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Target misalignment can cause beam pointing deviations and degradation of sidelobe performance. In order to eliminate the effect of target misalignment, we formulate the jamming sub-space recovery problem as a linearly modified atomic norm-based optimization. Then, we develop a deep-unfolding network based on the alternating direction method of multipliers (ADMM), which effectively improves the applicability and efficiency of the algorithm. By using the back-propagation process of deep-unfolding networks, the proposed method could optimize the hyper-parameters in the original atomic norm. This feature enables the adaptive beamformer to adjust its weight according to the observed data. Specifically, the proposed method could determine the optimal hyper-parameters under different interference noise matrix conditions. Simulation results demonstrate that the proposed network could reduce computational cost and achieve near-optimal performance with low complexity. [ABSTRACT FROM AUTHOR]

Recently, deep unfolding networks have been widely used in direction of arrival (DOA) estimation because of their improved estimation accuracy and reduced computational cost. However, few have considered the existence of a nested array (NA) with off-grid DOA estimation. In this study, we present a deep sparse Bayesian learning (DSBL) network to solve this problem. We first establish the signal model for off-grid DOA with NA. Then, we transform the array output into a real domain for neural networks. Finally, we construct and train the DSBL network to determine the on-grid spatial spectrum and off-grid value, where the loss function is calculated using reconstruction error and the sparsity of network output, and the layers correspond to the steps of the sparse Bayesian learning algorithm. We demonstrate that the DSBL network can achieve better generalization ability without training labels and large-scale training data. The simulation results validate the effectiveness of the DSBL network when compared with those of existing methods. [ABSTRACT FROM AUTHOR]

Agroforestry systems are considered effective methods of carbon sequestration. In these systems, most of the carbon is stored in the soil, and the pattern of tree planting can influence the spatial distribution of organic matter input into the soil. However, limited information is available about the extent of this influence. In this study, the horizontal and vertical distributions of soil organic carbon (SOC) and labile fractions were investigated in four planting systems: a pure poplar (Populus deltoides cv. "35") planting system, a wide-row (14 m spacing) poplar and wheat (Triticum aestivum L.) agroforestry system, a narrow-row (7 m spacing) poplar and wheat agroforestry system, and a pure wheat field. The results showed that although the poplar system had the highest vegetation biomass (147.50 t ha−1), the agroforestry systems overall had higher SOC contents than the pure poplar system and wheat fields. Especially in the wide-row agroforestry system, the SOC, readily oxidizable carbon, and dissolved organic carbon contents were, respectively, 25.3%, 42.4%, and 99.3% higher than those of the pure poplar system and 60.3%, 148.7%, and 6.3% higher than those of the wheat field in a 1 m soil profile, and it also had the highest fine root biomass. However, the microbial biomass carbon content was highest in the pure poplar system. The SOC of the three poplar planting systems was spatially heterogeneous, with the highest values occurring at 1.5 m in the narrow-row systems and within the tree rows in the wide-row system, similar to the distribution of fine root biomass. Additionally, we found that the larger the diameter at the breast height of the trees, the greater their positive effect on SOC at greater distances. [ABSTRACT FROM AUTHOR]

Sirtuin 1 (SIRT1) overexpression significantly inhibits lipid deposition during yak intramuscular preadipocyte (YIMA) differentiation; however, the regulatory mechanism remains unknown. We elucidated the role of SIRT1 in YIMA differentiation using lentivirus-mediated downregulation technology and conducted mRNA-seq and ChIP-seq assays using H3K9ac antibodies after SIRT1 overexpression in order to reveal SIRT1 targets during YIMA adipogenesis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed in order to identify the functional annotation of common genes. In addition, a potential target of SIRT1 was selected to verify its effects on the differentiation and proliferation of YIMAs. SIRT1 interfered with lipid deposition and promoted YIMA differentiation. In total, 143,518 specific peaks were identified after SIRT1 overexpression, where genes associated with downregulation peaks were enriched in transcription, gene expression, lipid-related processes, and classical lipid-related pathways. The H3K9ac signal in the whole genome promoter region (2 kb upstream and downstream of the transcription start site (TSS)) was weakened, and the peaks were distributed across all gene functional regions. Genes that lost signals in their TSS region or gene body region were enriched in both biological processes and pathways associated with lipogenesis. The ChIP-seq results revealed 714 common differential genes in mRNA-seq, which were enriched in "MAPK signaling", "lipid and atherosclerosis", "mTOR signaling", and "FoxO signaling" pathways. A total of 445 genes were downregulated in both their H3K9ac signals and mRNA expression, and one of their most significantly enriched pathways was FoxO signaling. Nine genes (FBP2, FPGT, HSD17B11, KCNJ15, MAP3K20, SLC5A3, TRIM23, ZCCHC10, and ZMYM1) lost the H3K9ac signal in their TSS regions and had low mRNA expression, and three genes (KCNJ15, TGM3, and TRIM54) had low expression but lost their H3K9ac signal in the gene body region. The interference of TRIM23 significantly inhibited fat deposition during preadipocyte differentiation and promoted cell proliferation by increasing S-phase cell numbers. The present study provides new insights into the molecular mechanism of intramuscular fat content deposition and the epigenetic role of SIRT1 in adipocyte differentiation. [ABSTRACT FROM AUTHOR]

Parameter estimation is significant to prediction and estimation in the field of radar clutter characteristics. Therefore, it is necessary to study the problem of parameter estimation. The K-distribution is a commonly used model in sea clutter, which is a two-parameter model with shape parameters and scale parameters. The value of the shape parameters should be greater than 0. Moment estimation is usually used to estimate the parameters of the K-distribution. It overcomes the disadvantage of large computation compared with the maximum likelihood estimation method. However, the moment estimation usually uses two different order origin moments to solve the parameters. The joint solution of different order will cause large calculation errors, and sometimes the shape parameter is estimated to be less than 0. In the origin moment expression, the order k can be regarded as a continuous variable. By calculating the relationship between the k-order origin moment and its derivative, a parameter estimation method based on the origin moment derivative is proposed. The estimation efficiency and accuracy are compared with some moment estimation methods. Both simulation data and measured clutter data show that this method can achieve 100% estimation efficiency, can obtain higher estimation accuracy, and can also avoid the situation where the estimated value of the shape parameter is less than 0. Using the same idea to estimate the parameters in the two-parameter models, log–normal and Weibull distribution, we can also obtain the parameters with higher estimation accuracy. The experiments show that the higher-order origin moments are sensitive to the data, and the lower-order moments should be selected as far as possible. By selecting the appropriate order k, we can obtain ideal estimation parameters. [ABSTRACT FROM AUTHOR]

Due to its widespread application in communications, radar, etc., the orthogonal frequency division multiplexing (OFDM) signal has become increasingly urgent in the field of localization. Under uniform circular array (UCA) and near-field conditions, this paper presents a closed-form algorithm based on phase difference for estimating the three-dimensional (3-D) location (azimuth angle, elevation angle, and range) of the OFDM signal. In the algorithm, considering that it is difficult to distinguish the frequency of the OFDM signal's subcarriers and the phase-based method is always affected by errors of the frequency estimation, this paper employs sparse representation (SR) to obtain the super-resolution frequencies and the corresponding phases of subcarriers. Further, as the phase differences of the adjacent sensors including azimuth angle, elevation angle and range parameters can be expressed as indefinite equations, the near-field OFDM signal's 3-D location is obtained by employing the least square method, where the phase differences are based on the average of the estimated subcarriers. Finally, the performance of the proposed algorithm is demonstrated by several simulations. [ABSTRACT FROM AUTHOR]

Stereocomplex-polylactic acid (SC-PLA) is obtained in poly(d-lactic) acid/poly(l-lactic) acid (PDLA/PLLA) blends under adjusting processing conditions. It is found that the degree of crystallinity of overall SC-PLA is up to 43.7% in PDLA/PLLA blends of 1:1 mass ratio. Formation of stereocomplex (SC) crystals forces molecular chains in the blends to be more closely arranged and further enhances interaction between molecular chains, thus forming a physical cross-linking network in the SC crystals, resulting in the blends having a special microstructure. The mechanism of formation of the SC crystal physical cross-linking network is elucidated by dielectric spectroscopy, and the relationships between homocomplex (HC) crystals, SC crystals, and amorphous regions in the blends are also analyzed. Interestingly, mechanical properties of the blends are significantly improved due to formation of an SC crystal cross-linking network. [ABSTRACT FROM AUTHOR]

Polyethylene glycol (PEG)/hybrid carbon foam (CF) phase change materials (PCMs) were prepared by integrating PEG into CF via dynamic-vacuum impregnation. The hybrid CF was first synthesized by mixtures of graphene oxide (GO) and carbon nanotubes (CNTs) with different volume ratios. The morphologies, chemical structures, thermal conductivities, shape-stabilization levels, and photo-thermal energy conversion levels of these composite PCMs were characterized systematically. The prepared composite PCMs exhibited good shape-stabilization levels and showed their original shapes without any PEG leakage. It was found that the polyethylene glycol/carbon foam with multi-walled carbon nanotubes (PEG/MCF) composite PCMs had a better shape-stable performance below the temperature of 250 °C, and the thermal conductivity of the PEG/MCF composite PCMs reached as high as 1.535 W/(mK), which was obviously higher than that of polyethylene glycol/carbon foam with single-walled carbon nanotubes (PEG/SCF, 1.159 W/(mK)). The results of the photo-thermal simulation tests showed that the composite PCMs had the ability to absorb light energy and then convert it to thermal energy, and the maximum thermal energy storage efficiency of the PEG/MCF composite PCMs and the PEG/SCF composite PCMs was 92.1% and 90.6%, respectively. It was considered that a valuable technique to produce high-performance composite PCMs was developed. [ABSTRACT FROM AUTHOR]

A high-accuracy algorithm is presented for the localization of mixed incoherent near-field and far-field narrow-band sources under uniform circular array (UCA). Herein, considering that it is difficult to classify the mixed sources, we first decouple mixed sources’ angles and ranges by calculating centro-symmetric sensors’ phase differences. Then, as the phase differences including only sources’ angles can be transformed as indefinite equations, each source’s azimuth angle and elevation angle are obtained by performing the least squares method. After that, on the basis of the estimated angles of the mixed sources, one-dimensional (1-D) multiple signal classification (MUSIC) method and corresponding spatial spectrum are utilized to identify the mixed sources and estimate the ranges of the near-field sources. Finally, simulation and comparison results verify the superior performance of our proposed algorithm. [ABSTRACT FROM AUTHOR]