Results 191 to 200 of about 221,152 (303)

Propensity Score Matching in Non-Interventional Studies: A Step-by-Step Guide for Clinicians and Researchers. [PDF]

Iran J Med Sci
Pourahmad S, Madadizadeh F.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Estimated Impact of Pressure to Conceive on Maternal Depression in Lesotho: A Quasi-experimental Propensity Score Matching using DHS Data. [PDF]

Arch Womens Ment Health
Jejaw M, Kelkay JM, Demissie KA, Hagos A, Teshale G, Baykemagn ND, Tadele A, Tafere TZ, Aschalew AY, Yehuala TZ.   +9 more
europepmc   +1 more source

Modulating Two‐Photon Absorption in a Pyrene‐Based MOF Series: An In‐Depth Investigation of Structure–Property Relationships

Advanced Functional Materials, EarlyView.
This study investigates H4TBAPy‐based metal–organic frameworks (MOFs) ‐ NU‐1000, NU‐901, SrTBAPy, and BaTBAPy ‐ for multiphoton absorption (MPA) performance. It observes topology‐dependent variations in the 2PA cross‐section, with BaTBAPy exhibiting the highest activity.
Simon N. Deger, Helmy Pacheco Hernandez, Yang Cui, Hongxing Hao, Vanessa Ramm, David C. Mayer, Khai‐Nghi Truong, Roland A. Fischer, Jürgen Hauer, Mariana Kozlowska, Alexander Pöthig   +10 more
wiley   +1 more source

Characteristics and Prognosis of Early-Onset vs. Late-Onset Colon Cancer: A Propensity Score Matching Analysis Based on Histology. [PDF]

Cancer Med
Cheng X, Xiang T, Wang S, Wang J.
europepmc   +1 more source

Flexibility and Dynamicity Enhances and Controls Supramolecular Self‐Assembly of Zinc(II) Metallogels

Advanced Functional Materials, EarlyView.
Zinc(II) coordination complexes with tunable aryloxy‐imine ligands exhibit controllable supramolecular self‐assembly into hierarchical fibrous structures. Coordination‐driven stacking, not π–π interactions, enables gelation, dynamic assembly/disassembly, and enhanced nanomechanical properties.
Merlin R. Stühler, Hesam Makki, Dorothee Silbernagl, Mathias Dimde, Kai Ludwig, Bengt E. Tegner, Christopher Greve, Konstantin Rausch, Eva M. Herzig, Anna Köhler, Alex J. Plajer   +10 more
wiley   +1 more source

Impact of COVID-19 on ischemic stroke patterns and outcomes: a multicenter retrospective study using propensity score matching. [PDF]

Front Med (Lausanne)
Almarghalani DA, Almehmadi KA, Hammad AM, Alzahrani MS, Qadri M, Sindi JA, Alharthi RA, Aloudah MN, Alghamdi SA, Alsuwat SJ, Almutairi MB, Hakami AY, Alamri FF, Makkawi S.   +13 more
europepmc   +1 more source

On‐Surface Indigo‐Based Bimolecular Coordination Networks with Programmable Regular or Vitreous Structure

Advanced Functional Materials, EarlyView.
A previously unreported coordination motif stabilising single Fe atoms by indigo chelation and pyridyl coordination on Au(111) has been revealed. By using planar tritopic pyridyl linkers (TPyB), extended 2D porous networks of indigo3(TPyB)2Fe6 form. These networks can be crystalline or vitreous and offer an environment where individual coordination ...
Hongxiang Xu, Ritam Chakraborty, Biao Yang, Dennis Meier, Joachim Reichert, Svetlana Klyatskaya, Mario Ruben, Shobhana Narasimhan, Johannes V. Barth, Anthoula C. Papageorgiou   +9 more
wiley   +1 more source

Impact of Age on Early CAR T-Cell Therapy Toxicity: A Propensity Score Matching Analysis. [PDF]

Cancer Med
Tan JY, Yeo YH, Ang QX, Chen G, Chan KH.
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