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  • 1
    Online Resource
    Online Resource
    Current and Future Trends of Rainfall Erosivity and Soil Erosion in Central Asia (2021)
    Cham :Springer International Publishing :
    Details
    Keywords: Environment. ; Soil science. ; Physical geography. ; Geographic information systems. ; Environmental Sciences. ; Soil Science. ; Physical Geography. ; Geographical Information System.
    Description / Table of Contents: Chapter 1. Introduction and Background on Rainfall Erosivity Processes and Soil Erosion -- Chapter 2. Natural Conditions of Central Asia -- Chapter 3. Data sources on Rainfall and RUSLE model -- Chapter 4. Projected Rainfall Erosivity and Soil Erosion in Central Asia -- Chapter 5. Spatio-temporal variations and Projected Rainfall Erosivity and Erosivity Density in Kazakhstan -- Chapter 6. Conclusions and Recommendations.
    Abstract: This book analyses climate change influences on rainfall erosivity and soil erosion across Central Asia, provides an overview (past and projections) on the Central Asian countries where projected changes in rainfall erosivity and erosivity density are the greatest, and discusses the potential impacts on the environment across the region. This analysis is accomplished primarily using the RUSLE model with past and future climate projections, spatiotemporal variations of rainfall erosivity and soil erosion based on WorldClim, and Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models (for Central Asia and separately Kazakhstan). The relationship between precipitation characteristics and erosion has been well established, but spatial and temporal projections of future rainfall erosivity in a changing climate in Central Asia have not been published significantly. Therefore, assessing rainfall erosivity and its consequences can assist specialists and researchers in achieving the best practices for soil conservation. The result of this type of research is all-encompassing, and may reflect normal variations in other parts of the world (for example, the arid and semi-arid regions) and is inherently limited to the Central Asian region.
    Type of Medium: Online Resource
    Pages: XVIII, 84 p. 36 illus., 35 illus. in color. , online resource.
    Edition: 1st ed. 2021.
    ISBN: 9783030635091
    Series Statement: SpringerBriefs in Environmental Science,
    URL: https://doi.org/10.1007/978-3-030-63509-1
    DOI: 10.1007/978-3-030-63509-1
    DDC: 333.7
    Language: English
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    E-BOOKS (AWI ONLY)
  • 2
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    Requirements and concepts for future automotive electronic architectures from the view of integrated safety (2021)
    KIT Scientific Publishing
    Details
    Publication Date: 2023-12-21
    Description: In this dissertation, concepts of the electronic architecture of automotive Integrated Safety System are developed as a cooperative approach of engineering process, dependable hardware architecture and software platform. The development process covers distributed rapid prototyping with virtual front-loading and correct-by-construction. Concepts of fault tolerant hardware architecture and dependability software services are discussed with design guidelines.
    Keywords: T1-995 ; dependable software platform ; Agreement ; dependability software services ; E/E-Architecture ; Integrated Safety System ; SW-Watchdog ; fault-tolerant hardware ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues
    Language: English
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    E-BOOK
  • 3
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    Tensor Network Contractions (2021)
    Springer Nature
    Details
    Publication Date: 2024-04-05
    Description: Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from the numerical renormalization group approach proposed by K. G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum many-body physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.
    Keywords: Physics ; Physics ; Quantum physics ; Quantum optics ; Statistical physics ; Machine learning ; Elementary particles (Physics) ; Quantum field theory ; thema EDItEUR::P Mathematics and Science::PH Physics::PHJ Optical physics ; thema EDItEUR::P Mathematics and Science::PH Physics::PHQ Quantum physics (quantum mechanics and quantum field theory) ; thema EDItEUR::P Mathematics and Science::PH Physics::PHS Statistical physics ; thema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::P Mathematics and Science::PH Physics::PHJ Optical physics ; thema EDItEUR::P Mathematics and Science::PH Physics::PHQ Quantum physics (quantum mechanics and quantum field theory) ; thema EDItEUR::P Mathematics and Science::PH Physics::PHS Statistical physics ; thema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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    E-BOOK
  • 4
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    Stoichiometric and sterol responses of dinoflagellates to changes in temperature, nutrient supply in the exponential phase (2019)
    PANGAEA
    In:  Supplement to: Chen, Mengna; Bi, Rong; Chen, Xi; Ding, Yang; Zhang, Hailong; Li, Li; Zhao, Meixun (2019): Stoichiometric and sterol responses of dinoflagellates to changes in temperature, nutrient supply and growth phase. Algal Research, 42, 101609, https://doi.org/10.1016/j.algal.2019.101609
    Details
    Publication Date: 2023-06-19
    Description: We investigated the responses of elemental stoichiometry (POC, PON) and sterol contents (brassicasterol, dinosterol) in three dinoflagellate species (Prorocentrum donghaiense, Prorocentrum minimum and Karenia mikimotoi) to changes in temperatures (15, 20 and 25 °C), N:P supply ratios (molar ratios 10:1, 24:1 and 63:1) in the exponential phase in batch culture experiments.
    Keywords: 24-Methylcholesta-5,22E-dien-3beta-ol per cell; 4alpha,23,24-Trimethyl-5alpha-cholest-22E-en-3beta-ol per cell; Carbon; Dinoflagellates; nitrogen; Nitrogen, organic, particulate/Carbon, organic, particulate ratio; Particulate organic carbon production per cell; Particulate organic nitrogen production per cell; Ratio; Species; sterols; Temperature, technical; Treatment; warming
    Type: Dataset
    Format: text/tab-separated-values, 800 data points
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    DATA PANGAEA
  • 5
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    Water-mass control on phytoplankton spatiotemporal variations (2018)
    PANGAEA
    In:  Supplement to: Bi, Rong; Chen, Xi; Zhang, Jing; Ishizaka, Joji; Zhuang, Yanpei; Jin, Haiyan; Zhang, Hailong; Zhao, Meixun (2018): Water Mass Control on Phytoplankton Spatiotemporal Variations in the Northeastern East China Sea and the Western Tsushima Strait Revealed by Lipid Biomarkers. Journal of Geophysical Research: Biogeosciences, 123(4), 1318-1332, https://doi.org/10.1002/2017JG004340
    Details
    Publication Date: 2023-11-07
    Description: Continental margin ecosystems in the western North Pacific Ocean are subject to strong climate forcing and anthropogenic impacts. To evaluate mechanisms controlling phytoplankton biomass and community structure variations in marginal sea-open ocean boundary regions, brassicasterol, dinosterol and C37 alkenones were measured in suspended particles in summer and autumn from 2012 to 2013 in the northeastern East China Sea and the western Tsushima Strait (NEECS-WTS). In summer, the concentrations of brassicasterol (40 - 1535 ng L-1) and dinosterol (4.2 - 94 ng L-1) were higher in the southwest of Cheju Island, while C37 alkenones (0 - 30 ng L-1) were higher in the south of Cheju Island. In autumn, brassicasterol (12 - 106 ng L-1), dinosterol (2.4 - 21 ng L-1) and C37 alkenones (0.7 - 7.0 ng L-1) were higher in the southwest of Cheju Island and the WTS, and higher C37 alkenones also occurred in the Okinawa Trough. Correlation analysis of biomarkers and environmental conditions (temperature, salinity and inorganic nutrient concentrations) clearly demonstrated that phytoplankton biomass and community structure variations can be well elucidated by water masses as indexed by temperature and salinity. High nutrients from the Changjiang River were the main cause of high biomass in summer, while nutrients from subsurface water were likely the key factor regulating phytoplankton biomass in open ocean water stations in autumn. This study indicates that mechanisms controlling phytoplankton biomass in marginal sea-open ocean boundary regions should be classified by various water masses with different nutrient concentrations, instead of by geography.
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 6
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    (Figure 6) Physical oceanography and nutrients across the water column in the northeastern East China Sea and the western Tsushima Strait (2018)
    PANGAEA
    Details
    Publication Date: 2023-11-07
    Keywords: Chlorophyll a; DEPTH, water; East China Sea; ECS_1; ECS_11; ECS_12; ECS_13; ECS_14; ECS_17; ECS_18; ECS_2; ECS_22; ECS_23; ECS_24; ECS_3; ECS_7; ECS_8; ECS_9; Event label; Japan Sea; Latitude of event; Longitude of event; MULT; Multiple investigations; Nitrogen, inorganic, dissolved; Phosphorus, inorganic, dissolved; Salinity; Silicon; Temperature, water; Yellow Sea
    Type: Dataset
    Format: text/tab-separated-values, 862 data points
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    DATA PANGAEA
  • 7
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    (Figure 5) Temperature, salinity and density across the water column in the northeastern East China Sea and the western Tsushima Strait (2018)
    PANGAEA
    Details
    Publication Date: 2023-11-07
    Keywords: Density, sigma-theta (0); DEPTH, water; East China Sea; ECS_1; ECS_11; ECS_12; ECS_13; ECS_14; ECS_17; ECS_18; ECS_2; ECS_22; ECS_23; ECS_24; ECS_3; ECS_7; ECS_8; ECS_9; Event label; Japan Sea; Latitude of event; Longitude of event; MULT; Multiple investigations; Salinity; Temperature, water, potential; Yellow Sea
    Type: Dataset
    Format: text/tab-separated-values, 7950 data points
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    DATA PANGAEA
  • 8
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    (Figure 2-4) Physical oceanography, nutrients and lipid biomarkers in surface waters of the northeastern East China Sea and the western Tsushima Strait (2018)
    PANGAEA
    Details
    Publication Date: 2023-11-07
    Keywords: 24-Methylcholesta-5,22E-dien-3beta-ol of water; 4alpha,23,24-Trimethyl-5alpha-cholest-22E-en-3beta-ol of water; Alkenones/lipid biomarkers ratio; Alkenones of water; Brassicasterol/lipid biomarkers ratio; Chlorophyll a; Dinosterol/lipid biomarkers ratio; East China Sea; ECS_1; ECS_11; ECS_12; ECS_13; ECS_14; ECS_17; ECS_18; ECS_2; ECS_22; ECS_23; ECS_24; ECS_3; ECS_7; ECS_8; ECS_9; ECS_A07; ECS_B2; ECS_Buoy-1; ECS_Buoy-2; ECS_CK-10; ECS_CK-3; ECS_CK-6; ECS_F06; ECS_F07; ECS_F08; ECS_F09; ECS_F10; ECS_F11; ECS_F12; ECS_FP01; ECS_FP02; ECS_FP03; ECS_FP04; ECS_FP06; ECS_GN-1; ECS_GN-5; ECS_GS-2; ECS_GS-6; ECS_GW-2; ECS_GW-7; ECS_HR10; ECS_HR11; ECS_HR2; ECS_HR6; ECS_HR8; ECS_HR9; ECS_I-2; ECS_LS1; ECS_LS2; ECS_MC1; ECS_MC4; ECS_MT4; ECS_MT5; ECS_P07; ECS_P08; ECS_P09; ECS_P11; ECS_TE-2; ECS_TE-5; ECS_TE-8; ECS_TW-1; ECS_TW-3; ECS_TW-6; ECS_YS1; ECS_YS2; ECS_Z3; Event label; Japan Sea; Latitude of event; Longitude of event; MULT; Multiple investigations; Nitrogen, inorganic, dissolved; Phosphorus, inorganic, dissolved; Salinity; Season; Silicon; Sum lipid biomarkers of water; Temperature, water; Yellow Sea
    Type: Dataset
    Format: text/tab-separated-values, 867 data points
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    DATA PANGAEA
  • 9
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    Stable carbon isotope data in surface sediments in the eastern China marginal seas (2022)
    PANGAEA
    Details
    Publication Date: 2023-10-16
    Keywords: Alkenones; Biomarkers; DATE/TIME; Eastern China Marginal Seas; ECMS_sediment_d13C_200808; ECMS_sediment_d13C_200812; ECMS_sediment_d13C_201106; ECMS_sediment_d13C_201110; ECMS_sediment_d13C_201205; Elementar IsoPrime 100 isotope ratio mass spectrometer; Event label; LATITUDE; LONGITUDE; marginal seas; Reference/source; Station label; sterol; surface sediments; surface water; Thermo Delta V mass spectrometer; Thermo Delta V mass spectrometer and Elementar IsoPrime 100 isotope ratio mass spectrometer; δ13C, total organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 807 data points
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  • 10
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    The lipid biomarker contents and TOC data of the surface sediments in the eastern China marginal seas (2022)
    PANGAEA
    Details
    Publication Date: 2023-10-16
    Keywords: 24-Methylcholesta-5,22E-dien-3beta-ol; 4alpha,23,24-Trimethyl-5alpha-cholest-22E-en-3beta-ol; Alkenone, C37; Alkenones; Biomarkers; Box corer/grab; Carbon, organic, total; DATE/TIME; Depth, bottom/max; DEPTH, sediment/rock; Depth, top/min; Eastern China Marginal Seas; ECMS_sediment_200604_08; ECMS_sediment_200808; ECMS_sediment_200812; ECMS_sediment_201006; ECMS_sediment_201106_1; ECMS_sediment_201106_2; ECMS_sediment_201108; ECMS_sediment_201204; ELEVATION; Event label; LATITUDE; LONGITUDE; marginal seas; n-Alkane C27+C29+C31; Reference/source; see description in data abstract; Station label; sterol; Sum Brassicasterol, Dinosterol and C37 alkenones; surface sediments; surface water
    Type: Dataset
    Format: text/tab-separated-values, 2526 data points
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    DATA PANGAEA
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