Investigation of absorption of laser radiation by copperand nickel-based powders to obtain gradient materials by selective laser melting

The absorption of laser radiation of powder materials based on copper and nickel is studied. The dependence of the temperature of the powder material on the power of the incident laser radiation is obtained. A comparison of the values obtained by different measurement methods is presented

Investigation of absorption of laser radiation by copperand nickel-based powders to obtain gradient materials by selective laser melting

The absorption of laser radiation of powder materials based on copper and nickel is studied. The dependence of the temperature of the powder material on the power of the incident laser radiation is obtained. A comparison of the values obtained by different measurement methods is presented

Analysis of ¹⁶O/¹⁸O and H/D Exchange Reactions between Carbohydrates and Heavy Water Using High-Resolution Mass Spectrometry

Mono- and polysaccharides are an essential part of every biological system. Identifying underivatized carbohydrates using mass spectrometry is still a challenge because carbohydrates have a low capacity for ionization. Normally, the intensities of protonated carbohydrates are relatively low, and in order to increase the corresponding peak height, researchers add Na+, K+, or NH4+to the solution. However, the fragmentation spectra of the corresponding ions are very poor. Based on this, reliably identifying carbohydrates in complex natural and biological objects can benefit frommeasuring additional molecular descriptors, especially those directly connected to the molecular structure. Previously, we reported that the application of the isotope exchange approach (H/D and 16O/18O) to high-resolution mass spectrometry can increase the reliability of identifying drug-like compounds. Carbohydrates possess many –OH and –COOH groups, making it reasonable to expect that the isotope exchange approach would have considerable potential for detecting carbohydrates. Here, we used a collection of standard carbohydrates to investigate the isotope exchange reaction (H/D and 16O/18O) in carbohydrates and estimate its analytical applications

Active learning with deep pre-trained models for sequence tagging of clinical and biomedical texts

Active learning is a technique that helps to minimize the annotation budget required for the creation of a labeled dataset while maximizing the performance of a model trained on this dataset. It has been shown that active learning can be successfully applied to sequence tagging tasks of text processing in conjunction with deep learning models even when a limited amount of labeled data is available. Recent advances in transfer learning methods for natural language processing based on deep pre-trained models such as ELMo and BERT offer a much better ability to generalize on small annotated datasets compared to their shallow counterparts. The combination of deep pre-trained models and active learning leads to a powerful approach to dealing with annotation scarcity. In this work, we investigate the potential of this approach on clinical and biomedical data. The experimental evaluation shows that the combination of active learning and deep pre-trained models outperforms the standard methods of active learning. We also suggest a modification to a standard uncertainty sampling strategy and empirically show that it could be beneficial for annotation of very skewed datasets. Finally, we propose an annotation tool empowered with active learning and deep pre-trained models that could be used for entity annotation directly from Jupyter IDE

Curing of DER-331 Epoxy Resin with Arylaminocyclotriphosphazenes Based on o-, m-, and p-methylanilines

As a result of this research, it was established that the chlorine atom replacement rates in hexa-chlorocyclotriphosphazene by o-, m-, and p-methylanilines’ temperatures are crucial in determining which reaction is made. The speed of reaction practically does not affect the polarity of the synthesis solvent. For the formation of fully substituted o-, m-, and p-arilaminocyclotriphosphazenes, the reaction takes 5 h and is carried out in the diglyme at its boiling temperature. The structure of the synthesized AAP was confirmed by 31P and 1H NMR spectroscopy and MALDI-TOF mass spectrometry. By means of synchronous DSK and TGA, it is found that the synthesized AAP are crystalline and their thermal destruction has a stepped character. Thermal destruction is shown to be accompanied by the simultaneous removal of three aniline molecules from the AAP molecules. Conducted curing of epoxy resin DER-331 is carried out using the AAP as a curing agent. It has been established that due to steric difficulties, o- AAP does not interact with epoxy resin, unlike m- and p- AAP. The gel fraction in curing resin is measured, and the AAP relate to the stage processes of macromolecule formation. The result is that polymers based on DER-331 and m-, p-AAP have a gel fraction content up to 97 mass. %. These polymers have glass-transition temperatures 80 and 85 °C (m- and p-AAP-based, respectively) and demonstrate fire resistance to standard UL-94 of category V-0

Active learning with deep pre-trained models for sequence tagging of clinical and biomedical texts

Active learning is a technique that helps to minimize the annotation budget required for the creation of a labeled dataset while maximizing the performance of a model trained on this dataset. It has been shown that active learning can be successfully applied to sequence tagging tasks of text processing in conjunction with deep learning models even when a limited amount of labeled data is available. Recent advances in transfer learning methods for natural language processing based on deep pre-trained models such as ELMo and BERT offer a much better ability to generalize on small annotated datasets compared to their shallow counterparts. The combination of deep pre-trained models and active learning leads to a powerful approach to dealing with annotation scarcity. In this work, we investigate the potential of this approach on clinical and biomedical data. The experimental evaluation shows that the combination of active learning and deep pre-trained models outperforms the standard methods of active learning. We also suggest a modification to a standard uncertainty sampling strategy and empirically show that it could be beneficial for annotation of very skewed datasets. Finally, we propose an annotation tool empowered with active learning and deep pre-trained models that could be used for entity annotation directly from Jupyter IDE

Synthesis and biological evaluation of aromatic analogues of conduritol F, L-chiro-inositol, and dihydroconduritol F structurally related to the amaryllidaceae anticancer constituents

Pancratistatin is a potent anticancer natural product, whose clinical evaluation is hampered by the limited natural abundance and the stereochemically complex structure undermining practical chemical preparation. Fifteen aromatic analogues of conduritol F, L-chiro-inositol, and dihydroconduritol F that possess four of the six pancratistatin stereocenters have been synthesized and evaluated for anticancer activity. These compounds serve as truncated pancratistatin analogues lacking the lactam ring B, but retaining the crucial C10a-C10b bond with the correct stereochemistry. The lack of activity of these compounds provides further insight into pancratistatin's minimum structural requirements for cytotoxicity, particularly the criticality of the intact phenanthridone skeleton. Significantly, these series provide rare examples of simple aromatic conduritol and inositol analogues and, therefore, this study expands the chemistry and biology of these important classes of compounds