Results 91 to 100 of about 108,466 (288)

Rheumatoid Arthritis and Coronary Artery Calcium Progression: A Case Cohort Analysis from ELSA‐Brasil

Arthritis Care &Research, Accepted Article.
Objective To investigate the association between rheumatoid arthritis (RA) and coronary artery calcium (CAC) prevalence, incidence, and progression over four years in adults without prior cardiovascular disease. Methods A case‐cohort study within ELSA‐Brasil included 585 participants (86 RA, 499 controls). Longitudinal analyses were restricted to those
Patrícia Fonseca Estrada, Marcos R. N. Cavalcante, Itamar de Souza Santos, Márcio S. Bittencourt, Vandrize Meneghini, Paulo Andrade Lotufo, Alessandra C.Goulart, Isabela M. Bensenor   +7 more
wiley   +1 more source

A Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller

International Journal of Adaptive Control and Signal Processing, EarlyView.
Block Diagram of the Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller. ABSTRACT This article introduces a discrete‐time robust adaptive one‐sample‐ahead preview super‐twisting sliding mode controller. A stability analysis of the controller by Lyapunov criteria is developed to demonstrate its robustness in handling both ...
Guilherme Vieira Hollweg, Paulo Jefferson Dias de Oliveira Evald, Shivam Chaturvedi, Hilton Abilio Gründling, Rodrigo Varella Tambara, Wencong Su   +5 more
wiley   +1 more source

Power short-term load forecasting based on big data and optimization neural network

Tongxin xuebao, 2016
With the reduction of the cost of power data acquisition and the interconnection of large scale power systems,the types of data available in the power network are becoming more and more abundant.In the past,the centralized fore-casting method was limited
Xin JIN, Long-wei LI, Jia-nan JI, Zhi-qi LI, Yu HU, Yong-bin ZHAO   +5 more
doaj   +2 more sources

Optimizing Models and Data Denoising Algorithms for Power Load Forecasting

Energies
To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed.
Yanxia Li, Ilyosbek Numonov Rakhimjon Ugli, Yuldashev Izzatillo Hakimjon Ugli, Taeo Lee, Tae-Kook Kim   +4 more
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Short-term Electricity Load Forecasting Based on Improved Seagull Algorithm Optimized Gated Recurrent Unit Neural Network

EAI Endorsed Transactions on Energy Web
INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads.
Mengfan Xu, Junyang Pan
doaj   +1 more source

Planar Solid‐State Nanopores Toward Scalable Nanofluidic Integration Based on CMOS Technology

Advanced Engineering Materials, EarlyView.
We present a scalable silicon‐based fabrication strategy for planar solid‐state nanopores to enable their integration with complex nanofluidic systems. Prototype devices demonstrate normal voltage‐current characteristics, good noise performance, and appreciable streaming currents. Our CMOS‐compatible fabrication process offers precise geometric control
Ngan Hoang Pham, Chenyu Wen, Mohammad Hadi Khaksaran, Won‐Yong Lee, Javier Cruz, Klas Hjort, Dongping Wu, Shi‐Li Zhang   +7 more
wiley   +1 more source

A power load forecasting method using cosine similarity and a graph convolutional network

Zhejiang dianli
To address the challenges of existing power load forecasting models， which struggle to deeply extract spatiotemporal correlation features and exhibit weak generalization capabilities—failing to simultaneously manage both short-term and long-term ...
JI Shan, JIANG Wei, JING Xin
doaj   +1 more source

Forecasting wholesale electricity prices: A review of time series models [PDF]

In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. We calibrate autoregression (AR) models, including specifications with a fundamental (exogenous) variable - system load, to ...
Weron, Rafal
core   +1 more source

On the Lightweight Potential of Laser Additive Manufactured NiTi Triply Periodic Minimal Sheet Lattices

Advanced Engineering Materials, EarlyView.
This study explores the lightweight potential of laser additive‐manufactured NiTi triply periodic minimal surface sheet lattices. It systematically investigates the effects of relative density and unit cell size on surface quality, deformation recovery, compression behavior, and energy absorption.
Haoming Mo, Shuo Qu, Shiming Gao, Xu Song   +3 more
wiley   +1 more source