Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO₂-NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.     

Use of a smartphone-based Student Response System in large active-learning Chemical Engineering Thermodynamics classrooms

Smartphones are a promising tool as student response systems (SRS) for interactive teaching due to their widespread diffusion. Here, the main purpose is to assess the efficacy of smartphone-based SRS in large classroom settings of undergraduate Thermodynamics, as representative of engineering courses requiring high-level cognitive skills for problem solving. Four sets of multiple-choice questions were presented during the course. Overall, the results refer to 1055 students between control and SRS classes, each corresponding to a3 years period.One of the main results of this work is the strong linear correlation between the average questionnaire score and the final exam grade (R² = 0.91). A similar correlation, although with a lower value of R², is already found in the first questionnaire, thus showing the SRS high predictive power of class performance. The results of this study provide guidance for a quantitative use of smartphone-based SRS in teaching basic disciplines. The SRS monitoring capability allows early detection of struggling students, thus paving the way to personalized tutoring and improved student engagement in active learning practices. This approach is especially important in emergency situations, such as the SARS-Cov-2 pandemic, when distance learning is widely adopted, and remote interactive tools are highly needed.     

A practical development of engineering simulation-assisted educational AR environments

Engineering simulations have opened several gates for today’s chemical engineers. They are powerful tools to provide technical content as physics-based numerical solvers. Augmented reality (AR) and virtual reality (VR), on the other hand, are already underway to digitize environments in many fields. The combination of AR/VR environments and simulations in engineering education has been attracting widespread interest. Literature has demonstrated a massive amount of digital educational environments in several contexts as being complementary to conventional educational methods. Nevertheless, hosting technical content produced by engineering simulations with educational AR/VR is still challenging and requires expertise from multiple disciplines throughout the technical development. Present work provides a facile and agile methodology for low-cost hardware but content-wise rich AR software development. Inspired by the Covid-19 pandemic, a case study is developed to teach chemical-engineering concepts using a liquid-soap synthesis process. Accordingly, we assess and conclude the digital development process to guide inexperienced developers for the digitalization of teaching content. The present contribution serves as an example of the power of integrating AR/VR with traditional engineering simulations for educational purposes. The digital tool developed in this work is shared in the online version.     

基于PMU的电力系统监控体系结构优化设计

文中提出了一种利用有限数量的相量测量单元(PMU)和相量数据集中器(PDC)设计最优监控结构的方法。通过在大量的设定值场景下,使电力系统可观测性曲线的期望值最大化,同时使通信基础设施成本最小化,最终确定PMU和PDC的最佳位置。提出了一种非线性动态扩展卡尔曼滤波(EFK)状态观测器。这种状态观测器可以将暂态行为转换为由代数微分方程描述的广域电力系统,而无需非线性反演技术。最后以IEEE-5电力系统为例,说明了该方法的有效性。     

“感知与人机交互”教学实践与PBL模式探索

机器人工程是在国家新工科发展战略背景下为了适应机器人专业人才需求而新近创建的专业。本文依托东南大学机器人工程专业教学体系，介绍了“感知与人机交互”课程的教学实践，探索了PBL教学模式在本门课程中的应用。具体介绍了课程教学内容设置、PBL教学方案例设计及存在问题分析等，可为自动化类机器人工程专业的相关课程教学实践提供参考。     

电信专业学术英文写作模块化教学实践

学术英文写作是当今电子信息专业研究生必修的一门课程。然而传统的以教师为中心的灌输式教学已不能跟上网络迅猛发展的趋势,尤其是当疫情来临的特殊形势下。此外,现有的学术英文写作教学研究仅提供宏观的设计,缺乏具体的实际可操作性。为此,本文以引言写作逻辑为教学模块案例,探索了如何充分利用网络资源的优势,开展以学生为中心的课前课中课下有机融合的灵活的教学设计。所探索的引言写作技巧和详实的教学设计,为模块化网络教学提供一个切实可行的参考方案。     

An optimal configuration for hybrid SOFC,gas turbine,and Proton Exchange Membrane Electrolyzer using a developed Aquila Optimizer

In this research, a technical, economic and environmental analysis has been proposed to a Hybrid Solid Oxide Fuel Cell (SOFC) system-based hybrid system including biomass, gas turbine, and Proton Exchange Membrane Electrolyzer. A multi-objective optimization technique has been utilized to improve the overall product cost and the exergy effectiveness based on a developed version of Aquila Optimizer (DAO). The main idea of using the developed version is to improve the accuracy and the precision of the original Aquila optimizer. The system is then authenticated in terms of energy/exergy effectiveness, and energy-economic efficiency. The achievements indicate that employing the optimization algorithm for different configurations provided satisfying results for the system.