The Grid is a rapidly developing computing structure that allows components of our information technology infrastructure, computational capabilities, databases, sensors, and people to be shared flexibly as true collaborative tools. Over the last 3 years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed computing systems. After the advances made in distributed system design, collaborative environments, high performance computing and high throughput computing, the Grid is the logical next step.

The new Aims and Scope of FGCS will cover new developments in:

[1] Grid Applications and application support:

    Novel applications
    eScience and eBusiness applications
    Problem solving environments and virtual laboratories
    Grid economy
    Semantic and knowledge based grids
    Collaborative Grids and virtual organizations
    High Performance and high throughput computing on grids
    Complex application workflows
    Scientific, industrial and social implications
    Grids in education

[2] Grid methods and middleware:

    Tools for grid development: monitoring and scheduling
    Distributed dynamic resource management
    Grid- and web-services
    Information management
    Protocols and emerging standards
    Peer to peer and internet computing
    Pervasive computing
    Grid Security

[3] Grid Theory:

    Process specification; program and algorithm design
    Theoretical aspects of wide area communication and computation
    Scaling and performance theory
    Protocol verification

Special Issue on Harnessing the Convergence of HPC and AI in Bioinformatics: Challenges and Opportunities
提出日: 2024-06-30

High-performance computing (HPC) is of utmost importance in the field of bioinformatics due to the vast amount of data generated and the complex computational tasks involved. HPC offers parallel processing capabilities, large memory resources, and high-speed interconnects, enabling researchers to execute complex algorithms and simulations efficiently. In bioinformatics, HPC plays a crucial role in various applications. For instance, it facilitates genome sequencing and assembly, where massive amounts of DNA sequences need to be aligned, compared, and reconstructed. HPC also aids in protein structure prediction, molecular dynamics simulations, and drug discovery, where complex calculations and simulations are performed to understand protein-ligand interactions and predict drug efficacy.

For the last decade, the development of AI models in bioinformatics is rapidly evolving. For example, the development of AlphaFold2 for proteins structure prediction and large language models (LLMs) such as BioBert for bio-medical research. The success of AI in bioinformatics heavily relies on the availability of computing power and development of efficient training algorithms. These enable the efficient training and execution of AI models. The combination of AI and HPC has opened up new possibilities in genomics, drug discovery, and precision medicine, leading to ground breaking advancements in the understanding and treatment of diseases.

In this special issue, we invite research articles and review articles aiming at presenting the most recent development and trend of HPC and AI algorithms/technologies for biomedical and biological research.

Topics of interest include, but are not limited to algorithm and system development in the following areas:
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Guest Editors

Yanjie WeiShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, P.R. Chinayj.wei@siat.ac.cn

Weiguo LiuSchool of Software, Shandong University, P.R. Chinaweiguo.liu@sdu.edu.cn

Bertil SchmidtInstitute of Computer Science, Johannes Gutenberg-Universität Mainz, Germanybertil.schmidt@uni-mainz.de  

Quan ZouInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology, P.R. Chinazouquan@nclab.net 

Limin JiangDepartment of Public Health Sciences, University of Miami, United Stateslxj423@med.miami.edu 

Special Issue on Next-Generation Web 3.0 for Digitalized Industrial Applications in the 5G/6G Era
提出日: 2024-07-15

With the rapid development of 5G/6G communication networks, billions of IoT devices are being connected to the network, thus generating a large amount of data. The collection, processing, and analysis of this vast amount of data are essential to help people and enterprises gain valuable information, make sensible decisions, and improve people’s lives. However, the underlying communication networks also face many new challenges. Managing these large number of devices in a scalable and secure manner brings significant challenges to the infrastructure construction, maintenance, and management of the communication networks. Recurring data privacy breaches and the lack of control make Internet users and enterprises less willing to provide valuable data for processing and analysis.

In the context of the rapidly evolving 5G/6G era, the relevance of Web 3.0 to digital industrial applications is multifaceted and transformative. By emphasizing enhanced security and decentralized data management, Web 3.0 technologies offer robust solutions to safeguard sensitive industrial information and ensure resilient data infrastructure. These technologies enable streamlined supply chain and asset management through transparent and tamper-resistant data exchange, while the integration of smart contracts facilitates automated agreements, expediting operational efficiency. Furthermore, the implementation of self-sovereign identity systems empowers users to manage and authorize data access, ensuring heightened privacy compliance. The compatibility of Web 3.0 with advanced 5G/6G network capabilities enables seamless integration, promoting efficient communication, computation, and data processing within the industrial landscape. Overall, the integration of Web 3.0 technologies fosters a dynamic, secure, and interconnected industrial ecosystem poised for sustainable growth and innovation.

Authors are invited to submit high-quality papers containing original work from either academia or industry reporting novel advances in (but not limited to) the following topics on Web 3.0 for digitalized industrial applications in the 5G/6G era:

Guest Editors
Qingqi PeiXidian University, Chinaqqpei@mail.xidian.edu.cn
F. Richard YuCarleton University, Canadarichard.yu@carleton.ca 
Kaou OtaMuroran Institute of Technology, Japan ota@csse.muroran-it.ac.jp
Mohammed AtiquzzamanUniversity of Oklahoma, USAatiq@ou.edu
Youshui LuXi’an Jiaotong University, Xi’an, Chinayolu6176@uni.sydney.edu.au

Important Dates
Submission portal opens: January 15, 2024
Deadline for paper submission: July 15, 2024
Latest acceptance deadline for all papers: November 15, 2024 

Special Issue on AIFI – Artificial Intelligence for Interoperability
提出日: 2024-11-15

Motivation and Scope

With the increasing number of data processing technologies, interoperability represents one of the biggest challenges in gathering information from heterogeneous sources. Data collection involves different hardware and software solutions, the adoption of different formats, and the sharing of different meanings. Data interoperability encompasses how diverse datasets are exchanged, merged or aggregated in seamless and meaningful ways, enabling the extraction of knowledge that can be inferred from the whole dataset but not from single sources. While research in this domain explored diverse strategies, the advent of Artificial Intelligence (AI) represents a new frontier in enhancing data interoperability.

AI can intervene at different steps of the data processing lifecycle: at acquisition time to adapt data collection for future use or compatibility with other technologies, during the storing process to perform semantic enrichment and alignment with a broader dataset, during the data analysis to federate knowledge from multiple repositories.

This special issue explores diverse approaches, challenges, and benefits in AI-driven data interoperability, focusing on the domains of the Internet of Things, Big Data, and Knowledge Graphs. This special issue can raise valuable insights into how AI can enhance data interoperability, enabling efficient use of diverse datasets to drive innovation and ease knowledge discovery.

The topics of this special issue include (but are not limited to):

Middleware Strategies empowered by AI
AI-Driven Standardization and Adaptive Data Formats
Federated Knowledge Extraction
Interoperability solutions based on Generative AI
Interoperable architecture, protocols, and standards for large-scale IoT deployments enabled by AI.
Automatic design and integration of IoT standards, such as the W3C Web of Things (WoT) specifications, powered by AI techniques.
Methods and algorithms to improve interoperability at the knowledge level
AI solutions for blockchain interoperability
Explainable AI for enhanced interoperability
AI-based data privacy and security in interoperable systems
AI-driven ontology alignment and semantic interoperability

Guest Editors

Luca SciulloUniversity of Bologna, Italyluca.sciullo@unibo.it
Ivan ZyrianoffUniversity of Bologna, Italyivandimitry.ribeiro@unibo.it
Ronaldo C. PratiFederal University of ABC, Brazilronaldo.prati@ufabc.edu.br
Lionel MediniUniversity of Claude Bernard Lyon 1, France lionel.medini@liris.cnrs.fr

Important Dates
Submission portal opens: May 15th, 2024
Deadline for paper submission: November 15th, 2024
Latest acceptance deadline for all papers: March 15th, 2025 

Special Issue on Explainable Artificial Intelligence in Drug Discovery and Development
提出日: 2024-12-15

Motivation and Scope

'Artificial Intelligence' (AI) has recently revolutionized the field of drug discovery and development, achieving breakthroughs in areas such as molecular design, chemical synthesis planning, protein structure prediction, and macromolecular target identification. Despite various computational methods proposed to address practical challenges, the complexity of these algorithms often results in limited explainability of the models, hindering our ability to understand and explain their underlying mechanisms. Given the rapid advancement of AI in drug discovery and related fields, there is an increasing demand for methods that help us understand and interpret the underlying models. Consequently, proposing 'Explainable Artificial Intelligence' (XAI) methods to address the challenge posed by the lack of explainability in deep learning models and enhancing human reasoning and decision-making capabilities have become imperative.

This special issue aims to gather papers that focus on integrating and applying advanced XAI algorithms to address the most fundamental questions in drug discovery and development, including drug repositioning, potential drug target identification, and small drug molecule target interaction and binding affinity prediction, etc. We expect the articles covering this special issue can effectively promote the drug discovery in methodology and meanwhile provide interesting insights or new biological observations.

The topics of this special issue include but not limited to:

Prediction of drug properties with XAI
Explaining drug-drug/target interaction through XAI
Development of explainable large language models for drug discovery
XAI for drug and target feature representation
XAI for ab initio drug design
XAI for virtual screening drugs

Guest Editors

Leyi WeiShandong University, Chinaweileyi@sdu.edu.cn 
Balachandran ManavalanSungkyunkwan University, South Koreabala2022@skku.edu 
Xiucai YeUniversity of Tsukuba, Japan yexiucai@cs.tsukuba.ac.jp 
Dariusz MrozekSilesian University of Technology, PolandDariusz.Mrozek@polsl.pl 

Important Dates
Submission portal opens: March 20, 2024
Deadline for paper submission: Dec. 15, 2024
Latest acceptance deadline for all papers: March 1, 2025