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THIS PAPER FOCUSES ON THE COMMUNIST STATE AS THE ARCHITECT OF NATIONALISM IN CHINA AND EXPLAINS THE RISE OF CHINESE NATIONALISM BY EXAMINING THE STATE'S PATRIOTIC EDUCATION CAMPAIGN. IT BEGINS WITH AN ANALYSIS OF HOW NATIONALISM REPLACED THE OFFICIAL IDEOLOGY AS THE COALESCING FORCE IN THE AFTERMATH OF THE TIANANMEN SQUARE MASSACRE. THEN IT EXAMINES THE PROCESS, CONTENTS, METHODS, AND EFFECTIVENESS OF THE PATRIOTIC EDUCATION CAMPAIGN.

EARLY SCHOLARSHIP OF CHINESE POLITICS SAW THE COMMUNIST REGIME AS A COERCIVE STATE THAT NOT ONLY PENETRATED SOCIETY AT ALL LEVELS BUT ALSO ENFORCED COMPLIANCE FROM LOCAL GOVERNMENTS TO ACHIEVE THE GOALS AND IMPLEMENT THE POLICIES SET BY THE CENTER. BUT RECENT SCHOLARSHIP HAS FOUND THAT CHINESE COMMUNIST POLITICS IS ESSENTIALLY CONFLICT-RIDDEN. THE TRANSFORMATION OF THE COERCIVE STATE AND THE DYNAMICS OF THE CONFLICT MAY BE OBSERVED FROM AN EXAMINATION OF THE CENTRAL-LOCAL ECONOMIC RELATIONSHIP. DECENTRALIZATION SINCE THE LATE 1950'S HAS UNDERMINED THE EFFICACY OF THE COERCIVE INSTRUMENTS AND UNDERMINED THE CAPACITY OF THE CENTRAL AUTHORITIES TO ENFORCE COMPLIANCE FROM LOCAL GOVERNMENTS. THE DEVELOPMENT OF A NEGOTIATORY RELATIONSHIP BETWEEN LOCAL GOVERNMENTS AND THE CENTER ILLUSTRATES THE TRANSFORMATION OF THE COERCIVE STATE.

Hypothesizes that questions asked as part of a series with identical formats should cause departures from the question text. More than most items, questions with a repetitive format violate conventional conversation norms. Thus there may be a temptation to vary the wording so as not to seem mechanical. (RSM)

We propose a conceptual framework for the political-economic geography of foreign exchange trading, focusing on the causes and consequences of the relationship between the international currency system and international financial centres. The framework is used to analyse data for 1995-2013 demonstrating that while the trading activity has boomed, its structure by currency, with USD in the lead, has remained remarkably stable, and its concentration in the NYLON axis has grown. We show that Asian currencies and financial centres have made little progress in the foreign exchange market, and argue that any challenge of RMB to USD would require nothing short of an unprecedented geo-political and geo-economic transformation.

Facing a high birth rate, a falling mortality rate, and inconsistent policies on family planning from the 1950s to the early 1970s, the People's Republic of China (PRC) launched its widely known one‐child policy in 1979. The intention was to restrict population growth by reducing fertility through family planning and thereby to conserve the nation's resources to advance economic development. The effectiveness of the one‐child policy has varied greatly because policy regulations are differentially carried out by officials of provinces, municipalities, counties, communes, and minority regions. Generally speaking, the state policy has had greater acceptance in urban areas but is far less rigidly enforced by local officials in rural areas and for certain national minorities, which can have a second child under certain circumstances (Chow and Chen, 1994).

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.