In these few years, gradually Internet develops to wideband, multimedia is being used on video or music. In addition, the use of IP Multicast must be based on the deployment of routers, which is too difficult to arrange. Utilities of Application Layer Multicast is in the middle and just between IP Multicast and Unicast.Therefore, in this paper, Application Layer Multicast is still worth to study it. In this paper, is applied effectively build the Application Layer Multicast. Control through the IP Anycast Technique, we can lower the time for host join the Multicast Tree. Every host can join the nearest cluster. We use the hierarchical cluster-based Method in order to serve more hosts. This concept about cluster can substantially decrease control overhead. The Complete Binary Trees lower the cluster leader¡¦s burden, also phased RTT decided effectively the transit sequence. In Summary, associate techniques with methods, to make up the defects from NICE and I-Zigzag.

Many studies have shown that intersections are among the most dangerous locations of a roadway network. Therefore, there is a need to understand the factors that contribute to traffic crashes at such locations. One approach is to model crash occurrences based on configuration, geometric characteristics and traffic. Instead of combining all variables and crash types to create a single statistical model, this analysis created several models that address the different factors that affect crashes, by type of collision as well as injury level, at signalized intersections. The first objective was to determine if there is a difference between important variables for models based on individual crash types or severity levels and aggregated models. The second objective of this research was to investigate the quality and completeness of the crash data and the effect that incomplete data has on the final results. A detailed and thorough data collection effort was necessary for this research to ensure the quality and completeness of this data. Multiple agencies were contacted and databases were crosschecked (i.e. state and local jurisdictions/agencies). Information (including geometry, configuration and traffic characteristics) was collected for a total of 832 intersections and over 33,500 crashes from Brevard, Hillsborough and Seminole Counties and the City of Orlando. Due to the abundance of data collected, a portion was used as a validation set for the tree-based regression. Hierarchical tree-based regression (HTBR) and ordered probit models were used in the analyses. HTBR was used to create models for the expected number of crashes for collision type as well as injury level. Ordered probit models were only used to predict crash severity levels due to the ordinal nature of this dependent variable. Finally, both types of models were used to predict the expected number of crashes. More specifically, tree-based regression was used to consider the difference in the relative importance of each variable between the different types of collisions. First, regressions were only based on crashes available from state agencies to make the results more comparable to other studies. The main finding was that the models created for angle and left turn crashes change the most compared to the model created from the total number of crashes reported on long forms (restricted data usually available at state agencies). This result shows that aggregating the different crash types by only estimating models based on the total number of crashes will not predict the number of expected crashes as accurately as models based on each type of crash separately. Then, complete datasets (full dataset based on crash reports collected from multiple sources) were used to calibrate the models. There was consistently a difference between models based on the restricted and complete datasets. The results in this section show that it is important to include minor crashes (usually reported on short forms and ignored) in the dataset when modeling the number of angle or head-on crashes and less important to include minor crashes when modeling rear-end, right turn or sideswipe crashes. This research presents in detail the significant geometric and traffic characteristics that affect each type of collision. Ordered probit models were used to estimate crash injury severity levels for three different types of models; the first one based on collision type, the second one based on intersection characteristics and the last one based on a significant combination of factors in both models. Both the restricted and complete datasets were used to create the first two model types and the output was compared. It was determined that the models based on the complete dataset were more accurate. However, when compared to the tree-based regression results, the ordered probit model did not predict as well for the restricted dataset based on intersection characteristics. The final ordered probit model showed that crashes involving a pedestrian/bicyclist have the highest probability of a severe injury. For motor vehicle crashes, left turn, angle, head-on and rear-end crashes cause higher injury severity levels. Division (a median) on the minor road, as well as a higher speed limit on the minor road, was found to lower the expected injury level. This research has shed light on several important topics in crash modeling. First of all, this research demonstrated that variables found to be significant in aggregated crash models may not be the same as the significant variables found in models based on specific crash types. Furthermore, variables found to be significant in crash type models typically changed when minor crashes were added to complete the dataset. Thirdly, ordered probit models based on significant crash-type and intersection characteristic variables have greater crash severity prediction power, especially when based on the complete dataset. Lastly, upon comparison between tree-based regression and ordered probit models, it was found that the tree-based regression models better predicted the crash severity levels.

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This thesis studies the relationship between the first generation biofuels and selected commodities and assets in the USA, Europe, and Brazil. It is the first attempt to combine the taxonomy and wavelet analyses in a single research application. Our unique dataset comprises 32 weekly price series covering the 2003--2015 time period. First, we employ a method of minimum spanning trees and hierarchical trees to model a biofuel-related price network. We demonstrate a development phase shift between Brazilian and the US/EU biofuel industries. We reveal a strong and stable connection between Brazilian ethanol and its main production factor, local sugarcane. We further find that US ethanol is closely linked to corn. In the contrary, European biodiesel exhibits only moderate ties to its production factors. Subsequent wavelet analysis scrutinizes the identified price connections both in time and frequency domains. Both Brazilian and US ethanols are found to be positively related to their respective feedstock commodities. In particular, feedstock proves to lead the price of the biofuel and not vice versa. Moreover, the dynamics remains qualitatively unchanged when controlled for the influence of crude oil.

<p>Device Management Applications are applications which are used to manage software on devices such as mobile phones. OMSI Forum provides such an application which is used to update the software on a phone. Software updates can be done at device management stations placed in stores or other service locations. Whenever a phone's software is updated, information about the update process is stored in a log. These logs can then be analyzed to generate statistics about updates such as the number of successful or failed updates or which faults that are common.</p><p>This master thesis project solves the problem of manually collecting and compiling logs from several stores by making this process automatic. Rather than collecting logs manually, each device management station sends its logs to a centralized server which stores all collected logs in a database. This makes it possible to generate charts and statistics in a simple manner from a web application. This solution makes the analysis more e ective, allowing users to concentrate on analyzing data by removing the work task of collecting logs.</p>

Device Management Applications are applications which are used to manage software on devices such as mobile phones. OMSI Forum provides such an application which is used to update the software on a phone. Software updates can be done at device management stations placed in stores or other service locations. Whenever a phone's software is updated, information about the update process is stored in a log. These logs can then be analyzed to generate statistics about updates such as the number of successful or failed updates or which faults that are common. This master thesis project solves the problem of manually collecting and compiling logs from several stores by making this process automatic. Rather than collecting logs manually, each device management station sends its logs to a centralized server which stores all collected logs in a database. This makes it possible to generate charts and statistics in a simple manner from a web application. This solution makes the analysis more e ective, allowing users to concentrate on analyzing data by removing the work task of collecting logs.

股票市場對投資者而言是以極大化自有資產為目的，因此如何辨別不同景氣區間對股市的影響為投資者感興趣的議題。傳統上，使用統計資料來幫助我們比較不同景氣區間之差異，然而股票市場之複雜、非線性及不可預測性也經常成為各統計資料失準的關鍵，因此，本篇論文以複雜網路作為分析股票市場之模型，並將各個股票表示成節點、股價變化之關聯性作為連結下，建立出複雜網路，藉此探討股市中的景氣差異。 　　在本研究中，先利用國發會制定的景氣對策信號，來幫助我們選取四段景氣區間，接著將台積電作為網路核心建構個股的相關網路。並以最小生成樹(Minimum Spanning Tree) 將複雜的股票網路簡單化。同時我們計算出各股相關網路之全域網路參數(Global Network Parameters)及區域網路參數(Regional Network Parameters)，以利我們討論兩段景氣好區間與兩段景氣差區間之差異。最後，我們將股市相關網路以分層樹(Hierarchical Tree)來表示，以了解網路分群的結果。 　　結果顯示，我們建構的個股相關網路符合小世界網路特性，在全域網路參數中，景氣好相關網路之常規化平均特徵路徑(Normalization Average Characteristic Path Length)及景氣差相關網路中之平均群聚係數(Average Clustering Coefficient)、平均特徵路徑(Average Characteristic Path Length)、常規化平均特徵路徑(Normalization Average Characteristic Path Length)有顯著差異。 在區域網路參數中，在景氣好相關網路中，被選為網路樞紐並有顯著差異之個股有台達化、宜進與華通，景氣差相關網路則有瑞利、日月光、矽品及萬企。在景氣好相關網路比較時，台積電的連結度與點效率皆具有顯著差異。