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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.

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.

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.

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.

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 −4 –10 −1  S cm −1 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. Predictions of new solid-state Li-ion conductors are challenging due to the diverse chemistries and compositions involved. Here the authors combine unsupervised learning techniques and molecular dynamics simulations to discover new compounds with high Li-ion conductivity.

The abuse of conventional antibiotics leads to increasing bacterial resistance. Nanozyme is a new kind of ultra-efficient and safe nanomaterial with intrinsic enzyme-like activities, showing remarkable potential as a next generation nanobactericide. Graphdiyne (GDY) is a burgeoning two-dimensional (2D) carbon allotrope with intriguing physicochemical properties, holding a great promise as a metal-free nanozyme. In this study, a boron doped GDY nanosheet (B-GDY) was constructed to simulate the performance of peroxidase (POD). By promoting the decomposition of H 2 O 2 to produce reactive oxygen species (ROS), the bactericidal efficacies against both Gram-positive and Gram-negative bacteria were substantially enhanced attributed to the extremely high catalytic activity of B-GDY. In-depth density functional theory (DFT) calculations illuminate that doping of boron can introduce more active B-defect sites as well as lower Gibbs free energy during the H 2 O 2 decomposition reaction. Notably, B-GDY contributes to significant wound healing and excellent biocompatibility, reducing the biological burden. The design of this nanozyme opens a new avenue for the development of alternative antibiotics.

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 lo 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. Functional diversity of tissue-resident macrophages and signals governing their ontogeny and turnover remain unknown for the majority of tissues. Here the authors describe two phenotypically and functionally distinct long-lived populations of lung interstitial macrophages and their putative blood-derived monocytic precursor.

This paper develops a more comprehensive theoretical model for functionally graded material (FGM) piezoelectric nanobeams. The model incorporates a Winkler–Pasternak linear elastic foundation and fully accounts for the effects of dynamic flexoelectric, surface effects, and higher-order electric fields. The purpose of this study is to investigate the bending behavior and free vibration characteristics of Euler–Bernoulli beam models considering functionally graded materials. The governing equations and boundary conditions are produced using Hamilton’s variational principle. The Fourier series expansion approach is used to create the analytical solution for the bending problem. Then the analytical equation for the natural frequencies is obtained using the Navier method. The bending performance, electromechanical coupling characteristics, and normalized natural frequencies of FGM piezoelectric nanobeams are all significantly impacted by higher-order electric fields, gradient index, dynamic flexoelectric effects, surface effects, and the Winkler–Pasternak elastic foundation, according to numerical analysis. For the design and optimization of micro/nano energy harvesters and resonators, this paper offers theoretical insights and references.

Infrastructure assets, serving everyone’s daily life, are an essential foundation of any society. Their management faces a wide range of challenges. Hence optimization methods are increasingly applied to assist making management decisions in infrastructure asset management (IAM). A large number of articles apply a broad range of optimization methods in their decision making (DM) and achieve great results. However, they mainly focus on individual methods and a comprehensive knowledge, given the broad range of optimization methods, is hardly discussed. Hence it is valuable to analyze and graphically present the existing knowledge on this subject. This paper, based on a total of 337 articles, provides an overall review of the applications of optimization when making management decisions in IAM, with the intension of enhancing the optimization application and method selection and guiding the future research in this field. More specifically, this paper introduces the application process of optimization when assisting DM in IAM, summarizes the previous application research, and discusses the popular optimization methods applied in DM in IAM. According to the literature review, this paper confirms optimization can effectively assist DM in IAM and a wide range of optimization methods are applicable to assist a variety of DM problems. The recommendations on the applications and selection of optimization methods in the context of IAM are also made to facilitate the applications.

In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.