Results 131 to 140 of about 34,245 (290)
The multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by many authors.
Mohamed Reda Abonazel
doaj +1 more source
Reevaluating the Activity of ZIF‐8 Based FeNCs for Electrochemical Ammonia Production
Though receiving much attention, the field of electrochemical nitrogen reduction reaction (eNRR) to ammonia is marked by doubts about whether this reaction is possible in aqueous media. This work sheds light on this question for iron single‐atom on N‐doped carbon (FeNC) catalysts—a class of well‐known catalysts that is also worth testing for the sister
Caroline Schneider +6 more
wiley +1 more source
A food‐grade cooling composite made from starch and recycled eggshell powder offers a scalable, ultra‐low‐cost solution for passive daytime radiative cooling. Easily prepared using basic kitchen tools, this material empowers communities, even in areas with limited infrastructure, to stay cooler during worsening summer heat waves.
Qimeng Song +3 more
wiley +1 more source
On the performance of the new minimax shrinkage estimators for a normal mean vector
This paper explores new classes of estimators for a multivariate normal mean (MNM) with an unknown variance and evaluating their performance based on the risk relative to the balanced loss function (BLF).
Benkhaled Abdelkader +3 more
doaj +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
On Data-Enriched Logistic Regression
Biomedical researchers typically investigate the effects of specific exposures on disease risks within a well-defined population. The gold standard for such studies is to design a trial with an appropriately sampled cohort.
Cheng Zheng +4 more
doaj +1 more source
Peptide Sequencing With Single Acid Resolution Using a Sub‐Nanometer Diameter Pore
To sequence a single molecule of Aβ1−42–sodium dodecyl sulfate (SDS), the aggregate is forced through a sub‐nanopore 0.4 nm in diameter spanning a 4.0 nm thick membrane. The figure is a visual molecular dynamics (VMD) snapshot depicting the translocation of Aβ1−42–SDS through the pore; only the peptide, the SDS, the Na+ (yellow/green) and Cl− (cyan ...
Apurba Paul +8 more
wiley +1 more source
In this study, we investigate the issue of estimating the mean vector of a multivariate normal distribution. We introduce two new families of shrinkage estimators derived from both the maximum likelihood estimator and the James-Stein estimator.
Najla M. ALoraini +2 more
doaj +1 more source
3D porous carbons with tunable density are crucial for energy storage, separations, and load‐bearing applications; however, their fabrication is often constrained by shrinkage during pyrolysis. This study optimizes and demonstrates the versatility of a template–coating pair strategy, producing materials that largely retain their shape and hierarchical ...
Adarsh Suresh +7 more
wiley +1 more source

