Browsing by Author "Zhang, Zhe"

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    From Individual Motivation to Geospatial Epidemiology: A Novel Approach Using Fuzzy Cognitive Maps and Agent-Based Modeling for Large-Scale Disease Spread
    Song, Zhenlei; Zhang, Zhe; Lyu, Fangzheng; Bishop, Michael; Liu, Jikun; Chi, Zhaohui (MDPI, 2024-06-13)
    In the past few years, there have been many studies addressing the simulation of COVID-19’s spatial transmission model of infectious disease in time. However, very few studies have focused on the effect of the epidemic environment variables in which an individual lives on the individual’s behavioral logic leading to changes in the overall epidemic transmission trend at larger scales. In this study, we applied Fuzzy Cognitive Maps (FCMs) to modeling individual behavioral logistics, combined with Agent-Based Modeling (ABM) to perform “Susceptible—Exposed—Infectious—Removed” (SEIR) simulation of the independent individual behavior affecting the overall trend change. Our objective was to simulate the spatiotemporal spread of diseases using the Bengaluru Urban District, India as a case study. The results show that the simulation results are highly consistent with the observed reality, in terms of trends, with a Root Mean Square Error (RMSE) value of 0.39. Notably, our approach reveals a subtle link between individual motivation and infection-recovery dynamics, highlighting how individual behavior can significantly impact broader patterns of transmission. These insights have potential implications for epidemiologic strategies and public health interventions, providing data-driven insights into behavioral impacts on epidemic spread. By integrating behavioral modeling with epidemic simulation, our study underscores the importance of considering individual and collective behavior in designing sustainable public health policies and interventions.
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    Gender differences in educational adaptation of immigrant-origin youth in the United States
    Qian, Yue; Buchmann, Claudia; Zhang, Zhe (Demographic Research, 2018)
    Immigrant-origin students (i.e., first- and second-generation immigrants) comprise roughly 20% of the US school-age population. Despite growing awareness of a female favorable gender gap in educational performance, quantitative research on immigrant educational adaptation rarely considers whether there are differences in the educational adaptation patterns between men and women. Using a nationally representative sample of 2002 high school sophomores from the Educational Longitudinal Study, the authors examine gender-specific patterns of generational differences in high school grades and investigate racial/ethnic variation in these patterns.
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    MOON: MapReduce on Opportunistic eNvironments
    Lin, Heshan; Archuleta, Jeremy; Ma, Xiaosong; Feng, Wu-chun; Zhang, Zhe; Gardner, Mark K. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009)
    MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments.