The effect of sodium sulfate crystallization on soil freezing temperature, and the changes in salt expansion and frost heave pressure with temperature were analyzed experimentally and using ...crystallization theory. The Pitzer ion model was used to find the supersaturation ratio of a sodium sulfate solution in soil. The relationship between the initial supersaturation ratio and temperature was obtained, which provided criteria for the presence of salt crystals over the entire temperature range. Experiments were carried out to determine the effect of cooling the sodium sulfate saline soil as well as the location of salt crystallization. Analysis of the location and shape change of salt crystals based on the experiments clarified the interaction between salt crystallization and ice-water phase change. A formula relating the surface free energy of a crystal to its critical nucleation radius was deduced based on the assumption of homogeneous nucleation. Finally, equations for the salt expansion force and frost heave pressure were obtained and their rationalizations were provided.
•The initial supersaturated ratio of saline solution is discussed below 0°C.•The effect of salt crystals on the freezing point of soil is studied.•The critical nucleation radius of salt crystals is derived by physical-chemical method.•The relation between salt expansion pressure and supersaturated ratio is investigated.
Strong earthquakes can trigger mountain landslides, which can produce long-term effects on subsequent landslide activities. Therefore, understanding the spatiotemporal evolution characteristics of ...post-earthquake landslides is crucial for risk assessment of long-term geological hazards. In light of this, the current study aims to analyze the spatiotemporal evolution law, decay modes, and susceptibility changes of post-earthquake landslides, using the post-hazard condition of the Jiuzhaigou earthquake in 2017 as the reference for research. An integrated monitoring technology known as "space-sky-ground" was utilized to create a comprehensive multi-temporal dataset of post-earthquake typical landslide disasters. The spatiotemporal distribution characteristics of landslides were then analyzed to construct a quantitative predictive model for landslide spatiotemporal evolution and to deduce the long-term spatiotemporal evolution law of landslide disasters in seismic areas. Following this, the typical influencing factors were introduced to construct a coupled post-earthquake landslide susceptibility model (CF + LR), summarizing the spatiotemporal evolution patterns of landslide susceptibility. The results show that post-earthquake landslides gradually transitioned from large and medium-sized to small-scale slides, exhibiting an overall power-law decay pattern, with an estimated recovery to pre-earthquake levels projected by 2033. Additionally, the CF + LR coupled model demonstrated higher accuracy and reliability in identifying the high and extremely high susceptibility areas, with the susceptibility zones showing an evolution trend towards lower altitudes, gentler slopes, windward slopes, and closer proximity to channels. This study provides important guidance for the staging, zoning, and long-term risk assessment and prevention of post-earthquake landslides.
Shared decision making (SDM) improves the health status of patients with chronic diseases, especially in the condition of poly-medicated patients. This study aims to find the factors associated with ...participation of patients with chronic diseases in SDM on medication.
A total of 1,196 patients with chronic diseases were selected in Hubei Province of China using cluster sampling methods. The random forest method was applied to rank the importance of independent variables by Mean Decrease Gini and out-of- bag (OOB) curve. Multivariate logistic regression was used to explore the independent variables' effect direction and relative hazard.
In this study, 5.18% of patients used patient-directed decision making (PDM, a decision-making model led by patients), 37.79% of patients used SDM (a collaborative decision-making model by patients and doctors), and 57.02% of patients used doctor-directed decision making (DDM, or paternalistic decision making, a decision-making model led by doctors). The random forest analysis demonstrated that the top 5 important factors were age, education, exercise, disease course, and medication knowledge. The OOB curve showed that the error rate reached minimum when top 5 variables in importance ranking composed an optimal variable combination. In multivariate logistic regression, we chose SDM as a reference group, and identified medication knowledge (OR = 2.737, 95%CI = 1.524 ~ 4.916) as the influencing factor between PDM and SDM. Meanwhile, the influencing factors between DDM and SDM were age (OR = 0.636, 95%CI = 0.439 ~ 0.921), education (OR = 1.536, 95%CI = 1.122 ~ 2.103), exercise (OR = 1.443, 95%CI = 1.109 ~ 1.877), disease course (OR = 0.750, 95%CI = 0.584 ~ 0.964), and medication knowledge (OR = 1.446, 95%CI = 1.120 ~ 1.867).
Most Chinese patients with chronic diseases used DDM during their medication decision-making, and some patients used PDM and SDM. The participation in SDM should be taken seriously among elderly patients with lower education levels. The SDM promotion should focus on transformation of patients' traditional perception and enhance their medication knowledge.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods ...for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering.
The mechanical behavior of mudstones is significantly affected by their mesostructure. This study investigates the damage evolution of red mudstone mesostructures under uniaxial compression through ...U-Net image segmentation, mesoscopic representative elementary area (mREA), meso-element equivalent method, and linearly superimposed model. Results show that the mREAs can be simplified as a binary structure comprising nonclayey minerals and a porous matrix. The inclusion-matrix interfaces constituted by the dominant inclusions, such as large inclusions and a series of inclusions with specific arrangements, are the weakest regions at the postpeak stage. The shear failure of these interfaces leads to general shear failure with a penetrating principal fracture or local shear with several nonpenetrating local fractures in the mREAs. The damage evolution and failure mode are sensitive to the inclusion geometric features, e.g., inclination, grain size, and spacing. However, the uniaxial compressive strength (UCS) is mainly determined by the included angle between the dominant inclusion inclination and the loading direction. With consistent composition, an increase in the included angle from 0 to 90° results in an increase in the UCS of the muddy facies and a decrease in the sandy facies, with the UCS of the sandy facies being more sensitive to changes in the included angle. Our findings indicate that the structural failure of red mudstones from mesoscopic to macroscopic is driven by the accumulation and expansion of shear failure at multiple scales due to the presence of hard–soft interfaces. This study enhances our understanding of the structural failure mechanism of mudstones and serves as a practical reference for the mesomechanical testing of clay rocks.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The paper presents an intelligent real-time slope surface deformation monitoring system based on binocular stereo-vision. To adapt the system to field slope monitoring, a design scheme of concentric ...marking point is proposed. Techniques including Zernike moment edge extraction, the least squares method, and k-means clustering are used to design a sub-pixel precision localization method for marker images. This study is mostly focused on the tracking accuracy of objects in multi-frame images obtained from a binocular camera. For this purpose, the Upsampled Cross Correlation (UCC) sub-pixel template matching technique is employed to improve the spatial-temporal contextual (STC) target-tracking algorithm. As a result, the tracking accuracy is improved to the sub-pixel level while keeping the STC tracking algorithm at high speed. The performance of the proposed vision monitoring system has been well verified through laboratory tests.
Targeted proteomics is a convenient method determining enzyme expression levels, but a quantitative analysis of these proteomic data has not been fully explored yet. Here, we present and demonstrate ...a computational tool (principal component analysis of proteomics, PCAP) that uses quantitative targeted proteomics data to guide metabolic engineering and achieve higher production of target molecules from heterologous pathways. The method is based on the application of principal component analysis to a collection of proteomics and target molecule production data to pinpoint specific enzymes that need to have their expression level adjusted to maximize production. We illustrated the method on the heterologous mevalonate pathway in Escherichia coli that produces a wide range of isoprenoids and requires balanced pathway gene expression for high yields and titers. PCAP-guided engineering resulted in over a 40% improvement in the production of two valuable terpenes. PCAP could potentially be productively applied to other heterologous pathways as well.
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•We report quantitative analysis method for proteomics data using PCA.•Principal component analysis of proteomics (PCAP) can direct metabolic engineering.•PCAP provides information not apparent by the simple observation of proteomics data.•PCAP was successfully applied to improve the mevalonate pathway (MEV) in E. coli.•Limonene and bisabolene production increased about 40% using PCAP-based strategy.
This paper studies the limitations of binocular vision technology in monitoring accuracy. The factors affecting the surface displacement monitoring of the slope are analyzed mainly from system ...structure parameters and environment parameters. Based on the error analysis theory, the functional relationship between the structure parameters and the monitoring error is studied. The error distribution curve is obtained through laboratory testing and sensitivity analysis, and parameter selection criteria are proposed. Corresponding image optimization methods are designed according to the error distribution curve of the environment parameters, and a large number of tests proved that the methods effectively improved the measurement accuracy of slope deformation monitoring. Finally, the reliability and accuracy of the proposed system and method are verified by displacement measurement of a slope on site.
Characterizing the surface deformation during the inter-survey period could assist in understanding rock mass progressive failure processes. Moreover, 3D reconstruction of rock mass surface is a ...crucial step in surface deformation detection. This study presents a method to reconstruct the rock mass surface at close range in a fast way using the improved structure from motion—multi view stereo (SfM) algorithm for surface deformation detection. To adapt the unique feature of rock mass surface, the AKAZE algorithm with the best performance in rock mass feature detection is introduced to improve SfM. The surface reconstructing procedure mainly consists of image acquisition, feature point detection, sparse reconstruction, and dense reconstruction. Hereafter, the proposed method was verified by three experiments. Experiment 1 showed that this method effectively reconstructed the rock mass model. Experiment 2 proved the advanced accuracy of the improved SfM compared with the traditional one in reconstructing the rock mass surface. Eventually, in Experiment 3, the surface deformation of rock mass was quantified through reconstructing images before and after the disturbance. All results have shown that the proposed method could provide reliable information in rock mass surface reconstruction and deformation detection.
Bus Rapid Transit (BRT) has become an increasing source of concern for public transportation of modern cities. Traditional contact sensing techniques during the process of health monitoring of BRT ...viaducts cannot overcome the deficiency that the normal free-flow of traffic would be blocked. Advances in computer vision technology provide a new line of thought for solving this problem. In this study, a high-speed target-free vision-based sensor is proposed to measure the vibration of structures without interrupting traffic. An improved keypoints matching algorithm based on consensus-based matching and tracking (CMT) object tracking algorithm is adopted and further developed together with oriented brief (ORB) keypoints detection algorithm for practicable and effective tracking of objects. Moreover, by synthesizing the existing scaling factor calculation methods, more rational approaches to reducing errors are implemented. The performance of the vision-based sensor is evaluated through a series of laboratory tests. Experimental tests with different target types, frequencies, amplitudes and motion patterns are conducted. The performance of the method is satisfactory, which indicates that the vision sensor can extract accurate structure vibration signals by tracking either artificial or natural targets. Field tests further demonstrate that the vision sensor is both practicable and reliable.
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