Effects of alkali metal ion on imprinting GRIN microstructure in GeS2-Ga2S3-MCl (M=Na,K, Cs) glasses for visible to mid-infrared microgratings

Gradient refractive index (GRIN) micro-optics present unique opportunities for control of the chromatic properties, new degrees of freedom for optical design as well as the potential for use in new optical system applications. GRIN microgratings were imprinted in GeS₂-Ga₂S₃-MCl (M = Na, K, Cs) chalcohalide glasses by microthermal poling, and the effects of the type and concentration of alkali cations on their performance were investigated. Two effective imprinting formation regions of the GRIN microstructure based on the poling saturation voltage (U_s) and glass composition are observed at fixed poling time and temperature. The U_s increases from 140 to 750 and 2600 V in accordance with the activation energy (E_a) of alkali ions (Na⁺ to K⁺ and Cs⁺) increasing from 45.15 to 58.62 and 92.58 kJ/mol for studied samples. The saturated numbers of diffraction order (N_s) of the gratings in these samples are 7, 9 and 6, respectively, the highest number being provided by the K⁺-containing sample. This is in accordance with imprinting-induced phase differences (0.14λ, 0.19λ and 0.09λ) measured in the fabricated samples containing Na⁺, K⁺ and Cs⁺ ions. Furthermore, the U_s of samples decreases from 1500 to 300 V with four concentrations of K⁺ from 10 to 30%, associated with their E_a of K⁺ decreasing from 69.62 to 53.46 kJ/mol, while N_s increases from 8 to 14, which is attributed to the increase of the phase difference in the GRIN structures. The controllable GRIN microstructures are realized by adjusting the type and concentration of alkali cations in chalcohalide glasses, which is expected to drive the design of broadband GRIN microgratings.     

Prediction of rockhead using a hybrid N-XGBoost machine learning framework

The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e. borehole drilling) could provide local engineering geological information, the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved. With the development of computer science, machine learning (ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically. However, few studies have been reported on the adoption of ML models for the prediction of the rockhead position. In this paper, we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information. The framework of the natural gradient boosting (NGBoost) algorithm combined with the extreme gradient boosting (XGBoost) is used as the basic learner. The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree (GBRT), the light gradient boosting machine (LightGBM), the multivariate linear regression (MLR), the artificial neural network (ANN), and the support vector machine (SVM). The results demonstrate that the XGBoost algorithm, the core algorithm of the probabilistic N-XGBoost model, outperformed the other conventional ML models with a coefficient of determination (R²) of 0.89 and a root mean squared error (RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data. The probabilistic N-XGBoost model not only achieved a higher prediction accuracy, but also provided a predictive estimation of the uncertainty. Thus, the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.     

Numerical study of flow distribution uniformity for the optimization of gradient porosity configuration of porous copper fiber sintered felt for hydrogen production through methanol steam reforming micro-reactor

A macroscopic numerical method is proposed to study the flow distribution uniformity of a novel porous copper fiber sintered felt (PCFSF), which has gradient porosities and was developed as the methanol steam reforming micro-reactor catalyst support for hydrogen production for fuel cell applications. The macroscopic porous media developed by the ANSYS/FLUENT software is used to represent the PCFSF. Our results indicate that the gradient porosity can reshape the flow distribution of PCFSFs greatly, thus producing significant influence on their performance. It is further revealed that, for a PCFSF with a determined gradient porosity configuration but different reactant feed directions, the velocity uniformity can be used as a quantitative criterion to evaluate the performance of hydrogen production. Furthermore, new gradient PCFSFs are produced according to the flow distribution of original gradient PCFSFs. The preliminary experimental results of the new gradient PCFSFs of 0.8-0.9-0.7 and 0.7-0.9-0.8 exhibit better methanol conversion and H₂ flow rate. This indicates that the numerical method can be used for the optimization of PCFSFs' gradient porosity configuration, which consists of the shape and position of the interfaces between different porosity portions, the number of interfaces and the porosity distribution in different portions.     

Microstructure and interface-adhesion of thermally sprayed continuous gradient elastic modulus FeCrAl-ceramic coatings

The bond strength between thermally sprayed metal bond-coats and ceramic top-coats is a key factor in determining their service life. However, most studies focus on interface modifications. In this research, based on FeCrAl bond-coats prepared by arc spraying, top-coats (Al₂O₃-40 wt% TiO₂) were prepared by plasma spraying, and heat treatment was carried out in a hypoxic atmosphere. Continuous gradient elastic modulus FeCrAl-ceramic coatings were successfully prepared, and the microstructural and mechanical properties from the substrate to the top-coats were systematically investigated. The Al₂O₃ content gradually decreased from the top-coats to the substrate, forming continuous gradient elastic modulus FeCrAl-ceramic coatings. The oxide formed during the heat treatment filled the defects in the bond-coats and greatly improved the mechanical properties of the coating. The bonding strength of the continuous gradient elastic modulus coating was 21.7% greater than that of the as-received coating.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.     

Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels

Having accurate information about the hydrogen solubility in hydrocarbon fuels and feedstocks is very important in petroleum refineries and coal processing plants. In the present work, extreme gradient boosting (XGBoost), multi-layer perceptron (MLP) trained with Levenberg–Marquardt (LM) algorithm, adaptive boosting support vector regression (AdaBoost?SVR), and a memory-efficient gradient boosting tree system on adaptive compact distributions (LiteMORT) as four novel machine learning methods were used for estimating the hydrogen solubility in hydrocarbon fuels. To achieve this goal, a database containing 445 experimental data of hydrogen solubilities in 17 various hydrocarbon fuels/feedstocks was collected in wide-spread ranges of operating pressures and temperatures. These hydrocarbon fuels include petroleum fractions, refinery products, coal liquids, bitumen, and shale oil. Input parameters of the models are temperature and pressure along with density at 20 °C, molecular weight, and weight percentage of carbon (C) and hydrogen (H) of hydrocarbon fuels. XGBoost showed the highest accuracy compared to the other models with an overall mean absolute percent relative error of 1.41% and coefficient of determination (R²) of 0.9998. Also, seven equations of state (EOSs) were used to predict hydrogen solubilities in hydrocarbon fuels. The 2- and 3-parameter Soave-Redlich-Kwong EOS rendered the best estimates for hydrogen solubilities among the EOSs. Moreover, sensitivity analysis indicated that pressure owns the highest influence on hydrogen solubilities in hydrocarbon fuels and then temperature and hydrogen weight percent of the hydrocarbon fuels are ranked, respectively. Finally, Leverage approach results exhibited that the XGBoost model could be well trusted to estimate the hydrogen solubility in hydrocarbon fuels.     

电场作用下磺化聚苯乙烯微球在弹性体中梯度结构构建及性能研究

先采用硫酸对聚苯乙烯微球经过磺化得到带正电荷的磺化聚苯乙烯(s PS),将s PS分散在聚二甲基硅氧烷(PDMS)预聚体中,在高压直流电场下通过电泳作用形成梯度结构,再通过PDMS的热固化后将梯度结构固定下来。采用马尔文Zeta电位仪分析了s PS微球电荷,扫描电镜(SEM)对s PS以及s PS/PDMS梯度膜的结构进行表征,动态热机械分析仪测试了材料的动态力学行为和介电损耗。结果表明,电压强度为500 V/mm时,s PS能够在PDMS预聚体中通过电泳作用形成梯度结构,s PS总质量分数为20%时,s PS/PDMS梯度膜相比相同组成的均匀膜断裂伸长率提高了9. 41%,损耗因子在所测得温度范围(-70~200℃)均提高。在s PS的玻璃化转变温度附近,s PS/PDM S梯度膜比均匀膜损耗因子提高了30%。进一步对梯度结构膜力学行为及介电损耗增强效应进行了详细分析。     

Joint model of gradient magnitude and Gabor features via Spatio-Temporal slice

To form a high-performance video quality predictor, we developed a framework for full-reference (FR) video quality assessment that integrates Spatio-temporal slice analysis (STS) to create a high-performance predictor of video quality. However, both gradient and Gabor are spatial–temporal structural capturers used for the simultaneous extraction of both spatial and temporal features. In this paper, we proposed a novel VQA algorithm via a joint model of gradient magnitude and Gabor features (JMG) between the STS images of the reference videos and their distorted counterparts to assess the degradation of video quality effectively. Firstly, gradient magnitude and the Gabor filter were constructed to extract the spatiotemporal features of the video sequence. However, the two-feature model combined to predict the perceptual quality of frames. This new proposed VQA model is known as the horizontal and time STS (HT-JMG) model. To further investigate the influence of spatial dissimilarity, we combined the frame-by-frame spatial T-JMG(S) factor with the HT-JMG and propose another VQA model, called the time, horizontal, and vertical STS (THV-JMG) model. Finally, the results of the experiment showed that the proposed method has a strong correlation with subjective perception and is competitive with state-of-the-art full reference VQA models.     

水平受荷桩的截面优化设计

运用有限单元法和梯度法 ,尝试性地对端承桩进行变截面优化设计 ,编制了相应程序 ;并以一人工挖孔灌注桩为例 ,计算比较截面优化前后的内力、挠度。计算结果表明 ,变截面桩可节省混凝土材料约 45 %。     

上海滨江森林公园规划设计研究

“自然、生态、野趣、保护、创新、人性化”一直是城市郊野森林公园的建设主题。从一个工程项目实例——上海滨江森林公园一期工程入手,以生态保护、生态恢复为出发点,结合地景梯度、地景密度的应用,以及公园规划布局、公园主体植物群落的实践操作、湿地景观的营造等诸多方面,全面阐述了当今城市郊野森林公园规划设计的方向,并提出了在环保节能技术运用方面存在的诸多困难因素,还有待于设计方和建设方共同努力解决。