Results 131 to 140 of about 96,811 (275)

Correlates of burnout and dropout intentions in medical students: A cross-sectional study

Journal of Affective Disorders
Burnout is a pervasive issue among medical students, exhibiting a high prevalence that jeopardizes their academic success and may also predispose them to more severe affective disorders such as depression. This study aims to explore the complex relationships between psychological capital (PsyCap), general social support, educational satisfaction, and ...
Sinval, Jorge, Oliveira, Pedro, Novais, Filipa, Almeida, Carla Maria, Telles-Correia, Diogo   +4 more
openaire   +4 more sources

Sequential Portal Vein–Hepatic Vein Embolization: Progress Yet Unaccounted Pitfalls

Annals of Gastroenterological Surgery, EarlyView.
Syeda Rabiah Shahid, Ahmad Furqan Anjum
wiley   +1 more source

Cryogenic Neuromorphic Synaptic Behavior in 180 nm Silicon Transistors for Emerging Computing Systems

Advanced Intelligent Systems, EarlyView.
This study investigates the neuromorphic plasticity behavior of 180 nm bulk complementary metal oxide semiconductor (CMOS) transistors at cryogenic temperatures. The observed hysteresis data reveal a signature of synaptic behavior in CMOS transistors at 4 K.
Fiheon Imroze, Bhavani Yalagala, Naveen Kumar, Mostafa Elsayed, Meraj Ahmad, Robert Graham, Vihar Georgiev, Hadi Heidari, Martin Weides   +8 more
wiley   +1 more source

Systematic review on inhaled corticosteroid monotherapy and its efficacy and safety in longterm treatment of patients with chronic obstructive pulmonary disease (COPD) [PDF]

--chronic obstructive pulmonary disease,COPD,corticosteroids,systematic review,chronisch obstruktive Lungenerkrankung,COPD,Kortikosteroid,systematischer ...
Buchberger, Barbara, Kossmann, Beate, Neumann, Anja, Niebuhr, Dea, Wasem, Jürgen   +4 more
core  

PREDICTORS OF PROFESSIONAL AND INSTITUTIONAL DROPOUT INTENTION IN TEACHERS

Psicologia, Saúde & Doença, 2023
Pricila Kuhn, Mary Sandra Carlotto
openaire   +1 more source

Machine Learning‐Based Standard Compact Model Binning Parameter Extraction Methodology for Integrated Circuit Design of Next‐Generation Semiconductor Devices

Advanced Intelligent Systems, EarlyView.
This study presents a neural network‐based methodology for Berkeley Short‐Channel IGFET Model–Common Multi‐Gate parameter extraction of gate‐all‐around field effect transistors, integrating binning adaptive sampling and transformer neural networks to efficiently capture current–voltage and capacitance–voltage characteristics.
Jaeweon Kang, Johyeon Kim, Sueyeon Kim, Hyunbo Cho, Jongwook Jeon   +4 more
wiley   +1 more source

Multitarget Recognition of Flower Images Based on Lightweight Deep Neural Network and Transfer Learning

Advanced Intelligent Systems, EarlyView.
This article proposes a lightweight YOLOv4‐based detection model using MobileNetV3 or CSPDarknet53_tiny, achieving 30+ FPS and higher mAP. It also presents a ShuffleNet‐based classification model with transfer learning and GAN‐augmented images, improving generalization and accuracy.
Qingyang Liu, Yanrong Hu, Hongjiu Liu
wiley   +1 more source

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source

RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non‐Small Cell Lung Cancer

Advanced Intelligent Systems, EarlyView.
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley   +1 more source