Radioisotope analysis and Digital Elevation Model (DEM) method were combined to examine sedimentation rates and associated sedimentary processes in the Yangtze River Estuary. The major depocenter is ...validated at the delta front sedimentary facies above the normal wave base (NWB), where accumulation exceeds erosion. This alternated sedimentation does not accommodate Pb-210 and Cs-137 measurement, although sedimentation rates of less than 0.2–5.0
cm
yr
−1 were recorded in the fine-grained (silty) sediments, which were interbedded with coarse-grained (sandy) sediments. However, historical DEM data provide more detailed information on sedimentation in the delta front facies, where accumulation is dominant in the sandy shoals (1.73–8.30
cm
yr
−1) and delta front slope (5.22
cm
yr
−1) facies. The DEM data also show that erosion (1.61–7.32
cm
yr
−1) dominates in the northern estuarine distributaries, and accumulation (3.01–4.97
cm
yr
−1) prevails in the southern ones, primarily owing to the superimposed runoff and ebb tidal currents. Pb-210 and Cs-137 measurements reveal sedimentation rate from 2.0
cm
yr
−1 to 6.3–6.6
cm
yr
−1 in the delta front slope facies, which progressively decreases to <0.8
cm
yr
−1 in the prodelta facies and is unmeasurable in the delta-shelf transition zone. DEM analysis detects minor erosion in the delta front slope and prodelta facies, although accumulation predominates there. The present sedimentological database will be useful for estuarine environmental assessment after the Three-Gorges Dam is completed in 2009.
Abstract
Comparing the prediction effects of traditional econometric algorithm model and deep learning algorithm model, taking regional GDP as an example, two prediction models of ARMA-ECM and ...LSTM-SVR are established for prediction, and the prediction results of different models are compared and analyzed. The results show that there are some deviations in the prediction results of the two models, but the prediction trends are the same. The prediction accuracy of LSTM-SVR model will decrease significantly with the reduction of time series data samples, while ARMA-ECM model is not so sensitive.
Abstract
Urbanization is a key part of achieving a overall well-off society in it’s decisive stage. Accurately predicting the developing trend of urbanization rate has important practical ...significance for relevant departments to make strategic decisions. This article uses Stata software and the relevant data released by Data Center of the National Bureau of Statistics, selects the urbanization rate time series data of 31 provinces of China from 1990 to 2019, establishes an ARIMA model of urbanization rate and time, and predicts 2020 to 2050. Trend of urbanization rate in various regions. The data shows that, exclude individual areas, the urbanization rate of each area can reach 72% in 2030 and over 90% in 2050. However, the development of urbanization rates among regions is uneven, and relevant departments should formulate a reasonable development strategy on the basis of sustainable and balanced development
The Yingjisu Sag was petroliferous for normal oil, condensate oil, reservoir bitumen and natural gases. Geochemical studies showed that natural gases in the Yingjisu Sag were a gas mixture consisting ...mainly of Cambrian pyrolysis gas, Jurassic condensate oil in well Yingnan 2 and normal oil in well Tadong 2, reflecting the characteristics of marine-phase gases and oils, while crude oils in well Longkou 1 demonstrated the characteristics of both marine and terrestrial oils, which were derived from lower algae and higher plants. Jurassic oils from wells Longkou 1 and Huayingcan 1 and Cambrian crude oils from well Tadong 2 were derived mainly from Cambrian-Lower Ordovician source rocks. Jurassic and Silurian reservoir bitumens from well Yingnan 2 were biodegradated, suggesting they are of marine and terrestrial origins. The bitumens have similar geochemical characteristics, which are correlated well with Ordovician crude oils from well Tadong 2 and Jurassic condensate oil from well Yingnan 2. Based on the characteristics of tectonic evolution in this area and the analysis of hydrocarbon accumulation, the constraints on the mixed source and hydrocarbon filling process in the Yingjisu Sag were brought forward.
Interstitial lung disease (ILD) is a specific form of chronic fibrosing interstitial pneumonia with various etiology. The severity and progression of ILD usually predict the poor outcomes of ILD. ...Otherwise, Krebs von den Lungen-6 (KL-6) is a potential immunological biomarker reflecting the severity and progression of ILD. This meta-analysis is to clarify the predictive value of elevated KL-6 levels in ILD.
EBSCO, PubMed, and Cochrane were systematically searched for articles exploring the prognosis of ILD published between January 1980 and April 2021. The Weighted Mean Difference (WMD) and 95% Confidence Interval (CI) were computed as the effect sizes for comparisons between groups. For the relationship between adverse outcome and elevated KL-6 concentration, Hazard Ratio (HR), and its 95%CI were used to estimate the risk factor of ILD.
Our result showed that ILD patients in severe and progressive groups had higher KL-6 levels, and the KL-6 level of patients in the severe ILD was 703.41 (U/ml) than in mild ILD. The KL-6 level in progressive ILD group was 325.98 (U/ml) higher than that in the non-progressive ILD group. Secondly, the KL-6 level of patients in acute exacerbation (AE) of ILD was 545.44 (U/ml) higher than stable ILD. Lastly, the higher KL-6 level in ILD patients predicted poor outcomes. The KL-6 level in death of ILD was 383.53 (U/ml) higher than in survivors of ILD. The pooled HR (95%CI) about elevated KL-6 level predicting the mortality of ILD was 2.05 (1.50-2.78), and the HR (95%CI) for progression of ILD was 1.98 (1.07-3.67).
The elevated KL-6 level indicated more severe, more progressive, and predicted the higher mortality and poor outcomes of ILD.
Targeting translation factor proteins holds promise for developing innovative anti-tuberculosis drugs. During protein translation, many factors cause ribosomes to stall at messenger RNA (mRNA). To ...maintain protein homeostasis, bacteria have evolved various ribosome rescue mechanisms, including the predominant trans-translation process, to release stalled ribosomes and remove aberrant mRNAs. The rescue systems require the participation of translation elongation factor proteins (EFs) and are essential for bacterial physiology and reproduction. However, they disappear during eukaryotic evolution, which makes the essential proteins and translation elongation factors promising antimicrobial drug targets. Here, we review the structural and molecular mechanisms of the translation elongation factors EF-Tu, EF-Ts, and EF-G, which play essential roles in the normal translation and ribosome rescue mechanisms of
(Mtb). We also briefly describe the structure-based, computer-assisted study of anti-tuberculosis drugs.
As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important ...tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. Drones are aerial operations and shoot small targets. So the detection accuracy of drones is less. To address the problem of low accuracy of Unmanned Aerial Vehicles (UAVs) in detecting small targets, we designed a more suitable algorithm for UAV detection and called GBS-YOLOv5. It was an improvement on the original YOLOv5 model. Firstly, in the default model, there was a problem of serious loss of small target information and insufficient utilization of shallow feature information as the depth of the feature extraction network deepened. We designed the efficient spatio-temporal interaction module to replace the residual network structure in the original network. The role of this module was to increase the network depth for feature extraction. Then, we added the spatial pyramid convolution module on top of YOLOv5. Its function was to mine small target information and act as a detection head for small size targets. Finally, to better preserve the detailed information of small targets in the shallow features, we proposed the shallow bottleneck. And the introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3Formula: see text and the mAP@0.5:0.95 was 20.0Formula: see text. Compared to the default YOLOv5 algorithm was boosted by 4.0Formula: see text and 3.5Formula: see text, respectively.
The cotter pin (CP) is a vital fastener for the catenary support components (CSCs) of high-speed electrified railways. Due to the vibration and excitation caused by the passing of railway vehicles, ...some CPs may be broken or fallen off over time, which poses a significant safety hazard to the railway systems. Currently, the CP defect detection is primarily conducted by humans, which is inefficient and inconsistent. Therefore, there is an urgent need for automatic CP defect detection to ensure railway safety. However, this task is very challenging as it requires covering hundreds or thousands of miles in limited times when the railway stops running. To this end, we first design a traffic track intelligent imaging device to capture catenary images at various angles at high speed. Then, inspired by the success of deep learning-based object detection, we develop a CP detection model based on an improved Faster R-CNN with a multi-scale region proposal network (MS-RPN) and propose the positive sample adaptive loss function (PSALF) to enhance detection accuracy. Finally, we propose a module to recognize the CP defect based on dilated convolution. The experimental results show that our method can effectively detect the CP defect in the catenary image, achieving 99.05 % precision and 98.40 % recall rate on CP defect detection. Furthermore, CP detection method and CP defect detection are significantly faster than baseline method, with FPS improvements of 2.76 and 24.67, respectively, thus making it more suitable for real-time applications in railway systems.
•A CP detection model based on an improved Faster R-CNN is proposed.•The proposed method achieves 99.05 % precision and 98.40 % recall rate on CP defect detection.•The proposed method is more accurate, faster, and robust than the existing methods.
PDF
