The International Journal of eScience

Computing infrastructures and systems are rapidly developing and so are novel ways to map, control and execute scientific applications which become more and more complex and collaborative.
Computational and storage capabilities, databases, sensors, and people need true collaborative tools. Over the last years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed sensing and computing systems. Big Data in all its guises require novel methods and infrastructures to register, analyze and distill meaning.

FGCS aims to lead the way in advances in distributed systems, collaborative environments, high performance and high performance computing, Big Data on such infrastructures as grids, clouds and the Internet of Things (IoT).

The Aims and Scope of FGCS cover new developments in:

[1] Applications and application support:

Novel applications for novel e-infrastructures
Complex workflow applications
Big Data registration, processing and analyses
Problem solving environments and virtual laboratories
Semantic and knowledge based systems
Collaborative infrastructures and virtual organizations
Methods for high performance and high throughput computing
Urgent computing
Scientific, industrial, social and educational implications
Education

[2] Methods and tools:

Tools for infrastructure development and monitoring
Distributed dynamic resource management and scheduling
Information management
Protocols and emerging standards
Methods and tools for internet computing
Security aspects

[3] Theory:

Process specification;
Program and algorithm design
Theoretical aspects of large scale communication and computation
Scaling and performance theory
Protocols and their verification

Special Issue on MLOps Advancements: Improving Development, Management, and Interpretability in AI and Machine Learning
Día de Entrega: 2026-03-31

Motivation and Scope

The rapid advancement of data-driven artificial intelligence has accelerated the integration of machine learning (ML) models into real-world applications across diverse domains. This transition from experimental ML to production-ready AI systems has unveiled significant operational challenges in model development, deployment, monitoring, and maintenance. MLOps has emerged as a critical framework of practices and tools designed to streamline the entire ML lifecycle, ensuring automation, reproducibility, and scalability while bridging the gap between data science experimentation and the reliable operation of ML components in production.

As AI adoption continues to expand, the need for robust, scalable, and transparent MLOps practices has become increasingly vital to guarantee the reliability and trustworthiness of deployed ML models. This Special Issue seeks to gather cutting-edge research, case studies, and insights from both academia and industry that address the key challenges and emerging trends in MLOps, with particular emphasis on practical solutions for improving AI and machine learning workflows.

We invite high-quality submissions that explore, but are not limited to, the following topics:

MLOps Frameworks and Best Practices
ML pipelines orchestration
ML model reproducibility, traceability, and explainability
Continuous integration/continuous delivery (CI/CD) practices for ML models
ML model monitoring and observability
MLOps practices to ensure Interpretability and Explainability in AI systems
Application of MLOps principles to Knowledge and Semantic Representation
Application of MLOps principles to large language models (LLMOps)
ML-specific architecture design and patterns
Experience reports on real-world MLOps applications
Challenges in applying MLOps to specific domains (e.g., healthcare and finance)
Ethics and Accountability in MLOps
AutoML applications in MLOps
Collaboration and team dynamics in MLOps
Regulatory and policy aspects of MLOps
MLOps strategies for Green AI
Security and data privacy in MLOps

The proponents of this Special Issue are also the organizers of the Workshop on Machine Learning Operations – MLOps'25, which will be held at ECAI 2025, one of the premier conferences in artificial intelligence. As part of this initiative, we aim to provide an opportunity for authors of papers accepted at the workshop to submit an extended version of their work to this Special Issue. This will enable further development of innovative research contributions presented at MLOps'25, fostering deeper discussions and broader dissemination of advancements in MLOps methodologies, tools, and applications.

Guest Editors

Antonella Carbonaro
University of Bologna, Bologna, Italy
antonella.carbonaro@unibo.it

Luigi Quaranta
University of Bari, Bari, Italy
luigi.quaranta@uniba.it

Giulio Mallardi
University of Bari, Bari, Italy
giulio.mallardi@uniba.it

Fabio Calefato
University of Bari, Bari, Italy
fabio.calefato@uniba.it

Important Dates

Submission portal opens: January 7th, 2026
Deadline for paper submission: March 31st, 2026
Latest acceptance deadline for all papers: June 15th, 2026

Special Issue on Emerging Technologies in Distributed Intelligence for Natural Disaster Management
Día de Entrega: 2026-03-31

Motivation and Scope

The increasing complexity and unpredictability of environmental phenomena and emergency situations demand advanced sensing, analysis, and response capabilities. Distributed intelligent systems --- networks of interconnected sensors, edge devices, and AI-powered analytics --- are transforming how we monitor natural and human-built environments and manage emergencies in real time. As climate change and urbanization intensify the frequency and impact of natural and human-induced hazards, there is a growing need for integrated, real-time sensing and decision-making frameworks for Natural Disaster Management (NDM). This special issue aims to explores the transformative role and to showcase cutting-edge research and innovative applications of distributed intelligent sensing and decision-making systems in environmental monitoring, disaster prediction and response, public safety, and critical infrastructure resilience. A particular emphasis is placed on cutting-edge developments in Distributed Artificial Intelligence, enabling decentralized and privacy-aware model training across sensor networks; Distributed Remote Sensing, integrating satellite, aerial, and in-situ data sources for real-time environmental monitoring; and Structural Monitoring systems, which ensure the safety and resilience of critical infrastructure through intelligent sensing and analytics.
We welcome contributions that present theoretical advances, system architectures, algorithmic innovations, and real-world applications that leverage distributed intelligence for resilience, sustainability, and safety in complex environments.

Contributions are invited on topics including, but not limited to:

Real-time distributed sensing networks for environmental hazard detection (e.g., floods, wildfires, air and water quality)
AI-driven distributed decision support for emergency response coordination
Integration of IoT, edge computing, and 6G communications in emergency management systems
Federated Learning for privacy-preserving data analysis and model training across distributed sensing nodes
Distributed remote sensing architectures combining satellite, UAV (Unmanned Aerial Vehicles), mobile robots and ground sensor data for environmental awareness
Structural Health Monitoring using intelligent sensor networks and AI for infrastructure resilience
Case studies on distributed systems for urban resilience and smart city emergency preparedness
Novel algorithms for anomaly detection, event prediction, and adaptive system reconfiguration in distributed sensing environments
Ethical, privacy, and security considerations in distributed intelligent emergency systems

By bringing together interdisciplinary perspectives and state-of-the-art technologies, this issue seeks to advance the development of robust, scalable, and intelligent distributed systems that enhance situational awareness, enable timely interventions, and ultimately save lives and protect ecosystems.

Guest editors:

Roberto Marino, Ph.D. - University of Messina, roberto.marino@unime.it
Lorenzo Carnevale, Ph.D. - University of Messina, lorenzo.carnevale@unime.it
Daniel Balouek, Ph.D. - Inria, Nantes, daniel.balouek@inria.fr
Manish Parashar, Ph.D. - University of Utah, manish.parashar@utah.edu

Manuscript submission information:

Important Dates

Submission portal opens: December 1st, 2025
Deadline for paper submission: March 31st, 2026
Latest acceptance deadline for all papers: May 31th, 2026

https://www.sciencedirect.com/special-issue/327640/emerging-technologies-in-distributed-intelligence-for-natural-disaster-management

Special Issue on Advances in QuantComputing: Methods, Algorithms, and Systems. Vol. III
Día de Entrega: 2026-04-15

Motivation and Scope

Quantum computing is rapidly evolving, with quantum systems requiring integration with HPC and QPUs as accelerators. This special Issue spotlights foundations and practice: large-scale simulators; quantum compilers, runtimes, workflow managers, schedulers, and orchestrators; quantum algorithms and applications (including QML, quantum chemistry, …); quantum data and memories; error-correction codes; hybrid QC-HPC methods; performance modeling and characterization; cloud-based quantum services; quantum technologies for computation; speed-up and supremacy assessment; and benchmarking. We welcome contributions that tackle hardware-software co-design, classical-quantum interfaces, and validation at scale, showcasing both current results and emerging directions.

Guest editors:

Prof. Stefano Markidis
KTH Royal Institute of Technology, Stockholm, Sweden
markidis@kth.se

Prof. Michela Taufer
University of Tennessee, Knoxville, TN, USA
mtaufer@utk.edu

Prof. Lucio Grandinetti
Universita’ della Calabria, Rende, Italy
lucio.grandinetti@unical.it

Manuscript submission information:

Important Dates

Submission portal opens: August 15th, 2025
Deadline for paper submission: April 15th, 2026
Latest acceptance deadline for all papers: August 30th, 2026

https://www.sciencedirect.com/special-issue/326513/special-collection-on-advances-in-quantcomputing-methods-algorithms-and-systems-vol-iii

Special Issue on Digital Twins and Emerging Technologies for Industrial and Cyber-Physical Systems: From IoT to Decentralized Intelligence
Día de Entrega: 2026-05-31

Motivation and Scope

Overview section: Industrial and cyber-physical systems are undergoing a profound transformation enabled by the convergence of Digital Twin (DT) paradigms, Industrial IoT (IIoT), edge/fog computing, and decentralized intelligence techniques such as federated learning, blockchain coordination, and gossip protocols. These technologies support high-fidelity simulations, real-time monitoring, and secure, data-driven decision-making aacross manufacturing, logistics, energy, and infrastructure networks.

This Special Issue aims to gather cutting-edge research exploring both theoretical advances and applied solutions, with particular emphasis on secure, robust, and intelligent digital infrastructures. While the primary focus remains on industrial domains, we also explicitly welcome contributions addressing complex cyber-physical environments —including urban mobility, city-scale infrastructures, and smart energy systems —when these are treated with approaches transferable to, or interoperable with, industrial and mission-critical settings.

Guest editors:

Dr. Marcello Pietri (Executive Guest Editor), Department of Sciences and Methods for Engineering (DISMI) - University of Modena and Reggio Emilia, Reggio nell’Emilia, Italy, marcello.pietri@unimore.it

Dr. Matteo Martinelli, Department of Sciences and Methods for Engineering (DISMI) - University of Modena and Reggio Emilia, Reggio nell’Emilia, Italy, matteo.martinelli@unimore.it

Prof. Andreas Wortmann, Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) - University of Stuttgart, Germany, andreas.wortmann@isw.uni-stuttgart.de

Prof. JaeSeung Song, Department of Computer and Information Security - Sejong University, Sejong, South Korea, jssong@sejong.ac.kr

Manuscript submission information:

Important Dates

Submission portal opens: January 15th, 2026
Deadline for paper submission: May 31st, 2026
Latest acceptance deadline for all papers: 30th Nov, 2026

Manuscript Submission Instructions

Must have article type/name in EM

The FGCS’s submission system (Editorial Manager®) will be open for submissions to our Special Issue from January 15th, 2026. When submitting your manuscript please select the article type VSI: DT-CPS.

All submissions deemed suitable by the editors to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production to be published in the special issue.

Keywords:

Industrial IoT; Digital Twins; Cyber-Physical Systems; Edge Computing; Federated Learning; Secure Distributed Intelligence

https://www.sciencedirect.com/special-issue/328906/digital-twins-and-emerging-technologies-for-industrial-and-cyber-physical-systems-from-iot-to-decentralized-intelligence

Special Issue on High-Performance Scientific Workflows in the Compute Continuum
Día de Entrega: 2026-07-31

Motivation and scope:

The convergence of High-Performance Computing (HPC), Cloud, and Edge infrastructures, such as Compute Continuum, is transforming eScience by enabling scientific workflows to run across heterogeneous, distributed environments, from exascale systems to edge devices. This paradigm shift raises new challenges in interoperability, scalability, performance, and reproducibility.

This Special Issue focuses on high-performance scientific workflows designed for the Compute Continuum with topics including workflow models and description languages, orchestration and resource-aware scheduling, resilience and adaptive execution, data streaming and in-transit processing, energy- and cost-aware policies, and performance monitoring. Emphasis is placed on FAIR-compliant methodologies that support interoperability, provenance, security, and the reusability of data and services. The Special Issue welcomes both foundational research and practical contributions, including algorithms, middleware, open frameworks, and reproducible case studies demonstrating next-generation Compute-Continuum-aware eScience workflows.

Guest editors:

Dr. Raffaele Montella
Department of Science and Technology, University of Naples “Parthenope”, Naples, Italy, raffaele.montella@uniparthenope.it

Dr. Iacopo Colonnelli
Department of Computer Science, University of Turin, Turin, Italy, iacopo.colonnelli@unito.it

Dr. Daniela Cassol
DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, dcassol@lbl.gov

Dr. Diana Di Luccio
Department of Science and Technology, University of Naples “Parthenope”, Naples, Italy, diana.diluccio@uniparthenope.it

Manuscript submission information:

Important dates:

Submission portal opens: February 16, 2026Deadline for paper submission: July 31, 2026
Latest acceptance deadline for all papers: March 31st, 2027

The FUTURE's submission system (Editorial Manager®) will be open for submissions to our Special Issue from February 16, 2026. When submitting your manuscript, please select the article type VSI: HPSWCC.

All submissions deemed suitable by the editors to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production to be published in the special issue.

Keywords:

Compute Continuum; High-Performance Scientific Workflows; eScience; Workflow Orchestration; FAIR Principles

https://www.sciencedirect.com/special-issue/330423/special-issue-on-high-performance-scientific-workflows-in-the-compute-continuum