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A Cost-Sharing Scheme for the k-Level Facility Location Game with Penalties

Journal of the Operations Research Society of China 2022年第1期10卷 173-182页

作者： Feng-Min Wang Jia-Jia Wang Na Li Yan-Jun Jiang Shi-Cheng Li Beijing Jinghang Research Institute of Computing and Communication Beijing 100074China School of Mathematics and Statistics Science Ludong UniversityYantai 264025ShandongChina The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing Beijing 100074China

In the k-level facility location problem with penalties,each client will be either serviced or rejected *** if the client is planned to be serviced,then it must be connected to a sequence of k different kinds of facilities located in k levels of *** total cost including the facility cost,connection cost and penalty cost will be jointly paid by all the *** the corresponding game of the k-level facility location problem with penalties,called the k-level facility location game with penalties,the total cost should be allocated to different *** work set out a cost-sharing scheme for the k-level facility location game with penalties that is cross-monotonic,competitive,and the approximate cost recovery is 6.

关键词： Cost-sharing scheme Cross-monotonic Competitive k-level facility location game

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NEW C0 INTERIOR PENALTY METHOD FOR MONGE-AMPÈRE EQUATIONS

arXiv

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arXiv 2024年

作者： Chu, Tianyang Guo, Hailong Zhang, Zhimin LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematics and Statistics The University of Melbourne ParkvilleVIC3010 Australia Department of Mathematics Wayne State University DetroitMI48202 United States

Monge-Ampère equation is a prototype second-order fully nonlinear partial differential equation. In this paper, we propose a new idea to design and analyze the C0 interior penalty method to approximation the viscosity solution of the Monge-Ampère equation. The new methods is inspired from the discrete Miranda-Talenti estimate. Based on the vanishing moment representation, we approximate the Monge-Ampère equation by the fourth order semi-linear equation with some additional boundary conditions. We will use the discrete Miranda-Talenti estimates to ensure the well-posedness of the numerical scheme and derive the error estimates. © 2024, CC BY.

关键词： Partial differential equations

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A Dynamic Pyramidal Relaxation Method of AC OPF

A Dynamic Pyramidal Relaxation Method of AC OPF

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Power and Electrical engineering (ACPEE), 5th Asia Conference on

作者： Yu-Yang Tang Liang Chen Hongbo Du Sheng-Jie Chen Caixia Kou Xiaomeng Ai Bo Zhou Academy of Mathematics and Systems Science Chinese Academy of Sciences Institute of Computational Mathematics and Scientific/Engineering Computing Beijing China College of Sciences Beijing University of Posts and Telecommunications Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Institute of Applied Mathematics Beijing China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Industrial and Operations Engineering University of Michigan Ann Arbor United States

ISBN: (数字)9798350309638

ISBN: (纸本)9798350309645

The Alternating Current Optimal Power Flow (AC OPF) is crucial for power system analysis, yet existing algorithms face challenges in meeting the diverse requirements of practical applications. This paper presents a Pyramidal Relaxation (PR) formulation of the Branch Flow Model (BFM) for AC OPF, where surface conical constraints are approximated through a union of tetrahedrons in a Mixed Integer Linear Programming (MILP) relaxation. To reduce problem size and to enhance computational efficiency, we proposed a Dynamic Pyramidal Relaxation (DPR) method, where the cuts (inner approximation and outer approximation) are adaptively added. In practice, the DPR method is embedded into a MILP commercial solver based on the branch-and-cut framework. Case studies in different test systems validate the effectiveness in terms of feasibility, accuracy, and efficiency of our proposed method.

关键词： Electrical engineering computational modeling Power system dynamics Asia Linear approximation Relaxation methods Mixed integer linear programming

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Optimal-rate error estimates and a twice decoupled solver for a backward Euler finite element scheme of the Doyle-Fuller-Newman model of lithium-ion batteries

arXiv

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arXiv 2024年

作者： Xu, Shu Cao, Liqun School of Mathematical Sciences Peking University Beijing100871 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Scientific and Engineering Computing Beijing100190 China National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing100190 China

We investigate the convergence of a backward Euler finite element discretization applied to a multi-domain and multi-scale elliptic-parabolic system, derived from the Doyle-Fuller-Newman model for lithium-ion batteries. We establish optimal-order error estimates for the solution in the norms l2(H1) and l2(L2(Hrq)), q = 0,1. To improve computational efficiency, we propose a novel solver that accelerates the solution process and controls memory usage. Numerical experiments with realistic battery parameters validate the theoretical error rates and demonstrate the significantly superior performance of the proposed solver over existing solvers. Copyright © 2024, The Authors. All rights reserved.

关键词： computational efficiency

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A Robust Deep-Learning Model to Detect Major Depressive Disorder Utilizing EEG Signals

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4938-4947页

作者： Anik, Israq Ahmed Kamal, A.H.M. Kabir, Muhammad Ashad Uddin, Shahadat Moni, Mohammad Ali Jatiya Kabi Kazi Nazrul Islam University Department of Computer Science and Engineering Trishal2224 Bangladesh Jatiya Kabi Kazi Nazrul Islam University Trishal2224 Bangladesh Charles Sturt University School of Computing Mathematics and Engineering BathurstNSW2795 Australia The University of Sydney School of Project Management Faculty of Engineering SydneyNSW2037 Australia Charles Sturt University Institute of Ai and Cyber Future BathurstNSW2795 Australia Charles Sturt University Rural Health Research Institute OrangeNSW2800 Australia

Major depressive disorder (MDD), commonly called depression, is a prevalent psychiatric condition diagnosed via questionnaire-based mental status assessments. However, this method often yields inconsistent and inaccurate results. Furthermore, there is currently a lack of a comprehensive diagnostic framework for MDD that assesses various brainwaves (alpha, theta, gamma, etc.) of electroencephalogram (EEG) signals as potential biomarkers, aiming to identify the most effective one for achieving accurate and robust diagnostic outcomes. To address this issue, we propose an innovative approach employing a deep convolutional neural network (DCNN) for MDD diagnosis utilizing the brainwaves present in EEG signals. Our proposed model, an extended 11-layer 1-D convolutional neural network (Ex-1DCNN), is designed to automatically learn from input EEG signals, foregoing the need for manual feature selection. By harnessing intrinsic brainwave patterns, our model demonstrates adaptability in classifying EEG signals into depressive and healthy categories. We have conducted an extensive analysis to identify optimal brainwave features and epoch duration for accurate MDD diagnosis. Leveraging EEG data from 34 MDD patients and 30 healthy subjects, we have identified that the Gamma brainwave at a 15-s epoch duration is the most effective configuration, achieving an accuracy of 99.60%, sensitivity of 100%, specificity of 99.21%, and an F1 score of 99.60%. This study highlights the potential of deep-learning techniques in streamlining the diagnostic process for MDD and offering a reliable aid to clinicians in MDD diagnosis. © 2024 IEEE.

关键词： Feature extraction

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Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks

arXiv

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arXiv 2025年

作者： Buehler, Markus J. Laboratory for Atomistic and Molecular Mechanics Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology CambridgeMA02139 United States

We present an agentic, autonomous graph expansion framework that iteratively structures and refines knowledge in situ. Unlike conventional knowledge graph construction methods relying on static extraction or single-pass learning, our approach couples a reasoning-native large language model with a continually updated graph representation. At each step, the system actively generates new concepts and relationships, merges them into a global graph, and formulates subsequent prompts based on its evolving structure. Through this feedback-driven loop, the model organizes information into a scale-free network characterized by hub formation, stable modularity, and bridging nodes that link disparate knowledge clusters. Over hundreds of iterations, new nodes and edges continue to appear without saturating, while centrality measures and shortest path distributions evolve to yield increasingly distributed connectivity. Our analysis reveals emergent patterns—such as the rise of highly connected "hub" concepts and the shifting influence of "bridge" nodes—indicating that agentic, self-reinforcing graph construction can yield open-ended, coherent knowledge structures. Applied to materials design problems, we present compositional reasoning experiments by extracting node-specific and synergy-level principles to foster genuinely novel knowledge synthesis, yielding cross-domain ideas that transcend rote summarization and strengthen the framework’s potential for open-ended scientific discovery. We discuss other applications in scientific discovery and outline future directions for enhancing scalability and interpretability. © 2025, CC BY-NC-ND.

关键词： Knowledge graph

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Prediction of Rental Demands using different Machine Learning Algorithms

Prediction of Rental Demands using different Machine Learnin...

引用

2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, S. Vijay, K. Pavithra, S. Prithi, S. Kannan, Sriram Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineeirng Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Chennai India SRM IST School of Computing Department of Computational Intelligence Kattankulathur India Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348217

A Bike rental expectation conjectures the interest for bikes rentals in reliance of climate conditions like the temperature and calendric data. In bike rental frameworks, it is vital that the managers have to realize the number of bikes to be placed in each and every station. This include empowers them in or-ganizing the necessary number of bikes at each station and choose whether a specific station needs to have additional number of bikes or it is not re-quired. In this venture we will foresee the no of bikes required for explicit climate and climatic conditions. © 2023 IEEE.

关键词： Machine learning

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Scalable AI Solutions for IoT-based Healthcare Systems using Cloud Platforms 8

Scalable AI Solutions for IoT-based Healthcare Systems using...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Dankan Gowda, V. Chaithra, S.M. Gujar, Shantanu Sudhir Shaikh, Salman Firoz Ingole, Balaji Shesharao Reddy, N. Sudhakar Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Mathematics Karnataka India Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of CyberSecurity RochesterNY14623 United States Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of Computer Science RochesterNY14623 United States India Mohan Babu University School of Engineering Department of Ece Tirupati India

ISBN: (纸本)9798350376425

This research offers a solution for AI in IoT-based health care systems that can help large scale implementation of AI-based cloud platforms for real-time data processing and analysis. The IoT sensors with the cloud-enabled environment supported by the CNNs and LSTM networks for health condition monitoring allows real-time management of patient health status. Based on the experimental context, the system was able to prove that the system can actually filter large data stream and work even through the fluctuating network environment. Specifically, the performance targets such as the time taken to process the data, AI output accuracy, the ability of the system to scale and the robustness of the system were assessed. The results demonstrate that the proposed solution has accuracy of up to 96% in health predictions and is easily scalable in terms of the number of IoT devices involved. Network latency was also well controlled, whereby the lowest network latency for networks was measured in 5G networks. Such conclusions prove that the application of cloud-based AI systems can enhance the conditions of the healthcare provisioning, in terms of timely identification of patients with certain health conditions. © 2024 IEEE.

关键词： Electronic health record

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Blood Cancer Detection and Classification using Auto Encoder and Regularized Extreme Learning Machine 8

Blood Cancer Detection and Classification using Auto Encoder...

引用

8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kanimozhi, N. Nayak, Swikruti Keshav Kumar, K. Manjramkar, Vinda Kumar, Ranjeet Suganthi, D. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai Tamilnadu Kattankulathur India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India Department of Humanities and Mathematics Shaikpet Telangana Hyderabad India Zoology Maharashtra Thane India University College of Engineering & Technology Vinoba Bhave University Department of Electronics & Communication Jharkhand Hazaribagh India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350396638

Leukaemia is considered as the most common type of blood cancer. The length of time one requires to recover from this disease is determined by how quickly it is recognized and treated. Leukaemia develops when the bone marrow generates an abnormally large amount of white blood cells. Since images of human blood samples are studied under the microscope, haematologists employ techniques such as microscopic colour imaging, image segmentation, grouping, and classification to diagnose patients as fast as possible. Microscopy may identify blood cancer in visible and immature white blood cells in a variety of methods. Early and accurate diagnosis of leukaemia is critical for clinicians to properly treat patients. After the input image is provided, the gaussian filter is used to perform image enhancement, and the Wiener filter is used in image Deblurring, Adaptive K-Means clustering technique, Colour feature, Texture feature, GLCM and Hausdroff are also used, and lastly the Model is trained using AE and RELM. The proposed system outperforms conventional models such as RELM and ELM, which generates an accuracy of 98.45%. © 2023 IEEE.

关键词： Image enhancement

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Automated Decision-Making System for Decentralized Stock Trading using Blockchain Smart Contracts and Quaternion Generative Adversarial Networks 4

Automated Decision-Making System for Decentralized Stock Tra...

引用

4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Mohideen AbdulKader, M. Nallusamy, Kalaichelvi Jebamalar Zulaiha Maryam, M. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamilnadu Chengalpattu India Tamilnadu Kalavakkam India Mohamed Sathak A. J. College of Engineering Department of Information Technology Tamilnadu Siruseri India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Mathematics & Actuarial Science GST Road Vandalur Tamilnadu Chennai India

ISBN: (纸本)9798331529635

In the field of financial technology, the creation of an automated decision system for the decentralized execution of stock market operations is a noteworthy development. The challenges of the generic approach, such as transparency, security, and efficiency in stock trading, are effectively solved with the help of predicting analytics combined with smart contracts based on blockchain technology. In this research, a Blockchain Smart Contract Adoption with Quaternion Generative Adversarial Networks, (BSCA-QGAN), is presented for decentralized stock market prediction over Yahoo Finance data. The first pre-processing is data cleaning and normalization of data collected and then feature extraction is done by the Spike Driven Transformer (SDT) which is the integration of Transformers and Spiking Neural Networks. Also, the Quaternion Generative Adversarial Networks (QGANs) forecast the stock prices whereby time series with built-in quaternion math is modelled. The QGAN's hyperparameters are optimized using the White Shark Optimizer (WSO). An automated decision system for decentralized stock markets is integrated with blockchain smart contracts. The system achieved 99.72% prediction accuracy and minimal memory usage of 10MB. © 2024 IEEE.

关键词： Smart contract

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