The use of treatment process variables to differentiate between completers and dropouts for a guided self -change adolescent substance abuse intervention

This study documented differences between substance using adolescent participants who either completed or dropped out of a brief motivational intervention. Therapeutic alliance, working alliance and patient involvement were used to describe differences in treatment process ratings in a sample of majority Latino males who either (a) completed a adolescent substance abuse intervention called Alcohol Treatment Targeting Adolescents In Need (ATTAIN) or (b) dropped out after the first or second Guided Self-Change therapy session. Fifteen-minute segments were copied from the midpoint of previously recorded audio-tapes of Guided Self-Change therapy sessions. Raters were trained to a criterion level of interrater reliability for both the Working Alliance Inventory-Short and Vanderbilt Psychotherapy Process Scale. Correlations among Working Alliance Inventory- Short and Vanderbilt Psychotherapy Process Scale subscales reflected a general similarity in the assignment of ratings to client-therapist dyads. Findings underscore why these concepts are often used interchangeably in the treatment process literature. The Vanderbilt Psychotherapy Process Scale patient participation subscale demonstrated substantial empirical differentiation from overall therapeutic alliance. Discriminant function analysis demonstrated the Working Alliance Inventory-Short goal subscale and the Vanderbilt Psychotherapy Process Scale patient participation and therapist warmth and friendliness subscales as successful classifiers of groups of mostly Latino youth based on completion status. Follow-up logistic regression analyses confirmed major findings and successfully predicted group membership. Treatment process constructs can be used as clinical tools to identify participants who may be susceptible to dropping out of treatment services. Further investigation of treatment process may enhance understanding of the influence of alliance between clients and Guided Self-Change therapists. Investigating the role of treatment process as a critical component of brief motivational interventions for substance-using adolescents will inform both practitioners and researchers regarding the effectiveness of community-based substance abuse interventions for adolescents.

Background traffic modeling for large-scale network simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.

Background Traffic Modeling for Large-Scale Network Simulation

Network simulation is an indispensable tool for studying Internet-scale networks due to the heterogeneous structure, immense size and changing properties. It is crucial for network simulators to generate representative traffic, which is necessary for effectively evaluating next-generation network protocols and applications. With network simulation, we can make a distinction between foreground traffic, which is generated by the target applications the researchers intend to study and therefore must be simulated with high fidelity, and background traffic, which represents the network traffic that is generated by other applications and does not require significant accuracy. The background traffic has a significant impact on the foreground traffic, since it competes with the foreground traffic for network resources and therefore can drastically affect the behavior of the applications that produce the foreground traffic. This dissertation aims to provide a solution to meaningfully generate background traffic in three aspects. First is realism. Realistic traffic characterization plays an important role in determining the correct outcome of the simulation studies. This work starts from enhancing an existing fluid background traffic model by removing its two unrealistic assumptions. The improved model can correctly reflect the network conditions in the reverse direction of the data traffic and can reproduce the traffic burstiness observed from measurements. Second is scalability. The trade-off between accuracy and scalability is a constant theme in background traffic modeling. This work presents a fast rate-based TCP (RTCP) traffic model, which originally used analytical models to represent TCP congestion control behavior. This model outperforms other existing traffic models in that it can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding packet-oriented simulation. Third is network-wide traffic generation. Regardless of how detailed or scalable the models are, they mainly focus on how to generate traffic on one single link, which cannot be extended easily to studies of more complicated network scenarios. This work presents a cluster-based spatio-temporal background traffic generation model that considers spatial and temporal traffic characteristics as well as their correlations. The resulting model can be used effectively for the evaluation work in network studies.