Numerical Modelling for Process Investigation of a Single Coal Particle Combustion and Gasification

Combustion and Gasification are commercial processes of coal utilization, and therefore continuous improvement is needed for these applications. The difference between these processes is the reaction mechanism, in the case of combustion the reaction products are CO2 and H2O, whereas in the case of gasification the products are CO, H2 and CH4. In order to investigate these processes further, a single coal particle model has been developed. The definition of the chemical reactions for each process is key for model development. The developed numerical model simulation uses CFD (Computational Fluid Dynamic) techniques with an Eddy Break Up (EBU) model and a kinetics parameter for controlling the process reaction. The combustion model has been validated and extended to model the gasification process by inclusion of an additional chemical reaction. Finally, it is shown that the single coal particle model could describe single coal particle combustion and gasification. From the result, the difference between single coal particle combustion and gasification can clearly be seen. This simulation model can be considered for further investigation of coal combustion and gasification application processes

Growing Attributed Networks through Local Processes

This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of rich structural properties found in real-world networks. We make three contributions. First, we propose a parsimonious and accurate model of attributed network growth that jointly explains the emergence of in-degree distributions, local clustering, clustering-degree relationship and attribute mixing patterns. Second, our model is based on biased random walks and uses local processes to form edges without recourse to global network information. Third, we account for multiple sociological phenomena: bounded rationality, structural constraints, triadic closure, attribute homophily, and preferential attachment. Our experiments indicate that the proposed Attributed Random Walk (ARW) model accurately preserves network structure and attribute mixing patterns of six real-world networks; it improves upon the performance of eight state-of-the-art models by a statistically significant margin of 2.5-10x.Comment: 11 pages, 13 figure

A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework

Institutions, Inequality and Growth: A review of theory and evidence on the institutional determinants of growth and inequality

The difference in the development experiences between the most developed countries and the least developed countries of today is vast. Luxembourg’s per capita income is 200 times larger than Liberia’s. Even within the developing world, growth is very unequal. East Asia and parts of Latin America are growing at impressive rates, while many other countries - especially in Sub-Saharan Africa - struggle with sluggish and volatile growth. This study discusses the theoretical challenge posed in identifying the mechanisms that link institutions and equitable economic growth at various levels of aggregation. The relationship between governance modes and institutions on the one hand, and economic growth and development on the other hand, may take very different forms. This relates to the question of whether a single and unique combination of institutions and governance modes is optimal for (equitable) growth, or whether different governance modes and institutions may lead to good or equitable growth performance in different locations and historical contexts.development administration; growth policy; institutional framework;

Social networks: evolving graphs with memory dependent edges

The plethora, and mass take up, of digital communication tech- nologies has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the ex- istence or otherwise of certain infinite products and series involving age dependent model parameters. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here

Information is not a Virus, and Other Consequences of Human Cognitive Limits

The many decisions people make about what to pay attention to online shape the spread of information in online social networks. Due to the constraints of available time and cognitive resources, the ease of discovery strongly impacts how people allocate their attention to social media content. As a consequence, the position of information in an individual's social feed, as well as explicit social signals about its popularity, determine whether it will be seen, and the likelihood that it will be shared with followers. Accounting for these cognitive limits simplifies mechanics of information diffusion in online social networks and explains puzzling empirical observations: (i) information generally fails to spread in social media and (ii) highly connected people are less likely to re-share information. Studies of information diffusion on different social media platforms reviewed here suggest that the interplay between human cognitive limits and network structure differentiates the spread of information from other social contagions, such as the spread of a virus through a population.Comment: accepted for publication in Future Interne

The transmission of foreign financial crises to South Africa: a firm-level study

The process of financial integration has increased the exposure of South African financial markets to foreign financial crises. This paper contributes to the understanding of crisis transmission by evaluating several hypotheses that claim to explain how financial crises are transmitted to South African financial markets. The study proceeds from a firm-level perspective, which it argues overcomes the potential loss of information when using aggregate economic data. Consequently, the different transmission hypotheses are evaluated for the East Asian, Russian and Argentinean crises using firm-level daily stock return data from the JSE Securities Exchange. A multivariate regression model, supplemented by sensitivity tests, forms the core of the empirical methodology.financial contagion; crisis; South Africa; financial linkages