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Phyllotaxis-inspired nanosieves with multiplexed orbital angular momentum

eLight 2021年第1期1卷 46-56页

作者： Zhongwei Jin David Janoschka Junhong Deng Lin Ge Pascal Dreher Bettina Frank Guangwei Hu Jincheng Ni Yuanjie Yang Jing Li Changyuan Yu Dangyuan Lei Guixin Li Shumin Xiao Shengtao Mei Harald Giessen Frank Meyer zu Heringdorf Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore College of Optical and Electronic Technology China Jiliang UniversityHangzhou 310018China Faculty of Physics and Center for Nanointegration Duisburg-Essen(CENIDE) University of Duisburg-EssenLotharstrasse 1-2147057 DuisburgGermany Department of Materials Science and Engineering Shenzhen Institute for Quantum Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Beijing Qianjunyide Technology Co.Ltd Beijing 100031China. Physics Institute and Stuttgart Center of Photonics Engineering(SCoPE) University of Stuttgart70569 Stutt-gartGermany. School of Physics University of ElectronicScience and Technology of ChinaChengdu 611731China. Key Laboratory of Optoelectronic Materials Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China. Department of Electronic and Information Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong SARChina. Department of Materials Science and Engineering City University of Hong Kong83 Tat Chee AvenueKowloonHong Kong SARChina. State Key Laboratory On Tunable Laser Technology Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information SystemShenzhen Graduate SchoolHarbin Institute of TechnologyShenzhen 518055China.

Nanophotonic platforms such as metasurfaces,achieving arbitrary phase profiles within ultrathin thickness,emerge as miniaturized,ultracompact and kaleidoscopic optical vortex ***,it is often required to segment or interleave independent sub-array metasurfaces to multiplex optical vortices in a single nano-device,which in turn affects the device’s compactness and channel ***,inspired by phyllotaxis patterns in pine cones and sunflowers,we theoretically prove and experimentally report that multiple optical vortices can be produced in a single compact phyllotaxis nanosieve,both in free space and on a chip,where one meta-atom may contribute to many vortices *** time-resolved dynamics of on-chip interference wavefronts between multiple plasmonic vortices was revealed by ultrafast time-resolved photoemission electron *** nature-inspired optical vortex generator would facilitate various vortex-related optical applications,including structured wavefront shaping,free-space and plasmonic vortices,and high-capacity information metaphotonics.

关键词： Phyllotaxis vortex nanosieve Free-space Plasmonic vortices Ultrafast time-resolved

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Engineered Modular Neuronal Networks-on-Chip Represent Structure-Function Relationship

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SSRN 2024年

作者： Habibey, Rouhollah Striebel, Johannes Latiftikhereshki, Roshanak Schmieder, Felix Nasiri, Rohollah Latifi, Shahrzad Universitäts-Augenklinik Bonn University of Bonn Dep. of Ophthalmology Ernst-Abbe-Straße 2 BonnD-53127 Germany CRTD - Center for Regenerative Therapies TU Dresden Dresden01307 Germany Dept. Neuroscience Italian Institute of Technology Genova Italy Department of Computer Engineering Faculty of Engineering Kermanshah Branch Azad University Kermanshah Iran Laboratory of Measurement and Sensor System Technique Faculty of Electrical and Computer Engineering TU Dresden Helmholtzstraße 18 Dresden01069 Germany Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Neurology David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Neuroscience Rockefeller Neuroscience Institute West Virginia University MorgantownWV26506 United States

Brain function is substantially linked to the highly organized structure of neuronal networks. The structures of in vitro assembled neuronal circuits often vary between samples and change over time, making it challenging to reliably record network functional output and link it to network structure. Therefore, engineering neuronal structures with predefined geometry and reproducible functional features is essential to robustly model in vivo neuronal circuits. Here, we engineered thin microchannel devices to assemble 2D and 3D modular networks. The microchannel devices were coupled with a multi-electrode array (MEA) electrophysiology system to enable long-term electrophysiology recordings from microengineered circuits. Each network consisted of 64 modules connected to their adjacent modules by micron-sized channels. The microstructures physically confined neurons to the recording electrodes, which significantly improved the efficiency of electrophysiology readout. In addition, the microstructures maintained the modular network structure for weeks. Modular circuits within microfluidic devices showed consistent spatial patterns of activity over weeks, which was lacking in the randomly formed circuits. The number of physical connections per module was shown to influence the measured activity and functional connectivity parameters, which represents the impact of network structure on its functional output. We show that microengineered 3D modular networks with profound activity and higher number of functional connections recapitulate key functional features of developing cortex. Structurally and functionally stable 2D and 3D networks mimic the modular architecture of brain circuits and provide a robust and reproducible in vitro microphysiological system for basic and translational neuroscience research. © 2024, The Authors. All rights reserved.

关键词： Microfluidics

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Fault Diagnosis Model of Switching Power Supply Based on Improved DBN

Fault Diagnosis Model of Switching Power Supply Based on Imp...

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International Conference on Intelligent system and Knowledge engineering, ISKE

作者： Lei Zhou Cheng Yu Hu Liu Xin Li Fenfen Li Haiyan Zhang Faculty of Computer and Software Engineering Research Center for logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Huai’an Jiangsu China College of Computer Science and Technology Nanjing University of Technology Nanjing Jiangsu China Faculty of Chemical Engineering Huaiyin Institute of Technology Huai’an Jiangsu China

ISBN: (纸本)9781665405546

Switching power supply has wide application in various electrical equipment. The failure of high-voltage filter capacitors is the main cause of frequent faults, accounting for more than 60% of switching power supply failures. In order to realize the real-time detection of switching power supply failure without disassembling the power supply, a fault diagnosis model for improving the deep belief network (DTDBN) is proposed. The model uses the data acquisition device to isolate, filter, and collect data on the grid voltage of the switching power supply, judge the failure ratio of the filter capacitor according to the degree of voltage distortion of the power supply. Experimental results show that the proposed fault detection method achieves a 100% recognition rate for the filter capacitor normal state, 50% and 90% state recognition rate is 90%. The superiority of this method for the classification and early warning of power capacitor faults is proved.

关键词： Fault diagnosis Power supplies Fault detection Capacitors Switches Voltage Distortion

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The Bankable Economic Potential of Utility-Scale Solar Pv: A Novel Gis-Based Framework with Application to Indonesia

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SSRN 2023年

作者： Langer, Jannis Ashqar, Ziad Zhou, Yilong Isabella, Olindo Praktiknjo, Aaron Quist, Jaco Kwee, Zenlin Blok, Kornelis Delft University of Technology Faculty of Technology Policy and Management Department of Engineering Systems and Services Jaffalaan 5 Delft2628 BX Netherlands Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Electrical Sustainable Energy Photovoltaic Materials and Devices group Mekelweg 4 Delft2628 CD Netherlands RWTH Aachen University Energy System Economics Institute for Future Energy Consumer Needs and Behavior E.ON Energy Research Center Mathieustraße 10 Aachen52074 Germany Delft University of Technology Faculty of Technology Policy and Management Department of Values Technology and Innovation Jaffalaan 5 Delft2628 BX Netherlands

Geospatial analysis is useful to calculate and map solar photovoltaic’s (PV) economic potential. However, recent studies do not address PV’s bankable potential for practical implementation and show limitations in (1) potential and input reporting, (2) studied metrics, (3) uncertainty analysis, and (4) policy analysis. To tackle these limitations, we propose a novel framework for utility-scale PV’s bankable potential, which combines geospatial analysis and project finance. The framework involves 20 years of hourly solar electricity production, a debt sizing and cash flow analysis model, and Monte Carlo simulation. We report a conservative technical potential of 9.3 TWpeak and 12.1 PWh/year in Indonesia. Considering expected 2030 demand, PV’s bankable potential is up to 40.8 TWh/year mostly situated in East Indonesia where population and demand are low. PV’s bankability is currently limited by strict loan maturities and low electricity tariffs, and could be improved to up to 232 TWh/year (52% of 2030 demand) via a feed-in tariff of 11.5 US¢(2021)/kWh and a temporary lift of local content for solar panels. Despite their effectiveness, policymakers should be aware of possible side effects of PV policies, e.g. for local coal-based industries and rural communities with limited electricity access. © 2023, The Authors. All rights reserved.

关键词： Economic analysis

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Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

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Intelligent Automation & Soft Computing 2024年第3期39卷 417-436页

作者： Muhammad S.Alam Farhan B.Mohamed Ali Selamat Faruk Ahmed AKM B.Hossain Department of Emergent Computing School of ComputingUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Malaysia-Japan International Institute of Technology(MJIIT) Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia Media and Game Innovation Centre of Excellence(MaGICX) Universiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Engineering Technology University of MemphisMemphisTN38152USA Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Department of Information System and Cyber Security College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance *** scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and *** research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image *** annotated image dataset trains the proposed system and predicts the camera pose in *** system mainly improved the localization performance of indoor cameras by more accurately predicting the camera *** also recognizes the scene changes during the sequence and evaluates the effects of these *** system achieved high accuracy and real-time *** scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact ***,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

关键词： Camera pose estimation indoor camera localization real-time localization scene change detection simultaneous localization and mapping(SLAM)

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The Discussion Corpus toward Argumentation Quality Assessment in Multi-Party Conversation

The Discussion Corpus toward Argumentation Quality Assessmen...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Tsukasa Shiota Kazutaka Shimada Graduate School of Computer Science and System Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan Kyushu Institute of Technology Iizuka Fukuoka Japan

ISBN: (纸本)9781728173986

Recently, many studies in NLP, especially argument mining, have focused on revealing what a good argument is with written texts. However, there are a few studies to challenge argumentation quality assessment in face-to-face conversations due to a lack of corpora, as compared with written texts. Therefore, we construct a multimodal multi-party discussion corpus toward argumentation quality assessment in multi-party conversation. The corpus consists of 10 multi-party discussions (200 minutes) in total. Each participant is annotated for 6 scores about rhetoric, which is one of the classes of argumentation quality. In this paper, we explain the procedure of our corpus construction and report the results of the annotation.

关键词： Annotations Predictive models Rhetoric Quality assessment Reliability Data mining Informatics

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The Phenology and Water Level Time-Series Mangrove Index for Improved Mangrove Monitoring

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SSRN 2024年

作者： Sun, Weiwei Huang, Ke Yang, Gang Fu, Bolin Chen, Chao Meng, Xiangchao Feng, Tian Ge, Yong Department of Geography and Spatial Information Techniques Ningbo University Ningbo315211 China Zhejiang Collaborative Innovation Center Ningbo Universities Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences & Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Geomatics and Geoinformation Guilin University of Technology Guilin541006 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China

Mangroves are declining and degrading under the influence of human activities and natural factors, making their accurate mapping and dynamic monitoring essential. However, most of the existing mangrove indices based on the spectral characteristics of multispectral images have some limitations in recognition accuracy and universality. Therefore, this study aimed to develop a robust and efficient Phenology and Water level Time-series Mangrove Index (PWTMI) for mangrove monitoring. PWTMI is constructed by combining spectral and temporal characteristics from dense time-series multispectral data, wherein phenology and water level time-series characteristics are extracted from NDVI and MNDWI time series. The results show that PWTMI outperforms existing multispectral-based mangrove indices and has an accuracy similar to a hyperspectral-based mangrove index, with overall accuracy ranging from 91.49% to 98.83% and F1 score ranging from 0.91 to 0.98 in four typical areas in China, indicating great potential for long time-series and large-scale mangrove monitoring. © 2024, The Authors. All rights reserved.

关键词： Water levels

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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Phase transitions of heliconical smectic-C and heliconical nematic phases in banana-shaped liquid crystals

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Physical Review E 2020年第5期101卷 050701(R)-050701(R)页

作者： Akihiko Matsuyama Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Kawazu 680-4 Iizuka Fukuoka 820-8502 Japan

A mean-field theory is introduced to describe heliconical nematic (NTB), heliconical smectic-C (SmCTB), and biaxial heliconical smectic-C (SmCTB,b) phases with mirror symmetry breaking. We extend our previous theories of the NTB phase to the heliconical smectic-C phases, by taking into account one-dimensional spatial ordering of smectic layers. The calculated phase diagrams on the temperature–alkyl chain length plane show a rich variety of phase transitions: first- and second-order NTB−SmCTB transitions, etc., including tricritical, tetracritical, and multicritical points. Our theory is qualitatively consistent with an experimental phase diagram.

关键词： Liquid crystal phase transitions Biaxial liquid crystals Liquid crystals Smectic liquid crystals

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Prewavy instability-originated dielectric chevrons of electroconvection in nematic liquid crystals

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Physical Review E 2020年第4期102卷 042704-042704页

作者： Masato Shiomi E-Joon Choi Jong-Hoon Huh Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka 820-8502 Japan Department of Polymer Science and Engineering Kumoh National Institute of Technology Gumi Gyungbuk 730-701 Korea

This paper reports the pattern formation and evolution in a high-frequency dielectric regime observed in the AC-driven electroconvection of nematic liquid crystals (NLCs). It is determined that prewavy (PW) instability is essential for the formation and evolution. Specifically, in a twisted-aligned NLC cell, we observe a unique pattern that is comparable to the dielectric chevrons (DCVs) typically observed in a conventional planarly aligned pattern. It is determined to be a combined PW pattern with dielectric normal rolls (DNRs). Moreover, the power indices (αPW, αDNR, and αDCV) are determined from the frequency-dependent thresholds (Vth∝fα) of PW, DNR, and DCV. By taking into account the appearance of PW and the different power indices, the pattern evolution scenarios for DCV are proposed, which can well explain the previous experimental results. Specifically, the PW-originated DCV2 clearly differs from the defect-mediated DCV1, which is known as a typical pattern evolution.

关键词： Pattern formation

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