Families in the United States: Their Current Status and Future Prospects

1992 ◽  
Vol 41 (4) ◽  
pp. 430 ◽  
Author(s):  
Mark A Fine
Keyword(s):  
United States ◽  
The United States ◽  
Current Status ◽  
Future Prospects

The Linguistic Vitality of Chinese in the United States

2013 ◽  
Vol 10 (3) ◽  
pp. 294-304
Author(s):  
Na Liu
Keyword(s):  
United States ◽  
Heritage Language ◽  
Chinese Immigrants ◽  
The United States ◽  
Current Status ◽  
Future Prospects ◽  
Heritage Speakers ◽  
Linguistic Vitality

This article examines the current status of Chinese as a heritage language (CHL) in the United States, referring to the Capacity-Opportunity-Desire (COD) framework (Grin, 1990, 2003; LoBianco, 2008). After briefly describing the linguistic profile of Chinese immigrants in the United States, the current status and future prospects of CHL in the United States are discussed. Thearticle concludes that a wide variety of programs are available to CHLspeakers, compared to those available decades ago. However, heritage speakers’ capacity in Chinese will be developed only when they have opportunities to use thelanguage and a desire to learn it.

The Current Status of Retirement Mentoring in Academic Surgery in the United States

2019 ◽  
Vol 02 (03) ◽  
Author(s):  
Sherif Aly ◽  
Allan Stolarski ◽  
Patrick O’Neal ◽  
Edward Whang ◽  
Gentian Kristo
Keyword(s):  
United States ◽  
The United States ◽  
Current Status ◽  
Academic Surgery

scholarly journals Marine harmful algal blooms (HABs) in the United States: History, current status and future trends

Harmful Algae ◽  
2021 ◽  
pp. 101975
Author(s):  
Donald M. Anderson ◽  
Elizabeth Fensin ◽  
Christopher J. Gobler ◽  
Alicia E. Hoeglund ◽  
Katherine A. Hubbard ◽  
...  
Keyword(s):  
United States ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
United States History ◽  
The United States ◽  
Current Status ◽  
Future Trends ◽  
States History

scholarly journals Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks

2021 ◽  
Vol 18 (7) ◽  
pp. 3834
Author(s):  
Mohammad Reza Davahli ◽  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Awad M. Aljuaid ◽  
Redha Taiar
Keyword(s):  
United States ◽  
Neural Networks ◽  
Graph Theory ◽  
Predictive Model ◽  
Short Term Memory ◽  
The United States ◽  
Graphical Analysis ◽  
Economic Consequences ◽  
Current Status ◽  
The Usa

The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the US states. While the GTNN model’s edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with Rt numbers collected over the previous four days and asked them to predict the following day for all states in the USA. The performance of these models was evaluated with the datasets that included Rt values reflecting conditions from 22 January through 26 November 2020 (before the start of COVID-19 vaccination in the USA). To determine the efficiency, we compared the results of two models with each other and with those generated by a baseline Long short-term memory (LSTM) model. The results indicated that the GTNN model outperformed both the NGNN and LSTM models for predicting Rt.

638: Current status of the OBGYN hospitalist workforce in the United States

2016 ◽  
Vol 214 (1) ◽  
pp. S339-S340
Author(s):  
Dotun Ogunyemi ◽  
Alma Aurioles ◽  
Rob Olson ◽  
Nathaniel Sugiyama ◽  
Ray Bahado-Singh
Keyword(s):  
United States ◽  
The United States ◽  
Current Status
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