Modeling and simulation of bulk gallium nitride power semiconductor devices

Bulk gallium nitride (GaN) power semiconductor devices are gaining significant interest in recent years, creating the need for technology computer aided design (TCAD) simulation to accurately model and optimize these devices. This paper comprehensively reviews and compares different GaN physical models and model parameters in the literature, and discusses the appropriate selection of these models and parameters for TCAD simulation. 2-D drift-diffusion semi-classical simulation is carried out for 2.6 kV and 3.7 kV bulk GaN vertical PN diodes. The simulated forward current-voltage and reverse breakdown characteristics are in good agreement with the measurement data even over a wide temperature range.

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.

A Proposed Framework for Simulation Based Learning of Inheritance

Different types of serious games have been used in elucidating computer science areas such as computer games, mobile games, Lego-based games, virtual worlds and webbased games. Different evaluation techniques have been conducted like questionnaires, interviews, discussions and tests. Simulation have been widely used in computer science as a motivational and interactive learning tool. This paper aims to evaluate the possibility of successful implementation of simulation in computer programming modules. A framework is proposed to measure the impact of serious games on enhancing students understanding of key computer science concepts. Experiments will be held on the EEECS of Queen’s University Belfast students to test the framework and attain results.

Oil Market Modelling: A Comparative Analysis of Fundamental and Latent Factor Approaches

We formally compare fundamental factor and latent factor approaches to oil price modelling. Fundamental modelling has a long history in seeking to understand oil price movements, while latent factor modelling has a more recent and limited history, but has gained popularity in other financial markets. The two approaches, though competing, have not formally been compared as to effectiveness. For a range of short- medium- and long-dated WTI oil futures we test a recently proposed five-factor fundamental model and a Principal Component Analysis latent factor model. Our findings demonstrate that there is no discernible difference between the two techniques in a dynamic setting. We conclude that this infers some advantages in adopting the latent factor approach due to the difficulty in determining a well specified fundamental model.

Quantifying Aflatoxin B1 in peanut oil using fabricating fluorescence probes based on upconversion nanoparticles

Rare earth doped upconversion nanoparticles convert near-infrared excitation light into visible emission light. Compared to organic fluorophores and semiconducting nanoparticles, upconversion nanoparticles (UCNPs) offer high photochemical stability, sharp emission bandwidths, and large anti-Stokes shifts. Along with the significant light penetration depth and the absence of autofluorescence in biological samples under infrared excitation, these UCNPs have attracted more and more attention on toxin detection and biological labelling. Herein, the fluorescence probe based on UCNPs was developed for quantifying Aflatoxin B₁ (AFB₁) in peanut oil. Based on a specific immunity format, the detection limit for AFB₁ under optimal conditions was obtained as low as 0.2 ng·ml^{- 1}, and in the effective detection range 0.2 to 100 ng·ml^{- 1}, good relationship between fluorescence intensity and AFB₁ concentration was achieved under the linear ratios up to 0.90. Moreover, to check the feasibility of these probes on AFB₁ measurements in peanut oil, recovery tests have been carried out. A good accuracy rating (93.8%) was obtained in this study. Results showed that the nanoparticles can be successfully applied for sensing AFB₁ in peanut oil.