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Aggregate 2021年第5期2卷 100-114页

作者： Qian Wang Zhen Qi Meng Chen Da-Hui Qu Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering Feringa Nobel Prize Scientist Joint Research CenterSchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghaiChina

A rich variety of smart materials developed via supramolecular assembly strategies have been introduced in the past ***,most materials reside in the thermodynamic equilibrium state,opposed to those nonequilibrium structures with sophisticated functions that are observed in living *** develop advanced synthetic systems,chemists have begun to focus on how to use strategies similar to those used in biological systems for fabricating artificial out-of-equilibrium ***,a rich variety of artificial out-of-equilibrium systems have been *** this review,we have summarized the recent progress of artificial out-of-equilibrium systems and categorized them in terms of the chemical fuel used,including adenosine triphosphate(ATP),acid/base,carbodiimide reagents,and many *** these self-assembling systems,their design strategies,potential applications,as well as advantageous features have been *** the end of this review,the remaining challenges and an outlook of the chemical-fuel-driven out-of-equilibrium systems were also *** is believed that this review has provided some insights and could be useful for those who are interested in the out-of-equilibrium supramolecular assembling systems and their subsequent constructing strategies for various transient materials.

关键词： chemical fuel out-of-equilibrium supramolecular chemistry

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN Computer science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Work in Progress: Assessment of the Querétaro Beca Embajadores Summer Program and Implications for Self-Efficacy and Interest in Graduate School

Work in Progress: Assessment of the Querétaro Beca Embajado...

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IEEE World Engineering Education Conference (EDUNINE)

作者： Wilson González-Espada Jorge Ortega-Moody Neftalí Villanueva Pérez Nilesh N. Joshi Miescha Bycura Department of Engineering Sciences Morehead State University Morehead KY USA Research Center for Applied Science & Advanced Technology National Polytechnic Institute Querétaro Mexico Department of Engineering & Technology Eastern Kentucky University Richmond KY USA Department of English & Modern Languages Morehead State University Morehead KY USA

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

STEM-interested Latino students often experience significant push factors leading to attrition and college departures. Effective summer interventions can enhance their graduation rates and self-efficacy. This paper presents ongoing work to describe the key features and assessment of a 10-day residential summer enrichment opportunity designed by a faculty team at Morehead State University. The researchers applied a mixed methods approach to collect assessment data, mediated by locally designed pre and postsurveys. Participants reported increased self-efficacy and motivation to complete their undergraduate studies and pursue graduate studies. For the workshops, most pre and postsurvey content scores by objective showed significant improvements, as measured by effect size and Hake gain statistics. Detailed inferential statistics and thematic analyses of qualitative data are planned to identify best practices and improvement areas.

关键词： Atmospheric measurements Conferences Gain measurement Size measurement Particle measurements Engineering education Best practices STEM

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FLASH: Low-Latency Serverless Model Inference with Multi-Core Parallelism in Edge

FLASH: Low-Latency Serverless Model Inference with Multi-Cor...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yanbo Li Yanying Lin Shijie Peng Yingfei Tang Tian Xiang Wei Song Kejiang Ye Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Southern University of Science and Technology University of Chinese Academy of Sciences Zhejiang Laboratory Intelligent Robot Research Center

Low response latency holds a pivotal role in the landscape of edge deep learning model inference, yet the constrained resources within edge computing environments often limit its full potential. Within the scope of this paper, we substantiate that even amid the resource limitations inherent to edge computing environments, it remains feasible to curtail model response latency by elevating multi-core parallel efficiency. Our research encompasses a comprehensive analysis of the parallel acceleration effects observed across models featuring diverse parameter magnitudes during the inference process. This analysis culminates in the development of FLASH, an online deep model inference system tailored for Serverless edge inference, strategically optimized through the utilization of multi-core parallelism. FLASH exhibits dynamic adaptability by modulating the number of CPU cores within computational instances in accordance with traffic request loads. It also employs a dynamic scaling mechanism to finely adjust model placement, ultimately facilitating inference acceleration and mitigating the concomitant cold start overhead. Empirical experimentation conducted across a spectrum of burst-level workloads serves to underscore FLASH’s capacity, resulting in an average reduction in response latency by 33% and a maximum reduction of 75%, while concurrently realizing a throughput enhancement of 2.94x.

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Geomimicry—Liberating high-pressure research by encapsulation

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Matter and Radiation at Extremes 2022年第6期7卷 79-81页

作者： Ho-Kwang Mao Wendy L.Mao Center for High Pressure Science and Technology Advanced Research 10 Dongbeiwang West RoadHaidianBeijing 100094China Department of Geological Sciences Stanford UniversityStanfordCalifornia 94305USA

High pressures induce changes of properties and structures that could greatly impact materials science if such changes were preserved for ambient *** the geological process of diamond formation that the pressures and high-pressure phases in diamond inclusions can be preserved by the strong diamond envelope,we discuss the perspectives that such process revolutionizes high-pressure science and technology and opens a great potential for creation of functional materials with extremely favorable properties.

关键词： properties ambient process

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Magnon-induced scalar spin chirality in Kagome and honeycomb ferromagnets

arXiv

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arXiv 2025年

作者： Esaki, Nanse Go, Gyungchoon Kim, Se Kwon Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Tokyo113-0033 Japan Advanced Science Research Center Japan Atomic Energy Agency Tokai Ibaraki319-1195 Japan Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

The scalar spin chirality (SSC), defined as a triple product of spins, is essential for describing noncoplanar spin structures and understanding chiral physics in magnetic systems. Traditionally, SSC has been discussed primarily in the context of noncoplanar ground-state spin configurations at zero temperature, as collinear spin systems are generally thought to lack SSC. Consequently, whether the SSC can emerge at finite temperatures in spin systems with collinear ground states remains an open question and has yet to be fully understood. In this study, we theoretically demonstrate that thermally excited magnons can induce SSC even in collinear spin systems. By considering 2D ferromagnets on Kagome and honeycomb lattices, we demonstrate that the Dzyaloshinskii-Moriya interactions (DMI) which break the effective time-reversal symmetry in the magnon Hamiltonian can lead to finite SSC at finite temperatures. Using a simple spin model, we show both numerically and analytically that the SSC increases with the magnitude of DMI and temperature. Furthermore, calculations based on realistic material parameters reveal that the magnon-induced SSC can achieve a magnitude comparable to those observed in non-coplanar spin configurations. These findings suggest that SSC plays a significant role even in collinear spin systems, providing new insights into the chiral physics of magnetic materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Honeycomb structures

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FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks

arXiv

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arXiv 2023年

作者： Omae, Yuto Sakai, Yusuke Takahashi, Hirotaka Artificial Intelligence Research Center College of Industrial Technology Nihon University Chiba275-8575 Japan Research Center for Space Science Advanced Research Laboratories Tokyo City University Tokyo158-8557 Japan

Recently, many convolutional neural networks (CNNs) for classification by time domain data of multisignals have been developed. Although some signals are important for correct classification, others are not. When data that do not include important signals for classification are taken as the CNN input layer, the calculation, memory, and data collection costs increase. Therefore, identifying and eliminating nonimportant signals from the input layer are important. In this study, we proposed features gradient-based signals selection algorithm (FG-SSA), which can be used for finding and removing nonimportant signals for classification by utilizing features gradient obtained by the calculation process of grad-CAM. When we define ns as the number of signals, the computational complexity of the proposed algorithm is linear time O(ns), that is, it has a low calculation cost. We verified the effectiveness of the algorithm using the OPPORTUNITY Activity Recognition dataset, which is an open dataset comprising acceleration signals of human activities. In addition, we checked the average 6.55 signals from a total of 15 acceleration signals (five triaxial sensors) that were removed by FG-SSA while maintaining high generalization scores of classification. Therefore, the proposed algorithm FG-SSA has an effect on finding and removing signals that are not important for CNN-based *** Codes 68T07, 68W25 Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Protein engineering in the deep learning era

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mLife 2024年第4期3卷 477-491页

作者： Bingxin Zhou Yang Tan Yutong Hu Lirong Zheng Bozitao Zhong Liang Hong Institute of Natural Sciences Shanghai Jiao Tong UniversityShanghaiChina Shanghai National Center for Applied Mathematics(SJTU center) Shanghai Jiao Tong UniversityShanghaiChina School of Information Science and Engineering East China University of Science and TechnologyShanghaiChina Shanghai Artificial Intelligence Laboratory ShanghaiChina School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMichiganUSA School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Zhangjiang Institute for Advanced Study Shanghai Jiao Tong UniversityShanghaiChina

Advances in deep learning have significantly aided protein engineering in addressing challenges in industrial production,healthcare,and environmental *** review frames frequently researched problems in protein understanding and engineering from the perspective of deep *** provides a thorough discussion of representation methods for protein sequences and structures,along with general encoding pipelines that support both pre‐training and supervised learning *** summarize state‐of‐the‐art protein language models,geometric deep learning techniques,and the combination of distinct approaches to learning from multi‐modal biological ***,we outline common downstream tasks and relevant benchmark datasets for training and evaluating deep learning models,focusing on satisfying the particular needs of protein engineering applications,such as identifying mutation sites and predicting properties for candidates'virtual *** review offers biologists the latest tools for assisting their engineering projects while providing a clear and comprehensive guide for computer scientists to develop more powerful solutions by standardizing problem formulation and consolidating data *** research can foresee a deeper integration of the communities of biology and computer science,unleashing the full potential of deep learning in protein engineering and driving new scientific breakthroughs.

关键词： artificial intelligence geometric deep learning protein engineering protein language model synthetic biology

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Gbps key rate passive-state-preparation continuous-variable quantum key distribution within an access-network area

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Photonics research 2024年第7期12卷 1485-1493页

作者： FEIYU JI PENG HUANG TAO WANG XUEQIN JIANG GUIHUA ZENG State Key Laboratory of Advanced Optical Communication Systems and Networks Center for Quantum Sensing and Information ProcessingSchool of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Shanghai Research Center for Quantum Sciences Shanghai 201315China Hefei National Laboratory Hefei 230088China College of Information Science and Technology Donghua UniversityShanghai 201620China

The conventional Gaussian-modulated coherent-state quantum key distribution(QKD) protocol requires the sender to perform active modulations based on a true random number generator. Compared with it, the passive-state-preparation(PSP) continuous-variable quantum key distribution(CVQKD) equivalently performs modulations passively by exploring the intrinsic field fluctuations of a thermal source, which offers the prospect of chip integration QKD with low cost. In this paper, we propose and experimentally demonstrate a high-rate PSP-CVQKD scheme within an access-network area using high-bandwidth detectors in a continuous wave encoding and decoding way. By proposing effective methods for suppressing the noises during the PSP process and polarization multiplexing to decrease the photon leakage noises, we realize the high-intensity local oscillator transmission, thereby achieving coherent detection with high efficiency, low noise, and high bandwidth. The secure key rates over transmission distance of 5.005 km with and without consideration of the finite-size effect are 273.25 Mbps and 1.09 Gbps. The use of the PSP method boosts the asymptotic secret key rate of CVQKD to Gbps level for the first time, to our knowledge, within the range of the access network, which provides an effective and secure key distribution strategy for high-speed quantum cryptography access communication.

关键词： passive network distribution

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Anionic MOF derived Bimetallic Ni_(x)Co_(y)@Nano-porous carbon composites toward strong and efficient electromagnetic wave absorption

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Journal of Materiomics 2022年第4期8卷 852-862页

作者： Fan Zhang Yefu Chen Yujie Ren Qi Zheng Lianjun Wang Wan Jiang State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai201620China Engineering Research Center of Advanced Glasses Manufacturing Technology Ministry of EducationDonghua UniversityShanghai201620China Institute of Functional Materials Donghua UniversityShanghai201620China

Metal-organic frameworks (MOFs) derived metallic nanoparticles embedded nano-porous carbon (NPC)composites have shown to be promising microwave absorbing materials. Although MOF precursors withdiverse compositions and morphologies have been extensively investigated, anionic MOFs are rarelyexplored for this utility. In addition to the metal sites and ligands on the MOF framework, the guestcounter-cations in the void provide supplementary parameters to tune the capability of microwaveabsorption. Herein, we applied an anionic NixCoy-MOF featuring N-rich ligands and hierarchical porousstructures as a precursor for microwave absorption. The obtained dielectric-magnetic Ni_(x)Co_(y)@NPCcomposites with rich N dopants and multiple hetero-interfaces promote the microwave attenuationcapability through enhanced dipole/interfacial relaxation. The synergistic effects of magnetic loss andconduction loss induced by metallic nanoparticles (NPs) and porous graphitic layers further facilitate themicrowave dissipation. More significantly, impedance matching can be effectively improved by tuningthe Co/Ni amount in the precursors to realize the modulation of electromagnetic parameters. Conse-quently, Ni@NPC exhibits the optimal electromagnetic wave (EMW) absorption with minimum reflectionloss of 66 dB at only 2 mm and broad effective absorption band covering 4.56 GHz at a thin thickness of2.07 mm, making this material promising absorber for EMW elimination.

关键词： Anionic MOFs MOF-Derived carbon composites N-rich ligands Dielectric loss Magnetic loss Electromagnetic wave absorption

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