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Modeling of Composition and Channel Length-Dependent Transient Characteristics in Short-Channel IGZO Field-Effect Transistors.
ACS Appl Mater Interfaces
January 2025
School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.
In this study, we analyze the characteristics of fast transient drain current () in IGZO-based field-effect transistors (FETs) with different composition ratios (device O: ratio of 1:1:1 for In, Ga, Zn, device G: ratio of 0.307:0.39:0.
View Article and Find Full Text PDFAssociation Between Four Non-Insulin-Based Insulin Resistance Indices and the Risk of Post-Stroke Depression.
Clin Interv Aging
January 2025
Department of Neurology, the Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, Jiangsu, People's Republic of China.
Purpose: Research suggests that insulin resistance (IR) is associated with acute ischemic stroke (AIS) and depression. The use of insulin-based IR assessments is complicated. Therefore, we explored the relationship between four non-insulin-based IR indices and post-stroke depression (PSD).
View Article and Find Full Text PDFInterventions and implementation considerations for reducing pre-treatment loss to follow-up in adults with pulmonary tuberculosis: A scoping review.
F1000Res
January 2025
Faculty of Medicine and Health Sciences, Division of Epidemiology and Biostatistics, Stellenbosch University Centre for Evidence-Based Health Care, Cape Town, South Africa.
Background: Tuberculosis (TB) is a leading cause of death worldwide with over 90% of reported cases occurring in low- and middle-income countries (LMICs). Pre-treatment loss to follow-up (PTLFU) is a key contributor to TB mortality and infection transmission.
Objectives: We performed a scoping review to map available evidence on interventions to reduce PTLFU in adults with pulmonary TB, identify gaps in existing knowledge, and develop a conceptual framework to guide intervention implementation.
Exploring emotional patterns in social media through NLP models to unravel mental health insights.
Healthc Technol Lett
January 2025
This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts. The research question was: can an ensemble of fine-tuned transformer models (XLNet, RoBERTa, and ELECTRA) with Bayesian hyperparameter optimization improve the accuracy of mental health disorder classification in social media text. Three transformer models (XLNet, RoBERTa, and ELECTRA) were fine-tuned on a dataset of social media posts labelled with 15 distinct mental health disorders.
View Article and Find Full Text PDFOptimal extreme learning machine for diagnosing brain tumor based on modified sailfish optimizer.
Heliyon
July 2024
College of Engineering and IT, University of Dubai, Academic City, 14143, Dubai, United Arab Emirates.
This study proposes a hierarchical automated methodology for detecting brain tumors in Magnetic Resonance Imaging (MRI), focusing on preprocessing images to improve quality and eliminate artifacts or noise. A modified Extreme Learning Machine is then used to diagnose brain tumors that are integrated with the Modified Sailfish optimizer to enhance its performance. The Modified Sailfish optimizer is a metaheuristic algorithm known for efficiently navigating optimization landscapes and enhancing convergence speed.
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