Used by more than a million people throughout the world, this highly readable book provides a comprehensive examination of the applied behavioral sciences, and focuses on fundamental ideas which have stood the test of years of application in academic, business, not-for-profit and administrative environments. Complete coverage of motivation and behavior, situational leadership, building effective relationships, planning and implementing change, leadership strategies, the organizational cone and integrating situational leadership with the Classics. For individuals interested in expanding their knowledge of, and proficiency in leadership strategies.

In this paper, we propose the use of cases and instructional modules to teach invention, engineering design, and elements of technology management. One way to learn is to study and reflect upon the experience of others. Such experience may be captured in a case. Cases promote active learning by requiring students to assume the roles of participants in the decision making process. Cases are also a vehicle for raising business issues and human resources concerns not usually considered in traditional engineering courses. Real world design and engineering involves risk and uncertainty, tradeoffs and priorities, ethical issues, human elements, and impact assessment. Cases expose students to open ended, ill defined problems whose solution often depends on making assessments, judgments, and decisions about the technical competencies of the organization.

Many states have implemented large-scale transportation management systems to improve mobility in urban areas. These systems are highly prone to missing and erroneous data, which results in drastically reduced data sets for analysis and real-time operations. Imputation is the practice of filling in missing data with estimated values. Currently, the transportation industry generally does not use imputation as a means for handling missing data. Other disciplines have recognized the importance of addressing missing data and, as a result, methods and software for imputing missing data are becoming widely available. The feasibility and applicability of imputing missing traffic data are addressed, and a preliminary analysis of several heuristic and statistical imputation techniques is performed. Preliminary results produced excellent performance in the case study and indicate that the statistical techniques are more accurate while maintaining the natural characteristics of the data.

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We propose a zero-intelligence agent-based model of the E-Mini S&P 500 futures market, which allows for a close examination of the market microstructure. Several classes of agents are characterized by their order speed and order placement within the limit order book. These agents' orders populate the simulated market in a way consistent with real world participation rates. By modeling separate trading classes the simulation is able to capture interactions between classes, which are essential to recreating market phenomenon. The simulated market is validated against empirically observed characteristics of price returns and volatility. We therefore conclude that our agent based simulation model can accurately capture the key characteristics of the nearest months E-Mini S&P 500 futures market. Additionally, to illustrate the applicability of the simulation, experiments were run, which confirm the leading hypothesis for the cause of the May 6 th 2010 Flash Crash.