Parameters Optimization of Curtain Grouting Reinforcement Cycle in Yonglian Tunnel and Its Application

For practical purposes, the curtain grouting method is an effective method to treat geological disasters and can be used to improve the strength and permeability resistance of surrounding rock. Selection of the optimal parameters of grouting reinforcement cycle especially reinforcement cycle thickness is one of the most interesting areas of research in curtain grouting designs. Based on the fluid-structure interaction theory and orthogonal analysis method, the influence of reinforcement cycle thickness, elastic modulus, and permeability on water inflow of tunnel after grouting and stability of surrounding rock was analyzed. As to the water inflow of tunnel after grouting used as performance evaluation index of grouting reinforcement cycle, it can be concluded that the permeability was the most important factor followed by reinforcement cycle thickness and elastic modulus. Furthermore, pore water pressure field, stress field, and plastic zone of surrounding rock were calculated by using COMSOL software under different conditions of reinforcement cycle thickness. It also can be concluded that the optimal thickness of reinforcement cycle and permeability can be adopted as 8 m and 1/100 of the surrounding rock permeability in the curtain grouting reinforcement cycle. The engineering case provides a reference for similar engineering

AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation

Methods: In this study, a benchmark \emph{Abdominal Adipose Tissue CT Image Dataset} (AATTCT-IDS) containing 300 subjects is prepared and published. AATTCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3,213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATTCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. Results: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATTCT-IDS reveals three adipose distributions in the subject population. Conclusion: AATTCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: \url{https://figshare.com/articles/dataset/AATTCT-IDS/23807256}.Comment: 17 pages, 7 figure

The First Polarimetric View on Quasi-Periodic Oscillations in a Black Hole X-ray Binary

We present the first polarimetric analysis of Quasi-Periodic Oscillations (QPO) in a black hole binary utilizing \textit{IXPE} data. Our study focuses on Swift J1727.8--1613, which experienced a massive outburst that was observed by various telescopes across different wavelengths. The \textit{IXPE} observation we studied was conducted during the Hard-Intermediate state. The polarization degree (PD) and polarization angle (PA) were measured at 4.28$\pm$0.20\% and $1.9^{\circ}\pm1.4^{\circ}$, respectively. Remarkably, significant QPO signals were detected during this observation, with a QPO frequency of approximately 1.34 Hz and a fractional root-mean-square (RMS) amplitude of about 12.3\%. Furthermore, we conducted a phase-resolved analysis of the QPO using the Hilbert-Huang transform technique. The photon index showed a strong modulation with respect to the QPO phase. In contrast, the PD and PA exhibit no modulations in relation to the QPO phase, which is inconsistent with the expectation of the Lense-Thirring precession of the inner flow. Further theoretical studies are needed to conform with the observational results.Comment: Accepted for publication in APJ

Numerical analysis of the behavior of special double-row support structure

A special double-row support structure used for braced excavation was modeled numerically using finite element method. The performance of the braced excavation depends on the interaction between the two walls of the support structure. Comprehensive parametric studies were carried out to investigate the influence factors on the performance. It was ascertained that the support structure behavior was largely influenced by overlapping length of two support walls, embedment ratio of inner support wall and spacing between two support walls. Appropriate parameters should be chosen to limit wall deflection and to maintain the stability of the support structure

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting

A Malicious Mining Code Detection Method Based on Multi-Features Fusion

With the continuous increase in the economic value of new digital currencies represented by Bitcoin, more and more cybercriminals use malicious code to occupy victims system resources and network resources for mining without the victims permission, thereby obtaining cryptocurrency. This type of malicious code named malicious mining code has brought considerable influence and harm to society, enterprises and users. The mining code always conceals the fact that it consumes computer resources in a way that is difficult for ordinary people to discover. This paper proposes a malicious mining code detection method based on feature fusion and machine learning. First, we analyze from static analysis methods and statistical analysis methods to extract multi-dimensional features. Then for multi-dimensional text features, feature vectors are extracted through the n-gram model and TF-IDF, and best feature vectors are selected through the classifier and we fuse these best feature vectors with other statistic features to train our detection model. Finally, automatic detection is performed based on the machine learning framework. The experimental results show that the recognition accuracy of our method can reach 98.0%, its f1 score reach 0.969, and the ROCs AUC reach 0.973

An efficient multi-scale waveform inversion method in Laplace-Fourier domain

Aiming at the problem that large computational resources and long computation time are required for the conventional Laplace-Fourier domain waveform inversion, an efficient multi-scale grid algorithm with variable computed area is proposed, and used in inversion modeling of the Marmousi and Overthrust model. This algorithm can choose a proper grid spacing automatically according to the different frequency, and adjust the depth of computing area according to the Laplace damping constant. This algorithm not only improves inversion efficiency significantly without the loss of inversion precision, but also improves the stability due to the decrease of grid number. Inversion results of the Marmousi and Overthrust model demonstrate the validity of the algorithm. In addition, the inversion results by the algorithm still can be approximate to the real model when low frequency information is missing. Key words: Laplace-Fourier domain, full waveform inversion, multi-scale grid, multi-scale computation, inversion precision, inversion efficienc