Výsledky vyhledávání

Akademický článek

Was 2016 a Realigning Election in Taiwan?

Autor: CLARK, CAL, TAN, ALEXANDER C., HO, KARL

Publikováno v: Asian Survey, 2020 Nov 01. 60(6), 1006-1028.

Externí odkaz: https://www.jstor.org/stable/48735719

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Dissertation/ Thesis

Dealignment Decades on: Partisanship and Party Support in Great Britain, 1979-1996

Autor: Ho, Karl Ka-yiu

This dissertation surveys electoral change in Great Britain during the period between 1979 and 1996. It analyzes the long-term factors and the short-term dynamics underlying the evolution of three aspects of the electorate: party identification, voti

Externí odkaz: https://digital.library.unt.edu/ark:/67531/metadc278532/

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Dissertation/ Thesis

The Subjective Economy and Political Support: The Case of the British Labour Party

Autor: Ho, Karl Ka-yiu

During the past two decades, extensive research efforts have focused on the conventional wisdom that the economy has a direct influence on a party's destiny. This hypothesis rests on the implicit assumption that the linkages between macroeconomic var

Externí odkaz: https://digital.library.unt.edu/ark:/67531/metadc500261/

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Kniha

What If We Don’t Party? Political Partisanship in Taiwan and Korea in the 1990s

Autor: Lee, Wei-Chin, author, C. Tan, Alexander, author, Ho, Karl, author, Kang, Kyung-Tae, author, Yu, Tsung-Chi, author

Publikováno v: Taiwan in Perspective. :67-84

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Akademický článek

Global Patterns in the Achievement of Women's Human Rights to Equality

Autor: Poe, Steven C., Wendel-Blunt, Dierdre, Ho, Karl

Publikováno v: Human Rights Quarterly, 1997 Nov 01. 19(4), 813-835.

Externí odkaz: https://www.jstor.org/stable/762687

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Akademický článek

Accurate Prediction of Hourly Energy Consumption in a Residential Building Based on the Occupancy Rate Using Machine Learning Approaches

Autor: Le Hoai My Truong, Ka Ho Karl Chow, Rungsimun Luevisadpaibul, Gokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Ben Horan, Saad Mekhilef, Alex Stojcevski

Publikováno v: Applied Sciences, Vol 11, Iss 5, p 2229 (2021)

In this paper, a novel deep neural network-based energy prediction algorithm for accurately forecasting the day-ahead hourly energy consumption profile of a residential building considering occupancy rate is proposed. Accurate estimation of residenti

Externí odkaz: https://doaj.org/article/151356cd3b2e4986a6a06575a3cbcf4c

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