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Characteristics of nanoepoxy composite through structural, thermal and machine learning-enhanced dielectric analysis

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Journal of Polymer research 2025年第5期32卷 1-13页

作者： Thakor, Sanketsinh Jain, Prince Joshi, Anand Vaja, Chandan R. Parikh, Swapnil Panchal, Piyush Department of Applied Science and Humanities Parul Institute of Technology Parul University Gujarat Vadodara391760 India Department of Mechatronics Engineering Parul Institute of Technology Parul University Gujarat Vadodara391760 India Department of Physics School of Sciences Gujarat University Gujarat Ahmedabad380009 India Department of Computer Science Engineering Parul Institute of Technology Parul University Gujarat Vadodara391760 India Department of Physics Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Micro Nano Research and Development Center Parul University Gujarat Vadodara391760 India

This study aims to comprehensively explore the characteristics of hybrid nano fillers incorporated epoxy composites (NFIEC) using various analytical techniques. The research encompasses machine learning-enhanced dielectric analysis across a wide frequency spectrum ranging from 100 Hz to 2 MHz. The structural properties of the nano-fillers, pristine epoxy (PE), and NFIEC are investigated through X-ray Diffraction (XRD). Hybrid nano fillers, including combinations like SiO2 + Fe3O4, ZnO + Fe3O4, TiO2 + Fe3O4 and Al2O3 + Fe3O4, are added to the PE at a concentration of 1 wt.% to form NFIEC. Additionally, Differential Scanning Calorimetry are employed to gain further insights into the thermal behaviour of the NFIEC. This study also demonstrates the effectiveness of machine learning models, particularly Extra Trees, CatBoost, XGBoost, and Ensemble Averaging, in predicting dielectric properties (Ε′ and Ε′′) of NFIEC using data from LCR and VNA measurements. The models exhibited high predictive accuracy, with Ensemble Averaging consistently delivering robust performance across datasets, highlighting the potential of ML techniques in advancing material science by reducing experimental efforts and enhancing predictive precision. © The Polymer Society, Taipei 2025.

关键词： Hybrid composites

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Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking

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IEEE Transactions on Biomedical engineering 2025年 PP卷 PP页

作者： Su, Bai-Quan Ma, Xu-Dong Li, Weihan Kuang, Zi-Ao Gong, Yi Wang, Gang Zhang, Qingqian Liu, Wenyong Li, Changsheng Gao, Li Wang, Junchen Beijing University of Posts and Telecommunications Medical Robotics Laboratory School of Automation Beijing100876 China University of Tokyo School of Mechanical Engineering Tokyo113-0033 Japan Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China Peking University General Surgery Department Beijing100191 China Beihang University Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beijing100083 China Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China Peking University Department of Periodontology National Stomatological Center School and Hospital of Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing100081 China Beihang University School of Mechanical Engineering and Automation Beijing100083 China

The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscopic surgery, it is not commonly applied to open surgery. Instead, open surgery skill assessment relies on observing the trajectory of surgical tools on tissue. This observation-based method often lacks clear standards, leading to inaccurate assessments. This paper presents a method for evaluating cutting skill in open surgery through scalpel motion analysis. A 3D multiple-facet ArUco code cube is designed, and a dataset of tip coordinate system poses for various scalpels in the ArUco code coordinate system (ACS) is established using the pivot calibration method. The YOLOv8 model and an image dataset of different scalpels are used to identify the scalpel type and select its tip position. The tip position is then transformed from ACS to a binocular camera coordinate system (BCS), representing the incision curve made by the scalpel. Five assessment metrics are proposed to quantify the surgeon's cutting skill: average incision curvature deviation, incision length difference, incision endpoint deviation, average incision deviation, and average cutting jerk. Experiments involving twenty expert and novice surgeons performing four common incisions (straight line, polyline, semicircle, and cross line) demonstrate the metrics' effectiveness. The metrics provide a clear, objective display of individual cutting skills, and a combined ranking reveals comparative skill levels. This study offers a precise method for evaluating surgeons' cutting skills with a scalpel in open surgery. © 1964-2012 IEEE.

关键词： Surgery

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On-chip multi-timescale spatiotemporal optical synchronization

arXiv

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arXiv 2025年

作者： Xu, Lida Mehrabad, Mahmoud Jalali Flower, Christopher J. Moille, Gregory Restelli, Alessandro Suarez-Forero, Daniel G. Chembo, Yanne Mittal, Sunil Srinivasan, Kartik Hafezi, Mohammad Joint Quantum Institute Quantum Technology Center University of Maryland College ParkMD20742 United States Joint Quantum Institute University of Maryland National Institute of Standards and Technology College ParkMD20742 United States Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States

Mode-locking mechanisms are key resources in nonlinear optical phenomena, such as micro-ring solitonic states, and have transformed metrology, precision spectroscopy, and optical communication. However, despite significant efforts, mode-locking has not been demonstrated in the independently tunable multi-timescale regime. Here, we vastly expand the nonlinear mode-locking toolbox into multi-timescale synchronization on a chip. We use topological photonics to engineer a 2D lattice of hundreds of coupled silicon nitride ring resonators capable of hosting nested mode-locked states with a fast (≈ 1 THz) single-ring and a slow (≈ 3 GHz) topological super-ring timescales. We demonstrate signatures of multi-timescale mode-locking including quadratic distribution of the pump noise with the two-time azimuthal mode dimensions, as expected by mode-locking theory. Our observations are further corroborated by direct signatures of the near-transform-limit repetition beats and the formation of the temporal pattern on the slow timescale. Moreover, we show that these exotic properties of edge-confined mode-locked states are in sharp contrast to bulk and single-ring counterparts and establish a clear pathway for their identification. Our unprecedented demonstration of mode-locking in topological combs unlocks the implementation of lattice-scale synchronization and independently tunable multi-timescale mode-locking phenomena, also the exploration of the fundamental nonlinearity-topology interplay on a chip. © 2025, CC BY.

关键词： Silicon nitride

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Sunway supercomputer architecture towards exascale computing: analysis and practice

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Science China(Information Sciences) 2021年第4期64卷 177-197页

作者： Jiangang GAO Fang ZHENG Fengbin QI Yajun DING Hongliang LI Hongsheng LU Wangquan HE Hongmei WEI Lifeng JIN Xin LIU Daoyong GONG Fei WANG Yan ZHENG Honghui SUN Zhou ZHOU Yong LIU Hongtao YOU National Research Center of Parallel Computer Engineering and Technology

In recent years, the improvements of system performance and energy efficiency for supercomputers have faced increasing challenges, which create more intensive demands on the architecture design for realizing exascale computing. This paper first analyzes the main requirements of exascale computing on the aspects of the parallel computing application and supercomputing center operation. Afterwards, a mapping scheme of "demands-challenges-architecture" is proposed. Then, the major challenges of exascale supercomputer, such as scalability, power consumption, data movement, programming and availability, are thoroughly analyzed, and the corresponding appropriate solutions are proposed. Moreover, this paper proposes the Sunway computer architecture towards exascale computing in which the many-core processor, network chipset and software system are all domestically-designed. The technology roadmap of Sunway supercomputer will hold the comprehensive design methods for the architecture, including the processor, interconnect network,assembly structure, power supply, cooling system, system software, parallel algorithm and application support, promising great advances for exascale supercomputing.

关键词： supercomputer exascale Sunway scalability power consumption data movement programming availability

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A survey on scalable consensus algorithms for blockchain technology

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Jain, Ankit Kumar Gupta, Nishant Gupta, Brij B. National Institute of Technology Kurukshetra India Department of Computer Science and Information Engineering Asia University Taiwan Taichung 413 China Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India

The process of reaching an agreement on a value within a distributed network, known as a consensus problem, is a defining feature of blockchain. This consensus problem can be seen in various applications like load balancing, transaction validation in blockchain, and distributed computing. In recent years, many researchers have provided solutions to this problem. Hence we have presented a survey in which we delved into blockchain consensus algorithms and conducted a comparative analysis of all the consensus algorithms to provide information about each protocol's advantages and drawbacks. This survey starts with the standard proof-of-work consensus protocol applied in bitcoin cryptocurrency and its limitations on the ground of the following parameters: throughput (transactions per second), latency, forks, fault tolerance, double spending attacks, and power consumption. The rest of the consensus algorithms in this paper have been systematically covered to address the limitations of proof-of-work. This paper also covered Raft and PBFT consensus algorithms suitable for permissioned networks. Although the PBFT consensus protocol has a high throughput and a low latency, it has limited node scalability. The PBFT has a low byzantine fault tolerant rate. This paper also covers PoEWAL for blockchain-based IoT applications and WBFT, which prevents corrupt nodes from taking part in consensus. A comparative analysis of the consensus algorithms provides an explicit knowledge of the present research, which also offers guidance for future study. © 2024

关键词： Blockchain technology Byzantine faults Consensus algorithm Latency Throughput

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Scalability and efficiency challenges for the exascale supercomputing system:practice of a parallel supporting environment on the Sunway exascale prototype system

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Frontiers of Information technology & Electronic engineering 2023年第1期24卷 41-58页

作者： Xiaobin HE Xin CHEN Heng GUO Xin LIU Dexun CHEN Yuling YANG Jie GAO Yunlong FENG Longde CHEN Xiaona DIAO Zuoning CHEN National Research Center of Parallel Computer Engineering and Technology Beijing 100190China

With the continuous improvement of supercomputer performance and the integration of artificial intelligence with traditional scientific computing,the scale of applications is gradually increasing,from millions to tens of millions of computing cores,which raises great challenges to achieve high scalability and efficiency of parallel applications on super-large-scale *** the Sunway exascale prototype system as an example,in this paper we first analyze the challenges of high scalability and high efficiency for parallel applications in the exascale *** overcome these challenges,the optimization technologies used in the parallel supporting environment software on the Sunway exascale prototype system are highlighted,including the parallel operating system,input/output(I/O)optimization technology,ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision *** operating systems and I/O optimization technology mainly support largescale system scaling,while the ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision method mainly enhance the efficiency of large-scale ***,the contributions to various applications running on the Sunway exascale prototype system are introduced,verifying the effectiveness of the parallel supporting environment design.

关键词： parallel computing Sunway Ultra-large-scale Supercomputer

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Integrative Multi-Source Mapping of Coastal Environments Using Hy-perspectral Data Fusion Platforms

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Han, Huimin Por, Lip Yee Talpur, Bandeh Ali Hashim, Muhammad Yang, Jing Saidani, Taoufik Djumaniyazova, Mukhayya Xusinovna Madrakhimovich, Khudoynazarov Egambergan Talpur, Kazim Raza Hainan Vocational University of Science and Technology Mechanical and Electrical Engineering Col-lege Haikou571126 China Uni-versiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Trinity Col-lege Dublin School of Computer Science and Statistics Ireland National Textile Uni-versity Faisalabad Faisalabad Business School Pakistan Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur50603 Malaysia Northern Border University Center for Scientific Research and Entrepreneurship Arar73213 Saudi Arabia Urganch State University Pedagogy and Psychology Department Uzbekistan Mamun University Uzbekistan Jamshoro Institute of Health Management and Re-search Sciences Sindh Pakistan

Hyperspectral images (HSIs) not only have a large number of spectral features, but also show a comprehensive spa-tial distribution of land cover and offer substantial advantages in the fine classification of ground materials. However, due to the high dimensionality and redundancy of HSIs and class imbalance in hyperspectral datasets, improving classification performance is still a huge challenge. Recently, graph-based learning algorithms are proven to be better classifiers for high dimensional data. Learning graph data and modeling spatial topological links be-tween features are significantly more accessible and more effective with graph-based deep learning, which has been demonstrated to be a promising approach. In the past few years, convolutional neu-ral networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs strug-gle to achieve good accuracy in low samples, while GCNs have a substantial computational cost. To resolve these issues, this paper proposes a feature fusion model using a 3D-CNN and hybrid-GAT named FFCHG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in im-proving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classifica-tion time with accuracy of more than 90% in all datasets and kappa with more than 91% for each dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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engineering Andreev Bound States for Thermal Sensing in Proximity Josephson Junctions

arXiv

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arXiv 2025年

作者： Jung, Woochan Arnault, Ethan G. Huang, Bevin Park, Jinho Jang, Seong Watanabe, Kenji Taniguchi, Takashi Englund, Dirk Fong, Kin Chung Lee, Gil-Ho Physics Department Pohang University of Science and Technology Pohang37673 Korea Republic of Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Research Center for Functional Materials National Institute for Materials Science Tsukuba Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Department of Physics Northeastern University BostonMA02115 United States Quantum Materials and Sensing Institute BurlingtonMA01803 United States

The thermal response of proximity Josephson junctions (JJs) is governed by the temperature (T)- dependent occupation of Andreev bound states (ABS), making them promising candidates for sensitive thermal detection. In this study, we systematically engineer ABS to enhance the thermal sensitivity of critical current (lc) of proximity JJs, quantified as |dlc/dT/| for threshold readout scheme and |dlc/***−1| for inductive readout scheme. Using a gate-tunable graphene-based JJ platform, we explore the impact of key parameters—including channel length, transparency, carrier density, and superconducting material—on the thermal response. Our results reveal that the proximity-induced superconducting gap plays a crucial role in optimizing thermal sensitivity. Notably, we see a maximum |dlc/***−1| value of 0.6 K−1 at low temperatures with titanium-based graphene JJs. By demonstrating a systematic approach to engineering ABS in proximity JJs, this work establishes a versatile framework for optimizing thermal sensors and advancing the study of ABS-mediated transport. © 2025, CC BY.

关键词： Superconducting materials

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Modest functional diversity decline and pronounced composition shifts of microbial communities in a mixed waste-contaminated aquifer

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Microbiome 2025年第1期13卷 1-13页

作者： Fan, Yupeng Wang, Dongyu Yang, Joy X. Ning, Daliang He, Zhili Zhang, Ping Rocha, Andrea M. Xiao, Naijia Michael, Jonathan P. Walker, Katie F. Joyner, Dominique C. Pan, Chongle Adams, Michael W. W. Fields, Matthew W. Alm, Eric J. Stahl, David A. Hazen, Terry C. Adams, Paul D. Arkin, Adam P. Zhou, Jizhong Institute for Environmental Genomics University of Oklahoma 101 David L Boren Blvd Norman 73019 OK United States School of Biological Sciences University of Oklahoma Norman 73019 OK United States Southern Marine Science and Engineering Guangdong Laboratory Guangdong Zhuhai 519080 China Alkek Center for Metagenomics and Microbiome Research Department of Molecular Virology and Microbiology Baylor College of Medicine Houston 77030 TX United States Biosciences Division Oak Ridge National Laboratory Oak Ridge 37830 TN United States Department of Civil and Environmental Engineering University of Tennessee Knoxville 37996 TN United States School of Computer Science University of Oklahoma Norman 73019 OK United States Department of Biochemistry and Molecular Biology University of Georgia Athens 30602 GA United States Center for Biofilm Engineering and Department of Microbiology and Cell Biology Montana State University Bozeman 59717 MT United States Department of Biological Engineering Center for Microbiome Informatics and Therapeutics Massachusetts Institute of Technology Cambridge 02139 MA United States Department of Civil and Environmental Engineering University of Washington Seattle 98195 WA United States Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory Berkeley 94720 CA United States Department of Bioengineering University of California Berkeley 94720 CA United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory Berkeley 94720 CA United States Earth and Environmental Sciences Lawrence Berkeley National Laboratory Berkeley 94720 CA United States School of Civil Engineering and Environmental Sciences University of Oklahoma Norman 73019 OK United States

Background: Microbial taxonomic diversity declines with increased environmental stress. Yet, few studies have explored whether phylogenetic and functional diversities track taxonomic diversity along the stress gradient. Here, we investigated microbial communities within an aquifer in Oak Ridge, Tennessee, USA, which is characterized by a broad spectrum of stressors, including extremely high levels of nitrate, heavy metals like cadmium and chromium, radionuclides such as uranium, and extremely low pH (< 3). Results: Both taxonomic and phylogenetic α-diversities were reduced in the most impacted wells, while the decline in functional α-diversity was modest and statistically insignificant, indicating a more robust buffering capacity to environmental stress. Differences in functional gene composition (i.e., functional β-diversity) were pronounced in highly contaminated wells, while convergent functional gene composition was observed in uncontaminated wells. The relative abundances of most carbon degradation genes were decreased in contaminated wells, but genes associated with denitrification, adenylylsulfate reduction, and sulfite reduction were increased. Compared to taxonomic and phylogenetic compositions, environmental variables played a more significant role in shaping functional gene composition, suggesting that niche selection could be more closely related to microbial functionality than taxonomy. Conclusions: Overall, we demonstrated that despite a reduced taxonomic α-diversity, microbial communities under stress maintained functionality underpinned by environmental selection. ^{6Db3s6ZxkfDo1r5g77KnzZVideo Abstract} © The Author(s) 2025.

关键词： Contaminated aquifer Functional diversity Phylogenetic diversity Taxonomic diversity

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