Designing and building structures that will withstand the unique challenges that exist in subsea operations is no easy task. As deepwater wells are drilled to greater depths, engineers are confronted ...with a new set of problems such as water depth, weather conditions, ocean currents, equipment reliability, and well accessibility, to name just a few. A definitive reference for engineers designing, analyzing and instilling offshore structures, this book provides an expert guide to the key processes, technologies and equipment that comprise contemporary offshore structures. Written in a clear and easy to understand language, the book is based on the authors 30 years of experience in the design, analysis and instillation of offshore structures. This book answers the above mentioned crucial questions as well as covers the entire spectrum of subjects in the discipline, from route selection and planning to design, construction, installation, materials and corrosion, inspection, welding, repair, risk assessment, and applicable design solutions. It yields a roadmap not only for the subsea engineer but also the project managers, estimators and regulatory personnel hoping to gain an appreciation of the overall issues and directed approaches to subsea engineering design solutions.
Marine pipelines for the transportation of oil and gas have become a safe and reliable part of the expanding infrastructure put in place for the development of the valuable resources below the worlds ...seas and oceans. The design of these pipelines is a relatively new technology and continues to evolve as the design of more cost effective pipelines becomes a priority and applications move into deeper waters and more hostile environments. This updated edition of a best selling title provides the reader with a scope and depth of detail related to the design of offshore pipelines and risers not seen before in a textbook format.
Image classification has always been a hot research direction in the world, and the emergence of deep learning has promoted the development of this field. Convolutional neural networks (CNNs) have ...gradually become the mainstream algorithm for image classification since 2012, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. In the wake of these successes, CNN-based methods have emerged in remote sensing image scene classification and achieved advanced classification accuracy. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art (SOAT) network architectures. Along the way, we analyze (1) the basic structure of artificial neural networks (ANNs) and the basic network layers of CNNs, (2) the classic predecessor network models, (3) the recent SOAT network algorithms, (4) comprehensive comparison of various image classification methods mentioned in this article. Finally, we have also summarized the main analysis and discussion in this article, as well as introduce some of the current trends.
This book is based on the authors' 30 years of experience in offshore. The authors provide rigorous coverage of the entire spectrum of subjects in the discipline, from pipe installation and routing ...selection and planning to design, construction, and installation of pipelines in some of the harshest underwater environments around the world. All-inclusive, this must-have handbook covers the latest breakthroughs in subjects such as corrosion prevention, pipeline inspection, and welding, while offering an easy-to-understand guide to new design codes currently followed in the United States, United Kingdom, Norway, and other countries.
New fractional gradient representation of generalized Birkhoff system is studied. New definition of fractional gradient system for generalized Birkhoff system is given. New condition for generalized ...Birkhoff system transforms into arbitrary fractional-order (integer or non-integer) gradient system is deduced. The fractional dynamical modelling method by fractional gradient system is also discussed. We apply our results into several classical nonlinear models, such as van der Pol equation, Hénon–Heiles equation and four-order generalized Birkhoff’s equation.
Ceramics are an important class of materials with widespread applications because of their high thermal, mechanical, and chemical stability. Computational predictions based on first principles ...methods can be a valuable tool in accelerating materials discovery to develop improved ceramics. It is essential to experimentally confirm the material properties of such predictions. However, materials screening rates are limited by the long processing times and the poor compositional control from volatile element loss in conventional ceramic sintering techniques. To overcome these limitations, we developed an ultrafast high-temperature sintering (UHS) process for the fabrication of ceramic materials by radiative heating under an inert atmosphere. We provide several examples of the UHS process to demonstrate its potential utility and applications, including advancements in solid-state electrolytes, multicomponent structures, and high-throughput materials screening.
Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here ...we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10
-10
S cm
predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.
Resident tissue macrophages (RTM) can fulfill various tasks during development, homeostasis, inflammation and repair. In the lung, non-alveolar RTM, called interstitial macrophages (IM), importantly ...contribute to tissue homeostasis but remain little characterized. Here we show, using single-cell RNA-sequencing (scRNA-seq), two phenotypically distinct subpopulations of long-lived monocyte-derived IM, i.e. CD206
and CD206
IM, as well as a discrete population of extravasating CD64
CD16.2
monocytes. CD206
IM are peribronchial self-maintaining RTM that constitutively produce high levels of chemokines and immunosuppressive cytokines. Conversely, CD206
IM preferentially populate the alveolar interstitium and exhibit features of antigen-presenting cells. In addition, our data support that CD64
CD16.2
monocytes arise from intravascular Ly-6C
patrolling monocytes that enter the tissue at steady-state to become putative precursors of CD206
IM. This study expands our knowledge about the complexity of lung IM and reveals an ontogenic pathway for one IM subset, an important step for elaborating future macrophage-targeted therapies.
We report a new type of coherent perfect absorption that is determined by the phase coupling between metaatoms and is referred to as the phase-coupled simultaneous coherent perfect absorption ...(PC-SCPA) for antisymmetric and symmetric incidences and especially the PC-SCPA for antisymmetric and symmetric incidences can be simultaneously achieved in the same bi-layered Schottky junction metamaterial possessing the phase coupling. Our proposed mechanism exploits the phase coupling between metaatoms, which is in contrast with the existing mechanism which depends on the near-field coupling. The theory of PC-SCPA is provided using coupled mode theory with the phase coupling. The operating wavelengths of PC-SCPA are insensitive to the variations of the spacing distances between metaatoms in the lateral and vertical directions. An infrared PC-SCPA-based hot-electron photodetection with dynamically switchable operating wavelengths and dynamically tunable bandwidth is theoretically and numerically verified in the same bi-layered Schottky junction metamaterial. The peak of spectrum of responsivity for antisymmetric and symmetric incidences can be switched to the same wavelength only by altering the phase coupling. Our study may build the bridge among the new type of PC-SCPA, metamaterial, and hot electron and may find potential and significant applications in hot-electron photodetection.
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