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  • Foods | Open Access Journal | MDPI
    Foods is an international, peer-reviewed, open access journal on food science published semimonthly online by MDPI
  • Foods | 2023 - Browse Issues - MDPI
    Foods, an international, peer-reviewed Open Access journal
  • Foods | Announcements - MDPI
    Food and nutrition play a critical role in preventing noncommunicable diseases such as diabetes, obesity, and cardiovascular disease and maintaining overall health Throughout all stages of life, but especially during key developmental stages, nutrition can have a lasting impact For example, children that have access to a nutritious diet, within the first 2 years of life, have improved
  • Foods | Editorial Board - MDPI
    Foods, an international, peer-reviewed Open Access journal
  • Foods | Aims Scope - MDPI
    About Foods Aims Foods (ISSN 2304-8158) is an international, peer-reviewed scientific open access journal that provides an advanced forum for studies related to all aspects of food research, with major emphasis on the “science of food” Our goal is to strive for the publication of exceptionally high-quality articles with rigor and depth We will publish comprehensive reviews, research
  • Foods | Special Issues - MDPI
    Special Issues Foods publishes Special Issues to create collections of papers on specific topics, with the aim of building a community of authors and readers to discuss the latest research and develop new ideas and research directions Special Issues are led by Guest Editors, who are experts on the topic and all Special Issue submissions follow MDPI's standard editorial process The journal
  • Foods | Instructions for Authors - MDPI
    Foods, an international, peer-reviewed Open Access journal
  • GNN-DRL Optimization Scheduling Method for Damaged Equipment . . .
    Aiming at the problems that traditional heuristic algorithms struggle to capture the complex correlations between damaged equipment and dynamically adjust maintenance task requirements in different task scenarios, the Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) optimization scheduling method for damaged equipment maintenance tasks is proposed, the purpose is to enhance the




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