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Shipbuilding and Ship Repair Workers around the World

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ISBN: 9789462981157 Year: DOI: 10.5117/9789462981157 Language: Undetermined
Publisher: Amsterdam University Press
Subject: Economics
Added to DOAB on : 2017-03-14 11:01:13
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Maritime trade is the backbone of the world’s economy. Around ninety percent of all goods are transported by ship, and since World War II, shipbuilding has undergone major changes in response to new commercial pressures and opportunities. Early British dominance, for example, was later undermined in the 1950s by competition from the Japanese, who have since been overtaken by South Korea and, most recently, China. The case studies in this volume trace these and other important developments in the shipbuilding and ship repair industries, as well as workers’ responses to these historic transformations.

Across the Oceans: Development of the overseas business information transmission

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Book Series: Studia Fennica Historica ISBN: 9789517469043 9789522228086 9789522228093 Year: Pages: 459 DOI: 10.21435/sfh.13 Language: English
Publisher: Finnish Literature Society / SKS Grant: Jane and Aatos Erkko Foundation grant and SKS
Subject: History
Added to DOAB on : 2016-09-27 11:01:24
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"In the early 19th century, the only way to transmit information was to send letters across the oceans by sailing ships or across land by horse and coach. Growing world trade created a need and technological development introduced options to improve general information transmission. Starting in the 1830s, a network of steamships, railways, canals and telegraphs was gradually built to connect different parts of the world. The book explains how the rate of information circulation increased many times over as mail systems were developed. Nevertheless, regional differences were huge. While improvements on the most significant trade routes between Europe, the Americas and East India were considered crucial, distant places such as California or Australia had to wait for gold fever to become important enough for regular communications. The growth of passenger services, especially for emigrants, was a major factor increasing the number of mail sailings.
 The study covers the period from the Napoleonic wars to the foundation of the Universal Postal Union (UPU) and includes the development of overseas business information transmission from the days of sailing ships to steamers and the telegraph."

Lipid Signaling in T Cell Development and Function

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196975 Year: Pages: 142 DOI: 10.3389/978-2-88919-697-5 Language: English
Publisher: Frontiers Media SA
Subject: Medicine (General) --- Allergy and Immunology
Added to DOAB on : 2016-08-16 10:34:25
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Lipids are best known as energy storing molecules and core-components of cellular membranes, but can also act as mediators of cellular signaling. This is most prominently illustrated by the paramount importance of the phospholipase C (PLC) and phosphoinositide 3-kinase (PI3K) signaling pathways in many cells, including T cells and cancer cells. Both of these enzymes use the lipid phosphatidylinositol(4,5)bisphosphate (PIP2) as their substrate. PLCs produce the lipid product diacylglycerol (DAG) and soluble inositol(1,4,5)trisphosphate (IP3). DAG acts as a membrane tether for protein kinase C and RasGRP proteins. IP3 is released into the cytosol and controls calcium release from internal stores. The PI3K lipid product phosphatidylinositol(3,4,5)trisphosphate (PIP3) controls signaling by binding and recruiting effector proteins such as Akt and Itk to cellular membranes. Recent research has unveiled important signaling roles for many additional phosphoinositides and other lipids. The articles in this volume highlight how multiple different lipids govern T cell development and function through diverse mechanisms and effectors. In T cells, lipids can orchestrate signaling by organizing membrane topology in rafts or microdomains, direct protein function through covalent lipid-modification or non-covalent lipid binding, act as intracellular or extracellular messenger molecules, or govern T cell function at the level of metabolic regulation. The cellular activity of certain lipid messengers is moreover controlled by soluble counterparts, exemplified by symmetric PIP3/inositol(1,3,4,5)tetrakisphosphate (IP4) signaling in developing T cells. Not surprisingly, lipid producing and metabolizing enzymes have gained attention as potential therapeutic targets for immune disorders, leukemias and lymphomas.

Keywords

Lipid --- T cell --- eicosanoid --- PI3K --- Vitamin D --- diacylglyerol --- Inositol --- Pten --- SHIP --- Adipokine

Acoustical Impact of Ships and Harbours: Airborne and Underwater N&V Pollution

Authors: ---
Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889457106 Year: Pages: 57 DOI: 10.3389/978-2-88945-710-6 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Oceanography
Added to DOAB on : 2019-01-23 14:53:43
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Noise and vibrations generated by ships affect a wide range of receivers: crew and passengers inside the vessel, inhabitants of the coastal areas and marine fauna outside it. Recent studies suggest that a large percentage of people living in urban areas close to harbors and a number of marine species, at different evolutionary levels (in particular mammals and cephalopods), suffer from ship N&V emissions in air and in water. The present degree of knowledge of the phenomena involved in the noise emissions inside and outside ships is quite different, as a result also of the time elapsed since the negative effects were realized and therefore studied. The development of the normative framework in the various areas reflects these differences, but there are expectations for improvements on all fronts that need to be supported by the scientific community presenting the latest research results in this particular field of acoustics.

Modeling and Simulation of Carbon Emission Related Issues

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ISBN: 9783039213115 / 9783039213122 Year: Pages: 420 DOI: 10.3390/books978-3-03921-312-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Economics
Added to DOAB on : 2019-12-09 11:49:15
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Carbon emissions reached an all-time high in 2018, when global carbon dioxide emissions from burning fossil fuels increased by about 2.7%, after a 1.6% increase in 2017. Thus, we need to pay special attention to carbon emissions and work out possible solutions if we still want to meet the targets of the Paris climate agreement. This Special Issue collects 16 carbon emissions-related papers (including 5 that are carbon tax-related) and 4 energy-related papers using various methods or models, such as the input–output model, decoupling analysis, life cycle impact analysis (LCIA), relational analysis model, generalized Divisia index model (GDIM), forecasting model, three-indicator allocation model, mathematical programming, real options model, multiple linear regression, etc. The research studies come from China, Taiwan, Brazil, Thailand, and United States. These researches involved various industries such as agricultural industry, transportation industry, power industry, tire industry, textile industry, wave energy industry, natural gas industry, and petroleum industry. Although this Special Issue does not fully solve our concerns, it still provides abundant material for implementing energy conservation and carbon emissions reduction. However, there are still many issues regarding the problems caused by global warming that require research.

Keywords

household consumption --- total carbon emissions --- CLA Model --- influence factor --- STIRPAT model --- carbon emissions --- influencing factors --- decoupling elasticity --- Generalized Divisia Index --- Tapio’s model --- household CO2 emissions (HCEs) --- per capita household CO2 emissions (PHCEs) --- input–output model --- refined oil distribution --- inventory routing problem --- hybrid genetic algorithm --- carbon emissions --- carbon tax --- decoupling analysis --- greenhouse gas emissions --- carbon footprint --- low-carbon agriculture --- causal factors --- CO2 emissions forecasting --- VARIMAX-ECM model --- sustainable development --- economic growth --- population growth --- carbon price fluctuation --- renewable energy --- real options analysis --- investment under uncertainty --- carbon emissions --- carbon tax --- activity-based costing (ABC) --- capacity expansion --- green quality management --- product-mix decision model --- mathematical programming --- long-term --- final energy consumption --- LT-ARIMAXS model --- sustainable development --- economic growth and the environment --- error correction mechanism model --- activity-based costing (ABC) --- mathematical programming --- textile industry --- green manufacturing --- Industry 4.0 --- carbon emissions --- Activity-Based Costing (ABC) --- carbon emissions --- tire industry --- carbon trading --- mathematical programming --- quotas allocation --- carbon emissions --- electric power industry --- fairness --- agricultural-related sectors --- carbon emissions --- carbon tax --- China --- power industry --- carbon emissions --- Generalized Divisia Index --- scenario forecast --- Monte Carlo method --- wave energy converter --- life cycle assessment --- energy intensity --- carbon intensity --- aircraft --- taxi time --- takeoff rate --- pushback control --- green transportation --- carbon emissions --- reducing carbon emissions --- carbon intensity target --- energy structure --- gray model (GM (1, 1)) --- generalized regression neural network (GRNN) --- Markov forecasting model --- non-linear programming --- ethylene supply --- shale gas --- non-energy uses of fossil fuels --- socio-economic scenarios --- climate change --- tea --- climate change --- sustainable agriculture --- environmental impact --- carbon footprint --- CO2 emissions --- HOMER software --- hybrid ship power systems --- Li-ion battery --- shipping --- n/a

Learning to Understand Remote Sensing Images

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ISBN: 9783038976844 / 9783038976851 Year: Volume: 1 Pages: 426 DOI: 10.3390/books978-3-03897-685-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

Learning to Understand Remote Sensing Images

Author:
ISBN: 9783038976981 / 9783038976998 Year: Volume: 2 Pages: 376 DOI: 10.3390/books978-3-03897-699-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

Keywords

hyperspectral image classification --- SELF --- SVMs --- Segment-Tree Filtering --- multi-sensor --- change feature analysis --- object-based --- multispectral images --- heterogeneous domain adaptation --- transfer learning --- multi-view canonical correlation analysis ensemble --- semi-supervised learning --- canonical correlation weighted voting --- ensemble learning --- image classification --- spatial attraction model (SAM) --- subpixel mapping (SPM) --- land cover --- mixed pixel --- spatial distribution --- hard classification --- building damage detection --- Fuzzy-GA decision making system --- machine learning techniques --- optical remotely sensed images --- sensitivity analysis --- texture analysis --- quality assessment --- ratio images --- Synthetic Aperture Radar (SAR) --- speckle --- speckle filters --- ice concentration --- SAR imagery --- convolutional neural network --- urban surface water extraction --- threshold stability --- sub-pixel --- linear spectral unmixing --- Landsat imagery --- image registration --- image fusion --- UAV --- metadata --- visible light and infrared integrated camera --- semantic segmentation --- CNN --- deep learning --- ISPRS --- remote sensing --- gate --- hyperspectral image --- sparse and low-rank graph --- tensor --- dimensionality reduction --- semantic labeling --- convolution neural network --- fully convolutional network --- sea-land segmentation --- ship detection --- hyperspectral image --- target detection --- multi-task learning --- sparse representation --- locality information --- remote sensing image correction --- color matching --- optimal transport --- CNN --- very high resolution images --- segmentation --- multi-scale clustering --- vehicle localization --- vehicle classification --- high resolution --- aerial image --- convolutional neural network (CNN) --- class imbalance --- deep learning --- convolutional neural network (CNN) --- fully convolutional network (FCN) --- classification --- remote sensing --- high resolution --- semantic segmentation --- deep convolutional neural networks --- manifold ranking --- single stream optimization --- high resolution image --- feature extraction --- hypergraph learning --- morphological profiles --- hyperedge weight estimation --- semantic labeling --- convolutional neural networks --- remote sensing --- deep learning --- aerial images --- hyperspectral image --- feature extraction --- dimensionality reduction --- optimized kernel minimum noise fraction (OKMNF) --- hyperspectral remote sensing --- endmember extraction --- multi-objective --- particle swarm optimization --- image alignment --- feature matching --- geostationary satellite remote sensing image --- GSHHG database --- Hough transform --- dictionary learning --- road detection --- Radon transform --- geo-referencing --- multi-sensor image matching --- Siamese neural network --- satellite images --- synthetic aperture radar --- inundation mapping --- flood --- optical sensors --- spatiotemporal context learning --- Modest AdaBoost --- HJ-1A/B CCD --- GF-4 PMS --- hyperspectral image classification --- automatic cluster number determination --- adaptive convolutional kernels --- hyperspectral imagery --- 1-dimensional (1-D) --- Convolutional Neural Network (CNN) --- Support Vector Machine (SVM) --- Random Forests (RF) --- machine learning --- deep learning --- TensorFlow --- multi-seasonal --- regional land cover --- saliency analysis --- remote sensing --- ROI detection --- hyperparameter sparse representation --- dictionary learning --- energy distribution optimizing --- multispectral imagery --- nonlinear classification --- kernel method --- dimensionality expansion --- deep convolutional neural networks --- road segmentation --- conditional random fields --- satellite images --- aerial images --- THEOS --- land cover change --- downscaling --- sub-pixel change detection --- machine learning --- MODIS --- Landsat --- very high resolution (VHR) satellite image --- topic modelling --- object-based image analysis --- image segmentation --- unsupervised classification --- multiscale representation --- GeoEye-1 --- wavelet transform --- fuzzy neural network --- remote sensing --- conservation --- urban heat island --- land surface temperature --- climate change --- land use --- land cover --- Landsat --- remote sensing --- SAR image --- despeckling --- dilated convolution --- skip connection --- residual learning --- scene classification --- saliency detection --- deep salient feature --- anti-noise transfer network --- DSFATN --- infrared image --- image registration --- MSER --- phase congruency --- hashing --- remote sensing image retrieval --- online learning --- hyperspectral image --- compressive sensing --- structured sparsity --- tensor sparse decomposition --- tensor low-rank approximation

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