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Optimizing the efficiency of photovoltaic-thermoelectric systems equipped with hybrid nanofluid channels: Environmental and economic considerations

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 194卷 497-510页

作者： Boora, Faranack M. Ebrahimpourboura, Javad Sheikholeslami, M. Khalili, Z. Babol Noshirvani Univ Technol Renewable Energy Syst & Nanofluid Applicat Heat Tr Babol Iran Babol Noshirvani Univ Technol Dept Mech Engn Babol Iran

This study aims to optimize a solar Photovoltaic (PV) and thermoelectric (TE) unit utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The system incorporates a hybrid nanofluid jet, composed of water and ND-Co3O4 nanoparticles. Optimization, conducted in python, utilizes data from an extensive 3D numerical model. Key factors under consideration include solar irradiation, the jet's injection location, tube and jet inlet velocities, and the proportion of hybrid nanoparticles. The primary goals are to reduce pumping power (Ep), maximize the system's overall gain over a 10-year span, and improve CO2 reduction. This research is significant for its comprehensive approach to enhancing solar energy technology, boosting system performance and efficiency, while addressing environmental concerns by lowering CO2 emissions. By combining advanced numerical simulations with NSGA-II optimization, this work advances sustainable energy solutions, providing valuable insights for the design of well-organized and environmentally friendly solar energy units. The optimization successfully balanced system gain, CO2 reduction, and pumping power, achieving optimal results of $12,508.8 for system gain, 431.59 tons for CO2 reduction, and 0.2097 for pumping power. The Mean Squared Error (MSE) percentages for the training data are under 1 % for system gain, approximately 1.6 % for CO2 reduction, and around 1.1 % for pumping power, underscoring the effectiveness of the optimization process.

关键词： NSGA-II Photovoltaic solar panel Machine learning Hybrid nanofluid Jet impingement python programming

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Developing a program for quantification and characterization of microstructure: Application to BSE images of geopolymer recycled concrete

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JOURNAL OF BUILDING ENGINEERING 2025年 104卷

作者： Huo, Wangwen Li, Rita Yi Man Zhu, Zhiduo Nanjing Inst Technol Sch Civil Engn & Architecture Nanjing 211167 Jiangsu Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Jiangsu Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 210098 Peoples R China Hong Kong Shue Yan Univ KSYU Real Estate & Econ Res Lab Hong Kong Peoples R China

This study aims to achieve efficient, accurate, and automated identification of phase features in the backscattered electron (BSE) images of geopolymer recycled concrete (GRC), enhancing the understanding of its microstructural characteristics and facilitating the investigation of the relationship between microstructure and macroscopic mechanical performance. A system for phase recognition and quantitative analysis of GRC, named CMAS, is developed. This system performs quantitative analysis and calculations based on various microstructural images. The program first automatically removes noise, enhances contrast, and corrects image defects. It then applies the multi-Otsu thresholding method and binarization to segment and identify the phase components. Finally, the system employs connectivity analysis and geometric operations to derive ten microstructural parameters, including area, length, orientation, perimeter, fractal dimension, uniformity coefficient, and curvature coefficient. Additionally, the system generates corresponding rose diagrams. The proposed system can automatically process large numbers of images, significantly improving data processing efficiency and user-friendliness. All operations are nondestructive to the images, making it a powerful tool for the analysis of material microstructural images and providing valuable reference for the development of similar image processing systems.

关键词： Geopolymer recycled concrete Microstructure image Image segmentation Geometric measurement python programming

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Enhancing programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a python Course 25th

Enhancing Programming Education with ChatGPT: A Case Study o...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Mal, Boxuan Chen, Li Konomi, Shin'ichi Kyushu Univ Fac Arts & Sci Fukuoka Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka Japan

ISBN: (纸本)9783031643149;9783031643156

The integration of ChatGPT as a supportive tool in education, notably in programming courses, addresses the unique challenges of programming education by providing assistance with debugging, code generation, and explanations. Despite existing research validating ChatGPT's effectiveness, its application in university-level programming education and a detailed understanding of student interactions and perspectives remain limited. This paper explores ChatGPT's impact on learning in a python programming course tailored for first-year students over eight weeks. By analyzing responses from surveys, open-ended questions, and student-ChatGPT dialog data, we aim to provide a comprehensive view of ChatGPT's utility and identify both its advantages and limitations as perceived by students. Our study uncovers a generally positive reception toward ChatGPT and offers insights into its role in enhancing the programming education experience. These findings contribute to the broader discourse on AI's potential in education, suggesting paths for future research and application.

关键词： Generative AI ChatGPT python programming

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Thermal analysis of unsteady viscous flow in medical engineering: A comparative analysis of numerical and semi-analytical methods

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MODERN PHYSICS LETTERS B 2025年第25期39卷

作者： Shateri, Amirali Mahboobtosi, Mehdi Ahmad, Irshad Jalili, Payam Jalili, Bahram Ganji, Davood Domiri Islamic Azad Univ Dept Mech Engn North Tehran Branch Tehran Iran Babol Noshirvani Univ Technol Dept Mech Engn POB 484 Babol Iran King Khalid Univ Coll Appl Med Sci Dept Med Rehabil Sci Abha Saudi Arabia

The study of thermal and mass transfer in unsteady squeezing flows between parallel plates is crucial for biomedical fluid systems, including drug delivery and medical device design. This research employs a hybrid approach integrating the Akbari-Ganji method (AGM) with the fourth-order Runge-Kutta method, utilizing similarity transformation to convert partial differential equations into ordinary differential equations (ODEs). The analysis focuses on key parameters such as the Prandtl number (0.7-1.5), Squeeze number (-2-2), Schmidt number (0.5-1.2) and Eckert number (1-2.5). Results show that as the squeeze number increases from -2 to 2, the velocity profile f(eta) at eta = 0.5 decreases from 0.78 to 0.65, while the temperature profile theta(eta) at eta = 0.5 decreases from 2.75 to 1.52. Increasing the Prandtl number from 0.7 to 1.5 raises the temperature, with theta(eta) at eta = 0.5 increasing from 1.45 to 1.9. The concentration profile phi(eta) at eta = 0.5 increases from 0.7 to 0.74 as the squeeze number increases from -1.5 to 1.5 while decreasing from 0.84 to 0.71 as the Schmidt number increases from 0.5 to 1.2. The AGM results show excellent agreement with numerical methods, with maximum relative errors of approximately 0.00107% for f(eta), 0.14991% for theta(eta) and 0.06368% for phi(eta), validating the accuracy of the semi-analytical approach. The theoretical convergence analysis, supported by python programming simulations, validates the robustness of the methods used. These results provide a solid foundation for the optimization of biomedical fluid systems, particularly in the design of advanced medical devices.

关键词： Unsteady squeezing flow heat and mass transfer similarity transformation semi-analytical method python programming

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Motivation in first year engineering students of the online course "Computer programming in python" in the context of a pandemic by COVID-19 6

Motivation in first year engineering students of the online ...

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16th Latin American Conference on Learning Technologies (LACLO)

作者： Bustos, Solange Barros Alvarez-Gonzalez, Luis Univ San Sebastian Fac Psicol Valdivia Chile Univ Austral Chile Inst Informat Valdivia Chile

ISBN: (纸本)9781665423588

Teaching-learning has changed drastically since the coronavirus pandemic and motivation in a pandemic context where students have had to radically change their learning system. The main goal of this research is to identify the relationship between the motivation of students in an online course of Computer programming in python, in the context of pandemic with sociodemographic variables (Internet connection, number of people living at home, employment status) and school variables (number of parallel subjects, grades and time spent). 75 first year engineering students participated and motivation was measured through the EME scale that provides Internal Motivation, External Motivation and Amotivation. The results indicate that there are no mean differences between motivation and socio-demographic variables, while significant differences are found in the Amotivation factor compared to students' final grades and the total time taken to complete the learning units. The course in question took place in the second semester of 2020, which ended in January 2021, given the irregularity of the semester

关键词： Intrinsic Motivation Extrinsic Motivation Amotivation python programming online course LAMS

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Design, Implementation and Evaluation of a Computer Science Teacher Training Programme for Learning and Teaching of python Inside and Outside School Establishing and Supporting Code Clubs to Learn Computer programming by Self-contained Examples 11th

Design, Implementation and Evaluation of a Computer Science ...

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11th International Conference on Interactive Mobile Communication Technologies and Learning (IMCL)

作者： Moumoutzis, Nektarios Boukeas, George Vassilakis, Vassilis Pappas, Nikos Xanthaki, Chara Maragkoudakis, Ioannis Deligiannakis, Antonios Christodoulakis, Stavros Tech Univ Crete Sch Elect & Comp Engn Lab Distributed Multimedia Informat Syst & Applic Khania Greece

ISBN: (纸本)9783319751757;9783319751740

We present the design, implementation and evaluation of a training programme for Computer Science teachers on the educational use of the python programming language inside and outside school. The programme used educational resources centered on meaningful, self-contained programming projects. The training programme followed a blended-learning approach thus offering an opportunity to many computer science teachers make their first steps towards the educational use of the python language within a very promising learner-centered pedagogical framework. Using initially an online course made it possible to reach a much greater number of Computer Science teachers, especially those living in remote areas and through them, have a considerable impact on students through the subsequent establishment of local code clubs. The synchronous interaction with the course facilitators during the monthly sessions, the forming of local groups and the systematic communication through the learning platform used, made it possible to alleviate many of the disadvantages usually linked with online courses as it is evident from the evaluation results.

关键词： python programming Computer science in secondary education Code clubs

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Utilizing the open-source programming language python to create interactive Quality Assurance dashboards for diagnostic and screening performance in Cytology

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Journal of the American Society of Cytopathology 2024年第4期13卷 309-318页

作者： Kovács, István Székely, Tamás Pogány, Péter Takács, Szabolcs Erős, Mónika Járay, Balázs Eurofins-Medserv Ltd Budapest Hungary

Introduction: Effective feedback on cytology performance relies on navigating complex laboratory information system data, which is prone to errors and lacks flexibility. As a comprehensive solution, we used the python programming language to create a dashboard application for screening and diagnostic quality metrics. Materials and methods: Data from the 5-year period (2018-2022) were accessed. Versatile open-source python libraries (user developed program code packages) were used from the first step of LIS data cleaning through the creation of the application. To evaluate performance, we selected 3 gynecologic metrics: the ASC/LSIL ratio, the ASC-US/ASC-H ratio, and the proportion of cytologic abnormalities in comparison to the total number of cases (abnormal rate). We also evaluated the referral rate of cytologists/cytotechnologists (CTs) and the ratio of thyroid AUS interpretations by cytopathologists (CPs). These were formed into colored graphs that showcase individual results in established, color-coded laboratory “goal,” “borderline,” and “attention” zones based on published reference benchmarks. A representation of the results distribution for the entire laboratory was also developed. Results: We successfully created a web-based test application that presents interactive dashboards with different interfaces for the CT, CP, and laboratory management (https://***/app). The user can choose to view the desired quality metric, year, and the anonymized CT or CP, with an additional automatically generated written report of results. Conclusions: python programming proved to be an effective toolkit to ensure high-level data processing in a modular and reproducible way to create a personalized, laboratory specific cytology dashboard. © 2024 The Author(s)

关键词： Cytology Dashboard Fine-needle biopsy Papanicolaou test python programming Quality improvement

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GRAD-AI: An automated grading tool for code assessment and feedback in programming course

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EDUCATION AND INFORMATION TECHNOLOGIES 2025年第7期30卷 9859-9899页

作者： Gambo, Ishaya Abegunde, Faith-Jane Gambo, Omobola Ogundokun, Roseline Oluwaseun Babatunde, Akinbowale Natheniel Lee, Cheng-Chi Obafemi Awolowo Univ Dept Comp Sci & Engn Ife 220282 Osun State Nigeria Lead City Univ Dept Arts & Social Sci Educ Ibadan 200005 Nigeria Kaunas Univ Technol Dept Software Engn LT-44249 Kaunas Lithuania Landmark Univ Dept Comp Sci Omu Aran 251103 Nigeria Tshwane Univ Technol TUT Dept Comp Syst Engn Pretoria South Africa Kwara State Univ Dept Comp Sci Malete 241103 Nigeria Fu Jen Catholic Univ Dept Lib & Informat Sci New Taipei City 24206 Taiwan Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan

The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading, timely feedback, and personalized support, enhancing the education process. This paper aims to develop "GRAD-AI," an automated grading tool for computer programming assignments. The objective is to overcome the limitations of manual grading by harnessing AI's capabilities to deliver accurate and timely assessments, thus creating a more interactive and supportive learning environment. The results show that GRAD-AI provides unbiased grading and timely and accurate feedback delivery for programming assignments by using the Halstead Complexity Measure, Term Frequency - Inverse Document Frequency Measure, Abstract Syntax Tree Process, and K-means Clustering. GRAD-AI marks a substantial stride in improving grading and feedback delivery within the education sector. Its real-time feedback provision and gap identification contribute to enhanced learning outcomes. As AI's role expands, integrating automated grading tools like GRAD-AI becomes crucial for fostering personalized learning and adaptability. The paper underscores AI's potential to revolutionize assessment and grading processes, supporting global students' growth and development.

关键词： Automated grading tool Feedback programming assignments Assessment Automated Manual python programming Artificial intelligence Characteristic extraction K-means clustering Plagiarism detection User Interface

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On Urban Development Index of a Country: A Fuzzy Logic and python Approach

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NEW MATHEMATICS AND NATURAL COMPUTATION 2025年第0期

作者： Kerre, Etienne E. Ashraf, Samina Ghias, Sadia Rehman, Atiq Ur Univ Ghent Dept Appl Math Comp Sci & Stat Krijgslaan 281-S9 B-9000 Ghent Belgium Univ Educ Dept Math Bank Rd Campus Lahore 54000 Punjab Pakistan COMSATS Univ Islamabad Dept Math Lahore Campus Islamabad 53710 Punjab Pakistan

The development index (DI) of a region is defined by various elements such as geography, population density, gross domestic product (GDP), GDP per capita, natural resources, markets, economic development, and other relevant factors. The traditional approach to classification usually depends on the country's total land area measured in square kilometers. Nevertheless, the transition points between different levels of development size (small, medium, and giant) are highly subjective, resulting in frequent inconsistencies and conflicts. Fuzzy logic, a superset of conventional (Boolean) logic, extends the framework to include partial truth values, ranging from "fully true" to "totally false". This development is especially essential since human reasoning, particularly commonsense reasoning, is approximate rather than precise. This study offers a model that uses fuzzy logic and the Mamdani fuzzy inference system (MFIS) to evaluate nine main cities in Pakistan based on their populations (POPs), gross domestic products (GDPs), and literacy rates (LR). The model includes variables for the antecedents (inputs) and the consequences (outputs). The input variables are the population (POP), GDP, and LR, while the output variable is the DI. The MFIS is used with python programming tools, including the scikit-fuzzy library for inference and aggregation and matplotlib for graphics.

关键词： Fuzzy logic Mamdani fuzzy inference system population gross domestic product literacy rate development index python programming

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An improved adaptive personalization model for instructional video-based e-learning environments

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JOURNAL OF COMPUTERS IN EDUCATION 2025年第1期12卷 267-313页

作者： Sanal Kumar, T. S. Thandeeswaran, R. Vellore Inst Technol VIT Sch Comp Sci Engn & Informat Syst Vellore Tamil Nadu India

Due to the unexpected COVID-19 pandemic, video-based e-learning environments for programming education have disrupted traditional classroom teaching methods. The major drawbacks of these environments are that they never consider the individual differences and personal traits of the learner while building a challenging course like programming, having high dropout and failure rates. To address this issue, this paper proposed a learning style-enabled novel rule-based personalized instructional video delivery model for programming education. The model used the following four learning parameters for delivering the instructional videos: (a) most recent instructional video, (b) assessment score, (c) complexity level, and (d) weight (variance of two recent assessments) score. This work was designed using a paired pre-test-post-test experimental approach with first-year undergraduate students. For the experimental evaluation, students were randomly classified into three groups. Learner scores and feedback were taken as evaluation metrics. Results revealed that the proposed model-driven group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver personalized instructional videos based on learners' receptive pace, cognitive level, and learning preference.

关键词： Blended learning Higher education Learning style model Personalized instructional videos python programming Statistical analysis

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