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Enhancing the Sustainability Content of Courses in Environmental engineering: Reports of Workshops at the 2022 St. Louis and the 2023 Boston AEESP conferences

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ENVIRONMENTAL engineering SCIENCE 2025年

作者： Davidson, Cliff I. Hardin, Rebecca D. Powers, Susan E. Zhang, Qiong Zhou, Jim Syracuse Univ Link Hall 151 Syracuse NY 13244 USA Univ Michigan Ann Arbor MI USA Clarkson Univ Potsdam NY USA Univ S Florida Tampa FL USA Southern Illinois Univ Edwardsville Edwardsville IL USA

The AEESP Research and Education conference series has for many years included workshops to improve teaching and learning in environmental engineering but has only recently focused on incorporating sustainability into environmental engineering programs at both undergraduate and graduate levels. Here, we summarize two recent workshops with a total of 69 participants from 44 colleges and universities, which were built on two previous pilot workshops and could be a foundation for future sustainability efforts. Approximately 44% of the participants were graduate students and postdocs with limited or no teaching experience, while the remainder were either nontenure-track or tenure-track instructors. Sixteen of the participants, along with the workshop organizers, had taught sustainability in at least five courses, and this experience enabled them to contribute valuable ideas throughout the workshops. Although all areas of engineering can incorporate sustainability concepts and principles, some topics within environmental engineering seem most popular for teaching sustainability to our students. When asked to indicate topics they have used for teaching sustainability from a list of 21 topic areas, the one identified by the most participants was water, with life cycle assessment in second place and with climate change and energy/power generation tied for third place. For the six topics identified by the greatest number of participants in each workshop, only the first three of these topics were included in the top six in both workshops. An identical list of 21 topics given in an online U.S. survey in 2014 with 140 respondents showed that the six most frequently chosen topics included all four of these. The workshops described here are part of an international effort to enhance the teaching of sustainability to engineering students on a global scale.

关键词： curriculum engineering engineering education sustainability sustainable

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teaching Assistance Measures for Foreign Students in China in Higher Vocational Colleges Based on Knowledge Map 3rd

Teaching Assistance Measures for Foreign Students in China i...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Li, Yuting Austria, Ramir Santos Zhou, Quan University of the Cordilleras Weifang Vocational College of Electromechanical Engineering Shandong Weifang262737 China University of the Cordilleras Baguio City2600 Philippines Weifang Vocational College of Electromechanical Engineering Shandong Weifang262737 China

ISBN: (纸本)9789819791279

With the continuous development of international education, the number of international students in China is increasing year by year. How to provide them with effective teaching Audio-visual Instructional Development System (AIDS) has become an important topic. As a new knowledge representation and reasoning tool, knowledge map provides a new solution for foreign students’ teaching assistance. In this paper, the framework of teaching aid system based on knowledge map is constructed, and the construction process of knowledge map and its application scenario in foreign students’ teaching are expounded in detail. At the same time, a series of simulation experiments were designed and implemented to simulate the learning environment and teaching assistance process of international students. By collecting the data of foreign students’ learning history, hobbies, ability level, etc., the user portrait is constructed, and the intelligent recommendation of learning resources is realized based on knowledge map and CF (Collaborative filtering) algorithm. The experimental results show that this scheme can significantly improve the learning effect and satisfaction of international students. By comparing the effects of different recommendation strategies, it is found that this method has obvious advantages in personalized recommendation. Therefore, the teaching assistance measures for international students in China based on knowledge map proposed in this paper have high practical value and promotion prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Apprentices

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Performance Analysis of Machine learning Algorithms with and without Hyperparameter tuning for Dry Bean Classification 3

Performance Analysis of Machine Learning Algorithms with and...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Jenitta, J. Swetha Rani, L. Manasa, S. Amc Engineering College Department of Ece Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Classification of dry beans is very important task in food and agriculture industry. Traditionally, classification was done manually. To reduce the task of human and to automate the separation, algorithms of machine learning are employed. All the supervised learning algorithms are trained and tested with and without hyperparameter tuning and the results are presented as result. Machine learning algorithms which are used for this study are Naïve Bayes, K Nearest Neighbour, Decision Tree, Random Forest, Support Vector Machine, XG Boost, Multi-Layer Perceptron. Accuracy of all the algorithms were compared and it is evident that Support Vector Machine with Grid search hyper tuning is giving highest accuracy of all other algorithms for dry bean classification. © 2025 IEEE.

关键词： Adversarial machine learning

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Streamlined Machine learning Workflow for Accurate Rice Yield Estimation Using Remote Sensing and Meteorological Data 3

Streamlined Machine Learning Workflow for Accurate Rice Yiel...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sinchana, H.S. Pallavi, N.R. Bgs Institute of Technology Adichunchanagiri University Department of Computer Science Engineering B.G Nagar Mandya District India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The goal of this project is to use machine learning (ML), meteorological data, and remote sensing data to create an effective methodology for accurate rice yield estimation. The project entails creating a customized machine learning model with the aim of forecasting rice yields while accounting for the unique agricultural characteristics of the target region. The goal of the research is to determine the best approach for estimating rice yields by utilizing sophisticated data processing techniques and carefully assessing various machine learning models. By combining these strategies, crop production forecasts should become more accurate and provide insightful information for agricultural planning and management in a variety of geographical areas. © 2025 IEEE.

关键词： Adversarial machine learning

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Exploring Quantum Processors in Decentralized Edge computing for Improved Data Processing 2

Exploring Quantum Processors in Decentralized Edge Computing...

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2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Pitkar, Harshad Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798331505745

The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems. © 2025 IEEE.

关键词： Qubits

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7th international conference on Creative Mathematical Sciences Communication, CMSC 2024

7th International Conference on Creative Mathematical Scienc...

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7th international conference on Creative Mathematical Sciences Communication, CMSC 2024

ISBN: (纸本)9783031732560

The proceedings contain 17 papers. The special focus in this conference is on Creative Mathematical Sciences Communication. The topics include: Unplugging Dijkstra’s Algorithm as a Mechanical Device;unplugged Decision Tree learning – A learning Activity for Machine learning Education in K-12;tactile Kolam Patterns – Communicating Art and Mathematics to Students with Vision Impairments;quBobs teaching Kits to Explain Quantum computing;solid Geometry Modeling: 3D Printing Is Not Always the Best Option;the Algorithm Experience at Primary Schools: An Experience Report;curricular Choices for Computational Thinking in Large Scale Low Resource Environments;why Teach About Binary Numbers?;teaching Tangible Division Algorithms or Going from Concrete to Abstractions in Math Education by the Genetic Socratic Method;mathematical Versus Computational Thinking with a Computer in the Background;Computational Thinking Based STEM Art Exhibits;BeLLE: Detecting National Differences in Computational Thinking and Computer Science Through an international Challenge;From Caesar Shifts to Kid-Enigma. The CS Unplugged-Like Path in the MuMa Science Centre;large and Parallel Human Sorting Networks;distance teaching of Mathematical and Computer Disciplines During the War in Ukraine.

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Optimized Biometric-Based Anonymous Authentication for Secure Mobile Edge computing 3

Optimized Biometric-Based Anonymous Authentication for Secur...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Dinesh, E. Akash, R. Bhagavathi, R. Harrish Sundar, A. M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

We propose a novel biometric-based anonymous authentication system for facilitating operation in mobile edge computing networks. Hence, the proposed approach incorporates FaceNet to perform face embedding extraction for better reliability and Federated learning (FL) for privacy-preserving model training. Therefore, dynamic learning rate optimization makes the system adjust effectively to the fluctuations in data distribution enhancing the models. An Android-based application acts as a front-end through which users can safely take and send templates to the back end for real-time processing. The performances obtained from experiments show a very high identification accuracy as well as effectively safeguarding users' privacy;therefore, making the realisation of the system well suited for applications that requires high levels of privacy in edge computing. © 2025 IEEE.

关键词： Differential privacy

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Research on the Application of Intelligent Education in Practical teaching for Mechanical and Electrical engineering Undergraduates 1

Research on the Application of Intelligent Education in Prac...

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1st international conference on Intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Xie, Yi Zheng, Liming Navy Submarine Academy Shangdong Qingdao China

ISBN: (纸本)9781643685779

With the rapid development of technology, intelligent education has gradually become a research hotspot in the field of education. The Third Plenary Session of the 20th Central Committee of the Communist Party of China explicitly proposed to promote the digitalization of education and align with the global "Education 4.0". This article explores the application of intelligent education in practical teaching for undergraduate students majoring in mechanical and electrical engineering, aiming to improve teaching effectiveness and students’ operational skills. By analyzing relevant literature, this article proposes specific application strategies for establishing a complete practical teaching system for undergraduate students majoring in mechanical and electrical engineering, constructing an intelligent teaching platform for practical teaching in this field, enhancing teachers’ intelligent literacy in mechanical and electrical engineering, and innovating the intelligent teaching evaluation methods for practical teaching. These strategies can provide references for the cultivation of undergraduate students majoring in mechanical and electrical engineering in colleges and universities. © 2025 The Authors.

关键词： Students

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Concurrent and Learned Data Structures 25

Concurrent and Learned Data Structures

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26th international conference on Distributed computing and Networking, ICDCN 2025

作者： Chatterjee, Bapi Peri, Sathya Department of Computer Science and Engineering IIIT Delhi Delhi India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798400710629

We explore the landscape of modern data structures that employ two powerful scaling mechanisms simultaneously: (a) concurrency to harness the power of today's multicore processors, and (b) learned queries - the operations employing machine learning inference by models trained on the underlying data. We focus on advancements, challenges, and potential future directions in this field. We delve into the motivation for incorporating learned queries in concurrent data structures. We discuss the key techniques, applications, and open research questions. On the theoretical side, we discuss the mathematical foundations for building the learned data structures in addition to the correctness of queries in a concurrent setting. On the practical side, we present an overview of techniques used for managing concurrency with learned queries that need training the models with concurrent real-time updates. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Adaptive AI-Based Personalized learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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international Journal of Advanced Computer Science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (Science and Information Organization). All rights reserved.

关键词： Students

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