Essays on China\u27s Cigarette Industry

This dissertation comprises three chapters on the Chinese cigarettes industry. The China State Tobacco Monopoly Administration (STMA) regulates this industry, allocating quotas of production across manufacturers. Between 2006 and 2007, it mandated all cigarette firms within a province merge into a single state-owned, province-level firm. After the merger, the province-level firms allocate their quotas for the maximum number of cartons they can produce directly. In the first chapter, I examine how the mandated change in market structure resulting from the STMA affected allocation on the quality dimension. To assess the pre-merger differences in market structure in quota allocation, I compare the changes in cigarette quality in provinces that initially had only one firm, hence whose market structure did not change, with those that initially had multiple firms. I construct a theoretical model for the monopoly market and the duopoly market. The model predicts that when there is regional competition, the proportion of high-quality cigarettes is lower than in a monopoly market. I use an event study method and a triple-differences model to identify the changes in the quality composition at the province-level before and after the merger by comparing two types of reorganization. I find that the consolidation mandated by the merger is associated with increases in product quality. I use the incentives of managers in monopoly and oligopoly markets to explain the shift in quality choices of firms in the provinces affected by the STMA mandate. My second paper presents the analysis of the effect of the mandated merger on inventory. The Chinese cigarette industry provides an excellent opportunity to study a market with the characteristics of inflexible prices and uncertain demand. In this paper, I provide a theoretical model to take into account the demand uncertainty and different market structures to predict how the mandated consolidation as an exogenous shock affects the inventory. Based on the theoretical model, if there are competitors in the region, which is a duopoly market, managers choose non-cooperative strategies by producing more high-quality cigarettes to steal their competitors\u27 high-segment markets for higher profit margins, which leads to higher inventories. My empirical analysis confirms these effects of high-quality cigarettes and medium-quality cigarettes. My third chapter presents the welfare analysis of the effect of horizontal mergers. Based on the theoretical model in Chapter One, after the reform, the consumers who can buy the cigarettes with desired characteristics increases. The consumer\u27s welfare increases as a result of the consolidation. On the other hand, producer welfare increases because of the lower dollar value of the inventory

Enhanced first-order methods for convex and nonconvex optimization

First-order methods for convex and nonconvex optimization have been an important research topic in the past few years. This talk studies and develops efficient algorithms of first-order type, to solve a variety of problems. We first focus on the widely studied gradient-based methods in composite convex optimization problems that arise extensively in compressed sensing and machine learning. In particular, we discuss an accelerated first-order scheme and its variants, which enjoy the “optimal” convergence rate for the gradient methods in terms of complexity, and their practical behavior.In the second part of the talk, we present alternating direction type of methods solving structured nonlinear nonconvex problems. The problem we are interested in has special structure which allows convenient 2-block variable splitting. Our methods rely on solving convex subproblem and the limit point obtained can be guaranteed to satisfy KKT conditions. Our approach includes the alternating directions method of multipliers (ADMM) and the alternating linearization method (ALM) and we provide convergence rate results for both classes of methods. Moreover, global optimization techniques from polynomial optimization literature are applied to complement our local methods and to provide lower bounds. The application includes some nonconvex problems that have recently arisen in portfolio selection, power system, etc

Peer-to-peer, multi-agent interaction adapted to a web architecture

The Internet and Web have brought in a new era of information sharing and opened up countless opportunities for people to rethink and redefine communication. With the development of network-related technologies, a Client/Server architecture has become dominant in the application layer of the Internet. Nowadays network nodes are behind firewalls and Network Address Translations, and the centralised design of the Client/Server architecture limits communication between users on the client side. Achieving the conflicting goals of data privacy and data openness is difficult and in many cases the difficulty is compounded by the differing solutions adopted by different organisations and companies. Building a more decentralised or distributed environment for people to freely share their knowledge has become a pressing challenge and we need to understand how to adapt the pervasive Client/Server architecture to this more fluid environment. This thesis describes a novel framework by which network nodes or humans can interact and share knowledge with each other through formal service-choreography specifications in a decentralised manner. The platform allows peers to publish, discover and (un)subscribe to those specifications in the form of Interaction Models (IMs). Peer groups can be dynamically formed and disbanded based on the interaction logs of peers. IMs are published in HTML documents as normal Web pages indexable by search engines and associated with lightweight annotations which semantically enhance the embedded IM elements and at the same time make IM publications comply with the Linked Data principles. The execution of IMs is decentralised on each peer via conventional Web browsers, potentially giving the system access to a very large user community. In this thesis, after developing a proof-of-concept implementation, we carry out case studies of the resulting functionality and evaluate the implementation across several metrics. An increasing number of service providers have began to look for customers proactively, and we believe that in the near future we will not search for services but rather services will find us through our peer communities. Our approaches show how a peer-to-peer architecture for this purpose can be obtained on top of a conventional Client/Server Web infrastructure

Social Media Research in Hospitality and Tourism: A Causal Chain Framework of Literature Review

Purpose – The present study aims to conduct a systematic literature review to und erstand how current social media studies have adopted theories, used research constructs, and developed conceptual frameworks. Design – The current study examined 149 articles on social media published in the top eight hospitality and tourism journals between 2007 and 2017. Methodology – First, descriptive statistics were presented to show the status quo of theories and constructs used in social media-related articles. Second, three causal chain frameworks are developed based on the antecedent-moderator–mediator-outcome model. Findings – First, psychological theory is the predominant theory that has been applied to explain the behavior of social media users. Second, platform-related antecedents have been identified as the most prevalent antecedents. Third, consumer outcomes have attracted the most research interest. Fourth, only about half of the selected social media publications used moderators or mediators in their research models. Finally, there is a lack of cross-category causal relationships among the three causal chain frameworks. Originality – It is expected that the causal chain frameworks developed in this study will provide a research roadmap for academia as well as insights for the hospitality and tourism industry

Tourism Destination Governance: A Systematic Literature Review and Future Research Agenda

Tourism is regarded as one of the most novel and contemporary fields of governance research and practices. The efforts to define, assess, and explore destination governance have mainly been made in political science and business disciplines. However, destination governance distinguishes itself by its complex nature and context-related feature. Research in this area is mostly fragmented, lacking synergies in its definition, scope, and dimensions. This research plans to adopt a systematic quantitative review to investigate the status quo of knowledge and contributes to the current literature by summarizing significant aspects, discussing incongruity issues, identifying research gaps, and highlighting future research directions

Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs

Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality, graph structures are often incomplete and noisy, which hinders temporal graph networks (TGNs) from learning informative representations. Graph contrastive learning uses data augmentation to generate plausible variations of existing data and learn robust representations. However, rule-based augmentation approaches may be suboptimal as they lack learnability and fail to leverage rich information from downstream tasks. To address these issues, we propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs, which learns better graph structures for downstream tasks through adding potential temporal edges. In particular, it predicts time-aware context embedding based on previously observed interactions and uses the Gumble-Top-K to select the closest candidate edges to this context embedding. Additionally, several candidate sampling strategies are proposed to ensure both efficiency and diversity. Furthermore, we jointly learn the graph structure and TGNs in an end-to-end manner and perform inference on the refined graph. Extensive experiments on temporal link prediction benchmarks demonstrate that TGSL yields significant gains for the popular TGNs such as TGAT and GraphMixer, and it outperforms other contrastive learning methods on temporal graphs. We will release the code in the future.Comment: 10 pages,4 figures,5 table

Effects of nitrogen-free species on NO removal performance by coal pyrolysis gas via reactive molecular dynamics simulations

Coal splitting and reburning is a promising technology to control NO emissions during coal combustion. During this process, coal pyrolysis gas is used as reburn fuel to convert NO to N2. Nitrogen-containing compounds (HCN and NH3) play dominant roles in the NO reduction performance. In this study, we investigated the influence of nitrogen-free species (CH4, CO and H2) in coal pyrolysis gas on the NO reduction by HCN and NH3 via reactive force field (ReaxFF) molecular dynamics (MD) simulations. The nitrogen distribution in products is determined and monitored during the process of NO removal by HCN and NH3 under different additives. In addition, mechanisms of NO reduction by HCN and NH3 are revealed, accounting for the changes of nitrogen distribution in the products at the atomic level. The present research provides new insights into the influence of CH4, CO and H2 on the NO reduction by HCN and NH3, which may be helpful to reduce the NOx emissions during coal combustion by optimising the nitrogen-free components of coal pyrolysis gas

elcome@12Impact of oxygen and nitrogen-containing species on performance of NO removal by coal pyrolysis gas

Coal pyrolysis gas is considered a promising reburn fuel with excellent NO reduction performance because of the present of nitrogen-containing species (HCN and NH3) in the pyrolysis gas. In this study, we explored the effects of oxygen and nitrogen-containing species on NO removal performance with HCN and NH3 by reactive force field (ReaxFF) molecular dynamics (MD) simulations. Results indicate that appropriately reducing O2 concentrations and increasing the amount of nitrogen-containing species can benefit the NO reduction performance by coal pyrolysis gas. In addition, the effects of oxygen and nitrogen-containing species content on the NO removal and mechanisms of NO consumption and N2 formation are illustrated during NO reduction with HCN and NH3, respectively. Finally, based on the simulations results, practical operating strategies are proposed to optimize the NO reduction efficiency. In summary, this study provides new insights into NO reduction performance, which may contribute to optimizing the operating parameters to decrease NOx emissions during coal combustion