Results 151 to 160 of about 315,081 (269)

Machine Learning Applied to High Entropy Alloys under Irradiation

Advanced Engineering Materials, EarlyView.
Designing alloys for extreme environments demands fast, trustworthy prediction. This review charts how machine learning—especially machine‐learned interatomic potentials and predictive models based on experiment‐informed datasets—captures the complexity of high‐entropy alloys in extreme environments, predicts phase formation, mechanical properties, and
Amin Esfandiarpour, Sri Tapaswi Nori, Silvia Bonfanti, Mikko Alava, Antoni Wadowski, Wenyi Huo, Łukasz Kurpaska, Michał Pecelerowicz, Jan S. Wróbel   +8 more
wiley   +1 more source

Retraction Notice: 3D DWT-DCT and Logistic MAP Based Robust Watermarking for Medical Volume Data. [PDF]

Open Biomed Eng J, 2016
Li J, Liu Y, Zhong J.
europepmc   +1 more source

Numerical Model and Experimental Validation of Composite Metal Foam in Protecting Carbon Steel Against Puncture

Advanced Engineering Materials, EarlyView.
The puncture prevention and energy absorption performance of composite metal foam (CMF) are studied experimentally and numerically for hazardous materials transportation protection. The numerical model implementing air within the CMF (nonhomogeneous model using fluid cavity technique) predicts puncture more accurately compared to the homogeneous CMF ...
Aman Kaushik, Afsaneh Rabiei
wiley   +1 more source

Using the Logistic Map to Generate Scratching Sounds [PDF]

, 2013
Yasushi Miki
openalex   +1 more source

Cholesterol in mRNA‐Lipid Nanoparticles can be Replaced with the Synthetic Mycobacterial Monomycoloyl Glycerol Analogue MMG‐1

Advanced Functional Materials, EarlyView.
This study demonstrates that cholesterol in messenger RNA‐lipid nanoparticles (mRNA‐LNPs) can be completely replaced with an immunopotentiating lipid, i.e., a synthetic analogue of the C‐type lectin receptor agonist monomycoloyl glycerol (MMG‐1), without compromising physicochemical properties, in vivo transfection efficiency, and immunogenicity of the
Abhijeet G. Lokras, Saahil S. Baghel, Rune F. Jensen, Aneesh Thakur, Henrik Franzyk, Oskar Thofte, Marco Herrera‐Barrera, Madeleine Landry, Melike Ongun, Ahmad Tami, Marlene P. Thanthrige, Eva Callens, Corinne Beinat, Thomas Rades, Søren R. Paludan, Kristian Riesbeck, Ida Rosenkrands, Dennis Christensen, Gabriel K. Pedersen, Camilla Foged   +19 more
wiley   +1 more source

Mitigating security flaws in Baptista's chaotic cryptosystem through superior and alternated logistic map approaches. [PDF]

Sci Rep
Verma DK, Rani M, Dikshit R, Gupta H, Kumar A.   +4 more
europepmc   +1 more source

Modified and Extended Logistic Map in Superior Orbit

, 2015
Anju Yadav, Mamta Rani
openalex   +1 more source

Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives

Advanced Functional Materials, EarlyView.
AI is transforming the research paradigm of battery materials and reshaping the entire landscape of battery technology. This comprehensive review summarizes the cutting‐edge applications of AI in the advancement of battery materials, underscores the critical challenges faced in harnessing the full potential of AI, and proposes strategic guidance for ...
Qingyun Hu, Junyuan Lu, Jian Hui, Ziyuan Rao, Yang Ren, Hong Wang   +5 more
wiley   +1 more source

A dynamic image encryption scheme through 2-D cellular automata and chaotic logistic map. [PDF]

Sci Rep
Abdul Y, Ramasamy V, Kukaram G, Boulaaras S, Alharbi A.   +4 more
europepmc   +1 more source

Chaotic-Map-Based Image Steganography Using Bernoulli and Logistic Functions

Erick Malcoaccha-D ́ ıaz, Roxana Flores-Quispe, Yuber Velazco-Paredes   +2 more
openalex   +1 more source