Relationship between the shear viscosity and heating rate of metallic glasses below Tg

金沢大学大学院自然科学研究科機能創成システムIt has been shown that the shear viscosity of bulk and ribbon glassy Pd40 Cu30 Ni10 P20 at temperatures T< Tg (Tg is the glass transition temperature) follows a simple relationship, ln η (T) =B (T) -ln = T, where T - is the heating rate and B depends only on temperature. This means, in particular, that ln η (T) dependencies measured at different heating rates can be superposed by a simple vertical shift and the derivative a∂ ln η/∂ ln T T=const =-1. Such a behavior is indeed found for glassy Pd40 Cu30 Ni10 P20. The experimental viscosity data derived earlier on other metallic glasses follow the same relationship. It is argued that this relationship originates from stress-oriented irreversible structural relaxation with distributed activation energies. © 2006 The American Physical Society

Low Frequency Internal Friction Induced by Structural Relaxation of Metallic Glasses

A quantitative model of visco-plastic damping induced by structural relaxation of metallic glasses is developed. The model is shown to be consistent with the experiment

Predicting of fentanyl-associated neurotoxicity in pancreatic cancer with clical, genetic model [Прогнозирование фентанил-ассоциированной нейротоксичности у больных с раком поджелудочной железы с помощью клинико-генетической модели]

Aim. To develop a model for the implementation of opioid - associated neurotoxicity in patients with pancreatic cancer based on an analysis of the relationship of clinical and genetic factors. Materials and methods. In 45 patients with pancreatic cancer, 54 clinical and genetic factors were studied for predicting the implementation of opioid-associated neurotoxicity, receiving a transdermal form of fentanyl. Results. A clinical genetic model of the implementation of opioid - associated neurotoxicity in patients with pancreatic cancer was developed using the example of a transdermal form of fentanyl Conclusion. The clinical genetic model for predicting the risk of opioid-associated neurotoxicity in patients with pancreatic cancer is important from the perspective of personalized medicine. © 2021 Global Media Tekhnologii. All rights reserved

Evolutionary algorithm for automated formation of decision-making models for predicting the safety of opioid therapy

In this paper, an evolutionary algorithm for solving the problem of predicting the safety of opioid therapy for patients with pancreatic cancer is proposed. Opioid analgesics such as fentanyl and morphine are used as a therapy for pain syndromes. Using the patient database, based on the results of the therapy applied to them, it is determined whether there is a correlation between the outcome and the combination of input data taken into account. To find a set of informative features, it is proposed to use the genetic algorithm for multi-criterion optimization, in which two criteria are reduced to one generalized criterion using the method of "additive convolution". The formed combination of the selected input features, which affects the outcome, is used to build a decision support model and to evaluate it afterwards. © Published under licence by IOP Publishing Ltd