Results 171 to 180 of about 213,623 (266)

A Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller

International Journal of Adaptive Control and Signal Processing, EarlyView.
Block Diagram of the Robust Adaptive One‐Sample‐Ahead Preview Super‐Twisting Sliding Mode Controller. ABSTRACT This article introduces a discrete‐time robust adaptive one‐sample‐ahead preview super‐twisting sliding mode controller. A stability analysis of the controller by Lyapunov criteria is developed to demonstrate its robustness in handling both ...
Guilherme Vieira Hollweg, Paulo Jefferson Dias de Oliveira Evald, Shivam Chaturvedi, Hilton Abilio Gründling, Rodrigo Varella Tambara, Wencong Su   +5 more
wiley   +1 more source

Shared burden of ultra-rare genetic variants across a spectrum of motor neuron diseases. [PDF]

Transl Neurodegener
Wu G, Chen W, Wuu J, Jain A, Myers J, Cordts I, Rampersaud E, Heckmann JM, Nel M, Granit V, Statland J, Swenson A, Ravits J, McMillan CT, Elman L, Caress J, Burns TM, Pioro EP, Trivedi J, Katz J, Jackson C, Maiser S, Walk D, So Y, McCauley JL, Baker MC, Taylor JP, Zuchner S, Rademakers R, van Blitterswijk M, Benatar M.   +30 more
europepmc   +1 more source

Harnessing Fungal Biowelding for Constructing Mycelium‐Engineered Materials

Advanced Engineering Materials, EarlyView.
Mycelium‐bound composites (MBCs) offer low‐carbon alternatives for construction, yet interfacial bonding remains a critical challenge. This review examines fungal biowelding as a biocompatible adhesive, elucidating mycelium‐mediated interfacial mechanisms and their role in material assembly. Strategies to optimize biowelding are discussed, highlighting
Xue Brenda Bai, Xinrui Sarah Niu, Zhao Qin   +2 more
wiley   +1 more source

Motor neuron axonal excitability changes in the clinical course of amyotrophic lateral sclerosis. [PDF]

Neurol Sci
Kokotis P, Bakola E, Schmelz M, Rentzos M, Papagiannopoupou G, Tsivgoulis G.   +5 more
europepmc   +1 more source

3D (Bio) Printing Combined Fiber Fabrication Methods for Tissue Engineering Applications: Possibilities and Limitations

Advanced Functional Materials, EarlyView.
Biofabrication aims at providing innovative technologies and tools for the fabrication of tissue‐like constructs for tissue engineering and regenerative medicine applications. By integrating multiple biofabrication technologies, such as 3D (bio) printing with fiber fabrication methods, it would be more realistic to reconstruct native tissue's ...
Waseem Kitana, Indra Apsite, Leonid Ionov   +2 more
wiley   +1 more source

Detour ahead: possible causes of corticospinal tract truncation in upper motor neuron-predominant amyotrophic lateral sclerosis. [PDF]

Brain Commun
Rajagopalan V, Pioro EP.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

A critical affinity window for IgSF proteins DIP-α and Dpr10 is required for proper motor neuron arborization. [PDF]

Genes Dev
Lopez DH, Rostam KD, Zamurrad S, Xu S, Mann RS.   +4 more
europepmc   +1 more source

Cardiac vagal motor neurons

Current Opinion in Neurobiology
Wang, Yoko Brigitte, Saunders, Sandy E., Campbell, John N., Boychuk, Carie R.   +3 more
openaire   +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