Advanced Geological Prediction

Due to the particularity of the tunnel project, it is difficult to find out the exact geological conditions of the tunnel body during the survey stage. Once it encounters unfavorable geological bodies such as faults, fracture zones, and karst, it will bring great challenges to the construction and will easily cause major problems, economic losses, and casualties. Therefore, it is necessary to carry out geological forecast work in the tunnel construction process, which is of great significance for tunnel safety construction and avoiding major disaster accident losses. This lecture mainly introduces the commonly used methods of geological forecast in tunnel construction, the design principles, and contents of geological forecast and combines typical cases to show the implementation process of comprehensive geological forecast. Finally, the development direction of geological forecast theory, method, and technology is carried out. Prospects provide a useful reference for promoting the development of geological forecast of tunnels

MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization

Video summarization intends to produce a concise video summary by effectively capturing and combining the most informative parts of the whole content. Existing approaches for video summarization regard the task as a frame-wise keyframe selection problem and generally construct the frame-wise representation by combining the long-range temporal dependency with the unimodal or bimodal information. However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content. Thus, it is critical to construct a more powerful and robust frame-wise representation and predict the frame-level importance score in a fair and comprehensive manner. To tackle the above issues, we propose a multimodal hierarchical shot-aware convolutional network, denoted as MHSCNet, to enhance the frame-wise representation via combining the comprehensive available multimodal information. Specifically, we design a hierarchical ShotConv network to incorporate the adaptive shot-aware frame-level representation by considering the short-range and long-range temporal dependency. Based on the learned shot-aware representations, MHSCNet can predict the frame-level importance score in the local and global view of the video. Extensive experiments on two standard video summarization datasets demonstrate that our proposed method consistently outperforms state-of-the-art baselines. Source code will be made publicly available

Simulation and analysis of microring electric field sensor based on a lithium niobate-on-insulator

With the increasing sensitivity and accuracy of contemporary high-performance electronic information systems to electromagnetic energy, they are also very vulnerable to be damaged by high-energy electromagnetic fields. In this work, an all-dielectric electromagnetic field sensor is proposed based on a microring resonator structure. The sensor is designed to work at 35 GHz RF field using a lithium niobate-on-insulator (LNOI) material system. The 2.5-D variational finite difference time domain (varFDTD) and finite difference eigenmode (FDE) methods are utilized to analyze the single-mode condition, bending loss, as well as the transmission loss to achieve optimized waveguide dimensions. In order to obtain higher sensitivity, the quality factor (Q-factor) of the microring resonator is optimized to be 106 with the total ring circumference of 3766.59 μm. The lithium niobate layer is adopted in z-cut direction to utilize TM mode in the proposed all-dielectric electric field sensor, and with the help of the periodically poled lithium niobate (PPLN) technology, the electro-optic (EO) tunability of the device is enhanced to 48 pm·μm/V

Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism

With the propagation of cyberbullying in social networks as a trending subject, cyberbullying detection has become a social problem that researchers are concerned about. Developing intelligent models and systems helps detect cyberbullying automatically. This work focuses on text-based cyberbullying detection because it is the commonly used information carrier in social networks and is the widely used feature in this regard studies. Motivated by the documented success of neural networks, we propose a complete model combining the bidirectional gated recurrent unit (Bi-GRU) and the self-attention mechanism. In detail, we introduce the design of a GRU cell and Bi-GRU’s advantage for learning the underlying relationships between words from both directions. Besides, we present the design of the self-attention mechanism and the benefit of this joining for achieving a greater performance of cyberbullying classification tasks. The proposed model could address the limitation of the vanishing and exploding gradient problems. We avoid using oversampling or downsampling on experimental data which could result in the overestimation of evaluation. We conduct a comparative assessment on two commonly used datasets, and the results show that our proposed method outperformed baselines in all evaluation metrics

Association between nocturnal sleep duration and midday napping and the incidence of sarcopenia in middle-aged and older adults: a 4-year longitudinal study

Background: Identifying treatment targets for sarcopenia is a public health concern. This study aimed to examine the association of nocturnal sleep duration and midday napping with the presence of sarcopenia in middle-aged and older adults, utilizing data from the China Health and Retirement Longitudinal Study in 2011 and 2015. Methods: A sum of 7,926 individuals (≥40 years) took part in this study. Sarcopenia was diagnosed according to the Asian Working Group for Sarcopenia. A self-reported questionnaire was used to collect data on nocturnal sleep duration and midday napping. Nocturnal sleep duration was categorized into three groups: short sleepers (8 h). Midday napping was coded as a dichotomous outcome (yes/no). Results: The incidence of sarcopenia was 5.3% during the 4-year follow-up. Short sleep duration (<6 h) was substantially linked to an increased incidence of sarcopenia (OR: 1.50, 95% CI: 1.21–1.87) as compared to nocturnal sleep length (6–8 h). Adults with midday napping had a lower risk of developing sarcopenia than non-nappers (OR: 0.78, 95% CI: 0.63–0.95). We further found that short sleepers with midday napping did not have a significantly higher risk of subsequent diagnosis of sarcopenia compared to normal sleepers without midday napping. Conclusion: These findings imply that short sleep duration in middle-aged and older persons is related to an increased incidence of sarcopenia. However, the adverse effect of short sleep duration on sarcopenia can be compensated by midday napping

An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine

The fast and accurate classification of surrounding rock mass is the basis for tunnel design and construction and has significant value in engineering applications. Therefore, this paper proposes a method for classifying and predicting surrounding rock mass based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). The premise of the research is that the data acquired from digital drilling technology are divided into a training group and a test group; the training group continuously optimizes the algorithm for the particle swarm optimization least squares support vector machine, and then the test group is used for verification. Moreover, the fast searching abilities of the particle swarm significantly accelerate the computational power and computational accuracy of the least squares support vector machine, making it a high-speed analog search tool. Taking the Jiaozhou Bay undersea tunnel in China as an example, a comparison of the evaluation results of PSO-LSSVM and QGA-RBF (quantum genetic algorithm-radical basis function neural network) is undertaken. The results show that PSO-LSSVM matches well with the field-measured surrounding rock grade. Applying the method in an engineering context proves that it has good self-learning abilities, even when the sample size is small and the prediction accuracy is high; as such, it meets the engineering requirements. The technique has the advantages of small sample prediction, pattern recognition, and nonlinear prediction

Synchronous Retrieval of Wheat Cab and LAI from UAV Remote Sensing: Application of the Optimized Estimation Inversion Framework

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training

Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework

UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing

Lactate dehydrogenase D serves as a novel biomarker for prognosis and immune infiltration in lung adenocarcinoma

Abstract Background Lung cancer is reported to be the leading cause of death in males and females, globally. Increasing evidence highlights the paramount importance of Lactate dehydrogenase D (LDHD) in different types of cancers, though it’s role in lung adenocarcinoma (LUAD) is still inadequately explored. In this study, we aimed to investigate and determine the relationship between LDHD and LUAD. Methods The collection of the samples was guided by The Cancer Genome Atlas (TCGA) datasets and Gene Expression Omnibus (GEO). To ascertain various aspects around LDHD function, we analyzed different expression genes (DEGs), functional enrichment, and protein–protein interaction (PPI) networks. The predictive values for LDHD were collectively determined using the Kaplan–Meier method, Cox regression analysis, and a nomogram. Evaluation of the immune infiltration analysis was completed using Estimate and ssGSEA. The prediction of the immunotherapy response was based on TIDE and IPS. The LDHD expression levels in LUAD were validated through Western blot, qPCR, and immunohistochemistry methods. Wound healing and transwell assays were also performed to illustrate the aggressive features in LUAD cell lines. Results The results showed that LDHD was generally downregulated in LUAD patients, with the low LDHD group presenting a decline in OS, DSS, and PFI. Enriched pathways, which include pyruvate metabolism, central carbon metabolism, and oxidative phosphorylation were observed through KEGG analysis. It was also noted that the expression of LDHD expression was inversely related to immune cell infiltration and typical checkpoints. The high LDHD group’s response to immunotherapy was remarkable, particularly in CTAL4 + /PD1- therapy. In vitro studies revealed that the overexpression of LDHD caused tumor migration and invasion to be suppressed. Conclusion In conclusion, our study revealed that LDHD might be an effective predictor of prognosis and immune filtration, possibly leading to better choices for immunotherapy