Results 121 to 130 of about 186,322 (298)

AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes

Advanced Intelligent Discovery, EarlyView.
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song, Xinmin Li, Hongbo Yu, Le Xiong, Guixia Liu, Weihua Li, Yun Tang   +6 more
wiley   +1 more source

Sublingual versus subcutaneous immunotherapy: patient adherence at a large German allergy center

Patient Preference and Adherence, 2017
Marie-Luise Lemberg,1 Till Berk,2 Kija Shah-Hosseini,1 Elena-Manja Kasche,1,3 Ralph Mösges1 1Faculty of Medicine, Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne, Cologne, Germany; 2Department of Trauma Surgery,
Lemberg M, Berk T, Shah-Hosseini K, Kasche E, Mösges R   +4 more
doaj  

Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Advanced Intelligent Discovery, EarlyView.
Artificial neural networks enable autonomous extraction of onset times from transient heating curves in luminescence thermometry. Using Ln3+‐doped upconverting nanoparticles as luminescent thermometers, we combine experimental transients with physically motivated synthetic curves to enhance data diversity and improve generalization.
David J. Sousa, Erving Ximendes, Luís D. Carlos, Carlos D. S. Brites   +3 more
wiley   +1 more source

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article describes a multimodal fusion data acquisition and processing system about electromyography for dynamic movement recognition and bioelectrical impedance for key posture recognition. In addition, a new dynamic–static fusion algorithm strategy is designed.
Chenhao Cao, Gang Ma, Zelin Chen, Yiming Ouyang, Hu Jin, Shiwu Zhang   +5 more
wiley   +1 more source

A survey on dropout risk factors among medical students, Shiraz Medical University, 1999

Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Bābul, 2003
Background and Objective: Dropout of medical students is an important problem in medical education. If not controlled, it will result a low scientific knowledge of physicians in future years.
GH.R Dehbozorgi
doaj  

Impact of Changes to the Delivery of Opioid Agonist Treatment, Introduced During the COVID-19 Pandemic, On Treatment Access and Dropout in Ireland: An Interrupted Time Series Analysis

, 2022
Louise Durand, Fiona Boland, Norma Harnedy, Íde Delargy, Mike Scully, Margaret Bourke, William Ebbitt, María Otero Vázquez, Eamon Keenan, Gráinne Cousins   +9 more
openalex   +1 more source

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A wireless wearable sweat rate sensor system is presented, featuring digital 3D direct‐write printing on a flexible substrate with microfluidic layers for continuous, real‐time monitoring. Printed encapsulated metal electrodes are used for capacitance measurements, achieving high sensitivity (0.01 μL min−1) while maintaining a compact and lightweight ...
Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan   +6 more
wiley   +1 more source

Educational Policy Impacts on Secondary School Dropout Rates in Nigerian Villages: A Randomized Controlled Trial Analysis

, 2005
Nneka Chikwendiu, Obi Ejike, Chidera Nnamdi   +2 more
openalex   +1 more source

A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

Advanced Intelligent Systems, EarlyView.
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng, Hyobin Kim, Patrick Martin, Junil Kim, Tae‐Hyung Kim, Gi‐Hoon Nam, Dongha Kim, Kyoung Jae Won   +7 more
wiley   +1 more source

Dropout in Testing Phase Makes Adversarial Samples Generation Difficult

, 2019
Yuan Wang, Zhiming Wang, Xu-Cheng Yin, Chao Zhu, Yuan Wang, C Szegedy, W Zaremba, I Sutskever, A Rozsa, M Gnther, Terrance Boult, N Carlini, D Wagner, I Goodfellow, J Shlens, C Szegedy, N Papernot, P Mcdaniel, S Jha, S.-M Moosavi-Dezfooli, A Fawzi, P Frossard   +21 more
openalex   +1 more source