Розмірний ефект у провідності полікристалічних плівок нікелю

Експериментально досліджено провідність полікристалічних плівок нікелю при кімнатних температурах. Шляхом обробки експериментальних даних із використанням моделі Маядаса і Шацкеса встановлені розмірні залежності імовірності дзеркального відбиття електронів зовнішніми поверхнями зразка та імовірності дифузного розсіяння носіїв заряду міжкристалічними межами. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/21794Экспериментально исследована зависимость проводимости поликристал- лических пленок никеля при комнатных температурах. Путем обработки экспериментальных данных с использованием модели Маядаса и Шацкеса установлены размерные зависимости вероятности зеркального отражения электронов внешними поверхностями образца и вероятности диффузного рассеяния носителей заряда на границах кристаллов. При цитировании документа, используйте ссылку http://essuir.sumdu.edu.ua/handle/123456789/21794Dependence of conductivity of polycrystalline nickel films is observationally explored at room temperatures. By processing experimental data with use of model Mayadas and Shazkes the size dependences of probability of regular reflection of electron by external borders of the sample and probability of diffuse scattering of charge carries on borders of crystals are erected. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2179

Fleroff goes digital: georeferenced records from "Flora des Gouvernements Wladimir" (Fleroff, 1902)

Global Biodiversity Information Facility (GBIF) has uneven data coverage across taxonomic, spatial and temporal dimensions. Temporal imbalances in the data coverage are particularly dramatic. Thus, 188.3M GBIF records were made in 2020, more than the whole lot of the currently available pre-1986 electronic data. This underscores the importance of reliable and precise biodiversity spatial data collected in early times. Biological collections certainly play a key role in our knowledge of biodiversity in the past. However, digitisation of historical literature is underway, being a modern trend in biodiversity data mining. The grid dataset for the flora of Vladimir Oblast, Russia, includes many historical records borrowed from the "Flora des Gouvernements Wladimir" by Alexander F. Fleroff (also known as Flerov or Flerow). Intensive study of Fleroff's collections and field surveys exactly in the same localities where he worked, showed that the quality of his data is superb. Species lists collected across hundreds of localities form a unique source of reliable information on the floristic diversity of Vladimir Oblast and adjacent areas for the period from 1894 to 1901. Since the grid dataset holds generalised data, we made precise georeferencing of Fleroff's literature records and published them in the form of a GBIF-mediated dataset.A dataset, based on "Flora des Gouvernements Wladimir. I. Pflanzengeographische Beschreibung des Gouvernements Wladimir" by Fleroff (1902), includes 8,889 records of 654 taxa (mainly species) from 366 localities. The majority of records originate from Vladimir Oblast (4,611 records of 534 taxa from 195 localities) and Yaroslavl Oblast (2,013 records of 409 taxa from 66 localities), but also from Nizhny Novgorod Oblast (942 records), Ivanovo Oblast (667 records) and Moscow Oblast (656 records). The leading second-level administrative units by the number of records are Pereslavsky District (2,013 records), Aleksandrovsky District (1,318 records) and Sergievo-Posadsky District (599 records). Georeferencing was carried out, based on the expert knowledge of the area, analysis of modern satellite images and old topographic maps. For 2,460 records, the georeferencing accuracy is 1,000 m or less (28%), whereas for 6,070 records it is 2,000 m or less (68%). The mean accuracy of records of the entire dataset is 2,447 m. That accuracy is unattainable for most herbarium collections of the late 19th century. Some localities of rare plants discovered by Fleroff and included into the dataset were completely lost in the 20th century due to either peat mining or development of urban areas

Anisotropic giant magnetoresistive effect in the sandwich based Fe

The anisotropic effect of a giant magnetoresistance (GMR) in the three-layer FexNi1−x/Cu/FexNi1−x/Sub (x ≈ 0.5, Sub is the substrate) magnetically ordered film is analyzed experimentally and theoretically using the phenomenological approach. It is shown that in the case when the direction of the current density vector j coincides with the direction of the local magnetization vector M (j∥M) in magnetic layers, the consideration of the resistance anisotropy results in a decrease in the magnitude of the GMR effect; and while the vectors are mutually perpendicular in the film plane (j⊥M), the GMR value increases. The analysis of the size dependence (dependence of the top magnetic layer on the thickness dm2) of the magnetoresistance ratio (MR ratio) shows that, in the case of the isotropic GMR effect, when inequalities dm2 > dm1) (dm1 is the thickness of the bottom magnetic layer) hold, the indicated effect is negligible due to shunting of the top layer resistance by the base layer resistance (shunting of the base layer resistance by the top-layer resistance). In the case when the magnetic layer thicknesses are comparable in size (dm1 ~ dm2), the magnitude of the anisotropic giant magnetoresistance (AGMR) acquires its maximum (amplitude) value because of the absence of the shunting effect. The method for calculating the asymmetry parameter αlj=l0j+∕l0j– αlj=l0j+∕l0j– ( l0js l0js is the spin-dependent free path of charge carriers in the s = ±th spin channel of the jth magnetic layer), which characterizes the difference in the free paths of electrons in the spin conduction channels, is proposed for the first time

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities