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Biomass and respiration activity of soil microorganisms in anthropogenically transformed ecosystems (Moscow region)

Ivashchenko K.V., Ananyeva N.D., Vasenev V.I., Kudeyarov V.N., Valentini R.

separated sites (3-5 points per site) have been randomly selected and soil samples have been taken from

Методика расчета доверительного интервала оценки случайной величины, подчиненной композиции нормального и равномерного законов распределения

Крейдик, Е. Л., Kreidik, E. L.

. The general case of determining of probability of hit by a given section of a random variable

Randomized Evidence for Reduction of Perioperative Mortality: An Updated Consensus Process

Landoni Giovanni

all the nonsurgical interventions, supported by randomized evidence, that may help reduce

MONTE CARLO OUTPUT ANALYSIS

Marcinkiewicz-type law of large numbers for double arrays

Hong D., Volodin A.

of Marcinkiewicz proving that if Xn is a sequence of independent, identically distributed random variables with E

Случайные гауссовы волны в изотропной упругой среде

Максимов, Дмитрий Н., Садреев, Алмаз Ф., Maksimov, Dmitry N., Sadreev, Almas F.

longitu- dinal and transverse random waves. Based on that we derive the intensity correlation function

Probability Distribution Functions of the Sum of Squares of Random Variables in the Non-zero Mathematical Expectations

Fateev, Yuri L., Shaidurov, Vladimir V., Garin, Evgeny N., Dmitriev, Dmitry D., Tyapkin, Valeriy N., Фатеев, Юрий Л., Шайдуров, Владимир В., Гарин, Евгений Н., Дмитриев, Дмитрий Д., Тяпкин, Валерий Н.

The article concluded probability distribution functions sum of the squares of the random variables

Marcinkiewicz-type law of large numbers for double arrays

Hong D., Volodin A.

of Marcinkiewicz proving that if Xn is a sequence of independent, identically distributed random variables with E

Performance Evaluation of Antivirus Systems for Computer Networks

Anisimov E.G., Saurenko T.N., Anisimov V.G., Los’ V.P.

sample from a general set of values of a random variable that characterizes the number of network

Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia

Chinilin A.V., Savin I.Yu.

variables and remote sensing data. The article provided the results of 3D modeling of SOC content in several

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