Case study on deformation control of upper-soft and lower-hard large span tunnel station using combined control technology and monitoring demonstration

A large number of shallow buried tunnels are built in the city nowadays and the special strata such as large upper-soft and lower-hard ground often encountered. Deformation control of strata is the focus issue related to the construction safety. Based on Dalian metro Hing Street station with the classical geological condition of upper-soft and lower-hard ground, this paper fully used a combined control method including six different support measures to control the deformation of surrounding rock. 3D finite element model was setup to analyze the construction effect of combined control measures and the monitoring in-site was carried out to verify the deformation control effect of combined control method. It shows that the maximum surface subsidence value is gradually reduced with the support measures gradually increasing. In the case of various supports the maximum sedimentation value is 2.67 cm, which is 42. 1% lower than that of not using control method and the control effect is obvious. In addition, it can be seen that the two-layer initial support and additional large arch foot have the best effect on controlling the ground surface settlement with reduction of 11.7% and 20.2%, respectively. The research results can provide practical experience for the construction of such tunnels, and guide the design and construction of the tunnel in the future

Detecting extreme-mass-ratio inspirals for space-borne detectors with deep learning

One of the primary objectives for space-borne gravitational wave detectors is the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a substantial challenge because of the complex and long EMRI signals, further complicated by their inherently faint signal. In this research, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors. Our method employs the Q-transform for data preprocessing, effectively preserving EMRI signal characteristics while minimizing data size. By harnessing the robust capabilities of CNNs, we can reliably distinguish EMRI signals from noise, particularly when the signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden'' EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure practical utility. We assess our model's performance using a 0.5-year dataset, achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and 1\% FPR at an SNR of 50. This study underscores the promise of incorporating deep learning methods to advance EMRI data analysis, potentially leading to rapid EMRI signal detection.Comment: 12 pages, 8 figures, 2 table

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by their inherently faint nature. In this letter, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 % false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as mass and spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can be inferred directly by employing a VGG network. The mass and spin of the SMBH can be determined at 99 % and 92 % respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation .Comment: 6 pages, 5 figure

Establishment of an Efficient in Vitro Propagation System for Iris Sanguinea

Iris sanguinea is a perennial flowering plant that is typically cultivated through seeds or bulbs. However, due to limitations in conventional propagation, an alternate regeneration system using seeds was developed. The protocol included optimization of sterilization, stratification and scarification methods as iris seeds exhibit physiological dormancy. In addition to chlorine-based disinfection, alkaline or heat treatment was used to break seed dormancy and reduce contamination. When seeds were soaked in water at 80 °C overnight, and sterilized with 75% EtOH for 30 s and 4% NaOCl solution for 20 minutes, contamination was reduced to 10% and a 73.3% germination was achieved. The germinated seedlings with 2-3 leaves and radicle were used as explants to induce adventitious buds. The optimal MS medium with 0.5 mg L−1 6-benzylaminopurine, 0.2 mg L−1 NAA, and 1.0 mg L−1 kinetin resulted in 93.3% shoot induction and a proliferation coefficient of 5.30. Medium with 0.5 mg L−1 NAA achieved 96.4% rooting of the adventitious shoots. The survival rate was more than 90% after 30 days growth in the cultivated matrix. In conclusion, a successful regeneration system for propagation of I. sanguinea was developed using seeds, which could be utilized for large-scale propagation of irises of ecological and horticultural importance

Bragg spectroscopy of a superfluid Bose-Hubbard gas

Bragg spectroscopy is used to measure excitations of a trapped, quantum-degenerate gas of 87Rb atoms in a 3-dimensional optical lattice. The measurements are carried out over a range of optical lattice depths in the superfluid phase of the Bose-Hubbard model. For fixed wavevector, the resonant frequency of the excitation is found to decrease with increasing lattice depth. A numerical calculation of the resonant frequencies based on Bogoliubov theory shows a less steep rate of decrease than the measurements.Comment: 11 pages, 4 figure

Genome Sequencing Reveals Unique Mutations in Characteristic Metabolic Pathways and the Transfer of Virulence Genes between V. mimicus and V. cholerae

Vibrio mimicus, the species most similar to V. cholerae, is a microbe present in the natural environmental and sometimes causes diarrhea and internal infections in humans. It shows similar phenotypes to V. cholerae but differs in some biochemical characteristics. The molecular mechanisms underlying the differences in biochemical metabolism between V. mimicus and V. cholerae are currently unclear. Several V. mimicus isolates have been found that carry cholera toxin genes (ctxAB) and cause cholera-like diarrhea in humans. Here, the genome of the V. mimicus isolate SX-4, which carries an intact CTX element, was sequenced and annotated. Analysis of its genome, together with those of other Vibrio species, revealed extensive differences within the Vibrionaceae. Common mutations in gene clusters involved in three biochemical metabolism pathways that are used for discrimination between V. mimicus and V. cholerae were found in V. mimicus strains. We also constructed detailed genomic structures and evolution maps for the general types of genomic drift associated with pathogenic characters in polysaccharides, CTX elements and toxin co-regulated pilus (TCP) gene clusters. Overall, the whole-genome sequencing of the V. mimicus strain carrying the cholera toxin gene provides detailed information for understanding genomic differences among Vibrio spp. V. mimicus has a large number of diverse gene and nucleotide differences from its nearest neighbor, V. cholerae. The observed mutations in the characteristic metabolism pathways may indicate different adaptations to different niches for these species and may be caused by ancient events in evolution before the divergence of V. cholerae and V. mimicus. Horizontal transfers of virulence-related genes from an uncommon clone of V. cholerae, rather than the seventh pandemic strains, have generated the pathogenic V. mimicus strain carrying cholera toxin genes