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Taking loess as the research object, the influence of cement content on mechanical properties of loess was studied. Firstly, the basic physical parameters of loess were obtained through laboratory physical index test, and improved by using Portland cement. Secondly, under different incorporation ratios (3%, 5%, 7%, 9%), heavy compaction test and liquid-plastic limit test were carried out respectively to obtain mechanical parameters of cement-improved loess. Finally, the shear strength parameters and compressive strength of the improved soil samples are obtained by unconsolidation undrained triaxial shear test and unconfined compressive strength test. The results show that the liquid limit, plastic limit and plastic index of loess improved by adding cement increase compared with the original soil sample, indicating that the binding force between soil particles is improved and the plasticity is enhanced. The variation range of dry density and water content has no obvious relationship with the content of cement. At the same time, the compressibility of soil is reduced and the collapsibility of soil is eliminated. With the increase of age, the strength of cement-modified soil also increases. Through unconsolidated and undrained triaxial shear tests and unconfined compressive strength tests, the strength of loess improved significantly after adding cement, and the strength of soil-cement increased with the increase of cement content, and water stability was obviously improved.

The frequency stability of a passive rubidium atomic frequency standard (RAFS) largely depends on the performance of the microwave cavity used in the physics package. In this paper, a large-size slotted tube microwave cavity with an inner diameter of 40 mm, as well as the physics package based on this cavity, were designed and built. Theoretical calculations and experimental results showed that the magnetic lines inside this cavity were highly parallel to the quantization axis. The frequency discrimination slope and the shot noise of the physics package were measured. The results showed that the cavity could be used to realize an RAFS with frequency stability of .

Progresses have been made in the development of digitalized and miniaturized rubidium atomic frequency standard (RAFS). In the traditional RAFS circuits, the 6 840 MHz microwave signal is mixed with the 5.312 5 MHz signal generated by the frequency synthesizer to obtain 6 834.687 5 MHz microwave signal, which is used to excite rubidium atom transition. Early RAFS frequency synthesizer used a large number of discrete analog devices, and showed disadvantages such as low degree of digitization, complicated parameter optimization and large physical size. The direct digital synthesizer (DDS) scheme used currently generates the 5.312 5 MHz signal directly, but often needs to multiply the 10 MHz signal. It has the disadvantages of low spectral purity and high phase noise. This paper introduces a digital frequency synthesizer solution that generates the 5.312 5 MHz signal. The design eliminates the need for a 10 MHz multiplier circuit when using a DDS device. It has the advantages of high spectral purity, low phase noise and adjustable output frequency/phase.

INTRODUCTION:Non-intubated anesthesia (NIA) has been proposed for video-assisted thoracoscopic surgery (VATS), although how the benefit-to-risk of NIA compares to that of intubated general anesthesia (IGA) for certain types of patients remains unclear. Therefore, the aim of the present meta-analysis was to understand whether NIA or IGA may be more beneficial for patients undergoing VATS. METHODS:A systematic search of Cochrane Library, Pubmed and Embase databases from 1968 to April 2019 was performed using predefined criteria. Studies comparing the effects of NIA or IGA for adult VATS patients were considered. The primary outcome measure was hospital stay. Pooled data were meta-analyzed using a random-effects model to determine the standard mean difference (SMD) with 95% confidence intervals (CI). RESULTS AND DISCUSSION:Twenty-eight studies with 2929 patients were included. The median age of participants was 56.8 years (range 21.9-76.4) and 1802 (61.5%) were male. Compared to IGA, NIA was associated with shorter hospital stay (SMD -0.57 days, 95%CI -0.78 to -0.36), lower estimated cost for hospitalization (SMD -2.83 US, 95% CI -4.33 to -1.34), shorter chest tube duration (SMD -0.32 days, 95% CI -0.47 to -0.17), and shorter postoperative fasting time (SMD, -2.76 days; 95% CI -2.98 to -2.54). NIA patients showed higher levels of total lymphocytes and natural killer cells and higher T helper/T suppressor cell ratio, but lower levels of interleukin (IL)-6, IL-8 and C-reactive protein (CRP). Moreover, NIA patients showed lower levels of fibrinogen, cortisol, procalcitonin and epinephrine. CONCLUSIONS:NIA enhances the recovery from VATS through attenuation of stress and inflammatory responses and stimulation of cellular immune function.

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention. [ABSTRACT FROM AUTHOR]

Rock moisture, which is a hidden component of the terrestrial hydrological cycle, has received little attention. In this study, frequency domain reflectometry is used to monitor fluctuating rock water content (RWC) in a sandstone cave of the Yungang Grottoes, China. We identified two major cycles of rock moisture addition and depletion, one in summer affected by air vapour concentration and the other in winter caused by freezing–thawing. For the summer-time RWC, by using the long short-term memory (LSTM) network and the SHapley Additive exPlanations (SHAP) method, we find relative humidity, air temperature and wall temperature have contributions to rock moisture, and there is a good match between predicted and measured RWC using the three variables as model inputs. Moreover, by using summer-time vapour concentration and the difference between dew point temperature and wall temperature as input variables of the LSTM network, which belongs to physics-informed machine learning, the predicted RWC has a better agreement with the measured RWC, with increased Nash–Sutcliffe efficiency (NSE) and decreased mean absolute error (MAE) and root mean square error (RMSE). After identifying the causal factors of RWC fluctuations, we also identified the mechanism controlling the inter-day fluctuations of vapour condensation. The increased vapour concentration accompanying a precipitation event leads to transport of water vapour into rock pores, which is subsequently adsorbed onto the surface of rock pores and then condensed into liquid water. With the aid of the physics-informed deep learning model, this study increases understanding of sources of water in caves, which would contribute to future strategies of alleviating weathering in caves. [ABSTRACT FROM AUTHOR]

In practical engineering applications, there is a high demand for inverting parameters for various materials, and obtaining monitoring data can be costly. Traditional inverse methods often involve tedious computational processes, require significant computational effort, and exhibit slow convergence speeds. The recently proposed Physics-Informed Neural Network (PINN) has shown great potential in solving inverse problems. Therefore, in this paper, we propose a transfer learning-based coupling of the Smoothed Finite Element Method (S-FEM) and PINN methods for the inversion of parameters in elastic-plasticity problems. The aim is to improve the accuracy and efficiency of parameter inversion for different elastic-plastic materials with limited data. High-quality small datasets were synthesized using S-FEM and subsequently combined with PINN for pre-training purposes. The parameters of the pre-trained model were saved and used as the initial state for the PINN model in the inversion of new material parameters. The inversion performance of the coupling of S-FEM and PINN is compared with the coupling of the conventional Finite Element Method (FEM) and PINN on a small data set. Additionally, we compared the efficiency and accuracy of both the transfer learning-based and non-transfer learning-based methods of the coupling of S-FEM and PINN in the inversion of different material parameters. The results show that: (1) our method performs well on small datasets, with an inversion error of essentially less than 2%; (2) our approach outperforms the coupling of conventional FEM and PINN in terms of both computational accuracy and computational efficiency; and (3) our approach is at least twice as efficient as the coupling of S-FEM and PINN without transfer learning, while still maintaining accuracy. Our method is well-suited for the inversion of different material parameters using only small datasets. The use of transfer learning greatly improves computational efficiency, making our method an efficient and accurate solution for reducing computational cost and complexity in practical engineering applications. [ABSTRACT FROM AUTHOR]

Debris‐covered glacier mapping for monitoring glacier fluctuations is necessary to prevent geohazards caused by glaciers. Recently, deep learning‐based methods have been widely utilized for the identification of debris‐covered glaciers. Compared with conventional geospatial methods, deep learning‐based approaches have the advantage of large‐scale coverage and outstanding accuracies in identifying glaciers. However, there are two main difficulties when using deep learning‐based approaches: (1) object misclassification, which causes the misclassifications of the surrounding bedrock, snow, and mountain‐shadowed areas as glaciers; and (2) the inaccurate segmentation of boundaries. In addition, the sample sets for training deep learning models are generally insufficient, which leads to unsatisfactory results. To address the above problems, a deep learning‐based approach is proposed for accurate and automatic mapping of the complex debris‐covered glacier from remote sensing imagery for glacial hazard prevention. First, high‐quality remote sensing imagery is acquired and pre‐processed. Second, we adopt a weight‐optimized glacier semantic segmentation model to our approach. Then, the post‐processing procedures can be used to obtain the glacier outline. Finally, an accurate and automatic mapping of complex debris‐covered glaciers can be achieved. To demonstrate the effectiveness, our approach is applied to observe spatiotemporal changes in the glacier outline in the Nyenchen Tanglha region. Results show that most evaluation metrics of our model are higher than 90%, which demonstrates that it is reasonable for the glacier boundary extraction. We also verify that the proposed approach can be used to observe the changes in glaciers to prevent and control glacial hazards in high‐mountain regions. [ABSTRACT FROM AUTHOR]