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PROSPER (Project, Sustainability, and Perseverance) Learning Model in English for Computer Science
PROSPER (Project, Sustainability, and Perseverance) Learning Model in English for Computer Science
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PROSPER (Project, Sustainability, and Perseverance) Learning Model in English for Computer Science

Wahyuni, Dewi Sari ; Rozimela, Yenni ; Ardi, Havid ; Mukhaiyar, Mukhaiyar ; Darmansyah, Darmansyah

Sustainability, 2022-12, Vol.14 (24), p.16749 [Periódico revisado por pares]

Basel: MDPI AG

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Supporting and Sustaining Equitable STEAM Activities in High School Classrooms: Understanding Computer Science Teachers’ Needs and Practices When Implementing an E-Textiles Curriculum to Forge Connections across Communities
Supporting and Sustaining Equitable STEAM Activities in High School Classrooms: Understanding Computer Science Teachers’ Needs and Practices When Implementing an E-Textiles Curriculum to Forge Connections across Communities
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Supporting and Sustaining Equitable STEAM Activities in High School Classrooms: Understanding Computer Science Teachers’ Needs and Practices When Implementing an E-Textiles Curriculum to Forge Connections across Communities

Fields, Deborah ; Kafai, Yasmin

Sustainability, 2023-05, Vol.15 (11), p.8468 [Periódico revisado por pares]

Basel: MDPI AG

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3
WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs
WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs
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WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs

Galiez, Clovis ; Siebert, Matthias ; Enault, François ; Vincent, Jonathan ; Söding, Johannes Birol, Inanc

Bioinformatics (Oxford, England), 2017-10, Vol.33 (19), p.3113-3114 [Periódico revisado por pares]

England: Oxford University Press (OUP)

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4
LearningRlab: Educational R Package for Statistics in Computer Science Engineering
LearningRlab: Educational R Package for Statistics in Computer Science Engineering
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LearningRlab: Educational R Package for Statistics in Computer Science Engineering

Cuadrado-Gallego, Juan J ; Gómez, Josefa ; Tayebi, Abdelhamid ; Usero, Luis ; Hellín, Carlos J ; Valledor, Adrián

Sustainability, 2023-05, Vol.15 (10), p.8246 [Periódico revisado por pares]

Basel: MDPI AG

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5
Improved electrochemical performance using well-dispersed carbon nanotubes as conductive additive in the Ni-rich positive electrode of lithium-ion batteries
Improved electrochemical performance using well-dispersed carbon nanotubes as conductive additive in the Ni-rich positive electrode of lithium-ion batteries
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Improved electrochemical performance using well-dispersed carbon nanotubes as conductive additive in the Ni-rich positive electrode of lithium-ion batteries

Choi, Jae Hong ; Lee, Chaewon ; Park, Sungwoo ; Hwang, Mingi ; Embleton, Tom James ; Ko, Kyungmok ; Jo, Mina ; Saqib, Kashif Saleem ; Yun, Jeongsik ; Jo, Minki ; Son, Yoonkook ; Oh, Pilgun

Electrochemistry communications, 2023-01, Vol.146, p.107419, Article 107419 [Periódico revisado por pares]

Elsevier B.V

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6
Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques
Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques
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Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques

Erfani, Seyed Mohammad Hassan ; Goharian, Erfan

Journal of hydroinformatics, 2023-05, Vol.25 (3), p.835-850 [Periódico revisado por pares]

IWA Publishing

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7
Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets
Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets
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Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets

Le, Xuan-Hien ; Thu Hien, Le Thi ; Ho, Hung Viet ; Lee, Giha

Journal of hydroinformatics, 2024-07, Vol.26 (7), p.1572-1588 [Periódico revisado por pares]

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8
Using computer vision to monitor ice conditions in water supply infrastructure: a study of salient image features
Using computer vision to monitor ice conditions in water supply infrastructure: a study of salient image features
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Using computer vision to monitor ice conditions in water supply infrastructure: a study of salient image features

Chen, Junjie ; Liu, Donghai

Journal of hydroinformatics, 2023-07, Vol.25 (4), p.1139-1152 [Periódico revisado por pares]

IWA Publishing

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9
Predicting total upland sediment yield using regression and machine learning models for improved land management and water conservation
Predicting total upland sediment yield using regression and machine learning models for improved land management and water conservation
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Predicting total upland sediment yield using regression and machine learning models for improved land management and water conservation

S. M., Shravan Kumar ; Pandey, Manish ; Umamahesh, N. V.

Journal of hydroinformatics, 2024-08 [Periódico revisado por pares]

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10
An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin
An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin
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An integrated cyberinfrastructure system for water quality resources in the Upper Mississippi River Basin

Mount, Jerry ; Sermet, Yusuf ; Jones, Christopher S. ; Schilling, Keith E. ; Gassman, Philip W. ; Weber, Larry J. ; Krajewski, Witold F. ; Demir, Ibrahim

Journal of hydroinformatics, 2024-08 [Periódico revisado por pares]

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