Growth curve of Nile tilapia from different families of the AquaAmérica variety

Selection can affect growth, changing performance and asymptotic values. However, there is little information about the growth of families in fish breeding programs. The aim of this study was to evaluate the performance and growth of families of Nile tilapia AquaAmérica. Twenty AquaAmérica families cultivated in a net cage (13.5 m3) for 181 days were evaluated. The nonlinear Gompertz regression model was fitted to the data by the weighted least squares method, taking the inverse of the variance of weight in different families and at different ages as the weighting variable. The model was adjusted to describe the growth in weight and morphometric characteristics. Two families showed highest (P<0.05) weights at both 133 days (family AA10: 743.2 g; family AA16: 741.2 g) and 181 days (family AA10: 1,422.1 g; family AA16: 1,393.4 g) of the experiment. In both experimental periods, the males showed a heavier weight, with the greatest contrast between the sexes occurring at 181 days. The analysis of the three most contrasting families (AA1, AA9 and AA14) showed that the asymptotic value for weight was higher (P<0.05) in family AA9 (3,926.3 g) than in family AA14 (3,251.6 g), but specific growth rate and age at the inflection point did not differ significantly between families. In conclusion, two of the 20 families were superior; males exhibited a greater growth, mainly in the period of 181 days; and the growth curve differed between the families, especially for asymptotic weight.

ESTIMATION OF THE DYNAMICS OF FACTOR CONTRIBUTIONS IN A LINEAR REGRESSION MODEL

Разработаны две алгоритмические схемы оценивания параметров линейной регрессии с требованием равенства нулю ошибки аппроксимации для заданного наблюдения и на их основе способы расчета динамических оценок вкладов факторов, входящих в состав правой части линейной регрессионной модели, в значения зависимой переменной. Одна из этих схем основана на решении задачи квадратичного программирования, а вторая предусматривает использование взвешенного метода наименьших квадратов. Организованный при этом итерационный процесс предполагает пересчет матрицы весовых коэффициентов для каждого наблюдения обрабатываемой выборки данных. Рассчитаны вклады следующих факторов для регрессионной модели погрузки на железнодорожном транспорте: объема добычи угля, объема вывезенной древесины, рабочего парка груженых железнодорожных вагонов (в среднем в сутки). Установлено, что наибольшее влияние на выходную переменную оказывает объем добычи угля, хотя это влияние и имеет некоторую общую тенденцию к снижению: почти на 4 пункта за 14 лет. Также несколько ослабевает, на 3 пункта, влияние и второго по значимости фактора - рабочего парка груженых железнодорожных вагонов. А наименее значимый показатель (объем вывезенной древесины) имеет явную тенденцию к усилению своего влияния, которое выросло почти на 7 пунктов I developed two algorithmic schemes for estimating the parameters of linear regression with the requirement that the approximation error for a given observation is zero and, on their basis, methods for calculating the dynamic estimates of the contributions of the factors included in the right side of the linear regression model to the values of the dependent variable. One of these schemes is based on solving a quadratic programming problem, and the second involves the use of a weighted least squares method. The iterative process organized in this case involves recalculating the matrix of weighting coefficients for each observation of the processed data sample. I calculated the contributions of the following factors for the regression model of loading on railway transport: the volume of coal production, the volume of exported timber, the working fleet of loaded railway cars (on average per day). I found that the largest influence on the output variable is exerted by the volume of coal production, although this influence has some general tendency to decrease - by almost 4 points over 14 years. Also, the influence of the second most important factor - the working fleet of loaded railway cars, is also weakening by 3 points. But the least significant indicator - the volume of exported timber - has a clear tendency to increase its influence, which has grown by almost 7 points

Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro

In this work we fit an epidemiological model SEIAQR (Susceptible - Exposed - Infectious - Asymptomatic - Quarantined - Removed) to the data of the first COVID-19 outbreak in Rio de Janeiro, Brazil. Particular emphasis is given to the unreported rate, that is, the proportion of infected individuals that is not detected by the health system. The evaluation of the parameters of the model is based on a combination of error-weighted least squares method and appropriate B-splines. The structural and practical identifiability is analyzed to support the feasibility and robustness of the parameters' estimation. We use the bootstrap method to quantify the uncertainty of the estimates. For the outbreak of March-July 2020 in Rio de Janeiro, we estimate about 90% of unreported cases, with a 95% confidence interval (85%, 93%).

A COMPARISON OF ESTIMATION METHODS FOR ONE PARAMETER INVERSE GOMPERTZ DISTRIBUTION

In this paper, we compare the methods of estimation for one parameter lifetime distribution, which is a special case of inverse Gompertz distribution. We discuss five different estimation methods such as maximum likelihood method, least-squares method, weighted least-squares method, the method of Anderson-Darling, and the method of Crámer–von Mises. It is evaluated the performances of these estimators via Monte Carlo simulations according to the bias and mean-squared error. Furthermore, two real data applications are performed.

Development of organizational and technical methods for predicting emergency situations and possible losses as their results

Emergency prevention is based on analysis, forecasting and early response to emergencies. A systematic approach to solving the problem of preventing emergencies envisages forecasting emergencies by type, level and possible losses caused as a their results both in the state as a whole and in its regions. To implement a systematic approach based on a formalized mathematical model, an organizational and technical method has been developed for predicting emergencies and possible losses caused as their results. The method is a combination of a variable order polynomial regression method, a weighted least squares method, and a probabilistic statistical method. This allows to compensate for the shortcomings of some at the expense of others, which will lead to an increase in forecasting accuracy. A control algorithm has been developed for the implementation of an organizational and technical method for predicting emergency situations and possible losses caused as their results. Its use involves the implementation of a number of interrelated procedures. At the first stage, the collection, processing and analysis of information on emergency situations in the country for a certain period of monitoring is carried out. This is the basis for predicting the processes of emergencies in general, in nature, level and types, as well as losses due to them both in the state and its regions. The information received is taken into account when forming a decision on the actions of civil protection units in order to adequately respond to emergency situations and eliminate their consequences. Based on the analysis of the effectiveness of the actions of the response units, the decisions on the elimination of emergency situations are adjusted. The developed method makes it possible to reasonably approach the planning and implementation of organizational and technical measures to prevent emergency situations, taking into account the potential threats to the territories and population of the country's regions

Research on Location Algorithm Based on Beacon Filtering Combining DV-Hop and Multidimensional Support Vector Regression

The DV-Hop algorithm is widely used because of its simplicity and low cost, but it has the disadvantage of a large positioning error. In recent years, although some improvement measures have been proposed, such as hop correction, distance-weighted correction, and improved coordinate solution, there is room for improvement in location accuracy, and the accuracy is affected in anisotropic networks. A location algorithm based on beacon filtering combining DV-Hop and multidimensional support vector regression (MSVR) is proposed in this paper. In the process of estimating the coordinates of unknown nodes, received signal strength indication (RSSI), MSVR, and weighted least squares method are combined. In addition, the verification error of beacon nodes is proposed, which can select the beacon nodes with smaller errors to reduce the location error. Simulation results show that in different distributions, the location accuracy of the proposed algorithm is at least 34% higher than that of the classical DV-Hop algorithm and at least 28% higher than that of the localization based on multidimensional support vector regression (LMSVR) algorithm. The proposed algorithm has the potential of application in small-scale anisotropic networks.

Novel Optimization-Based Parameter Estimation Method for the Bass Diffusion Model

The Bass model is the most popular model for forecasting the diffusion process of a new product. However, the controlling parameters in it are unknown in practice and need to be determined in advance. Currently, the estimation of the controlling parameters has been approached by various techniques. In this case, a novel optimization-based parameter estimation (OPE) method for the Bass model is proposed in the theoretical framework of system dynamics ( SD). To do this, the SD model of the Bass differential equation is first established and then the corresponding optimization mathematical model is formulated by introducing the controlling parameters as design variable and the discrepancy of the adopter function to the reference value as objective function. Using the VENSIM software, the present SD optimization model is solved, and its effectiveness and accuracy are demonstrated by two examples: one involves the exact solution and another is related to the actual user diffusion problem from Chinese Mobile. The results show that the present OPE method can produce higher predicting accuracy of the controlling parameters than the nonlinear weighted least squares method and the genetic algorithms. Moreover, the reliability interval of the estimated parameters and the goodness of fitting of the optimal results are given as well to further demonstrate the accuracy of the present OPE method.

Monitoring observations of meteor echo at the EKB ISTP SB RAS radar: algorithms, validation, statistics

The paper considers the implementation of algorithms for automatic search for signals scattered by meteor trails according to EKB ISTP SB RAS radar data. In general, the algorithm is similar to the algorithms adopted in specialized meteor systems. The algorithm is divided into two stages: detecting a meteor echo and determining its parameters. We show that on the day of the maximum Geminid shower, December 13, 2016, the scattered signals detected by the algorithm are foreshortening and correspond to scattering by irregularities extended in the direction of the meteor shower radiant. This confirms that the source of the signals detected by the algorithm is meteor trails. We implement an additional program for indirect trail height determination. It uses a decay time of echo and the NRLMSIS-00 atmosphere model to estimate the trail height. The dataset from 2017 to 2019 is used for further testing of the algorithm. We demonstrate a correlation in calculated Doppler velocity between the new algorithm and FitACF. We present a solution of the inverse problem of reconstructing the neutral wind velocity vector from the data obtained by the weighted least squares method. We compare calculated speeds and directions of horizontal neutral winds, obtained in the three-dimensional wind model, and the HWM-14 horizontal wind model. The algorithm allows real-time scattered signal processing and has been put into continuous operation at the EKB ISTP SB RAS radar.

Monitoring observations of meteor echo at the EKB ISTP SB RAS radar: algorithms, validation, statistics

The paper considers the implementation of algorithms for automatic search for signals scattered by meteor trails according to EKB ISTP SB RAS radar data. In general, the algorithm is similar to the algorithms adopted in specialized meteor systems. The algorithm is divided into two stages: detecting a meteor echo and determining its parameters. We show that on the day of the maximum Geminid shower, December 13, 2016, the scattered signals detected by the algorithm are foreshortening and correspond to scattering by irregularities extended in the direction of the meteor shower radiant. This confirms that the source of the signals detected by the algorithm is meteor trails. We implement an additional program for indirect trail height determination. It uses a decay time of echo and the NRLMSIS-00 atmosphere model to estimate the trail height. The dataset from 2017 to 2019 is used for further testing of the algorithm. We demonstrate a correlation in calculated Doppler velocity between the new algorithm and FitACF. We present a solution of the inverse problem of reconstructing the neutral wind velocity vector from the data obtained by the weighted least squares method. We compare calculated speeds and directions of horizontal neutral winds, obtained in the three-dimensional wind model, and the HWM-14 horizontal wind model. The algorithm allows real-time scattered signal processing and has been put into continuous operation at the EKB ISTP SB RAS radar.