Results 121 to 130 of about 1,602,708 (346)

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

Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study

BMC Infectious Diseases
Background Invasive fungal disease (IFD) is characterized by its capacity to rapidly escalate to life-threatening conditions, even when patients are hospitalized.
Wei Wang, Yan Li, Hua Wang, Yumeng Du, Mengyuan Cheng, Jinyan Tang, Mingliang Wu, Chaomin Chen, Qingwen Lv, Weibin Cheng   +9 more
doaj   +1 more source

Multicolor Optoelectronic Synapse Enabled by Photon‐Modulated Remote Doping in Solution‐Processed Van Der Waals Heterostructures

Advanced Functional Materials, EarlyView.
Multicolor optoelectronic synapses are realized by vertically integrating solution‐processed MoS2 thin‐film and SWCNT. The electronically disconnected but interactive MoS2 enables photon‐modulated remote doping, producing a bi‐directional photoresponse.
Jihyun Kim, Hyunbin Kim, Gang Jin Cheon, Dohyun Lee, In Soo Kim, Uiyeon Won, Jongseok Lee, Sang Min Won, Joohoon Kang   +8 more
wiley   +1 more source

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers

Communications Medicine
Background Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography.
Borek Foldyna, Ibrahim Hadzic, Roman Zeleznik, Marcel C. Langenbach, Vineet K. Raghu, Thomas Mayrhofer, Michael T. Lu, Hugo J. W. L. Aerts   +7 more
doaj   +1 more source

An architectural approach to modeling artificial general intelligence. [PDF]

Heliyon, 2023
Slavin BB.
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

Psychological model of representation, generation, and adjustion of belief for artificial general intelligence [PDF]

, 2021
Zhizhong Kai, Xiaolan Fu
openalex   +1 more source

Machine Intelligence, Artificial General Intelligence, Super-Intelligence, and Human Dignity

Religions
Our temptation to personify machine intelligence is not unexpected. As a child we named our dolls and took our Teddy Bear to bed with us. Today we ask death bots to comfort us with post-mortem conversation. All the while we know this to be pretend.
Ted F. Peters
doaj   +1 more source

Towards the Neuroevolution of Low-level artificial general intelligence. [PDF]

Front Robot AI, 2022
Pontes-Filho S, Olsen K, Yazidi A, Riegler MA, Halvorsen P, Nichele S.   +5 more
europepmc   +1 more source

Advanced training systems [PDF]

Training is a major endeavor in all modern societies. Common training methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training.
Loftin, R. Bowen, Savely, Robert T.
core   +1 more source