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Bottomland hardwood forests are highly productive ecosystems which perform many important ecological services. Unfortunately, many bottomland hardwood forests have been degraded or lost. Accurate land cover mapping is crucial for management decisions affecting these disappearing systems. SPOT-5 imagery from 2005 was combined with Light Detection and Ranging (LiDAR) data from 2006 and several ancillary datasets to map a portion of the bottomland hardwood system found in the Sulphur River Basin of Northeast Texas. Pixel-based classification techniques, rulebased classification techniques, and object-based classification techniques were used to distinguish nine land cover types in the area. The rule-based classification (84.41% overall accuracy) outperformed the other classification methods because it more effectively incorporated the LiDAR and ancillary datasets when needed. This output was compared to previous classifications from 1974, 1984, 1991, and 1997 to determine abundance trends in the area’s bottomland hardwood forests. The classifications from 1974-1991 were conducted using identical class definitions and input imagery (Landsat MSS 60m), and the direct comparison demonstrates an overall declining trend in bottomland hardwood abundance. The trend levels off in 1997 when medium resolution imagery was first utilized (Landsat TM 30m) and the 2005 classification also shows an increase in bottomland hardwood from 1997 to 2005, when SPOT-5 10m imagery was used. However, when the classifications are re-sampled to the same resolution (60m), the percent area of bottomland hardwood consistently decreases from 1974-2005. Additional investigation of object-oriented classification proved useful. A major shortcoming of object-based classification is limited justification regarding the selection of segmentation parameters. Often, segmentation parameters are arbitrarily defined using general guidelines or are determined through a large number of parameter combinations. This research justifies the selection of segmentation parameters through a process that utilizes landscape metrics and statistical techniques to determine ideal segmentation parameters. The classification resulting from these parameters outperforms the classification resulting from arbitrary parameters by approximately three to six percent in terms of overall accuracy, demonstrating that landscape metrics can be successfully linked to segmentation parameters in order to create image objects that more closely resemble real-world objects and result in a more accurate final classification.

This thesis explores, through fictional storytelling, the cultural duality of individuals inhabiting Singapore prior to World War II. The primary locale in many of these stories-an actual residence known as Eu Villa-interconnects each narrative and helps to uncover the hybridization of a Chinese family (and servants) living in a British colony. Many of the stories are imparted from different perspectives: wives, children and amahs, each of them pieced together to bridge the space between Chinese heritage overlaid and intermixed with British culture. In this way, the stories of this thesis reflect on the history that preceded the distinct multiculturalism of contemporary Singapore.

Since the World Wide Web (WWW) appeared, more and more useful information has been available on the WWW. In order to find the information, one application of data mining techniques on the WWW, referred to as Web mining, has become a research area with increasing importance. Mining traversal patterns is one of the important topics in Web mining. It focuses on how to find the Web page sequences which are frequently browsed by users. Although the algorithms for mining association rules (e.g., Apriori and DHP algorithms) could be applied to mine traversal patterns, they do not utilize the property of Web transactions and generate too many invalid candidate patterns. Thus, they could not provide good performance. Wu et al. proposed an algorithm for mining traversal patterns, SpeedTracer, which utilizes the property of Web transactions, i.e., the continuous property of the traversal patterns in the Web structure. Although they decrease the number of candidate patterns generated in the mining process, they do not efficiently utilize the property of Web transactions to decrease the number of checks while checking the subsets of each candidate pattern. In this thesis, we design three algorithms, which improve the SpeedTracer algorithm, for mining traversal patterns. For the first algorithm, SpeedTracer*-I, it utilizes the property of Web transactions to directly generate and count all candidate patterns from user sessions. Moreover, it utilizes this property to improve the checking step, when candidate patterns are generated. Next, according to the SpeedTracer*-I algorithm, we propose SpeedTracer*-II and SpeedTracer*-III algorithms. In these two algorithms, we improve the performance of the SpeedTracer*-I algorithm by decreasing the number of times to scan the database. In the SpeedTracer*-II algorithm, given a parameter n, we apply the SpeedTracer*-I algorithm to find Ln first, and use Ln to generate all Ck, where k > n. After generating all candidate patterns, we scan the database once to count all candidate patterns and then the frequent patterns could be determined. In the SpeedTracer*-III algorithm, given a parameter n, we also apply the SpeedTracer*-I algorithm to find Ln first, and directly generate and count Ck from user sessions based on Ln, where k > n. The simulation results show that the performance of the SpeedTracer*-I algorithm is better than that of the Speed- Tracer algorithm in terms of the processing time. The simulation results also show that SpeedTracer*-II and SpeedTracer*-III algorithms outperform SpeedTracer and SpeedTracer*-I algorithms, because the former two algorithms need less times to scan the database than the latter two algorithms. Moreover, from our simulation results, we show that all of our proposed algorithms could provide better performance than Apriori-like algorithms (e.g., FS and FDLP algorithms) in terms of the processing time.

This dissertation is mainly anchored in police administrative neutrality. First of all, according to political literature, it analyzes the role of the police when they execute the governance power in a state. Secondly, from police science literature, it explores the reasons the police involve with politics. Thirdly, based on legislative procedure, it analyzes the different opinions a government and legislature have regarding to ¡§civil service administrative neutrality.¡¨ This dissertation also, according to the study of administrative neutrality from foreign administrative law and professors, seeks the criterion of police administrative neutrality. Finally, throughout the previous media reports, it sorts out the police administrative bias and turns it into study material and judgmental source for this thesis. The author discovers three steps to examine police administrative neutrality, and delivers a case study: mass media expose police administrative bias during the police enforcement tasks of the 11th presidential election. 1. Rule of law: to analyze if the police achieves tasks based on law, and due process of law or standard operating procedures. 2. Execution Neutrality: to analyze if administrative discretion is proper and impartial according to administrative principles. 3. Administration Neutrality: to analyze if politics interferes with police duties. If this occurs, it may result in a partial administration. The police symbolize the governance power of a state, and play different roles as government changes. In an authoritarian state, the police are a tool to suppress people against the ruler; in a democratic state, police authority is restricted to a certain level. Lately, political environment and political party change has become normal. The police ought to acknowledge a truth; governments change but administrations remain. To strengthen democratic functions as the police performs duties, they should prioritize public interest, follow rule of law, adopt political policies, assure legality of human rights, maintain equality of political parties, and present the appropriate assistance to people in the specialization area of public order maintenance.

In the coming knowledge-based economy era, knowledge structure map (KSM) has becoming more and more important. If learners doing learning without the support of knowledge structure map, it will cause learning alone problem. In order to construct a real KSM, we targeted the MIS domain. By using the National Dissertation and Thesis Abstract System as input source, we first extract different research subjects from keywords and then calculate the relation strength between each keyword pairs. An automatic approach has been developed for constructing KSM for different periods of time. The constructed KSM can help learners to reduce learning alone and provide a good reference for new researchers to seek for related research directions. The proposed method can also be applied to enterprises. They can adopt this method to construct any specific KSM corresponding to their professional domain, the constructed KSM would help new employee to learn better. Furthermore, with the support of KSM, CEO can make a better decision as the KSM would contain internal and external competitive advantages about future directions.

Interoperability between Geographic Information System (GIS) of different infrastructure companies is still a problem to be handled. Infrastructure companies deal with many operations as a part of their daily routine such as a regular maintenance, or sometimes they deal with unexpected situations such as a malfunction due to natural event, like a flood or an earthquake. These situations may affect all companies and affected infrastructure companies response to these effects. Responses may result in consequences and in order to model these consequences on GIS, GISs are able to share information, which brings the interoperability problem into the scene. The present research, aims at finding an answer to interoperability problem between GISs of different companies by considering contextual information. During the study, the geographical features are handled as the major concern and interoperability problem is examined by targeting them. The model constructed in this research is based on the ontology and because the meaning of the terms in the ontology depends on the context, ontology based context modeling is also used. v In this research, a system implementation is done for two different GISs of two

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This thesis try to contribute a legal research of the commercial arbitration system in Mainland China . With the developing of the Mainland China¡¦s economy, China¡¦s government is devoted to establish the legal system of the foreign investors don¡¦t trust the legal system of PRC. The commercial arbitration is a common and important way of the international disputes resolution. It will be more popular in the future.This article discusses about the international arbitration and the China¡¦s new commercial arbitration system. Not only introduces the arbitration law of PRC, but also discusses the internati -onal arbitration,for example UNCITRAL MODEL LAW and THE NEW YORK CONVENTION. This article also discusses relation between arbitration and the courts.

With the computerization of businesses, more and more data are generated and stored in databases for many business applications. Finding interesting patterns among those data may lead to useful knowledge that provides competitive advantage in business. Knowledge discovery in database has thus become an important issue to help business acquire knowledge that assists managerial and operational work. Among many types of knowledge, classification knowledge is widely used. Most classification rules learned by induction algorithms are in the crisp form. Fuzzy linguistic representation of rules, however, is much closer to the way human reasons. The objective of this research is to propose a method to mine classification knowledge from the database with fuzzy descriptions. The procedure contains five steps, starting from data preparation to rule pruning. A rule induction algorithm, RITIO, is employed to generate the classification rules. Fuzzy inference mechanism that includes fuzzy matching and output reasoning is specified to yield the output class. An experiment is conducted using several databases to show advantages of this work. The proposed method is justified with good system performance. It can be easily implemented in various business applications on classification tasks.