Sea Ice Kinematics and Thickness from RGPS: Observations and Theory

The RADARSAT Geophysical Processor System (RGPS) has produced a wealth of data on Arctic sea ice motion, deformation, and thickness with broad geographical coverage and good temporal resolution. These data provide unprecedented spatial detail of the structure and evolution of the sea ice cover. The broad purpose of this study was to take advantage of the strengths of the RGPS data set to investigate sea ice kinematics and thickness, which affect the climate through their influence on ice production, ridging, and transport (i.e. mass balance); heat flux to the atmosphere; and structure of the upper ocean mixed layer. The objectives of this study were to: (1) Explain the relationship between the discontinuous motion of the ice cover and the large-scale, smooth wind field that drives the ice; (2) Characterize the sea ice deformation in the Arctic at different temporal and spatial scales, and compare it with deformation predicted by a state-of-theart ice/ocean model; and (3) Compare RGPS-derived sea ice thickness with other data, and investigate the thinning of the Arctic sea ice cover as seen in ULS data obtained by U.S. Navy submarines. We briefly review the results of our work below, separated into the topics of sea ice deformation and sea ice thickness. This is followed by a list of publications, meetings and presentations, and other activities supported under this grant. We are attaching to this report copies of all the listed publications. Finally, we would like to point out our community service to NASA through our involvement with the ASF User Working Group and the RGPS Science Working Group, as evidenced in the list of meetings and presentations below

A Highway-Driving System Design Viewpoint using an Agent-based Modeling of an Affordance-based Finite State Automata

This paper presents an agent-based modeling framework for affordance-based driving behaviors during the exit maneuver of driver agents in human-integrated transportation problems. We start our discussion from one novel modeling framework based on the concept of affordance called the Affordance-based Finite State Automata (AFSA) model, which incorporates the human perception of resource availability and action capability. Then, the agent-based simulation illustrates the validity of the AFSA framework for the Highway-Lane-Driver System. Next, the comparative study between real driving data and agent-based simulation outputs is provided using the transition diagram. Finally, we perform a statistical analysis and a correlation study to analyze affordance-based driving behavior of driver agents. The simulation results show that the AFSA model well represents the perception-based human actions and drivers??? characteristics, which are essential for the design viewpoint of control framework of human driver modeling. This study is also expected to benefit a designed control for autonomous/self-driving car in the future

Molecular Determinants and Genetic Modifiers of Aggregation and Toxicity for the ALS Disease Protein FUS/TLS

A combination of yeast genetics and protein biochemistry define how the fused in sarcoma (FUS) protein might contribute to Lou Gehrig's disease

Performance measures and outcome analyses of dynamic decision making in real-time supervisory control

Ph.D.Alexander C. Kirli

Modeling skilled decision-making using artificial neural network and genetic-based machine learning techniques

M.S.Alexander C. Kirli

Performance assessment in an interactive call center workforce simulation

In this paper a new performance assessment methodology for human-in-the-loop call center systems at the level of customer-agent interactions (CAI) is proposed We develop a team-in-the-loop simulation test bed to analyze CAI-level performance of a service system using a temporal performance measure with time windows The proposed framework should allow researchers to collect and analyze individual as well as team performance at a finer granularity than current call center efforts which focus on queue-centered analysis The software framework is object-oriented and has been designed to be configurable A sample simulation study in different scenarios is illustrated to provide the usages and advantages of the proposed method with index of Interactive Service Performance.close

Using the Lens Model Framework to Characterize Compensatory and Noncompensatory Decision Making Strategies Abstract

This paper presents an effort to use the Lens Model Framework, as originally conceived by Egon Brunswik to characterize a functional relationship between a decision maker and his environment which is mediated through the lens of probabilistic variables in the environment. In particular, the authors are interested in using the Lens Model to explore the hypothesis that human judgments oscillate between compensatory and noncompensatory decision making strategies. We submit that a continuum can be constructed to characterize this oscillation. Our claim for the existence of a continuum is motivated by psychological research which suggests that humans tend to adjust their decision strategies to cope with the change of information demands and time available. We propose a framework within which to investigate strategy shifts. A cornerstone of our framework is a rule-based formulation of the lens model called the noncompensatory lens model (NLM) to investigate decision strategies under time-stressed and information-rich task environments. In addition to the NLM, we also discuss the development and expansion of a noncompensatory policy capturing algorithm to complement the traditional multiple-linear regression-based implementation of the Lens Model. We conclude the paper with a discussion of a planned empirical investigation to validate our framework. We submit that, while the models discussed in this paper do not directly concern human-computer interaction (HCI), the outcomes of the research program can have broad implications. By providing HCI practitioners with means to determine the salient perceptual cues for decision making as well as to find whether a decision maker is consistent with a compensatory versus noncompensatory mode of judgment, the development of adaptive interfaces which provide the correct information at the appropriate time may be within reach.

AN AFFORDANCE-BASED FORMALISM FOR MODELING HUMAN-INVOLVEMENT IN COMPLEX SYSTEMS FOR PROSPECTIVE CONTROL

We propose a predictive modeling framework for human-involved complex systems in which humans play controlling roles. Affordance theory provides definitions of human actions and their associated properties, and the affordance-based Finite State Automata (FSA) model is capable of mapping the nondeterministic human actions into computable components in modeling formalism. In this paper, we further investigate the role of perception in human actions and examine the representation of perceptual elements in affordance-based modeling formalism. We also propose necessary and sufficient conditions for mapping perception-based human actions into systems theory to develop a predictive modeling formalism in the context of prospective control. A driving example is used to show how to build a formal model of human-involved complex system for prospective control. The suggested modeling frameworks will increase the soundness and completeness of a modeling formalism as well as can be used as guide to model human activities in a complex system