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Until now, there have been no empirical studies published investing the link between citizens' satisfaction with government performance and its impact on public trust towards the government. Here, we explore the case of the Yemeni government. Public trust in government is considered very low among Arabic countries. Public trust in government in Yemen was found to be much lower than expected. Furthermore, the variables of citizen satisfaction with the quality of public services, the transparency of government and government performance in fighting corruption were found to be positively associated with public trust in the transitional Yemeni government at both the city and central levels. This study also found that some demographic features in Yemeni citizens such as males with low and middle school education showed a higher possibility of trust in city government. These findings suggest that maintaining political trust will be a challenge for the transitional Yemeni government especially because Yemen is now experiencing civil war.

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There are many challenges in implementation of wireless sensor network systems: clustering and grouping are two of them. The grouping of sensors is computational process intended to partition the sensors of network into groups. Each group contains a number of sensors and a sensor can be an element of multiple groups. In this paper, we provided a Sensors Grouping Hierarchy Structure (GHS) to split the nodes in wireless sensor network into groups to assist the collaborative, dynamic, distributed computing and communication of the system. Our idea is to partition the nodes according to their geographical maximum covered regions such that each group contains a number of nodes and a number of leaders. To evaluate the performance of our proposed grouping structure, we have implemented a Grouped based routing and Grouped based object tracking. The proposed grouping structure shows a good performance in energy consumption and energy dissipation during data routing and it generates a little redundant data during object tracking.