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.     

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。     

Parallel Multipopulation Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control

This paper presents parallel multipopulation differential evolutionary particle swarm optimization (DEEPSO) for voltage and reactive power control (VQC). The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Since VQC is one of the online controls, speed‐up of computation is required. Moreover, there is still room for improvement in solution quality. This paper applies parallel multipopulation DEEPSO in order to speed up the calculation and improve solution quality. The proposed method is applied to IEEE 30, 57, and 118 bus systems. The results indicate that the proposed method can realize fast computation and minimize more active power losses than the conventional evolutionary computation techniques.     

绿色住区参数化优化设计平台的开发进展与展望

绿色住区设计是建设宜居城市和实现节能减排目标的重要手段与方法。文章首先回顾绿色住区参数化优化设计方法及平台概况，在此基础上，重点介绍平面生成、噪声计算、通风计算、可视度计算等新增功能和天空遮挡算法升级，以及广州市的工程应用，最后展望平台研发与应用的未来发展方向。     

论游戏的核心艺术评价标准

数字游戏常被认为是继文学、绘画、音乐等传统艺术之后的“第九艺术”。然而,长期以来,由于游戏的评价标准被商业体系左右,造成了游戏创作在思想层面的偏颇,所以设计师有必要建立一套针对数字游戏的艺术评价标准,以促进游戏的艺术创作和理论研究。从艺术思潮、艺术哲学和设计创新的角度出发,通过美学历史文献查阅、游戏艺术作品案例分析、艺术理论辨析等方法,就游戏的核心艺术因素展开论述。游戏的艺术标准应包括情感表现、艺术反思和创新性三个主要方面。其中,艺术情感是所有艺术的共性和核心;艺术反思是艺术理念的升华和艺术创作的动机;创新性则是艺术更替发展的内在动力。该标准独立于商业体系,将有助于促进和引导数字游戏在艺术层面的发展。     

The role of Persian-language word exercise games in improving spelling of students with dyslexia: Word exercise games in improving spelling

Children with dyslexia have reduced sensitivity to phonological sounds and words, and this deficiency in lexical processing causes many problems for them. Word exercise games based on phonological awareness have emphasized the mistakes of students with dyslexia, attempting to help children avoid these mistakes. This study was conducted to investigate the effect of Persian-language word exercise games on the spelling of students with dyslexia. The design of the present study was quasi-experimental, with a pretest and posttest of an experimental group and a control group. Participants were 30 students with dyslexia from second grade to fifth grade in elementary schools in Bojnord, who completed the spelling test as a pretest and posttest. The experimental group played eleven 40-min sessions of word exercise games. The results showed that the word exercise programme improved the spelling of the children with dyslexia. This suggests that basing training on the phonological mistakes of students with dyslexia and using the word exercise games can improve their spelling.