11,218 research outputs found
Interview with Irene Y. Wong - OH 774
No full textThis interview was conducted by Dr. O. Jennifer Dixon-McKnight with Irene Y. Wong as part of Project 2020: A Collaborative Oral History. Ms. Wong shares her experiences amid the COVID-19 pandemic and critical year 2020, particularly as a retired Asian American and Sun City resident. She discusses the collaborative work of her neighborhood at the onset of the pandemic, notably their efforts to make face masks and to increase technological literacy—e.g. online grocery shopping. She also shares her journey of grief after the loss of her husband and other loved ones during this period of social isolation. Other notable topics of conversation include vaccination, race, social unrest, and the 2020 political climate.
Irene Y. Wong (b. 1950) is a native of Hong Kong and former business professional living in Sun City Carolina Lakes in Indian Land, SC. At the age of seven, she immigrated to the United States as a refugee and resettled in the Washington, D.C. area. She later attended university in Massachusetts and worked for many years in both New England and the Southeast. Following retirement, Ms. Wong moved to Sun City.
Spearheaded by Dr. O. Jennifer Dixon-McKnight, an Assistant Professor of History & African American Studies at Winthrop University, Project 2020 is best summarized in her words: “The goal was to conduct interviews that explored the various ways in which Americans were experiencing and being impacted by the various watershed moments that emerged during 2020 (the global pandemic, social unrest, financial challenges, issues with healthcare, etc.).”https://digitalcommons.winthrop.edu/oralhistoryprogram/1689/thumbnail.jp
SC author and illustrator Kate Salley Palmer signing book
No full textPhotograph of SC author and illustrator Kate Salley Palmer signing boo
Book signing by SC author and illustrator Kate Salley Palmer
No full textPhotograph of Book signing by SC author and illustrator Kate Salley Palme
Medical Care and Rehabilitation Services providing to Children with Cerebral Palsy in Northern Taiwan
No full textEpidemiology of respiratory syncytial virus infection among paediatric inpatients in northern Taiwan
No full textHyperemesis Gravidarum Presenting as Jaundice and Transient Hyperthyroidism Complicated with Acute Pancreatitis.
No full textEstimation methods for fundamental and topological parameters in area-wide macroscopic traffic flow models
No full textArea-wide macroscopic traffic flow models have recently drawn a great deal of attention due to their tremendous potential benefits in various applications, such as network traffic control and initial land use planning. These models can be generally classified into two categories: (a) area-wide macroscopic cost flow (MCF) functions and (b) area-wide macroscopic fundamental diagrams (MFDs). For this study’s in-depth examinations, the selected MCF functions are the macroscopic Bureau of Public Road (MBPR) functions, and the selected MFDs are macroscopic Underwood models. For the beneficial application of these models, the prerequisite is to obtain well-established, accurate, area-wide macroscopic traffic flow models. However, due to various limitations of the high-tech detectors and sensors used in direct area-wide traffic data collection, it remains challenging to estimate these models based on real-world traffic data.
This study develops estimation methods for the two classes of area-wide macroscopic traffic flow models that use two different approaches. In the first approach, direct methods for estimating the fundamental parameters and standard errors of these models (based on traffic data) are evaluated by using linear data projection. Linear data projection is a timely, commonly adopted data scaling method that can compatibly fuse data from various sources for unbiased traffic data estimations. This projection can infer unobservable traffic data by projecting the observable traffic data, using the mean of a set of scaling factors. Linearly projected data may be unbiased in nature, but direct model calibrations based on such data without considering the effects of scaling factor variability can lead to systematically biased estimates of parameters and standard errors. This study unveils the origin of such biases, and it generically proves the necessary and the sufficient conditions for their introduction. To remove or reduce such biases, different estimation methods are proposed that can incorporate the higher order moments of the scaling factors for the two classes of models. Simulations reveal that the proposed methods can accurately estimate the parameters and standard errors. To illustrate these methods, MBPR functions and macroscopic Underwood models are estimated for networks sampled from Hong Kong, according to the proposed methods, with the use of traffic data retrieved from global positioning system-equipped taxis and counting stations.
The second approach is to establish indirect estimation methods for the two classes of area-wide macroscopic traffic flow models by using network topological metrics as inputs. Area-wide macroscopic traffic flow models depict a network’s performance at different levels of traffic demand and at different loadings. Network topologies are apparently the primary factors determining the shapes of these models. This study identifies the determining topological factors, and unveils their relationships with the parameters of the two classes of models based on empirical data. By applying these unveiled relationships, the spatially variable area-wide macroscopic traffic flow models can be established. These models provide simple, fast, straightforward alternatives to the estimations of MCF functions and MFDs using the governing topological metrics. Compared with traffic data, network topological data are much more easily obtainable, and therefore substantially simplify the estimation procedures for these models.published_or_final_versionCivil EngineeringDoctoralDoctor of Philosoph
High-resolution clean-sc
No full textIn this paper a high-resolution extension of CLEAN-SC is proposed: HR-CLEAN-SC. Where CLEAN-SC uses peak sources in “dirty maps” to define so-called source components, HR-CLEAN-SC takes advantage of the fact that source components can likewise be derived from points at some distance from the peak, as long as these “source markers” are on the main lobe of the Point Spread Function (PSF). This is very useful when sources are closely spaced together, such that their PSFs interfere. Then, alternative markers can be sought in which the relative influence by PSFs of other source positions is minimised. For those markers the source components better agree with the actual sources, which allows for better estimation of their locations and strengths. This paper outlines the theory needed to understand this approach and discusses applications to 2D and 3D microphone array simulations with closely spaced sources
SC author and illustrator Kate Salley Palmer talking to event attendees
No full textPhotograph of SC author and illustrator Kate Salley Palmer talking to Rita Lewi
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