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  • 1
    Keywords: Environmental management. ; Mineralogy. ; Energy policy. ; Energy and state. ; Mining engineering. ; Environmental Management. ; Mineralogy. ; Energy Policy, Economics and Management. ; Mining and Exploration.
    Description / Table of Contents: Chapter 1- Introduction -- Chapter 2- Prerequisites for Green Mine Selection -- Chapter 3- Score Sheet of Green Mine Construction -- Chapter 4- Mining Area Environment -- Chapter 5- Resource Development Methods -- Chapter 6- Comprehensive Utilization of Ore Resources -- Chapter 7- Energy Saving and Emission Reduction -- Chapter 8- Technological Innovation and Smart Mines -- Chapter 9- Corporate Management and Corporate Image -- Chapter 10- Related Knowledge.
    Abstract: This book focuses on green mine evaluation. It includes green mine evaluation methods, evaluation content, evaluation indicators, etc. The "Green Mine Evaluation Index" has been issued by the Ministry of Natural Resources of China. In order to promote mining enterprises, green mine consulting service agencies, third-party evaluation agencies and mining administration personnel to better understand and practice the provisions of green mine evaluation indicators, the authors wrote this "Interpretation of Green Mine Evaluation Index". The content of this book specifically includes introduction, prerequisites for green mine selection, score sheet of green mine construction, related knowledge, as well as introduction of specific green mine evaluation items, including mining area environment, resource development methods, comprehensive utilization of ore resources, energy saving and emission reduction, technological innovation and smart mines, corporate management and corporate image, etc. The relevant concepts, relevant laws and policies, implementation measures, inspection points, and materials that enterprises should provide, have been vividly expounded based on the actual situation and specific cases of green mine construction. This book is useful as a reference for managers, engineering and technical personnels, teachers and students from mining enterprises, government departments, consulting services and evaluation agencies, colleges and secondary professional schools.
    Type of Medium: Online Resource
    Pages: XII, 348 p. 14 illus., 5 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9789811654336
    DDC: 333.7
    Language: English
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  • 2
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    Springer Nature | Apress
    Publication Date: 2024-04-14
    Description: This unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library. You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will Learn Optimize core operations based on N-dimensional arrays Design and implement an industry-level algorithmic differentiation module Implement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiation Design and optimize a computation graph module, and understand the benefits it brings to the numerical computing library Accommodate the growing number of hardware accelerators (e.g. GPU, TPU) and execution backends (e.g. web browser, unikernel) of numerical computation Use the Zoo system for efficient scripting, code sharing, service deployment, and composition Design and implement a distributed computing engine to work with a numerical computing library, providing convenient APIs and high performance Who This Book Is For Those with prior programming experience, especially with the OCaml programming language, or with scientific computing experience who may be new to OCaml. Most importantly, it is for those who are eager to understand not only how to use something, but also how it is built up.
    Keywords: programming language ; OCaml ; scientific computing ; computational ; debugging ; open source ; source ; code ; numerical ; data science ; big data ; owl ; functional ; math ; scientific ; engineering ; thema EDItEUR::U Computing and Information Technology ; thema EDItEUR::U Computing and Information Technology::UY Computer science ; thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
    Language: English
    Format: image/jpeg
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  • 3
    Publication Date: 2023-05-18
    Description: The dataset comprises X-ray fluorescent (XRF) core scanning, TOC, C/N, δ13Corg, and macro-charcoal counts of bulk sediment from the sediment core CFL-3. The purpose of this dataset is to reconstruct the sedimentation environment change after the large-scale deforestation. The lake sediment core CFL-3 was taken in Cueifong Lake, northeastern Taiwan in 2017, with a Russian Corer set. The XRF core scanning signals were normalized as described in Lin et al., 2023. The age model was established with 210Pb dating results, augmented by 137Cs dating results. The experiment and analyze detail were described Lin et al., 2023.
    Keywords: 13C; Anthropogenic disturbances; Anthropogenic impact; C/N; charcoal; Deforestation; freshwater lake; Lake sediment core; mountain lakes; Taiwan; TOC; XRF core scanner data; XRF-core scanning
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 4
    Publication Date: 2023-05-18
    Keywords: 13C; AGE; Anthropogenic disturbances; Anthropogenic impact; C/N; Carbon, organic, total; Carbon, organic, total/Nitrogen, total ratio; CFL-3; charcoal; Cueifong Lake; Deforestation; DEPTH, sediment/rock; freshwater lake; Lake sediment core; mountain lakes; RUSC; Russian corer; Taiwan; TOC; XRF core scanner data; XRF-core scanning; δ13C, organic carbon
    Type: Dataset
    Format: text/tab-separated-values, 30 data points
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  • 5
    Publication Date: 2023-05-18
    Keywords: 13C; AGE; Anthropogenic disturbances; Anthropogenic impact; C/N; CFL-3; charcoal; Counting; Cueifong Lake; Deforestation; DEPTH, sediment/rock; freshwater lake; Lake sediment core; Macrocharcoal; mountain lakes; RUSC; Russian corer; Taiwan; TOC; XRF core scanner data; XRF-core scanning
    Type: Dataset
    Format: text/tab-separated-values, 14 data points
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  • 6
    Publication Date: 2023-09-23
    Description: The diatom valves were counted at magnifications of 1000 times; at least 400 valves were counted per sample. Percentages are based on the total diatom sum. The samples with poor diatom preservation (〈100 diatom valves) were only used for estimation of the diatom abundance, while were not used for the discussions of diatom percentages.
    Keywords: AGE; Calculated; DEPTH, sediment/rock; environmental magnetism; Giant piston corer; GPC; IMAGES VII - WEPAMA; Index; marine sediments; Marion Dufresne (1995); MD012414; MD01-2414; MD122; Mid-Brunhes Transition; mid-Pleistocene transition; Okhotsk Sea; paleomagnetism; Sea ice; Sea of Ochotsk; Super-interglacial; terrigenous detritus
    Type: Dataset
    Format: text/tab-separated-values, 401 data points
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  • 7
    Publication Date: 2023-09-23
    Description: The abundance of iron and manganese were detected by using non-destructive X-ray Fluorescence (XRF) scanning technique. Continuous downcore measurements of element variations were performed in the ITRAX XRF Core Scanner Laboratory, Department of Geosciences, National Taiwan University. All U-channels were scanned by using the 3 kW Mo source and were analyzed at 30 kV/24 mA, 2 mm resolution with a exposure time of 30 s. The count value of each element was calculated from element peak areas of original XRF spectra by the Q-Spec software provided by COX Analytical Systems.
    Keywords: AGE; DEPTH, sediment/rock; environmental magnetism; Giant piston corer; GPC; IMAGES VII - WEPAMA; Iron; Iron/Titanium ratio; log-Iron/Titanium ratio; log-Manganese/Titanium ratio; Manganese; Manganese/Titanium ratio; marine sediments; Marion Dufresne (1995); MD012414; MD01-2414; MD122; Mid-Brunhes Transition; mid-Pleistocene transition; Okhotsk Sea; paleomagnetism; Sea ice; Sea of Ochotsk; Super-interglacial; terrigenous detritus; Titanium; X-ray fluorescence (XRF)
    Type: Dataset
    Format: text/tab-separated-values, 165823 data points
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  • 8
    Publication Date: 2023-09-23
    Description: Magnetic measurements of U-channel samples were measured by the Bartington MS2 magnetic susceptibility system with an ASC auto-tracking rail and a cryogenic magnetometer (2G 755 SRM) in the shielding room of the paleomagnetic laboratory at the Institute of Earth Sciences, Academia Sinica, Taiwan. The Anhysteretic Remanent Magnetization (ARM) was applied with a DC bias field of 1 Gauss under a 100 mT alternating field. All U-channel measurements are within 1 cm interval.
    Keywords: AGE; Anhysteretic remanent magnetization; Anhysteretic remanent magnetization/magnetic susceptibility ratio; Bartington MS2C sensor; Declination; DEPTH, sediment/rock; environmental magnetism; Giant piston corer; GPC; IMAGES VII - WEPAMA; Inclination; Magnetic susceptibility; marine sediments; Marion Dufresne (1995); MD012414; MD01-2414; MD122; Mid-Brunhes Transition; mid-Pleistocene transition; Okhotsk Sea; paleomagnetism; Relative paleointensity proxy; Sea ice; Sea of Ochotsk; Super-interglacial; terrigenous detritus
    Type: Dataset
    Format: text/tab-separated-values, 31659 data points
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  • 9
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    PANGAEA
    In:  Supplement to: Shumilovskikh, Lyudmila S; Hopper, Kristen; Djamali, Morteza; Ponel, Philippe; Demory, François; Rostek, Frauke; Tachikawa, Kazuyo; Bittmann, Felix; Golyeva, Alexandra; Guibal, Frédéric; Talon, Brigitte; Wang, Liang-Chi; Nezamabadi, Masoud; Bard, Edouard; Lahijani, Hamid A K; Nokandeh, Jebrael; Omrani Rekavandi, Hamid; de Beaulieu, Jacques-Louis; Sauer, Eberhard; Andrieu-Ponel, Valerie (2016): Landscape evolution and agro-sylvo-pastoral activities on the Gorgan Plain (NE Iran) in the last 6000 years. The Holocene, 26(10), 1676-1691, https://doi.org/10.1177/0959683616646841
    Publication Date: 2024-02-21
    Description: The Gorgan Plain (NE Iran) is characterized by fertile soils formed on a loess plateau and is at present primarily exploited for intensive agriculture. However, the timing and intensity of the human impact on the landscape in the past are still unclear. A sediment core, taken from the centre of the eastern Gorgan Plain in the Kongor Lake covering the major part of the Holocene from 6.1 to 0.8 ka (all ages are calibrated before present), has been studied for pollen, non-pollen palynomorphs, botanical macroremains, insects, charcoal, geochemistry, biomarkers and magnetism in order to provide new insights into the evolution of the landscape and to estimate the intensity of human activities. The data obtained suggest a dry period between 5.9 and 3.9 ka and an increase in regional humidity afterwards with a maximum between 2.7 and 0.7 ka, during the period of the Persian empires (Achaemenid through Sasanian) and the Islamic era. The eastern part of the Gorgan Plain was characterized by open steppe landscapes during the last 6 ka, which most likely were used for pasture and at least since 2.7 ka for agriculture including arboriculture. The strongest anthropogenic impact on the environment around the Kongor site is documented during the Parthian and Sasanian Empires (200 BC–651 AD) and the Islamic era up to the eve of the Mongol invasion.
    Keywords: Kongor_Lake; pollen; radiocarbon; SEDCO; Sediment corer
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 10
    Publication Date: 2024-02-21
    Keywords: Acer; AGE; Alchemilla pentaphyllea-type; Allium ursinum-type; Alnus; Anagallis-type; Apiaceae; Artemisia; Asteraceae; Ballota-type; Betula; Brassicaceae; Bryonia/Leonotis; Bupleurum-type; Calystegia; Cannabis/Humulus; Carpinus betulus; Caryophyllaceae; Castanea; Centaurea cyanus-type; Centaurea jacea-type; Centaurea montana-type; Centaurea scabiosa-type; Cerealia-type; cf. Chrozophora; cf. Cornus mas; cf. Cornus sanguinea; cf. Filipendula; cf. Sausurea; cf. Solanum dulcamara; cf. Styrax; Chenopodiaceae; Cichorioideae; Cirsium; Convolvulus arvensis-type; Corylus; Counting, palynology; Cousinia; Cyperaceae; Daphne; DEPTH, sediment/rock; Diospyrus lotus; Eleagnus; Ephedra distachya-type; Ephedra fragilis-type; Epilobium; Eremurus; Euphorbia; Fabaceae; Fagus; Fraxinus excelsior-type; Galium-type; Genista group; Geranium; Geum-type; Hippophae; Hypericum hyssopifolium; Jasione montana-type; Juglans; Juniperus-type; Kongor_Lake; Lemna; Lycopodium (added); Lycopodium spores counted; Lycopodium tablets; Lythrum; Malvaceae; Matricaria-type; Mentha-type; Morus alba; Myrica; Myriophyllum spicatum; Nitraria; Number of batch; Olea; Onobrychis; Ornithogalum umbellatum-type; Ostrya-type; Papaver rhoeas-type; Parrotia persica; Pinus diploxylon-type; Pistacia; Plantago lanceolata-type; Plantago major/media; Plantago maritima-type; Platanus; Plumbaginaceae; Poaceae; pollen; Pollen, reworked; Pollen indeterminata; Polygonum aviculare-type; Polygonum bistorta-type; Populus; Potamogeton natans-type/Triglochin; Potentilla-type; Primulaceae; Primula clusiana-type; Prosopis farcta; Pterocarya; Pteropyrum; Pulmonaria-type; Quercus robur-type; radiocarbon; Ranunculus acris-type; Rhamnus-type; Rheum; Rhinanthus group; Rumex acetosa-type; Salix; Salvia verticillata; Sambucus ebulus; Sambucus nigra-type; Sample volume; Sanguisorba minor-type; Saussurea-type; Secale; SEDCO; Sediment corer; Senecio-type; Solanum dulcamara-type; Solanum nigrum-type; Sorbus group; Sparganium-type; Tamarix; Taxus; Thalictrum; Tilia; Tribulus terrestris; Typha latifolia-type; Ulmus; Valerianella; Varia; Viscum; Vitis; Xanthium spinosum-type; Xanthium strumarium-type; Zelkova
    Type: Dataset
    Format: text/tab-separated-values, 6943 data points
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