Results 161 to 170 of about 451,587 (286)

Health facility and contextual correlates of HIV test positivity: a multilevel model of routine programmatic data from Malawi. [PDF]

BMJ Public Health
Niwa M, Green D, Smith T, Klyn B, Kamgwira Y, Allinder S, Hoege D, Likumbo S, Holmes CB, Kawalazira G, Chewere L.   +10 more
europepmc   +1 more source

Survey implementation process and interviewer effects on skipping sequence of maternal and child health indicators from National Family Health Survey: An application of cross-classified multilevel model. [PDF]

SSM Popul Health, 2022
Sharma R, Dwivedi LK, Jana S, Banerjee K, Mishra R, Mahapatra B, Sahu D, Singh SK.   +7 more
europepmc   +1 more source

Non‐Destructive, Reference‐Free Quantitative Analysis of TaOx Memristive Devices Using Soft X‐Ray Radiation

Advanced Electronic Materials, EarlyView.
A non‐destructive, quantitative approach has been developed to explore the nanoscale dynamics of TaOx‐based memristive devices. The utilization of nano‐X‐ray fluorescence analysis enables the direct probing of spatially resolved elemental distributions, including those present in buried layers, that are critical for the resistive switching.
André Wählisch, Xin Zheng, Andreas Haidl, Jan Weser, Christian Stadelhoff, Marc Dummin, Leona J. Bauer, Rainer Unterumsberger, Ilia Valov, Burkhard Beckhoff   +9 more
wiley   +1 more source

Surviving a crisis: A multilevel model of leadership styles, employees' psychological capital and organizational resilience. [PDF]

PLoS One
Njaramba F, Olukuru J.
europepmc   +1 more source

Risk factors for scabies, tungiasis, and tinea infections among schoolchildren in southern Ethiopia: A cross-sectional Bayesian multilevel model. [PDF]

PLoS Negl Trop Dis, 2021
Amare HH, Lindtjorn B.
europepmc   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Challenges in reducing grand multiparity rates in Ethiopia: an analysis of 2019 EDHS data using a multilevel model approach. [PDF]

Contracept Reprod Med
Dibaba D, Charkos TG.
europepmc   +1 more source

SPICE‐Compatible Compact Modeling of Cuprate‐Based Memristors Across a Wide Temperature Range

Advanced Electronic Materials, EarlyView.
A physics‐guided compact model for YBCO memristors is introduced, incorporating carrier trapping, field‐induced detrapping, and a differential balance equation to describe their switching dynamics. The model is compared with experiments and implemented in LTspice, allowing realistic circuit‐level simulations.
Thomas Günkel, Aleix Barrera, Lluís Balcells, Narcís Mestres, Enrique Miranda, Anna Palau, Jordi Suñé   +6 more
wiley   +1 more source

A Bayesian multilevel model for populations of networks using exponential-family random graphs. [PDF]

Stat Comput
Lehmann B, White S.
europepmc   +1 more source

Efficient In‐Hardware Matrix–Vector Multiplication and Addition Exploiting Bilinearity of Schottky Barrier Transistors Processed on Industrial FDSOI

Advanced Electronic Materials, EarlyView.
ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez, Niladri Bhattacharjee, Yuxuan He, Giulio Galderisi, Brojogopal Sapui, Sara Mannaa, Ian O'Connor, Violetta Sessi, Peter Javorka, Thomas Mikolajick, Jens Trommer   +10 more
wiley   +1 more source