The Chesapeake Bay is the largest estuary in the United States, where its watershed is home to more than 3,600 species of plants and animals and more than 16.6 million people. However one of its major issues is water pollution. Good water quality is vital for the health of all these plants, animals and people. In order to act upon this problem and restore the water, there is the need to monitor the water quality. There are currently several organizations and agencies monitoring different parts of the bay and working to restore the bay.[1]This project analyzes innovative ways to improve water quality monitoring in the West and Rhode rivers. The water in these rivers is currently monitored by a River Keeper. There is the need for an improved system with higher frequency of data input, higher accuracy of higher quality sensors, and a wider range of parameters being monitored.The motivation behind this project is to develop a transfer function between water quality and source of pollution. An improved model will allow the river keeper to have a better understanding of the conditions of the water and track the sources of pollution. With this new system, he will be able to act upon this acquired data and help to restore these rivers and subsequently the Chesapeake Bay.This design evaluated various sensor alternatives, transmission technologies, and used GIS mapping software in order to implement an automated water monitoring system for the West and Rhode rivers. A notional utility curve between available sensors and transmission techniques was developed where preliminary results indicate a system will fit the river keepers' needs and desired goals

Team collaboration and decision making have a significant role in the overall performance of complex system of systems (SoS). Improved systems engineering (SE) processes and tools are needed during the systems development lifecycle to make decisions and rapidly evaluate and assess multiple design alternatives to effectively select architectural design strategies that result in the highest mission performance. Therefore, the early and consistent evaluation of these strategies and collaborative team decisions are essential. The degree of utility of alternative decision strategy designs may vary given the condition, but conditions are not always considered when these decisions are being made. This article applies the SE principles of design for change and flexibility in an agent-based model for simulation of design alternatives for systems analysis and decision making in evaluating and selecting the decision(s) for SoS comprised of collaborative teams that result in higher mission performance. The authors apply the proposed agent-based model to test the flight delay within the SoS structure of the air transportation domain. The flight delays are due to the management strategies initiated by the air traffic facilities to balance the air demand and capacity when conditions are not normal (such as severe weather). The experimental results suggest that:(1) the current strategies may result in unnecessary delays and underdelivery of flights to the airport during the hours where demand is well below capacity; (2) aggressive strategies may have the reverse effect; and (3) longer-duration strategies have a more significant impact on delays during severe weather conditions. K E Y W O R D Sagent-based simulation, system design and evaluation, system of systems 20

The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

No abstract

INTRODUCTION: In-flight medical emergencies (IFMEs) average 1 of every 604 flights and are expected to increase as the population ages and air travel increases. Flight diversions, or the rerouting of a flight to an alternate destination, occur in 2 to 13% of IFME cases, but may or may not be necessary as determined after the fact. Estimating the effect of IFME diversions compared to nonmedical diversions can be expected to improve our understanding of their impact and allow for more appropriate decision making during IFMEs.METHODS: The current study matched multiple disparate datasets, including medical data, flight plan and track data, passenger statistics, and financial data. Chi-squared analysis and independent samples t-tests compared diversion delays and costs metrics between flights diverted for medical vs. nonmedical reasons. Data were restricted to domestic flights between 1/1/2018 and 6/30/2019.RESULTS: Over 70% of diverted flights recover (continue on to their intended destination after diverting); however, flights diverted due to IFMEs recover more often and more quickly than do flights diverted for nonmedical reasons. IFME diversions introduce less delay overall and cost less in terms of direct operating costs and passenger value of time (averaging around 38,000) than do flights diverted for nonmedical reasons.DISCUSSION: Flights diverted due to IFMEs appear to have less impact overall than do flights diverted for nonmedical reasons. However, the lack of information related to costs for nonrecovered flights and the decision factors involved during nonmedical diversions hinders our ability to offer further insights.Lewis BA, Gawron VJ, Esmaeilzadeh E, Mayer RH, Moreno-Hines F, Nerwich N, Alves PM. Data-driven estimation of the impact of diversions due to in-flight medical emergencies on flight delay and aircraft operating costs. Aerosp Med Hum Perform. 2021; 92(2):99105.