Analysis of directions for application of artificial intelligence systems in soil condition analysis

The most significant characteristics of the soil are water-physical, biological and other properties, by influencing which it is possible to increase the productivity of agricultural crops. Analyzing various soil characteristics is a very labor-intensive and expensive process. The authors reviewed research on the use of artificial intelligence systems in the field of soil analysis. The purpose of the study is to analyze research on the use of artificial intelligence systems in the field of soil analysis. The article presents an analysis of artificial intelligence systems based on artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), multiple regression (MR) methods, particle swarm algorithm (PSO) and also their ensembles (various combinations). The use of these systems (their models) makes it possible to predict various types of soil erosion, predict soil temperature and moisture depending on various conditions, and also predict such complex indicators as the cation exchange capacity of the soil. The scope of application of artificial intelligence systems is to increase the awareness of managers and specialists of agricultural organizations and industry management bodies about various soil characteristics. This, in turn, allows you to make optimal decisions on various agricultural activities. Artificial intelligence systems can process significant amounts of data with high accuracy and speed compared to humans, which makes it possible to more effectively analyze many factors affecting the condition of the soil. The use of artificial intelligence systems can significantly reduce the cost of analyzing various soil characteristics and is a necessary condition for the use of precision farming systems

The VEPP-5 injection complex modulator revision and operational experience

For the VEPP-5 injection complex klystrons (5045, SLAC Lab., USA) power supply the four modulators producing a pulse voltage of 23.5 kV, a current of 6.3 kA, a pulse-top-duration of 3.7 µs are used. The modulator scheme designed over ten years ago has proved its reliability and let us to find out and study the weak spots of the modulators. The output voltage stability and pulse-to-pulse time jitter improvements are made. The results of the thyratrons TGI1-2500/50 and TGI1-5000/50A long-term exploitation in the VEPP-5 injection complex modulators are presented. The test results of the thyratron TGI1-2500/50 operating in the mode of more then twice pulse current overload at the nominal mode of modulator operation are also presented.Для живлення клістронів 5045 (виробництво SLAC) інжекційного комплексу ВЕПП-5 використовуються модулятори, що формують імпульси напругою 23,5 кВ, струмом 6,3 кА, тривалістю 3,7 мкс у кількості 4 штук. Конструкція модуляторів, розроблена близько 10 років тому, довела свою надійність і, разом з тим, дозволила вивчити і виявити слабкі місця модуляторів. Проведена робота з підвищення стабільності напруги на клістроні, зменшенню часового розкиду від імпульсу до імпульсу. Приводяться результати тривалої експлуатації тиратронів ТГІ1-2500/50 і ТГІ1-5000/50 у модуляторах для живлення клістронів 5045. Викладено результати ресурсних іспитів тиратрона ТГІ1-2500/50 при більш ніж подвійному перевантаженню по амплітуді струму в номінальному режимі роботи модулятора.Для питания клистронов 5045 (производство SLAC) инжекционного комплекса ВЭПП-5 используются модуляторы, формирующие импульсы напряжением 23,5 кВ, током 6,3 кА, длительностью 3,7 мкс в количестве 4 штук. Конструкция модуляторов, разработанная около 10 лет назад, доказала свою надежность и, вместе с тем, позволила изучить и выявить слабые места модуляторов. Проведена работа по повышению стабильности напряжения на клистроне, уменьшению временного разброса от импульса к импульсу. Приводятся результаты длительной эксплуатации тиратронов ТГИ1-2500/50 и ТГИ1-5000/50 в модуляторах для питания клистронов 5045. Изложены результаты ресурсных испытаний тиратрона ТГИ1-2500/50 при более чем двойной перегрузке по амплитуде тока в номинальном режиме работы модулятора

Search for narrow resonances in e+ e- annihilation between 1.85 and 3.1 GeV with the KEDR Detector

We report results of a search for narrow resonances in e+ e- annihilation at center-of-mass energies between 1.85 and 3.1 GeV performed with the KEDR detector at the VEPP-4M e+ e- collider. The upper limit on the leptonic width of a narrow resonance Gamma(R -> ee) Br(R -> hadr) < 120 eV has been obtained (at 90 % C.L.)

Measurement of \Gamma_{ee}(J/\psi)*Br(J/\psi->e^+e^-) and \Gamma_{ee}(J/\psi)*Br(J/\psi->\mu^+\mu^-)

The products of the electron width of the J/\psi meson and the branching fraction of its decays to the lepton pairs were measured using data from the KEDR experiment at the VEPP-4M electron-positron collider. The results are \Gamma_{ee}(J/\psi)*Br(J/\psi->e^+e^-)=(0.3323\pm0.0064\pm0.0048) keV, \Gamma_{ee}(J/\psi)*Br(J/\psi->\mu^+\mu^-)=(0.3318\pm0.0052\pm0.0063) keV. Their combinations \Gamma_{ee}\times(\Gamma_{ee}+\Gamma_{\mu\mu})/\Gamma=(0.6641\pm0.0082\pm0.0100) keV, \Gamma_{ee}/\Gamma_{\mu\mu}=1.002\pm0.021\pm0.013 can be used to improve theaccuracy of the leptonic and full widths and test leptonic universality. Assuming e\mu universality and using the world average value of the lepton branching fraction, we also determine the leptonic \Gamma_{ll}=5.59\pm0.12 keV and total \Gamma=94.1\pm2.7 keV widths of the J/\psi meson.Comment: 7 pages, 6 figure

Measurement of main parameters of the \psi(2S) resonance

A high-precision determination of the main parameters of the \psi(2S) resonance has been performed with the KEDR detector at the VEPP-4M e^{+}e^{-} collider in three scans of the \psi(2S) -- \psi(3770) energy range. Fitting the energy dependence of the multihadron cross section in the vicinity of the \psi(2S) we obtained the mass value M = 3686.114 +- 0.007 +- 0.011 ^{+0.002}_{-0.012} MeV and the product of the electron partial width by the branching fraction into hadrons \Gamma_{ee}*B_{h} = 2.233 +- 0.015 +- 0.037 +- 0.020 keV. The third error quoted is an estimate of the model dependence of the result due to assumptions on the interference effects in the cross section of the single-photon e^{+}e^{-} annihilation to hadrons explicitly considered in this work. Implicitly, the same assumptions were employed to obtain the charmonium leptonic width and the absolute branching fractions in many experiments. Using the result presented and the world average values of the electron and hadron branching fractions, one obtains the electron partial width and the total width of the \psi(2S): \Gamma_{ee} =2.282 +- 0.015 +- 0.038 +- 0.021 keV, \Gamma = 296 +- 2 +- 8 +- 3 keV. These results are consistent with and more than two times more precise than any of the previous experiments

Invasive aspergillosis in patients with COVID-19 in intensive care units: results of a multicenter study

Objective. To study risk factors, clinical and radiological features and effectiveness of the treatment of invasive aspergillosis (IA) in adult patients with COVID-19 (COVID-IA) in intensive care units (ICU). Materials and Methods. A total of 60 patients with COVID-IA treated in ICU (median age 62 years, male – 58%) were included in this multicenter prospective study. The comparison group included 34 patients with COVID-IA outside the ICU (median age 62 years, male – 68%). ECMM/ISHAM 2020 criteria were used for diagnosis of CAPA, and EORTC/MSGERC 2020 criteria were used for evaluation of the treatment efficacy. A case-control study (one patient of the main group per two patients of the control group) was conducted to study risk factors for the development and features of CAPA. The control group included 120 adult COVID-19 patients without IA in the ICU, similar in demographic characteristics and background conditions. The median age of patients in the control group was 63 years, male – 67%. Results. 64% of patients with COVID-IA stayed in the ICU. Risk factors for the COVID-IA development in the ICU: chronic obstructive pulmonary disease (OR = 3.538 [1.104–11.337], p = 0.02), and prolonged (> 10 days) lymphopenia (OR = 8.770 [4.177–18.415], p = 0.00001). The main location of COVID-IA in the ICU was lungs (98%). Typical clinical signs were fever (97%), cough (92%), severe respiratory failure (72%), ARDS (64%) and haemoptysis (23%). Typical CT features were areas of consolidation (97%), hydrothorax (63%), and foci of destruction (53%). The effective methods of laboratory diagnosis of COVID-IA were test for galactomannan in BAL (62%), culture (33%) and microscopy (22%) of BAL. The main causative agents of COVID-IA are A. fumigatus (61%), A. niger (26%) and A. flavus (4%). The overall 12-week survival rate of patients with COVID-IA in the ICU was 42%, negative predictive factors were severe respiratory failure (27.5% vs 81%, p = 0.003), ARDS (14% vs 69%, p = 0.001), mechanical ventilation (25% vs 60%, p = 0.01), and foci of destruction in the lung tissue on CT scan (23% vs 59%, p = 0.01). Conclusions. IA affects predominantly ICU patients with COVID-19 who have concomitant medical conditions, such as diabetes mellitus, hematological malignancies, cancer, and COPD. Risk factors for COVID-IA in ICU patients are prolonged lymphopenia and COPD. The majority of patients with COVID-IA have their lungs affected, but clinical signs of IA are non-specific (fever, cough, progressive respiratory failure). The overall 12-week survival in ICU patients with COVID-IA is low. Prognostic factors of poor outcome in adult ICU patients are severe respiratory failure, ARDS, mechanical ventilation as well as CT signs of lung tissue destruction

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Identification of the features of the regions that are most preferable for the use of precision farming technologies in agricultural production

As the main hypothesis, it is suggested that the existing unevenness in the number of precision farming elements used in agriculture in the subjects of the Russian Federation is related to regional characteristics and specific features of the agricultural sector of the regional economy. The purpose of the study is to identify the geographical features of the regions that are most preferable for the use of precision farming technology in agricultural production. Mathematical modeling uses data from 20 subjects of the Russian Federation on the dynamics of the introduction and use of precision farming elements and the characteristics of these regions for 14 different indicators that can in one way or another affect the introduction of these technologies. Multiple correlation was obtained using 5 characteristics of regions (the correlation coefficient was r=0.89±0.1). At the same time, two indicators (the change in the level of registered unemployment and the amount of subsidies per 1 ha of agricultural land) were inversely dependent on the result of the introduction of precision farming elements. The selected indicators determined the intensity of introduction of precision farming elements in the regions by almost 80% (the coefficient of determination was D=0.798). The identification of these features and the construction of an appropriate model allows to predict the most preferred regions for the use of precision farming elements in agricultural production based on the generalization of existing experience