Results 241 to 250 of about 345,517 (350)

A Study Protocol for the X-SIAGA Intervention: A Cluster Randomized Trial to Improve Household Preparedness for Disease X and Outbreaks in Selangor's Indigenous Community. [PDF]

Cureus
Abdul Shakor AS, Mohamad M, Ibrahim K, Mohamad Sayed I.   +3 more
europepmc   +1 more source

Three‐dimensional Antimony Sulfide Based Flat Optics

Advanced Functional Materials, EarlyView.
This work presents the development of a grayscale electron beam lithography (g‐EBL) method for fabricating antimony trisulfide (Sb2S3) nanostructures with customizable 3D profiles. The refractive index of g‐EBL patterned Sb2S3 is determined based on the synergy of genetic algorithm and transfer matrix method.
Wei Wang, Uwe Hübner, Anne Gärtner, Tao Chen, Joseph Köbel, Franka Jahn, Daniel Repp, Abhrodeep Dey, Henrik Schneidwind, Andrea Dellith, Jan Dellith, Torsten Wieduwilt, Matthias Zeisberger, Tanveer Ahmed Shaik, Astrid Bingel, Markus A. Schmidt, Jer‐Shing Huang, Ulf Peschel, Volker Deckert   +18 more
wiley   +1 more source

A standardized educational intervention ameliorates deleterious effects of mandatory service on lifestyle habits in Austrian conscripts: a cluster-randomized trial. [PDF]

BMC Public Health
Helk O, Kitl-Sarbandi N, Lim CJM, Widhalm K.   +3 more
europepmc   +1 more source

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

Advanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha, Yingshan Ma, Yong Nam An, Sung‐Un An, Hyeon Hak Jung, Suvi‐Tuuli Varjamo, Jiwon Yoo, Junho Min, Hanvit Kim, Faisal Ahmed, Sang Hoon Chae, Young Min Song, Weiwei Cai, Tawfique Hasan, Zhipei Sun, Dong‐Ho Kang, Hyeon‐Jin Shin, Yunyun Dai, Hoon Hahn Yoon   +18 more
wiley   +1 more source

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning

Advanced Functional Materials, EarlyView.
Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu, Rui Zheng, Quan Liu, Jianwen Jiang   +3 more
wiley   +1 more source

Maintenance of Sugar-Sweetened Beverage Behaviors Among Adolescents and Their Caregivers: A Cluster Randomized Controlled Trial

Annie L. Reid, Wen You, Theresa Markwalter, Kathleen Porter, Donna‐Jean P. Brock, Philip I. Chow, Lee M. Ritterband, Jamie Zoellner   +7 more
openalex   +1 more source

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

Advanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo, Ewa Anna Oprzeska‐Zingrebe, Mariana Kozlowska, Lennart Hilbert, Iliya D. Stoev   +4 more
wiley   +1 more source

A cluster-randomized trial of household vs incentive-based tuberculosis contact investigation in rural South Africa: implementation reach. [PDF]

PLOS Glob Public Health
Machavariani E, Cox SR, Nonyane BAS, Gvelesiani T, Martinson N, Dowdy DW, Hanrahan CF.   +6 more
europepmc   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source