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Advanced Technologies and Applications of Machine Learning, Cybersecurity, Cloud Computing and Blockchain

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 309

Editors


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Guest Editor
Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: artificial intelligence; electric vehicles; energy storage; mathematical modeling; control theory and applications; smart cities and smart grids; power electronic converters; power electronic systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: deep learning; IOU; motor imagery; brain computer interface; electroencephalogram; computer graphics; isosurface; transfer functions; facial animation; emotion; speech synthesis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Computer Systems and Technologies, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: fog computing; network protocol; internet of things; cybercrime; cyberattack; warfare; neural networks; RRAM; chaotic circuit; object detection; deep learning; IOU; model-based testing; computer interface
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advances in machine learning, cybersecurity, cloud computing and blockchain are reshaping the design, deployment and governance of modern digital systems. These technologies increasingly operate together: machine learning enables intelligent automation and data-driven decision-making; cybersecurity protects critical assets, data and services; cloud computing provides scalable and flexible infrastructure and blockchain offers decentralized trust, transparency and secure record management. Their convergence creates new opportunities for smart industry, energy systems, healthcare, finance, education, transportation, public services and Internet of Things applications.

This Special Issue is prepared in connection with the 2026 14th International Scientific Conference COMPUTER SCIENCE (ComSci-2026), while remaining fully open to all relevant high-quality manuscripts and not restricted to conference papers. It aims to collect theoretical, methodological and applied research addressing advanced technologies and practical applications in these interconnected domains. Topics of interest include intelligent algorithms, secure machine learning, privacy-preserving data processing, cloud and edge architectures, blockchain-based platforms, cyber–physical security, digital twins, smart contracts, distributed applications, resilient infrastructure, AI-enabled threat detection and trustworthy computing. Contributions presenting novel models, experimental validation, case studies, prototypes, reviews, benchmarks and interdisciplinary applications are welcome, especially works demonstrating real-world impact, technical robustness, scalability, security, interoperability and sustainability.

Prof. Dr. Nikolay Hinov
Prof. Dr. Milena Lazarova
Prof. Dr. Ognyan Nakov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • cybersecurity
  • cloud computing
  • blockchain
  • artificial intelligence
  • secure systems
  • internet of things
  • edge computing
  • smart contracts
  • digital transformation

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Published Papers (1 paper)

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Research

40 pages, 13861 KB  
Article
Non-Compensatory Security and Utility Gates for Blockchain Lifecycle Assessment: Framework Development and an Operational-Energy Application to the Ethereum Merge
by Nikolay Hinov
Appl. Sci. 2026, 16(15), 7820; https://doi.org/10.3390/app16157820 - 5 Aug 2026
Viewed by 119
Abstract
Environmental comparisons of blockchain systems are often reduced to electricity per transaction, although operational services also depend on validators, cloud gateways, storage, monitoring, key management, recovery, and hardware replacement. This study develops a lifecycle assessment framework with non-compensatory security and utility gates and [...] Read more.
Environmental comparisons of blockchain systems are often reduced to electricity per transaction, although operational services also depend on validators, cloud gateways, storage, monitoring, key management, recovery, and hardware replacement. This study develops a lifecycle assessment framework with non-compensatory security and utility gates and applies its operational-energy module to Ethereum’s transition from proof of work (PoW) to proof of stake (PoS). Three units are separated: 24 h of observed network operation (FU-O), 24 h of fully security- and utility-qualified service (FU-Q), and one million included layer-1 transactions (FU-B, an attributional diagnostic). FU-Q is not evaluated because several mandatory gates remain UNRESOLVED. Matched 28-day activity windows are combined with dated network-energy estimates, not continuous metering over those windows. Using the independent Cambridge baseline, daily operational electricity decreased from 58,617.39 to 5.376 MWh, a factor of 10,903.5 and a reduction of 99.99083%. The CCRI replication factor was 8804.9, while an adverse bounded pairing still yielded a factor of 3424.7. Across 100,000 Monte Carlo realizations generated by the supplied executable workflow, the median FU-O reduction was 99.98698%, with a central 95% interval of 99.97492–99.99441%. Jansen sensitivity analysis identified post-Merge annual energy as the dominant input to the FU-O factor. The additional post-Merge cloud and annualized embodied burden required to eliminate FU-O parity was 21,408 GWh/year. The result is a bounded operational-energy application and does not establish the complete lifecycle, cloud, cybersecurity, or functional-equivalence framework. Full article
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