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Advances in Natural Sciences: Nanoscience and Nanotechnology (ANSN) is an international, peer-reviewed journal publishing articles on all aspects of nanoscience and nanotechnology, including the fundamental physics, optics, photonics, chemistry, biology and technology of nanometer-scale materials and devices, for applications in quantum computation, smart lighting, energy generation and storage, sensors, health-care, agricultural production, environmental protection.

Frequency: 4 issues per year.Subject coverage. The journal aims to promote research and developments in geophysics and related areas of engineering.It has a predominantly applied science and engineering focus, but solicits and accepts high-quality contributions in all earth physics, including geodynamics, natural and controlled-source seismology, oil, gas and mineral exploration, petrophysics and reservoir geophysics.It covers those aspects of engineering that are closely related to geophysics or on the targets and problems that geophysics addresses. Typically this is engineering focused on the subsurface, particularly petroleum engineering, rock mechanics, geophysical software engineering, drilling technology, remote sensing, instrumentation and sensor design.Articles. Original contributions, not normally more than 8000 words (14 journal pages).Invited articles. Commissioned by the Editorial Board.

Laser Physics offers a comprehensive view of theoretical and experimental laser research and applications. Articles cover every aspect of modern laser physics and quantum electronics, emphasizing physical effects in various media (solid, gaseous, liquid) leading to the generation of laser radiation; peculiarities of propagation of laser radiation; problems involving impact of laser radiation on various substances and the emerging physical effects, including coherent ones; the applied use of lasers and laser spectroscopy; the processing and storage of information; and more.

Machine Learning: Engineering is a multidisciplinary open access journal dedicated to the application of machine learning (ML), artificial intelligence (AI) and data-driven computational methods across all areas of engineering. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to engineering.