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A Proof of the Collatz Conjecture via Complete Set Classification and Unique Cycle Analysis
Amarachukwu Nwankpa
Posted: 14 March 2025
The Collatz Infinite Tree: Inclusion of Natural Numbers and Nonexistence of Nontrivial Cycles
Eyob Solomon Getachew
Posted: 14 March 2025
Theory of Relationships Between Spaces to Obtain Numerical Knowledge
Carlos Eduardo Ramos Cardoso
Posted: 14 March 2025
Real-Time Monitoring of Oil and Gas Pipeline Leakages Identification System Based on Deep Learning Approaches: A Systematic Review
Gabriel James,
Imeh Umoren,
Anietie Ekong,
Ifeoma Ohaeri,
Saviour Inyang
Posted: 14 March 2025
Designing a Secure and Efficient Campus Carpooling System: A Comprehensive Software Requirement Specification
Mya Eirdina Sharn Kamel,
Mark Aldrich Vincent Bin Buyun,
Mohamad Fayyadh Bin Abdul Aziz,
Brighton Moronda Moronda,
Riyadh Usman Abdulqadir,
Parishad Banaei Arani,
Samia Islam,
Noor Ul Amin
This study presents an official Software Requirement Specification (SRS) of a campus carpooling system to enhance transport efficiency, reduce traffic congestion, and ensure user safety. The report outlines functional and non-functional requirements, quality requirements, and acceptance conditions for system deployment. Some of the key features include real-time ride updates, user registration, security features, multi-platform compatibility, and an incentive-based reward scheme to promote user engagement. Through mapping requirements to stakeholders' goals, including syntactic requirement patterns, and defining acceptance criteria, the study ensures that there is a stable and convenient carpooling experience. The findings are favorable to green transportation solutions, upholding environmental awareness and operational effectiveness in universities.
This study presents an official Software Requirement Specification (SRS) of a campus carpooling system to enhance transport efficiency, reduce traffic congestion, and ensure user safety. The report outlines functional and non-functional requirements, quality requirements, and acceptance conditions for system deployment. Some of the key features include real-time ride updates, user registration, security features, multi-platform compatibility, and an incentive-based reward scheme to promote user engagement. Through mapping requirements to stakeholders' goals, including syntactic requirement patterns, and defining acceptance criteria, the study ensures that there is a stable and convenient carpooling experience. The findings are favorable to green transportation solutions, upholding environmental awareness and operational effectiveness in universities.
Posted: 14 March 2025
Lightweight Interpretable Deep Learning Model for Nutrient Analysis in Mobile Health Applications
Zvinodashe Revesai,
Okuthe P. Kogeda
Posted: 14 March 2025
Optimizing the Reduction of Streaking Artifacts in Routine Non-Contrast Chest CT with a Guided Diffusion Deep Learning Method
Jingxin Liu,
Xinran Zhu,
Zhangzhen Shi,
Donghong An,
Lihui Zu,
Kailiang Cheng,
Zhong Zhang
Posted: 14 March 2025
Performance Enhancing Market Risk Calculation Through Gaussian Process Regression and Multi-Fidelity Model
Noureddine Lehdili,
Pascal Oswald,
Hoang Dung Nguyen
Posted: 14 March 2025
Universal Invariant Framework for Emotion Recognition in Incomplete Multimodality
Maximilian Neumann,
Emily Marwood,
Leonie Schneider
Posted: 14 March 2025
Multi-Examiner: A Knowledge Graph-Driven System for Generating Comprehensive IT Questions with Higher-Order Thinking
Yonggu Wang,
Zeyu Yu,
Zengyi Yu,
Zihan Wang,
Jue Wang
The Question Generation System (QGS) for Information Technology (IT) education, designed to create, evaluate, and improve Multiple-Choice Questions (MCQs) using Knowledge graphs (KGs) and Large Language Models (LLMs), encounters three major needs: ensuring the generation of contextually relevant and accurate distractors, enhancing the diversity of generated questions, and balancing the higher-order thinking of questions to match various learning levels. To address these needs, we proposed a multi-agent system named Multi-Examiner, which integrates knowledge graphs, domain-specific search tools, and local knowledge bases, categorized according to Bloom’s taxonomy, to enhance the contextual relevance, diversity, and higher-order thinking of automatically generated information technology multiple-choice questions. We designed a multidimensional evaluation rubric to assess the semantic coherence, answer correctness, question validity, distractor relevance, question diversity, and higher-order thinking, and applied it to questions generated for six knowledge points from the second chapter of the "Information Systems and Society" textbook using both the Multi-Examiner system and GPT-4, alongside real exam questions, evaluated by 30 high school IT teachers. The results demonstrated that: (i) overall, questions generated by the Multi-Examiner system outperformed those generated by GPT-4 across all dimensions and closely matched the quality of human-crafted questions in several dimensions; (ii) domain-specific search tools significantly enhanced the diversity of questions generated by Multi-Examiner; (iii) GPT-4 generated better questions for knowledge points at the "remembering" and "understanding" levels, while Multi-Examiner significantly improved the higher-order thinking of questions for "evaluating" and "creating" levels. This study highlights the potential of multi-agent systems in advancing question generation.
The Question Generation System (QGS) for Information Technology (IT) education, designed to create, evaluate, and improve Multiple-Choice Questions (MCQs) using Knowledge graphs (KGs) and Large Language Models (LLMs), encounters three major needs: ensuring the generation of contextually relevant and accurate distractors, enhancing the diversity of generated questions, and balancing the higher-order thinking of questions to match various learning levels. To address these needs, we proposed a multi-agent system named Multi-Examiner, which integrates knowledge graphs, domain-specific search tools, and local knowledge bases, categorized according to Bloom’s taxonomy, to enhance the contextual relevance, diversity, and higher-order thinking of automatically generated information technology multiple-choice questions. We designed a multidimensional evaluation rubric to assess the semantic coherence, answer correctness, question validity, distractor relevance, question diversity, and higher-order thinking, and applied it to questions generated for six knowledge points from the second chapter of the "Information Systems and Society" textbook using both the Multi-Examiner system and GPT-4, alongside real exam questions, evaluated by 30 high school IT teachers. The results demonstrated that: (i) overall, questions generated by the Multi-Examiner system outperformed those generated by GPT-4 across all dimensions and closely matched the quality of human-crafted questions in several dimensions; (ii) domain-specific search tools significantly enhanced the diversity of questions generated by Multi-Examiner; (iii) GPT-4 generated better questions for knowledge points at the "remembering" and "understanding" levels, while Multi-Examiner significantly improved the higher-order thinking of questions for "evaluating" and "creating" levels. This study highlights the potential of multi-agent systems in advancing question generation.
Posted: 14 March 2025
Nonconforming Finite Elements and Multigrid Methods for Maxwell Eigenvalue Problem
Xuerong Zhong,
Meifang Yang,
Jintao Cui
In this paper, we demonstrate that the Maxwell eigenvalue problem can be solved by a nonconforming finite element and multigrid method. By using an appropriate operator, the eigenvalue problem can be viewed as a curl-curl problem. We obtain the approximate optimal error estimates on graded mesh. We also prove the convergence of the W-cycle and full multigrid algorithms for the corresponding discrete problem. The performance of these algorithms is illustrated by numerical experiments.
In this paper, we demonstrate that the Maxwell eigenvalue problem can be solved by a nonconforming finite element and multigrid method. By using an appropriate operator, the eigenvalue problem can be viewed as a curl-curl problem. We obtain the approximate optimal error estimates on graded mesh. We also prove the convergence of the W-cycle and full multigrid algorithms for the corresponding discrete problem. The performance of these algorithms is illustrated by numerical experiments.
Posted: 14 March 2025
IDEAL-Enhanced DevOps: A Structured Framework for Continuous Improvement in Software Engineering
Mohammed Nazeh Alimam,
Sami Kudsi
Recent advances in DevOps have dramatically reshaped software development and operations by emphasizing automation, continuous integration/delivery, and rapid feedback. However, organizations still struggle to achieve predictable improvements despite widespread adoption. In this study, we propose an “IDEAL-Enhanced DevOps” framework that integrates the five-phase IDEAL model—Initiate, Diagnose, Establish, Act, and Learn—into a DevOps transformation process. The proposed method lays out a structured approach to applying incremental improvements throughout the software delivery process. By a review of literature, in-depth analysis of case studies, and dis-semination of a questionnaire to practitioners in the field, this research explains how the IDEAL stages can be mapped to key processes in DevOps, address automation and scalability challenges, and facilitate a learning-centered, cooperative culture. The results show that a well-defined process-improvement approach can effectively reduce error incidence, enhance usability of tools, and significantly shorten time to get products to market. Our analysis shows that coupling IDEAL with DevOps not only clarifies responsibilities and organizational roles, but also lays a foundation for more resilient, high-quality, and adaptable software engineering methods.
Recent advances in DevOps have dramatically reshaped software development and operations by emphasizing automation, continuous integration/delivery, and rapid feedback. However, organizations still struggle to achieve predictable improvements despite widespread adoption. In this study, we propose an “IDEAL-Enhanced DevOps” framework that integrates the five-phase IDEAL model—Initiate, Diagnose, Establish, Act, and Learn—into a DevOps transformation process. The proposed method lays out a structured approach to applying incremental improvements throughout the software delivery process. By a review of literature, in-depth analysis of case studies, and dis-semination of a questionnaire to practitioners in the field, this research explains how the IDEAL stages can be mapped to key processes in DevOps, address automation and scalability challenges, and facilitate a learning-centered, cooperative culture. The results show that a well-defined process-improvement approach can effectively reduce error incidence, enhance usability of tools, and significantly shorten time to get products to market. Our analysis shows that coupling IDEAL with DevOps not only clarifies responsibilities and organizational roles, but also lays a foundation for more resilient, high-quality, and adaptable software engineering methods.
Posted: 14 March 2025
Enabling Future Maritime Traffic Management: A Decentralized Architecture for Sharing Data in the Maritime Domain
Dennis Höhn,
Lorenz Mumm,
Benjamin Reitz,
Christina Tsiroglou,
Axel Hahn
Posted: 14 March 2025
Beyond Signatures: Leveraging Sensor Fusion for Contextual Handwriting Recognition
Alen Salkanovic,
Diego Sušanj,
Luka Batistić,
Sandi Ljubic
Posted: 14 March 2025
AI Predictive Simulation for Low-Cost Hydrogen Production
Allan Butler,
Akhtar Kalam
Green hydrogen, produced via renewable-powered electrolysis, has the potential to revolutionize energy systems, but its widespread adoption hinges on achieving competitive production costs. A critical challenge lies in optimising the hydrogen production process to address solar and wind energy's high variability and intermittency. This paper explores the role of artificial intelligence (AI) in reducing and streamlining hydrogen production costs by enabling advanced process optimisation, focusing on electricity cost management and system-wide efficiency improvements.
Green hydrogen, produced via renewable-powered electrolysis, has the potential to revolutionize energy systems, but its widespread adoption hinges on achieving competitive production costs. A critical challenge lies in optimising the hydrogen production process to address solar and wind energy's high variability and intermittency. This paper explores the role of artificial intelligence (AI) in reducing and streamlining hydrogen production costs by enabling advanced process optimisation, focusing on electricity cost management and system-wide efficiency improvements.
Posted: 14 March 2025
A Systematic Survey on Federated Sequential Recommendation
Yichen Li,
Qiyu Qin,
Gaoyang Zhu,
Wenchao Xu,
Haozhao Wang,
Yuhua Li,
Rui Zhang,
Ruixuan Li
Posted: 13 March 2025
Bridging Vision and Texts: An External Graph Framework for Enhanced Language Comprehension
Martínez Pérez,
Emily Marwood,
Martina Fernández Gómez
Posted: 13 March 2025
Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN
Marcos Villar García,
José-Benito Bouza Rodríguez,
Alberto Comesaña Campos
Posted: 13 March 2025
Enhancing Campus Mobility: A Requirement Engineering Approach to a Carpool System for University Students and Staff
MYA EIRDINA SHARN KAMEL,
MARK ALDRICH VINCENT BIN BUYUN,
MOHAMAD FAYYADH BIN ABDUL AZIZ,
BRIGHTON MORONDA MORONDA,
RIYADH USMAN ABDULQADIR,
PARISHAD BANAEI ARANI,
SAMIA ISLAM,
NOOR UL AMIN
Posted: 13 March 2025
YOLO-UFS: A Novel Detection Model for UAVs to Detect Early Forest Fires
Zitong Luo,
Haining Xu,
Yanqiu Xing,
Chuanhao Zhu,
Zhupeng Jiao,
Chengguo Cui
Posted: 13 March 2025
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