Geologic mapping of the Urvara and Yalode Quadrangles of Ceres

We conducted geologic mapping of the Urvara (Ac-13) and Yalode (Ac-14) Quadrangles (21–66°S, 180–360°E) of the dwarf planet Ceres utilizing morphologic, topographic, and compositional information acquired by NASA's Dawn mission. The geologic characteristics of the two large impact basins Urvara (170 km diameter) and Yalode (260 km diameter) and their surroundings were investigated using Dawn Framing Camera datasets, including Survey (415 m/pixel), HAMO (140 m/pixel), and LAMO (35 m/pixel) images and mosaics, color and color ratio images, and DTMs derived from stereo-photogrammetry. Geologic mapping demonstrates that impact cratering has dominated the geologic history of the Urvara and Yalode Quadrangles, with early cratered terrain formation followed by formation of the large basins and widespread emplacement of basin-related smooth material. Impact craters display a wide range of preservation states from nearly completely buried/degraded forms to more recent pristine craters with terraced inner walls and lobate ejecta deposits. Cross-cutting relationships and morphologic signatures show that the Urvara impact followed the Yalode impact, consistent with ages derived from crater size-frequency distributions (580 ± 40 Ma for Yalode and 550 ± 50 Ma for Urvara). Observed differences in basin materials and rim morphology suggest heterogeneities in the substrate excavated by impact. Smooth deposits that cover large areas of the quadrangles, including the basin floors, rims, and exterior zones, are interpreted to be dominated by Urvara ejecta but Yalode ejecta and localized ice-rich flow material may be minor components. Geologic mapping results and simulations of ejecta emplacement suggest that Urvara and Yalode ejecta deposits extend for large distances (more than two crater diameters from the basin centers) and may serve as important stratigraphic markers for the geologic record of Ceres

Application of constrained optimization methods in health services research: Report 2 of the ISPOR Optimization Methods Emerging Good Practices Task Force

Background Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity. Objectives In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available. Conclusions Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force’s first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods

The HAMO-Based Global Geologic Map of Ceres

The HAMO-Based Global Geologic Map of Cere

Fault Populations on Alba Mons, Mars, and their Age Relationships to Volcanic, Fluvial, and Glacial Processes

We map the cross-cutting relationships between faults, valley networks, and (volcanic) flows on Alba Mons to infer the temporal sequences of the major processes that influenced the area. Preliminary mapping on the northwestern flank suggests that lava flows predate the valley networks, and the faults postdate both the flows and the fluvial valleys. We also mapped individual fault segments for future quantitative fault population analysis