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  • 1
    Online Resource
    Online Resource
    CyberKnife NeuroRadiosurgery : A practical Guide (2020)
    Cham :Springer International Publishing :
    Details
    Keywords: Nervous system Surgery. ; Nervous system Radiography. ; Oncology. ; Medical physics. ; Neurology . ; Interventional radiology. ; Neurosurgery. ; Neuroradiology. ; Oncology. ; Medical Physics. ; Neurology. ; Interventional Radiology.
    Description / Table of Contents: Creating the Future -- CyberKnife Warfare in America: Battles at the Border Between Neurosurgery and Radiation Oncology -- The CyberKnife Robotic Radiosurgery System -- The Target Locating System for CyberKnife NeuroRadiosurgery -- Treatment Planning -- Small Field Dosimetry -- Quality Control -- Morphological Imaging -- Functional Imaging -- Diffusion Tensor Imaging (DTI) tractography -- Metabolic Imaging -- Radiobiology of Radiosurgery and Hypofractionated Treatments -- Organs at Risk (OAR) Tolerance in Hypofractionated Radiosurgery -- Brain Metastasis: Therapeutic, Diagnostic and Strategic Considerations -- Brain Metastasis: The Experience of the Burdenko Institute of Neurosurgery -- Multiple Brain Metastases -- Large Metastases and Tumor Bed -- Parasagittal and Convexity Meningiomas -- Skull Base Meningiomas -- High Grade Meningiomas and Hemagiopericytomas -- Perioptic Meningiomas -- Optic Nerve Sheath Meningiomas -- Vestibular Schwannomas -- Large Vestibular Schwannomas -- Pituitary Adenomas -- Craniopharyngiomas -- Malignant Gliomas -- Pilocytic Astrocytomas -- Pineal Tumors -- Reirradiation of Skull Base Tumors -- Chordomas and Chondrosarcomas -- Paragangliomas of Head and Neck -- Paragangliomas: a case series from Burdenko Institute -- Brainstem Tumors -- Uveal Melanoma -- Pediatric Radiosurgery -- Immunotherapy and Radiosurgery -- Spinal Metastases -- Reirradiation of Spinal Metastases -- Benign Spinal Tumors -- Intradural Spinal Lesions -- Cerebral Arteriovenous Malformations -- Large Arteriovenous Malformations -- Cerebral Cavernous Malformations -- Dural Arteriovenous Fistulas -- Cavernous Sinus Hemangiomas -- Trigeminal Neuralgia -- Movement Disorders -- Epilepsy -- Psychiatric/Behavioral Disorders. .
    Abstract: This book is a practical guide on image-guided robotic (CyberKnife®) radiosurgery of the brain and the spine. The volume introduces the radiosurgical community to the potential of image-guidance in the treatment of neurosurgical diseases including neuro-oncological, vascular and functional disorders. Principles of image-guided radiosurgery, including physics and radiobiology are considered. Each chapter provides a critical review of the literature and analyses of several aspects to offer an assessment of single and hypofractionated treatments. Based on the authors’ experience, tables or summaries presenting the treatment approaches and associated risks are included as well. Providing a practical guide to define the selection of dose, fractionation schemes, isodose line, margins, imaging, constraints to the structures at risk will support safe practice of neuroradiosurgery. This book aims to shed new light on the treatment of neoplastic and non-neoplastic diseases of the central nervous system using the CyberKnife® image-guided robotic radiosurgery system. It will be adopted by neurosurgery residents and neurosurgery consultants as well as residents in radiation oncology and radiation oncologists; medical physicists involved in radiosurgery procedures may also benefit from this book. .
    Type of Medium: Online Resource
    Pages: XVI, 588 p. 147 illus., 120 illus. in color. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9783030506681
    URL: https://doi.org/10.1007/978-3-030-50668-1
    DOI: 10.1007/978-3-030-50668-1
    DDC: 617.48
    Language: English
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  • 2
    Online Resource
    Online Resource
    Deep Learning for Hydrometeorology and Environmental Science (2021)
    Cham :Springer International Publishing :
    Details
    Keywords: Water. ; Hydrology. ; Artificial intelligence. ; Environmental sciences Mathematics. ; Environment. ; Neural networks (Computer science) . ; Human ecology Study and teaching. ; Water. ; Artificial Intelligence. ; Mathematical Applications in Environmental Science. ; Environmental Sciences. ; Mathematical Models of Cognitive Processes and Neural Networks. ; Environmental Studies.
    Description / Table of Contents: Introduction -- Mathematical Background -- Data Preprocessing -- Neural Network -- Training a Neural Network -- Updating Weights -- Improving model performance -- Advanced Neural Network Algorithms -- Deep learning for time series -- Deep learning for spatial datasets -- Tensorflow and Keras Programming for Deep Learning -- Hydrometeorological Applications of deep learning -- Environmental Applications of deep learning.
    Abstract: This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
    Type of Medium: Online Resource
    Pages: XIV, 204 p. 189 illus., 133 illus. in color. , online resource.
    Edition: 1st ed. 2021.
    ISBN: 9783030647773
    Series Statement: Water Science and Technology Library, 99
    URL: https://doi.org/10.1007/978-3-030-64777-3
    DOI: 10.1007/978-3-030-64777-3
    DDC: 551.48
    Language: English
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  • 3
    Monograph available for loan
    Monograph available for loan
    Atmospheric physics (1980)
    Dordrecht/Holland [u.a.] : Reidel
    Details Availability
    Call number: MOP 45298 / Mitte
    Type of Medium: Monograph available for loan
    Pages: XII, 212 S.
    ISBN: 9027710333
    Location: MOP - must be ordered
    Branch Library: GFZ Library
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  • 4
    Monograph available for loan
    Monograph available for loan
    Thermal conductivity : proceedings of the eigth conference, held at Perdue University, West Lafayette, Indiana, October 7-10, 1968 (1969)
    New York : Plenum Press
    Details Availability
    Call number: 7765
    Type of Medium: Monograph available for loan
    Pages: XX, 1169 S. : Ill.
    Location: Upper compact magazine
    Branch Library: GFZ Library
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  • 5
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    Deep learning for hydrometeorology and environmental science (2021)
    Cham : Springer
    Associated volumes
    Details
    Call number: 9783030647773 (e-book)
    In: Water science and technology library, volume 99
    Description / Table of Contents: This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
    Type of Medium: 12
    Pages: 1 Online-Ressource (xiv, 204 Seiten) , Illustrationen, Diagramme, Karten
    ISBN: 9783030647773 , 978-3-030-64777-3
    ISSN: 0921-092X , 1872-4663
    Series Statement: Water science and technology library volume 99
    URL: Ebook (access only within the AWI network)
    DOI: 10.1007/978-3-030-64777-3
    Language: English
    Note: Contents 1 Introduction 1.1 What is Deep Learning? 1.2 Pros and Cons of Deep Learning 1.3 Recent Applications of Deep Learning in Hydrometeorological and Environmental Studies 1.4 Organization of Chapters 1.5 Summary and Conclusion References 2 Mathematical Background 2.1 Linear Regression Model 2.1.1 Simple Linear Regression 2.1.2 Multiple Linear Regression 2.2 Time Series Model 2.2.1 Autoregressive Model (AR) 2.3 Probability Distributions 2.3.1 Normal Distributions 2.3.2 Gamma Distribution 2.4 Exercises References 3 Data Preprocessing 3.1 Normalization 3.2 Data Splitting for Training and Testing 3.3 Exercises 4 Neural Network 4.1 Terminology in Neural Network 4.1.1 Components of Neural Network 4.1.2 Activation Functions 4.1.3 Error and Loss Function 4.1.4 Softmax and One-Hot Encoding 4.2 Artificial Neural Network 4.2.1 Simplest Network 4.2.2 Feedforward and Backward Propagation 4.2.3 Network with Multiple Input and Output Variables 4.2.4 Python Coding of the Simple Network 4.3 Exercises 5 Training a Neural Network 5.1 Initialization 5.2 Gradient Descent 5.3 Backpropagation 5.3.1 Simple Network 5.3.2 Full Neural Network 5.3.3 Python Coding of Network 5.4 Exercises Reference 6 Updating Weights 6.1 Momentum 6.2 Adagrad 6.3 RMSprop 6.4 Adam 6.5 Nadam 6.6 Python Coding of Updating Weights 6.7 Exercises References 7 Improving Model Performance 7.1 Batching and Minibatch 7.2 Validation 7.2.1 Python Coding of K-Fold Cross-Validation 7.3 Regularization 7.3.1 L-Norm Regularization 7.3.2 Dropout 7.3.3 Python Coding of Regularization 7.4 Exercises Reference 8 Advanced Neural Network Algorithms 8.1 Extreme Learning Machine (ELM) 8.1.1 Basic ELM 8.1.2 Generalized ELM 8.1.3 Python Coding 8.2 Autoencoder 8.2.1 Vanilla Autoencoder 8.2.2 Regularized Autoencoder 8.2.3 Python Coding of Regularized AE 8.3 Exercises Reference 9 Deep Learning for Time Series 9.1 Recurrent Neural Network 9.1.1 Backpropagation 9.1.2 Backpropagation Through Time (BPTT) 9.2 Long Short-Term Memory (LSTM) 9.2.1 Basics of LSTM 9.2.2 Example of LSTM 9.2.3 Backpropagation of a Simple LSTM 9.2.4 Backpropagation Through Time (BPTT) 9.3 Gated Recurrent Unit (GRU) 9.3.1 Basics of GRU 9.3.2 Example of GRU 9.3.3 Backpropagation of a Simple GRU Model 9.4 Exercises References 10 Deep Learning for Spatial Datasets 10.1 Convolutional Neural Network (CNN) 10.1.1 Definition of Convolution 10.1.2 Elements of CNN 10.2 Backpropagation of CNN 10.3 Exercises 11 Tensorflow and Keras Programming for Deep Learning 11.1 Basic Keras Modeling 11.2 Temporal Deep Learning (LSTM and GRU) 11.3 Spatial Deep Learning (CNN) 11.4 Exercises References 12 Hydrometeorological Applications of Deep Learning 12.1 Stochastic Simulation with LSTM 12.1.1 Mathematical Description for Stochastic Simulation with LSTM 12.1.2 Colorado Monthly Streamflow 12.1.3 Results of Colorado River 12.1.4 Python Coding 12.1.5 Matlab Coding 12.2 Forecasting Daily Temperature with LSTM 12.2.1 Preparing the Data 12.2.2 Methodology 12.2.3 Results 12.2.4 Python Coding 12.3 Exercises References 13 Environmental Applications of Deep Learning 13.1 Remote Sensing of Water Quality Using CNN 13.1.1 Introduction 13.1.2 Study Area and Monitoring 13.1.3 Field Data Collection 13.1.4 Point-Centered Regression CNN (PRCNN) 13.1.5 Results and Discussion 13.1.6 Conclusion 13.1.7 Python Coding References
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  • 6
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    Global estimate of submarine groundwater discharge based on an observationally constrained radium isotope model (2015)
    John Wiley & Sons
    Details
    Publication Date: 2022-05-25
    Description: © The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Geophysical Research Letters 41 (2014): 8438–8444, doi:10.1002/2014GL061574.
    Description: Along the continental margins, rivers and submarine groundwater supply nutrients, trace elements, and radionuclides to the coastal ocean, supporting coastal ecosystems and, increasingly, causing harmful algal blooms and eutrophication. While the global magnitude of gauged riverine water discharge is well known, the magnitude of submarine groundwater discharge (SGD) is poorly constrained. Using an inverse model combined with a global compilation of 228Ra observations, we show that the SGD integrated over the Atlantic and Indo-Pacific Oceans between 60°S and 70°N is (12 ± 3) × 1013 m3 yr−1, which is 3 to 4 times greater than the freshwater fluxes into the oceans by rivers. Unlike the rivers, where more than half of the total flux is discharged into the Atlantic, about 70% of SGD flows into the Indo-Pacific Oceans. We suggest that SGD is the dominant pathway for dissolved terrestrial materials to the global ocean, and this necessitates revisions for the budgets of chemical elements including carbon.
    Description: This work was supported by the Ministry of Oceans and Fisheries, Korea, through the Korea Institute of Marine Science and Technology (KIMST) (20120176) and National Research Foundation (NRF) of Korea (2013R1A2A1A05004343 and 2013R1A1A1058203). Charette and Moore's contributions were supported by the US National Science Foundation through the GEOTRACES project.
    Keywords: Submarine groundwater discharge ; Radium ; Inverse modeling ; Land-ocean interaction ; Brackish groundwater ; Coastal flux
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/postscript
    Format: application/pdf
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  • 7
    Electronic Resource
    Electronic Resource
    Characterization of Cold Drawn Gold bonding Wire with EBSD (2002)
    s.l. ; Stafa-Zurich, Switzerland
    Materials science forum Vol. 408-412 (Aug. 2002), p. 499-504 
    Details
    ISSN: 1662-9752
    Source: Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Type of Medium: Electronic Resource
    URL: http://www.tib-hannover.de/fulltexts/2011/0528/02/06/transtech_doi~10.4028%252Fwww.scientific.net%252FMSF.408-412.499.pdf
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  • 8
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    Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (2016)
    Taylor & Francis
    Details
    Publication Date: 2016-01-02
    Print ISSN: 1554-8627
    Electronic ISSN: 1554-8635
    Topics: Biology
    Published by Taylor & Francis
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  • 9
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    The KSTAR project: An advanced steady state superconducting tokamak experiment (2000)
    Institute of Physics
    Details
    Publication Date: 2000-03-01
    Print ISSN: 0029-5515
    Electronic ISSN: 1741-4326
    Topics: Physics
    Published by Institute of Physics
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  • 10
    Electronic Resource
    Electronic Resource
    Conductivity and ion density of a plasma channel induced by a mildly relativistic electron beam from a gas-filled diode (1998)
    [S.l.] : American Institute of Physics (AIP)
    Physics of Plasmas 5 (1998), S. 1514-1521 
    Details
    ISSN: 1089-7674
    Source: AIP Digital Archive
    Topics: Physics
    Notes: Conductivity and ion density of a plasma channel induced by a mildly relativistic electron beam (300 kV, ∼2 kA, 10–50 ns) have been experimentally investigated under various gas pressures. Pressures of filling gas (air) in this experiment ranged from 10 mTorr to 100 mTorr. The net currents of the beam-induced plasma channel were measured by four Rogowski coils located along the propagating region, while the electron beam currents were measured by a Faraday cup. The inductive plasma currents observed at the above pressure regimes have been characterized by magnetic decay time. Plasma-channel conductivity and ion density induced by the beam are measured along the propagating axial positions under various gas pressures. The numerical result of the ion density is also obtained at the charge neutralization time when the ion density is just the same as the electron beam density, and the digitizing experimental data of the beam current Ib(t) and voltage Vd(t) have been used. As expected, in both numerical and experimental results the ion density increases to a peak value of about 3.0×1011 cm−3 and 3.3×1011 cm−3, respectively, at 50 mTorr and slowly decreases for both cases as the gas pressure increases from 50 mTorr to 100 mTorr. Moreover, the results of ion density predicted by the theoretical model developed here are also found to be in remarkably good agreement with experimental and numerical results at pressure regimes from 10 mTorr to 100 mTorr. © 1998 American Institute of Physics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1063/1.872824
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