Publications by authors named "A Norambuena"
Article Synopsis
- The study examines the effects of anisotropy on a system of three qubits using the antiferromagnetic Heisenberg XXX model in a magnetic field, focusing on Stirling and Otto cycles.
- Results show that easy-axis anisotropy boosts engine efficiency, with the ring topology outperforming the chain at low temperatures in the Stirling cycle.
- The Stirling cycle achieves Carnot efficiency with useful work at quantum critical points, while the quasistatic Otto cycle reaches Carnot efficiency but doesn't produce useful work, and the Stirling cycle operates across various thermal regimes compared to the Otto cycle.
Article Synopsis
- Alterations in NADH and NADPH metabolism are linked to aging, cancer, and Alzheimer's Disease.
- Research shows that tau oligomers increase mitochondrial NADPH production through an enzyme called NADK2 in both human neurons and mouse brains.
- This increase in NADPH contributes to a harmful cycle that enhances the internalization of tau oligomers, leading to increased toxicity in neurons.
Introduction: Reduced brain energy metabolism, mammalian target of rapamycin (mTOR) dysregulation, and extracellular amyloid beta (Aβ) oligomer (xcAβO) buildup are some well-known Alzheimer's disease (AD) features; how they promote neurodegeneration is poorly understood. We previously reported that xcAβOs inhibit nutrient-induced mitochondrial activity (NiMA) in cultured neurons. We now report NiMA disruption in vivo.
View Article and Find Full Text PDFIntroduction: Reduced brain energy metabolism, mTOR dysregulation, and extracellular amyloid-β oligomer (xcAβO) buildup characterize AD; how they collectively promote neurodegeneration is poorly understood. We previously reported that xcAβOs inhibit N utrient-induced M itochondrial A ctivity (NiMA) in cultured neurons. We now report NiMA disruption .
View Article and Find Full Text PDFQuantum control is a ubiquitous research field that has enabled physicists to delve into the dynamics and features of quantum systems, delivering powerful applications for various atomic, optical, mechanical, and solid-state systems. In recent years, traditional control techniques based on optimization processes have been translated into efficient artificial intelligence algorithms. Here, we introduce a computational method for optimal quantum control problems via physics-informed neural networks (PINNs).
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