The overhead caused by broadcast schemes cannot be neglected in mobile ad hoc networks. It is known that the probabilistic broadcast scheme is an effective approach to reduce overhead, in which nodes forward packets with a probability to reduce redundant retransmissions. In hostile environments, malicious nodes degrade network efficiency, and they should be prevented from route discovery and data transmission. In this case, routing packets forwarded by malicious nodes are redundant. This paper presents a Trust-based Probabilistic Broadcast scheme (TPB) focusing on reducing overhead, especially caused by malicious nodes, in which rebroadcast probability is determined based on nodes’ trustworthiness. In order to obtain nodes’ trust level, a light-weight trust management model is designed based on direct and recommended trust evidence. According to nodes’ trust level, rebroadcast delay is calculated for rearranging the rebroadcast order of routing packets. Furthermore, a novel method based on nodes’ trustworthiness is proposed to calculate rebroadcast probability, which prevents untrusted nodes from route discovery. The proposed trust-based probabilistic broadcast scheme is incorporated with existing routing protocol and its performance is evaluated by simulations. The numerical results show that the proposed scheme can secure network communication and reduce the routing overhead.

The gateway selection is an essential issue in hybrid mobile ad hoc networks (MANETs). Current independent and random selections without supporting of routing negotiation protocol may cause links or gateways overloaded when they are selected by multiple nodes simultaneously. Therefore, the network performance may not be in a proper way. Furthermore, the ad hoc nature of MANETs makes the topology change dynamically, so that the gateway selection becomes even more difficult. This paper presents a mathematical model for gateway capability and a novel approach, called bio-inspired gateway selection, where the gateways are selected according to the network status and associated with a cooperative mechanism for optimization. The novelty includes the use of attractor selection model, the self-adaptability and the autonomy of biological system. The performance of the proposed approach is evaluated by simulation with different scenarios and compared with the conventional approaches being currently used in hybrid MANETs. The illustrated numerical results present the performance of the proposed approach in terms of packet delivery ratio, average delivery latency, normalized routing overhead and gateway load balance under different network conditions. Furthermore, the numerical results are also able to demonstrate the significant performance gain compared with the conventional gateway selection approaches.

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

This paper presents a fast SAR imagery algorithm for Ground Moving Target Imaging (GMTIm) based on the slope detection strategy combined with Time-Frequency Representation (TFR), which is known as Lv’s Distribution (LVD). This fast imagery algorithm focuses on the solution of the ambiguity problems and relevant heavy computing load in SAR imagery. Firstly, according to the relationship between the slope of the range walk trajectory and the cross-track velocity of moving target, a new high-efficiency slope detection strategy based on gradient and level-line angle is presented in the image domain. Then, the Doppler centroid shift induced by cross-track velocity can also be obtained. Secondly, owing to the cross-track velocity estimated before, the Range Walk Migration Correction (RWMC) can be performed to concentrate the echo response of the moving target into a single range cell. Finally, due to the superior performance in representing multi-component Linear Frequency Modulation (LFM) signal, LVD is adopted here to represent the Doppler chirp rate of multiple moving targets in a Doppler Centroid Frequency and Chirp Rate domain (CFCR). The performance of the proposed algorithm is evaluated in terms of superiority and effectiveness using simulations, and the comparison between the proposed algorithm and the other conventional algorithms is also presented.

This paper presents the quantitative measurement through an experimental test of 640 Gbps 16-QAM coherent-optical orthogonal frequency-division multiplexing (CO-OFDM) over 800 km optical fiber with mid-link optical phase conjugation (OPC) using highly nonlinear fiber (HNLF). The first focus is the OPC parameter optimization, including the optimization of HNLF length and signal/pump power that inputs into OPC. Four different HNLFs, as the illustrative examples, are investigated. The second focus is to investigate the effects of fiber dispersion, nonlinearity, and amplified spontaneous emission (ASE) noise on the long-haul transmission of 16-QAM CO-OFDM signal, and the OPC compensation efficiency. The performance evaluation focuses on the conversion efficiency (CE), received signal constellation, Q-factor improvement, and bit error rate (BER) at the receiver end. Such end-to-end performance evaluation is important because the 16-QAM CO-OFDM signal status is heterogeneous and the mitigation of transmission impairments to the signal is still unclear. The OPC parametric optimization is achieved experimentally using commercially available HNLFs with different scenarios and the numerical results are interpreted in conjunction with simulations.