A model of response efficiency in the hierarchical control system based on the assessment of readiness of fire departments

Introduction. The readiness of all levels of subsystems that comprise the Unified State System for Emergency Prevention and Liquidation (USSEPL) is one of the most important characteristics that determine its effectiveness. To support decision-making at the upper levels of the management hierarchy, it is important to have a set of models that adequately represent the dependence between key response efficiency indicators and particular indicators of lower levels of the system (fire and rescue departments). In most cases, a regulatory approach to the construction of such models, by virtue of which analysts set their structure and parameters, turns out to be unproductive due to their non-adaptive nature in the context of dynamically changing external conditions and technological capabilities of modern devices. The use of an approach based on solving inverse problems that close the feedback loop and provide for an adaptive adjustment of parameters and the structure of models, ensures the current adequacy of models amid changing conditions.The relevance of the study lies in the development of a technology for constructing polynomial models that allow to assess the USSEPL response effectiveness based on estimated indicators of readiness of subsystems at lower levels obtained using expert evaluation techniques (testing) by means of internal control.Goals and objectives. The aim of the work is to build and test the technology for developing analytical polynomial models that allow to adequately assess performance indicators of the USSEPL response depending on the readiness indicators of lower-level subsystems (fire and rescue departments). In compliance with this goal, the tasks of choosing the type of model and methods of obtaining the necessary initial data are also set.Methods. The study uses methods of analysis of hierarchically organized systems, mathematical statistics, simulation modelling, and methods of expert evaluation. The research is backed by materials from domestic and foreign publications.Results and discussion. The proposed method of constructing an efficiency model of the USSEPL operation, relying on the readiness of subsystems, serves as the basis for constructing models that can take into account other indicators of subsystems.Conclusions. The solution to the problem of constructing a polynomial model, that features dependence between the USSEPL response efficiency and lower-level readiness indicators, serves as the basis for other similar models that will support decision making systems.

Estimating moisture content variation in kiln dried Pacific coast hemlock

Kiln drying is admittedly a significant value-adding step in timber processing where the importance of predicting moisture within a dried batch cannot be overemphasized. This study predicts and characterizes the moisture variation in kiln-dried wood based on the initial and target moisture values using polynomial models. Four polynomial models are used to correlate initial and final moisture characteristics. First model is linear while the three others are nonlinear. The robustness of the three best models is analyzed and a closed formula is proposed to evaluate the final moisture coefficient of variation based on the target moisture and initial moisture coefficient of variation. Three models could successfully characterize the final moisture variation with the best one showing an R 2 > 96%. However, the first (linear) model is the most resilient and, thus recommended for estimating final moisture variation.

Time Series Methods and Repeated Sample Surveys

<p>When applied to a sequence of repeated surveys, the traditional sample survey estimators of means or totals for one time period only, fail to take advantage of any time series structure. Such structure may result from correlation between successive responses for resampled individuals, or from time series properties in the parameters of interest. Historically, the initial published papers on time series improvement of repeated sample survey estimates allowed only the first possibility, treating the sum over the population of the individual responses as fixed; individual responses were seen as having stochastic properties only with respect to the sampling scheme. The alternative and later development allowed that both individual responses and their sum have stochastic properties with respect to a superpopulation from which the population of individual responses are drawn. Superpopulations allowed the application of mainstream time series techniques, including signal extraction and stochastic least squares, to repeated sample survey data. These developments in their historical perspective are the topic of Chapter 1. Superpopulation models may also be applied to sample surveys from a single time period, and superpopulation and design properties of the one period linear non-homogeneous sample survey estimator form the topic of Chapter 2; this estimator is sufficiently general to subsume almost all single period non-informative sample survey estimators, and Chapter 2 allows systematisation of a wide range of previously disparate results. This linear estimator may also be extended beyond one time period to include the known estimators for repeated surveys, and this topic, together with a consideration of the effects of data agqregation on non-stochastic and stochastic least squares, is the subject of Chapter 3. Given the central role of the general linear model, and the time series nature of repeated surveys, projection and parameter updating formulae for linear models should form an integral part of repeated survey analysis. The correlation of sample survey errors however, invalidates the formulae appropriate to the known iid error case, and Chapters 4 and 5 develop the general formulae to allow correlated error structure. Chapter 4 considers parameter vectors of fixed length, as for example, for polynomial models, and provides formulae for estimating the length of the parameter vector, and for calculating independent recursive residuals and cusums when further data are added to the model. Chapter 5 considers updating and projection formulae in a wider context, and allows that the parameter vector may be stochastic or non-stochastic and that its length may increase with additional data; it consequently provides a general extension of the Kalman filter to the case of coloured noise over time. The paucity of suitable data has limited data analysis to that contained in Chapter 6, where a simulation study and an analysis of medical data gauge the efficacy of polynomial models in time with multiple observations per time point and autocorrelated errors. The formulae of Chapter 4 allow testing for the constancy of the regression relationships over time. The appendix details SAS computer programs for fitting the polynomial models of Chapter 6.</p>

Time Series Methods and Repeated Sample Surveys

<p>When applied to a sequence of repeated surveys, the traditional sample survey estimators of means or totals for one time period only, fail to take advantage of any time series structure. Such structure may result from correlation between successive responses for resampled individuals, or from time series properties in the parameters of interest. Historically, the initial published papers on time series improvement of repeated sample survey estimates allowed only the first possibility, treating the sum over the population of the individual responses as fixed; individual responses were seen as having stochastic properties only with respect to the sampling scheme. The alternative and later development allowed that both individual responses and their sum have stochastic properties with respect to a superpopulation from which the population of individual responses are drawn. Superpopulations allowed the application of mainstream time series techniques, including signal extraction and stochastic least squares, to repeated sample survey data. These developments in their historical perspective are the topic of Chapter 1. Superpopulation models may also be applied to sample surveys from a single time period, and superpopulation and design properties of the one period linear non-homogeneous sample survey estimator form the topic of Chapter 2; this estimator is sufficiently general to subsume almost all single period non-informative sample survey estimators, and Chapter 2 allows systematisation of a wide range of previously disparate results. This linear estimator may also be extended beyond one time period to include the known estimators for repeated surveys, and this topic, together with a consideration of the effects of data agqregation on non-stochastic and stochastic least squares, is the subject of Chapter 3. Given the central role of the general linear model, and the time series nature of repeated surveys, projection and parameter updating formulae for linear models should form an integral part of repeated survey analysis. The correlation of sample survey errors however, invalidates the formulae appropriate to the known iid error case, and Chapters 4 and 5 develop the general formulae to allow correlated error structure. Chapter 4 considers parameter vectors of fixed length, as for example, for polynomial models, and provides formulae for estimating the length of the parameter vector, and for calculating independent recursive residuals and cusums when further data are added to the model. Chapter 5 considers updating and projection formulae in a wider context, and allows that the parameter vector may be stochastic or non-stochastic and that its length may increase with additional data; it consequently provides a general extension of the Kalman filter to the case of coloured noise over time. The paucity of suitable data has limited data analysis to that contained in Chapter 6, where a simulation study and an analysis of medical data gauge the efficacy of polynomial models in time with multiple observations per time point and autocorrelated errors. The formulae of Chapter 4 allow testing for the constancy of the regression relationships over time. The appendix details SAS computer programs for fitting the polynomial models of Chapter 6.</p>

Adaptation Parameters of Time Series Models for Forecasting

Prospects for using time series to predict changes in technical parameters in real time are considered. The task is to assess the trend dynamics of the parameter. Adaptive polynomial models of the first and second order, based on the method of multiple exponential smoothing, were selected for forecasting. The models have been modified to adapt to the peculiarities of the computing process in the microcontroller. The initial data, the acceleration values in three axes, were obtained using a three-axis accelerometer installed on the vehicle. Comparison of the forecasting results showed that the second-order adaptive polynomial model is generally more preferable from the point of view of the reduced error. Both models can be used to estimate the change in a parameter for an arbitrary number of prediction intervals. The efficiency of using the models for the forecasting problem largely depends on the determination of the adaptation parameters, such as the smoothing constant and the initial estimates of the coefficients of the time series model. The paper considers the features of the behavior of the models and defines the rules for the selection of adaptation parameters depending on the nature of the change in the technical parameter over time.

KINETIC REGULARITIES OF NICKEL SULFURIC ACID LEACHING PROCESSES

В работе представлены результаты исследования кинетики сернокислого выщелачивания никеля из его сульфидов. В качестве модельных образцов выбраны синтезированные сульфиды никеля по составу и строению идентичные природным минералам: миллериту и хизлевудиту. Получены зависимости влияния скорости извлечения металла от концентрации серной кислоты, температуры, частоты вращения диска и продолжительности взаимодействия. Рассчитаны полиномиальные модели изучаемого процесса, преобразованные в уравнения скорости. Вычислены константы скорости и эмпирические значения энергии активации. The paper presents the results of a study of the kinetics of sulfuric acid leaching of nickel from its sulfides. Synthesized nickel sulfides were selected as model samples, identical in composition and structure to natural minerals: millerite and heazlewudite. The dependences of the influence of the metal extraction rate on the concentration of sulfuric acid, temperature, disk rotation frequency and duration of interaction are obtained. The polynomial models of the process under study, transformed into velocity equations, are calculated. The rate constants and empirical values of the activation energy are calculated.

Optinalysis: A New Method of Data Analysis and Comparison

The key concepts in symmetry detection and similarity, identity measures are automorphism and isomorphism respectively. Therefore, methods for symmetry detection and similarity, identity measures should be functionally bijective, inverse, and invariance under a set of mathematical operations. Nevertheless, few or no existing method is functional for these properties. In this paper, a new methodological paradigm, called optinalysis, is presented for symmetry detections, similarity, and identity measures between isoreflective or autoreflective pair of mathematical structures. The paradigm of optinalysis is the re-mapping of isoreflective or autoreflective pairs with an optical scale. Optinalysis is characterized as invariant under a set of transformations and its isoreflective polymorphism behaves on polynomial and non-polynomial models.

A dual perspective (conventional and solar-enhanced) on advanced oxidation processes for the treatment of Agave cocui vinasses

Agave cocui vinasse was physicochemically characterized with reference to the relevant environmental regulations. The following results were obtained: COD: 71,000 mg.L-1, total solids: 21,000 mg.L-1, dissolved solids: 17,000 mg.L-1; pH: 4.06, conductivity: 9.45 mhoscm-1, total Fe: 48.83 mg.L-1, total phenols: 8.66 mg.L-1; BOD: 30,000 mg.L-1. Fenton and photo-Fenton reactions were applied to treat the wastewater produced. For the Fenton process, the optimal oxidation conditions found were pH = 3.48, [COD]:[H2O2] mass ratio = 1:5, and [Fe+2]: [H2O2] mass ratio = 1:6. For the photo-Fenton process, the optimal parameters found were: pH = 3.98, [COD]:[H2O2] = 1:7.86, and [Fe+2]: [H2O2] = 1:5. The experimental data were adjusted to fit second order polynomial models with R2 = 0.88 for the Fenton process and R2 = 0.91 for the photo-Fenton process, respectively. The sludge produced featured the following characteristics: average COD: 41,000 mg.L-1, total Fe: 296,000 mg.L-1, pH: 7.7. The variables with the greatest influence in both processes were [Fe+2]:[H2O2] and [COD]:[H2O2].

Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity

Ising machines based on nonlinear analog systems are a promising method to accelerate computation of NP-hard optimization problems. Yet, their analog nature is also causing amplitude inhomogeneity which can deteriorate the ability to find optimal solutions. Here, we investigate how the system’s nonlinear transfer function can mitigate amplitude inhomogeneity and improve computational performance. By simulating Ising machines with polynomial, periodic, sigmoid and clipped transfer functions and benchmarking them with MaxCut optimization problems, we find the choice of transfer function to have a significant influence on the calculation time and solution quality. For periodic, sigmoid and clipped transfer functions, we report order-of-magnitude improvements in the time-to-solution compared to conventional polynomial models, which we link to the suppression of amplitude inhomogeneity induced by saturation of the transfer function. This provides insights into the suitability of nonlinear systems for building Ising machines and presents an efficient way for overcoming performance limitations.