Results 71 to 80 of about 117,319,651 (289)

Nonparametric regression for dependent data in the errors-in-variables problem [PDF]

We consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression ...
Toshio Honda
core  

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption

Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2020
Uganda seeks to transform its society from a peasant to a modern and largely urban society by the year 2040. To achieve this, electricity as a form of modern and clean energy has been identified as a driving force for all the sectors of the economy.
Kürşat Ayan, Abdal Kasule
doaj   +1 more source

UDP‐Glucose‐6‐Dehydrogenase Mediated O‐GlcNAcylation of Tight Junction Protein 1 Suppresses Metastasis in Renal Cell Carcinoma

Advanced Science, EarlyView.
Our research unveiled a regulatory paradigm wherein TRIM25 orchestrates the ubiquitin‐mediated degradation of UGDH. UGDH modulates the protein stability of TJP1 by regulating O‐GlcNAcylation levels, effectively impeding the metastasis of ccRCC. Our insights elevate UGDH to a pivotal biomarker and tumor suppressor, marking the first demonstration that ...
Xiaolin Chen, Yuhang Bao, Tao Liu, Jianhui Qiu, Yuanyuan Ma, Zedan Zhang, Chuandong Wang, Ruiyi Deng, Zheng Song, Tianyi He, Haode Liu, Jingcheng Zhou, Yizhou Wang, Kan Gong   +13 more
wiley   +1 more source

GAN-KANInformer-Based Prediction of Underwater Temperature-Pressure Sensor Data

IEEE Access
Accurately predicting short-term changes in interconnected temperature and pressure variables is essential for effective monitoring of subsea oil and gas transportation systems.
Hao Liu, Xinyi Miao, Yinglian Lin
doaj   +1 more source

Integrative Multi‐Omics Analysis Reveals a Mitochondrial–Immune Axis Associated With Neoadjuvant Chemotherapy Response in High‐Grade Serous Ovarian Cancer

Advanced Science, EarlyView.
Integrative multi‐omics analysis delineates a mitochondrial–immune axis governing neoadjuvant chemotherapy response in high‐grade serous ovarian cancer. Immune‐active tumors exhibit enhanced B‐cell infiltration and favorable sensitivity, whereas metabolically rewired tumors display oxidative phosphorylation dependency and resistance.
Wei Jiang, Yi Wang, Xiaohang Lu, Qianlan Yao, Libing Xiang, Xu Cai, Xiaoyu Tu, Shaoxian Tang, Ying Xu, Yufan Cheng, Liming Shen, Huijuan Yang   +11 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter

Kurdistan Journal of Applied Research
Outliers in multivariate linear regression models can significantly distort parameter estimates, leading to biased results and reduced predictive accuracy.
Amira Wali Omer, Taha Hussein Ali
doaj   +1 more source

New multivariate central limit theorems in linear structural and functional error-in-variables models

Electronic Journal of Statistics, 2007
This paper deals simultaneously with linear structural and functional error-in-variables models (SEIVM and FEIVM), revisiting in this context generalized and modified least squares estimators of the slope and intercept, and some methods of moments estimators of unknown variances of the measurement errors.
openaire   +3 more sources