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In offshore engineering, the phenomenon of free span often occurs, and the pipeline may have multiple free spans adjacent to each other, forming a multi-span pipeline. The interaction of different spans makes the structural vibration characteristics more complex, which may change the fatigue characteristics of the pipeline and affect the safety of the structure. In this paper, model tests were designed to explore the vortex-induced vibration (VIV) characteristics of multi-span pipelines and investigate the multi-span interaction mechanism. The experimental study mainly focused on the dynamic response of double-span pipelines, and further extended to triple-span pipelines, hoping that the results can be applied to more complex environment. The effects of span-length ratio, buried depth and axial force on VIV of the pipeline were investigated and discussed. The dynamic response of the pipeline varied with the span length. There was obvious interaction between different spans of multi-span pipelines, and the pipe-sediment interaction obviously affected the vibration characteristics of pipeline. The differences of pipeline burial depth and axial force changed the structural stiffness. With the increase of buried depth, the response amplitude presented a downward trend. The spanwise evolutions were asymmetric caused by the pipe-sediment interaction and multi-span interaction. The results can help to identify multi-span pipelines in engineering, and realize the prevention and control of free spans.

Background The COVID-19 pandemic has increased the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases. These opportunities create a “double-edge sword,” as the ethical governance of such approaches often lags behind technological achievements. Objective The aim was to investigate ethical issues identified from utilizing artificial intelligence–augmented surveillance or early warning systems to monitor and detect common or novel infectious disease outbreaks. Methods In a number of databases, we searched relevant articles that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems, and/or big data analytics technology for detecting, monitoring, or tracing infectious diseases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and further identified and analyzed them with a theoretical framework. Results This systematic review identified 29 articles presented in 6 major themes clustered under individual, organizational, and societal levels, including awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. While these measures were understandable during a pandemic, the public had concerns about receiving inadequate information; unclear governance frameworks; and lack of privacy protection, data integrity, and autonomy when utilizing infectious disease digital surveillance. The barriers to engagement could widen existing health care disparities or digital divides by underrepresenting vulnerable and at-risk populations, and patients’ highly sensitive data, such as their movements and contacts, could be exposed to outside sources, impinging significantly upon basic human and civil rights. Conclusions Our findings inform ethical considerations for service delivery models for medical practitioners and policymakers involved in the use of digital surveillance for infectious disease spread, and provide a basis for a global governance structure. Trial Registration PROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180