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Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023

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arXiv 2024年

作者： Smith, C. Estelle Shiekh, Kylee Cooreman, Hayden Rahman, Sharfi Zhu, Yifei Siam, Md Kamrul Ivanitskiy, Michael Ahmed, Ahmed M. Hallinan, Michael Grisak, Alexander Fierro, Gabe Colorado School of Mines Department of Computer Science GoldenCO United States Colorado School of Mines Department of Physics GoldenCO United States Colorado School of Mines Applied Mathematics & Statistics GoldenCO United States Colorado School of Mines Department of Geophysics GoldenCO United States Colorado School of Mines Chemical & Biological Engineering GoldenCO United States Colorado School of Mines Department of Mechanical Engineering GoldenCO United States

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. Yet there is limited prior research on computing students’ use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists;(2) describe computing students’ GenAI-related needs and concerns for their education and careers;and (3) provide recommendations for GenAI policy development and educational tooling. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education. © 2024, CC BY.

关键词： Adversarial machine learning

Tailoring Charge Donor-Acceptor Interaction in CsPbBr3 Perovskite Nanocrystals through Ligand Exchange

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arXiv 2024年

作者： Shah, Syed Abdul Basit Ghimire, Sushant Lesyuk, Rostyslav Diamanti, Maria Vittoria Lughi, Vanni Klinke, Christian Department of Engineering and Architecture Università degli studi di Trieste Trieste Italy Institute of Physics University of Rostock Rostock18059 Germany Munich80539 Germany Pidstryhach Institute for Applied Problems of Mechanics and Mathematics of NAS of Ukraine Lviv Ukraine Department of Chemistry Materials and Chemical Engineering "Giulio Natta" Politecnico di Milano Italy Department of Chemistry Swansea University Swansea United Kingdom Department Life Light & Matter University of Rostock Rostock18059 Germany

The surface ligands in colloidal metal halide perovskites influence not only their intrinsic optoelectronic properties but also their interaction with other materials and molecules. We explore donor-acceptor interactions of CsPbBr3 perovskite nanocrystals with TiO2 nanoparticles and nanotubes by replacing long-chain oleylamine ligands with short-chain butylamines. Through post-synthesis ligand exchange, we functionalize the nanocrystals with butylamine ligands while maintaining their intrinsic properties. In solution, butylamine-capped nanocrystals exhibit reduced photoluminescence intensity with increasing TiO2 concentration but without any change in photoluminescence lifetime. Intriguingly, the Stern-Volmer plot depicts different slopes at low and high TiO2 concentrations, suggesting mixed static and sphere-of-action quenching interactions. Oleylamine-capped nanocrystals in solution, on the other hand, show no interaction with TiO2, as indicated by consistent photoluminescence intensities and lifetimes before and after TiO2 addition. In films, both types exhibit decreased photoluminescence lifetime with TiO2, indicating enhanced donor-acceptor interactions, which are discussed in terms of trap state modification and electron transfer. TiO2 nanotubes enhance nonradiative recombination more in butylamine-capped CsPbBr3 perovskite nanocrystals, emphasizing the role of ligand chain length. © 2024, CC BY-NC-ND.

关键词： Quenching

Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems

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IFAC-PapersOnLine 2022年第6期55卷 673-678页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.

关键词： Gaussian Process Regression (GPR) Noise-Signal-Dependence Random Forest (RF) Feature Extraction Selection Fault Detection Diagnosis (FDD) Wind Energy Conversion (WEC) Systems

Past, present, and futures of non destructive techniques

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Past, present, and futures of non destructive techniques

IEEE Metrology for Aerospace

作者： Ciro Tortora Fabiana Graziano Veronica Vespini Sara Coppola Simonetta Grilli Pietro Ferraro Department of engineering of industrial product and chemical processes CNR-ISASI Institute of Applied Sciences and Intelligent Systems “E. Caianiello” University of Naples “Federico II” Napoli Italy Department of Mathematics and Physics CNR-ISASI Institute of Applied Sciences and Intelligent Systems “E. Caianiello” University of Campania “Luigi Vanvitelli” Caserta Italy CNR-ISASI Institute of Applied Sciences and Intelligent Systems “E. Caianiello” Pozzuoli Italy

Recent progress in manufacturing techniques has led to a wide range of new materials and components. Advance in material development has resulted in a wide range of new kind of materials as carbon composite and carbon fiber reinforced. The need, already consolidated, to investigate its properties (mechanical, electrical, thermal, etc.) arises and characterize the possible innovations by industrial sector such as aeronautics and aerospace, through the use of analysis techniques non-destructive (NDT). Common NDT techniques can be classified into seven major categories: visual (visual inspection), penetrating radiation (X-ray, and neutron imaging), magnetic-electrical (magnetic particle), mechanical vibration (ultrasonic, acoustic emission, and tapping), chemical/electrochemical (chemical spot testing), thermal (infrared thermography) and other optical methods (interferometry, holography, and shearography). More traditional non-destructive techniques (NDT) are not always effective as expected, and there is therefore a long-term need to use innovative NDT methods capable of provide reliable and quantitative information regarding damage status and integrity of materials. In the present work we focus on the development of specific tools of NDT for assuring the quality related to the fabrication of industrial components. The potential tool that will be take into consideration for inspection and detection of defects will mainly based on the following two main methods: ‐full-field optical techniques (such as profilometry, interferometry, sherography and thermography); ‐Laser ultrasound testing (LUT); The integrated and combinated use of different NDT (thermography, shearography and ultrasound) was necessary for a complete characterization of some types of composite materials.

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Fabrication and characterization of biological biosensors in sports injury treatment: High sensitivity of silver oxide using artificial neural network modeling

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Wu, Youliang Moghadas, B. Kamyab Gu, Dongqiang Salahshour, S. Hashemi, M. Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Hefei Normal University Hefei230601 China Department of Chemical Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Applied Researches Chemical Petroleum & Polymer Engineering Research Center Shiraz Branch Islamic Azad University Shiraz Iran Department of Sports Medicine Senior Department of Orthopedics The Fourth Medical Center of PLA General Hospital Beijing100048 China Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

This study focuses on the potential of biosensor technology to revolutionize sports injury management. Conventional methods for sports injury management, such as physical examinations and imaging techniques, often lack the sensitivity and real-time monitoring capabilities required to track the healing process effectively. These methods are also invasive, relying on blood sampling, which can be uncomfortable for athletes. In contrast, the proposed wearable biosensor offers a non-invasive, painless alternative by measuring biomarkers like myoglobin and creatine kinase in sweat. This study introduces a novel graphene field-effect transistor biosensor integrated into a wristband, combined with an artificial neural network (ANN) model to predict material properties and optimize biomarker detection. The results show the potential of this technology to revolutionize sports injury management by providing real-time, accurate, and non-invasive monitoring of injury progression and recovery. The results indicate that changes in compressive strength and porosity have an impact on dissolution rate, pore size growth, and chemical stability. Lower compressive strength leads to an increase in dissolution rate, while higher compressive strength promotes pore size growth and chemical stability. The accuracy of the ANN model's predictions was evaluated using linear regression and demonstrated acceptable error levels compared to experimental testing. Among the nanocomposite hydrogel scaffolds containing silver oxide nanoparticles, a specific sample showed noteworthy characteristics, including a compressive strength of 2.4 Mega Pascal, 55 % porosity, 22 % dissolution rate, 27 % pore size growth, and 65 % chemical stability. © 2025 Elsevier Ltd

关键词： Enzyme sensors

Beyond potential energy surface benchmarking: a complete application of machine learning to chemical reactivity

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arXiv 2023年

作者： Guan, Xingyi Heindel, Joseph Ko, Taehee Yang, Chao Head-Gordon, Teresa Kenneth S. Pitzer Theory Center Department of Chemistry Departments of Bioengineering and Chemical and Biomolecular Engineering University of California BerkeleyCA94720 United States Chemical Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mathematics Penn State University University ParkPA16802 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory United States

We train an equivariant machine learning model to predict energies and forces for a real-world study of hydrogen combustion under conditions of finite temperature and pressure. This challenging case for reactive chemistry illustrates that ML-learned potential energy surfaces (PESs) are always incomplete as they are overly reliant on chemical intuition of what data is important for training, i.e. stable or metastable energy states. Instead we show here that a "negative design" data acquisition strategy is necessary to create a more complete ML model of the PES, since it must also learn avoidance of unforeseen high energy intermediates or even unphysical energy configurations. Because this type of data is unintuitive to create, we introduce an active learning workflow based on metadynamics that samples a lower dimensional manifold within collective variables that efficiently creates highly variable energy configurations for further ML training. This strategy more rapidly completes the ML PES such that deviations among query by committee ML models helps to now signal occasional calls to the external ab initio data source to further molecular dynamics in time without need for retraining the ML model. With the hybrid ML-physics model we predict the change in transition state and/or reaction mechanism at finite temperature and pressure for hydrogen combustion, thereby delivering on the promise of real application work using ML trained models of an ab initio PES with two orders of magnitude reduction in cost. © 2023, CC BY.

关键词： Quantum chemistry

Synthesis of iron-doped zinc oxide (Fe-ZnO) nanoparticles by using several stabilizers 1

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Synthesis of iron-doped zinc oxide (Fe-ZnO) nanoparticles by...

1st South East Asia Science, Technology, engineering and mathematics International Conference, SEA- STEM 2020

作者： Muktaridha, O. Adlim, M. Suhendrayatna, S. Ismail, I. Graduate School of Mathematics and Applied Science Universitas Syiah Kuala Darussalam Banda Aceh23111 Indonesia Chemical Engineering Department Faculty of Engineering Universitas Syiah Kuala Darussalam Banda Aceh23111 Indonesia Physics Department Faculty of Math and Natural Science Universitas Syiah Kuala Darussalam Banda Aceh23111 Indonesia

Studies on the effect of the stabilizer of chitosan, citric acid and urea for preparation Fe-doped ZnO have been conducted. Both ZnO and Fe were prepared from each salt precursor solution. The Zn2+ was added fist and subsequently Fe2+ by dropwise method into each stabilizer solution before converted into the hydroxide compounds before annealing. All metal ion solutions precipitated out after 7 minutes of NaOH addition except in urea stabilizer, where the solution remained in the colloidal form for more than 30 minutes. The hydroxyl ions are required for ZnO formation, but it weakens chitosan and citric acid stabilizing capacity, which instigated ZnO particles to be highly agglomerated. Each stabilizer generated a smaller ZnO crystallite size compared to the control sample. Fe-ZnO crystallite size prepared using chitosan, citric acid and urea as the stabilizer was 14.75, 23.29 and 22.51 nm, respectively. The crystallite size of Fe-ZnO that was prepared without stabilizer (control) was 32.89 nm. © Published under licence by IOP Publishing Ltd.

关键词： Urea

Classical and Quantum Distributed Algorithms for the Survivable Network Design Problem

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arXiv 2024年

作者： Kerger, Phillip A. Bernal Neira, David E. Izquierdo, Zoe Gonzalez Rieffel, Eleanor G. Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Research Institute of Advanced Computer Science USRA Mountain ViewCA United States Quantum Artificial Intelligence Laboratory NASA Ames Research Center Mountain ViewCA United States Department of Chemical Engineering Purdue University West LafayetteIN United States

We investigate distributed classical and quantum approaches for the survivable network design problem (SNDP), sometimes called the generalized Steiner problem. These problems generalize many complex graph problems of interest, such as the traveling salesperson problem, the Steiner tree problem, and the k-connected network problem. To our knowledge, no classical or quantum algorithms for the SNDP have been formulated in the distributed settings we consider. We describe algorithms that are heuristics for the general problem but give concrete approximation bounds under specific parameterizations of the SNDP, which in particular hold for the three aforementioned problems that SNDP generalizes. We use a classical, centralized algorithmic framework first studied in (Goemans & Bertsimas, 1993) and provide a distributed implementation thereof. Notably, we obtain asymptotic quantum speedups by leveraging quantum shortest path computations in this framework, generalizing recent work of (Kerger, Bernal Neira, Gonzalez Izquierdo, & Rieffel, 2023). These results raise the question of whether there is a separation between the classical and quantum models for application-scale instances of the problems considered. © 2024, CC BY.

关键词： Trees (mathematics)

Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation

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arXiv 2024年

作者： Rhyu, Jinwook Schaeffer, Joachim Li, Michael L. Cui, Xiao Chueh, William C. Bazant, Martin Z. Braatz, Richard D. Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Control and Cyber-Physical Systems Laboratory Technical University of Darmstadt 64289 Germany Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Applied Energy Division SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (∼100 days) for cells to reach the end of life. We propose a systematic feature design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. Two simple Q(V ) features designed from our framework, extracted from formation data without any additional diagnostic cycles, achieved a median of 9.20% error for cycle life prediction, outperforming thousands of autoML models using pre-defined features. We attribute the strong performance of our designed features to their physical origins – the voltage ranges identified by our framework capture the effects of formation temperature and microscopic particle resistance heterogeneity. By designing highly interpretable features, our approach can accelerate formation research, leveraging the interplay between data-driven feature design and mechanistic *** Codes 15-04 Copyright © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries