Results 111 to 120 of about 691,873 (239)

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets. [PDF]

Bioinformatics, 2020
Häkkinen A, Koiranen J, Casado J, Kaipio K, Lehtonen O, Petrucci E, Hynninen J, Hietanen S, Carpén O, Pasquini L, Biffoni M, Lehtonen R, Hautaniemi S.   +12 more
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

An automated planning optimization framework for cervical intensity-modulated radiation therapy using voxel dose prediction and adaptive parameter tuning. [PDF]

Phys Imaging Radiat Oncol
Jia Q, Zhen C, Cai L, Zhu J, Wang X.
europepmc   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
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

Globalized parameter tuning of microwave passives by dimensionality-reduced surrogates and multi-fidelity simulations. [PDF]

Sci Rep
Koziel S, Pietrenko-Dabrowska A.
europepmc   +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

Improved Manta Ray Foraging Optimization for PID Control Parameter Tuning in Artillery Stabilization Systems. [PDF]

Biomimetics (Basel)
Wang X, Li X, Sun Q, Xia C, Chen YH.
europepmc   +1 more source

Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison. [PDF]

PLoS One, 2020
Günther J, Reichensdörfer E, Pilarski PM, Diepold K.   +3 more
europepmc   +1 more source

Expanding Chemical Space of Nucleic Acid Nanoparticles for Tunable Antiviral‐Like Immunomodulatory Responses and Potent Adjuvant Activity

Advanced Functional Materials, EarlyView.
We introduce a nucleic acid nanoparticle (NANP) platform designed to be rrecognized by the human innate immune system in a regulated manner. By changing chemical composition while maintaining constant architectural parameters, we identify key determinants of immunorecognition enabling the rational design of NANPs with tunable immune activation profiles
Martin Panigaj, Hannah S. Newton, Jian Wang, Laxmi Pandey, Yelixza I. Avilla, Phong Nguyen, Morgan Chandler, Justin Halman, Yasmine Radwan, Stephanie Smith, Elijah Edmondson, Simone Difilippantonio, Chelsea Sanders, Bao Tran, Yongmei Zhao, Shaojun Xie, Edward Cedrone, Barry W. Neun, Meni Wanunu, Nikolay V. Dokholyan, Marina A. Dobrovolskaia, Kirill A. Afonin   +21 more
wiley   +1 more source

Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning. [PDF]

Sci Rep
Koziel S, Pietrenko-Dabrowska A, Szczepanski S.   +2 more
europepmc   +1 more source