Machines | An Open Access Journal from MDPI Machines is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions
Aims Scope | Machines | MDPI Aims Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering It publishes research articles, reviews and communications Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible There is no restriction on the maximum length of the papers Full experimental and or methodical details must be
Machines | Special Issues - MDPI Special Issues Machines 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
Instructions for Authors | Machines | MDPI Machines is a member of the Committee on Publication Ethics (COPE) We fully adhere to its Code of Conduct and to its Best Practice Guidelines The editors of this journal enforce a rigorous peer review process together with strict ethical policies and standards to ensure to add high quality scientific works to the field of scholarly publication
Machines | Article Processing Charges - MDPI All articles published in Machines (ISSN 2075-1702) are published in full open access An article processing charge (APC) of CHF 2400 (Swiss francs) applies to papers accepted after peer review This article processing charge is to cover the costs of peer review, copyediting, typesetting, long-term archiving, and journal management
General Theory of Information and Mindful Machines - MDPI As artificial intelligence advances toward unprecedented capabilities, society faces a choice between two trajectories One continues scaling transformer-based architectures, such as state-of-the-art large language models (LLMs) like GPT-4, Claude, and Gemini, aiming for broad generalization and emergent capabilities This approach has produced powerful tools but remains largely statistical
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