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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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From simulations to reality: enhancing multi-robot exploration for urban search and rescue

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International Journal of Information Technology (Singapore) 2025年 1-12页

作者： Kashyap, Gautam Siddharth Mahajan, Deepkashi Phukan, Orchid Chetia Kumar, Ankit Brownlee, Alexander E. I. Gao, Jiechao IIIT Delhi New Delhi India GeeksforGeeks Noida India Division of Computing Science & Mathematics University of Stirling Stirling United Kingdom University of Virginia Charlottesville VA United States

In this paper, we propose a novel hybrid algorithm that combines Levy Flight (LF) and Particle Swarm Optimization (PSO) (LF-PSO), designed for efficient multi-robot exploration in unknown environments without Global Positioning Systems (GPS) or detailed sensor data. The focus of the study is to address challenges in autonomous multi-robot exploration for general-purpose tasks rather than specifically targeting Urban Search and Rescue (USAR) scenarios. Although multi-robot systems offer advantages in terms of scalability, flexibility, and area coverage, many existing approaches rely on assumptions that may not translate well to real-world applications, such as perfect communication or target information. Our hybrid algorithm leverages LF for large-area exploration while integrating PSO-based inter-robot repulsion to prevent clustering and ensure diverse coverage. Experimental results in a controlled obstacle-free environment demonstrate the algorithm's potential for general exploration tasks, though further studies are required to adapt this approach for complex, obstacle-rich environments relevant to USAR scenarios. These simulations highlight improved area coverage compared to traditional methods but do not yet address challenges posed by sensor-based navigation or real-world USAR conditions. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Levy flight Multi-robot exploration Particle swarm optimization

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Fuzzy-Based Optimization Techniques for Segmenting the Tumors in Multimodal MRI Images

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Operations Research Forum 2025年第1期6卷 1-27页

作者： Alagarsamy, Saravanan Nagarajan, D. Govindaraj, Vishnuvardhan Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Rajiv Gandhi Salai (OMR) Tamilnadu Chennai India Department of Mathematics Rajalakshmi Institute of Technology Chennai India Division of Data Science of School of Computing Science and Education VIT Bhopal University Kothrikalan Madhya Pradesh Sehore 466114 India

Accurate brain tumor prediction is essential when approaching the field of health care;wherever accuracy in decision-making is crucial, the issues also need to be resolved right away. Many artificial intelligence and machine learning-based techniques have been developed in the last couple of years in the field of healthcare. The intention of this work is to create an algorithm that combines the features of Crow Search (CS) and Interval Type-II Fuzzy Logic System (IT2FLS) algorithms to distinguish the area of tumor from complex brain tissues. The ability of oncologists to make decisions is critical for any therapy sequence to be successful, and the methodology proposed in the research work considerably influences a conclusion through technology interruption. The suggested approach is flexible enough to work with a diversity of image sequences found in the BRATS Challenge 2020 dataset that presents varying degrees of obstacles, challenges, and difficulties in locating the anomalous regions, and it produces improved demarcation results that have been instinctively evaluated and supported by Dice score (98 ± 1.3), specificity (98 ± 1.4), and sensitivity (98 ± 1.7) as average metrics. The motive of this study is the enhancement of the visual perception of oncologists, which gives them improved insight into and comprehension of the condition of the patient. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Crow Search (CS) Interval Type-II Fuzzy Logic System (IT2FLS) Magnetic Resonance Imaging (MRI) Tumor segmentation

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A generalized hybrid distance measure for SVNS with an application in medical diagnosis

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Neutrosophic Sets and Systems 2025年 76卷 189-204页

作者： Mustapha, Norzieha Alias, Suriana Md Yasin, Roliza Zulkifli, Alia Nur Izzah Shafii, Noorazliyana Smarandache, Florentin School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Kelantan Machang Campus Kelantan Malaysia Delphina Technik Sdn Bhd Selangor Petaling Jaya47301 Malaysia School of Health Sciences Health Campus Universiti Sains Malaysia Kubang Kerian Kelantan 16150 Malaysia The University of New Mexico Mathematics Physics and Natural Science Division 705 Gurley Ave GallupNM87301 United States

A generalized hybrid distance measure of a single value neutrosophic set (SVNS) is proposed to analyze the patient’s factors and diseases. Since Neutrosophic Set (NS) may communicate contradictory and ambiguous information, it is a crucial and useful tool for modelling uncertainty information. A distance measure for NS information is a key tool that is always employed in many approaches including medical diagnostics. Several distance measures in NS can be found in the literature, however, only a few of them proposed hybrid techniques. This paper aims to develop a new distance measure by considering exponential function and to define new hybrid distance measures for NS. The work also includes the formulation of the properties and application in the medical diagnosis for the validity phase. This study examines the possibility of cardiac problems in pregnant women. The eight factors considered in this study are age, obesity, smoking, family pathological history, personal pathological history, electrocardiogram, ultrasound, and functional class. The generalized hybrid distance measure has been used to discuss the factors of six different diseases, present consistent results. © 2025, University of New Mexico. All rights reserved.

关键词： Exponential functions

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Bayesian classifier based on cancer prognostic markers using accelerated failure time model with frailty effect

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Quality and Quantity 2025年 1-27页

作者： Vishwakarma, Gajendra K. Kumari, Pragya Bhattacharjee, Atanu Ong, Seng Huat Department of Mathematics & Computing Indian Institute of Technology Dhanbad Jharkhand Dhanbad 826004 India Division of Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur 56000 Malaysia Institute of Mathematical Sciences University of Malaya Kuala Lumpur 50603 Malaysia

This work presents a Bayesian classifier technique to categorize patients based on predictive biomarkers of time-to-event data utilizing the Accelerated Failure Time (AFT) model incorporating the frailty effect. Before classification, efficient and significant markers from a high-dimensional gene expression dataset need to be identified. Currently, it is an emerging area in oncology. A conventional three-step feature selection approach is introduced to select the most relevant markers from high-dimensional data. A threshold value for each selected marker is obtained using the proposed Bayesian classification procedure incorporating the AFT model with the frailty effect. The frailty effect is incorporated to account for unobserved heterogeneity in the expression values of subjects, allowing for a more accurate investigation of the risk of cancer-related outcomes. A simulation study is performed to validate the proposed classification approach, and the classification’s performance is evaluated using the Brier score, and the result indicates that it is relatively high. Application of the proposed classification technique is provided for two high-dimensional TCGA datasets of lung cancer patients. Our results provide evidence about the effect of classified gene expression values on the survival risk of lung cancer patients. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Accelerated failure time model Bayesian Cancer’s biomarker Classification Survival analysis

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Optimization of the rectangle area inside a concave polygon using PSO and Tabu Search

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Soft computing 2025年 1-23页

作者： Kashyap, Gautam Siddharth Malik, Karan Wazir, Samar Brownlee, Alexander E. I. Department of CSE SEST Jamia Hamdard New Delhi New Delhi India USICT GGSIP University New Delhi India Division of Computing Science and Mathematics University of Stirling Stirling United Kingdom

Optimising a figure inside any shape is an important problem with many applications. Several studies tried to find the largest area of the rectangle inside the convex polygon in mathematics, as there are polynomial-time solutions available i.e. there exists an algorithm for finding the solution bounded by a polynomial function, but not for concave polygon as there are no polynomial-time solutions available. This makes this problem an NP (Nondeterministic Polynomial)-complete problem. Generally, the availability of the solution depends upon the length of the input for the problem. However, when solving an NP-complete problem, one method is to apply a polynomial algorithm to estimate the solution;the result will not always be optimal, but it will be close. Therefore, this paper attempts to find the maximum area of the rectangle inside the non-convex polygon by using two heuristic algorithms that are: 1) PSO (Particle Swarm Optimization), and 2) Tabu Search. Where Tabu Search has been utilised for comparative purposes to demonstrate the PSO's exceptional performance. These two algorithms work fast and find the optimal rectangle more efficiently than conventional mathematical approaches. These approximation algorithms have an O(n3) time complexity and O(n2) space complexity for PSO, and O(n4) time complexity and O(n2) space complexity for Tabu Search. In addition to this, the already available literature is also employed for comparative purposes, with PSO displaying competitive results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Area Computational geometry optimization Concave Polygon PSO Rectangle Tabu search

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Decoder-Only Image Registration

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Jia, Xi Lu, Wenqi Cheng, Xinxing Duan, Jinming University of Birmingham School of Computer Science United Kingdom Manchester Metropolitan University Department of Computing and Mathematics United Kingdom University of Manchester Division of Informatics Imaging and Data Sciences United Kingdom

In unsupervised medical image registration, encoder-decoder architectures are widely used to predict dense, full-resolution displacement fields from paired images. Despite their popularity, we question the necessity of making both the encoder and decoder learnable. To address this, we propose LessNet, a simplified network architecture with only a learnable decoder, while completely omitting a learnable encoder. Instead, LessNet replaces the encoder with simple, handcrafted features, eliminating the need to optimize encoder parameters. This results in a compact, efficient, and decoder-only architecture for 3D medical image registration. We evaluate our decoder-only LessNet on five registration tasks: 1) inter-subject brain registration using the OASIS-1 dataset, 2) atlas-based brain registration using the IXI dataset, 3) cardiac ES-ED registration using the ACDC dataset, 4) inter-subject abdominal MR registration using the CHAOS dataset, and 5) multi-study, multi-site brain registration using images from 13 public datasets. Our results demonstrate that LessNet can effectively and efficiently learn both dense displacement and diffeomorphic deformation fields. Furthermore, our decoder-only LessNet can achieve comparable registration performance to benchmarking methods such as Voxel-Morph and TransMorph, while requiring significantly fewer computational resources. © 1982-2012 IEEE.

关键词： Image registration

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Initial Experiences with DAOS Object Storage on Aurora 24

Initial Experiences with DAOS Object Storage on Aurora

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Rob Latham Robert B. Ross Philip Carns Shane Snyder Kevin Harms Kaushik Velusamy Paul Coffman Gordon McPheeters Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9798350355543

The storage subsystem of the Aurora platform at Argonne National Laboratory offers the potential for unprecedented application I/O performance. Its hardware stack combines NVMe drives, persistent memory devices, a large collection of storage servers, and a unified RDMA-capable network fabric. Its software stack is based on the DAOS object storage system, thereby putting into practice decades of I/O research into minimizing overhead, maximizing access concurrency, and streamlining application interfaces. Taken together, these elements combine to present up to 230 PiB of projected storage capacity and up to 31 TiB/s of projected I/O throughput to applications executing on *** storage platform is unique, however, and presents a new set of challenges to practitioners. To unlock the full potential of this system, we must revisit fundamental issues such as hardware topology, network concurrency, and I/O APIs to understand their impact on real-world I/O performance. In this paper we present our initial experiences with the DAOS storage system on Aurora and characterize its sensitivity to these parameters at small scale. We discuss our initial impressions and offer recommendations for how to extract maximum effective performance from this class of storage system.

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A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems

arXiv

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arXiv 2025年

作者： Ryu, Minseok Byeon, Geunyeong Kim, Kibaek School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States

We propose a GPU-accelerated distributed optimization algorithm for controlling multi-phase optimal power flow in active distribution systems with dynamically changing topologies. To handle varying network configurations and enable adaptable decomposition, we advocate a componentwise decomposition strategy. However, this approach can lead to a prolonged computation time mainly due to the excessive iterations required for achieving consensus among a large number of fine-grained components. To overcome this, we introduce a technique that segregates equality constraints from inequality constraints, enabling GPU parallelism to reduce per-iteration time by orders of magnitude, thereby significantly accelerating the overall computation. Numerical experiments on IEEE test systems ranging from 13 to 8500 buses demonstrate the superior scalability of the proposed approach compared to its CPU-based counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Computer graphics equipment

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FIDSUS: Federated Intrusion Detection for Securing UAV Swarms in Smart Aerial computing

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IEEE Internet of Things Journal 2025年第9期12卷 11312-11328页

作者： Deng, Jiangtao Wang, Wei Wang, Lina Bashir, Ali Kashif Gadekallu, Thippa Reddy Feng, Hailin Lv, Meilei Fang, Kai Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Quzhou University College of Electrical and Information Engineering Quzhou324000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BX United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India Lovely Professional University Division of Research and Development Phagwara144411 India Chitkara University Center of Research Impact and Outcome Rajpura140401 India

The dynamic environment of UAV swarms in forest management is characterized by communication instability, heterogeneous nodes, and frequent topology changes due to challenging terrain. These systems are vulnerable to network attacks, requiring advanced intrusion detection technologies. Traditional methods struggle with rapid changes due to data privacy concerns and centralized computational limits, while existing federated learning (FL) algorithms lack robustness against client heterogeneity and dynamic data distribution, especially in complex forest environments. To address these challenges, we propose federated intrusion detection for securing UAV swarms (FIDSUS). FIDSUS improves intrusion detection systems by leveraging collaborative sensing among UAVs, enabling better monitoring and response to security threats in forestry. By quantifying the similarity between UAVs’ local feature extractors through an affinity matrix, FIDSUS guides the aggregation of feature extractors, improving detection capabilities. It also uses AI-driven aerial and distributed computing to enhance data processing efficiency and decision-making speed. The framework addresses data heterogeneity by cross-round feature fusion, improving detection in dynamic environments. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that FIDSUS outperforms existing FL methods with a 4%–34% accuracy improvement. FIDSUS shows robustness and accuracy in dynamic environments, providing an effective solution for securing UAV swarms in forestry. © 2014 IEEE.

关键词： Network intrusion

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