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Optimizations and applications in large-scale scenes of Monte Carlo geometry conversion code CMGC

Nuclear Science and Techniques 2025年第4期36卷 220-232页

作者： Xin Wang Ling-Yu Zhang Xue-Ming Shi Zhen Wu Jun-Li Li Gui-Ming Qin Yuan-Guang Fu CAEP Software Center for High Performance Numerical Simulation Beijing 100088China Institute of Applied Physics and Computational Mathematics Beijing 100088China Department of Engineering Physics Tsinghua UniversityBeijing 100084China

In response to the demand for rapid geometric modeling in Monte Carlo radiation transportation calculations for large-scale and complex geometric scenes,functional improvements,and algorithm optimizations were performed using CAD-to-Monte Carlo geometry conversion(CMGC)*** representation(BRep)to constructive solid geometry(CSG)conversion and visual CSG modeling were combined to address the problem of non-convertible geometries such as spline *** splitting surface assessment method in BRep-to-CSG conversion was optimized to reduce the number of Boolean operations using an Open ***,in turn,reduced the probability of CMGC conversion *** auxiliary surface generation algorithm was optimized to prevent the generation of redundant auxiliary surfaces that cause an excessive decomposition of CAD geometry *** optimizations enhanced the usability and stability of the CMGC model *** was applied successfully to the JMCT transportation calculations for the conceptual designs of five China Fusion engineering Test Reactor(CFETR)*** rapid replacement of different blanket schemes was achieved based on the baseline CFETR *** geometric solid number of blankets ranged from hundreds to tens of *** correctness of the converted CFETR models using CMGC was verified through comparisons with the MCNP calculation *** CMGC supported radiation field evaluations for a large urban scene and detailed ship *** enabled the rapid conversion of CAD models with thousands of geometric solids into Monte Carlo CSG *** analysis of the JMCT transportation simulation results further demonstrated the accuracy and effectiveness of the CMGC.

关键词： Monte Carlo CAD BRep to CSG CMGC

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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Revealing atomic strengthening mechanism in CoNiV medium-entropy alloy via machine learning-guided simulations

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Journal of Materials Science and Technology 2025年 238卷 66-77页

作者： Li, Wenyue Liu, Xiongjun Liu, Leqing Du, Qing Lin, Deye Chen, Xin He, Dong Wang, Shudao Wu, Yuan Wang, Hui Jiang, Suihe Zhang, Xiaobin Lu, Zhaoping Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China School of Intelligent Manufacturing and Mechanical Engineering Hunan Institute of Technology Hengyang421002 China School of Materials Science and Engineering Ocean University of China Qingdao266100 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100094 China School of Materials Science and Engineering Dalian University of Technology Dalian116024 China

High/medium entropy alloys (H/MEAs) have shown unique strengthening behavior and mechanical properties because of the presence of massive local chemical orderings. Nevertheless, dynamic interactions between chemical short-range orders (CSROs) and dislocations, and the underlying atomic strengthening mechanism remain elusive. In this work, we first developed a novel machine learning-embedded atom method (ML-EAM) potential of the CoNiV system, trained on a comprehensive first-principles dataset, which enables accurate and efficient modeling of CSRO formation and dislocation dynamics. Then, we investigated the strengthening mechanisms of CSROs in CoNiV MEA through machine learning-augmented molecular dynamics (MD) simulations. Hybrid MD/Monte Carlo simulations reveal that CSRO domains possess an L12 (NiCo)3 V structure, whose size increases with lowering annealing temperatures. These domains significantly enhance strength by impeding dislocation motion through complex energy pathways, increasing depinning forces, and reducing mobility. Moreover, the MD simulations combined with theoretical analysis elucidate the competition between CSRO-assisted strengthening (via antiphase boundary formation) and solid solution weakening (via reduced atomic misfit volume). Phonon-drag effects are also amplified by CSROs, further resisting dislocation glide. Our results demonstrate that L12-CSROs strengthen CoNiV MEA primarily through antiphase boundary and phonon-drag contributions, providing new insights for designing high-performance multi-principal-element alloys via tailoring CSROs. © 2025

关键词： Turing machines

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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Automatic Diagnosis Model of Gastrointestinal Diseases Based on Tongue Images 27th

Automatic Diagnosis Model of Gastrointestinal Diseases Based...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Fu, Baochen Duan, Miao Li, Zhen Zuo, Xiuli Qiao, Xu School of Software Shandong University Shandong Jinan250101 China School of Control Science and Engineering Shandong University Shandong Jinan250061 China Department of Gastroenterology Qilu Hospital Shandong University Shandong Jinan250012 China Shandong National Center for Applied Mathematics Shandong Jinan250100 China

ISBN: (纸本)9783031781278

Gastrointestinal diseases have become a significant global health issue, with early diagnosis and timely intervention being crucial for improving human health. Although endoscopy is considered the primary examination tool, it is time-consuming and invasive, limiting its widespread implementation. According to Traditional Chinese Medicine (TCM) theories, the tongue reflects the body's physiological and pathological conditions. Therefore, tongue images can be used to develop a non-invasive and rapid diagnostic model for gastrointestinal diseases. We collected tongue images from 1123 patients using a smartphone and categorized them into normal and abnormal groups based on their diagnostic reports. Comparison experiments were conducted using four models: ResNet-50, RepVGG,Deit-B and CrossViT. To address dataset imbalances, we introduced a hybrid loss function to enhance the models’ learning for underrepresented classes. The experimental results indicate that Vision Transformer demonstrates superior performance in early diagnosis of gastrointestinal diseases. Specifically, the dualbranch cross-attention model with a hybrid loss function achieved an AUC of 0.87 and an accuracy of 0.84. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Physiological models

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Optimizing Secure Multi-User ISAC Systems With STAR-RIS: A Deep Reinforcement Learning Approach for 6G Networks

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IEEE Access 2025年 13卷 31472-31484页

作者： Kamal, Mian Muhammad Zain Ul Abideen, Syed Al-Khasawneh, M.A. Alabrah, Amerah Sohail Ahmed Larik, Raja Irfan Marwat, Muhammad Southeast University School of Electronic Science and Engineering Jiangning Jiangsu Nanjing211189 China Qingdao University College of Computer Science and Technology Qingdao266071 China Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19111 Jordan Skyline University College School of Computing University City Sharjah Sharjah United Arab Emirates King Saud University College of Computer and Information Science Department of Information Systems Riyadh11543 Saudi Arabia Ilma University Department of Computer Science Sindh Karachi75190 Pakistan University of Science and Technology Bannu Department of Software Engineering Bannu28100 Pakistan

The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications. © 2025 The Authors.

关键词： Deep reinforcement learning

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Development of a Hypersonic Entry Test Platform

Development of a Hypersonic Entry Test Platform

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Johnson, Breanna J. Johnson, Kate E. Jang, Peter Steffes, Stephen R. Fritz, Matthew P. Hawke, Veronica Garcia, Joseph A. Morr, Donald E. NASA Johnson Space Center Flight Mechanics and Trajectory Design Branch HoustonTX77058 United States NASA Johnson Space Center Applied Aeroscience and CFD Branch HoustonTX77058 United States Aerodyne Industries Applied Aeroscience and CFD Branch HoustonTX77058 United States Charles Stark Draper Laboratory Navigation Algorithms and Software Space GNC Group HoustonTX77058 United States Science Technology Corporation Systems Analysis Office Mountain ViewCA United States NASA Ames Research Center NASA Systems Analysis Office Mountain ViewCA94035 United States Millennium Engineering and Integration Company ArlingtonVA22202 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

NASA is designing and building a low-cost, scalable, rapidly developed test platform to lower the barrier to entry for entry, descent, and landing (EDL) technologies. This HyperSonic Testing for Entry Platform, HyperSTEP, will enable the low-cost flight demonstration of hypersonic entry technologies, leveraging a publishable government reference design to further enable future government and industry landers. The HyperSTEP vehicle leverages a lifting body shape that was originally designed for a NASA human-class Mars mission concept vehicle, the CobraMRV. Lifting body vehicles offer increased volume efficiency and greater range control capabilities over capsules, making them an attractive class of vehicles to further develop for small to large payload return missions from the Moon, Mars, and beyond. Progress has been made in the development of the HyperSTEP vehicle in the areas of aerodynamics, aerothermodynamics, structural design and packaging, propulsion, guidance navigation and control (GN&C), decelerators (i.e., parachutes), and the thermal protection system (TPS). This paper details progress made in refining the mission concept of operations (ConOps) for the first notional missions and the development of the aerodynamic and aerothermodynamic databases, GN&C, and parachute system, as the project moves into its build and test phase. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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DIFFERENTIABLE FOLDING FOR NEAREST NEIGHBOR MODEL OPTIMIZATION

arXiv

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arXiv 2025年

作者： Krueger, Ryan K. Aviran, Sharon Mathews, David H. Zuber, Jeffrey Ward, Max School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Biomedical Engineering and Genome Center University of California Davis DavisCA95616 United States Department of Biochemistry and Biophysics and Center for RNA Biology University of the Rochester Medical Center RochesterNY14642 United States Department of Computer Science and Software Engineering The University of Western Australia PerthWA Australia

The Nearest Neighbor model is the de facto thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains ≈ 13, 000 underlying thermodynamic parameters, and fitting these to both experimental and structural data is computationally challenging. Here, we leverage recent advances in differentiable folding, a method for directly computing gradients of the RNA folding algorithms, to devise an efficient, scalable, and flexible means of parameter optimization that uses known RNA structures and thermodynamic experiments. Our method yields a significantly improved parameter set that outperforms existing baselines on all metrics, including an increase in the average predicted probability of ground-truth sequence-structure pairs for a single RNA family by over 23 orders of magnitude. Our framework provides a path towards drastically improved RNA models, enabling the flexible incorporation of new experimental data, definition of novel loss terms, large training sets, and even treatment as a module in larger deep learning pipelines. We make available a new database, RNAometer, with experimentally-determined stabilities for small RNA model systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Federated transfer learning for distributed drought stage prediction

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Discover Artificial Intelligence 2025年第1期5卷 1-13页

作者： Raza, Muhammad Owais Umar, Aqsa Rasheed, Jawad Asuroglu, Tunc Alsubai, Shtwai Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan Department of Software Engineering Istanbul Nisantasi University Istanbul Turkey Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Medicine and Health Technology Tampere University Tampere Finland VTT Technical Research Centre of Finland Tampere Finland Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with drought modeling is the limited availability of centralized datasets. To address this problem, we are a novel proposing federated learning based transfer learning models for the prediction of drought stages. In this study, satellite image dataset was collected from the Tharparkar district (prone to drought) of Pakistan. We trained the dataset using traditional and federated learning approaches, comparing centralized ML models, pre-trained models, and their respective federated learning models (FL-ResNet, FL-DenseNet, FL-MobileNet). The development of these models is the novel aspect of the study specifically for the use case of drought stage prediction. Based on the final evaluation, FL-MobileNet achieved 82% precision while baseline MobileNet scored 68%. The results show the effectiveness of novelty (federated learning), that our proposed framework improves the performance of the drought stage classification task.

关键词： Federated learning

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A multi-objective comprehensive framework for predicting protein-peptide interactions and binding residues

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Wang, Ruheng Yang, Xuetong Pang, Chao Wei, Leyi University of Texas Southwestern Medical Center Department of Biomedical Engineering DallasTX United States Shandong University School of Software Jinan China Jinan China Macao Polytechnic University Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science China Xiamen University School of Informatics Xiamen China

The identification of protein-peptide interacting pairs and their corresponding binding residues is fundamentally crucial and can greatly facilitate the peptide therapeutics designing and understanding the mechanisms of protein functions. Recently, several computational approaches have been proposed to solve the protein-peptide interaction prediction problem. However, most existing prediction methods cannot directly predict the protein-peptide interacting pairs as well as the binding residues from protein and peptide sequences simultaneously. Here, we developed a Comprehensive Protein-Peptide Interaction prediction Framework (CPPIF), to predict both binary protein-peptide interaction and their binding residues. We also constructed a benchmark dataset containing more than 8,900 protein-peptide interacting pairs with non-covalent interactions and their corresponding binding residues to systematically evaluate the performances of existing models. Comprehensive evaluation on the benchmark datasets demonstrated that CPPIF can successfully predict the non-covalent protein-peptide interactions that cannot be effectively captured by previous prediction methods. Moreover, CPPIF outperformed other state-of-the-art methods in predicting binding residues in the peptides and achieved good performance in the identification of important binding residues in the proteins. © 2025 IEEE.

关键词： Prediction models

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