Background. Dermatology residency programs are relatively diverse in their resident selection process. The authors investigated the importance of 25 dermatology residency selection criteria focusing on differences in program directors' (PDs') perception based on specific program demographics. Methods. This cross-sectional nationwide observational survey utilized a 41-item questionnaire that was developed by literature search, brainstorming sessions, and online expert reviews. The data were analyzed utilizing the reliability test, two-step clustering, and K-means methods as well as other methods. The main purpose of this study was to investigate the differences in PDs' perception regarding the importance of the selection criteria based on program demographics. Results. Ninety-five out of 114 PDs (83.3%) responded to the survey. The top five criteria for dermatology residency selection were interview, letters of recommendation, United States Medical Licensing Examination Step I scores, medical school transcripts, and clinical rotations. The following criteria were preferentially ranked based on different program characteristics: “advanced degrees,” “interest in academics,” “reputation of undergraduate and medical school,” “prior unsuccessful attempts to match,” and “number of publications.” Conclusions. Our survey provides up-to-date factual data on dermatology PDs' perception in this regard. Dermatology residency programs may find the reported data useful in further optimizing their residency selection process.
The objective of this study was to investigate the influence of aggregate characteristics and gradation on the skid resistance of various asphalt mixtures. Asphalt mixture slabs with different combinations of aggregate sources and gradations were fabricated in the laboratory. These slabs were polished with a wheel-polishing device developed by the National Center for Asphalt Technology. The frictional characteristics of each slab were then measured by the sand patch method, British pendulum, dynamic friction tester, and circular texture meter. Aggregates used in these mixtures were characterized by petrographic analysis, conventional test methods (acid insolubility, magnesium soundness, Micro-Deval, and British polish value), and the aggregate imaging system (AIMS). In addition, the aggregate gradation of each mixture was described by the two-parameter cumulative Weibull distribution function. Statistical analysis of test results led to the development of a function for predicting the International Friction Index, which is a measure of skid resistance of asphalt mixtures, after different intervals of polishing. The parameters of this function were found to be related to ( a) initial and terminal aggregate texture measured by using AIMS, ( b) rate of change in aggregate texture measured by using AIMS after different polishing intervals in the Micro-Deval, and ( c) the Weibull distribution parameters describing aggregate gradation. This function can be useful for estimating the frictional characteristics of an asphalt mixture surface during the mixture design stage.
ABSTRACT:Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.
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