Discover Applied Sciences (formerly SN Applied Sciences) is a fully open access (OA) journal. All content published in the journal is published under an open access licence, and freely available to readers worldwide, enabling the widest possible dissemination and reuse.

It is a multi-disciplinary, peer-reviewed journal for the disciplines of Applied Life Sciences, Chemistry, Earth and Environmental Sciences, Engineering, Materials Science and Physics, fostering sound scientific discovery to solve practical problems. The journal encourages quality, scientifically valid, original research which presents a good understanding of scientific knowledge, experiments and theories, methods and techniques, as well as interdisciplinary linkages.

The Applied Life Sciences section of Discover Applied Sciences aims to publish relevant articles that focus on modern applications of life sciences research in Biological Sciences, Chemistry, Health Sciences, Physics, Engineering and Environmental Sciences. We are particularly interested in applied research that makes significant scientific and societal contributions across the broad spectrum of life sciences.
The topics covered in this section include but are not limited to:

    Agriculture and Agronomy
    Aquatic and Marine Biology
    Behavioral Sciences
    Biochemistry
    Bioengineering
    Biomaterials
    Biomedical Sciences
    Biophysics
    Biotechnology
    Cell Biology
    Ecology
    Environmental Life Science
    Evolutionary and Developmental Biology
    Food Science
    Forestry
    Genetics and Genomics
    Microbiology
    Plant Sciences
    Population dynamics
    Systems Biology and Bioinformatics
    Zoology and Veterinary Sciences

The Engineering section provides a forum for various sub-disciplines of Engineering Technology, e.g. Bioengineering, Applied Engineering, Robotics and Control, Signal Processing, Data Analysis, Experiments, Computational Mechanics, and Fluid Mechanics. We are looking for contributions covering theoretical and practical problems associated with:

    Aerospace Engineering
    Big Data, Data Science and Data Analysis
    Chemical Engineering and Mechanical Engineering
    Communication Systems
    Complex Structures
    Computer Engineering
    Computing Systems
    Electrical and Electronics Engineering, Electromagnetics
    Energy, Power and Industrial Applications
    Fluid Mechanics
    Image Processing and Signal Processing
    Industrial Technology and Manufacturing
    Machine Learning
    Artificial Intelligence and Deep Learning
    Multiscale Modelling and Multiscale Analysis
    Optics
    Structural Integrity and Technologies in Construction
    Sustainable Inventive Systems

The Interdisciplinary section provides a forum for multi-disciplinary projects that cover the expertise of experiments, numerical modelling, and theory to provide a better understanding of key processes. It welcomes manuscripts combining or linking disciplines of applied sciences and exploring theoretical or practical problems.

The Chemistry and Materials Science section deals with the key research fields of chemistry and material science. Among many topics that our section covers, we promote the topics relating to catalysis, activation of small molecules, electrochemistry and energy materials. The following keywords are only few of the key topics in our section:

    Electrocatalysis
    Click Chemistry
    Heterogeneous and Homogeneous Catalysis
    Biochemistry
    Green Synthesis Of Nanoparticles
    Energy Conversion
    Biomass
    Construction Materials
    Photocatalysis
    Water Splitting
    Fuel Cells
    Inorganic Chemistry
    Analytical Chemistry
    Oxygen Reduction Reaction
    Electrolysers
    Food Chemistry

The Physics and Materials Science section of Discover Applied Sciences targets the latest developments in the Applied Physics area and its intersection with the Material Sciences. We encourage topics that generate an interest or are related to global challenges, including among others, alternative energy sources, climate change, food and water safety and innovative health. Some keywords that we are considering are:

    Electronic Properties of Materials
    Structure-Property Relationship and Characterization
    Superconductivity and Magnetism
    Spintronics
    Organic Electronics
    Perovskite/ Silicon Solar Cells
    Biomaterials and Health Applications
    Bioelectronics and Sensors
    Applied Photochemistry
    Photonic Materials
    Mechanical Properties of Materials
    Thermoelectric Materials
    Nanomaterials/Nanoparticles
    Energy Transfer, Storage and Conversion
    Organic Materials Doping
    Batteries
    Optical Properties of Materials
    Alloys and Ceramics
    Applied Biophysics
    Sustainable Development in Material Sciences

The Earth and Environmental Sciences section of Discover Applied Sciences aims to publish relevant articles and case studies within Earth and Environmental System Sciences. We welcome disciplinary papers from the fields of Physical Geography, Geology, Climate, Oceanography and Natural Hazards, as well as those with strong interdisciplinary connections from the fields of Geophysics, Environmental Chemistry, Geochemistry and Environmental Engineering. We welcome contributions which demonstrate linkages between natural and anthropogenic processes, human-environmental interactions and environmental processes and applications.

Content types

Discover Applied Sciences welcomes full-length Research articles as well as Brief Communications of empirical findings, Reviews, Perspectives, Comments, Case Studies, Registered Reports, and Data Notes from across the full range of disciplines concerned with life science. The journal also publishes guest-edited Topical Collections of relevance to all aspects of life science and its applications. For more information, please follow up with our journal publishing contact.

Special Issue on Engineering: Applications of Machine Learning in Image Processing and Data Mining
Submission Date: 2024-10-10

This Topical Collection focuses on the challenge of image processing and data mining in applied sciences, which is expected to be solved by machine learning models, such as deep learning, transfer learning, zero shot learning, and few shot learning. Low quality data processing is a very challenging problem in computer science, which may include feature extraction, pattern recognition, classification, and quality enhancement. Compared with conventional methods, machine learning models usually have stronger ability of feature learning and classification. Recently, machine learning models, especially deep neural networks, are widely used in image processing and data mining to improve the performance in applied sciences. Especially, advanced machine learning models allow us to deal with complex problems with low quality data that were previously unsolvable. This Topical Collection welcomes the applications of machine learning in low quality image processing, medical data analysis, information fusion, Artificial Intelligence model security, and bioinformatics.