Biologically Significant Illinois Streams: An Evaluation of the Streams of Illinois based on Aquatic Biodiversity: Part 1

Part 1: Text. See Reference ID-1365 for Part 2: AtlasReport issued on: December 31, 1991INHS Technical Report prepared for Illinois Dept. of Conservation, Illinois Dept. of Energy and Natural Resource

Biological Assessments of Six Selected Fishes, Amphibians, and Mussels in Illinois

ID: 8758; issued November 1, 1996INHS Technical Report prepared for Illinois Department of Natural Resources, Division of Natural Heritag

The feasibility of a programmed heat shield for solar cell performance control

Feasibility of programmed heat shield for temperature and power control for spacecraft on-board powe

White Dwarf Cosmochronology in the Solar Neighborhood

The study of the stellar formation history in the solar neighborhood is a powerful technique to recover information about the early stages and evolution of the Milky Way. We present a new method which consists of directly probing the formation history from the nearby stellar remnants. We rely on the volume complete sample of white dwarfs within 20 pc, where accurate cooling ages and masses have been determined. The well characterized initial-final mass relation is employed in order to recover the initial masses (1 < M/Msun < 8) and total ages for the local degenerate sample. We correct for moderate biases that are necessary to transform our results to a global stellar formation rate, which can be compared to similar studies based on the properties of main-sequence stars in the solar neighborhood. Our method provides precise formation rates for all ages except in very recent times, and the results suggest an enhanced formation rate for the solar neighborhood in the last 5 Gyr compared to the range 5 < Age (Gyr) < 10. Furthermore, the observed total age of ~10 Gyr for the oldest white dwarfs in the local sample is consistent with the early seminal studies that have determined the age of the Galactic disk from stellar remnants. The main shortcoming of our study is the small size of the local white dwarf sample. However, the presented technique can be applied to larger samples in the future.Comment: 25 pages, 10 figures, accepted for publication in the Astrophysical Journa

Analytical model of non-Markovian decoherence in donor-based charge quantum bits

We develop an analytical model for describing the dynamics of a donor-based charge quantum bit (qubit). As a result, the quantum decoherence of the qubit is analytically obtained and shown to reveal non-Markovian features: The decoherence rate varies with time and even attains negative values, generating a non-exponential decay of the electronic coherence and a later recoherence. The resulting coherence time is inversely proportional to the temperature, thus leading to low decoherence below a material dependent characteristic temperature.Comment: 19 pages, 3 figure

Decision Support Design for Workload Mitigation in Human Supervisory Control of Multiple Unmanned Aerial Vehicles

As UAVs become increasingly autonomous, the multiple personnel currently required to operate a single UAV may eventually be superseded by a single operator concurrently managing multiple UAVs. Instead of lower-level tasks performed by today’s UAV teams, the sole operator would focus on high-level supervisory control tasks such as monitoring mission timelines and reacting to emergent mission events. A key challenge in the design of such single-operator systems will be the need to minimize periods of excessive workload that could arise when critical tasks for several UAVs occur simultaneously. To a certain degree, it is possible to predict and mitigate such periods in advance. However, actions that mitigate a particular period of high workload in the short term may create long term episodes of high workload that were previously non-existent. Thus some kind of decision support is needed that facilitates an operator’s ability to evaluate different options for managing a mission schedule in real-time. This paper describes two decision support visualizations designed for supervisory control of four UAVs performing a time-critical targeting mission. A configural display common to both visualizations, named the StarVis, was designed to highlight potential periods of high workload corresponding to the current mission timeline, as well as “what if” projections of possible high workload periods based upon different operator options. The first visualization design allows an operator to compare different high workload mitigation options for individual UAVs. This is termed the local visualization. The second visualization is indicates the combined effects of multiple high workload mitigation decisions on the timeline. This is termed the global visualization. The main advantage of the local visualization is that options can be compared directly; however, the possible effects of these options on the mission timeline are only indicated for the individual UAV primarily affected by the decision. For the global visualization, different decisions can be combined to show possible effects on the system propagated across all UAVs, but the different alternatives of a single decision option alternative cannot be directly compared. An experiment was conducted testing these visualizations against a control with no visualization. Results showed that subject using the local visualization had better performance, higher situational awareness, and no significant increase in workload over the other two experimental conditions. This occurred despite the fact that the local and global StarVis displays were very similar. Not only did the Global StarVis produce degraded results as compared to the local StarVis, but those participants with no visualization performed as well as those with the global StarVis. This disparity in performance despite strong visual similarities in the StarVis designs is attributed to operators’ inability to process all the information presented in the global StarVis as well as the fact that participants with the local StarVis were able to rapidly develop effective cognitive problem strategies. This research effort highlights a very important design consideration, in that a single decision support design can produce very different performance results when applied at different levels of abstraction.Prepared for Kevin Burns, The MITRE Corporatio

An Experimental Platform for Investigating Decision and Collaboration Technologies in Time-Sensitive Mission Control Operations

This report describes the conceptual design and detailed architecture of an experimental platform developed to support investigations of novel decision and collaboration technologies for complex, time-critical mission control operations, such as military command and control and emergency response. In particular, the experimental platform is designed to enable exploration of novel interface and interaction mechanisms to support both human-human collaboration and human-machine collaboration for mission control operations involving teams of human operators engaged in supervisory control of intelligent systems, such as unmanned aerial vehicles (UAVs). Further, the experimental platform is designed to enable both co-located and distributed collaboration among operations team members, as well as between team members and relevant mission stakeholders. To enable initial investigations of new information visualization, data fusion, and data sharing methods, the experimental platform provides a synthetic task environment for a representative collaborative time-critical mission control task scenario. This task scenario involves a UAV operations team engaged in intelligence, surveillance, and reconnaissance (ISR) activities. In the experimental task scenario, the UAV team consists of one mission commander and three operators controlling multiple, homogeneous, semi-autonomous UAVs. In order to complete its assigned missions, the UAV team must coordinate with a ground convoy, an external strike team, and a local joint surveillance and target attack radar system (JSTARS). This report details this task scenario, including the possible simulation events that can occur and the logic governing the simulation dynamics. In order to perform human-in-the-loop experimentation within the synthetic task environment, the experimental platform also consists of a physical laboratory designed to emulate a miniature command center. The Command Center Laboratory comprises a number of large-screen displays, multi-screen operator stations, and mobile, tablet-style devices. This report details the physical configuration and hardware components of this Command Center Laboratory. Details are also provided of the software architecture used to implement the synthetic task environment and experimental interface technologies to facilitate user experiments in this laboratory. The report also summarizes the process of conducting an experiment in the experimental platform, including details of scenario design, hardware and software instrumentation, and participant training. Finally, the report suggests several improvements that could be made to the experimental platform based on insights gained from initial user experiments that have been conducted in this environment.Prepared For Boeing, Phantom Work

Cognitive Task Analysis for the LCS Operator

In support of Plan Understanding for Mixed-initiative control of Autonomous systems (PUMA)The following Tables and Figures detail the cognitive task analysis (CTA) performed to determine the information requirements needed to support a single operator located aboard the futuristic Littoral Combat Ship (LCS). This operator is responsible for controlling four underwater unmanned vehicles in conjunction with a UAV operating on a shared network. • Table 1 is a scenario task overview that breaks the overall mission into 3 phases (planning, execution, and recovery) and then details the subtasks for each of the 3 mission phases. • Figure 1 is an event flow diagram that demonstrates what events must occur in a temporal order for each of the 3 phases. There are three basic event types in Figure 1: 1) a loop (L) that represents a process that occurs in a looping fashion until some predetermined event occurs, 2) a decision (D) that represents some decision that is required from the LCS operator, and 3) a process (P) which requires some human-computer interaction to support the required tasks. In each event block, an alphanumeric code is included which labels that particular event type (L#, D#, P#). This label is important because later information requirements will be mapped to one of these events. • Table 2, which details the situation awareness (SA) requirements for the LCS Operator for each of the 3 mission phases and associated subtasks. Each of these SA requirements is mapped directly to one or more events in Figure 1. Because the decisions in Figure 1 represent critical events that require detailed understanding of what information and knowledge is needed to support the operator’s decision-making process, decision ladders were constructed for the diamonds and loops in Figure 1 that correspond to an involved decision process to resolve the question being posed at that stage in the event flow (Figures 2-4). Decision ladders are modeling tools that capture the states of knowledge and information-processing activities necessary to reach a decision. Decision ladders can help identify the information that either the automation and/or the human will need to perform or monitor a task. Decision Ladders, illustrate the need not only for the same information identified by the cognitive task analysis, but the need for several other pieces of information such as the need for visual or aural alerts in contingency situations. In Figures 2-4, three versions are included that detail (a) the basic decision ladder, (b) the decision ladder with corresponding display requirements, and (c) the decision ladder with possible levels of automation. • Figure 2 represents the automated target recognition (ATR) decision ladder (D3 from Event Flow): (a) decision ladder, (b) decision ladder with corresponding display requirements, and (c) decision ladder with possible levels of automation. • Figure 3 shows the decision ladder information and knowledge requirements for the sentry handoff (L3 from Event Flow). • Figure 4, the UUV Recovery Decision Ladder (D7 from Event Flow), illustrates what information is nominally needed. Since this phase was not a major focus, the decision ladder is not as detailed as it could be. This should be a point of focus in Phase II. Lastly Figure 5 demonstrates the coordination loop that must occur in the case where a handoff failure occurs (for a number of reasons to include equipment failure, communication delays, etc.) Again, because the multi-player coordination issues are not a primary focus in Phase I but are a significant consideration for any follow-on phases.Prepared for Charles River Analytic