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Performance of a folk song 'Iniu ahuliu'
This is a Liangmai folk song composed by Liangmai folk singer Niureng of Kalalong village. The current song was sung by Wijotniliu of Rienta village. The song was video recorded in Chakha village.
Date:
January 16, 2022
Creator:
Mataina, Wichamdinbo
Object Type:
Video
System:
The UNT Digital Library
Performance of a folk song 'Niamning tu saojiu'
This is a Liangmai folk song composed by Liangmai folk singer Kaihuii (Kaiguiyang). The current song was sung by Wijotniliu of Rienta village. The song was recorded in Chakha village.
Date:
January 16, 2022
Creator:
Mataina, Wichamdinbo
Object Type:
Video
System:
The UNT Digital Library
Performance of a folk song 'Ting maleng wang'
This is a Liangmai folk song composed by Liangmai folk singer Namsongwi. The current song was sung by Wijotniliu of Rienta village. The song was recorded in Chakha village.
Date:
January 16, 2022
Creator:
Mataina, Wichamdinbo
Object Type:
Video
System:
The UNT Digital Library
Performance of a folk song 'Niamning hai ndah'
This is a Liangmai folk song composed by Liangmai folk singer Kaihuii (Kaiguiyang). The current song was sung by Wijotniliu of Rienta village. The song was recorded in Chakha village.
Date:
January 16, 2022
Creator:
Mataina, Wichamdinbo
Object Type:
Video
System:
The UNT Digital Library
Performance of a folk song 'Tase karia'
This is a Liangmai folk song composed by Liangmai folk singer Niureng of Kalalong village. The current song was sung by Wijotniliu of Rienta village. The song was recorded in Chakha village.
Date:
January 16, 2022
Creator:
Mataina, Wichamdinbo
Object Type:
Video
System:
The UNT Digital Library
Akha notebook 84
Handwritten notes and transcriptions of narratives told by Ásɔ̀q about clothing, spirits, marriage, how Akhas communicate, and death and song lyrics and word lists.
Date:
1978-01-16/1978-03-13
Creator:
Hansson, Inga-Lill
Object Type:
Text
System:
The UNT Digital Library
Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network
Article describes how accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. This study aims to assess the effectiveness of these algorithms for deck condition rating prediction at the national level.
Date:
January 16, 2024
Creator:
Fard, Fariba & Fard, Fereshteh Sadeghi Naieni
Object Type:
Article
System:
The UNT Digital Library