Results 191 to 200 of about 6,694,171 (343)
Enhancing the Ultrasonic Welding of Wood Using 3D Printed Lignin Energy Directors
Sustainable manufacturing for lightweight structures using ecofriendly materials will be key to reducing material consumption and lowering carbon footprints. Here, an approach is presented to weld wood using ultrasonic vibrations with material at the joint interface to direct energy.
Muhamad Amani +6 more
wiley +1 more source
New Janus interfaces from four-dimensional $$N=3$$ N = 3 gauged supergravity
We construct new supersymmetric Janus solutions from four-dimensional $$N=3$$ N = 3 gauged supergravity coupled to eight vector multiplets with $$SO(3)\times SU(3)$$ S O ( 3 ) × S U ( 3 ) gauge group.
Parinya Karndumri
doaj +1 more source
Light and compressed gluinos at the LHC via string theory. [PDF]
AbdusSalam SS.
europepmc +1 more source
New supersymmetric flux vacua of type II string theory and Generalized Complex Geometry [PDF]
David Andriot
openalex +1 more source
Brian R. Greene +2 more
semanticscholar +1 more source
Plasma proteomics is leveraged to decode the biological underpinnings of chronic widespread pain. A nested machine learning framework integrates proteomic signatures, prospective outcomes, and Mendelian randomization to uncover 18 causal proteins.
Li Chen +16 more
wiley +1 more source
We study holographic solutions describing RG flows across dimensions from five-dimensional $$N=2$$ N = 2 SCFT to SCFTs in three and two dimensions using matter-coupled F(4) gauged supergravity with $$ISO(3)\times U(1)$$ I S O ( 3 ) × U ( 1 ) gauge group.
Parinya Karndumri
doaj +1 more source
Entropy Contribution to the Line Tension: Insights from Polymer Physics, Water String Theory, and the Three-Phase Tension. [PDF]
Bormashenko E.
europepmc +1 more source
Non-perturbative thermodynamics in Matrix string theory [PDF]
Jesús Puente Peñialba
openalex +1 more source
Interpretable PROTAC Degradation Prediction With Structure‐Informed Deep Ternary Attention Framework
PROTAC‐STAN, a structure‐informed deep learning framework is presented for interpretable PROTAC degradation prediction. By modeling molecular hierarchies and protein structures, and simulating ternary interactions via a novel attention network, PROTAC‐STAN achieves significant performance gains over baselines.
Zhenglu Chen +11 more
wiley +1 more source

