Requirements, Issues, and Challenges for Sense and Avoid in Unmanned Aircraft Systems

The sense and avoid capability is one of the greatest challenges that has to be addressed to safely integrate unmanned aircraft systems into civil and nonsegregated airspace. This paper gives a review of existing regulations, recommended practices, and standards in sense and avoid for unmanned aircraft systems. Gaps and issues are identified, as are the different factors that are likely to affect actual sense and avoid requirements. It is found that the operational environment (flight altitude, meteorological conditions, and class of airspace) plays an important role when determining the type of flying hazards that the unmanned aircraft system might encounter. In addition, the automation level and the data-link architecture of the unmanned aircraft system are key factors that will definitely determine the sense and avoid system requirements. Tactical unmanned aircraft, performing similar missions to general aviation, are found to be the most challenging systems from an sense and avoid point of view, and further research and development efforts are still needed before their seamless integration into nonsegregated airspace

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income.