Results 91 to 100 of about 64,899 (169)

Challenges and enablers in fluidization technology

AIChE Journal, EarlyView.
Abstract Gas–solid fluidized beds provide excellent heat and mass transfer for high‐throughput operations from coating to catalytic conversion and underpin emerging low‐carbon technologies. Yet industrial reliability, scale‐up, and control lag scientific understanding, particularly as finer, stickier, and more variable feedstocks increasingly challenge
J. Ruud van Ommen, Jia Wei Chew
wiley   +1 more source

Factors affecting implementation of hospital inpatient-level care at home: a qualitative study of virtual wards in North West England. [PDF]

BMJ Open
Bradley F, Howells K, Gasteiger N, Sanders C, Blakeman T, Dowding D.   +5 more
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Public sentiment and thematic evolution in the metaverse: A large-scale computational analysis of Twitter discourse. [PDF]

PLoS One
Duraivel S, Rajendran L, Vasudevan S, Piazza A.   +3 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Lived Experiences of Social Isolation and Meaningful Relationships Among Older Adults Living with HIV with a Concurrent Mental Health Diagnosis: A Heideggerian Phenomenological Approach. [PDF]

Healthcare (Basel)
Kokorelias KM, Valentine D, Eaton AD, Munce SEP, Sheppard CL, Hitzig SL, Wasilewski MB, Zhabokritsky A, Abdelhalim R, Jamieson L, Harris MT, Sirisegaram L.   +11 more
europepmc   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Conceptualising the Patient Ecosystem: A Holistic Framework for Home Care. [PDF]

Scand J Caring Sci
Milavec Kapun M.
europepmc   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Research Partnerships in Rural and Remote Virtual Health Innovation Projects: A Scoping Review. [PDF]

Aust J Rural Health
Tjandra V, Yi J, Thomson E, Benbaruj J, Canfield C, Singh A, Jun J, Hoekstra F.   +7 more
europepmc   +1 more source