Thermodynamics and Kinetics of Defects at Surfaces

Fundamental understanding of the various electronic and structural properties at surfaces is a prerequisite for improved control of nanometer-scale patterning of surfaces for potential technological applications. In this dissertation, we have used multi-scale theoretical approaches to investigate the thermodynamic and kinetic properties of a few elemental types of surface defects. The multi-scale approaches range from first-principles calculations within density functional theory to empirical embedded atom method (EAM) to statistical analysis to kinetic Monte Carlo simulations. In studying the thermodynamic properties of intrinsic line defects on a vicinal TaC(910) surface, our Monte Carlo simulations in comparison with scanning tuning microscope (STM) images have established the existence of long-range attractive interaction between the steps. For extrinsic point defects underneath a GaAs surface, we have established through our theoretical analysis in comparison with STM observations that many-body effects in a system with purely repulsive interactions can give rise to an effective attractive interaction between the dopants at high dopant densities. In the study of the morphological evolution of monatomiclayer- high islands grown on metal surfaces, we have carried out Kinetic Monte Carlo simulations to demonstrate the importance of the island corner barriers. Our study has shown that if the island corner barrier effect is operational in preventing adatoms v located at an island edge to reach a neighboring edge defining the island corner, the islands thus formed must be non-compact, and develop fractal or dendritic shapes. Based on our EAM calculations of the diffusion barriers for various atomic processes and rate equation analysis, we have explained why fractal islands have rarely been observed on metal fcc(100) surfaces. For ideal surfaces, we have investigated the various driving forces for lattice relaxation based on first-principles calculations, and have proposed a new approach that has the promise to predict the direction of relaxation of the atoms in the surface layer strictly based on bulk properties of the given system. Finally, our fist-principles based interpretation of STM images within the framework of the Tersoff-Hamann theory has resulted in good agreement with STM experiments in revealing the anisotropy of electron density corrugations on several open metallic surfaces

Technological and Community Factors that Influence Online Trust and Knowledge Sharing ——A Model Based on Virtual Community

Knowledge sharing is common place online nowadays. Drawing from trust literature, we find trust mechanism behind knowledge sharing behavior may lead to useful implications. However, there exists a gap between theoretical and managerial perspective on the role of trust especially in online knowledge transfer. This study will try to study how technological and community factors influence trust formation and lead to knowledge sharing behaviors in online virtual communities. By exploring how extrinsic drivers affect trust elements, we combine practical technology and community design issues with theoretical trust foundations. Empirical research is under way to confirm our hypotheses

Who Teaches in Rural Schools in Underdeveloped Areas? An Investigation Based on a Survey of 5,554 Teachers from 117 Towns in H Province in Wuling Mountains Zone, China

Teacher shortage is a major hindrance to China’s rural education growth in underdeveloped areas, as well as one of the main causes of educational injustice. We conducted a survey of 5,554 teachers from 117 towns in H province in the Wuling Mountains Zone to investigate the issue of rural school teacher supply. From geographical, emotional, and institutional perspectives, we used a polynomial logit model to examine the validity of the “hometown effects” hypothesis. The findings showed that hometown effects exist in China in all three dimensions. The institutional hometown effects are the most pronounced; when compared to open recruitment, teachers sourced through teacher supply augmentation programs (such as the Secondary Normal Graduates Program, Special Position Program, and Targeted Position Program) are more likely to teach in rural schools, particularly more disadvantaged village primary schools or teaching sites. China’s policy of increasing teacher supply has had a considerable positive influence on rural school staffing. Students from rural areas make better teacher candidates; feelings for hometowns should be encouraged among normal school or university students in pre-service education; and the implementation of teacher support policies should be emphasized to retain rural teachers and improve their teaching quality

Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study

Benefiting from a relatively larger aperture's angle, and in combination with a wide transmitting bandwidth, near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises, hindering the information retrieval of the target. To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc. Thus, they achieve limited restoration performance in terms of the target's shape, especially for complex targets. To address these issues, a preliminary study is conducted on restoration with the recent promising deep learning inverse technique in this work. We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered. Adhering to it, a model-based deep learning network is designed to restore the image. A simulated degraded image dataset from multiple complex target models is constructed to validate the network. All the images are formulated using the electromagnetic simulation tool. Experiments on the dataset reveal their effectiveness. Compared with current methods, superior performance is achieved regarding the target's shape and energy estimation