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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Qizhen Wu Gaoxiang Liu Kexin Liu Lei Chen School of Automation Science and Electrical Engineering Beihang University Beijing China The State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beihang University Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

The bird-oid object (Boids) model proposes a control algorithm to make the positions between agents achieve cooperative stability. By changing the parameters of cohesion and repulsion in the algorithm, the agents in the swarm can be made to converge to different positions, causing expansion and contraction of the formation. But it is often more difficult to select the appropriate parameters to form the ideal formation. Therefore, this paper proposes a method to improve the cohesive and repulsive parameters in the Boids model based on Q-learning network to achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space.

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Bubble dynamics and structural damage caused by underwater explosion

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International Journal of Mechanical Sciences 2025年 297-298卷

作者： Li, Hailong Zhang, Jingyuan Wang, Longkan Li, Bing Zhang, Zhifan Zhang, Guiyong Zong, Zhi State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian116024 China China Ship Research and Development Academy Beijing100192 China 91439 Troops Liaoning Dalian116041 China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai200240 China Fuyao University of Science and Technology Fuzhou350100 China

In ocean engineering, curved structures are commonly used in various marine applications, including unmanned underwater vehicles (UUVs) and autonomous underwater vehicles (AUVs). However, the interaction between underwater explosions (UNDEX) and such elastic-plastic curved structures remains insufficiently understood, particularly regarding bubble dynamics and water jet loading near deformable boundaries. To address this gap, this study presents a combined experimental and numerical investigation of UNDEX near an elastic-plastic curved double-hull structure—a configuration that has received limited prior attention. Shockwave and bubble pulsation pressures were recorded using pressure sensors, and bubble motion and jet formation were captured via high-speed imaging. Numerical simulations using the coupled Eulerian-Lagrangian (CEL) method were performed to complement the experiments, covering 63 representative cases. Compared to shockwave loading alone, the combined action of shockwave, bubble, and water jet increased the outer hull deflection by 184 %, highlighting the critical role of multi-source interactions. Two key parameters—distance parameter (γ) and ring rib position (β)—were systematically varied to assess their influence on bubble behavior, jet dynamics, and structural damage. This study contributes new insights into the emergence of mutually deviating water jets and proposes a classification of six typical bubble shapes, four fundamental jet units, and five distinct jet types near elastic-plastic boundaries. These findings enhance the understanding of fluid-structure interaction (FSI) under multi-source loading and reveal the structural vulnerability associated with bubble pulsation and jet impact. The results underscore the importance of considering these coupled effects in the design and protection of marine structures exposed to underwater explosions. © 2025

关键词： Marine applications

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Predicting Grinding Mill Power Consumption in Mining: A Comparative Study

Predicting Grinding Mill Power Consumption in Mining: A Comp...

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International Conference on Information Science and Technology (ICIST)

作者： Chaimae Loudari Moha Cherkaoui Rachid Bennani Imad El Harraki Omar Fares Mohamed El Adnani El Hassan Abdelwahed Intissar Benzakour François Bourzeix Karim Baina dept. LMAID Laboratory National School of Mines of Rabat (ENSMR) Rabat Morocco dept. Digitalization and Microelectronic Smart Devices Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. EIA Reminex Research Center MANAGEM Group Casablanca Morocco dept. LISI Laboratory University of Cadi Ayyad (UCA) Marrakech Morocco dept. Embedded Systems and Artificial Intelligence Department Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Rabat Morocco dept. Al-Qualsadi Research & Development Team National Higher School for Computer Science and System analysis (ENSIAS) Rabat Morocco

The mining industry deals with complexity and high energy consumption in mineral production. Artificial intelligence technology has been integrated to manage and optimize energy usage in mining equipment. A special focus is given to grinding mills, which are essential but difficult to model due to their energy-intensive nature. Data-driven machine learning approaches have emerged as effective solutions, enabling energy consumption prediction and optimization. These methods offer valuable insights to improve the overall energy efficiency of grinding mills in mining. In this article, a comparative study is conducted to investigate the use of machine learning models for predicting the power consumption of grinding mills. The study starts by providing a detailed description of the dataset used and the predictive models employed in the research. Notably, the results of power prediction for grinding mills are compared, with the Random Forest model emerging as the top performer, boasting an impressive R2 value of 0.94. The article underscores the significance of this predictive capability in helping mining experts make informed decisions to optimize energy consumption in grinding mills. Finally, the article concludes by offering insightful recommendations and identifying promising avenues for future research in this field. Overall, this study sheds light on the potential of machine learning to enhance energy efficiency in the mining industry.

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Powerful Terahertz Emission from a Bi2Sr2CaCu2O8+δ Mesa Operating Above 77 K

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Physical Review Applied 2023年第3期19卷 034055-034055页

作者： K.J. Kihlstrom K.C. Reddy S. Elghazoly T.E. Sharma A.E. Koshelev U. Welp Y. Hao R. Divan M. Tsujimoto K. Kadowaki W.-K. Kwok T.M. Benseman Department of Physics Queens College of the City University of New York 6530 Kissena Blvd. Queens New York 11367 USA Materials Science Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Physics University of Illinois at Chicago Chicago Illinois 60607 USA Center for Nanoscale Materials Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8768 Japan Algae Biomass and Energy System (ABES) Research & Development Center University of Tsukuba Ibaraki 305-8573 Japan

Mesa-shaped structures of the high critical temperature (high-Tc) superconductor Bi2Sr2CaCu2O8+δ contain stacked intrinsic Josephson junctions. As such, they are a promising source of coherent radiation in the “terahertz gap” range, spanning from approximately 0.3 to 2.0 THz. Technological applications of these devices become far more practical if they can be operated at a cryogenic bath temperature of 77 K or higher. Previous works have reported emission from this type of device at high terahertz power levels at lower operating temperatures, 40–60 K, while at Tbath≥77K observed power levels have generally been low. Here we report generation of 130 μW of coherent power at 0.456 THz from a mesa of Bi2Sr2CaCu2O8+δ doped with 0.16 holes per Cu atom, at a bath temperature of 77.4 K. We find that the device radiates terahertz power when clearly identifiable cavity modes are excited, and that the frequency and bias voltage corresponding to each of these modes is almost independent of temperature. This is consistent with these modes having terahertz-frequency electric fields with very little dependence on vertical position within the mesa. We also find that the terahertz power radiated from any given mode decreases monotonically as the mesa temperature is increased. On the other hand, the low-frequency modes become inaccessible at low temperatures due to retrapping of the intrinsic Josephson junctions, and the maximum radiation power for the emitting mode is typically achieved at the temperature at which the retrapping voltage reaches the resonance voltage for this mode.

关键词： Collective dynamics Josephson effect Optoelectronics d-wave Cuprates Josephson junctions Optical sources & detectors Terahertz sources

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3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis

arXiv

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arXiv 2022年

作者： Zhou, Yang Yang, Zhiwen Zhang, Hui Chang, Eric I-Chao Fan, Yubo Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Biomedical Engineering Tsinghua University Beijing100084 China Microsoft Research Beijing100080 China

Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-dose. Previous studies based on deep learning methods usually directly extract hierarchical features for reconstruction. We notice that the importance of each feature is different and they should be weighted dissimilarly so that tiny information can be captured by the neural network. Furthermore, the synthesis on some regions of interest is important in some applications. Here we propose a novel segmentation guided style-based generative adversarial network (SGSGAN) for PET synthesis. (1) We put forward a style-based generator employing style modulation, which specifically controls the hierarchical features in the translation process, to generate images with more realistic textures. (2) We adopt a task-driven strategy that couples a segmentation task with a generative adversarial network (GAN) framework to improve the translation performance. Extensive experiments show the superiority of our overall framework in PET synthesis, especially on those regions of interest. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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All-In-One Medical Image Restoration via Task-Adaptive Routing

arXiv

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arXiv 2024年

作者： Yang, Zhiwen Chen, Haowei Qian, Ziniu Yi, Yang Zhang, Hui Zhao, Dan Wei, Bingzheng Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Biomedical Engineering Tsinghua University Beijing100084 China Department of Gynecology Oncology National Cancer Center National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100021 China ByteDance Inc. Beijing100098 China

Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider application. In this paper, we focus on the task of all-in-one medical image restoration, aiming to address multiple distinct MedIR tasks with a single universal model. Nonetheless, due to significant differences between different MedIR tasks, training a universal model often encounters task interference issues, where different tasks with shared parameters may conflict with each other in the gradient update direction. This task interference leads to deviation of the model update direction from the optimal path, thereby affecting the model’s performance. To tackle this issue, we propose a task-adaptive routing strategy, allowing conflicting tasks to select different network paths in spatial and channel dimensions, thereby mitigating task interference. Experimental results demonstrate that our proposed All-in-one Medical Image Restoration (AMIR) network achieves state-of-the-art performance in three MedIR tasks: MRI super-resolution, CT denoising, and PET synthesis, both in single-task and all-in-one settings. The code and data will be available at https://***/Yaziwel/AMIR. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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Region Attention Transformer for Medical Image Restoration

arXiv

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arXiv 2024年

作者： Yang, Zhiwen Chen, Haowei Qian, Ziniu Zhou, Yang Zhang, Hui Zhao, Dan Wei, Bingzheng Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Biomedical Engineering Tsinghua University Beijing100084 China Department of Gynecology Oncology National Cancer Center National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100021 China ByteDance Inc. Beijing100098 China

Transformer-based methods have demonstrated impressive results in medical image restoration, attributed to the multi-head self-attention (MSA) mechanism in the spatial dimension. However, the majority of existing Transformers conduct attention within fixed and coarsely partitioned regions (e.g. the entire image or fixed patches), resulting in interference from irrelevant regions and fragmentation of continuous image content. To overcome these challenges, we introduce a novel Region Attention Transformer (RAT) that utilizes a region-based multi-head self-attention mechanism (R-MSA). The R-MSA dynamically partitions the input image into non-overlapping semantic regions using the robust Segment Anything Model (SAM) and then performs self-attention within these regions. This region partitioning is more flexible and interpretable, ensuring that only pixels from similar semantic regions complement each other, thereby eliminating interference from irrelevant regions. Moreover, we introduce a focal region loss to guide our model to adaptively focus on recovering high-difficulty regions. Extensive experiments demonstrate the effectiveness of RAT in various medical image restoration tasks, including PET image synthesis, CT image denoising, and pathological image super-resolution. Code is available at https://***/RAT. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Spectroscopic observation of the crossover from a classical Duffing oscillator to a Kerr parametric oscillator

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Physical Review A 2022年第2期105卷 023519-023519页

作者： T. Yamaji S. Kagami A. Yamaguchi T. Satoh K. Koshino H. Goto Z. R. Lin Y. Nakamura T. Yamamoto System Platform Research Laboratories NEC Corporation Kawasaki Kanagawa 211-0011 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan College of Liberal Arts and Sciences Tokyo Medical and Dental University Ichikawa Chiba 272-0827 Japan Corporate Research and Development Center Toshiba Corporation Kawasaki Kanagawa 212-8582 Japan Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Meguro Tokyo 153-8904 Japan

We study microwave response of a Josephson parametric oscillator consisting of a superconducting transmission-line resonator with an embedded DC-superconducting quantum interference device (-SQUID). The DC-SQUID allows to control the magnitude of a Kerr nonlinearity over the ranges where it is smaller or larger than the photon loss rate. Spectroscopy measurements reveal the change in the microwave response from a classical Duffing oscillator to a Kerr parametric oscillator in a single device. In the single-photon Kerr regime, we observe parametric oscillations with a well-defined phase of either 0 or π, whose probability can be controlled by an externally injected signal.

关键词： Quantum measurements Quantum-to-classical transition

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Learnable Operational Design Condition Monitor for Failure Prediction in Autonomous Driving

SSRN

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SSRN 2025年

作者： Xu, Haowei Bian, Yougang Li, Yang Qin, Hongmao Zhou, Hanchu Chang, Fangrong Wang, Shaofei Ye, Qing State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Research and Development Center of Transport Industry of Self-driving Technology China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing China School of Traffic and Transportation Engineering Central South University Changsha China Department of Data and System Engineering The University of Hong Kong Hong Kong School of Resources and Safety Engineering Central South University Changsha China School of Data Science China

Ensuring the safety of Autonomous Driving systems (ADS) across diverse and unpredictable operational scenarios remains a significant challenge for accident prevention. Central to this effort is the continuous monitoring of Operational Design Conditions (ODC), which encompass both in-vehicle factors and external Operational Design Domain (ODD). However, the absence of robust methodologies to precisely identify ODC boundaries hinders the deployment and adoption of high-level ADS. This paper introduces a learnable ODC monitor designed to identify implicit operational design condition boundaries to prevent ADS failures. By integrating a state-space duality encoder and a Bayesian neural network decoder, we propose an efficient learnable ODC monitor, termed ODCNet. Furthermore, to further enhance prediction reliability, residual error analysis with linear discrimination is applied for calibrating prediction. Experimental results demonstrate ODCNet's superiority in failure prediction compared to baseline models, achieving a balance between safety and coverage. These findings contribute to advancing data-driven accident prediction and prevention strategies, ensuring safer deployments of ADS in complex environments. © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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