Given the fact that more and more governments invest heavily in e-government design and implementation, e-government has become an evolving and important research area in the IS field. Most, if not all, currently published e-government strategies are based on successful experiences from developed countries, which may not be directly applicable to developing countries. Based on a literature review, this study summarizes differences between developed/developing countries. It identifies key factors for a successful e-government implementation and proposes an implementation framework. As a demonstration, we follow the guidance of the proposed framework in conducting a case study to analyze the implementation strategies of e-government in developed and developing countries.

Apart from maximization of parametric yield, minimization of the spread in performance functions due to process variation is of extreme importance in very large scale integrated circuit design. To achieve efficient minimization of the spread, a novel algorithm based on the genetic algorithm and global approximation methods is proposed. The algorithm operates in two stages designated as coarse and fine optimization stages and adjusts design parameter set to simultaneously achieve the target performance and reduction in performance spread. The algorithm has distinctive features, such as global optimum design, subexponential complexity algorithm for N-P complete problem of global optimization, and simultaneous optimization of many functions. The algorithm is demonstrated using four design examples.

Process variations invariably give rise to a parametric yield below 100% for VLSI circuits. Improving the yield by choosing a set of optimum parameter values does not incur any extra cost, and it is a preferred method as it directly translates into profits. The paper presents an efficient and novel method to improve the VLSI parametric yield by selecting optimum parameter values. This method utilizes the worst-case distance analysis, design centering and gradient-dependent techniques. One circuit example is presented to demonstrate the optimization scheme.