks in RGB and NIR modalities in a self-supervised manner. Second, to use the flare-immunized TI information to enhance the masked RGB and NIR, we propose a Flare-Aware Cross-modal Enhancement module that adaptively guides feature extraction of masked RGB and NIR spectra with prior flare-immunized knowledge from the TI spectrum. Third, to extract common informative semantic information from RGB and NIR, we propose an Inter-modality Consistency loss that enforces semantic consistency between the two modalities. Finally, to evaluate the proposed FACENet in handling intense flare, we introduce a new multi-spectral vehicle re-ID dataset, called WMVEID863, with additional challenges such as motion blur, significant background changes, and particularly intense flare degradation. Comprehensive experiments on both the newly collected dataset and public benchmark multi-spectral vehicle re-ID datasets demonstrate the superior performance of the proposed FACENet compared to state-of-the-art methods, especially in handling strong flares. The code and dataset will be released soon