Precise determination of αS(MZ)\alpha_{S}(M_Z) from a global fit of energy-energy correlation to NNLO+NNLL predictions

We present a comparison of the computation of energy-energy correlation in $e^{+}e^{-}$ collisions in the back-to-back region at next-to-next-to-leading logarithmic accuracy matched with the next-to-next-to-leading order perturbative prediction to LEP, PEP, PETRA, SLC and TRISTAN data. With these predictions we perform an extraction of the strong coupling constant taking into account non-perturbative effects modelled with Monte Carlo event generators. The final result at NNLO+NNLL precision is $\alpha_{S}(M_{Z})=0.11750\pm 0.00018 {\text( exp.)}\pm 0.00102{\text(hadr.)}\pm0.00257{\text(ren.)}\pm 0.00078{\text(res.)}$.Comment: 35 pages, 10 figures, 2 table

Determination of αS\alpha_{S} beyond NNLO using event shape averages

We consider a method for determining the QCD strong coupling constant using fits of perturbative predictions for event shape averages to data collected at the LEP, PETRA, PEP and TRISTAN colliders. To obtain highest accuracy predictions we use a combination of perturbative ${\cal{O}}(\alpha_{S}^{3})$ calculations and estimations of the ${\cal{O}}(\alpha_{S}^{4})$ perturbative coefficients from data. We account for non-perturbative effects using modern Monte Carlo event generators and analytic hadronization models. The obtained results show that the total precision of the $\alpha_{S}$ determination cannot be improved significantly with the higher order perturbative QCD corrections alone, but primarily requires a deeper understanding of the non-perturbative effects.Comment: 29 pages, 4 figures, 4 tables, journal versio

Bioutilization of the distillery stillage of different grain species from bioethanol production

Wastewater from bioethanol plants is classified as highly concentrated in terms of organic pollution precisely due to distillery stillage. The main problem in the disposal of distillery stillage is the processing of the liquid phase, the volume of which is up to 92% of all wastewater from a bioethanol plant. The existing wastewater treatment technologies of a bioethanol plant can be conditionally divided into four types: evaporation, aerobic biological treatment with fodder yeast production, anaerobic stillage treatment with biogas production, combined schemes. The aim of our work was to study a combined method for cleaning grain stillage by the anaerobic-aerobic method with the immobilization of microorganisms on a fibrous carrier. Physicochemical parameters of grain stillage and purified methane mash were determined according to generally accepted methods for analyzing wastewater from distilleries. Under anaerobic conditions, biogas was formed from distillery stillage, including low molecular weight organic compounds – methane, carbon dioxide, organic acids. After the first anaerobic stage of treatment, the pollution of wastewater decreased by 8-10 times, after which it was fed to the aerobic stage of post-treatment, which was carried out by microorganisms immobilized on a fixed carrier, which reduced the removal of biomass with the flow of purified water and improved treatment performance. The chemical oxygen demand (COD) of methane mash after the 1st stage of anaerobic fermentation was 1360 mg/dm3 compared to the initial COD of grain stillage of 15800 mg/dm3, which ensured a purification efficiency of 91.4%. The purification efficiency according to biochemical oxygen demand in five days (BOD5) was 97.5%. After the aerobic stage, the purification efficiency was 98.2% in terms of COD and 99.8% in terms of BOD5. The values of the content of total phosphorus also decreased by almost 20 times, nitrogen – by 9 times, sulfates – by 5 times. The advantages of the proposed method of wastewater treatment of bioethanol plants over existing ones are the ability to treat wastewater with any concentration of pollutants and additional obtaining of fuel – biogas, which can be used to replace natural gas, solving the problem of removing the biomass of microorganisms from the purification zone due to their fixation on a fibrous fixed carrier

Особенности расчета спектра напряжения, модулированного по закону ШИМ І и ІІ на основе двойного ряда Фурье

Описано загальні алгоритми розрахунку спектральних характеристик напруги інвертора після фільтра з широтно-імпульсною модуляцією (ШІМ) І і ІІ родів. Виведено формули для розрахунку вільної і перехідної складової модульованої напруги. Запропоновано розраховувати спектральну характеристику перехідної складової ШІМ І і ІІ роду рядом Фур’є двох змінних, спектральну характеристику вільної складової ШІМ-І рядом Фур’є однієї змінної, вільної складової ШІМ-ІІ – рядом Фур’є двох змінних.Described general algorithms for calculating the spectral characteristics of the inverter voltage after the filter with PWM I and II kind. The formulas for the calculation of the free and the transient component of the modulated voltage are obtained. Perform calculations of the spectral characteristic of the transient component PWM I and II kind by double Fourier series are proposed. Calculations of spectral characteristic of PWM-I free component performed by one variable Fourier series, the PWM II free component performed by two variables Fourier series.Описано общие алгоритмы расчета спектральных характеристик напряжения инвертора после фильтра с широтно-импульсной модуляцией І и ІІ родов. Выведено формулы для расчета свободной и переходной составляющей модулированного напряжения. Предложено рассчитывать спектральную характеристику переходной составляющей ШИМ І и ІІ рода рядом Фурье двух переменных, спектральную характеристику свободной составляющей ШИМ-І рядом Фурье одной переменной, свободной составляющей ШИМ-ІІ – рядом Фурье двух переменных

Определение кратности модуляции ШИМ напряжения инвертора по значению коэффициента гармоник на основании двойного ряда Фурье

Описано стандартну процедуру розрахунку фільтра інверторів з широтно-імпульсною модуляцією (ШІМ) за значенням коефіцієнту гармонік. Зазначено, що за постійного значення кратності модуляції вихідна напруга має запас за значенням коефіцієнту гармонік при не повному навантаженні інвертора. Запропоновано динамічно змінювати кратність модуляції ШІМ для забезпечення заданого значення коефіцієнту гармонік і зменшення динамічних втрат у напівпровідникових приладах. Виведено формулу для розрахунку кратності модуляції за значенням коефіцієнту гармонік, параметрів фільтра і навантаження на основі подвійного ряду Фур’є.Standard procedure for calculating the filter of inverter with PWM according to THD value are discrabed. Indicated that the constant multiplicity modulation of output voltage has a margin on the value of the THD at partial load of inverter. Dynamically changing of the PWM multiplicity for providing a constant THD value and reducig the dynamic losses in semiconductor devices are proposed. The formula for calculating the value of the modulation multiplicity according to THD value harmonic ratio, the filter parameters and the load on the basis of the double Fourier series is obtained.Описано стандартную процедуру расчета фильтра инверторов с ШИМ по значению коэффициента гармоник. Указано, что при постоянной кратности модуляции выходное напряжение имеет запас по значению коэффициента гармоник при неполной нагрузке инвертора. Предложено динамически изменять кратность модуляции ШИМ для обеспечения заданного значения коэффициента гармоник и уменьшения динамических потерь в полупроводниковых приборах. Выведена формула для расчета кратности модуляции по значению коэффициента гармоник, параметрам фильтра и нагрузки на основании двойного ряда Фурье

Deeply Learning Deep Inelastic Scattering Kinematics

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q2 and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection of a training set, the neural networks sufficiently surpass all classical reconstruction methods on most of the kinematic range considered. Rapid access to large samples of simulated data and the ability of neural networks to effectively extract information from large data sets, both suggest that deep learning techniques to reconstruct DIS kinematics can serve as a rigorous method to combine and outperform the classical reconstruction methods