Predicting outcomes using neural networks in the intensive care unit DOI
GR Sridhar,

Venkat Yarabati,

Lakshmi Gumpeny

et al.

World Journal of Clinical Cases, Journal Year: 2024, Volume and Issue: 13(11)

Published: Dec. 25, 2024

Patients in intensive care units (ICUs) require rapid critical decision making. Modern ICUs are data rich, where information streams from diverse sources. Machine learning (ML) and neural networks (NN) can leverage the rich for prognostication clinical care. They handle complex nonlinear relationships medical have advantages over traditional predictive methods. A number of models used: (1) Feedforward networks; (2) Recurrent NN convolutional to predict key outcomes such as mortality, length stay ICU likelihood complications. Current exist silos; their integration into workflow requires greater transparency on that analyzed. Most accurate enough use operate 'black-boxes' which logic behind making is opaque. Advances occurred see through opacity peer processing black-box. In near future ML positioned help far beyond what currently possible. Transparency first step toward validation followed by trust adoption. summary, NNs transformative ability enhance accuracy improve patient management ICUs. The concept should soon be turning reality.

Language: Английский

Recent Advances in Polyvinylidene Fluoride with Multifunctional Properties in Nanogenerators DOI Open Access
Yueming Hu, Feijie Wang,

Yan Ma

et al.

Small, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

Abstract Amid the global energy crisis and rising emphasis on sustainability, efficient harvesting has become a research priority. Nanogenerators excel in converting abundant mechanical thermal into electricity, offering promising path for sustainable solutions. Among various nanogenerator's materials, Polyvinylidene fluoride (PVDF), with its distinctive molecular structure, exhibits multifunctional electrical properties including dielectric, piezoelectric pyroelectric characteristics. These combined excellent flexibility make PVDF prime candidate material nanogenerators. In nanogenerators, this is capable of efficiently collecting energy. This paper discusses how PVDF's are manifested three types nanogenerators compares performance these addition, strategies to improve output demonstrated, physical chemical modification as well structural optimization such hybrid structures external circuits. It also introduces application natural human harvesting, prospects medical technologies smart home systems. The aim promote use self‐powered sensing, monitoring, thereby providing valuable insights designing more versatile

Language: Английский

Citations

0
Nanogenerator-induced personalized wearable health monitoring electronics: a review DOI
Ahmed Shahat, Mohamed Ashraf Mahmoud, Islam M. El‐Sewify

et al.

Nano Energy, Journal Year: 2025, Volume and Issue: unknown, P. 110897 - 110897

Published: March 1, 2025

Language: Английский

Citations

0
Enhanced Monte Carlo Simulations for Electron Energy Loss Mitigation in Real-Space Nanoimaging of Thick Biological Samples and Microchips DOI Open Access
Xi Yang, Victor Smaluk, Timur Shaftan

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(3), P. 469 - 469

Published: Jan. 24, 2025

High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread other factors. In mode, where the objective projector lenses positioned downstream of sample, electron–sample interactions cause loss, which adversely impacts image quality resolution. This study introduces a simulation tool to estimate loss spectrum (EELS) function sample thickness, covering beam energies from 300 keV 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, includes state-of-the-art source with 2-picometer emittance, an 3×10−5, optimized characteristics, we aim minimize spread. By integrating EELS capabilities into BNL Monte Carlo (MC) code thicker samples, evaluate parameters mitigate resulting interactions. Based on our simulations, propose experimental procedure quantitively distinguishing between elastic inelastic scattering. The findings will guide selection optimal settings, thereby enhancing resolution nanoimaging thick samples microchips.

Language: Английский

Citations

0
Advances in brain computer interface for amyotrophic lateral sclerosis communication DOI Creative Commons
Yuchun Wang, Yuee Tang, Qianfeng Wang

et al.

Brain‐X, Journal Year: 2025, Volume and Issue: 3(1)

Published: March 1, 2025

Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that often results in the loss of speech, creating significant communication barriers. Brain–computer interfaces (BCIs) provide transformative solution for restoring and enhancing quality life ALS individuals. Recent advances implantable electrocorticographic systems have demonstrated feasibility synthesizing intelligible speech directly from neural activity. By recording high‐resolution signals motor, premotor, somatosensory cortices with decoding algorithms, these can transform patterns into acoustic features providing natural intuitive pathways Non‐invasive electroencephalography, while lacking spatial resolution systems, offers safer alternative high temporal capturing speech‐related dynamics. When combined robust feature extraction techniques, such as common pattern time‐frequency analyses, well multimodal integration functional near‐infrared spectroscopy or electromyography, it effectively enhances accuracy system robustness. Despite progress, challenges remain, including user variability, BCI illiteracy, impact fatigue on performance. Personalized models, adaptive secure frameworks brain data privacy are essential addressing limitations, enabling BCIs to enhance accessibility reliability. Advancing technologies methodologies holds immense promise independence bridging gap individuals ALS. Future research could focus long‐term clinical studies evaluate stability effectiveness development more unobtrusive paradigms.

Language: Английский

Citations

0
Multifunctional subwavelength device for wide-band sound absorption and acoustic-electric conversion DOI
Ming Yuan,

Bo Zhu,

Qingsong Jiang

et al.

Sensors and Actuators A Physical, Journal Year: 2025, Volume and Issue: unknown, P. 116554 - 116554

Published: April 1, 2025

Language: Английский

Citations

0
Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence DOI Creative Commons

Yi‐Feng Su,

D.L. Yin,

Xinmao Zhao

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2520 - 2520

Published: April 17, 2025

The integration of Deep Learning with sensor technologies has significantly advanced the field intelligent sensing and decision making by enhancing perceptual capabilities delivering sophisticated data analysis processing functionalities. This review provides a comprehensive overview synergy between sensors, particular focus on applications triboelectric nanogenerator (TENG)-based self-powered sensors combined artificial intelligence (AI) algorithms. First, evolution is reviewed, highlighting advantages, limitations, application domains several classical models. Next, innovative in autonomous driving, wearable devices, Industrial Internet Things (IIoT) are discussed, emphasizing critical role neural networks precision capabilities. then delves into TENG-based introducing their mechanisms based contact electrification electrostatic induction, material selection strategies, novel structural designs, efficient energy conversion methods. algorithms showcased through groundbreaking motion recognition, smart healthcare, homes, human–machine interaction. Finally, future research directions outlined, including multimodal fusion, edge computing integration, brain-inspired neuromorphic computing, to expand robotics, space exploration, other high-tech fields. offers theoretical technical insights collaborative innovation technologies, paving way for development next-generation systems.

Language: Английский

Citations

0
Predicting outcomes using neural networks in the intensive care unit DOI
GR Sridhar,

Venkat Yarabati,

Lakshmi Gumpeny

et al.

World Journal of Clinical Cases, Journal Year: 2024, Volume and Issue: 13(11)

Published: Dec. 25, 2024

Patients in intensive care units (ICUs) require rapid critical decision making. Modern ICUs are data rich, where information streams from diverse sources. Machine learning (ML) and neural networks (NN) can leverage the rich for prognostication clinical care. They handle complex nonlinear relationships medical have advantages over traditional predictive methods. A number of models used: (1) Feedforward networks; (2) Recurrent NN convolutional to predict key outcomes such as mortality, length stay ICU likelihood complications. Current exist silos; their integration into workflow requires greater transparency on that analyzed. Most accurate enough use operate 'black-boxes' which logic behind making is opaque. Advances occurred see through opacity peer processing black-box. In near future ML positioned help far beyond what currently possible. Transparency first step toward validation followed by trust adoption. summary, NNs transformative ability enhance accuracy improve patient management ICUs. The concept should soon be turning reality.

Language: Английский

Citations

0