Letter to Gerald Tomanek from LTC Andrew K. Kuschner

A letter to Gerald Tomanek from LTC Andrew K. Kuschner regarding the establishment of an extension agreement between Wichita State University and FHSU.https://scholars.fhsu.edu/military_science_rotc_docs/1011/thumbnail.jp

Letter to John D. Garwood

A letter from LTC Andrew K. Kuschner to Dr. John D. Garwood regarding course outlines for the military science courses.https://scholars.fhsu.edu/military_science_rotc_docs/1010/thumbnail.jp

Class Syllabus - MS113 United States Defense Establishment

A syllabus for MS113 course taught at Wichita State Universityhttps://scholars.fhsu.edu/military_science_rotc_docs/1014/thumbnail.jp

A Bayesian network approach to feature selection in mass spectrometry data

One of the key goals of current cancer research is the identification of biologic molecules that allow non-invasive detection of existing cancers or cancer precursors. One way to begin this process of biomarker discovery is by using time-of-flight mass spectroscopy to identify proteins or other molecules in tissue or serum that correlate to certain cancers. However, there are many difficulties associated with the output of such experiments. The distribution of protein abundances in a population is unknown, the mass spectroscopy measurements have high variability, and high correlations between variables cause problems with popular methods of data mining. to mitigate these issues, Bayesian inductive methods, combined with non-model dependent information theory scoring, are used to find feature sets and build classifiers for mass spectroscopy data from blood serum Such methods show improvement over existing measures, and naturally incorporate measurement uncertainties. Resulting Bayesian network models are applied to three blood serum data sets: one artificially generated, one from a 2004 leukemia study, and another from a 2007 prostate cancer study. Feature sets obtained appear to show sufficient stability under cross-validation to provide not only biomarker candidates but also families of features for further biochemical analysis

A Model of Carrying Capacity and Ecosystem Impacts in a Large-Scale, Bivalve-Dominated Agro-Ecosystem: Hard Clam Aquaculture in Cherrystone Inlet, VA

With the recent growth of the hard clam aquaculture industry, sites of intensive aquaculture have emerged as large-scale agro-ecosystems where the success of aquaculture production is dynamically linked to ecosystem function. Large scale clam aquaculture operations are associated with a range of potential positive and negative feedbacks related to nutrient dynamics, water and sediment quality, proliferation of macroalgae, and carrying capacity. Quantitative modeling tools are needed to support system-level planning related to site selection, scale of operations, production capacity and ecosystem function. The purpose of this study was to develop a model for Cherrystone Inlet, VA, where one-third (1.9 km2) of the sub-tidal bottom area is held as 37 separate, private shellfish leases with an estimated 100-150 million cultured clams. A reduced complexity estuarine ecosystem model was coupled with a hard clam energetics and growth model and a watershed loading model. The linked models facilitate ecosystem-based management and enable regional spatial planning in a full ecosystem context, through coupled simulations of aquaculture activities, land use changes, nutrient loading, climate change, and estuarine response. Modeled output for hard clam growth and water column chlorophyll-o, dissolved oxygen, and dissolved inorganic nitrogen and phosphorous reproduced in situ data. Simulations with increasing clam numbers up to 500 million resulted in diminishing returns in terms of reduced growth rates, increased time to harvestable size, and reduced harvestable biomass, confirming observations by Cherrystone farmers of reduced clam growth rates above 200 million cultured clams. Modeled hard clam production capacity decreased in the absence of benthic microalgal resuspension (6%) and without the input of external production from the Chesapeake Bay (41%), and increased in simulations with increased water column chlorophyll-o (11%) and the removal of predator exclusion nets (13%). Simulations to optimize siting indicated that the highest hard clam growth rates occurred up-estuary. Model simulations with changes in land use and climate indicated that clam growth is most sensitive to increasing temperature, with rates decreasing by 37% when temperatures were increased by 5°C, while changes in land use, sea level rise and salinity did not result in large changes in hard clam production. At the system scale hard clam aquaculture was predicted to account for 14% of total nitrogen inputs to the water column between sediment recycling of clam feces (13%) and direct clam excretion (1%). The Cherrystone ecosystem model fills a critical gap on the Eastern Shore of Virginia and in similar coastal systems, providing resource managers with the most current available science in a decision-support framework to promote effective regional spatial planning and sustainability of hard clam operations and the surrounding coastal ecosystems

On Permutation Selectors and their Applications in Ad-Hoc Radio Networks Protocols

Selective families of sets, or selectors, are combinatorial tools used to "isolate" individual members of sets from some set family. Given a set $X$ and an element $x\in X$, to isolate $x$ from $X$, at least one of the sets in the selector must intersect $X$ on exactly $x$. We study (k,N)-permutation selectors which have the property that they can isolate each element of each $k$-element subset of $\{0,1,...,N-1\}$ in each possible order. These selectors can be used in protocols for ad-hoc radio networks to more efficiently disseminate information along multiple hops. In 2004, Gasieniec, Radzik and Xin gave a construction of a (k,N)-permutation selector of size $O(k^2\log^3 N)$. This paper improves this by providing a probabilistic construction of a (k,N)-permutation selector of size $O(k^2\log N)$. Remarkably, this matches the asymptotic bound for standard strong (k,N)-selectors, that isolate each element of each set of size $k$, but with no restriction on the order. We then show that the use of our (k,N)-permutation selector improves the best running time for gossiping in ad-hoc radio networks by a poly-logarithmic factor.Comment: 9 pages, 2 figure

Human pulmonary responses to experimental inhalation of high concentration fine and ultrafine magnesium oxide particles.

Exposure to air polluted with particles less than 2.5 micron in size is associated epidemiologically with adverse cardiopulmonary health consequences in humans. The goal of this study was to characterize human pulmonary responses to controlled experimental high-dose exposure to fine and ultrafine magnesium oxide particles. We quantified bronchoalveolar lavage (BAL) cell and cytokine concentrations, pulmonary function, and peripheral blood neutrophil concentrations in six healthy volunteers 18 to 20 hr after inhalation of fine and ultrafine magnesium oxide particles produced from a furnace system model. We compared postexposure studies with control studies from the same six subjects. Mean +/- standard deviation (SD) cumulative magnesium dose was 4,138 +/- 2,163 min x mg/m3. By weight, 28% of fume particles were ultrafine (<0.1 micron in diameter) and over 98% of fume particles were fine (<2.5 micron in diameter). There were no significant differences in BAL inflammatory cell concentrations, BAL interleukin (IL)-1, IL-6, IL-8, tumor necrosis factor, pulmonary function, or peripheral blood neutrophil concentrations postexposure compared with control. Our findings suggest that high-dose fine and ultrafine magnesium oxide particle exposure does not produce a measurable pulmonary inflammatory response. These findings are in marked contrast with the well-described pulmonary inflammatory response following zinc oxide particle inhalation. We conclude that fine and ultrafine particle inhalation does not result in toxicity in a generic manner independent of particle composition. Our findings support the concept that particle chemical composition, in addition to particle size, is an important determinant of respiratory effects