Dynamic susceptibility of onion in ferromagnetic elliptical nanoring

Micromagnetic simulation was performed to investigate the equilibrium state and dynamic susceptibility spectra of magnetic elliptical nanoring. There are two equilibrium states (onion and vortex) obtained in elliptical nanoring. The onion state can be used to record information in MRAM. And it is important to investigate the dynamic susceptibility spectra of onion state, which is closely related to writing and reading speed of magnetic memory devices. Those results show that two or three resonance peaks are found under different thickness of elliptical nanoring with onion state, respectively. The low resonance frequency of two resonance peaks is increasing with the arm width of the elliptical ring, but is decreasing with the thickness. However, the high frequency of two resonance peaks is decreasing with the arm width of the elliptical ring

Numerical and Field Investigations of Tremors Induced by Thick-Hard Strata Fracture

Large energy mining-induced tremors are generally caused by the rupture of thick-hard overlying strata, and the roadway is prone to rockburst danger under its dynamic load. Through numerical simulations, the laws and influencing factors of stress, deformation, fracture and energy caused by mining-induced tremors (red bed breaking) were revealed, and the results were verified by field observations. The main conclusions were obtained: (1) the tensile stress and the compressive stress were concentrated in the middle of the thick-hard rock and both sides of the goaf, which were prone to tensile and shear failure type caused by mining-induced tremors, respectively; (2) after the goaf was formed, the compressive stress around the roadway was transformed into tensile stress. When a strong mining-induced tremor occurred, the tensile stress increased further. Meanwhile, when it was close to the tensile strength of the coal around the roadway, a rockburst occurred; (3) the energy of the mining-induced tremor released by shear failure was larger than that of the tensile failure. With the increase in shear strength of the roof, the released energy also increased; (4) according to the frequency-spectrum of the mining-induced tremors located in the extremely thick-hard overlying strata above the working face in the Baodian coal mine, the dynamic load generated by the tremors was superimposed on the high static load around the roadway, which was very likely to induce the instability of the roadway. The research conclusions have certain guiding significance for rockburst prevention in coal mines with thick-hard strata roofs

Numerical and Field Investigations of Tremors Induced by Thick-Hard Strata Fracture

Large energy mining-induced tremors are generally caused by the rupture of thick-hard overlying strata, and the roadway is prone to rockburst danger under its dynamic load. Through numerical simulations, the laws and influencing factors of stress, deformation, fracture and energy caused by mining-induced tremors (red bed breaking) were revealed, and the results were verified by field observations. The main conclusions were obtained: (1) the tensile stress and the compressive stress were concentrated in the middle of the thick-hard rock and both sides of the goaf, which were prone to tensile and shear failure type caused by mining-induced tremors, respectively; (2) after the goaf was formed, the compressive stress around the roadway was transformed into tensile stress. When a strong mining-induced tremor occurred, the tensile stress increased further. Meanwhile, when it was close to the tensile strength of the coal around the roadway, a rockburst occurred; (3) the energy of the mining-induced tremor released by shear failure was larger than that of the tensile failure. With the increase in shear strength of the roof, the released energy also increased; (4) according to the frequency-spectrum of the mining-induced tremors located in the extremely thick-hard overlying strata above the working face in the Baodian coal mine, the dynamic load generated by the tremors was superimposed on the high static load around the roadway, which was very likely to induce the instability of the roadway. The research conclusions have certain guiding significance for rockburst prevention in coal mines with thick-hard strata roofs

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively

Identification of functional butanol-tolerant genes from Escherichia coli mutants derived from error-prone PCR-based whole-genome shuffling

Abstract Background Butanol is an important biofuel and chemical. The development of butanol-tolerant strains and the identification of functional butanol-tolerant genes is essential for high-yield bio-butanol production due to the toxicity of butanol. Results Escherichia coli BW25113 was subjected for the first time to error-prone PCR-based whole-genome shuffling. The resulting mutants BW1847 and BW1857 were found to tolerate 2% (v/v) butanol and short-chain alcohols, including ethanol, isobutanol, and 1-pentanol. The mutants exhibited good stability under butanol stress, indicating that they are potential host strains for the construction of butanol pathways. BW1847 had better butanol tolerance than BW1857 under 0–0.75% (v/v) butanol stress, but showed a lower tolerance than BW1857 under 1.25–2% (v/v) butanol stress. Genome resequencing and PCR confirmation revealed that BW1847 and BW1857 had nine and seven single nucleotide polymorphisms, respectively, and a common 14-kb deletion. Functional complementation experiments of the SNPs and deleted genes demonstrated that the mutations of acrB and rob gene and the deletion of TqsA increased the tolerance of the two mutants to butanol. Genome-wide site-specific mutated strains DT385 (acrB C1198T) and DT900 (rob AT686–7) also showed significant tolerance to butanol and had higher butanol efflux ability than the control, further demonstrating that their mutations yield an inactive protein that enhances butanol resistance characteristics. Conclusions Stable E. coli mutants with enhanced short alcohols and high concentrations of butanol tolerance were obtained through a rapid and effective method. The key genes of butanol tolerance in the two mutants were identified by comparative functional genomic analysis

Identification of angiogenesis‐related genes signature for predicting survival and its regulatory network in glioblastoma

Abstract Glioblastoma (GBM) is notorious for malignant neovascularization that contributes to undesirable outcome. However, its mechanisms remain unclear. This study aimed to identify prognostic angiogenesis‐related genes and the potential regulatory mechanisms in GBM. RNA‐sequencing data of 173 GBM patients were obtained from the Cancer Genome Atlas (TCGA) database for screening differentially expressed genes (DEGs), differentially transcription factors (DETFs), and reverse phase protein array (RPPA) chips. Differentially expressed genes from angiogenesis‐related gene set were extracted for univariate Cox regression analysis to identify prognostic differentially expressed angiogenesis‐related genes (PDEARGs). A risk predicting model was constructed based on 9 PDEARGs, namely MARK1, ITGA5, NMD3, HEY1, COL6A1, DKK3, SERPINA5, NRP1, PLK2, ANXA1, SLIT2, and PDPN. Glioblastoma patients were stratified into high‐risk and low‐risk groups according to their risk scores. GSEA and GSVA were applied to explore the possible underlying GBM angiogenesis‐related pathways. CIBERSORT was employed to identify immune infiltrates in GBM. The Pearson's correlation analysis was performed to evaluate the correlations among DETFs, PDEARGs, immune cells/functions, RPPA chips, and pathways. A regulatory network centered by three PDEARGs (ANXA1, COL6A1, and PDPN) was constructed to show the potential regulatory mechanisms. External cohort of 95 GBM patients by immunohistochemistry (IHC) assay demonstrated that ANXA1, COL6A1, and PDPN were significantly upregulated in tumor tissues of high‐risk GBM patients. Single‐cell RNA sequencing also validated malignant cells expressed high levels of the ANXA1, COL6A1, PDPN, and key DETF (WWTR1). Our PDEARG‐based risk prediction model and regulatory network identified prognostic biomarkers and provided valuable insight into future studies on angiogenesis in GBM