Nation building in Bosnia and Herzegovina: Cooperation, coordination and collaboration

Cross-sectoral interorganizational relationships in post-conflict situations occur regularly. Whether formal task forces, advisory groups or other ad hoc arrangements, these relations take place in chaotic and dangerous situations with urgent and turbulent political, economic and social environments. Furthermore, they typically involve a large number of players from many different nations, operating across sectors, and between multiple layers of bureaucracy and diplomacy. The organizational complexity staggers many participants and observers, as do the tasks they are charged with completing. Reform efforts in Bosnia and Herzegovina starting in 1995 may serve as the archetype model of conflict, transition and development for the 21st century. It wins this honor due not to its particular programmatic successes and failures, rather to the interorganizational complexity of the International Community. From the massive response to the crisis, to the modern nation-building policies it spawned, and the development assistance practices and institutional arrangements it created, the Bosnian development experience has much to offer by way of lessons learned. This manuscript frames the unique Bosnian development situation, and provides lessons learned from the experience of nation building given local realities. Pettigrew (1992) called this "contextualizing." While network and/or organizational structure, strategy and process explain many interorganizational relationship issues, the development variables identified in this manuscript prove equally important, yet elusive and difficult to measure despite their very real and overt presence.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.