High-resolution measurements of the exited states (n,p n

Measurements of C12 cross sections for the excited states (n,p0) up to (n,p4) and (n,d0), (n,d1) have been carried out. The Van de Graaff neutron generator of the EC-JRC-IRMM laboratory has been used for these measurements. A very thin tritiated target (263 μg/cm2) was employed with deuteron beams energies impinging on the target in the range 2.5–4.0 MeV. Neutrons in the range 18.9–20.7 MeV were produced with an intrinsic energy spread of 0.2–0.25% FWHM. With such narrow neutron energy spread, using a high energy resolution device such as a single crystal diamond detector, several peaks from the outgoing charged particles produced by the (n,pn), (n,dn) and also (n,α0) reactions appear in the pulse height spectrum. The peaks can be identified using the reaction Q-values. The diamond detector used for these measurements has shown an intrinsic energy resolution lower than 0.9% FWHM. The analysis of the peaks has permitted to derive the partial carbon reaction cross sections for several excited states. The results are presented in this paper with the associated uncertainties and they are compared with different versions of TENDL compilation when these data are available (e.g. versions 2009, 2010, 2011 and 2015) and also with experimental results available in the EXFOR database

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia

Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response

There is a tendency to increase the use of demand response technology in the Russian Federation along with other developing countries, covering not only large industries, but also individual households and organizations. Reducing peak loads of electricity consumption and increasing energy efficient use of equipment in the power system is achieved by applying demand management technology based on modeling and predicting consumer behavior in an educational institution. The study proposes to consider the possibility of participating in the concept of demand management of educational institutions with a typical workload schedule of the work week. For the study, statistical data of open services and sources, Russian and foreign research on the use of digital and information technologies, analytical methods, methods of mathematical modeling, methods of analysis, and generalization of data and statistical methods of data processing are used. An algorithm for collecting and processing power consumption data and a load planning algorithm were developed, including all levels of interaction between devices. A comparison was made between the values of the maximum daily consumption before and after optimization, as well as the magnitude of the decrease in the maximum consumption after applying the genetic algorithm. The developed algorithm has the ability to scale, which will increase the effect of using the results of this study to more significant values. Load switching helps to reduce peak consumption charges, which often represent a significant portion of the electricity cost

Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response

There is a tendency to increase the use of demand response technology in the Russian Federation along with other developing countries, covering not only large industries, but also individual households and organizations. Reducing peak loads of electricity consumption and increasing energy efficient use of equipment in the power system is achieved by applying demand management technology based on modeling and predicting consumer behavior in an educational institution. The study proposes to consider the possibility of participating in the concept of demand management of educational institutions with a typical workload schedule of the work week. For the study, statistical data of open services and sources, Russian and foreign research on the use of digital and information technologies, analytical methods, methods of mathematical modeling, methods of analysis, and generalization of data and statistical methods of data processing are used. An algorithm for collecting and processing power consumption data and a load planning algorithm were developed, including all levels of interaction between devices. A comparison was made between the values of the maximum daily consumption before and after optimization, as well as the magnitude of the decrease in the maximum consumption after applying the genetic algorithm. The developed algorithm has the ability to scale, which will increase the effect of using the results of this study to more significant values. Load switching helps to reduce peak consumption charges, which often represent a significant portion of the electricity cost