The Hawaiian Islands have borne the brunt of numerous accidental and in~ tentional introductions of both plants and animals. Some have proved valuable, others undesirable. The mongoose was a deliberate importation, but the merit of this carnivore is a subject of controversy because opinions differ concerning its effectiveness as a destroyer of rodents, and much prejudice exists toward it because of its predation on domestic and wild birds.The need for detailed information on the mongoose in Hawaii arises partly from its considerable economic importance and also from the lack of any comprehensive account of the behavior of this mammal in Hawaii, in the West Indies where it was also introduced, or in its native Asiatic range. In Hawaiian and West Indian literature, the mongoose has been the subject of frequent comment concerning the role it plays as a rodent catcher. Field observations, food analyses, feeding experiments, and ecological data of limited scope have been reported, but much more extensive information than is now available must be gathered before an overall judgment of the practical value of the mongoose can be made. Of theoretical interest, the mongoose in Hawaii exemplifies the integration of a small carnivore into an insular fauna which had lacked animals of this type. La Rivers' (1948) work is the first attempt to describe the ecological role of the mongoose in a natural Hawaiian plant-animal community.The purpose of this study is to assemble information on the distribution, behavior, ecology, and human values of the mongoose in Hawaii. This report may be regarded as an outline suggesting fields for further exploration of the status of the mongoose in these islands. P. H. Baldwin obtained occasional records of the mongoose between 1938 and 1946 "and collected mongooses from September 1948 to September 1949 on the Island of Hawaii for a study of the reproductive cyle of this animal with Dr. Oliver P. Pearson. The latter project received financial aid from the Committee on the Joseph Henry Fund of the National Academy of Sciences. Special thanks are due to Dr. and Mrs. Chester K. Wentworth for facilities made available for field work on the island of Hawaii. C. W. Schwartz and E. R. Schwartz observed the mongoose during a survey of the game birds on t.he major Hawaiian Islands from February 1946 through July 1947 as a Federal Aid-Wildlife Program of the Board of Commissioners of Agriculture and Forestry, Territory of Hawaii.Mongooses were frequently noticed because of their diurnal habits; however, their tendency to retire to cover made extended observation difficult. Scats were collected and could easily be distinguished among those of the few mammalian species in Hawaii. The animals were trapped with number "0" steel traps baited with cooked meat (hotel scraps) suspended over the traps; results were best when traps were partially concealed with leaves. Feral cats frequently interfered with trap sets, and rats were caught in considerable numbers. A more
Various models have been developed over the past several decades to predict the dynamic modulus /E*/ of hot-mix asphalt (HMA) based on regression analysis of laboratory measurements. The models most widely used in the asphalt community today are the Witczak 1999 and 2006 predictive models. Although the overall predictive accuracies for these existing models as reported by their developers are quite high, the models generally tend to overemphasize the influence of temperature and understate the influence of other mixture characteristics. Model accuracy also tends to fall off at the low and high temperature extremes. Recently, researchers at Iowa State Univ. have developed a novel approach for predicting HMA /E*/ using an artificial neural network (ANN) methodology. This paper discusses the accuracy and robustness of the various predictive models (Witczak 1999 and2006 and ANN-based models) for estimating the HMA /E*/ inputs needed for the new mechanistic-empirical pavement design guide. The ANN-based /E*/ models using the same input variables exhibit significantly better overall prediction accuracy, better local accuracy at high and low temperature extremes, less prediction bias, and better balance between temperature and mixture influences than do their regression-based counterparts. As a consequence, the ANN models as a group are better able to rank mixtures in the same order as measured /E*/ for fixed (e.g., project-specific) environmental and design traffic conditions. The ANN models as a group also produced the best agreement between predicted rutting and alligator cracking computed using predicted versus measured /E*/ values for a typical pavement scenario. Abstract: Various models have been developed over the past several decades to predict the dynamic modulus |E*| of Hot-Mix Asphalt (HMA) based on regression analysis of laboratory measurements. The models most widely used in the asphalt community today are the Witczak (1999 and 2006) predictive models. Although the overall predictive accuracies for these existing models as reported by their developers are quite high, the models generally tend to overemphasize the influence of temperature and understate the influence of other mixture characteristics. Model accuracy also tends to fall off at the low and high temperature extremes.Recently, researchers at Iowa State University (ISU) have developed a novel approach for predicting HMA |E*| using an Artificial Neural Network (ANN) methodology. This paper discusses the accuracy and robustness of the various predictive models (Witczak 1999 and2006, and ANN-based models) for estimating the HMA |E*| inputs needed for the new MechanisticEmpirical Pavement Design Guide (MEPDG). The ANN-based |E*| models using the same input variables exhibit significantly better overall prediction accuracy, better local accuracy at high and low temperature extremes, less prediction bias, and better balance between temperature and mixture influences than do their regression-based counterparts. As a consequence, the ANN models a...
This report documents and presents the results of a study to evaluate the sensitivity of pavement performance predicted by the Mechanistic-Empirical Pavement Design Guide to the values of the design inputs. Global sensitivity analyses were performed for five pavement types under five climate conditions and three traffic levels. Design inputs evaluated in the analyses included traffic volume, layer thicknesses, material properties (e.g., stiffness, strength, HMA and PCC mixture characteristics, subgrade type), groundwater depth, geometric parameters (e.g., lane width), and others. Detailed traffic inputs were not considered. Depending on the base case, approximately 25 to 35 design inputs were evaluated in the analyses. Correlations among design inputs (e.g., between PCC elastic modulus and modulus of rupture) were considered where appropriate. A normalized sensitivity index defined as the percentage change of predicted distress relative to its design limit caused by a given percentage change in the design input. The analyses found that, for all pavement types and distresses, the sensitivities of the design inputs for the bound surface layers were consistently the highest. Additional findings are also reported for each specific pavement type. Disciplines Civil and Environmental Engineering DISCLAIMERThis is an uncorrected draft as submitted by the research agency. The opinions and conclusions expressed or implied in the report are those of the research agency. They are not necessarily those of the Transportation Research Board, the National Academies, or the program sponsors.i Correlations among design inputs (e.g., between PCC elastic modulus and modulus of rupture) were considered where appropriate. A normalized sensitivity index defined as the percentage change of predicted distress relative to its design limit caused by a given percentage change in the design input. The analyses found that, for all pavement types and distresses, the sensitivities of the design inputs for the bound surface layers were consistently the highest. Additional findings are also reported for each specific pavement type. LIST OF TABLES 1 EXECUTIVE SUMMARYThe Mechanistic-Empirical Pavement Design Guide Manual of Practice (AASHTO, 2008) and related MEPDG software provide a new, more theoretically grounded methodology for the analysis and performance prediction of different types of flexible and rigid pavements. The cracking, rutting, faulting, smoothness, and other distresses predicted by the MEPDG for the anticipated climatic and traffic conditions will depend on the values of the input parameters that characterize the pavement materials, layers, design features, and condition. Knowledge of the sensitivity of predicted performance to the design input values can help identify, for specific climatic region and traffic conditions, the inputs that most influence predicted performance. This will help pavement designers determine where additional effort is justified in developing higher quality and/or more certain input values.Th...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.