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Nanozymes regulated by nitrogen element:Mechanism,design,and application

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Advanced Powder Materials

advanced Powder Materials 2024年第4期3卷 9-26页

作者： Longhua Ding Longwei Wang Jian Zhang Na Ren Aizhu Wang Hong Liu Xin Yu Institute for Advanced Interdisciplinary Research(iAIR) School of Chemistry and Chemical EngineeringUniversity of JinanJinan 250022China CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety&CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology of ChinaUniversity of Chinese Academy of ScienceBeijing 100190China Division of Systems and Synthetic Biology Department of Life SciencesChalmers University of Technology41296GöoteborgSweden State Key Laboratory of Crystal Materials Shandong UniversityJinan 250100China Key Laboratory of Optic-electric Sensing and Analytical Chemistry for Life Science MOEQingdao University of Science and TechnologyQingdao 266042China

Nanozymes,a category of nanomaterials endowed with enzyme-mimicking capabilities,have exhibited considerable potential across diverse application *** comprehensive review delves into the intricacies of regulating nanozymes through N elements,elucidating the mechanisms governing N element control in the design and application of these *** initial sections introduce the foundational background and significance of *** exploration delves into the detailed discussion of N element regulation mechanisms on nanozymes,encompassing N vacancies,N doping,N coordination,and *** regulatory pathways play an instrumental role in fine-tuning the catalytic activity and specificity of *** review further scrutinizes practical applications of N element regulation on nanozymes,spanning sensing detection,infection therapy,tumor therapy,and pollutant *** conclusion,it succinctly summarizes the current research findings and proposes future directions for *** thorough investigation into the regulation of nanozymes by N elements anticipates precise control over their performance,thereby advancing the extensive utilization of nanozymes in the realms of biomedical and environmental applications.

关键词： Nnanozyme Nitrogen doping Nitrogen corrdination Infection therapy Tumor therapy

Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating advanced Parameter Initialization Strategies

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arXiv 2023年

作者： Kashif, Muhammad Rashid, Muhammad Al-Kuwari, Saif Shafique, Muhammad eBrain Lab Division of Engineering New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Center for Quantum and Topological Systems NYUAD Research Institute New York University Abu Dhabi United Arab Emirates Department of Computer Engineering Umm Al Qura University Makkah Saudi Arabia Qatar Center for Quantum Computing College of Science and Engineering Hamad bin Khalifa University Qatar

Parameterized quantum circuits (PQCs) have emerged as a foundational element in the development and applications of quantum algorithms. However, when initialized with random parameter values, PQCs often exhibit barren plateaus (BP). These plateaus, characterized by vanishing gradients with an increasing number of qubits, hinder optimization in quantum algorithms. In this paper, we analyze the impact of state-of-the-art parameter initialization strategies from classical machine learning in random PQCs from the aspect of BP phenomenon. Our investigation encompasses a spectrum of initialization techniques, including random, Xavier (both normal and uniform variants), He, LeCun, and Orthogonal methods. Empirical assessment reveals a pronounced reduction in variance decay of gradients across all these methodologies compared to the randomly initialized PQCs. Specifically, the Xavier initialization technique outperforms the rest, showing a 62% improvement in variance decay compared to the random initialization. The He, Lecun, and orthogonal methods also display improvements, with respective enhancements of 32%, 28%, and 26%. This compellingly suggests that the adoption of these existing initialization techniques holds the potential to significantly amplify the training efficacy of Quantum Neural Networks (QNNs), a subclass of PQCs. Demonstrating this effect, we employ the identified techniques to train QNNs for learning the identity function, effectively mitigating the adverse effects of BPs. The training performance, ranked from the best to the worst, aligns with the variance decay enhancement as outlined above. This paper underscores the role of tailored parameter initialization in mitigating the BP problem and eventually enhancing the training dynamics of QNNs. © 2023, CC BY.

关键词： Machine learning

Understanding User Perceptions and Knowledge: A Survey of Public Acceptance of Driving Automation

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SSRN

SSRN 2024年

作者： Zhou, Huiping Itoh, Makoto Kitazaki, Satoshi Autonomous Driving Research Division Japan Automobile Research Institute 2530 Karima Ibaraki Tsukuba305-0822 Japan Institute of Systems and Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Center for Artificial Intelligence Research University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan National Institute of Advanced Industrial Science and Technology 1-1-1 Higashi Tsukuba Ibaraki305-8566 Japan

Prior knowledge is crucial in determining public behavior toward the emerging technology of driving automation (DA). However, the mechanism by which knowledge influences such behavior remains unclear. This study explores the existing knowledge of consumers and their desire to learn more, aiming to elucidate the relationship between acquired and desired knowledge and public interest regarding DA in Japan. A web-based survey was administered, with 1,074 respondents completing the questionnaire. The findings indicate that 70.3% of participants expressed interest in DA, and 17.2% had seriously considered purchasing an automated vehicle. Statistical analysis revealed a significant correlation between public interest and both known and desired knowledge. As consumers’ willingness to purchase an automated vehicle (AV) increased, their interest shifted from general functions to specific applications. Additionally, approximately 60% of those who decided to purchase or had already purchased an AV reported not receiving any guidance from dealerships. This lack of educational resources and hands-on experience may contribute to the significant gap observed between acquired and desired knowledge. This gap also explains why approximately 40% of respondents with a high interest in using DA technologies could not correctly understand driver responsibilities. These results highlight the importance of effectively conveying knowledge to enhance public interest and acceptance and suggest the need to develop efficient educational initiatives for users in future work. © 2024, The Authors. All rights reserved.

关键词： Automation

Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

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arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data

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arXiv 2025年

作者： Nohel, Michal Ulrich, Constantin Suprijadi, Jonathan Wald, Tassilo Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic University hospital Ostrava Department of Deputy director for science research and education Czech Republic Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany NCT Heidelberg A partnership between DKFZ University Medical Center Heidelberg Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany

This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolkit comprises two main components: a segmentation network that delineates foreground regions to optimize data sampling and thus reduce training time, and a segmentation network that identifies anonymized regions, preventing erroneous supervision in reconstructionbased SSL methods. Experimental results demonstrate high robustness, with mean Dice scores exceeding 98.5 across all anonymization methods and surpassing 99.5 for foreground segmentation tasks, highlighting the toolkit's efficacy in supporting SSL applications in 3D medical imaging for both CT and MRI images. The weights and code is available at https://***/MIC-DKFZ/Foreground-and- Anonymization-Area-Segmentation. © 2025, CC BY.

关键词： Self-supervised learning

Recent Advancements in Solid State Transformer-based EV Fast Charging Stations

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Recent Advancements in Solid State Transformer-based EV Fast...

International Conference on Power Electronics and Drive systems (PEDS)

作者： Ruvini De Seram Anindita Golder Sheldon S. Williamson Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech University Oshawa ON Canada Ontario Tech-Automotive Center of Excellence (ACE) Ontario Tech University Oshawa ON Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

Growing deployments of ultra fast charging stations (XFCS) are putting a stress on the existing line-frequency transformer (LFT)-based medium voltage (MV) grid due to the high charging demands of exponentially growing electric vehicles (EV). The conventional LFT occupies a large space in the XFCS installation which introduces challenges in facilitating the XFCS infrastructure. As a result, solid-state transformer (SST)-based XFCS are being developed which can be directly connected to the MV grid and several advantages over LFT-based systems such as compactness, intelligence and availability of the DC link to connect renewable energy sources and storage system can be realized. As such, a detailed analysis of current developments in SST-based XFCS is presented and compared with one another in this paper. The challenges associated with the configurations are examined and direction for future research is identified.

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

Ornstein-Uhlenbeck process and generalizations: Particle dynamics under comb constraints and stochastic resetting

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Physical Review E 2023年第5期107卷 054129-054129页

作者： Pece Trajanovski Petar Jolakoski Kiril Zelenkovski Alexander Iomin Ljupco Kocarev Trifce Sandev Research Center for Computer Science and Information Technologies Macedonian Academy of Sciences and Arts Bul. Krste Misirkov 2 1000 Skopje Macedonia Department of Physics Technion Haifa 32000 Israel Max Planck Institute for the Physics of Complex Systems D-01187 Dresden Germany Faculty of Computer Science and Engineering Ss. Cyril and Methodius University PO Box 393 1000 Skopje Macedonia Institute of Physics & Astronomy University of Potsdam D-14776 Potsdam-Golm Germany Institute of Physics Faculty of Natural Sciences and Mathematics Ss. Cyril and Methodius University Arhimedova 3 1000 Skopje Macedonia

The Ornstein-Uhlenbeck process is interpreted as Brownian motion in a harmonic potential. This Gaussian Markov process has a bounded variance and admits a stationary probability distribution, in contrast to the standard Brownian motion. It also tends to a drift towards its mean function, and such a process is called mean reverting. Two examples of the generalized Ornstein-Uhlenbeck process are considered. In the first one, we study the Ornstein-Uhlenbeck process on a comb model, as an example of the harmonically bounded random motion in the topologically constrained geometry. The main dynamical characteristics (as the first and the second moments) and the probability density function are studied in the framework of both the Langevin stochastic equation and the Fokker-Planck equation. The second example is devoted to the study of the effects of stochastic resetting on the Ornstein-Uhlenbeck process, including stochastic resetting in the comb geometry. Here the nonequilibrium stationary state is the main question in task, where the two divergent forces, namely, the resetting and the drift towards the mean, lead to compelling results in the cases of both the Ornstein-Uhlenbeck process with resetting and its generalization on the two-dimensional comb structure.

关键词： Brownian motion Diffusion Fluctuations & noise Noise Random walks Stochastic processes

Room-temperature multiple ligands-tailored SnO_(2) quantum dots endow in situ dual-interface binding for upscaling efficient perovskite photovoltaics with high V_(OC)

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Light(science & Applications) 2021年第12期10卷 2467-2481页

作者： Zhiwei Ren Kuan Liu Hanlin Hu Xuyun Guo Yajun Gao Patrick W.K.Fong Qiong Liang Hua Tang Jiaming Huang Hengkai Zhang Minchao Qin Li Cui Hrisheekesh Thachoth Chandran Dong Shen Ming-Fai Lo Annie Ng Charles Surya Minhua Shao Chun-Sing Lee Xinhui Lu Frédéric Laquai Ye Zhu Gang Li Department of Electronic and Information Engineering Research Institute for Smart Energy(RISE)Guangdong-Hong Kong-Macao(GHM)Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and DevicesThe Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Electrical and Computer Engineering Nazarbayev UniversityNur-SultanKazakhstan The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518057China Hoffmann Institute of Advanced Materials Shenzhen Polytechnic7098 Liuxian BoulevardShenzhen 518055China Department of Applied Physics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Department of Chemical and Biological Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina King Abdullah University of Science and Technology(KAUST) KAUST Solar Center(KSC)Physical Sciences and Engineering Division(PSE)Material Science and Engineering Program(MSE)Thuwal 23955-6900Kingdom of Saudi Arabia Department of Physics The Chinese University of Hong KongShatin 999077 Hong KongChina Center of Super-Diamond and Advanced Films(COSDAF) Department of ChemistryCity University of Hong KongHong KongChina

The benchmark tin oxide(SnO_(2))electron transporting layers(ETLs)have enabled remarkable progress in planar perovskite solar cell(PSCs).However,the energy loss is still a challenge due to the lack of“hidden interface”*** report a novel ligand-tailored ultrafine SnO_(2) quantum dots(QDs)via a facile rapid room temperature ***,the ligand-tailored SnO_(2) QDs ETL with multi-functional terminal groups in situ refines the buried interfaces with both the perovskite and transparent electrode via enhanced interface binding and perovskite *** novel ETLs induce synergistic effects of physical and chemical interfacial modulation and preferred perovskite crystallization-directing,delivering reduced interface defects,suppressed non-radiative recombination and elongated charge carrier *** conversion efficiency(PCE)of 23.02%(0.04 cm^(2))and 21.6%(0.98 cm^(2),V_(OC) loss:0.336 V)have been achieved for the blade-coated PSCs(1.54 eV E_(g))with our new ETLs,representing a record for SnO_(2) based blade-coated ***,a substantially enhanced PCE(V_(OC))from 20.4%(1.15 V)to 22.8%(1.24 V,90 mV higher V_(OC),0.04 cm^(2) device)in the blade-coated 1.61 eV PSCs system,via replacing the benchmark commercial colloidal SnO_(2) with our new ETLs.

关键词： perovskite interface quantum