42 research outputs found
Recommended from our members
Experimental validation of Monte Carlo calculations for organ dose
The problem of validating estimates of absorbed dose due to photon energy deposition is examined. The computational approaches used for the estimation of the photon energy deposition is examined. The limited data for validation of these approaches is discussed and suggestions made as to how better validation information might be obtained. (ACR
The Oomph in Economic Philosophy: A Bibliometric Analysis of the Main Trends, from the 1960s to the Present
Entre défiance et amitié... Des relations politiques, diplomatiques et militaires tourmentées entre le roi de France et le prince-évêque de Liège au bas Moyen Âge (XIIIe-XVe siècles)
audience: researcher, professional, studen
Continuing education in structural biology for science teachers
The present paper sought to identify what perception teachers from Natural Science fields have on the use of instructional strategies that make use of models to represent biomolecules. The data presented are related to two continuing education courses\ud
carried out with teachers from public schools of the state of São Paulo (Brazil). Such data showed that the teachers approved the use of instructional materials such as the ones suggested in the courses (e.g., construction of a 3-D biomolecular structure) and\ud
they pointed out some advantages and obstacles to the use of such materials.\ud
© 2010 Elsevier Ltd. All rights reserved
Multi-fingered haptic palpation utilizing granular jamming stiffness feedback actuators
This paper describes a multi-fingered haptic palpation method using stiffness feedback actuators for simulating tissue palpation procedures in traditional and in robot-assisted minimally invasive surgery. Soft tissue stiffness is simulated by changing the stiffness property of the actuator during palpation. For the first time, granular jamming and pneumatic air actuation are combined to realize stiffness modulation. The stiffness feedback actuator is validated by stiffness measurements in indentation tests and through stiffness discrimination based on a user study. According to the indentation test results, the introduction of a pneumatic chamber to granular jamming can amplify the stiffness variation range and reduce hysteresis of the actuator. The advantage of multi-fingered palpation using the proposed actuators is proven by the comparison of the results of the stiffness discrimination performance using two-fingered (sensitivity: 82.2%, specificity: 88.9%, positive predicative value: 80.0%, accuracy: 85.4%, time: 4.84 s) and single-fingered (sensitivity: 76.4%, specificity: 85.7%, positive predicative value: 75.3%, accuracy: 81.8%, time: 7.48 s) stiffness feedback
Summary of the radiological assessment of the fuel cycle for a thorium-uranium carbide-fueled fast breeder reactor
A large fraction of the potential fuel for nuclear power reactors employing fissionable materials exists as ores of thorium. In addition, certain characteristics of a fuel system based on breeding of the fissionable isotope {sup 233}U from thorium offer the possibility of a greater resistance to the diversion of fissionable material for the fabrication of nuclear weapons. This report consolidates into a single source the principal content of two previous reports which assess the radiological environmental impact of mining and milling of thorium ore and of the reprocessing and refabrication of spent FBR thorium-uranium carbide fuel
The ‘Coase Theorem’ vs. Coase Theorem Proper: How an Error Emerged and Why it Remained Uncorrected so Long
Machine learning for estimation of building energy consumption and performance:a review
Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance