The development and testing of a multivariate model of differentiation of self among Hispanics

Individuals of Hispanic origin are the nation's largest minority (13.4%). Therefore, there is a need for models and methods that are culturally appropriate for mental health research with this burgeoning population. This is an especially salient issue when applying family systems theories to Hispanics, who are heavily influenced by family bonds in a way that appears to be different from the more individualistic non-Hispanic White culture. Bowen asserted that his family systems' concept of differentiation of self, which values both individuality and connectedness, could be universally applied. However, there is a paucity of research systematically assessing the applicability of the differentiation of self construct in ethnic minority populations. ^ This dissertation tested a multivariate model of differentiation of self with a Hispanic sample. The manner in which the construct of differentiation of self was being assessed and how accurately it represented this particular ethnic minority group's functioning was examined. Additionally, the proposed model included key contextual variables (e.g., anxiety, relationship satisfaction, attachment and acculturation related variables) which have been shown to be related to the differentiation process. ^ The results from structural equation modeling (SEM) analyses confirmed and extended previous research, and helped to illuminate the complex relationships between key factors that need to be considered in order to better understand individuals with this cultural background. Overall results indicated that the manner in which Hispanic individuals negotiate the boundaries of interconnectedness with a sense of individual expression appears to be different from their non-Hispanic White counterparts in some important ways. These findings illustrate the need for research on Hispanic individuals that provides a more culturally sensitive framework. ^

From Bilingualism to Interlinguistics: The Case Against the Deficit Model of Language Acquisition

The article argues against an ahistorical deficit model of Spanish/English bilingualism in educational practice based on interlinguistic research. The bidirectional facilitative effects of Hispanic bilingualism allow Spanish-speaking minorities to exploit their language background while learning academic English and integrating their language and culture into the American mainstream.

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.