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Quantum transient heat transport in the hyperparametric oscillator

Physical Review A 2021年第5期104卷 052220-052220页

作者： JungYun Han Daniel Leykam Dimitris G. Angelakis Juzar Thingna Center for Theoretical Physics of Complex Systems Institute for Basic Science (IBS) Daejeon 34126 Republic of Korea Basic Science Program University of Science and Technology (UST) Daejeon 34113 Republic of Korea Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 School of Electrical and Computer Engineering Technical University of Crete Chania Crete 73100 Greece

We explore nonequilibrium quantum heat transport in nonlinear bosonic systems in the presence of a non-Kerr-type interaction governed by hyperparametric oscillation due to two-photon hopping between the two cavities. We estimate the thermodynamic response analytically by constructing the su(2) algebra of the nonlinear Hamiltonian and predict that the system exhibits a negative excitation mode. Consequently, this specific form of interaction enables the cooling of the system by inducing a ground-state transition when the number of particles increases, even though the interaction strength is small. We demonstrate a transition of the heat current numerically in the presence of symmetric coupling between the system and the bath and show long relaxation times in the cooling phase. We compare with the Kerr-type Bose-Hubbard form of interaction induced via cross-phase modulation, which does not exhibit any such transition. We further compute the nonequilibrium heat current in the presence of two baths at different temperatures and observe that the cooling effect for the non-Kerr-type interaction persists. Our findings may help in the manipulation of quantum states using the system's interactions to induce cooling.

关键词： Quantum optics Quantum thermodynamics Quantum transport

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Dasom Kim Jin Hou Geon Lee Ayush Agrawal Sunghwan Kim Hao Zhang Di Bao Andrey Baydin Wenjing WU Fuyang Tay Shengxi Huang Daivd Hagenmüller Elbert E. M. Chia Dai-Sik Kim Minah Seo Aditya D. Mohite Junichiro Kono Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Physics and Astronomy Seoul National University Seoul Republic of Korea Department of Chemical and Biomolecular Engineering Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Smalley-Curl institute Rice University Houston TX USA CESQ-ISIS (UMR 7006) Universite de Strasbourg and CNRS Strasbourg France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Department of Physics and Astronomy Rice University Houston TX USA

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Couplings Phonons Laser modes Perovskites Electro-optical waveguides Photonics

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Whole-body phase plane analysis for standard maximum vertical jump assessment

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Gait & Posture 2021年 90卷 203-204页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University CICANT Centre for Research in Applied Communication Culture and New Technologies - MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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Lower limb joint angular phase plane coordination analysis on standard maximum vertical jump

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Gait & Posture 2021年 90卷 205-206页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University CICANT Centre for Research in Applied Communication Culture and New Technologies- MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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A Neural Network Based Modeling of Closed Room Thermal Comfort Environmental Prediction for Sensor Hub

A Neural Network Based Modeling of Closed Room Thermal Comfo...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Pornpawee Thongkhome Natasha Dejdumrong Science-Mathematics Program Triamudomsuksa Pattanakarn School Bangkok Thailand Dept. of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9781728164861

ISBN: (纸本)9781728164878

Currently, environmental protection and intelligence system are topics of interest in smart homes and intelligent buildings. This work represents an environmental prediction study of the closed room which installed a split-type air-conditioning system and a newly designed sensor hub using a feed-forward back-propagation artificial neural network-based prediction for human thermal comfort and indirectly concerned to the growth or existence of diseases, contagious diseases and allergies. The RPROP algorithm was utilized to train the closed room environmental parameters that get from sensor hub especial relative humidity in the ANN model and validate the model by using cohen's kappa and ROC curve to represents the degree of accuracy and reliability. The result obtained from the ANN trained data agreed closely with those obtained from a sensor hub with the high perfect agreement. The benefit of the ANN-based strategy in this study could be utilized for the design of a sensor hub that can realize the adaptive adjustment of environmental parameters through the air conditioning system.

关键词： Humidity Artificial neural networks Predictive models Buildings Temperature sensors Computational modeling Prediction algorithms

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TAROGE-M: Antarctic High-Mountain Radio Antenna Array for Detecting Ultra-High Energy ANITA Anomalous Events 38

TAROGE-M: Antarctic High-Mountain Radio Antenna Array for De...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Wang, Shih-Hao Chen, Pisin Chen, Yaocheng Chen, Ying-Chih Choi, Taejin Ham, Young-Bae Hsu, Shih-Ying Huang, Jian-Jung Huang, MingHuey A. Jee, Geonhwa Jung, Jongil Kim, Jieun Kuo, Chung-Yun Kwon, Hyuck-Jin Lee, Changsup Leung, Chung-Hei Liu, Tsung-Che Nam, Jiwoo Shiao, Yu-Shao J. Shin, Bok-Kyun Su, Shih-Chieh Wang, Min-Zu Wang, Shih-Hao Wang, Yu-Hsin Anker, Astrid Barwick, Steven W. Besson, Dave Z. Bouma, Sjoerd Cataldo, Maddalena Gaswint, Geoffrey Glaser, Christian Hallmann, Steffen Hanson, Jordan C. Henrichs, Jakob Kleinfelder, Stuart A. Lahmann, Robert Meyers, Zachary S. Nelles, Anna Novikov, Alexander Paul, Manuel P. Pyras, Lilly Persichilli, Christopher Plaisier, Ilse Rice-Smith, Ryan Seikh, Mohammad F. H. Tatar, Joulien Welling, Christoph Zhao, Leshan Department of Physics National Taiwan University Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan Kavli Institute for Particle Astrophysics and Cosmology SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Department of Energy Engineering National United University Miaoli Taiwan Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National Pingtung university Pingtung900 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Nano Device Laboratories Hsinchu300 Taiwan Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States DESY Zeuthen15738 Germany ECAP Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Whittier College Department of Physics WhittierCA90602 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Resea

TAROGE-M is a radio antenna array atop ∼ 2.7 km-high Mt. Melbourne in Antarctica for detecting ultra-high energy (UHE, E > 1017 eV) air showers in near-horizontal directions. Besides the detection of cosmic rays and Earth-skimming tau neutrinos, its primary goal is to reproduce the discovery and verify the origin of so-called ANITA anomalous events, having feature of upward-going UHE air showers but cannot be explained by tau neutrinos. The detection concept takes advantages of a high altitude for a broad view toward the horizon;strong and near-vertical geomagnetic field for enhancing the radio signal;and quiet radio environment in Antarctica. Its relatively simple design and high duty cycle make it easily to be extended and thus to achieve an exposure competitive with ANITA experiments within a few years. The first TAROGE-M station operating at 180-450 MHz frequencies deployed in 2020 detected seven UHE cosmic ray events within 25.3-day livetime. The events have a mean energy of ∼ 1 EeV and an estimated flux consistent with other experiments, and validate the station as an UHE particle detector. In 2022-2023 season, the system was upgraded for a robust long-term operation, with a more detailed calibration with a drone-borne pulser. We also discuss the planned upgrade, including the implementation of interferometric trigger, which is expected to increase the cosmic ray and tau neutrino acceptances by a factor of 10 at 0.1 EeV, corresponding to a lower energy threshold by a factor of 3. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2024年第2期2卷

作者： Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Ryo Maezono Study Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R 2 ) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1) , methyl quinoxaline-6-carboxylate (Q2) , (2 E ,3 E )-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3) , (2 E ,3 E ) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4) , ( E )-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5) , and 2-(4-methoxyphenyl)-7-methylthieno[3,2- b ] quinoxaline (Q6) , show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

关键词： Quantum neural network QSPR Corrosion inhibition Quinoxaline

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Computational prediction and validation of specific EmbR binding site on PknH

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Synthetic and Systems Biotechnology 2021年第4期6卷 429-436页

作者： Insung Na Huanqin Dai Hantian Li Anvita Gupta David Kreda Powell Zhang Xiangyin Chen Lixin Zhang Gil Alterovitz Computational Health Informatics Program Boston Children’s Hospital/Harvard Medical SchoolBostonMA 02115USA Department of Medicine Brigham and Women’s Hospital/Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Mycology Institute of MicrobiologyBeijing100101 China Department of Computer Sciences Stanford UniversityStanford CA 94305USA Center for Biomedical Informatics Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Bioreactor Engineering East China University of Science and TechnologyShanghaiChina Department of Biology The Johns Hopkins UniversityBaltimoreMD 21218USA Lexington High School BostonMA 02115USA

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not ***(EMB),one of the well-known first-line drugs in tuberculosis treatment is,unfortunately,not free from drug resistance *** studies have shown that some genetic mutations in Mycobacterium tuberculosis(Mtb)EmbR,and EmbC/A/B genes cause EMB ***-PknH pair controls embC/A/B operon,which encodes EmbC/A/B genes,and EMB interacts with EmbA/B ***,the EmbR binding site on PknH was *** conducted molecular simulation on the EmbR-peptides binding structures and discovered phosphorylated PknH 273-280(N′-HEALS^(P)DPD-C′)makesβstrand with the EmbR FHA domain,asβ-MoRF(MoRF;molecular recognition feature)does at its binding *** bond number analysis also supported the peptides’β-MoRF forming activity at the EmbR FHA ***,we discovered that previously known phosphorylation residues might have their chronological order according to the phosphorylation *** discovery validated that Mtb PknH 273-280(N′-HEALSDPD-C′)has reliable EmbR binding *** approach is revolutionary in the computer-aided drug discovery field,because it is the first trial to discover the protein-protein interaction site,and find binding partner in nature from this site.

关键词： Disorder-to-order transition Protein intrinsic disorder Binding site prediction Drug resistance Molecular simulation

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Decision Making using Internet of Things and Machine Learning: A bibliometric approach to tracking main research themes

Decision Making using Internet of Things and Machine Learnin...

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Gamboa-Rosales, N.K. Castorena-Robles, A. Casas-Valadez, M.A. Cobo, M.J. Castaneda-Miranda, R. Lopez-Robles, J.R. CONACYT-Autonomous University of Zacatecas Academic Unit of Electrical Engineering Zacatecas Mexico Monterrey Institute of Technology and Higher Education Biotechnology Engineering Queretaro Mexico Autonomous University of Zacatecas Academic Unit of Accounting and Administration Zacatecas Mexico University of Cadiz Department of Computer Science and Engineering Cadiz Spain Autonomous University of Zacatecas Post. Program of Eng. and Applied Tech. National Lab. CONACYT-SEDEAM Zacatecas Mexico

ISBN: (纸本)9781728196756

The internet has become an integral part of our life, we connect daily for informational, social, entertainment and even work and economic purposes. Therefore, it is completely normal to connect to the internet;But it is not only people, also these everyday objects or things in our environment who connect to the network to take advantage of its benefits. This next stage in the evolution of the internet, in which connectivity extends to the objects which surround us, is known as the Internet of Things. Sensors, Machines, Devices, Wearables are part of the Internet of Things and these can be identified to be recognized and interact without the need for a human being. With the Internet of Things, the connection extends to any type of object. All these interconnected devices and people produce an immense amount of data, when analyzed, they become a tool that allows organizations to make wise decisions in real time by having information on various market situations and ensuring a better understanding of behavior and consumer preferences. One of the disciplines of Artificial Intelligence responsible for analyzing this large amount of data is Machine Learning, whose sole purpose is to improve the Decision-Making process. To understand the relationship between Internet of Things, application of Machine Learning and the Decision Making process, a bibliometric analysis is carried out from 2013 to 2020 to know its evolution, trends, research areas, authors and other aspects using Scopus. © 2020 IEEE.

关键词： Decision making

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