scholarly journals Comparative Analysis on Current Status of Research University Libraries between Korea and the United States

Author(s):  
Hye-Young Han
2019 ◽  
Vol 02 (03) ◽  
Author(s):  
Sherif Aly ◽  
Allan Stolarski ◽  
Patrick O’Neal ◽  
Edward Whang ◽  
Gentian Kristo

Author(s):  
V. Iordanova ◽  
A. Ananev

The authors of this scientific article conducted a comparative analysis of the trade policy of US presidents Barack Obama and Donald Trump. The article states that the tightening of trade policy by the current President is counterproductive and has a serious impact not only on the economic development of the United States, but also on the entire world economy as a whole.


Harmful Algae ◽  
2021 ◽  
pp. 101975
Author(s):  
Donald M. Anderson ◽  
Elizabeth Fensin ◽  
Christopher J. Gobler ◽  
Alicia E. Hoeglund ◽  
Katherine A. Hubbard ◽  
...  

2021 ◽  
pp. 1-6
Author(s):  
Matias López ◽  
Juan Pablo Luna

ABSTRACT By replying to Kurt Weyland’s (2020) comparative study of populism, we revisit optimistic perspectives on the health of American democracy in light of existing evidence. Relying on a set-theoretical approach, Weyland concludes that populists succeed in subverting democracy only when institutional weakness and conjunctural misfortune are observed jointly in a polity, thereby conferring on the United States immunity to democratic reversal. We challenge this conclusion on two grounds. First, we argue that the focus on institutional dynamics neglects the impact of the structural conditions in which institutions are embedded, such as inequality, racial cleavages, and changing political attitudes among the public. Second, we claim that endogeneity, coding errors, and the (mis)use of Boolean algebra raise questions about the accuracy of the analysis and its conclusions. Although we are skeptical of crisp-set Qualitative Comparative Analysis as an adequate modeling choice, we replicate the original analysis and find that the paths toward democratic backsliding and continuity are both potentially compatible with the United States.


Author(s):  
Mohammad Reza Davahli ◽  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Awad M. Aljuaid ◽  
Redha Taiar

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.


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