The Period of Ignition Delay for Methane-Air Mixture with Hydrogen and Ethylene Additives

The article is devoted to estimating the intensifying efficiency of methane-air ignition by adding a small amount of hydrogen and/or ethylene. It presents features of the experimental determination of the ignition delay period for fuel-air mixtures using shock installation and methods of processing empirical data. The testing of the known ignition kinetic models for methane, hydrogen, and ethylene with air was carried out. The results of test calculations were compared with those previously published, as well as original experiments. The kinetic model was chosen to provide the minimum discrepancy between the calculated and experimental data. The regularities of the effect of hydrogen and ethylene additives on the ignition dynamics of the methane-air mixture for the range of initial pressures from 1 to 8 bar at temperatures from 900 to 1100 K were obtained with the use of non-stationary numerical modeling. Methane-air mixtures with the mass fraction of additives not exceeding 10% were studied. The quantitative indicators of possible reduction in the ignition delay period of methane-air mixtures were detected.

INDEEDopt: a deep learning-based ReaxFF parameterization framework

Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.

THE ANALYSIS OF FACTORS AFFECTING THE AUTHORITY OF UNIVERSITY PROFESSORS: A CASE OF IRAN, THE ISLAMIC REPUBLIC OF

The method employed in this study was a descriptive correlation in which two validated researcher-made questionnaires were used to collect data. The population involved 1770 university professors in the academic year 2019-2020. From the population, 240 were randomly selected. The data was analyzed using SPSS 21 and Amos 21. Pearson correlation was used to decide the relationship between variables, one-way ANOVA to compare means, Cronbach alpha to determine reliability, and factor analysis was to check the research model and substantiate construct validity. As a result, professor authority was found to have a significant negative correlation with relative deprivation. In addition, the academic status, age, and marital status of professors turned out to exert significant positive effects on their authorities. Furthermore, the chi-square minimum discrepancy value (CMIN) was equal to 523.414, the root mean square error of approximation (RMSEA) was 0.049, the root mean square of the residuals (RMR) was equal to 0.090, the minimum discrepancy per degree of freedom (CMIN/DF) in the model was 1.562, the comparative fit index (CFI) was 0.925, and finally, the parsimonious comparative fit index (PCFI) equaled 0.820. Thus, the comparative and parsimonious indices calculated to evaluate the solidarity of the constructs demonstrated that the collected data can be considered as supporting the research validity. JEL Classification Codes: I21, I23, I31, I32.

Extending the Basic Local Independence Model to Polytomous Data

A probabilistic framework for the polytomous extension of knowledge space theory (KST) is proposed. It consists in a probabilistic model, called polytomous local independence model, that is developed as a generalization of the basic local independence model. The algorithms for computing “maximum likelihood” (ML) and “minimum discrepancy” (MD) estimates of the model parameters have been derived and tested in a simulation study. Results show that the algorithms differ in their capability of recovering the true parameter values. The ML algorithm correctly recovers the true values, regardless of the manipulated variables. This is not totally true for the MD algorithm. Finally, the model has been applied to a real polytomous data set collected in the area of psychological assessment. Results show that it can be successfully applied in practice, paving the way to a number of applications of KST outside the area of knowledge and learning assessment.

Estimating Perceived Brightness in Low Luminance Conditions

Mesopic photometry, which studies visual perception of low-level optical radiation, is of great interest today in lighting engineering. It involves investigating human responses to visual observations in low light conditions in the object space, determining the optimum artificial illumination levels in industrial areas, and solving clinical perimetry problems. The estimation procedure for mesopic photometry recommended by the International Commission on Illumination (CIE) is based on computing a combination of photopic (daylight) and scotopic (nighttime) visual perception levels. This procedure being iterative makes it inconvenient to apply in engineering practice, as the number of iterative steps proves to be several dozens on average, exceeding a hundred in certain cases. As a result, the feasibility of using the CIE procedure instead of a purely photopic perception technique becomes questionable. The discrepancy in the results obtained via these methods informs the selection criterion. The paper compares computation results for perceived brightness in photopic and mesopic vision in low luminance conditions. We also establish whether it is possible to find analytical solutions using the CIE procedure. We show that, for radiation of a colour temperature in the range of 950--12000 K, the maximum computational discrepancy between photopic and mesopic vision scenarios lies in the --200--50 % range, while the minimum discrepancy is approximately 5 % for radiation characterised by a colour temperature of approximately 2000 K. We also present analytical solutions for several specific cases according to the CIE procedure