Dependence of sea ice yield-curve shape on ice thickness

In this note, the authors discuss the contribution that frictional sliding of ice floes (or floe aggregates) past each other and pressure ridging make to the plastic yield curve of sea ice. Using results from a previous study that explicitly modeled the amount of sliding and ridging that occurs for a given global strain rate, it is noted that the relative contribution of sliding and ridging to ice stress depends upon ice thickness. The implication is that the shape and size of the plastic yield curve is dependent upon ice thickness. The yield-curve shape dependence is in addition to plastic hardening/weakening that relates the size of the yield curve to ice thickness. In most sea ice dynamics models the yield-curve shape is taken to be independent of ice thickness. The authors show that the change of the yield curve due to a change in the ice thickness can be taken into account by a weighted sum of two thickness-independent rheologies describing ridging and sliding effects separately. It would be straightforward to implement the thickness-dependent yield-curve shape described here into sea ice models used for global or regional ice prediction

Initial Assessment of Canopy Fuel Factors in Forested Areas of the Netherlands

The changing climate in the Netherlands has created the need to better understand how the forested communities and their structures will be affected by fire and to better estimate the impact of those fires. This study is part of a continuing collaboration between the Stephen F. Austin State University’s Arthur Temple College of Forestry and Agriculture and the Instittut Fysieke Veiligheid to assess fuel loads in the Netherlands to develop and improve models to estimate fire behavior. Data to estimate canopy bulk density for modelling canopy fire behavior were collected in the Netherlands for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), Scots pine (Pinus sylvestris L.), and black pine (Pinus nigra Arn.) to be used in fire models estimating canopy fire behavior in that country. Leaf area index, gap fraction, and canopy bulk density were estimated using hemispherical photography, LI-COR LAI 2200c Plant Canopy Analyzer, spherical densiometer and allometeric methods. The crown allometry data collected was species, density (trees per ha), condition (live vs. dead), diameter breast height (DBH), height, and height to live crown base and canopy cover. Using SAS 9.3, statistical analyses were run employing one-way and two-way ANOVAs, Proc GLM, and a post-hoc Tukey test. Analysis of gap fraction found Scots pine and black pine were similar, but both were significantly different that Douglas-Fir. The gap fraction tests also found that between methods, data collected by the LI-COR and densiometer were similar but were both significantly different than the hemispherical photography. The analysis of leaf area index found no significant difference between species but there was a significant difference between the leaf area indexes calculated by the two methods. There was no significant difference in the canopy bulk density between species. The LAI values from the hemiphoto and LI-COR were compared to those of other studies. Previous black pine and Douglas-fir LAIs were more similar to the hemiphoto than the LI-COR, but Scots pine was more similar to the LAI from the LI-COR. There was a large range in the LAI depending on the stand density at each site. Continued research over a larger area should be pursued to increase the amount of data the Dutch fire spread models will have to use in the estimation of wild fire behavior. These results could be used to create a strong correlation between gap fraction and canopy bulk density for multiple sites, as well as one for leaf area index and canopy bulk density. Future destructive sampling would allow confirmation of the estimated LAI and CBD for the species

Screening Mammography Among Women With A Family History Of Breast Cancer

Screening mammography among women with a family history of breast cancer Lauren Hibler, Anees Chagpar Women with a first degree family history of breast cancer are at increased risk of developing this disease. Screening with mammography has been demonstrated to detect cancer early, when it is most treatable. We hypothesized that, due to their increased risk, women with a first degree family history of breast cancer would be more likely to obtain screening mammograms than the general population. We further sought to determine the self-reported reasons for failing to adhere to screening guidelines in this high risk population. The National Health Interview Survey (NHIS), conducted annually by the Centers for Disease Control, is designed to be representative of the US population. The 2010 NHIS Cancer Supplement was used to evaluate the rates of mammography, predictors of screening, and the most common reasons cited for not having a mammogram, in the previous two years in women with and without a first degree family history of breast cancer. Overall, 78.5% of women reported obtaining a screening mammogram in the previous two years. Women with a first degree family history had similar rates of screening mammography as average risk women. (79.5% vs 78.4% p=0.452). On multivariate analyses, income to poverty line ratio, and access to preventive health care services were independently associated with screening mammography in both average and high risk cohorts. The reasons women overall cited for not obtaining a screening mammogram (irrespective of risk cohort) were: Never thought about it, Doctor didn\u27t say I needed it, and Too expensive, or Didn\u27t have insurance. These data demonstrate that high risk populations are not more likely to adhere to screening guidelines for breast cancer than their average risk counterparts, and that the main factors influencing adherence were income, insurance, and access to preventive health services. These findings suggest that social determinants of health affect screening, and addressing key issues such as access and cost of healthcare is critical to improving rates of screening mammography, especially in high risk groups

Nutrition Education in Clark County, Nevada

Over 51 million school hours are missed annually by school-aged children due to a dental problem or visit, with 117 hours missed per 100 children (NIDCR, 2002). Approximately half a million of California\u27s 7.2 million school-age children missed at least one day of school in 2007 because of dental issues such as toothaches (Pourat, & Nicholson, 2009). Independent research regarding various educational and intervention programs have been conducted in elementary schools across the country that focus on nutrition and/or physical activity, some of which have found that the programs positively impacted students’ dietary behaviors (Edwards, Mauch, & Winkelman, 2011; Muth, Chatterjee, Williams, Cross, & Flower, 2008; Tuuri et al., 2009). However, there does not appear to be literature regarding the standardization of nutrition and oral health education curricula for elementary school children. Nevertheless, there are school districts that have well structured health education curricula that cover nutrition and oral health education (California Department of Education, 2009; Central Michigan University, 2005). All of these reports are in agreement with Dietz, et al (2008) which reported that nutrition and oral hygiene education should be an integral part of keeping growing children healthy and forming good nutrition habits that will carry into adulthood

Development of an electronic governor system for a small spark ignition engine

The use of electronic controls on engines has been widespread and has now reached the small utility engine. The evolution from carburetion to electronic fuel injection has presented opportunity to implement additional controls with the intent to improve engine control and performance. In this study, an electronic governor system is developed for use on a small twin cylinder utility engine. This system is to replace an existing mechanical governor system, and it should have the potential as a production based solution that integrates with an existing fuel injection system. It must improve the speed droop characteristics over the current mechanical system and should not carry a large cost increase to the engine --Abstract, page iii

Coyote Movement Patterns with Emphasis on Home Range Characteristics

Standard radio telemetry techniques via triangulation were used to determine coyote locations and to construct maps of coyote movement patterns in Curlew Valley (Utah and Idaho). Home range sizes were determined by establishing corresponding boundaries drawn on the basis of location, density and relative number of visitations (contour method), and then tracing the boundaries with a compensating polar planimeter. Mean home range sizes were determined directly for only those animals with home range values that reached an asymptote when plotted against corresponding time periods. This asymptotic value was considered the best estimate of the actual home range size. Four adult females and one adult male were in this category, with mean values of 18.3 km2 and 14.5 km2 respectively . The asymptote was estimated for home ranges which did not stabilize (mean values of 20.2 and 17.8 km for adult females and males respectively) using a home range estimator; validation of the estimator is discussed. Since no juvenile animal \u27s home range appeared to reach an asymptote, no asymptotic estimates were made . Various methods of constructing home range boundaries, their advantages and disadvantages, are listed. Standardization in the home range concept is necessary if meaningful comparisons are to be made between studies. Home ranges are dynamic, and must be considered in terms of specific time frames. Guidelines for adequate description of the home range are discussed with emphasis on quantity of data, time requirements and recognition of seasonal shifts in the home range. Map analysis suggested three general patterns with regard to home ranges; namely, animals with contiguous home range areas, those with disjunct home ranges, and wandering individuals. Fifty percent of the coyotes were trapped more than 0.5 km outside home range boundaries while an additional 42 percent were trapped on the periphery of the home range. Only 8 percent, all juveniles, were trapped within their home range. None of the 21 animals killed by hunters or trappers died well within their respective home ranges. Twenty-nine percent were killed on the periphery of their home ranges and 71 percent were killed an average of 11 km outside their home range ~n··~rl~Ties. Several movement patterns other than home range were discernible, including brief excursions away from the home range (sallies), dispersals, and total area utilized. Mean dispersal distances for adul t males, juvenile males and females respectively were 56, 9 and 54 km; no adult female was known to disperse. Juvenile females had the greatest tendency to disperse with 53 percent involved; juvenile males, 33 percent and adult males, 30 percent. Sallies were analyzed according to distance, duration and frequency, with adult females having the longest (4.9 km) and the greatest number of sallies (7.9 per month), and adult males spending the most time per sally (16.2 hours) as well as time per month (72.9 hours) in sally activity. The total area utilized by coyotes is discussed in terms of size and measurement. Mean values for total areas utilized are 138 km2 for adult females, 90 km 2 for adult males, 68 km2 for juvenile males and 46 km 2 for juvenile females. Home range configuration is discussed in terms of importance and variability in form, with the majority of shapes being ameboid in character. Linearity may be a function of the method used to establish home range boundaries and use of baseline data from fixed radio telemetry stations

Improving the spatial distribution of modeled Arctic sea ice thickness

The spatial distribution of ice thickness/draft in the Arctic Ocean is examined using a sea ice model. A comparison of model predictions with submarine observations of sea ice draft made during cruises between 1987 and 1997 reveals that the model has the same deficiencies found in previous studies, namely ice that is too thick in the Beaufort Sea and too thin near the North Pole. We find that increasing the large scale shear strength of the sea ice leads to substantial improvements in the model's spatial distribution of sea ice thickness, and simultaneously improves the agreement between modeled and ERS-derived 1993–2001 mean winter ice thickness

Robust Gene Expression Programming

AbstractGenetic/evolutionary methods are frequently used to deal with complex adaptive systems. The classic example is a Genetic Algorithm. A Genetic Algorithm uses a simple linear representation for possible solutions to a problem. This is usually a bit vector. Unfortunately, the natural representation for many problems is a tree structure. In order to deal with these types of problems many evolutionary methods make use of tree structures directly. Gene Expression Programming is a new, popular evolutionary technique that deals with these types of problems by using a linear representation for trees. In this paper we present and evaluate Robust Gene Expression Programming (RGEP). This technique is a simplification of Gene Expression Programming that is equally efficient and powerful. The underlying representation of a solution to a problem in RGEP is a bit vector as in Genetic Algorithms. It has fewer and simpler operators than those of Gene Expression Programming. We describe the basic technique, discuss its advantages over related methods, and evaluate its effectiveness on example problems

The Genetic Flock Algorithm

AbstractThe purpose of this paper is to describe and evaluate a new algorithm for optimization. The new algorithm is named the Genetic Flock Algorithm. This algorithm is a type of hybrid of a Genetic Algorithm and a Particle Swarm Optimization Algorithm. The paper discusses strengths and weaknesses of these two algorithms. It then explains how the Genetic Flock Algorithm combines features of both and gives details of the algorithm. All three algorithms are compared using eight standard optimization problems that are used in the literature. It is shown that the Genetic Flock Algorithm provides superior performance on 75% of the tested cases. In the remaining 25% of the cases it outperforms either the Genetic Algorithm or the Particle Swarm Optimization Algorithm; it is never worse than both. Possible future improvements to the Genetic Flock Algorithm are briefly described