The Experts below are selected from a list of 240 Experts worldwide ranked by ideXlab platform

Nelson J. Chao - One of the best experts on this subject based on the ideXlab platform.

  • Accidental or intentional exposure to ionizing radiation: Biodosimetry and treatment options
    Experimental Hematology, 2007
    Co-Authors: Nelson J. Chao
    Abstract:

    The potential risk of accidental and especially intentional radiation exposure in the form of a terrorist attack is growing. The dangers are potentially devastating. There is an urgent need for building a greater infrastructure for teaching and research in this area. Medical contingency planning and preparedness is also essential. Such planning should include an examination of our current resources, projected medical needs, management guidelines, and personnel training. Exposure to whole-body irradiation can induce acute radiation syndrome, with the Resultant Damage to hematopoiesis and immune suppression. In addition, acute toxicity to the skin, gut, and central nervous system are also prevalent. Complex injuries such as burns, multi-organ injury, and trauma will increase the morbidity and mortality from acute radiation syndrome. Our ability to understand and rapidly obtain data on the absorbed dose and have access to radiation mitigators are of critical importance if we are to have a beneficial impact in the exposed population.

Hiroshi Takagi - One of the best experts on this subject based on the ideXlab platform.

Richard Curran - One of the best experts on this subject based on the ideXlab platform.

  • Deducing the physical characteristics of an impactor from the Resultant Damage on aircraft structures
    International Journal of Solids and Structures, 2020
    Co-Authors: Philippe F.r. Massart, V.s. Viswanath Dhanisetty, Christos Kassapoglou, Wim J. C. Verhagen, Richard Curran
    Abstract:

    Abstract This paper proposes an analytical model that uses historical Damage dimension data to deduce physical impactor characteristics (size and energy) that has caused a certain resulting Damage. Maintenance tasks occur in operations due to impact, however the source of the Damage caused in the event remains in most cases unknown. Consequently, by inferring what has caused a certain type of Damage from the distribution of the Damage type and severity relative to impactor types, maintainers can be better prepared in terms of what to expect from a given impactor source. The developed model introduces a novel transition deformation region between the local deformation and the global plate deflection, allowing for fast and accurate predictions of the impact event. Using the known aluminium structural properties and Damage dimensions, the Damage data is converted into impactor data. The model is applied in a case study using 120 fuselage dent Damages dimensions (length, width, and depth) from a Boeing 777 fleet. The results show that the model deduces impactor characteristics for 94% of the considered Damages, ranging up to 240 J and 110 mm for impactor energy and radius respectively.

J. E. Dennis - One of the best experts on this subject based on the ideXlab platform.

  • Coefficient of Friction Patterns Can Identify Damage in Native and Engineered Cartilage Subjected to Frictional-Shear Stress
    Annals of Biomedical Engineering, 2015
    Co-Authors: G. A. Whitney, J. M. Mansour, J. E. Dennis
    Abstract:

    The mechanical loading environment encountered by articular cartilage in situ makes frictional-shear testing an invaluable technique for assessing engineered cartilage. Despite the important information that is gained from this testing, it remains under-utilized, especially for determining Damage behavior. Currently, extensive visual inspection is required to assess Damage; this is cumbersome and subjective. Tools to simplify, automate, and remove subjectivity from the analysis may increase the accessibility and usefulness of frictional-shear testing as an evaluation method. The objective of this study was to determine if the friction signal could be used to detect Damage that occurred during the testing. This study proceeded in two phases: first, a simplified model of biphasic lubrication that does not require knowledge of interstitial fluid pressure was developed. In the second phase, frictional-shear tests were performed on 74 cartilage samples, and the simplified model was used to extract characteristic features from the friction signals. Using support vector machine classifiers, the extracted features were able to detect Damage with a median accuracy of approximately 90%. The accuracy remained high even in samples with minimal Damage. In conclusion, the friction signal acquired during frictional-shear testing can be used to detect Resultant Damage to a high level of accuracy.

Jun Sasaki - One of the best experts on this subject based on the ideXlab platform.