Results 171 to 180 of about 444,093 (258)

A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data. [PDF]

Comput Struct Biotechnol J
An HE, Mun MH, Kim JI, Kim CB.
europepmc   +1 more source

Weather Attention in Online Discourse and Price Uncertainty in Agricultural Futures Markets

Agribusiness, EarlyView.
ABSTRACT Weather is a central determinant of production risk in agricultural markets, but markets respond not only to weather conditions themselves, but also to how those conditions are noticed, framed, and interpreted in public discourse. In this setting, real‐time online weather discussions may shape price uncertainty by influencing how market ...
Mario A. Ortez, Olga Isengildina Massa, Michael Liam Smith, Nicole Olynk Widmar   +3 more
wiley   +1 more source

Consistent and idiosyncratic pleiotropy in shaping genetic correlations. [PDF]

Mol Biol Evol
Cai H, Geiler-Samerotte K, Des Marais DL.   +2 more
europepmc   +1 more source

pyDMS: A Python package for the determination of physics‐informed dual‐mode sorption (DMS) parameters

AIChE Journal, EarlyView.
Abstract Sorption in glassy polymer membranes is commonly modeled with the dual‐mode sorption (DMS) model. Fitting the DMS model to sorption isotherms presents challenges, as multiple parameter sets may prove satisfactory. This work presents pyDMS, an open‐source Python package for the computation of DMS parameters obtained via a physics‐informed ...
Brandon C. Tapia, Pablo A. Dean, Jing Ying Yeo, Albert X. Wu, Zachary P. Smith   +4 more
wiley   +1 more source

A Quantitative Approach to Biodiversity Offset Multipliers: Managing Uncertainty in Complex Projects. [PDF]

Environ Manage
van der Lee AS, Brook ME, Koops MA.
europepmc   +1 more source

Machine learning driven many‐objective moving horizon scheduling optimization

AIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

Tightly Coupled LEO SOP/INS Integrated Navigation Using an Adaptive Unscented Kalman Filter Framework. [PDF]

Sensors (Basel)
Zhao Y, Feng J, Jiang Y, Fan Z, Xie B, Tang C, Liu Y.   +6 more
europepmc   +1 more source

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Advanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah, Muhammad Zulqarnain Zeb, Muhammad Husnain Zeb, Babar Shabbir   +3 more
wiley   +1 more source

A Decoupled Fractional-Order Kalman Filter for Accelerometer Tilt Angle Estimation. [PDF]

Sensors (Basel)
Wu N, Liu X, Han H, Wang J.
europepmc   +1 more source

Advancing Efficient Error Reduction in DNA Data Storage Systems with Deep Learning‐Based Denoising Models

Advanced Intelligent Discovery, EarlyView.
Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo, Anshula Tandon, Nguyen Kim Uyen, Quoc Nam Le, Jee‐Hyong Lee, Sung Ha Park   +5 more
wiley   +1 more source