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Differential Equations serve as mathematical models for virtually any natural or physical phenomena in science and technology and has applications even in diverse fields such as economics, medicine, ecology, etc. The seminar was organized to throw light on the recent advances in the applications of differential equations and to provide a platform for sharing the knowledge with experts in the field with young students and researchers. The Researchers and educators in the field of differential equations were invited to attend and share their rich experience. As for everything else. so for a mathematical theory. beauty can be perceived but not explained.
This book collects the latest results and new trends in the application of mathematics to some problems in control theory, numerical simulation and differential equations. The work comprises the main results presented at a thematic minisymposium, part of the 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019), held in Valencia, Spain, from 15 to 18 July 2019. The topics covered in the 6 peer-review contributions involve applications of numerical methods to real problems in oceanography and naval engineering, as well as relevant results on switching control techniques, which can have multiple applications in industrial complexes, electromechanical machines, biological systems, etc. Problems in control theory, as in most engineering problems, are modeled by differential equations, for which standard solving procedures may be insufficient. The book also includes recent geometric and analytical methods for the search of exact solutions for differential equations, which serve as essential tools for analyzing problems in many scientific disciplines.
This book constitutes the refereed proceedings of the Second International Conference on Intelligent Technologies and Applications, INTAP 2019, held in Bahawalpur, Pakistan, in November 2019. The 60 revised full papers and 6 revised short papers presented were carefully reviewed and selected from 224 submissions. Additionally, the volume presents 1 invited paper. The papers of this volume are organized in topical sections on AI and health; sentiment analysis; intelligent applications; social media analytics; business intelligence;Natural Language Processing; information extraction; machine learning; smart systems; semantic web; decision support systems; image analysis; automated software engineering.
This Research Topic is the second volume of this collection. You can find the original collection via https://www.frontiersin.org/research-topics/45485/deep-learning-for-marine-science Deep learning (DL) is a critical research branch in the fields of artificial intelligence and machine learning, encompassing various technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformer networks and Diffusion models, as well as self-supervised learning (SSL) and reinforcement learning (RL). These technologies have been successfully applied to scientific research and numerous aspects of daily life. With the continuous advancements in oceanographic observation equipment and technology, there has been an explosive growth of ocean data, propelling marine science into the era of big data. As effective tools for processing and analyzing large-scale ocean data, DL techniques have great potential and broad application prospects in marine science. Applying DL to intelligent analysis and exploration of research data in marine science can provide crucial support for various domains, including meteorology and climate, environment and ecology, biology, energy, as well as physical and chemical interactions. Despite the significant progress in DL, its application to the aforementioned marine science domains is still in its early stages, necessitating the full utilization and continuous exploration of representative applications and best practices.
The Special Issue (SI) “Recent Advances in GPR Imaging” offers an up-to-date overview of state-of-the-art research activities dealing with the development of Ground Penetrating Radar (GPR) technology and its recent advances in imaging in the different fields of application. In fact, the advances experimented with over the last few decades with regard to the appearance of new GPR systems and the need to manage large amounts of data suggest an increasing interest in the development of new signal processing algorithms and modeling, as well as in the use of three-dimensional (3D) imaging techniques.
This open access book contains review papers authored by thirteen plenary invited speakers to the 9th International Congress on Industrial and Applied Mathematics (Valencia, July 15-19, 2019). Written by top-level scientists recognized worldwide, the scientific contributions cover a wide range of cutting-edge topics of industrial and applied mathematics: mathematical modeling, industrial and environmental mathematics, mathematical biology and medicine, reduced-order modeling and cryptography. The book also includes an introductory chapter summarizing the main features of the congress. This is the first volume of a thematic series dedicated to research results presented at ICIAM 2019-Valencia Congress.
The series Advances in Stem Cell Biology is a timely and expansive collection of comprehensive information and new discoveries in the field of stem cell biology.Recent Advances in iPSC Technology, Volume 5 addresses the progress in induced pluripotent stem cells (iPSCs) technologies.Somatic cells can be reprogrammed into iPSCs by the expression of specific transcription factors. These cells are transforming biomedical research in the last 15 years. The volume teaches readers about current advances in the field. This book describes different technologies and strategies to use iPSCs for biological and clinical benefit. In recent years, remarkable progress has been made in the obtention of iPSCs and their differentiation into several cell types, tissues, and organs using state-of-the-art techniques. These advantages facilitated identification of key targets and definition of the molecular basis of several disorders. This volume will cover hot topics in the iPSC field, such as iPSCs for modeling the cardiovascular toxicities of anticancer therapies, iPSC differentiation through the lens of the noncoding genome, modeling of blood–brain barrier with iPSCs, mathematical modeling of iPSCs, iPSCs to study human brain evolution, selfrenewal in iPSCs, differences and similarities between iPSCs and embryonic stem cells, and more.The volume is written for researchers and scientists interested in stem cell therapy, cell biology, regenerative medicine, and organ transplantation and is contributed by world-renowned authors in the field. - Provides overview of the fast-moving field of induced pluripotent stem cell technology, regenerative medicine, and therapeutics - Covers the following topics: iPSCs for modeling the cardiovascular toxicities of anticancer therapies, iPSC differentiation through the lens of the non-coding genome, modeling of blood-brain barrier with iPSCs, mathematical modelling of iPSCs, iPSCs to study human brain evolution, self-renewal in iPSCs, differences and similarities between iPSCs and embryonic stem cells, and more - Contributed by world-renown experts in the field
Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.