Search results

The problem of missing children draws much attention of both governmental and nongovernmental organizations in China due to huge numbers of the missing children. According to the records of Baby Back Home network (BBHNet), a professional website to search missing children in China, 1,666 missing children have been found via releasing information on BBHNet; however, there are still 30,561 families searching for their children and 24,603 missing children are searching for biological parents through this website and have not succeeded yet. What is the difference between successful and unsuccessful cases in the aspect of released information? Motivated by this question, our research proposes to determine the crucial information in the process of searching missing children. A logistic regression model was developed on the data summarized from 500 succeed cases and 500 cases which have not succeed yet from BBHNet for forecasting success rate of searching missing children. The model identifies that the differences in terms of released information, number of children, address, and natural geographical features are the three most crucial factors that cause differences in the result of searching. This research can be used as a guide for improving the success rate of searching missing children and provide reference for developing a missing children information-sharing platform.

Since joining Bennett College in 2008, Dr. Oh has directed 17 undergraduate students’ research projects in applied mathematics. The National Science Foundation (NSF) awarded Dr. Oh grants from the Historically Black Colleges and Universities – Undergraduate Program (HBCU-UP). The grants allowed her to mentor eight mathematics majors/minors in summer research for four years (2009–2012). Based on the four years of successful undergraduate research (UGR) experiences, she, together with Dr. Jan Rychtar from the University of North Carolina at Greensboro (UNCG), received funding for two summers National Research Experience for Undergraduates (NREUP), an activity of Mathematical Association of America (MAA), funded by the NSF in 2013 and 2014. During the six years of funded UGR, Bennett students made 33 presentations at regional, state, and national conferences; two teams won the outstanding student presentation award and first place for presentation. Three papers were published; two of them by Dr. Oh and one of them with a UGR coauthor. Three projects resulted in manuscripts. As a result of the UGR experiences in 2015, Dr. Oh received three more grants: the MAA NREUP, the NSF’s Center for Undergraduate Research in Mathematics (CURM), and the NSF’s Preparation for Industrial Careers in Mathematical Sciences (PIC Math) program awarded grants. A grant was also submitted to HBC-UP-Targeted Infusion Projects: Computational Mathematics at Bennett College.

Overall, the six years of UGR at Bennett College attained the three goals of: (1) enhancing the quality of undergraduate STEM education and research for a deeper appreciation in those disciplines; (2) supporting increased graduation rates in STEM undergraduate education of females; and (3) broadening participation in the nation’s STEM workforce as well as enrollments in graduate schools.

This chapter proposes an optimized innovative information technology as a means for achieving operational functionalities of real-time portable electronic health records, system interoperability, longitudinal health-risks research cohort and surveillance of adverse events infrastructure, and clinical, genome regions – disease and interventional prevention infrastructure. In application to the Dod-VA (Department of Defense and Veteran's Administration) health information systems, the proposed modernization can be carried out as an “add-on” expansion (estimated at $288 million in constant dollars) or as a “stand-alone” innovative information technology system (estimated at $489.7 million), and either solution will prototype an infrastructure for nation-wide health information systems interoperability, portable real-time electronic health records (EHRs), adverse events surveillance, and interventional prevention based on targeted single nucleotide polymorphisms (SNPs) discovery.

Nowadays, most of our activities and personal details are recorded by one entity or another. These data are used for many applications that fundamentally enrich our lives, such as navigation systems, social networks, search engines, and health monitoring. On the darker side of data collection lie usages that can harm us and threaten our sense of privacy. Marketing, as an academic field and corporate practice, has benefited tremendously from this era of data abundance, but has concurrently heightened the risk of associated harms.

In this chapter, we discuss both the great advantages and potential harms ushered in by this era of data collection, as well as ways to mitigate the harms while maintaining the benefits. Specifically, we propose and discuss classes of potential solutions: methods for collecting less data overall, transparency of code and models, federated learning, and identity management tools, among others. Some of these solutions can be implemented now, others require a longer horizon, but all can begin through the advocacy of marketing research. We also discuss possible ways to improve on the benefits of data collection – by developing methods to assist individuals pursue their long-term goals while advocating for privacy in such pursuits.

We are interested in how and why people use or take part to crowdfunding projects.

Over the past four years, we have interviewed over 120 crowdfunding requesters and supporters of over 15 project types from dance to technology to publishing.

The key contributions of this research are: An understanding of the work involved, an understanding of motivations for participation, and an understanding of how the design of platforms influences engagement.

We adopt a computer-supported cooperative work approach from sociology, computer science, and design to provide a new perspective to researchers who seek to understand user behavior, motivations, and the mechanisms in place to support engagement with crowdfunding technology.