International Journal of Critical Infrastructures
Scope & Guideline
Advancing the future of critical infrastructure management.
Introduction
Aims and Scopes
- Critical Infrastructure Security and Resilience:
Research exploring methodologies and technologies to enhance the security and resilience of critical infrastructures, including energy, transport, and healthcare systems. - Data-Driven Decision Making:
Utilization of advanced data analytics, machine learning, and decision-making frameworks to optimize the performance and management of critical infrastructures. - Emerging Technologies in Infrastructure:
Investigation of the role of emerging technologies, such as IoT, AI, and cloud computing, in the development and enhancement of critical infrastructure systems. - Sustainable Infrastructure Development:
Focus on sustainable practices and technologies that contribute to the effective management and development of infrastructure, addressing environmental and societal impacts. - Public-Private Partnerships (PPP) and Financing Models:
Analysis of PPP frameworks and innovative financing models that support the development and maintenance of critical infrastructure projects. - Risk Assessment and Management:
Comprehensive studies on risk assessment methodologies and management strategies to mitigate vulnerabilities in critical infrastructure systems.
Trending and Emerging
- AI and Machine Learning Applications:
There is a significant increase in research applying AI and machine learning techniques to optimize infrastructure performance, enhance security, and improve decision-making processes. - IoT Integration in Infrastructure Systems:
The integration of IoT technologies into critical infrastructure has gained momentum, with studies focusing on smart cities, health care systems, and energy management, showcasing the potential for improved efficiency and monitoring. - Sustainable and Resilient Infrastructure Solutions:
Emerging themes around sustainability and resilience in infrastructure development are becoming prominent, emphasizing the need for environmentally friendly practices and disaster preparedness. - Cybersecurity in Critical Infrastructures:
With the rise in cyber threats, there is an increasing focus on cybersecurity measures tailored for critical infrastructures, encompassing both theoretical frameworks and practical applications. - Real-Time Monitoring and Predictive Analytics:
Research in real-time monitoring systems that leverage big data and predictive analytics for infrastructure management is trending, highlighting the importance of data-driven approaches in enhancing operational efficiency.
Declining or Waning
- Traditional Infrastructure Engineering Methods:
There has been a noticeable decrease in publications focusing on conventional engineering practices without incorporating modern technological solutions or innovative methodologies. - General Infrastructure Policy Analysis:
Research centered on broad policy analysis related to infrastructure without specific technological or operational insights has become less frequent, possibly indicating a shift towards more technology-driven studies. - Basic Risk Assessment Techniques:
The frequency of papers employing traditional risk assessment techniques without integration of advanced data analytics or machine learning has diminished, as researchers move towards more sophisticated approaches. - Static Infrastructure Models:
There is a reduction in the exploration of static models for infrastructure management, with a growing preference for dynamic models that account for real-time data and changing conditions.
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