The results of research on the development of automated classification of remote sensing images of the Earth for on-farm land use based on the use of an object-oriented approach, machine learning and geoinformation modeling are presented. The classification methodology included three stages: analysis of digital images with the selection of spatial objects through preliminary segmentation, classification of spatial objects using the ,Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms, and assessment of the overall accuracy of the result. For processing, satellite images Sentinel-2 from May to April for the land use area of the experimental station «Elitnaya» and Individual Enterprise of State Farm (Collective Farm) Kovalev S.M. of the Novosibirsk region with a spatial resolution of 10 m per pixel were used. The processing of the resulting multispectral images was carried out using the software product SAGA GIS version 8.5.1 and QGIS with opensource code, the creation of classification models was carried out in the package of the statistical programming language R. It was established that the overall accuracy of classification of land use objects displayed onsatellite images, for the territory of the experimental station «Elitnaya» the SVM algorithm was 87.1% (kappa coefficient 0.74), and using the RF algorithm – 90.3% (kappa coefficient 0.87). For the land use area of the Individual Enterprise of State Farm (Collective Farm) Kovalev S.M. using the SVM algorithm – 78.4% (kappa coefficient 0.78), and using the RF algorithm – 82.3% (kappa coefficient 0.82). The object-oriented approach, in integration with machine learning, facilitates efficient segmentation and classification of remote sensing images for the delineation of spatial objects, provides the ability to automate the mapping process of land use areas, and to incorporate this information into geoinformation modeling for evaluation and classification of agricultural lands.

The research was aimed at developing a conceptual model of digital nitrogen management in crops. Cognitive analysis of the knowledge structure in this subject area and conceptual modeling of digital nitrogen management in agrophytocenoses using an object-oriented approach and Unified Modeling Language (UML) were used. A detailed system of diagrams was created, encompassing class, process, and interaction diagrams. The model is anchored on three abstract objects: class (seven in total), attribute (32), and interclass relationships (18), which distribute the main concepts, emphasizing the complexity and multifaceted approaches to digital nitrogen management. The central class is “Agrophytocenosis”, which directly interacts with five classes and indirectly with one class. Attributes are integral to the classes and reflect their specific characteristics. To depict the interaction between the classes and their attributes, four types of relationships are employed in the model: “dependency,” “association,” “aggregation,” and “inner class.” In the process diagram of the digital nitrogen management system, two primary subsystems are highlighted: the analysis and planning subsystem and the adjustment subsystem, as well as tools and sources for data acquisition and processing. A distinctive feature of the developed conceptual model is the application of the temporality principle, integrating static and dynamic processes in the digital nitrogen management system within the agrophytocenosis. Subsystems for analysis, planning, and adjustment of emerging conditions in the management areas allow ensuring more efficient use of nitrogen fertilizers in crops. The conceptual model is aimed at developing a hardware-software complex for diagnostics of nitrogen nutrition of cultivated plants and management of fertilizer application based on modern digital monitoring and data-processing tools.

Widespread commercialization of sodium-ion batteries (SIB) is limited by the shortcomings of existing electrode materials, so the search and testing of various sodium compounds suitable for SIB are relevant. This paper presents the results of a study of the sodium diffusion mechanisms in quasi-layered oxides Na1-xV1-xMo1+xO6, which are potentially promising for applications for SIB. A simple synthesis procedure has been developed, which makes it possible to obtain compounds in a wide range of compositions up to x = 0.2. To elucidate the mechanisms of sodium diffusion, we applied a comprehensive approach that combines material characterization at the “macro” (XRD, impedance spectroscopy) and “atomic-scale” levels (NMR, ab-initio calculations). Our results reveal rather fast sodium dynamics: Ionic conductivity reaches the values of 10–3 S/cm at T > 730 K. It has been found moreover that the diffusion mechanism changes with increasing temperature. At T < 625 K, sodium motion occurs mainly along the crystallographic b axis due to atomic jumps with the shortest jump length ≈ 3.6 Å and activation energy Ea 1 eV. With increasing temperature, another type of jumps along a axis (in the ab plane) with a jump length of ≈ 5 Å and a barrier value of 2 eV is also activated.

The results of research on the development and assessment of the accuracy of predictive models of spring wheat yield based on the use of remote sensing data and machine learning methods are presented. Yield data of spring wheat variety Novosibirskaya 31 obtained in a field experiment in the central forest-steppe of the Novosibirsk region in 2019–2022 were used in this work. Both qualitative predictors (the level of agrotechnologies intensification) and quantitative predictors (atmospheric precipitation in critical phases of wheat plant development and indicators of vegetation indices characterizing the condition of crops) were taken into account when creating the models. The use of various methods of intellectual data analysis, as well as the combination of parametric and non-parametric approaches in the study provided a sufficiently high accuracy of spring wheat yield forecasting. The methods used to predict spring wheat yield included linear regression, nonlinear Regression Splines based model, decision tree (CART), Random Forest, Adaptive Boosting (AdaBoost) and Gradient boosting. It was found that the models based on random forest, gradient and adaptive boosting algorithms were characterized by the highest predictive capabilities of crop yield depending on the emerging conditions of vegetation and controlling influence (R2 = 0.74–0.80). The development of predictive yield models using remote sensing and machine learning represent a certain scientific novelty and practical significance for effective management of crop productivity in changing soil-climatic and economic conditions. Predictive modeling is faced with multilevel environmental uncertainty and high variability of the resulting indicators on a particular land plot. In this regard, the multilevel approach may represent a promising solution for effective forecasting of spring wheat yield.

An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

A software- and hardware-based monitoring system has been developed for metallic components, on the basis of eddy current scanning of nonmagnetic materials. Design details are presented. Experimental results confirm that this system may be used to monitor metallic nanofilms.

Министерство науки и высшего образования Российской Федерации Российское химическое общество им.Д.И.Менделеева Секция по химической термодинамике и термохимии Научного совета РАН по физической химии Сибирское Отделение Российской Академии Наук Институт неорганической химии им.А.В.Николаева СО РАН

Development of the technologies for energy storage and conversion requires a search for compounds with high diffusion of alkali and alkaline-earth ions. Here, we present the results of comprehensive studies, including synthesis, powder X-ray diffraction, experiments on impedance and Na-23 NMR spectroscopy, as well as ab initio calculations, which were carried out to explore the sodium diffusion in scheelite-like Na5M(MoO4)(4) with M = Y, La, Bi, and in related solid solutions Na5-xM1-xZrx(MoO4)(4) (0.05 <= x <= 0.1), which were synthesized for the first time. Our investigations reveal that the Na-ion mobility increases in the sequence Y -> La -> Bi and with growing x. For Na4.9Bi0.9Zr0.1(MoO4)(4) the highest ion conductivity was found: similar to 10(-4) S/cm at T = 450 degrees C, which is comparable to that of the NASICON-type molybdates. From the temperature variations of the Na-23 NMR spectra and DFT calculations, the mechanism of sodium-ion diffusion was established at the atomic-scale level.

Complex sodium-containing oxides are of interest for the search and development of new materials for many practical applications. This paper presents the results of multiscale experimental and theoretical studies aimed to explore the mechanism of sodium-ion diffusion in the scheelite-related Na2Zr(MoO4)(3) and Na4Zr(MoO4)(4). These molybdates were synthesized by the precursor (formate) method. Ab initio modeling of sodium migration predicts a barrier of 1.0-1.2 eV for long-range sodium diffusion, which is confirmed by the impedance spectroscopy and the Na-23 NMR experiments. Our results allow us to assume the existence in Na4Zr(MoO4)(4) of an additional, much faster motional process (with energy barrier of 0.5-0.6 eV) associated with localized ion jumps.

Cationic transport in triple molybdate Na25Cs8Sc5(MoO4)24 with an alluaudite structure has been studied. According to 23Na NMR, the ion jump frequency is as high as $$\tau _{d}^{{-1}}$$ ~ 104‒105 s–1 at Т = 550‒600 K, and the activation energy for sodium diffusion is Ea ~ 0.9 eV. The mobility of Na+ ions is slower than that found in double molybdate Na5Sc(MoO4)4, which is caused by structural features. In Na25Cs8Sc5(MoO4)24, some of sodium positions are replaced by Cs+ ions preventing the sodium diffusion in a channel along the c axis. As a result, the 2D mechanism of Na+ transport typical of the Na5R(MoO4)4 alluaudite family does not occur. The most probable mechanism of sodium diffusion in Na25Cs8Sc5(MoO4)24 involves successive atomic jumps along a zigzag path in the ab plane.

Research data for the diffusion mechanisms of Na+ ions in Na1 – xMg1 – xAl1 + x(XO4)3 (X = Mo, W) compounds with the NASICON-type structure (space group R $$\bar {3}$$ c, Z = 6) are reported. Solid solutions in the homogeneity range 0.1 ≤ x ≤ 0.5 for X = Mo and 0.4 ≤ x ≤ 0.6 for X = W have been prepared by solid-state synthesis. Conductivity measurements and NMR spectroscopy data indicate fast sodium diffusion in the studied samples: the ionic conductivity reaches the values of about 10–3 S/cm at T > 800 K. The frequency of elementary ionic jumps is on the order of 104 s–1 at T ≈ 500 K, and the activation energy is equal to 0.8–0.9 eV. The results have shown that the ionic conductivity in molybdates is higher than in tungstates. The growth of magnesium concentration increases the concentration of local coordinations Mg2+–Na+–Mg2+, acting as traps for moving sodium ions. The above conclusions are supported by ab initio calculations according to which the barrier for sodium diffusion from the Mg2+–Na+–Mg2+ position is expected to be higher than those for the Mg2+–Na+–Al3+ and Al3+–Na+–Al3+ ones.

Авторами рассматривается возможность использования нейросетевой модели (FFNN – нейронная сеть прямого распространения), для прогнозирования урожайности яровой пшеницы в условиях лесостепи Западной Сибири. В исследовании использованы материалы длительных полевых опытов СибНИИЗиХ – структурного подразделения СФНЦА РАН, проведенные в северной лесостепи Приобья, а также данные о метеорологических показателях Новосибирского поста метеонаблюдений за 2001-2018 гг. Работа выполнена с использованием общедоступных данных для универсальности системы при ее использовании в различных природно-сельскохозяйственных условиях. В качестве предикторов выделены качественные факторы (система обработки почвы, предшествующая культура, размещение культуры после пара применение средств интенсификации) и метеорологические показатели (среднедекадные температуры воздуха и суммы осадков), определяющие урожайность культуры на исследуемой территории. Выполнено построение модели, позволяющей осуществить прогноз урожайности яровой пшеницы на будущий вегетационный период в зависимости от заданных параметров. Коэффициент детерминации модели составил 0.93, а средняя абсолютная ошибка изменялась в пределах 0.05±0.03, что являются достаточно высоким результатом точности предиктивных моделей в постоянно изменяющихся условиях при совокупности абиотических факторов и управляющего воздействия. Полученные в ходе работы теоретические и практические результаты могут быть использованы при разработке систем поддержки принятия решений, а также при планировании и оценке эффективности размещения сельскохозяйственного производства растениеводческой продукции в изменяющихся погодно-климатических условиях на территории лесостепи Приобья. The authors consider the possibility of using a neural network model (FFNN – feed forward neural network) to predict the yield of spring wheat in the forest-steppe of Western Siberia. The study involved materials from long–term field experiments of SibNIIZiH, a structural subdivision of the SFSCA RAS, conducted in the northern forest-steppe of the Ob region, as well as data on meteorological indicators of the Novosibirsk meteorological observation post for 2001-2018. The work was carried out using publicly available data for the universality of the system when it is used in various natural and agricultural conditions. Qualitative factors (the tillage system, the previous crop, the placement of the crop after steam, the use of intensification means) and meteorological indicators (average decadal air temperatures and precipitation amounts) that determine the crop yield in the study area are identified as predictors. A model has been constructed that allows forecasting the yield of spring wheat for the future growing season, depending on the specified parameters. The coefficient of determination of the model was 0.93, and the mean absolute error varied within 0.05±0.03, which is a fairly high result of the accuracy of predictive models in constantly changing conditions with a combination of abiotic factors and control action. The theoretical and practical results obtained in the course of the work can be used in the development of decision support systems, as well as in planning and evaluating the effectiveness of the placement of agricultural production of crop production in changing weather and climatic conditions on the territory of the Ob region.

The species composition of the ground beetle community (Coleoptera, Carabidae) in the spring wheat crop area in the forest-steppe zone of Western Siberia was researched for the period of 2019-2020. The ground beetle population was represented by 36 species from 14 genera. The genera Poecilus, Dolichus, and Harpalus were the most abundant, while the genera Pterostichus, Poecilus, Harpalus, and Amara had the highest species diversity. It was revealed that the dominant species in sites with conventional technology of spring wheat cultivation, are: Harpalus rufipes, Poecilus cupreus, Dolichus halensis, Harpalus calceatus, and in 2020 Agonum gracilipes appeared along with them. In 2020, the species abundance increased by 25%, and the dynamic density of most species in the second year of the study was also higher than in the first year. This may be caused by the influence of agrometeorological conditions of the studied area. The vegetation period of 2020 differed by conditions of heat and moisture supply in comparison with 2019, as indicators of 2020 are much higher than the previous year and average long-term values for the area. The dominant and subdominant complex of carabidofauna was found to be relatively stable for two years.

The review summarizes and analyzes literary data on the distribution and host-parasite relations of the tick Ixodes trianguliceps Birula, 1895 (Ixodidae) in Northwestern Russia and adjacent European countries. Factors influencing population density of ticks, including habitats and main and additional hosts, are discussed.