Depolymerization of Pine Wood Organosolv Lignin in Ethanol Medium over NiCu/SiO and NiCuMo/SiO Catalysts: Impact of Temperature and Catalyst Composition.

The process of thermocatalytic conversion of pine ethanol lignin in supercritical ethanol was studied over NiCu/SiO and NiCuMo/SiO catalysts bearing 8.8 and 11.7 wt.

Reductive Fractionation of Flax Shives in Ethanol Medium over RuNi Bimetallic Catalysts.

The reductive catalytic fractionation of flax shives in the presence of bimetallic NiRu catalysts supported on oxidized carbon materials (CM) such as mesoporous Sibunit and carbon mesostructured by KAIST (CMK-3) was studied. The catalysts based on CMK-3 were characterized by a higher surface area (1216 m/g) compared to the ones based on Sibunit (315 m/g). The catalyst supported on CMK-3 (10Ni3RuC400) was characterized by a more uniform distribution of Ni particles, in contrast to the Sibunit-based catalyst (10Ni3RuS450), on the surface of which large agglomerated particles (300-400 nm) were presented.

Fractionation of Aspen Wood to Produce Microcrystalline, Microfibrillated and Nanofibrillated Celluloses, Xylan and Ethanollignin.

A new method for extractive-catalytic fractionation of aspen wood to produce microcrystalline (MCC), microfibrillated (MFC), nanofibrilllated (NFC) celluloses, xylan, and ethanollignin is suggested in order to utilize all of the main components of wood biomass. Xylan is obtained with a yield of 10.2 wt.

Mobile authentication of copy detection patterns.

In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect, this paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating the authentication aspects and the resistances to illegal copying of the modern CDP from machine learning perspectives.

Multivariate Time Series Information Bottleneck.

Time series (TS) and multiple time series (MTS) predictions have historically paved the way for distinct families of deep learning models. The temporal dimension, distinguished by its evolutionary sequential aspect, is usually modeled by decomposition into the trio of "trend, seasonality, noise", by attempts to copy the functioning of human synapses, and more recently, by transformer models with self-attention on the temporal dimension. These models may find applications in finance and e-commerce, where any increase in performance of less than 1% has large monetary repercussions, they also have potential applications in natural language processing (NLP), medicine, and physics.