Results 201 to 210 of about 517,907 (286)

Host‐Directed Biomaterials for Combatting Bloodstream Infections: From Macrocyclic Peptides to Immune‐Activating Cell Backpacks

Advanced Functional Materials, EarlyView.
Bloodstream infections (BSI) are one of the leading causes of mortality and morbidity in both civilian and military populations. This paper summarizes recent progress in novel treatment strategies to manage BSI arising from both bacterial and fungal pathogens using molecules, particles, and materials to elicit host‐directed immunity.
Thomas Thomou, Jagadish Krishnappa, Justin B. Schaal, Prasad Tongaonkar, Dat Q. Tran, Wenchao Gu, Yung‐Fu Chang, Michael E. Selsted, Shaoyi Jiang, Samir Mitragotri, Michael Super, Christopher J. Bettinger   +11 more
wiley   +1 more source

Consistency regularization for few shot multivariate time series forecasting. [PDF]

Sci Rep
She Y, Hong Y, Shen S, Yang B, Zhang L, Wang J.   +5 more
europepmc   +1 more source

Decoupling Size and Electronic Effects in Doped SrTiO3 Photocatalysts Through Surface Area–Normalized CO2 Hydrogenation Rates

Advanced Functional Materials, EarlyView.
Exploring the photocatalytic reverse water–gas shift (RWGS) reaction on doped SrTiO3 nanoparticle films, reveals that normalizing catalytic rates by the catalyst's specific surface area (SSA) disentangled surface area effects from the catalyst's intrinsic material properties.
Dikshita Bhattacharyya, Baliana Shani, Isaac Holmes‐Gentle, Gerardo T Martinez, Martyn McLachlan, Nicola Seriani, Ludmilla Steier   +6 more
wiley   +1 more source

Nonlinear time domain and multi-scale frequency domain feature fusion for time series forecasting. [PDF]

Sci Rep
Xiao K, Li Y, Dong Y, Yang W, Yao B, Chen L.   +5 more
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

TIC-FusionNet: A multimodal deep learning framework with temporal decomposition and attention-based fusion for time series forecasting. [PDF]

PLoS One
Chen L, Fan X.
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

Retraction Note: Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks. [PDF]

Pers Ubiquitous Comput
Rauf HT, Lali MIU, Khan MA, Kadry S, Alolaiyan H, Razaq A, Irfan R.   +6 more
europepmc   +1 more source

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source

A multiscale model for multivariate time series forecasting. [PDF]

Sci Rep
Naghashi V, Boukadoum M, Diallo AB.
europepmc   +1 more source