5,493 research outputs found
The Process of Acetonitrile Synthesis over Ξ³-Al[2]O[3] Promoted by Phosphoric Acid Catalysts
The influence of principal parameters (reaction temperature, ratio of acetic acid and ammonia, composition of reactionary mixture and promotion of catalysts) on the selectivity and yield of the desired product was studied in the reaction of catalytic acetonitrile synthesis by ammonolysis of acetic acid. The processing of [gamma]-Al[2]O[3] by phosphoric acid increases amount of the centers, on which carries out reaction of acetamide dehydration. The kinetic model of a limiting stage of reaction - the acetamide dehydration to acetonitrile was suggested. In the process of ammonolysis of acetic acid it was demonstrated that the use of catalysts promoted by phosphoric acid and ratio NH[3]:CH[3]COOH=(3-4):1 at temperatures of a reactor 360-390Β°Π‘ leads to the increase of acetonitrile productivity to 0.7-0.8 g/cm{3}Β·h and allows to minimize formation of by-products
Application of Humic Sorbents for Pb{2+}, Cu{2+} and Hg{2+} Ions Preconcentration from Aqueous Solutions
The sorbent prepared by sequential treatment of silica gel by polyhexamethylene guanidine of linear structure and humic acids is suggested for sorption concentration of metal ions (Pb{2+}, Cu{2+} and Hg{2+}) from aqueous solutions. Thermogravimetry and infrared spectroscopy have confirmed the success of the attachment of the humic acids onto modified silica surface. Sorption isotherms of lead (II), copper (II) and mercury (II) obtained in optimal conditions of metals sorption were analyzed by using Freundlich and Langmuir adsorption isotherms. Structural model of surface of humic sorbent was proposed based on the obtained results. The results demonstrated the potential applicability of supramolecular humic sorbent in the preconcentration of metal ions from aqueous solution
Engineered repeating prints: computer-aided design approaches to achieving continuity of repeating print across a garment using digital engineered print method
This Master’s research investigated approaches for engineering of repeating prints using digital textile printing technology and universally available computer-aided design software. Current practices for alignment of designs in yardage printed fabrics at garment seams are wasteful and do not allow for mass customisation. This inefficiency can be overcome with engineered digital printing, a method that allows for an integration of prints with garment patterns to generate Ready-to-Print images. Engineered printing offers more cost-effective use of materials, improved visual appearance, potential for mass customisation and more sustainable manufacturing. Still, technical difficulties exist in the integration of prints with garment patterns. As a result, application for apparel is limited to non-repeating prints and one-off fashion show garments. The integration of repeating prints presents even more difficulties. However, the advances in digital printing and computer-aided design technologies call for an examination of possible approaches for achieving improved continuity of a repeating print across a garment. The research used a three-stage mixed method approach. The first qualitative stage examined current practices for design and printing of repeating prints. By undertaking Applied Thematic Analysis, the diversity of meanings assigned to words describing attributes of repeating prints as a result of historical and current usage were identified and the terminology consolidated. A taxonomy of repeating print attributes was established, with three levels observed: a superordinate level for a surface, a basic for a repeat, and a subordinate for a motif. Quantifiable attributes of repeating prints were assigned to each level. The analysis also suggested three potential directions for engineered repeating prints: Modularity Design, Flexible Tiling and Distortion. The second quantitative stage evaluated suggested design directions in four experimental studies: one for each of the directions and a final study combining all three directions to engineer repeating prints for a graded garment. Practical computer-aided design techniques, based on accessible Adobe software tools, were developed for integration of repeating prints with garment patterns. The techniques were then tested in comparison with mainstream printing practices. In each experiment, repeating print attributes were examined for their impact on the adaptability of repeating prints for engineered printing. All three directions were validated as suitable for engineering of repeating prints. Statistical analyses revealed relationships between repeating print attributes and their impact on the adaptability of repeating prints for the engineered printing method. The final stage analysed the combined results of the previous two stages. Existing computer- aided design solutions were found to offer opportunities regarding their ability to be integrated into current digital production for innovative and sustainable engineered printing. While the suggested techniques require knowledge of more advanced dynamic editing tools, the research highlights the benefits for both fashion and textile designers to utilise such tools in order to fully embrace the potential digital printing technology has to offer. The research also highlights the need for dedicated software solutions for integration of repeating prints with garment patterns. The findings on the impact of repeating print attributes on the adaptability for engineered printing can help in the development of dedicated software
Optical Properties of Gallium-Doped Zinc Oxide-A Low-Loss Plasmonic Material: First-Principles Theory and Experiment
Searching for better materials for plasmonic and metamaterial applications is an inverse design problem where theoretical studies are necessary. Using basic models of impurity doping in semiconductors, transparent conducting oxides (TCOs) are identified as low-loss plasmonic materials in the near-infrared wavelength range. A more sophisticated theoretical study would help not only to improve the properties of TCOs but also to design further lower-loss materials. In this study, optical functions of one such TCO, gallium-doped zinc oxide (GZO), are studied both experimentally and by first-principles density-functional calculations. Pulsed-laser-deposited GZO films are studied by the x-ray diffraction and generalized spectroscopic ellipsometry. Theoretical studies are performed by the total-energy-minimization method for the equilibrium atomic structure of GZO and random phase approximation with the quasiparticle gap correction. Plasma excitation effects are also included for optical functions. This study identifies mechanisms other than doping, such as alloying effects, that significantly influence the optical properties of GZO films. It also indicates that ultraheavy Ga doping of ZnO results in a new alloy material, rather than just degenerately doped ZnO. This work is the first step to achieve a fundamental understanding of the connection between material, structural, and optical properties of highly doped TCOs to tailor those materials for various plasmonic applications
Cyclotron resonance of extremely conductive 2D holes in high Ge content strained heterostructures
Cyclotron resonance has been observed in steady and pulsed magnetic fields from high conductivity holes in Ge quantum wells. The resonance positions, splittings and linewidths are compared to calculations of the hole Landau levels
Π‘ΠΎΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² ΡΠΏΠΎΡ Ρ βΠ½Π°Π΄Π·ΠΎΡΠ½ΠΎΠ³ΠΎ ΠΊΠ°ΠΏΠΈΡΠ°Π»ΠΈΠ·ΠΌΠ°β: ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΡ ΠΈ Π²Π»Π°ΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ²
βSurveillance capitalismβ is not yet a sustainable term used in the social sciences, although there has long been scientific debate about the basic technologies of this economic order- digital information and communication technologies, algorithms, data, artificial intelligence, neural networks, the Internet of things, etc.Β At the time, Google had revolutionized the field of predictive analysis and promoted βsurveillance capitalismβ. The company began to pay special attention to extracting and analyzing data in translation operations, speech recognition, image processing, ranking, etc.Β Google began to turn data (raw materials) into intelligent products β algorithms designed to predict user behavior.Β These predictive products have been used for sale to other organizations that are increasing their profits by improving their predictive marketing capabilities.Β Smart machines are no longer just learning to know a man his behavior, they are trying to push him into making certain decisions, into programmed behavioral responses to some stimulus, which leads to an increase in the earnings of βsupervising capitalistsβ (and, above all, the income of owners of digital platforms).Β That is, by automating various processes, machines set the human behaviorΒ thatΒ Β leadsΒ to the emergence of a new type of power β the βinstrumentalβ power (automation of life of individuals by means of universal implementation of βsmartβ network devices, formation of βsmartβ space, βsmartβ houses). Algorithms penetrate into a variety of spheres, on the basis of algorithms management decisions are made and modern cities function. However, questions inevitably arise about the social consequences of widespreadΒ algorithmizationΒ and digitalization, the security of data storage, the limits of digitizing the social world.βΠΠ°Π΄Π·ΠΎΡΠ½ΡΠΉ ΠΊΠ°ΠΏΠΈΡΠ°Π»ΠΈΠ·ΠΌβ ΠΏΠΎΠΊΠ° Π΅ΡΠ΅ Π½Π΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΡΠΌ ΡΠ΅ΡΠΌΠΈΠ½ΠΎΠΌ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠΌ Π² ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
Π½Π°ΡΠΊΠ°Ρ
, Ρ
ΠΎΡΡ Π΄Π°Π²Π½ΠΎ Π²Π΅Π΄ΡΡΡΡ Π½Π°ΡΡΠ½ΡΠ΅ Π΄ΠΈΡΠΊΡΡΡΠΈΠΈ ΠΎΠ± ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΡ
ΡΠ°ΠΊΠΎΠ³ΠΎ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ° β ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎ-ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΡ
, Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°Ρ
, Π΄Π°Π½Π½ΡΡ
, ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠΌ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ΅, Π½Π΅ΠΉΡΠΎΡΠ΅ΡΡΡ
, ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠ΅ Π²Π΅ΡΠ΅ΠΉ ΠΈ Π΄Ρ. Π ΡΠ²ΠΎΠ΅ Π²ΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΡΒ GoogleΒ ΡΠΎΠ²Π΅ΡΡΠΈΠ»Π° ΡΠ΅Π²ΠΎΠ»ΡΡΠΈΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΠΎΠΉ Π°Π½Π°Π»ΠΈΡΠΈΠΊΠΈ ΠΈ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΠΎΠ²Π°Π»Π° ΡΠ°Π·Π²ΠΈΡΠΈΡ βΠ½Π°Π΄Π·ΠΎΡΠ½ΠΎΠ³ΠΎ ΠΊΠ°ΠΏΠΈΡΠ°Π»ΠΈΠ·ΠΌΠ°β. ΠΠΎΠΌΠΏΠ°Π½ΠΈΡ ΡΡΠ°Π»Π° ΡΠ΄Π΅Π»ΡΡΡ ΠΎΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ Π°Π½Π°Π»ΠΈΠ·Ρ Π΄Π°Π½Π½ΡΡ
ΠΏΡΠΈ ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ΠΈΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΠΏΠΎ ΠΏΠ΅ΡΠ΅Π²ΠΎΠ΄Ρ ΡΠ΅ΠΊΡΡΠΎΠ², ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠ΅ΡΠΈ, ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΡΠ°Π½ΠΆΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Ρ.ΠΏ.Β GoogleΒ Π½Π°ΡΠ°Π»Π° ΠΏΡΠ΅Π²ΡΠ°ΡΠ°ΡΡ Π΄Π°Π½Π½ΡΠ΅ (ΡΡΡΡΠ΅) Π² ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠ΅ ΠΏΡΠΎΠ΄ΡΠΊΡΡ β Π°Π»Π³ΠΎΡΠΈΡΠΌΡ, ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π½ΡΠ΅ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ. ΠΡΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΡΠ΅ ΠΏΡΠΎΠ΄ΡΠΊΡΡ ΡΡΠ°Π»ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ΄Π°ΠΆΠΈ Π΄ΡΡΠ³ΠΈΠΌ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠΌ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°ΡΡ ΡΠ²ΠΎΡ ΠΏΡΠΈΠ±ΡΠ»Ρ, ΡΠ»ΡΡΡΠ°Ρ ΡΠ²ΠΎΠΈ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΡΠΈ ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ΠΈΠΈ ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ΠΎΠ²ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. Π£ΠΌΠ½ΡΠ΅ ΠΌΠ°ΡΠΈΠ½Ρ ΡΠΆΠ΅ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π°ΡΡΠΈΠ»ΠΈΡΡ ΠΏΠΎΠ·Π½Π°Π²Π°ΡΡ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°, ΠΎΠ½ΠΈ ΠΏΡΡΠ°ΡΡΡΡ ΠΏΠΎΠ΄ΡΠΎΠ»ΠΊΠ½ΡΡΡ Π΅Π³ΠΎ ΠΊ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ, ΠΊ Π·Π°ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π°ΠΊΡΠΈΠΈ Π½Π° ΡΠΎΡ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠΉ ΡΡΠΈΠΌΡΠ», ΡΡΠΎ Π²Π΅Π΄Π΅Ρ ΠΊ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ Π΄ΠΎΡ
ΠΎΠ΄ΠΎΠ² βΠ½Π°Π΄Π·ΠΎΡΠ½ΡΡ
ΠΊΠ°ΠΏΠΈΡΠ°Π»ΠΈΡΡΠΎΠ²β (ΠΈ, ΠΏΡΠ΅ΠΆΠ΄Π΅ Π²ΡΠ΅Π³ΠΎ, Π΄ΠΎΡ
ΠΎΠ΄ΠΎΠ² Π²Π»Π°Π΄Π΅Π»ΡΡΠ΅Π² ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΏΠ»Π°ΡΡΠΎΡΠΌ). ΠΠ½ΡΠΌΠΈ ΡΠ»ΠΎΠ²Π°ΠΌΠΈ, ΠΌΠ°ΡΠΈΠ½Ρ Π·Π°Π΄Π°ΡΡ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°, Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΡ, Π²ΡΠ΅ ΡΡΠΎ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΠΏΠΎΡΠ²Π»Π΅Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° Π²Π»Π°ΡΡΠΈ β Π²Π»Π°ΡΡΠΈ βΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΠΉβ (Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΆΠΈΠ·Π½ΠΈ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΠΎΠ² Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΏΠΎΠ²ΡΠ΅ΠΌΠ΅ΡΡΠ½ΠΎΠ³ΠΎ Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ βΡΠΌΠ½ΡΡ
β ΡΠ΅ΡΠ΅Π²ΡΡ
ΡΡΡΡΠΎΠΉΡΡΠ², ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ βΡΠΌΠ½ΠΎΠ³ΠΎβ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π°, βΡΠΌΠ½ΡΡ
β Π΄ΠΎΠΌΠΎΠ²). ΠΠ»Π³ΠΎΡΠΈΡΠΌΡ ΠΏΡΠΎΠ½ΠΈΠΊΠ°ΡΡ Π² ΡΠ°ΠΌΡΠ΅ ΡΠ°Π·Π½ΡΠ΅ ΡΡΠ΅ΡΡ, Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΡΡΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ, ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΡΡΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ Π³ΠΎΡΠΎΠ΄Π°. Π Π½Π΅ΠΈΠ·Π±Π΅ΠΆΠ½ΠΎ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡ Π²ΠΎΠΏΡΠΎΡΡ ΠΎ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΡΡ
ΠΏΠΎΠ²ΡΠ΅ΠΌΠ΅ΡΡΠ½ΠΎΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ, Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ
, ΠΏΡΠ΅Π΄Π΅Π»Π°Ρ
ΠΎΡΠΈΡΡΠΎΠ²ΠΊΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΌΠΈΡΠ°
Π‘ΠΎΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΊΠ°ΠΊ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ΅ ΠΏΠΎΠ»Π΅ ΠΈ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½Ρ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΉ
This article attempts to represent social technologies as a research area of sociology and a practical field. Social technologies (as technology of government of social processes, agents, organizations, communities) are the complex social phenomenon. Nowadays β the days of radical technological changes (Internet of things, Big Data, virtual and augmented reality, blockchain technology, artificial intelligence, machine learning, robotization, transition to a shared economy), redefining a wide range of social fields and generating principally new social regimes ad configurations β the social technologies acquire almost universal character. The exploration and practices (design, implementation, modification) of social technologies mean the work with the widest possible range of social phenomena, deploying on very different spatial and time scales and in various social spheres. At the same time, there remains a need for conceptual and theoretical clarification of βsocial technologiesβ on the other hand, and for their institualization as research and practical fields (with its own standards, human and organizational resources and so on). The department of social technologies was opened in Moscow State University establishment on Faculty of Sociology in 2013 to address that need. The article outlines the whole number of research directions of this department since its establishment, through to the present day.Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΡΡΠ° ΠΏΠΎΠΏΡΡΠΊΠ° ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΠΈΡΡ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΊΠ°ΠΊ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΠΎΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠΎΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΈ ΠΊΠ°ΠΊ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΏΠΎΠ»Π΅. Π‘ΠΎΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ (ΠΊΠ°ΠΊ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠΌΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ°ΠΌΠΈ, ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠΌΠΈ Π°Π³Π΅Π½ΡΠ°ΠΌΠΈ, ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠΌΠΈ, ΠΎΠ±ΡΠ½ΠΎΡΡΡΠΌΠΈ ΠΈ Ρ.Π΄.) ΡΡΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ΅ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ΅ ΡΠ²Π»Π΅Π½ΠΈΠ΅. ΠΡΠΎ ΡΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΡΠΈΠΎΠ±ΡΠ΅ΡΠ°Π΅Ρ Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ β Π²ΡΠ΅ΠΌΡ ΡΠ°Π΄ΠΈΠΊΠ°Π»ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΉ (ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ Π²Π΅ΡΠ΅ΠΉ, Big Data, Π²ΠΈΡΡΡΠ°Π»ΡΠ½Π°Ρ ΠΈ Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½Π½Π°Ρ ΡΠ΅Π°Π»ΡΠ½ΠΎΡΡΡ, ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π±Π»ΠΎΠΊΡΠ΅ΠΉΠ½Π°, ΡΠΎΠ±ΠΎΡΠΈΠ·Π°ΡΠΈΡ, ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ, ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅, ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄ ΠΊ ΡΠ°Π·Π΄Π΅Π»ΡΠ΅ΠΌΠΎΠΉ (βΡΠ΅ΡΠΈΠ½Π³ΠΎΠ²ΠΎΠΉβ) ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅ ΠΈ Π΄Ρ.), ΠΏΠ΅ΡΠ΅ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡΠΈΡ
ΠΏΡΠΈΠ½ΡΠΈΠΏΡ ΠΈ ΡΠΏΠΎΡΠΎΠ±Ρ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΠΌΡΡ
ΡΠ°Π·Π½ΡΡ
ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΌΠΈΡΠ° ΠΈ ΠΏΠΎΡΠΎΠΆΠ΄Π°ΡΡΠΈΡ
ΡΠ°Π΄ΠΈΠΊΠ°Π»ΡΠ½ΠΎ Π½ΠΎΠ²ΡΠ΅ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅ΠΆΠΈΠΌΡ ΠΈ ΠΊΠΎΠ½ΡΠΈΠ³ΡΡΠ°ΡΠΈΠΈ β Π΅Π΄Π²Π° Π»ΠΈ Π½Π΅ ΡΠ½ΠΈΠ²Π΅ΡΡΠ°Π»ΡΠ½ΡΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅Ρ. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΡ ΠΈ ΠΏΡΠ°ΠΊΡΠΈΠΊΠΎΠ²Π°ΡΡ (ΡΠ°Π·ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡ, Π²Π½Π΅Π΄ΡΡΡΡ, ΠΈΠ·ΠΌΠ΅Π½ΡΡΡ) ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΎΠ·Π½Π°ΡΠ°Π΅Ρ ΡΠ°Π±ΠΎΡΡ Ρ ΡΠ°ΠΌΡΠΌΠΈ ΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·Π½ΡΠΌΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΠ΅Π½ΠΎΠΌΠ΅Π½Π°ΠΌΠΈ ΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ°ΠΌΠΈ, ΡΠ°Π·Π²ΠΎΡΠ°ΡΠΈΠ²Π°ΡΡΠΈΠΌΠΈΡΡ Π² ΡΠ°ΠΌΡΡ
ΡΠ°Π·Π½ΡΡ
Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΈ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΌΠ°ΡΡΡΠ°Π±Π°Ρ
ΠΈ Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΡΠ΅ΡΠ°Ρ
. ΠΡΠΈ ΡΡΠΎΠΌ Π΄ΠΎ ΡΠΈΡ
ΠΏΠΎΡ ΡΠΎΡ
ΡΠ°Π½ΡΠ΅ΡΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ, Ρ ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠΎΡΠΎΠ½Ρ, ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΡΡΠ½Π΅Π½ΠΈΡ βΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉβ, Π° Ρ Π΄ΡΡΠ³ΠΎΠΉ ΡΡΠΎΡΠΎΠ½Ρ, ΠΈΡ
ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΠΊΠ°ΠΊ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠΎΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΈ ΠΊΠ°ΠΊ ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠΈ (ΡΠΎ ΡΠ²ΠΎΠΈΠΌΠΈ ΡΡΠ°Π½Π΄Π°ΡΡΠ°ΠΌΠΈ, ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΎΠ½Π½ΡΠΌΠΈ ΠΈ ΠΊΠ°Π΄ΡΠΎΠ²ΡΠΌΠΈ ΡΠ΅ΡΡΡΡΠ°ΠΌΠΈ ΠΈ ΠΏΡΠΎΡΠ΅Π΅). ΠΡΠΊΡΡΡΠΈΠ΅ Π² 2013 Π³. ΠΊΠ°ΡΠ΅Π΄ΡΡ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π½Π° ΡΠΎΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ°ΠΊΡΠ»ΡΡΠ΅ΡΠ΅ ΠΠΠ£ ΠΈΠΌΠ΅Π½ΠΈ Π.Π. ΠΠΎΠΌΠΎΠ½ΠΎΡΠΎΠ²Π° ΡΡΠ°Π»ΠΎ ΡΠ²ΠΎΠ΅ΠΎΠ±ΡΠ°Π·Π½ΡΠΌ ΠΎΡΠ²Π΅ΡΠΎΠΌ Π½Π° Π΄Π°Π½Π½ΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ. Π ΡΡΠ°ΡΡΠ΅ ΠΎΡΠ΅ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΡΡΠ΄ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ ΡΠ°Π±ΠΎΡΡ ΠΊΠ°ΡΠ΅Π΄ΡΡ Ρ ΠΌΠΎΠΌΠ΅Π½ΡΠ° Π΅Π΅ ΡΡΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡ ΠΏΠΎ Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ
Π ΠΎΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ ΠΠ°ΠΏΠ°Π΄ΠΎΠΌ ΠΈ ΠΠΎΡΡΠΎΠΊΠΎΠΌ: ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡ, ΠΊΡΠ»ΡΡΡΡΠ°, ΠΏΡΠ°ΠΊΡΠΈΠΊΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ Π½Π΅ΡΠ°Π²Π΅Π½ΡΡΠ²Π°
This article is dedicated to the problems of formation of Russian business culture and the influence, which exert the national culture, on functioning of modern Russian organizations and organization behavior. Doing business involves work at the management level with heterogeneous elements, reducible to a single system, where the very heterogeneity (social, historical, cultural) is one of the major management problems that require the development and application of sometimes very trivial management technologies.Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ ΠΊΡΠ»ΡΡΡΡΡ, Π²Π»ΠΈΡΠ½ΠΈΡ Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΠΊΡΠ»ΡΡΡΡΡ Π½Π° ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ ΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠΠ΅Π΄Π΅Π½ΠΈΠ΅ Π±ΠΈΠ·Π½Π΅ΡΠ° ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π΅Ρ Π½Π° ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΎΠΌ ΡΡΠΎΠ²Π½Π΅ ΡΠ°Π±ΠΎΡΡ Ρ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΠΌΠΈ ΡΠ»Π΅ΠΌΠ΅Π½ΡΠ°ΠΌΠΈ, ΡΠ²ΠΎΠ΄ΠΈΠΌΡΠΌΠΈ Π² Π΅Π΄ΠΈΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ, Π² ΠΊΠΎΡΠΎΡΡΡ
ΡΠ°ΠΌΠ° Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΠΎΡΡΡ (ΡΠΎΡΠΈΠ°Π»ΡΠ½Π°Ρ, ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠ°Ρ, ΠΊΡΠ»ΡΡΡΡΠ½Π°Ρ) ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ· ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΠ±Π»Π΅ΠΌ, ΡΡΠ΅Π±ΡΡΡΠΈΡ
ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΠΎΡΠΎΠΉ Π²Π΅ΡΡΠΌΠ° Π½Π΅ΡΡΠΈΠ²ΠΈΠ°Π»ΡΠ½ΡΡ
ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ
Complexing of Sulfur(IV) Oxide with Hexamethylenetetramine and Hexamethylenediamine in Aqueous Solutions
Interaction in the sulfur(IV) oxideβhexamethylenetetramine (hexamethylenediamine)βwater systems was studied by pH-, redox-, and conductometric titration techniques. The structure and stability of the resulting molecular and ionic complexes were examined in relation to the nature and concentration of the components in solution, as well as to temperature
Manganese catalysts to obtain olefins from C1-C4 alkanes
Oxidative transformations of C1-C4 alkanes into olefins on oxide manganese catalysts were under study. We also studied oxidative coupling of methane (OCM) into ethylene on deposited and applied on the silicon dioxide catalysts. We studied the influence of chemical composition of catalyst and promotors on the OCM. Adding a little amount of ethane and propane hydrocarbons to methane allows increasing the concentration of ethylene in gases and significantly increasing productivity in ethylene. The study also shows the impact of the amount of manganese and promotors applied on SiO2 on the yield of olefins during the conversion of C3-C4 alkanes
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