Publication

Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"

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We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

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In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

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Total Flow Analysis (TFA) is a method for the worst-case analysis of time-sensitive networks. It uses service curve characterizations of the network nodes and arrival curves of flows at their sources; for tractability, the latter are often taken to be line ...

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The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, i ...

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Bromine radical (Br center dot) has been hypothesized to be a key intermediate of bromate formation during ozonation. Once formed, Br center dot further reacts with ozone to eventually form bromate. However, this reaction competes with the reaction of Br c ...

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Modeling, analysis, and design of complex biological networks

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Automating experimental procedures has resulted in an unprecedented increase in the volume of generated data, which, in turn, has caused an accumulation of unprocessed data. As a result, the need to develop tools to analyze data systematically has been ris ...

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Absorption in amine solutions is a well-established advanced technology for CO2 capture. However, the fundamental aspects of the chemical reactions occurring in solution still appear to be unclear. Our previous investigation of aqueous monoethanolamine (ME ...

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Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

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