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Future Generation Computer Systems (FGCS)

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インパクトファクター:
5.9
出版社:
Elsevier
ISSN:
0167-739X
閲覧:
182153
フォロー:
184
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論文募集

Future Generation Computer Systems (FGCS) is an academic journal published by Elsevier. (ISSN 0167-739X, impact factor 5.9, CCF C).

Aims & Scope 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
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特集号

特集号：Novel Paradigms and Scalable Computational Frameworks for Data-Intensive Research in Computational Biology and Medical Informatics 投稿締切日: 2027-01-03 This Special Issue explores novel paradigms and scalable computational frameworks for data-intensive research in computational biology and medical informatics. The rapid growth of clinical, molecular, and multi-omics data is creating unprecedented opportunities for discovery while simultaneously posing significant challenges for data processing, integration, and analysis. The contributions collected in this Special Issue focus on advanced computing solutions such as high-performance computing, distributed and GPU-accelerated architectures, AI-driven analytical workflows, and emerging paradigms including quantum and quantum-inspired computing. By connecting developments in computing systems with real-world life science applications, the Special Issue highlights methodologies and infrastructures that support the next generation of large-scale biomedical data analysis. Guest editors: Prof. Marco Beccuti, University of Turin, Turin, Italy, [email protected] Prof. Umberto Ferraro Petrillo, Sapienza University of Rome, Rome, Italy, [email protected] Dr. Lorenzo Di Rocco, Sapienza University of Rome, Rome, Italy, [email protected] Special issue information: The rapid growth of clinical, molecular, and omics data is increasingly driving new discoveries in biomedical and biological research. The combination of advanced computational infrastructures, such as GPU-accelerated and distributed computing systems, with machine learning and AI-driven workflows is accelerating the research process and enabling the extraction of meaningful insights from extensive datasets. Developing scalable, robust, and interpretable methods is essential to effectively manage the heterogeneity and complexity of modern large-scale datasets. As these data-intensive challenges become central to the fields of bioinformatics, biostatistics, and medical informatics, there is a growing demand for novel paradigms that effectively harness state-of-the-art learning techniques, as well as for engineering solutions that enable the efficient management of high-volume data. At the same time, engineering solutions capable of exploiting high-performance computing architectures and distributed environments are becoming increasingly important to support large-scale biomedical analysis pipelines. This Special Issue focuses on novel paradigms and scalable computational frameworks that leverages high-performance computing, distributed and GPU-accelerated architectures, as well as advanced machine learning and AI-based workflows, to address the challenges of modern data-intensive research in computational biology and medical informatics. Contributions exploring novel system architectures, scalable algorithms, integrated data processing pipelines, and emerging computing paradigms, including quantum and quantum-inspired approaches for complex data analysis, are particularly welcome. Important Dates Submission portal opens: October 5, 2026 Deadline for paper submission: February 1, 2027 Latest acceptance deadline for all paper: May 10, 2027 Manuscript submission information: The FGCS’s submission system (Editorial Manager®) will be open for submissions to our Special Issue from October 5, 2026. When submitting your manuscript, please select the article type VSI: SCF-CBMI. 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: high-performance computing, distributed computing, GPU-accelerated computing, artificial intelligence, machine learning, computational biology, multi-omics data analysis, medical informatics, quantum computing https://www.sciencedirect.com/special-issue/334600/novel-paradigms-and-scalable-computational-frameworks-for-data-intensive-research-in-computational-biology-and-medical-informatics
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特集号：Future Generation Information Technology for Social Good 投稿締切日: 2027-02-28 Technological innovation is increasingly pivotal in addressing global societal challenges and advancing the UN Sustainable Development Goals (SDGs): in healthcare, smart cities, environmental monitoring, and digital governance, to name just a few areas. Emerging computing paradigms—such as distributed systems, artificial intelligence, and data-driven infrastructures—are bringing digital social innovation to a new level of inclusivity and sustainability. This requires a user-centered approach where technological design, advanced hardware, and societal needs are holistically considered. This special issue aims to explore the multifaceted aspects of future generation computing, highlighting both technical advances and their broader societal implications for social good. Guest editors: Barbara Guidi, [email protected], University of Pisa, Italy Pietro Manzoni, [email protected], Polytechnic Univ. of Valencia, Spain Claudio E. Palazzi, [email protected], University of Padua, Italy Special issue information: Technological innovation plays a pivotal role in advancing the UN Sustainable Development Goals (SDGs) and the “Agenda 2030.” Emerging computing paradigms—such as distributed systems, artificial intelligence, and data-driven infrastructures—offer unprecedented opportunities to design scalable and impactful solutions for social good. Digital social innovation sits at the intersection of technology, societal challenges, and environmental sustainability. It emphasizes digital technologies in enabling communities to share data, collaborate, and scale solutions through open, distributed, and participatory approaches. A key challenge lies in effectively integrating emerging technologies, including advanced hardware (e.g., IoT, wearables, TinyML), digital paradigms (e.g., AI, blockchain, big data), and communication infrastructures (e.g., 5G). Furthermore, future ICT solutions must ensure inclusiveness, accessibility, affordability, and sustainability. Addressing these aspects requires a strictly user-centered perspective. This Special Issue focuses on novel IT solutions enabling collaborative and sustainable applications across domains like healthcare, smart cities, environmental monitoring, and digital governance. It aims to provide an interdisciplinary forum for researchers to present innovative models leveraging future generation computing. We strongly encourage multidisciplinary contributions exploring the intersection of computer science with social sciences and public policy. The Special Issue is open to submissions from the wider research community and will also consider extended versions of selected papers from the ACM International Conference on Information Technology for Social Good (GoodIT), provided they align with the scope. Our goal is to foster technology-driven solutions that advance the state of the art in computing and contribute to a socially responsible digital future. Topics of interest include, but are not limited to: - AI, Digital Twins, and Blockchain for social good - Accessible and Inclusive Technologies, bridging the digital divide - Citizen Science, Civic Intelligence, and Social Informatics - Digital Democracy, open data, and disinformation management - Ethical Computing, privacy, and trust in ICT solutions - Frugal Solutions and Sustainable IT - Gamification and Serious Games for social impact - IT for Health, Social Care, Education, and Development - Smart Governance, Sustainable Cities, and Environmental Monitoring - Human-Computer and Human-Robot Interaction - Decentralized Environments, networking, and social media Important Dates Submission portal opens: September 30, 2026 Deadline for paper submission: February 28, 2027 Latest acceptance deadline for all paper: July 01, 2027 Manuscript submission information: The FGCS’s submission system (Editorial Manager®) will be open for submissions to our Special Issue from September 30, 2026. When submitting your manuscript, please select the article type VSI:FUTURE_Social Good. 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: social good; digital social innovation; sustainable development goals; distributed systems; artificial intelligence; blockchain https://www.sciencedirect.com/special-issue/334718/future-generation-information-technology-for-social-good
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特集号：Advances in Quantum Computing: Methods, Algorithms, and Systems. Vol. IV 投稿締切日: 2027-04-30 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: Stefano S. Markidis, [email protected], KTH Royal Institute of Technology, Sweden. Michela Taufer, [email protected], The University of Tennessee Knoxville, United States Lucio L. Grandinetti, [email protected], University of Calabria, Italy Special issue information: We welcome contributions that tackle hardware-software co-design, classical-quantum interfaces, and validation at scale, showcasing both current results and emerging directions. Important Dates Submission portal opens: June 26, 2026 Deadline for paper submission: April 30, 2027 Latest acceptance deadline for all paper: June 30, 2027 https://www.sciencedirect.com/special-issue/334993/special-collection-on-advances-in-quantum-computing-methods-algorithms-and-systems-vol-iv
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