In this paper, we propose a new type of array antenna, termed the random frequency diverse array (RFDA), for an uncoupled indication of target direction and range with low system complexity. In RFDA, ...each array element has a narrow bandwidth and a randomly assigned carrier frequency. The beampattern of the array is shown to be stochastic but thumbtack-like, and its stochastic characteristics, such as the mean, variance, and asymptotic distribution are derived analytically. Based on these two features, we propose two kinds of algorithms for signal processing. One is matched filtering, due to the beampattern's good characteristics. The other is compressive sensing, because the new approach can be regarded as a sparse and random sampling of target information in the spatial-frequency domain. Fundamental limits, such as the Cramér-Rao bound and the observing matrix's mutual coherence, are provided as performance guarantees of the new array structure. The features and performances of RFDA are verified with numerical results.
This paper presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation ...between the array observation data and the output of the beamformer. First, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. Then, we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high-dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.
In this work, we propose a low-complexity robust adaptive beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) ...algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. LOCSME only requires prior knowledge of the angular sector in which the actual steering vector is located and the antenna array geometry. LOCSME does not require a costly optimization algorithm and does not need to know extra information from the interferers, which avoids direction finding for all interferers. Simulations show that LOCSME outperforms previously reported RAB algorithms and has a performance very close to the optimum.
•Soil salinity was the best predictors of soil bacterial and fungal community structure.•Fungi are more sensitive to grassland degradation than bacteria.•Grassland degradation increases interactions ...among microorganisms.
Grassland degradation is a retrogressive successionofgrasslandvegetation, which leads to the loss of biodiversity and the degradation of ecosystem functions. Soil microbiomes play critical roles in the functioning and services of grassland ecosystems, yet little is known about how their diversity, structure and co-occurrence network characteristics respond to grassland degradation. Here, we used lllumina Miseq technique to evaluate soil bacterial and fungal communities in a meadow steppe with different degrees of degradation in Northeastern China. Our results showed that Actinobacteria, Proteobacteria, and Chloroflexi and Acidobacteria were the dominant bacterial phyla, while Ascomycota, Basidiomycota, and Zygomycota were the predominant fungal phyla. The relative abundance of taxa assigned to Actinobacteria, Gemmatimonadetes, Firmicutes, and Deinococcus-Thermus increased with increasing degradation degrees, whereas those affiliated with Acidobacteria and Nitrospirae showed a decreasing pattern. Compared to bacteria, the relative abundance of most fungal phyla decreased gradually along the degradation gradient. Bacterial Shannon diversity index possessed a similar hump pattern, while fungal diversity decreased with increasing degree of grassland degradation. Bacterial and fungal communities have different responses to grassland degradation, indicating that fungi are more sensitive to grassland degradation than bacteria. Both bacterial and fungal community structures were significantly different among the three sites. Changes in soil bacterial and fungal community structures were best explained by soil salinity and pH. Plant diversity and nitrogen concentration in aboveground plant tissues were also important factors for regulating fungal communities. Co-occurrence network analysis revealed that microbial taxa increased positive interactions and average degree to strengthen the adaptability of microorganisms to grassland degradation. These findings could enhance our understanding of the formation and maintenance of microbial community diversity in degraded grasslands and the development of a new indicator for grassland ecosystem management.
Enhancer RNA (eRNA) is a type of noncoding RNA transcribed from the enhancer. Although critical roles of eRNA in gene transcription control have been increasingly realized, the systemic landscape and ...potential function of eRNAs in cancer remains largely unexplored. Here, we report the integration of multi-omics and pharmacogenomics data across large-scale patient samples and cancer cell lines. We observe a cancer-/lineage-specificity of eRNAs, which may be largely driven by tissue-specific TFs. eRNAs are involved in multiple cancer signaling pathways through putatively regulating their target genes, including clinically actionable genes and immune checkpoints. They may also affect drug response by within-pathway or cross-pathway means. We characterize the oncogenic potential and therapeutic liability of one eRNA, NET1e, supporting the clinical feasibility of eRNA-targeted therapy. We identify a panel of clinically relevant eRNAs and developed a user-friendly data portal. Our study reveals the transcriptional landscape and clinical utility of eRNAs in cancer.
The fatigue characteristics of rock materials are usually studied by cyclic load–unload tests, and the deformation and damage development reflect their weakening characteristics. In this paper, ...according to the mechanical characteristics of rock materials during load/unload cycles, the total strain can be separated into three types, that is, elastic strain, viscoelastic strain, and viscoplastic strain. The elastic strain is linear with stress, and viscoelastic strain exhibits a special behavior after unloading, the viscoplastic strain also displays its own unique features and reflects the damage in rocks. Based on their unique characteristics, we establish elastic, viscoelastic, and viscoplastic submodels, then an elastic-visco-plastic model can be obtained by connecting three submodels in series, which can reflect the development of the law of different strains. In order to verify the reliability of the model, red sandstone samples are selected for cyclic load/unload tests. The results show that the collected strain–time data are well fitted by the model. In addition, the characteristics of strain–time curves imply the deformation and damage development of rocks during load/unload cycles.
To improve the acquisition sensitivity of the weak and dynamic global navigation satellite systems signal, the fractional Fourier transform (FRFT) is introduced to deal with the acquisition process. ...The acceleration can be estimated using the FRFT approach, and the gain of coherent integration as well as the detection probability could be significantly improved. Partially matched filter technique is used to compare with FRFT. The digital computation complexity and the mean acquisition time are provided. Theoretical analysis for the detection probability has been proposed, and simulations are conducted to verify the high performance of the technique.
Automatic segmentation of salient objects in real-world images has gained increasing interests owing to its popularity in diverse real-world applications, such as autonomous driving, medical ...diagnosis, aviation security, and underwater surveillance. In this research, we propose Firefly Algorithm (FA)-enhanced evolving ensemble deep networks for semantic segmentation and visual saliency prediction. An improved FA model is proposed to optimize network hyper-parameters. Specifically, it employs mutation operators and a neighbouring search strategy with granular search steps to establish search intensification. It also emphasizes search diversification by adopting multiple dynamic hybrid leaders and diverse adaptive sine and cosine search trajectories in full and randomly selected sub-dimensions to overcome stagnation. Because of its competent segmentation performance, DeepLabV3+ is fine-tuned using transfer learning with FA-based hyper-parameter identification. We optimize the learning rate, momentum and weight decay of the transfer learning network. A number of optimized DeepLabV3+ networks with distinguishing learning configurations are yielded. An ensemble model is subsequently constructed by incorporating three optimized base networks to further strengthen segmentation performance. Evaluated using diverse challenging semantic segmentation and saliency prediction tasks using underwater and medical image data sets, our evolving ensemble deep network illustrates significant superiority over other state-of-the-art deep networks and existing studies. The proposed FA model also outperforms other search methods in solving diverse mathematical landscapes with statistical significance.
•We propose an evolving ensemble deep network for semantic segmentation.•An FA variant is proposed to devise optimized DeepLabV3+ segmenters.•It exploits intensification using fine-tuned mutation operations.•Elite signals in full and sub-dimensions are devised to escalate diversification.•Our system depicts a superior capability in semantic segmentation.
The present study aimed to investigate the correlation between weight status and mortality in mechanically ventilated patients and explore the potential mediators.
Three medical centers encompassing ...3301 critically ill patients receiving mechanical ventilation were assembled for retrospective analysis to compare mortality across various weight categories of patients using machine learning algorithms. Bioinformatics analysis identified genes exhibiting differential expression among distinct weight categories. A prospective study was then conducted on a distinct cohort of 50 healthy individuals and 193 other mechanically ventilated patients. The expression levels of the genes identified through bioinformatics analysis were quantified through enzyme-linked immunosorbent assay (ELISA).
The retrospective analysis revealed that overweight individuals had a lower mortality rate than underweight individuals, and body mass index (BMI) was an independent protective factor. Bioinformatics analysis identified matrix metalloproteinase 8 (MMP-8) as a differentially expressed gene between overweight and underweight populations. The results of further prospective studies showed that overweight patients had significantly lower MMP-8 levels than underweight patients ((3.717 (2.628, 4.191) vs. 2.763 (1.923, 3.753), ng/ml, P = 0.002). High MMP-8 levels were associated with increased mortality risk (OR = 4.249, P = 0.005), indicating that elevated level of MMP-8 predicts the mortality risk of underweight patients receiving mechanical ventilation.
This study provides evidence for a protective effect of obesity in mechanically ventilated patients and highlights the potential role of MMP-8 level as a biomarker for predicting mortality risk in this population.
A simplified rectangular differential spatial modulation (S-RDSM) scheme is conceived for massive multiple-input multiple-output (MIMO) systems dispensing with the channel state information (CSI). In ...the proposed S-RDSM scheme, the information bits are first mapped to a conventional SM symbol and then rectangular differential encoding is invoked between a pair of SM symbols. Then a non-coherent detector relying on a forgetting factor is developed, which requires no CSI at the receiver. Explicitly, a low-complexity hard limited maximum likelihood (HL-ML) detector is conceived for our generalized S-RDSM scheme, which is characterized by our theoretical analysis. Furthermore, we derive the optimal forgetting factor in closed form, which is capable of significantly reducing the complexity of the associated optimization. Finally, the upper bounds of the average bit error probability (ABEP) are derived using the moment generating function (MGF), and are validated by our simulation results. Both the theoretical and simulation results have shown that the proposed S-RDSM system outperforms the existing non-coherent schemes, despite operating at 10% of the benchmarker's complexity, whilst approaching the performance of its coherent SM counterpart at a comparable complexity.
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