206 research outputs found

    Geodetic estimations of the modern motions on Tien Shan

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    Using a hierarchical decision-making system in e-learning

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    It’s clear that not all factors affecting the effectiveness of training can be taken into account using only the information about the content. According to this there has been implemented a two-level decision making system (DMS). The first level of DMS is responsible for the choice of further training strategy and execution of an action related to the chosen strategy (reaction). The second level, if it required by the reaction, selects the desired content based on a model of knowledge and skills of the user. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2943

    ВзаимодСйствиС ΠΏΡ€ΠΎΡ‚ΠΈΠ²ΠΎΠΎΠΏΡƒΡ…ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π° Π°Ρ†Π΅Ρ‚Π°Ρ‚Π° Π°Π±ΠΈΡ€Π°Ρ‚Π΅Ρ€ΠΎΠ½Π° с Π΄Ρ†Π”ΠΠš

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    The electroanalytical characteristics of double-stranded DNA (dsDNA) and the complex of dsDNA and the antitumor drug abiraterone acetate (AA) were studied by differential pulse voltammetry. The effect of abiraterone acetate on dsDNA was shown, which was registered by alteration the intensity of electrochemical oxidation of purine heterocyclic bases guanine and adenine using screen printed electrodes modified with functionalized carbon nanotubes. The binding constants (Kb) of the [dsDNA-AA] complex for guanine and adenine were 1.63Γ—104 M-1 and 1.93Γ—104 M-1, respectively. The electrochemical coefficients of the toxic effect were calculated as the ratio of the intensity of the electrochemical oxidation signals of guanine and adenine, in the presence of abiraterone acetate to the intensity of the electrooxidation signals of these nucleobases Β without drug (%). At concentrations of abiraterone acetate exceeding 60 ΞΌM, a decrease in the currents of electrochemical oxidation of guanine and adenine by 50% or more is recorded. Based on the analysis of electrochemical parameters and values ​​of binding constants, an assumption was made about the mechanism of interaction of abiraterone acetate with DNA, mainly due to the formation of hydrogen bonds with the minor groove. An electrochemical DNA biosensor was first used to study the mechanism of interaction of the anticancer drug abiraterone acetate with dsDNA.ΠœΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ Π΄ΠΈΡ„Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½ΠΎΠΉ Π²ΠΎΠ»ΡŒΡ‚Π°ΠΌΠΏΠ΅Ρ€ΠΎΠΌΠ΅Ρ‚Ρ€ΠΈΠΈ исслСдованы элСктроаналитичСскиС характСристики Π΄Π²ΡƒΡ…Ρ†Π΅ΠΏΠΎΡ‡Π΅Ρ‡Π½ΠΎΠΉ Π”ΠΠš (Π΄Ρ†Π”ΠΠš) ΠΈ комплСкса Π΄Ρ†Π”ΠΠš ΠΈ ΠΏΡ€ΠΎΡ‚ΠΈΠ²ΠΎΠΎΠΏΡƒΡ…ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π° Π°Ρ†Π΅Ρ‚Π°Ρ‚Π° Π°Π±ΠΈΡ€Π°Ρ‚Π΅Ρ€ΠΎΠ½Π° (АА). Показано влияниС Π°Ρ†Π΅Ρ‚Π°Ρ‚Π° Π°Π±ΠΈΡ€Π°Ρ‚Π΅Ρ€ΠΎΠ½Π° Π½Π° Π΄Ρ†Π”ΠΠš, рСгистрируСмоС ΠΏΠΎ измСнСнию интСнсивности элСктрохимичСского окислСния ΠΏΡƒΡ€ΠΈΠ½ΠΎΠ²Ρ‹Ρ… гСтСроцикличСских азотистых оснований Π³ΡƒΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π° с использованиСм ΠΏΠ΅Ρ‡Π°Ρ‚Π½Ρ‹Ρ… элСктродов, ΠΌΠΎΠ΄ΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹ΠΌΠΈ ΡƒΠ³Π»Π΅Ρ€ΠΎΠ΄Π½Ρ‹ΠΌΠΈ Π½Π°Π½ΠΎΡ‚Ρ€ΡƒΠ±ΠΊΠ°ΠΌΠΈ. ΠšΠΎΠ½ΡΡ‚Π°Π½Ρ‚Ρ‹ связывания (Кb) комплСкса [Π΄Ρ†Π”ΠΠš-АА], для Π³ΡƒΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π°, составили 1.63Γ—104 М-1 ΠΈ 1.93Γ—104 М-1, соотвСтствСнно. Рассчитаны элСктрохимичСскиС коэффициСнты токсичСского эффСкта ΠΊΠ°ΠΊ ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠ΅ интСнсивности сигналов элСктроокислСния Π³ΡƒΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π°, входящих Π² состав Π΄Ρ†Π”ΠΠš, Π² присутствии Π°Ρ†Π΅Ρ‚Π°Ρ‚Π° Π°Π±ΠΈΡ€Π°Ρ‚Π΅Ρ€ΠΎΠ½Π° ΠΊ интСнсивности сигналов элСктроокислСния этих азотистых оснований Π±Π΅Π· лСкарства (%). ΠŸΡ€ΠΈ концСнтрациях Π°Ρ†Π΅Ρ‚Π°Ρ‚Π° Π°Π±ΠΈΡ€Π°Ρ‚Π΅Ρ€ΠΎΠ½Π°, ΠΏΡ€Π΅Π²Ρ‹ΡˆΠ°ΡŽΡ‰ΠΈΡ… 60 мкМ, рСгистрируСтся сниТСниС Ρ‚ΠΎΠΊΠΎΠ² элСктрохимичСского окислСния Π³ΡƒΠ°Π½ΠΈΠ½Π° ΠΈ Π°Π΄Π΅Π½ΠΈΠ½Π° Π½Π° 50% ΠΈ Π±ΠΎΠ»Π΅Π΅. На основании Π°Π½Π°Π»ΠΈΠ·Π° элСктрохимичСских ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² ΠΈ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ констант связывания сдСлано ΠΏΡ€Π΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΎ ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠ΅ взаимодСйствия Π°Ρ†Π΅Ρ‚Π°Ρ‚Π° Π°Π±ΠΈΡ€Π°Ρ‚Π΅Ρ€ΠΎΠ½Π° с Π”ΠΠš прСимущСствСнно Π·Π° счСт образования Π²ΠΎΠ΄ΠΎΡ€ΠΎΠ΄Π½Ρ‹Ρ… связСй с ΠΌΠ°Π»ΠΎΠΉ Π±ΠΎΡ€ΠΎΠ·Π΄ΠΊΠΎΠΉ. ЭлСктрохимичСский Π”ΠΠš-биосСнсор Π±Ρ‹Π» Π²ΠΏΠ΅Ρ€Π²Ρ‹Π΅ использован для исслСдования ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠ° взаимодСйствия ΠΏΡ€ΠΎΡ‚ΠΈΠ²ΠΎΠΎΠΏΡƒΡ…ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π° Π°Ρ†Π΅Ρ‚Π°Ρ‚Π° Π°Π±ΠΈΡ€Π°Ρ‚Π΅Ρ€ΠΎΠ½Π° с Π΄Ρ†Π”ΠΠš

    Enhancing functional efficiency in information-extreme machine learning with logistic regression ensembles

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    The subject matter of the article is the application of supervised machine learning for the task of object class recognition. The goal is enhancing functional efficiency in information-extreme technology (IET) for object class recognition. The tasks to be solved are: to analyse possible ways of increasing the functional efficiency IET approach; implement an ensemble of models that include logistic regression for prioritizing recognition features and an IEI learning algorithm; compare the functional efficiency of the resulting ensemble of models on well-known dataset with classic approach and results of other researchers. The methods: The method is developed within the framework of the functional approach to modelling natural intelligence applied to the problem of object classification. The following results were obtained: The study tries to augment existing IET to support feature prioritization as part of the object class recognition algorithm. The classical information-extreme algorithm treats all input features equivalently important in forming the decisive rule. As a result, the object features with strong correlation are not prioritized by the algorithm's decisive mechanism – resulting in decreasing functional efficiency in exam mode. The proposed approach is solving this problem by applying a two-stage approach. In the first stage the multiclass logistic regression applied to the input training features vectors of objects to be classified – formed the normalized training matrix. To prevent overfitting of the logistic regression, a model the L2(ridge) regularization method was used. On the second stage, the information-extreme method as input takes the result of the first stage. The geometrical parameters of class containers and the control tolerances on the recognition features were considered as the optimization parameters. Conclusions. The proposed approach increases MNIST (Modified National Institute of Standards and Technology) dataset classification accuracy compared with the classic information-extreme method by 26,44%. The proposed approach has a 3.77% lower accuracy compared to neural-like approaches but uses fewer resources in the training phase and allows retraining the model, as well as expanding the dictionary of recognition classes without model retraining

    ΠšΠΎΠ½ΡΡ‚Ρ€ΡƒΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ исслСдования саморазвития личности

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    Π£ статті Ρ€ΠΎΠ·Π³Π»ΡΠ΄Π°Ρ”Ρ‚ΡŒΡΡ Π°Π²Ρ‚ΠΎΡ€ΡΡŒΠΊΠΈΠΉ ΠΏΡ–Π΄Ρ…Ρ–Π΄ Π΄ΠΎ Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΈ діагностичного Ρ–Π½ΡΡ‚Ρ€ΡƒΠΌΠ΅Π½Ρ‚Π°Ρ€Ρ–ΡŽ вивчСння особливостСй саморозвитку особистості. ΠΠ½Π°Π»Ρ–Π·ΡƒΡŽΡ‚ΡŒΡΡ змістові ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚ΠΈ (психологічні рСсурси) особистісного саморозвитку. ΠΠ°Π²ΠΎΠ΄ΠΈΡ‚ΡŒΡΡ Ρ€ΠΎΠ·Ρ€ΠΎΠ±Π»Π΅Π½Π° діагностична ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠ° Π”Π₯БО. ΠžΠΏΠΈΡΡƒΡ”Ρ‚ΡŒΡΡ ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° ΠΏΠ΅Ρ€Π΅Π²Ρ–Ρ€ΠΊΠΈ валідності Ρ– надійності, Π° Ρ‚Π°ΠΊΠΎΠΆ стандартизації Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ–Π².In the article the author's approach to the development of diagnostic tools study features self-identity. Analyzed a substantial of components (psychological resources) personal self-development. A developed of diagnostic the technique DCSEP. A procedure of checking the validity and reliability, as well as standardization of results.Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ рассматриваСтся авторский ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ ΠΊ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ диагностичСского инструмСнтария изучСния особСнностСй саморазвития личности. ΠΠ½Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΡŽΡ‚ΡΡ ΡΠΎΠ΄Π΅Ρ€ΠΆΠ°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Ρ‹ (психологичСскиС рСсурсы) личностного саморазвития. ΠŸΡ€ΠΈΠ²ΠΎΠ΄ΠΈΡ‚ΡΡ разработанная диагностичСская ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠ° Π”Π₯Π‘Π›. ΠžΠΏΠΈΡΡ‹Π²Π°Π΅Ρ‚ΡΡ ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° ΠΏΡ€ΠΎΠ²Π΅Ρ€ΠΊΠΈ валидности ΠΈ надСТности, Π° Ρ‚Π°ΠΊΠΆΠ΅ стандартизации Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ²

    Бучасний стан Ρ‚Π° напрями Ρ€ΠΎΠ·Π²ΠΈΡ‚ΠΊΡƒ Education Data Mining Π² Π‘ΡƒΠΌΡΡŒΠΊΠΎΠΌΡƒ Π΄Π΅Ρ€ΠΆΠ°Π²Π½ΠΎΠΌΡƒ унівСрситСті

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    ΠžΠ΄Π½Ρ–Ρ”ΡŽ Π· ΠΏΠ΅Ρ€Π΅Π²Π°Π³ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·Π°Ρ†Ρ–Ρ— бізнСс процСсів Ρ” ΠΌΠΎΠΆΠ»ΠΈΠ²Ρ–ΡΡ‚ΡŒ Ρ—Ρ… Π³Π»ΠΈΠ±ΠΎΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»Ρ–Π·Ρƒ Π· ΠΌΠ΅Ρ‚ΠΎΡŽ ΠΏΠΎΡˆΡƒΠΊΡƒ ΠΏΡ€ΠΈΡ…ΠΎΠ²Π°Π½ΠΈΡ… зв’язків ΠΌΡ–ΠΆ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌΠΈ ΠΌΠΎΠ΄Π΅Π»Ρ– Ρ– Π΅Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŽ процСсів. ДослідТСння Ρƒ Ρ†ΡŒΠΎΠΌΡƒ напрямку Π½Π΅ ΠΏΡ€ΠΎΠΉΡˆΠ»ΠΈ ΠΎΡΡ‚ΠΎΡ€ΠΎΠ½ΡŒ систСм дистанційного навчання, особливо Π· появою Massive Open Online Courses (MOOC). На ряду Ρ–Π· Ρ‚Π΅Ρ€ΠΌΡ–Π½ΠΎΠΌ Data Mining – Π²ΠΈΠ΄ΠΎΠ±ΡƒΡ‚ΠΎΠΊ знань Π· масивів Ρ–Π½Ρ„ΠΎΡ€ΠΌΠ°Ρ†Ρ–Ρ—, Ρ€ΠΎΠ·Π³Π»ΡΠ΄Π°ΡŽΡ‚ΡŒ поняття Β«Education Data MiningΒ» (EDM)

    ЭлСктрохимичСский Π°Π½Π°Π»ΠΈΠ· ΠΌΠ΅Ρ‚Π°Π±ΠΎΠ»ΠΈΡ‚ΠΎΠ² ΠΊΠ°ΠΊ ΠΌΠ΅Ρ‚ΠΎΠ΄ опрСдСлСния активности Ρ†ΠΈΡ‚ΠΎΡ…Ρ€ΠΎΠΌΠΎΠ² P450

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    The review deals with the electrochemical methods for determination of metabolites of cytochromes P450 catalyzed reactions. We have focused on the electrochemical determination of metabolites of drugs and some endogenous compounds. We have reviewed bielectrode systems for determination of cytochrome P450 activity, where one electrode serves as a matrix for enzyme immobilization and a source of electrons for heme iron ion reduction and initialization of the catalytic reaction towards a substrate and the second one is being used for quantification of the products formed by their electrochemical oxidation. Such systems allow one to elude additional steps of separation of reaction substrates and products. The review also includes discussion of the ways to increase the analytical sensitivity and decrease the limit of detection of the investigated metabolites by chemical modification of electrodes. We demonstrate the possibilities of these systems for cytochrome P450 kinetics analysis and the perspectives of their further improvement, such as increasing the sensitivity of metabolite electrochemical determination by modern electrode modificators, including carbon-based, and construction of devices for automatic monitoring of the products.Π’ ΠΎΠ±Π·ΠΎΡ€Π΅ рассмотрСны ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ элСктрохимичСского опрСдСлСния ΠΌΠ΅Ρ‚Π°Π±ΠΎΠ»ΠΈΡ‚ΠΎΠ² Ρ€Π΅Π°ΠΊΡ†ΠΈΠΉ, ΠΊΠ°Ρ‚Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… Ρ†ΠΈΡ‚ΠΎΡ…Ρ€ΠΎΠΌΠ°ΠΌΠΈ P450. Основной Π°ΠΊΡ†Π΅Π½Ρ‚ сдСлан Π½Π° элСктрохимичСском ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΠΌΠ΅Ρ‚Π°Π±ΠΎΠ»ΠΈΡ‚ΠΎΠ² лСкарствСнных ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚ΠΎΠ² ΠΈ Π½Π΅ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… эндогСнных соСдинСний. РассмотрСны биэлСктродныС систСмы для опрСдСлСния активности Ρ†ΠΈΡ‚ΠΎΡ…Ρ€ΠΎΠΌΠΎΠ² P450, Π² ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… ΠΎΠ΄ΠΈΠ½ элСктрод выступаСт Π² качСствС ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ для ΠΈΠΌΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°Ρ†ΠΈΠΈ Ρ„Π΅Ρ€ΠΌΠ΅Π½Ρ‚Π° ΠΈ Π΄ΠΎΠ½ΠΎΡ€Π° элСктронов для восстановлСния ΠΈΠΎΠ½Π° ΠΆΠ΅Π»Π΅Π·Π° Π³Π΅ΠΌΠ° ΠΈ ΠΈΠ½ΠΈΡ†ΠΈΠ°Ρ†ΠΈΠΈ каталитичСской Ρ€Π΅Π°ΠΊΡ†ΠΈΠΈ ΠΏΠΎ ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡŽ ΠΊ субстрату, Π° Π²Ρ‚ΠΎΡ€ΠΎΠΉ – для количСствСнного опрСдСлСния ΠΎΠ±Ρ€Π°Π·ΡƒΡŽΡ‰ΠΈΡ…ΡΡ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ² ΠΏΡƒΡ‚Π΅ΠΌ ΠΈΡ… элСктрохимичСского окислСния. Π’Π°ΠΊΠΈΠ΅ систСмы Π² ΠΈΠ΄Π΅Π°Π»Π΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‚ ΠΈΠ·Π±Π΅ΠΆΠ°Ρ‚ΡŒ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… стадий раздСлСния субстратов ΠΈ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ² Ρ€Π΅Π°ΠΊΡ†ΠΈΠΉ. Π’ ΠΎΠ±Π·ΠΎΡ€Π΅ Ρ‚Π°ΠΊΠΆΠ΅ обсуТдСны способы ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ аналитичСской Ρ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ ΠΈ сниТСния ΠΏΡ€Π΅Π΄Π΅Π»Π° опрСдСляСмых ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΉ исслСдуСмых ΠΌΠ΅Ρ‚Π°Π±ΠΎΠ»ΠΈΡ‚ΠΎΠ² Π·Π° счСт химичСской ΠΌΠΎΠ΄ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ элСктродов. ΠŸΠΎΠΊΠ°Π·Π°Π½Ρ‹ возмоТности Ρ‚Π°ΠΊΠΈΡ… систСм для Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΠΈΠ½Π΅Ρ‚ΠΈΠΊΠΈ Ρ€Π΅Π°ΠΊΡ†ΠΈΠΉ, ΠΊΠ°Ρ‚Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… Ρ†ΠΈΡ‚ΠΎΡ…Ρ€ΠΎΠΌΠ°ΠΌΠΈ P450, ΠΈ пСрспСктивы ΠΈΡ… дальнСйшСго развития, Ρ‚Π°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠ΅ Ρ‡ΡƒΠ²ΡΡ‚Π²ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ элСктрохимичСского опрСдСлСния ΠΌΠ΅Ρ‚Π°Π±ΠΎΠ»ΠΈΡ‚ΠΎΠ² с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ соврСмСнных ΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»ΠΎΠ² для ΠΌΠΎΠ΄ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ элСктродов, Π² Ρ‚ΠΎΠΌ числС Π½Π° основС ΡƒΠ³Π»Π΅Ρ€ΠΎΠ΄Π°, Π° Ρ‚Π°ΠΊΠΆΠ΅ созданиС устройств, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰ΠΈΡ… Π² автоматичСском Ρ€Π΅ΠΆΠΈΠΌΠ΅ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΡ‚ΡŒ Ρ€Π΅Π³ΠΈΡΡ‚Ρ€Π°Ρ†ΠΈΡŽ ΠΎΠ±Ρ€Π°Π·ΡƒΡŽΡ‰ΠΈΡ…ΡΡ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ²
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