Doping Dependence of Thermal Oxidation on n-type 4H-SiC

The doping dependence of dry thermal oxidation rates in n-type 4H-SiC was investigated. The oxidation was performed in the temperature range 1000C to 1200C for samples with nitrogen doping in the range of 6.5e15/cm3 to 9.3e18/cm3, showing a clear doping dependence. Samples with higher doping concentrations displayed higher oxidation rates. The results were interpreted using a modified Deal-Grove model. Linear and parabolic rate constants and activation energies were extracted. Increasing nitrogen led to an increase in linear rate constant pre-exponential factor from 10-6m/s to 10-2m/s and the parabolic rate constant pre-exponential factor from 10e9m2/s to 10e6m2/s. The increase in linear rate constant was attributed to defects from doping-induced lattice mismatch, which tend to be more reactive than bulk crystal regions. The increase in the diffusion-limited parabolic rate constant was attributed to degradation in oxide quality originating from the doping-induced lattice mismatch. This degradation was confirmed by the observation of a decrease in optical density of the grown oxide films from 1.4 to 1.24. The linear activation energy varied from 1.6eV to 2.8eV, while the parabolic activation energy varied from 2.7eV to 3.3eV, increasing with doping concentration. These increased activation energies were attributed to higher nitrogen content, leading to an increase in effective bond energy stemming from the difference in C-Si (2.82eV) and Si-N (4.26eV) binding energies. This work provides crucial information in the engineering of SiO2 dielectrics for SiC MOS structures, which typically involve regions of very different doping concentrations, and suggests that thermal oxidation at high doping concentrations in SiC may be defect mediated.Comment: 13 pages. 9 figures, accepted as a transiction in IEEE electron device. TED MS#8035

Collective traffic-like movement of ants on a trail: dynamical phases and phase transitions

The traffic-like collective movement of ants on a trail can be described by a stochastic cellular automaton model. We have earlier investigated its unusual flow-density relation by using various mean field approximations and computer simulations. In this paper, we study the model following an alternative approach based on the analogy with the zero range process, which is one of the few known exactly solvable stochastic dynamical models. We show that our theory can quantitatively account for the unusual non-monotonic dependence of the average speed of the ants on their density for finite lattices with periodic boundary conditions. Moreover, we argue that the model exhibits a continuous phase transition at the critial density only in a limiting case. Furthermore, we investigate the phase diagram of the model by replacing the periodic boundary conditions by open boundary conditions.Comment: 8 pages, 6 figure

Institutions and entrepreneurship quality

Entrepreneurship contributes importantly to the economy. However, differences in the quality and quantity of entrepreneurship vary significantly across developing and developed countries. We use a sample of 70 countries over the period of 2005–2015 to examine how formal and informal institutional dimensions (availability of debt and venture capital, regulatory business environment, entrepreneurial cognition and human capital, corruption, government size, government support) affect the quality and quantity of entrepreneurship between developed and developing countries. Our results demonstrate that institutions are important for both the quality and quantity of entrepreneurship. However, not all institutions play a similar role; rather, there is a dynamic relationship between institutions and economic development

On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International Conference on Evaluation and Assessment in Software Engineering, 201

Attitude and Motivation for Learning English and their Impact on Performance: A Study on Engineering Students of Jessore University of Science and Technology

Learners\u27 cognitive, metacognitive, individual differences and demographic characteristics have been found having profound impact on their linguistic performance. This study has tried to observe two such factors namely motivation and attitude of the learners and their impact on the learners\u27 proficiency. An adapted version of AMTB and a TEEP test have been used to statistically measure the level of motivation and attitude of the learners for learning English and the correlation between these two learner factors and their language performance. The study has found that learners\u27 overall motivation level is average though instrumental motivation outscores integrative motivation and they have a mixed attitude towards learning English. Neither motivation nor attitude is significantly correlated with learners\u27 proficiency

Linkage between Corporate Governance and Financial Performance in an Emerging Economy

The objective of this study is to explore the Linkage between Corporate Governance and Financial Performance in an Emerging Economy in the banking sector of Bangladesh. The data have been taken from primary sources. Data were collected from 22 listed banks on the Dhaka Stock Exchange (DSE). This data were analyzed by using different statistical tools like structural equation model (SEM) with the help of SmartPLS-3 software. It is found that some important factors like enablers that improve corporate governance, obstacles that affect corporate governance those are the influential factors to build performance of selected bank. This study further supports the argument that when bank implement good corporate governance principles, it experiences improved financial performance. This study, with its emphasis on developing a corporate governance model, makes a significant contribution to the body of knowledge on corporate governance in emerging economies like Bangladesh

Calibrated quantum thermometry in cavity optomechanics

Cavity optomechanics has achieved the major breakthrough of the preparation and observation of macroscopic mechanical oscillators in peculiarly quantum states. The development of reliable indicators of the oscillator properties in these conditions is important also for applications to quantum technologies. We compare two procedures to infer the oscillator occupation number, minimizing the necessity of system calibrations. The former starts from homodyne spectra, the latter is based on the measurement of the motional sidebands asymmetry in heterodyne spectra. Moreover, we describe and discuss a method to control the cavity detuning, that is a crucial parameter for the accuracy of the latter, intrinsically superior procedure

Optimal Location of Energy Storage Systems with Robust Optimization

The integration of intermittent sources of energy and responsive loads in distribution system make the traditional deterministic optimization-based optimal power flow no longer suitable for finding the optimal control strategy for the power system operation. This paper presents a tool for energy storage planning in the distribution network based on AC OPF algorithm that uses a convex relaxation for the power flow equations to guarantee exact and optimal solutions with high algorithmic performances and exploits robust optimization approach to deal with the uncertainties related to renewables and demand. The proposed methodology is applied for storage planning on a distribution network that is representative of a class of networks