A quantum key distribution (QKD) network is proposed to allow QKD protocols to be the infrastructure of the Internet for distributing unconditional security keys instead of existing public-key cryptography based on computationally complex mathematical problems. Numerous countries and research institutes have invested enormous resources to execute correlation studies on QKD networks. Thus, in this study, we surveyed existing QKD network studies and practical field experiments to summarize the research results (e.g., type and architecture of QKD networks, key generating rate, maximum communication distance, and routing protocol). Furthermore, we highlight the three challenges and future research issues in the security of QKD networks and then provide some feasible resolution strategies for these challenges.

The papers in this special section focus on data analytics for public health care. Growing and intensive attention for the use of data analytics in healthcare due to its proven benefits to gain insights on the macro and micro depths to support decisionmaking at the patient, clinical, bench side and business levels. Health data analytics contributed to the improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment, narrow the gap between bedside and bench side and provide more informed decision-making. However, the recent pandemic of COVID-19 draw the world attention to need to extend the power of data analytics beyond the legacy data channels including Electronic Health Records (EHRs) and Patient Portals. Data analytics can assist population health management in improving patient outcomes, enhancing care management, and address social determinants of health. As the population health analytics continues to grow in the healthcare space, healthcare systems must be able to gather data from multiple sources, apply variety of data analytics (e.g., Descriptive Analytics, Predictive Analytics Prescriptive Analytics), and manage the provide insights for the population.

The articles in this special section focus on recent progress in developing artificial intelligence and machine learning methodologies.

The articles in this special section focus on current applications and innovations of artificial intelligence and machine learning in aerospace. Artificial intelligence (AI) and machine learning (ML) play an increasingly important role in aerospace applications and serve various military, commercial aviation, and space exploration sectors to ensure safety, dependability, and customer loyalty. AI/ML contributes to provide various automated systems used in aviation, such as fuel efficiency, smart maintenance, smart air traffic management, pilot training, passenger identification, threat identification, remote sensing, and fully autonomous aerial vehicles among other systems. AI/ML is concerned with algorithms and techniques that allow systems to “learn” and “reason” based on algorithms and techniques employing computational and statistical methods. It can significantly enhance speed, efficiency, workload, and safety to enable the integrating of more complex technologies, such as autonomous visionbased navigation and data ecosystems. Recently advanced data analytics provided the aviation industry a way to respond to COVID and advise airlines on when to swap aircraft for bigger or smaller planes and how the global health restrictions may change flight schedules. While there are many other innovative use cases of AI/ML in aviation and aerospace, the overarching conclusion is that the implementation must be driven by safety.