Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.     

草原石林：东北亚早白垩世花岗岩伸展穹隆和岩浆成矿爆发的地质遗迹及其形成过程

东北亚地区至少发育9个大型草原石林,属花岗岩石林的一种,是东北亚早白垩世巨量伸展和第四系冰川、风化作用的共同产物,主要特点是发育密集的近水平节理。本文通过整理岩浆岩专题填图和构造研究发现,这些石林的主体是早白垩世东北亚地区巨量伸展过程中形成众多的不对称花岗岩穹隆或同减薄岩体,岩体边部发育韧性剪切带,核部发布近水平岩浆流动面理,伸展和岩浆多期侵位导致与成矿密切相关的岩浆热液大量上涌,岩体抬升至地表,沿岩浆面理发育密集的近水平节理和部分垂直节理,后期沿这些构造面理发生的物理风化和化学作用(包括风蚀、冰川以及盐风化等)造就了千奇百怪的形态。草原石林是花岗岩地貌中的“另类”,不仅具有指示东北亚地区花岗岩形成时代(早白垩世)的特点,也是寻找晚中生代中国北方大规模成矿的良好标志体,而且风化形成的千姿百态的景色极具观赏价值,可以作为整个蒙古草原地区和大兴安岭南段地区特色的地学科普和生态旅游资源。     

Microenvironment-Sensitive Fluorescent Ligand Binds Ascaris Telomere Antiparallel G-Quadruplex DNA with Blue-Shift and Enhanced Emission

The development of small molecules that can selectively target G-quadruplex (G4) DNAs has drawn considerable attention due to their unique physiological and pathological functions. However, only a few molecules have been found to selectively bind a particular G4 DNA structure. We have developed a fluorescence ligand Q1 , a molecular scaffold with a carbazole–pyridine core bridged by a phenylboronic acid side chain, that acts as a selective ascaris telomere antiparallel G4 DNA ASC20 ligand with about 18 nm blue-shifted and enhanced fluorescence intensity. Photophysical properties revealed that Q1 was sensitive to the microenvironment and gave the best selectivity to ASC20 with an equilibrium binding constant K_a=6.04×10⁵ M⁻¹. Time-resolved fluorescence studies also demonstrated that Q1 showed a longer fluorescence lifetime in the presence of ASC20. The binding characteristics of Q1 with ASC20 were shown in detail in a fluorescent intercalator displacement (FID) assay, a 2-Ap titration experiment and by molecular docking. Ligand Q1 could adopt an appropriate pose at terminal G-quartets of ASC20 through multiple interactions including π–π stacking between aromatic rings; this led to strong fluorescence enhancement. In addition, a co-staining image showed that Q1 is mainly distributed in the cytoplasm. Accordingly, this work provides insights for the development of ligands that selectively targeting a specific G4 DNA structure.     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.     

A tool for mass transfer evaluation in packed-bed columns for chemical engineering students

This paper presents a Microsoft Excel tool to calculate liquid-gas mass transfer coefficients in packed towers to support numerical design activities in the courses of Unit Operations for Industrial Process and Sustainable Process Design for the Master’s degree in Chemical Engineering of the University of Naples Federico II (Italy).The Mass Transfer Solver Tool (MT Solver Tool) uses several available models to estimate, separately, the values of liquid and gas mass-transfer coefficients and the wet surface area for 144 random and structured packings of interest for absorption/stripping and distillation processes. In addition, a separate spreadsheet can be used in a user-defined mode, to evaluate the mass transfer coefficients with new packing types or to interpret experimental data when the geometrical and physical characteristics of the packing are known. Eventually, the tool is supplied with a data library, where packing geometry and model fitting parameters can be retrieved.The software is aimed to support students and educators in the Unit Operations for Industrial Process and Sustainable Process Design courses. In particular, this is meant to be an example on how the accuracy of design algorithms adopted in unit operation processes is affected by the use of the underpinning correlations for mass transfer rate or pressure drops. Besides, this is aimed to encourage comparison of different correlations when exact field data are not available. Besides, chemical engineers and researchers interested in packed columns design and modelling data may also benefit from the utilization of the software. The MT Solver Tool was introduced to students in a dedicated tutorial lesson after lecturers on packed column design algorithms for distillation, absorption and stripping. Most of the students of the course participated to a group training aimed to simulate the design of an absorption column supported by the MT Solver Tool providing feedback on its application.After the training, an anonymous survey was proposed to the students to monitor the approval rating of the proposed activity and the use of the MT Solver Tool software to support numerical calculations.     

基于改进的Random Subspace的客户投诉分类方法

电信业的客户投诉不断增多而又亟待高效处理。针对电信客户投诉数据的特点，提出了一种面向高维数据的改进的集成学习分类方法。该方法综合考虑客户投诉中的文本信息及客户通讯状态信息，基于Random Subspace方法，以支持向量机（Support Vector Machine，SVM）为基分类器，采用证据推理（Evidential Reasoning，ER）规则为一种新的集成策略，构造分类模型对电信客户投诉进行分类。所提模型和方法在某电信公司客户投诉数据上进行了验证，实验结果显示该方法能够显著提高客户投诉分类的准确率和投诉处理效率。     

一种基于深度森林的恶意代码分类方法

针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.     

Physicochemical structure characteristics and intrinsic reactivity of demineralized coal char rapidly pyrolyzed at elevated pressure

This study aims to investigate the effect of pyrolysis pressure on the physical and chemical structure characteristics and reactivity of subbituminous demineralized coal char. The pyrolysis experiments were studied under different pressures using a pressurized drop tube reactor (PDTR). Structural properties of coal chars were investigated by the application of scanning electron microscopy (SEM), nitrogen adsorption analyzer, automatic mercury porosimeter, and Raman spectroscopy, respectively. The Random Pore Model was used to determine kinetic parameters and intrinsic reactivity of chars. The specific pore volume of chars pyrolyzed at 900–1000 °C increased first and then decreased with pyrolysis pressure increasing, and the maximum value of the specific pore volume of chars occurred at 1.0 MPa. The degree of graphitization of chars deepened with the increase of temperature or pressure. Intrinsic activation energy of char-O₂ was within the range of 126–134 kJ/mol. The intrinsic reactivity of char-O₂ reaction showed strong correlation the Raman parameters with the change of pyrolysis conditions, and it suggested that the intrinsic reactivity of char-O₂ reaction was mainly affected by aromatic ring structures rather than pore structures.