Integrated Performance Assessment of Engineering Projects at the Interface of Emergent Properties and Uncertainty

Investigation of the performance of engineering project organizations is critical for understanding and eliminating inefficiencies in today’s dynamic global markets. The existing theoretical frameworks consider project organizations as monolithic systems and attribute the performance of project organizations to the characteristics of the constituents. However, project organizations consist of complex interdependent networks of agents, information, and resources whose interactions give rise to emergent properties that affect the overall performance of project organizations. Yet, our understanding of the emergent properties in project organizations and their impact on project performance is rather limited. This limitation is one of the major barriers towards creation of integrated theories of performance assessment in project organizations. The objective of this paper is to investigate the emergent properties that affect the ability of project organization to cope with uncertainty. Based on the theories of complex systems, we propose and test a novel framework in which the likelihood of performance variations in project organizations could be investigated based on the environment of uncertainty (i.e., static complexity, dynamic complexity, and external source of disruption) as well as the emergent properties (i.e., absorptive capacity, adaptive capacity, and restorative capacity) of project organizations. The existence and significance of different dimensions of the environment of uncertainty and emergent properties in the proposed framework are tested based on the analysis of the information collected from interviews with senior project managers in the construction industry. The outcomes of this study provide a novel theoretical lens for proactive bottom-up investigation of performance in project organizations at the interface of emergent properties and uncertainty

An agent-based model of team coordination and performance

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

An epistemic event -based correlation scheme for pervasive network management

Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.

Magnetic nanoparticle-based targeted drug delivery for treatment of neuro-AIDS and drug addiction

Brain is one of the safe sanctuaries for HIV and, in turn, continuously supplies active viruses to the periphery. Additionally, HIV infection in brain results in several mild-to-severe neuro-immunological complications termed neuroAIDS. One-tenth of HIV-infected population is addicted to recreational drugs such as opiates, alcohol, nicotine, marijuana, etc. which share common target-areas in the brain with HIV. Interestingly, intensity of neuropathogenesis is remarkably enhanced due to exposure of recreational drugs during HIV infection. Current treatments to alleviate either the individual or synergistic effects of abusive drugs and HIV on neuronal modulations are less effective at CNS level, basically due to impermeability of therapeutic molecules across blood-brain barrier (BBB). Despite exciting advancement of nanotechnology in drug delivery, existing nanovehicles such as dendrimers, polymers, micelles, etc. suffer from the lack of adequate BBB penetrability before the drugs are engulfed by the reticuloendothelial system cells as well as the uncertainty that if and when the nanocarrier reaches the brain. Therefore, in order to develop a fast, target-specific, safe, and effective approach for brain delivery of anti-addiction, anti-viral and neuroprotective drugs, we exploited the potential of magnetic nanoparticles (MNPs) which, in recent years, has attracted significant importance in biomedical applications. We hypothesize that under the influence of external (non-invasive) magnetic force, MNPs can deliver these drugs across BBB in most effective manner. Accordingly, in this dissertation, I delineated the pharmacokinetics and dynamics of MNPs bound anti-opioid, anti-HIV and neuroprotective drugs for delivery in brain. I have developed a liposome-based novel magnetized nanovehicle which, under the influence of external magnetic forces, can transmigrate and effectively deliver drugs across BBB without compromising its integrity. It is expected that the developed nanoformulations may be of high therapeutic significance for neuroAIDS and for drug addiction as well.

An Epistemic Event-based Correlation Scheme for Pervasive Network Management

Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.

Magnetic Nanoparticle-based Targeted Drug Delivery for Treatment of Neuro-AIDS and Drug Addiction

Brain is one of the safe sanctuaries for HIV and, in turn, continuously supplies active viruses to the periphery. Additionally, HIV infection in brain results in several mild-to-severe neuro-immunological complications termed neuroAIDS. One-tenth of HIV-infected population is addicted to recreational drugs such as opiates, alcohol, nicotine, marijuana, etc. which share common target-areas in the brain with HIV. Interestingly, intensity of neuropathogenesis is remarkably enhanced due to exposure of recreational drugs during HIV infection. Current treatments to alleviate either the individual or synergistic effects of abusive drugs and HIV on neuronal modulations are less effective at CNS level, basically due to impermeability of therapeutic molecules across blood-brain barrier (BBB). Despite exciting advancement of nanotechnology in drug delivery, existing nanovehicles such as dendrimers, polymers, micelles, etc. suffer from the lack of adequate BBB penetrability before the drugs are engulfed by the reticuloendothelial system cells as well as the uncertainty that if and when the nanocarrier reaches the brain. Therefore, in order to develop a fast, target-specific, safe, and effective approach for brain delivery of anti-addiction, anti-viral and neuroprotective drugs, we exploited the potential of magnetic nanoparticles (MNPs) which, in recent years, has attracted significant importance in biomedical applications. We hypothesize that under the influence of external (non-invasive) magnetic force, MNPs can deliver these drugs across BBB in most effective manner. Accordingly, in this dissertation, I delineated the pharmacokinetics and dynamics of MNPs bound anti-opioid, anti-HIV and neuroprotective drugs for delivery in brain. I have developed a liposome-based novel magnetized nanovehicle which, under the influence of external magnetic forces, can transmigrate and effectively deliver drugs across BBB without compromising its integrity. It is expected that the developed nanoformulations may be of high therapeutic significance for neuroAIDS and for drug addiction as well.