针对基山砂体沉积微相认识不清、控砂机制不明确的问题，在高分辨率层序地层格架划分的基础上，综合利用岩心、粒度、测井以及地震等资料，对惠民凹陷沙三中亚段的沉积特征进行识别，分析不同层序时期的沉积相及砂体展布情况，探讨砂体展布的控制因素。研究结果表明：1）沙三中亚段基山砂体可以划分为7个四级层序和21个五级层序。2）研究区以三角洲前缘沉积亚相为主，发育有水下分流河道、河口坝、席状砂、滑塌浊积等沉积微相。不同层序时期的沉积相及砂体展布情况不同，其中Psq7～Psq4层序时期，三角洲不断向南以及向东延伸，水下分流河道砂体逐渐增厚，浊积岩的分布范围逐步扩大。3）在高分辨率层序地层格架的控制下，砂体具有多种叠置样式——靠近物源发育坝上河砂体叠置，远离物源发育孤立河道砂体叠置，浊积砂体在垂向上多套叠置、平面上迭合连片。4）砂体展布受构造调节带及坡折带的控制，构造调节带控制着入盆水系的方向；断裂坡折带控制着滑塌浊积岩的分布，使其主要分布在低水位时期的斜坡低部位。
To avoid redundant data collection,break down data silos,and achieve cross-system information interoperability,data sharing have been implemented among many universities through technical means including data interfaces and data exchange protocols.This paper analyzes the existing problems in current sharing models,including insufficient data openness capabilities and uncoordinated data request processes.An architectural design for a data-sharing platform was carried out and a four-pronged approach was proposed to enhance data utility and security:constructing a data resource catalog,streamlining the data request workflow,implementing rigorous quality control mechanisms,and strengthening data security management systems.The purpose of these lies in improving data availability and security,standardizing data sharing procedures and ultimately improving the over-all sharing capacity of university data platforms.
Aerodynamic drag(FA)of high-speed trains increases significantly with the elevated speed,but a con-tradiction between space utilization and drag reduction efficiency exists in traditional streamlined design.Taking the CR400BF high-speed train(HST)as the research subject,the original streamlined smooth structure on the roof section was replaced with convex hull type non-smooth structure.The aerodynamic drag reduction perfor-mance was optimized using the response surface method(RSM).Through analysis of the impact of different structural parameters on FA,the key parameters influencing FA were determined.RSM was employed to con-struct a mathematical model between FA and the key parameters for the convex hull type roof structure of the HST.An accurate and reliable response surface model was obtained via designing experimental schemes,collect-ing data,and performing regression analysis.On this basis,an optimization algorithm was applied to enhance the aerodynamic drag reduction performance of the convex hull type roof structure.The results demonstrate that a multi-parameter collaborative optimization model for the convex hull structure was established based on RSM,improving optimization efficiency by 95%compared to traditional methods.The optimal parameter combination(convex radius:109.6 mm,convex height:580.1 mm,convex array spacing:347.8 mm)reduces aerodynamic drag by 25.1%,with a simulation-experimental error<3%.The convex hull structure achieves drag reduction by delaying boundary layer separation and reconstructing the wake vortex structure,resulting in an 18.1%reduc-tion in trailing vortex length.
As a chronic,systemic autoimmune disease,rheumatoid arthritis(RA)brings serious threats to patients'health and imposes a heavy burden on individuals and society.Despite the large variety of drugs for treating RA,problems still exist,such as poor therapeutic effects and strong side effects.The novel hydrogel drug delivery system with unique structure and properties such as good biocompatibility,large drug loading capacity,flexible drug loading methods,improved drug tar-geting,and controlled drug release,holds great potential to be widely used in the delivery of RA treatment drugs and exhibits a positive promoting effect on the treatment of RA.This article focuses on the research of novel intelligent hydrogels for intra-articular injection and their application in the treatment of RA,aiming to provide reference and guidance for the continuous research of hydrogel drug delivery systems for RA treatment.
In the present work,a dataset was formed by collecting 136 measured samples of the failure height of overlying rock strata to accurately predict the failure height of overlying rock strata in deep-thick coal seams under thick loose layers.The main influencing factors selected for the failure height of overlying rock strata include min-ing height,mining depth,the inclined length of working face,the proportion coefficient of hard rock,and the dip angle of coal seam.The weights of each factor were obtained through grey relational analysis and importance.Using the CS-RF optimized prediction model,the determination coefficient(R2),mean absolute error(MAE),mean square error(MSE),and out-of-bag error(OOB)were employed as evaluation indicators for the regres-sion model to determine the optimal hyperparameters of the RF model.A comparative analysis of the prediction performance of the CS-RF and RF prediction models was conducted using 10-fold cross-validation.The results show that compared to the RF prediction model,the CS-RF prediction model achieves an improvement of 0.31%in R2 on the training set,with reductions of 2.37%and 9.50%in MAE and MSE,respectively.On the cross-validation set,the CS-RF model improves R2 by 0.55%,with reductions of 90.16%and 5.23%in MAE and MSE,respectively.On the test set,the CS-RF model improves R2 by 0.43%,with reductions of 6.78%and 6.47%in MAE and MSE,respectively.The violin plot of relative errors on the training set has the largest width,indicating that the relative error data are the most concentrated in this region,and the mean relative error is the smallest.The mean relative error between the predicted and measured values of the failure height of overly-ing rock strata for the CS-RF prediction model is less than the corresponding value for the RF model.The aver-age predicted relative error for the failure height of overlying rock strata is 6.85%across 25 test working faces,demonstrating that the CS algorithm improves the accuracy of the RF model.This provides important insights for the training and prediction of failure height of overlying rock strata models.
