Results 121 to 130 of about 49,639 (176)

Predictive model of psychological factors influencing university students' intentions and behavior regarding physical exercise-based on machine learning algorithms. [PDF]

Front Psychol
Zhu L, Kong F, Liu T.
europepmc   +1 more source

Correlated Charge Transport in an Organic Coulomb Glass

Advanced Materials, EarlyView.
ABSTRACT Advances in the development of organic field‐effect transistors (OFETs), electrically gated organic semiconductors (EGOFETs), and organic electrochemical transistors (OECTs) allow for the operation of these devices at very high charge‐carrier densities, where Coulomb interactions between carriers can be expected to become significant.
Magdalena Sophie Dörfler, Heinz Bässler, Harald Oberhofer, Anna Köhler   +3 more
wiley   +1 more source

Development of a machine learning-based model for prediction of diabetes risk in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). [PDF]

BMJ Open
Yang S, Mo R, Wang W, Zhen P, Han W.
europepmc   +1 more source

Optimal Control Drives Ultrafast and Energy‐Efficient Magnetization Switching in Van der Waals Magnets

Advanced Materials, EarlyView.
ABSTRACT The accelerating expansion of data‐centric technologies is sharply increasing the energy burden of information storage, placing unprecedented pressure on the efficiency of magnetic switching. Conventional field‐driven reversal, once the foundation of magnetic memory, has become impractical in modern architectures due to its high energy cost ...
Mohammad H. Badarneh, PeiYu Cai, Elton J. G. Santos   +2 more
wiley   +1 more source

Correlation between cardiorespiratory fitness and body composition in individuals with different glucose metabolism statuses. [PDF]

Front Endocrinol (Lausanne)
Liu B, Li J, Niu Z, Lu Q.
europepmc   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Analyzing the association between ferritin levels and ICP using machine learning algorithms: a retrospective case-control study. [PDF]

Front Med (Lausanne)
Wei N, Guo S, Liu X, Zhang J, Liu J, Chen Q, Dai W.   +6 more
europepmc   +1 more source

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

Advanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

Data-Driven Chance Constrained Mixed Integer Nonlinear Bilevel Optimization via Copulas. [PDF]

Ind Eng Chem Res
Johnn SN, Nikkhah H, Tsai ML, Avraamidou S, Beykal B, Charitopoulos VM.   +5 more
europepmc   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source