Results 191 to 200 of about 24,034,047 (331)

G. T. Whyburn Topological Analysis, revised edition (Princeton Mathematical Series, no. 23, Princeton University Press; London: Oxford University Press, 1964), xii + 125 pp., 40s. [PDF]

, 1966
Dolores Moyano Martín
openalex   +1 more source

Amorphous High Entropy Alloy Nanosheets Enabling Robust Li–S Batteries

Advanced Functional Materials, EarlyView.
Amorphous ultrathin FeCoNiMoW high entropy alloy nanosheets are incorporated into the polypropylene separator of lithium‐sulfur batteries, enhancing their capacity, rate performance, and cycling stability. Abstract High‐entropy alloys (HEAs) show great potential for catalyzing complex multi‐step reactions, but optimizing their parameters, i.e ...
Ren He, Seungho Lee, Yang Ding, Chen Huang, Xuan Lu, Lirong Zheng, Ao Yu, Chaoyue Zhang, Canhuang Li, Xiaoyu Bi, Yaqiang Li, Yaqi Liao, Junshan Li, Ahmad Ostovari Moghaddam, Salimov Yernar, Ying Xu, Maria Ibáñez, Chaoqi Zhang, Linlin Yang, Yingtang Zhou, Andreu Cabot   +20 more
wiley   +1 more source

Mathematical analysis and control optimization of soluble and insoluble water pollutants dispersion. [PDF]

Heliyon
Oludoun OY, Salawu SO, Adesanya SO, Abiodun OE.   +3 more
europepmc   +1 more source

Mathematical analysis of a two-strain tuberculosis model in Bangladesh. [PDF]

Sci Rep, 2022
Kuddus MA, McBryde ES, Adekunle AI, White LJ, Meehan MT.   +4 more
europepmc   +1 more source

Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: a meta-analysis.

Developmental Science, 2017
Michael Schneider, Kassandra Beeres, Leyla Coban, Simon Merz, S. Susan Schmidt, Johannes Stricker, B. De Smedt   +6 more
semanticscholar   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Is it effective to do mathematical analysis for the etiology of nocturia using the nocturia indices derived from the frequency volume chart?: A retrospective observational study. [PDF]

Medicine (Baltimore)
Park S, Kang W, Lee DG, Bae H, Park EJ, Yoon JH, Kwon T, Moon KH, Cheon SH, Kim SC.   +9 more
europepmc   +1 more source

Optimal control strategies for the reliable and competitive mathematical analysis of Covid-19 pandemic model. [PDF]

Math Methods Appl Sci, 2022
Butt AIK, Imran M, Chamaleen DBD, Batool S.   +3 more
europepmc   +1 more source

MATHEMATICAL AND EXPERIMENTAL ANALYSIS OF HEAT DISSIPATION FROM CYLINDRICAL SOURCES BURIED IN SOIL.

, 1966
D.E. Black, B.R. Dickey
openalex   +1 more source

Role of Crosslinking and Backbone Segmental Dynamics on Ion Transport in Hydrated Anion‐Conducting Polyelectrolytes

Advanced Functional Materials, EarlyView.
Thermally crosslinked poly(2‐vinylpyridine)‐based anion conducting polyelectrolytes have been synthesized. The segmental dynamics of these polyelectrolytes have been systematically modified by varying the degree of crosslinking. Both the experimental and simulation results indicate that segmental dynamics have a limited influence on ion transport in ...
Zhongyang Wang, Kai Wang, Christopher Eom, Yuxi Chen, Ge Sun, Mincheol Kim, Joan M. Montes de Oca, Dongyue Liang, Kushal Bagchi, Shrayesh N. Patel, Juan J. de Pablo, Paul F. Nealey   +11 more
wiley   +1 more source