Results 111 to 120 of about 3,066,894 (297)

dropout

tools for dropout analysis when working with survey ...
Hendrik Mann (18115174)
core   +1 more source

Addressing School Dropout in Texas: A Summary for Administrators and Policymakers of "Dropout Prevention: A Practice Guide" [PDF]

, 2009
This booklet summarizes the information from: Dropout Prevention: A Practice Guide, available on the Institute of Education Sciences web site at http://ies.ed.gov/ncee/wwc ...
Clarke, L., Dynarksi, M., Finn, J., Cobb, B., Rumberger, R., Smink, J.   +5 more
core   +1 more source

Sustained Self‐Powered Real‐Time Vibration Monitoring Through Integrated Nonlinear Harvesting and Energy‐Aware Wireless Sensing

Advanced Science, EarlyView.
A self‐powered vibration sensing platform integrates a quasi‐zero‐stiffness piezoelectric harvester, synchronized electric charge extraction, energy management, and wireless ADC acquisition. Ambient vibration is simultaneously harvested as energy and digitized as information, enabling sustained real‐time waveform monitoring without external power.
Yizhou Li, Ye Zhang, Hao Tang, Yaozi Zheng, Yawei Wang, Chaoyang Zhao, Liwei Dong, Dewen Yu, Junlei Wang, Chunbo Lan, Guobiao Hu   +10 more
wiley   +1 more source

Key determinants of university dropout: academic, economic, and psychosocial factors in student retention

Frontiers in Education
IntroductionUniversity dropout remains a persistent challenge in higher education, reflecting structural tensions between inclusivity, student retention, and educational equity.
Alejandro Valencia-Arias, Julio César Valera Aredo, Jackeline Valencia, Sebastián Cardona-Acevedo, Juan Camilo Patiño-Vanegas, Hernán Uribe Bedoya   +5 more
doaj   +1 more source

Trustworthy Multimodal Attention Framework for Creep Rupture Life Prediction Under Data‐Scarce Conditions: A Case Study on IN718

Advanced Science, EarlyView.
An attention‐based multimodal deep learning framework is developed to predict the creep life of Ni‐based superalloys by fusing processing parameters with microstructural micrographs. The model achieves high accuracy (R2 = 0.92), aligns with metallurgical principles by capturing δ‐phase influence, and incorporates uncertainty quantification, offering a ...
Haopeng Lv, Dayong Wu, Ziyuan Rao, Qian Wang, Haikun Ma, Jie Kang, Wang Li, Huicong Dong, Yandong Wang, Zhinan Yang, Ru Su   +10 more
wiley   +1 more source

Addressing School Dropout in Texas: A Summary for Teachers and Practitioners of "Dropout Prevention: A Practice Guide" [PDF]

, 2009
This booklet summarizes the information from: Dropout Prevention: A Practice Guide, available on the Institute of Education Sciences web site at http://ies.ed.gov/ncee/wwc ...
Clarke, L., Dynarksi, M., Finn, J., Cobb, B., Rumberger, R., Smink, J.   +5 more
core   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

Schrödinger’s student: when students both do and do not drop out

Frontiers in Education
Student “drop out” is a key quality measure in European higher education, causing our understanding of dropout to depend heavily on statistics of what we choose to measure. Still, there is no shared operational definition of what dropout is.
Marius O. Johansen, Kjersti E. Lea, Helene M. K. Eide   +2 more
doaj   +1 more source

Uncoupling Type I Interferon Benefits From Inflammatory Toxicity: Transformer‐Prioritized Precision Agonists for Potent and Safer Cancer Immunotherapy

Advanced Science, EarlyView.
A Transformer‐based AI framework, DLINP, screens millions of compounds to identify Co68, a cobalt‐pincer organometallic complex that biases TLR4‐MD2 signaling toward antitumor interferon activation while suppressing inflammatory toxicity through an early TLR4‐SYK‐STAT1 axis.
Xuefei Guo, Yang Zhao, Xianle Rong, Xingyu Chen, Xiao Wang, Tianyi Liu, Yunfei Xie, Yushu Zou, Pingsen Zhao, Qiang Liu, Fuping You   +10 more
wiley   +1 more source

Factors, Prediction, Explainability, and Simulating University Dropout Through Machine Learning: A Systematic Review, 2012–2024

Computation
College dropout represents a significant challenge for universities, and despite advances in machine learning technologies, predicting dropout remains a complex task.
Mauricio Quimiz-Moreira, Rosa Delgadillo, Jorge Parraga-Alava, Nelson Maculan, David Mauricio   +4 more
doaj   +1 more source