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Correction to: Robust hand tracking for surgical telestration

International journal of computer assisted radiology and surgery 2022年第8期17卷 1487页

作者： Lucas-Raphael Müller Jens Petersen Amine Yamlahi Philipp Wise Tim J Adler Alexander Seitel Karl-Friedrich Kowalewski Beat Müller Hannes Kenngott Felix Nickel Lena Maier-Hein Intelligent Medical Systems (IMSY) German Cancer Research Center (DKFZ) Heidelberg Germany. lucas-raphael.mueller@dkfz.de. Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany. lucas-raphael.mueller@dkfz.de. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) Heidelberg Germany. Intelligent Medical Systems (IMSY) German Cancer Research Center (DKFZ) Heidelberg Germany. Department for General Visceral and Transplantation Surgery Mannheim University Hospital Heidelberg Germany. Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany. Department of Urology and Urosurgery Medical Faculty Mannheim Heidelberg University Hospital Heidelberg Germany. Department for General Visceral and Transplantation Surgery Mannheim University Hospital Heidelberg Germany. felix.nickel@med.uni-heidelberg.de. Medical Faculty Heidelberg University Heidelberg Germany.

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Biologically-induced synthetic manganese carbonate precipitate (BISMCP) for potential applications in heavy metal removal

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Heliyon 2023年第5期9卷

作者： Dewi, Anggraeni Kumala Sharma, Raju Kumar Das, Koyeli Sukul, Uttara Lin, Pin-Yun Huang, Yi-Hsun Lu, Chung Ming Lu, Cheng-Kang Chen, Tsung-Hsien Chen, Chien-Yen Department of Physics National Chung Cheng University University Road Minhsiung Chiayi County 62102 Taiwan Doctoral Program in Science Technology Environment and Mathematics Department of Earth and Environmental Sciences National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan Department of Chemistry and Biochemistry National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan Department of Biomedical Sciences Graduate Institute of Molecular Biology National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan Department of Chemical Engineering National Chung Cheng University University Road Minhsiung Chiayi County 62102 Taiwan Department of Chest Division Internal Medicine Ditmanson Medical Foundation Chiayi Christian Hospital 600566 Taiwan Department of Internal Medicine Ditmanson Medical Foundation Chiayi Christian Hospital 600566 Taiwan Center for Nano Bio-Detection Center for Innovative Research on Aging Society AIM-HI National Chung Cheng University 168 University Road Min-Hsiung Chiayi County 62102 Taiwan

Heavy metal pollution of water is a burning issue of today's world. Among several strategies involved for heavy metal remediation purpose, biomineralization has shown great potential. Of late, research has been focused on developing effective mineral adsorbents with reduced time and cost consumption. In this present paper, the Biologically-Induced Synthetic Manganese Carbonate Precipitate (BISMCP) was produced based on the biologically-induced mineralization method, employing Sporosarcina pasteurii in aqueous solutions containing urea and MnCl₂. The prepared adsorbent was characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), SEM-energy dispersive X-ray spectroscopy (SEM-EDX), X-ray diffraction (XRD) and BET surface area analyzer. EDX analysis showed the elements in the crystal BISMCP were Mn, C, and O. XRD result of BISMCP determined the crystal structure, which is close to rhodochrosite (MnCO₃). Spectral peaks of FTIR at 1641.79 cm⁻¹ confirmed the appearance of C[dbnd]O binding, with strong stretching of CO₃²⁻ in Amide I. From the six kinds of BISMCP produced, sample MCP-6 has the higher specific surface area by BET analysis at 109.01 m²/g, with pore size at 8.76 nm and higher pore volume at 0.178 cm³/g. These specifications will be suitable as an adsorbent for heavy metal removal by adsorption process. This study presents a preliminary analysis of the possibility of BISMCP for heavy metals adsorption using ICP multi-element standard solution XIII (As, Cr, Cd, Cu, Ni, and Zn). BISMCP formed from 0.1 MnCl₂ and 30 ml of bacteria volume (MCP-6) produced a better adsorbent material than others concentrations, with the adsorption efficiency of total As at 98.9%, Cr at 97.0%, Cu at 94.7%, Cd at 88.3%, Zn at 48.6%, and Ni at 29.5%. Future work could be examined its efficiency adsorbing individual heavy metals. © 2023

关键词： Adsorbent material Biologically induced mineralization Biomineralization Heavy metal removal Manganese carbonate (MnCO3) Sporosarcina pasteurii

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Simultaneous dimensionality and complexity model selection for spectral graph clustering

arXiv

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arXiv 2019年

作者： Yang, Congyuan Priebe, Carey E. Park, Youngser Marchette, David J. Department of Electrical and Computer Engineering Johns Hopkins University Department of Applied Math and Statistics Johns Hopkins University Center for Imaging Science Johns Hopkins University Applied Mathematics and Data Analytics Group Naval Surface Warfare Center Dahlgren Division

Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating both the embedding dimension and number of clusters. This paper attempts to address the issue by establishing a novel model selection framework specifically for vertex clustering on graphs under a stochastic block model. The first contribution is a probabilistic model which approximates the distribution of the extended spectral embedding of a graph. The model is constructed based on a theoretical result of asymptotic normality of the informative part of the embedding, and on a simulation result providing a conjecture for the limiting behavior of the redundant part of the embedding. The second contribution is a simultaneous model selection framework. In contrast with the traditional approaches, our model selection procedure estimates embedding dimension and number of clusters simultaneously. Based on our conjectured distributional model, a theorem on the consistency of the estimates of model parameters is stated and proven. The theorem provides statistical support for the validity of our method. Heuristic algorithms via the simultaneous model selection framework for vertex clustering are proposed, with good performance shown in the experiment on synthetic data and on the real application of connectome analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Thermodynamic Control of Intramolecular Singlet Fission and Exciton Transport in Linear Tetracene Oligomers

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Angewandte Chemie 2022年第8期135卷

作者： Dr. Shunta Nakamura Dr. Hayato Sakai Dr. Masaaki Fuki Rikuto Ooie Dr. Fumitaka Ishiwari Prof. Dr. Akinori Saeki Prof. Dr. Nikolai V. Tkachenko Prof. Dr. Yasuhiro Kobori Prof. Dr. Taku Hasobe Department of Chemistry Faculty of Science and Technology Keio University Yokohama Kanagawa 223-8522 Japan Molecular Photoscience Research Center Kobe University 1-1 Rokkodai-cho Nada-ku Kobe 657-8501 Japan Department of Chemistry Graduate School of Science Kobe University 1-1 Rokkodai-cho Nada-ku Kobe 657-8501 Japan Department of Applied Chemistry Graduate School of Engineering Osaka University Suita Osaka 565-0871 Japan Innovative Catalysis Science Division Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI) Osaka University Suita Osaka 565-0871 Japan Chemistry and Advanced Materials Group Faculty of Engineering and Natural Sciences Tampere University Korkeakoulunkatu 8 33720 Tampere Finland

We newly synthesized a series of homo- and hetero-tetracene (Tc) oligomers to propose a molecular design strategy for the efficient exciton transport in linear oligomers by promoting correlated triplet pair (TT) dissociation and controlling sequential exciton trapping process of individual doubled triplet excitons (T+T) by intramolecular singlet fission. First, entropic gain effects on the number of Tc units are examined by comparing Tc-homo-oligomers [(Tc) n : n =2, 4, 6]. Then, a comparison of (Tc) n and Tc-hetero-oligomer [TcF 3 -(Tc) 4 -TcF 3 ] reveals the vibronic coupling effect for entropic gain. Observed entropic effects on the T+T formation indicated that the exciton migration is rationalized by number of possible TT states increased both by increasing the number of Tc units and by the vibronic levels at the terminal TcF 3 units. Finally, we successfully observed high-yield exciton trapping process (trapped triplet yield: Φ TrT =176 %).

关键词： Exciton Transport Oligomers Singlet Fission Tetracene Vibronic Coupling

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A Hierarchical Multigrid Method for Oil Production Optimization

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IFAC-PapersOnLine 2019年第1期52卷 492-497页

作者： Steen Hørsholt Hamid Nick John Bagterp Jørgensen Department of Applied Mathematics and Computer Science & Center for Energy Resources Engineering (CERE) Technical University of Denmark DK-2800 Kgs. Lyngby Denmark Danish Hydrocarbon Research and Technology Centre Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

The large-scale optimization problems that arise from oil production optimization under geological uncertainty of industry-scale reservoir models poses a challenge even for modern computer architecture. In combination with ensemble-based methods for production optimization under uncertainty, gradient-based optimization algorithms provides a powerful approach that ensures a high convergence rate. However, the spatial resolution and complexity of typical industry-scale models has a significant computational impact that renders the optimization problem intractable. To reduce the computational burden model reduction is essential. In this paper, we introduce a grid coarsening method that maintains the overall dynamics of the flow, by preserving the geological features of the model. Furthermore, we present a hierarchical multigrid method for oil production optimization. The method utilizes a hierarchy of coarse level models based on the high-fidelity model. We present the workflow of the hierarchical multigrid optimization procedure and a numerical example that demonstrates the application of oil production optimization on a synthetic reservoir.

关键词： Production optimization Model reduction Reservoir management Work-flow Gradient based optimization

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Beyond Low Earth Orbit: Biological research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Electronic spin transition in FeO2: Evidence for Fe(II) with peroxide O22−

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Physical Review B 2019年第1期100卷 014418-014418页

作者： Bo Gyu Jang Jin Liu Qingyang Hu Kristjan Haule Ho-Kwang Mao Wendy L. Mao Duck Young Kim Ji Hoon Shim Department of Chemistry Pohang University of Science and Technology Pohang 37673 Korea Department of Geological Sciences Stanford University Stanford California 94305 USA Center for High Pressure Science and Technology Advanced Research Shanghai 201203 China Department of Physics and Astronomy Rutgers University Piscataway New Jersey 08854 USA Stanford Institute for Materials and Energy Sciences SLAC National Accelerator Laboratory Menlo Park California 94025 USA Division of Advanced Nuclear Engineering Pohang University of Science and Technology Pohang 37673 Korea

The discovery of FeO2 containing more oxygen than hematite (Fe2O3), which was previously believed to be the most oxygen rich iron compound, has important implications for the study of deep lower mantle compositions. Compared to other iron compounds, there are limited reports on FeO2, making studies of its physical properties of great interest in fundamental condensed matter physics and geoscience. Even the oxidation state of Fe in FeO2 is the subject of debate in theoretical works and there have not been reports from experimental electronic and magnetic properties measurements. Here, we report the pressure-induced spin state transition from synchrotron experiments and our computational results explain the underlying mechanism. Using density functional theory and dynamical mean field theory, we calculated spin states of Fe with volume and Hubbard interaction U change, which clearly demonstrate that Fe in FeO2 consists of Fe(II) and peroxide O22−. Our paper suggests that the localized nature of both Fe 3d orbitals and O2 molecular orbitals should be correctly treated for unveiling the structural and electronic properties of FeO2.

关键词： Magnetism Metal-insulator transition Pressure effects Spin state transition Density functional calculations

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Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

arXiv

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arXiv 2025年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai Guan

We report a dedicated study of the newly discovered extended UHE γ-ray source 1LHAASO J0056+6346u. Analyzing 979 days of LHAASO-WCDA data and 1389 days of LHAASO-KM2A data, we observed a significant excess of γ-ray events with both WCDA and KM2A. Assuming a point power-law source with a fixed spectral index, the significance maps reveal excesses of 12.65 σ, 22.18 σ, and 10.24 σ in the energy ranges of 1–25 TeV, 25–100 TeV, and > 100 TeV, respectively. We use a 3D likelihood algorithm to derive the morphological and spectral parameters, and the source is detected with significances of 13.72 σ by WCDA and 25.27 σ by KM2A. The best-fit positions derived from WCDA and KM2A data are (R.A. = 13.96◦ ± 0.09◦, Decl. = 63.92◦ ± 0.05◦) and (R.A. = 14.00◦ ± 0.05◦, Decl. = 63.79◦ ± 0.02◦), respectively. The angular size (r39) of 1LHAASO J0056+6346u is 0.34◦ ± 0.04◦ at 1-25 TeV and 0.24◦ ± 0.02◦ at > 25 TeV. The differential flux of this UHE γ-ray source can be described by an exponential cutoff power-law function: (2.67±0.25)×10−15 (E / 20 TeV)(−1.97±0.10) e−E / (55.1±7.2) TeV TeV−1cm−2s−1. To explore potential sources of γ-ray emission, we investigated the gas distribution around 1LHAASO J0056+6346u. 1LHAASO J0056+6346u is likely to be a TeV PWN powered by an unknown pulsar, which would naturally explain both its spatial and spectral properties. Another explanation is that this UHE γ-ray source might be associated with gas content illuminated by a nearby CR accelerator, possibly the SNR candidate G124.0+1.4. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

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In-gas-cell laser resonance ionization spectroscopy of Ir196,197,198

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Physical Review C 2020年第5期102卷 054307-054307页

作者： M. Mukai Y. Hirayama Y. X. Watanabe S. Schiffmann J. Ekman M. Godefroid P. Schury Y. Kakiguchi M. Oyaizu M. Wada S. C. Jeong J. Y. Moon J. H. Park H. Ishiyama S. Kimura H. Ueno M. Ahmed A. Ozawa H. Watanabe S. Kanaya H. Miyatake Nishina Center for Accelerator-Based Science RIKEN Wako Saitama 351-0198 Japan Graduate School of Pure and Applied Sciences University of Tsukuba Tsukuba Ibaraki 305-0006 Japan Wako Nuclear Science Center (WNSC) Institute of Particle and Nuclear Studies (IPNS) High Energy Accelerator Research Organization (KEK) Wako Saitama 351-0198 Japan Division of Mathematical Physics Department of Physics Lund University SE-22100 Lund Sweden Spectroscopy Quantum Chemistry and Atmospheric Remote Sensing (SQUARES) CP160/09 Université libre de Bruxelles B-1050 Brussels Belgium Materials Science and Applied Mathematics Malmö University S-20506 Malmö Sweden Rare Isotope Science Project Institute for Basic Science (IBS) Daejeon 305-811 Republic of Korea IRCNPC School of Physics and Nuclear Energy Engineering Beihang University Beijing 100191 China Department of Physics Osaka University Toyonaka Osaka 563-0043 Japan

Hyperfine structure (HFS) measurements of neutron-rich iridium isotopes Ir196,197,198 (Z=77,N=119–121) were performed via in-gas-cell laser resonance ionization spectroscopy at the KEK Isotope Separation System. Magnetic dipole moments μ and isotope shifts were determined from the HFS spectra. The variation of mean-square charge radii and quadrupole deformation parameters of these isotopes were evaluated from the isotope shifts. The μ value of Ir197 agreed with a theoretical value based on the strong coupling model, and the Ir nucleus was interpreted as prolately deformed by the theoretical calculations. The μ values of Ir196,198 were also compared with semiempirical values calculated based on the strong coupling model. From the comparison, we can suggest the possible spin values of Iπ=1,2− for Ir196 and Iπ=1− for Ir198.

关键词： Fine & hyperfine structure Laser spectroscopy Lifetimes & widths Nuclear structure & decays 190 ≤ A ≤ 219 Magnetic moment Spectrometers & spectroscopic techniques

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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