Nature Electronics will publish both fundamental and applied research across all areas of electronics, incorporating the work of scientists, engineers and industry. At its core, the journal will be concerned with the development of new technologies and understanding the impact of these developments on society.
npj 2D Materials and Applications is a new open access, online-only journal with broad coverage of 2D materials, including allotropes and compounds, ultralight composite materials, their properties, isolation/synthesis/manufacturing and applications.
Micro and Nano Systems Letters is a leading international peer-reviewed journal publishing original research, clinical updates, and reviews indexed in Clarivate Web of Science (SCIE) with Q1 quartile ranking.
Automotive Innovation is dedicated to the publication of innovative findings in the automotive field as well as other related disciplines, covering the principles, methodologies, theoretical studies, experimental studies, product engineering and engineering application. The main topics include but are not limited to: energy-saving, electrification, intelligent and connected, new energy vehicle, safety and lightweight technologies. The journal presents the latest trend and advances of automotive technology.
npj Regenerative Medicine is a new open access, online-only journal dedicated to publishing the highest quality research into ways to help the human body repair, replace, restore and regenerate damaged tissues and organs.
The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which are treated in this journal.
The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area. It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of big data,(f) storage, transmission, and management of big data,(g) methods and algorithms of data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.