2,139 research outputs found

    Automatic Look-Up Table Based Real-Time Phase Unwrapping for Phase Measuring Profilometry and Optimal Reference Frequency Selection

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    For temporal phase unwrapping in phase measuring profilometry, it has recently been reported that two phases with co-prime frequencies can be absolutely unwrapped using a look-up table; however, frequency selection and table construction has been performed manually without optimization. In this paper, a universal phase unwrapping method is proposed to unwrap phase flexibly and automatically by using geometric analysis, and thus we can programmatically build a one-dimensional or two-dimensional look-up table for arbitrary two co-prime frequencies to correctly unwrap phases in real time. Moreover, a phase error model related to the defocus effect is derived to figure out an optimal reference frequency co-prime to the principal frequency. Experimental results verify the correctness and computational efficiency of the proposed method

    Universal Phase Unwrapping for Phase Measuring Profilometry Using Geometry Analysis

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    Traditionally temporal phase unwrapping for phase measuring profilometry needs to employ the phase computed from unit-frequency patterned images; however, it has recently been reported that two phases with co-prime frequencies can be absolutely unwrapped each other. However, a manually man-made look-up table for two known frequencies has to be used for correctly unwrapping phases. If two co-prime frequencies are changed, the look-up table has to be manually rebuilt. In this paper, a universal phase unwrapping algorithm is proposed to unwrap phase flexibly and automatically. The basis of the proposed algorithm is converting a signal-processing problem into a geometric analysis one. First, we normalize two wrapped phases such that they are of the same needed slope. Second, by using the modular operation, we unify the integer-valued difference of the two normalized phases over each wrapping interval. Third, by analyzing the properties of the uniform difference mathematically, we can automatically build a look-up table to record the corresponding correct orders for all wrapping intervals. Even if the frequencies are changed, the look-up table will be automatically updated for the latest involved frequencies. Finally, with the order information stored in the look-up table, the wrapped phases can be correctly unwrapped. Both simulations and experimental results verify the correctness of the proposed algorithm

    Proteomic biomarkers of the apnea hypopnea index and obstructive sleep apnea: Insights into the pathophysiology of presence, severity, and treatment response

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    Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants

    ΠžΠ±Π»Ρ–ΠΊ Π·ΠΌΡ–Π½ΠΈ ΠΏΠ»ΠΎΡ‰ лісових ΡƒΠ³Ρ–Π΄ΡŒ Ρƒ структурі зСмСльного Ρ„ΠΎΠ½Π΄Ρƒ Π·Π° характСристиками супутникового Π·Π½Ρ–ΠΌΠΊΠ°

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    Using the example of a mountainous cultivated land environment, we have shown the use of Earth Remote Sensing Data (ERS) technology to control the change of forest land area due to their being cut down. The footage of the Quick Bird satellite image has been used for a simulation of the change in the forest area structure from "forest land" to "deforestation" in which the forest land area shifts into the cut down (deforested) area. It is proved that the histogram characteristics of the RGB Quick Bird satellite image contain information on changes in forest land are structure from "forest land" to "deforestation". It is shown that the median size of the RGB histogram responds to the change in the area of the forest land. The most sensitive out of all RGB channels to changing area dynamics is the channel Green. Physical explanation of this process is carried out using the test fragments "forest land" and "deforestation". We have found out that due to the different texture of the "forest land" and "deforestation" images, their histogram characteristics, the zones of pixel placement, differ in brightness. In the fragment "forest land" the majority of pixels in the histogram is concentrated in the range of 0–100, while for the fragment "deforestation" it shifts to the right to a range of 100–200. Mean square deviation (MSD) – 24 gives a more homogeneous texture of the deforestation, as it creates a graph with a clearly expressive extremum that becomes more and more visible as the cutting down spreads. An analytical dependence between the dynamics of the areas Ξ”S change at the boundary between "forest land" and "deforestation" and the number of pixels Ξ”N per the extremum, is obtained. Its considerably high sensitivity has been shown which allows solving the problems of recording the change of forest land areas in the land resource structure using the characteristics of satellite imagery.На ΠΏΡ€ΠΈΠΊΠ»Π°Π΄Ρ– Π³Ρ–Ρ€ΡΡŒΠΊΠΎΠ³ΠΎ Π°Π³Ρ€ΠΎΠ»Π°Π½Π΄ΡˆΠ°Ρ„Ρ‚Π½ΠΎΠ³ΠΎ сСрСдовища ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ застосування Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ— дистанційного зондування Π—Π΅ΠΌΠ»Ρ– (Π”Π—Π—) Π΄ΠΎ ΠΊΠΎΠ½Ρ‚Ρ€ΠΎΠ»ΡŽ Π·ΠΌΡ–Π½ΠΈ ΠΏΠ»ΠΎΡ‰ лісових ΡƒΠ³Ρ–Π΄ΡŒ Ρ‡Π΅Ρ€Π΅Π· Ρ—Ρ… вирубування. На підставі супутникового Π·Π½Ρ–ΠΌΠΊΠ° QuickBird здійснСно модСлювання Π²ΠΈΡ€ΡƒΠ±ΠΎΠΊ лісових ΡƒΠ³Ρ–Π΄ΡŒ Ρ– ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΎ зобраТСння Ρ‚Π΅Ρ€ΠΈΡ‚ΠΎΡ€Ρ–ΠΉ Π· Ρ€Ρ–Π·Π½ΠΈΠΌΠΈ Π·ΠΎΠ½Π°ΠΌΠΈ Π²ΠΈΡ€ΡƒΠ±ΠΎΠΊ. Π—ΠΌΡ–Π½ΠΈ Ρƒ структурі ΠΏΠ»ΠΎΡ‰ лісових ΡƒΠ³Ρ–Π΄ΡŒ Π²Ρ–Π΄Π±ΡƒΠ²Π°Ρ”Ρ‚ΡŒΡΡ способом пСрСміщСння Π»Ρ–Π½Ρ–Ρ— розмСТування "лісовий ΠΏΠΎΠΊΡ€ΠΈΠ² – Π²ΠΈΡ€ΡƒΠ±ΠΊΠ°", Π·Π° якого ΠΏΠ»ΠΎΡ‰Ρ– Π· лісового ΠΏΠΎΠΊΡ€ΠΈΠ²Ρƒ ΠΏΠ΅Ρ€Π΅Ρ…ΠΎΠ΄ΡΡ‚ΡŒ Π΄ΠΎ Π·ΠΎΠ½ΠΈ Π²ΠΈΡ€ΡƒΠ±ΠΎΠΊ. Π”ΠΎΠ²Π΅Π΄Π΅Π½ΠΎ, Ρ‰ΠΎ гістограмні характСристики RGB супутникового Π·Π½Ρ–ΠΌΠΊΠ° QuickBird ΠΌΠ°ΡŽΡ‚ΡŒ Ρ–Π½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŒ ΠΏΡ€ΠΎ Π·ΠΌΡ–Π½ΠΈ ΠΏΠ»ΠΎΡ‰ Ρƒ структурі "лісовий ΠΏΠΎΠΊΡ€ΠΈΠ² – Π²ΠΈΡ€ΡƒΠ±ΠΊΠ°". Показано, Ρ‰ΠΎ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½ΠΈ ΠΌΠ΅Π΄Ρ–Π°Π½ усіх RGB-ΠΊΠ°Π½Π°Π»Ρ–Π² ΠΎΠ΄Π½ΠΎΠ·Π½Π°Ρ‡Π½ΠΎ Ρ€Π΅Π°Π³ΡƒΡŽΡ‚ΡŒ Π½Π° Π·ΠΌΡ–Π½ΠΈ ΠΏΠ»ΠΎΡ‰ лісового ΠΏΠΎΠΊΡ€ΠΎΠ²Ρƒ. ΠΠ°ΠΉΠ±Ρ–Π»ΡŒΡˆ Ρ‡ΡƒΡ‚Π»ΠΈΠ²ΠΈΠΌ Π΄ΠΎ Π΄ΠΈΠ½Π°ΠΌΡ–ΠΊΠΈ Π·ΠΌΡ–Π½ΠΈ ΠΏΠ»ΠΎΡ‰ Π· усіх RGB-ΠΊΠ°Π½Π°Π»Ρ–Π² Ρ” ΠΊΠ°Π½Π°Π» Green. Π€Ρ–Π·ΠΈΡ‡Π½Π΅ пояснСння Ρ†ΡŒΠΎΠ³ΠΎ процСсу здійснСно Π½Π° підставі тСстових Ρ„Ρ€Π°Π³ΠΌΠ΅Π½Ρ‚Ρ–Π² "лісовий ΠΏΠΎΠΊΡ€ΠΈΠ²" Ρ– "Π²ΠΈΡ€ΡƒΠ±ΠΊΠ°". ВстановлСно, Ρ‰ΠΎ завдяки Ρ€Ρ–Π·Π½Ρ–ΠΉ тСкстурі "лісового ΠΏΠΎΠΊΡ€ΠΎΠ²Ρƒ" Ρ– "Π²ΠΈΡ€ΡƒΠ±ΠΊΠΈ" Ρ—Ρ…Π½Ρ– гістограмні характСристики Π²Ρ–Π΄Ρ€Ρ–Π·Π½ΡΡŽΡ‚ΡŒΡΡ Π·ΠΎΠ½Π°ΠΌΠΈ розміщСння піксСлів Π·Π° ΡΡΠΊΡ€Π°Π²Ρ–ΡΡ‚ΡŽ. Для Ρ„Ρ€Π°Π³ΠΌΠ΅Π½Ρ‚Ρƒ "лісовий ΠΏΠΎΠΊΡ€ΠΈΠ²" основна частина піксСлів Ρƒ гістограмі зосСрСдТСна Π² Π΄Ρ–Π°ΠΏΠ°Π·ΠΎΠ½Ρ– Π· ΠΊΠΎΠ΄ΠΎΠΌ 0–100, Ρ‚ΠΎΠ΄Ρ– як для Ρ„Ρ€Π°Π³ΠΌΠ΅Π½Ρ‚Ρƒ "Π²ΠΈΡ€ΡƒΠ±ΠΊΠ°" Π²ΠΎΠ½Π° Π·ΠΌΡ–Ρ‰ΡƒΡ”Ρ‚ΡŒΡΡ Π²ΠΏΡ€Π°Π²ΠΎ Π΄ΠΎ Π΄Ρ–Π°ΠΏΠ°Π·ΠΎΠ½Ρƒ 100–200. Π‘Ρ–Π»ΡŒΡˆ ΠΎΠ΄Π½ΠΎΡ€Ρ–Π΄Π½Π° тСкстура Π²ΠΈΡ€ΡƒΠ±ΠΎΠΊ Π΄Π°Ρ” Π‘ΠšΠ’ 24, Ρ‰ΠΎ ΡΡ‚Π²ΠΎΡ€ΡŽΡ” Π³Ρ€Π°Ρ„Ρ–ΠΊ Ρ–Π· Ρ‡Ρ–Ρ‚ΠΊΠΎ Π²ΠΈΡ€Π°Π·Π½ΠΈΠΌ СкстрСмумом, який стає Π΄Π΅Π΄Π°Π»Ρ– ΠΏΠΎΠΌΡ–Ρ‚Π½Ρ–ΡˆΠΈΠΌ Ρ–Π· ΠΏΠΎΡˆΠΈΡ€Π΅Π½Π½ΡΠΌ ΠΏΠ»ΠΎΡ‰Ρ– Π²ΠΈΡ€ΡƒΠ±ΠΎΠΊ. ΠžΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΎ Π°Π½Π°Π»Ρ–Ρ‚ΠΈΡ‡Π½Ρƒ Π·Π°Π»Π΅ΠΆΠ½Ρ–ΡΡ‚ΡŒ ΠΌΡ–ΠΆ Π΄ΠΈΠ½Π°ΠΌΡ–ΠΊΠΎΡŽ Π·ΠΌΡ–Π½ΠΈ ΠΏΠ»ΠΎΡ‰ Ξ”S Π½Π° Π³Ρ€Π°Π½ΠΈΡ†Ρ– "лісовий ΠΏΠΎΠΊΡ€ΠΈΠ² – Π²ΠΈΡ€ΡƒΠ±ΠΊΠ°" Ρ– ΠΊΡ–Π»ΡŒΠΊΠΎΡΡ‚Ρ– піксСлів NСкстр, Ρ‰ΠΎ ΠΏΡ€ΠΈΠΏΠ°Π΄Π°Ρ” Π½Π° СкстрСмум Π·Π° ΠΊΡ–Π»ΡŒΠΊΡ–ΡΡ‚ΡŽ піксСлів Ρƒ Π΄Ρ–Π°ΠΏΠ°Π·ΠΎΠ½Ρ– гістограми 100–200. Показано Ρ—Ρ— Π΄ΠΎΡΡ‚Π°Ρ‚Π½ΡŒΠΎ високу Ρ‡ΡƒΡ‚Π»ΠΈΠ²Ρ–ΡΡ‚ΡŒ, Ρ‰ΠΎ Π΄Π°Ρ” Π·ΠΌΠΎΠ³Ρƒ Π²ΠΈΡ€Ρ–ΡˆΡƒΠ²Π°Ρ‚ΠΈ Π·Π°Π΄Π°Ρ‡Ρ– ΠΎΠ±Π»Ρ–ΠΊΡƒ Π·ΠΌΡ–Π½ΠΈ ΠΏΠ»ΠΎΡ‰ лісових ΡƒΠ³Ρ–Π΄ΡŒ Ρƒ структурі зСмСльного Ρ„ΠΎΠ½Π΄Ρƒ Π·Π° характСристиками супутникового Π·Π½Ρ–ΠΌΠΊΠ°

    Mitofusin2 mutations disrupt axonal mitochondrial positioning and promote axon degeneration

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    Alterations in mitochondrial dynamics (fission, fusion and movement) are implicated in many neurodegenerative diseases, from rare genetic disorders such as Charcot-Marie-Tooth disease, to common conditions including Alzheimer’s disease. However, the relationship between altered mitochondrial dynamics and neurodegeneration is incompletely understood. Here we show that disease associated MFN2 proteins suppressed both mitochondrial fusion and transport, and produced classic features of segmental axonal degeneration without cell body death, including neurofilament filled swellings, loss of calcium homeostasis, and accumulation of reactive oxygen species. By contrast, depletion of Opa1 suppressed mitochondrial fusion while sparing transport, and did not induce axonal degeneration. Axon degeneration induced by mutant MFN2 proteins correlated with the disruption of the proper mitochondrial positioning within axons, rather than loss of overall mitochondrial movement, or global mitochondrial dysfunction. We also found that augmenting expression of MFN1 rescued the axonal degeneration caused by MFN2 mutants, suggesting a possible therapeutic strategy for Charcot-Marie-Tooth disease. These experiments provide evidence that the ability of mitochondria to sense energy requirements and localize properly within axons is key to maintaining axonal integrity, and may be a common pathway by which disruptions in axonal transport contribute to neurodegeneration

    KINETIC CHARACTERISTICS OF CONVECTION DRYING PROCESS OF CAPILLARY-POROUS COLLOIDAL MATERIALS

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    Π’ΠΈΠ·Π½Π°Ρ‡Π΅Π½ΠΎ, Ρ‰ΠΎ ΡΠΊΠ»Π°Π΄Π½Ρ–ΡΡ‚ΡŒ тСпломасообмінних процСсів Π΄Π΅Ρ€Π΅Π²ΠΎΠΎΠ±Ρ€ΠΎΠ±ΠΊΠΈ полягає самС Ρƒ складності явищ пСрСнСсСння Ρ‚Π΅ΠΏΠ»ΠΎΡ‚ΠΈ Ρ– Π²ΠΎΠ»ΠΎΠ³ΠΈ, як всСрСдині ΠΌΠ°Ρ‚Π΅Ρ€Ρ–Π°Π»Ρƒ, Ρ‚Π°ΠΊ Ρ– Π½Π° ΠΌΠ΅ΠΆΡ– Ρ€ΠΎΠ·ΠΏΠΎΠ΄Ρ–Π»Ρƒ Ρ„Π°Π· сСрСдовищС – Ρ‚Π²Π΅Ρ€Π΄Π΅ Ρ‚Ρ–Π»ΠΎ Ρ‚Π° складності Π·ΠΌΡ–Π½ΠΈ Ρ„Ρ–Π·ΠΈΠΊΠΎ-ΠΌΠ΅Ρ…Π°Π½Ρ–Ρ‡Π½ΠΈΡ… характСристик Π΄Π΅Ρ€Π΅Π²ΠΈΠ½ΠΈ ΠΏΡ–Π΄ час Ρ‚Π΅ΠΏΠ»ΠΎΠ²ΠΎΠ³ΠΎ оброблСння. Π—Π° дослідний ΠΌΠ°Ρ‚Π΅Ρ€Ρ–Π°Π» прийнято соснові ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€Ρ–Π°Π»ΠΈ Π·Π°Π²Ρ‚ΠΎΠ²ΡˆΠΊΠΈ 40Β ΠΌΠΌ, які Π²ΠΈΡΡƒΡˆΡƒΡŽΡ‚ΡŒ ΠΌ'якими Ρ€Π΅ΠΆΠΈΠΌΠ°ΠΌΠΈ. Π’ основу дослідТСння ΠΏΠΎΠΊΠ»Π°Π΄Π΅Π½ΠΎ ΠΏΠΎΠ±ΡƒΠ΄ΠΎΠ²Ρƒ ΠΊΡ€ΠΈΠ²ΠΎΡ— ΡΡƒΡˆΡ–Π½Π½Ρ, Π΄Π΅ Π²ΠΊΠ°Π·Π°Π½ΠΎ Π½Π° Π·ΠΌΡ–Π½Ρƒ ΡΠ΅Ρ€Π΅Π΄Π½ΡŒΠΎΡ— вологості Ρ‚Π° вологості ΠΏΠΎΠ²Π΅Ρ€Ρ…Π½Π΅Π²ΠΈΡ… Ρ– Ρ†Π΅Π½Ρ‚Ρ€Π°Π»ΡŒΠ½ΠΈΡ… ΡˆΠ°Ρ€Ρ–Π² Π΄Π΅Ρ€Π΅Π²ΠΈΠ½ΠΈ ΠΏΡ–Π΄ час ΡΡƒΡˆΡ–Π½Π½Ρ. Π—Π° Ρ†ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ Π²ΠΈΠ·Π½Π°Ρ‡Π΅Π½ΠΎ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½ΠΈ: ΡˆΠ²ΠΈΠ΄ΠΊΡ–ΡΡ‚ΡŒ ΡΡƒΡˆΡ–Π½Π½Ρ, ΠΊΠΎΠ΅Ρ„Ρ–Ρ†Ρ–Ρ”Π½Ρ‚ΠΈ ΡΡƒΡˆΡ–Π½Π½Ρ, вологопровідності Ρ‚Π° Π²ΠΎΠ»ΠΎΠ³ΠΎΠ²Ρ–Π΄Π΄Π°Ρ‡Ρ–, Π° Ρ‚Π°ΠΊΠΎΠΆ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½ΠΈ масообмінних ΠΊΡ€ΠΈΡ‚Π΅Ρ€Ρ–Ρ—Π² ΠΡƒΡΡΠ΅Π»ΡŒΡ‚Π°, Π€ΡƒΡ€'Ρ” Ρ‚Π° ΠšΡ–Ρ€ΠΏΡ–Ρ‡ΠΎΠ²Π°. Π—Π° Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Π°ΠΌΠΈ ΠΊΠΎΠ΅Ρ„Ρ–Ρ†Ρ–Ρ”Π½Ρ‚Π° ΡΡƒΡˆΡ–Π½Π½Ρ ΠΌΠΎΠΆΠ½Π° Π²ΠΈΠ·Π½Π°Ρ‡ΠΈΡ‚ΠΈ Ρ‚Ρ€ΠΈΠ²Π°Π»Ρ–ΡΡ‚ΡŒ ΡΡƒΡˆΡ–Π½Π½Ρ ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€Ρ–Π°Π»Ρ–Π² Π· Π΄Π΅Ρ€Π΅Π²ΠΈΠ½ΠΈ сосни Π·Π°Π²Ρ‚ΠΎΠ²ΡˆΠΊΠΈ 40Β ΠΌΠΌ Π±ΡƒΠ΄ΡŒ-якої ΠΏΠΎΡ‡Π°Ρ‚ΠΊΠΎΠ²ΠΎΡ— Ρ– ΠΊΡ–Π½Ρ†Π΅Π²ΠΎΡ— вологості Ρ‚Π° Π·Π° Π²Π΅Π»ΠΈΡ‡ΠΈΠ½ΠΎΡŽ ΠΊΠΎΠ΅Ρ„Ρ–Ρ†Ρ–Ρ”Π½Ρ‚Π° вологопровідності Π±ΡƒΠ΄ΡŒ-яких соснових ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€Ρ–Π°Π»Ρ–Π² Ρ€Ρ–Π·Π½ΠΎΡ— Ρ‚ΠΎΠ²Ρ‰ΠΈΠ½ΠΈ Ρ‚Π° ΠΏΠΎΡ‡Π°Ρ‚ΠΊΠΎΠ²ΠΎΡ— Ρ– ΠΊΡ–Π½Ρ†Π΅Π²ΠΎΡ— вологості ΠΉ Ρ–Π½ΡˆΠΈΡ… характСристик процСсу ΡΡƒΡˆΡ–Π½Π½Ρ. ВизначСння масообмінного ΠΊΡ€ΠΈΡ‚Π΅Ρ€Ρ–ΡŽ ΠšΡ–Ρ€ΠΏΡ–Ρ‡ΠΎΠ²Π° (ΠΊΡ€ΠΈΡ‚Π΅Ρ€Ρ–ΡŽ тріщиноутворСння) ΠΏΠΎΠΊΠ°Π·ΡƒΡ” Π½Π°ΡΠΊΡ–Π»ΡŒΠΊΠΈ ΠΎΠ±Ρ€Π°Π½ΠΈΠΉ Ρ€Π΅ΠΆΠΈΠΌ Ρ” Π±Π΅Π·ΠΏΠ΅Ρ‡Π½ΠΈΠΌ Π· Ρ‚ΠΎΡ‡ΠΊΠΈ Π·ΠΎΡ€Ρƒ виникнСння Π½Π°Π΄Π»ΠΈΡˆΠΊΠΎΠ²ΠΈΡ… Π²Π½ΡƒΡ‚Ρ€Ρ–ΡˆΠ½Ρ–Ρ… Π½Π°ΠΏΡ€ΡƒΠΆΠ΅Π½ΡŒ Ρƒ Π΄Π΅Ρ€Π΅Π²ΠΈΠ½Ρ–. Π—Π° ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡ–Π² ΠΊΡ–Π½Π΅Ρ‚ΠΈΠΊΠΈ процСсу ΡΡƒΡˆΡ–Π½Π½Ρ Ρ– масообмінних ΠΊΡ€ΠΈΡ‚Π΅Ρ€Ρ–Ρ—Π² ΠΡƒΡΡΠ΅Π»ΡŒΡ‚Π° Ρ‚Π° Π€ΡƒΡ€'Ρ” Π· доповнСнням Π΄ΠΎ Ρ†ΠΈΡ… Π°Π΅Ρ€ΠΎΠ΄ΠΈΠ½Π°ΠΌΡ–Ρ‡Π½ΠΈΡ… характСристик ΡΡƒΡˆΠΈΠ»ΡŒΠ½ΠΎΠ³ΠΎ обладнання, Ρ–Π· врахуванням ΠΊΡ€ΠΈΡ‚Π΅Ρ€Ρ–ΡŽ РСйнольдса, ΠΌΠΎΠΆΠ½Π° скласти Ρ„Ρ–Π·ΠΈΠΊΠΎ-ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Ρƒ модСль процСсу ΡΡƒΡˆΡ–Π½Π½Ρ ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€Ρ–Π°Π»Ρ–Π² Ρ– Π·Π°Π³ΠΎΡ‚ΠΎΠ²ΠΎΠΊ. Ця Ρ„Ρ–Π·ΠΈΠΊΠΎ-ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½Π° модСль Π²ΠΈΠ·Π½Π°Ρ‡Π°Ρ” Π²ΠΏΠ»ΠΈΠ² Π½Π° ΠΊΡ–Π½Π΅Ρ‚ΠΈΠΊΡƒ процСсу ΡΡƒΡˆΡ–Π½Π½Ρ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ–Π² ΡΡƒΡˆΠΈΠ»ΡŒΠ½ΠΎΠ³ΠΎ сСрСдовища, Ρ‚Π΅ΠΏΠ»ΠΎΡ„Ρ–Π·ΠΈΡ‡Π½ΠΈΡ… властивостСй Π΄Π΅Ρ€Π΅Π²ΠΈΠ½ΠΈ Ρ‚Π° Π°Π΅Ρ€ΠΎΠ΄ΠΈΠ½Π°ΠΌΡ–Ρ‡Π½ΠΈΡ… характСристик ΡΡƒΡˆΠΈΠ»ΡŒΠ½ΠΈΡ… ΠΊΠ°ΠΌΠ΅Ρ€.ΠžΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΎ, Ρ‡Ρ‚ΠΎ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡ‚ΡŒ тСпломассообмСнных процСссов Π΄Π΅Ρ€Π΅Π²ΠΎΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ состоит, собствСнно, Π² слоТности явлСний пСрСноса Ρ‚Π΅ΠΏΠ»Π° ΠΈ Π²Π»Π°Π³ΠΈ, ΠΊΠ°ΠΊ Π²Π½ΡƒΡ‚Ρ€ΠΈ ΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Π°, Ρ‚Π°ΠΊ ΠΈ Π½Π° Π³Ρ€Π°Π½ΠΈΡ†Π΅ раздСлСния Ρ„Π°Π· срСда – Ρ‚Π²Π΅Ρ€Π΄ΠΎΠ΅ Ρ‚Π΅Π»ΠΎ ΠΈ слоТности измСнСния Ρ„ΠΈΠ·ΠΈΠΊΠΎ-мСханичСских характСристик дрСвСсины Π²ΠΎ врСмя Ρ‚Π΅ΠΏΠ»ΠΎΠ²ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ. Как ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ‹ΠΉ ΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π» принято составныС ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ Ρ‚ΠΎΠ»Ρ‰ΠΈΠ½ΠΎΠΉ40Β ΠΌΠΌ, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ Π²Ρ‹ΡΡƒΡˆΠΈΠ²Π°ΡŽΡ‚ΡΡ мягкими Ρ€Π΅ΠΆΠΈΠΌΠ°ΠΌΠΈ. Π’ основу исслСдования ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΎ построСниС ΠΊΡ€ΠΈΠ²ΠΎΠΉ ΡΡƒΡˆΠΊΠΈ, Π³Π΄Π΅ ΡƒΠΊΠ°Π·Π°Π½ΠΎ Π½Π° ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ срСднСй влаТности ΠΈ влаТности повСрхностных ΠΈ Π²Π½ΡƒΡ‚Ρ€Π΅Π½Π½ΠΈΡ… слоСв дрСвСсины Π²ΠΎ врСмя ΡΡƒΡˆΠΊΠΈ. По этим Π΄Π°Π½Π½Ρ‹ΠΌ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Ρ‹ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Ρ‹: скорости ΡΡƒΡˆΠΊΠΈ, коэффициСнты ΡΡƒΡˆΠΊΠΈ, влагопроводности ΠΈ Π²Π»Π°Π³ΠΎΠΎΠ±ΠΌΠ΅Π½Π°, Π° Ρ‚Π°ΠΊΠΆΠ΅ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Ρ‹ массообмСнных ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠ΅Π² ΠΡƒΡΡΠ΅Π»ΡŒΡ‚Π°, Π€ΡƒΡ€ΡŒΠ΅ ΠΈ ΠšΠΈΡ€ΠΏΠΈΡ‡Π΅Π²Π°. Π—Π° Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Π°ΠΌ коэффициСнта ΡΡƒΡˆΠΊΠΈ ΠΌΠΎΠΆΠ½ΠΎ ΠΎΠΏΡ€Π΅Π΄Π΅Π»ΠΈΡ‚ΡŒ ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ ΡΡƒΡˆΠΊΠΈ ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»ΠΎΠ² ΠΈΠ· дрСвСсины сосны Ρ‚ΠΎΠ»Ρ‰ΠΈΠ½ΠΎΠΉ40Β ΠΌΠΌΠΊΠ°ΠΊΠΎΠΉ-Π»ΠΈΠ±ΠΎ Π½Π°Ρ‡Π°Π»ΡŒΠ½ΠΎΠΉ ΠΈ ΠΊΠΎΠ½Π΅Ρ‡Π½ΠΎΠΉ влаТности, Π° ΠΏΠΎ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Π°ΠΌ коэффициСнта влагопроводности ΠΌΠΎΠΆΠ½ΠΎ ΠΎΠΏΡ€Π΅Π΄Π΅Π»ΠΈΡ‚ΡŒ ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ процСсса ΠΊΠ°ΠΊΠΈΡ… – Π»ΠΈΠ±ΠΎ сосновых ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»ΠΎΠ² Ρ€Π°Π·Π»ΠΈΡ‡Π½ΠΎΠΉ Ρ‚ΠΎΠ»Ρ‰ΠΈΠ½Ρ‹, Π½Π°Ρ‡Π°Π»ΡŒΠ½ΠΎΠΉ ΠΈ ΠΊΠΎΠ½Π΅Ρ‡Π½ΠΎΠΉ влаТности ΠΈ Π΄Ρ€ΡƒΠ³ΠΈΡ… характСристик процСсса ΡΡƒΡˆΠΊΠΈ. ΠžΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ массообмСнного критСрия ΠšΠΈΡ€ΠΏΠΈΡ‡Π΅Π²Π° (критСрия трСщинообразования) ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚ насколько ΠΈΠ·Π±Ρ€Π°Π½Π½Ρ‹ΠΉ Ρ€Π΅ΠΆΠΈΠΌ сосны бСзопасСн с Ρ‚ΠΎΡ‡ΠΊΠΈ зрСния возникновСния ΠΈΠ·Π»ΠΈΡˆΠ½ΠΈΡ… Π²Π½ΡƒΡ‚Ρ€Π΅Π½Π½ΠΈΡ… напряТСний Π² дрСвСсинС. По ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹ΠΌ Π΄Π°Π½Π½Ρ‹ΠΌΠΈ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ ΠΊΠΈΠ½Π΅Ρ‚ΠΈΠΊΠΈ процСсса ΡΡƒΡˆΠΊΠΈ ΠΈ массообмСнных ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠ΅Π² ΠΡƒΡΡΠ΅Π»ΡŒΡ‚Π° ΠΈ Π€ΡƒΡ€ΡŒΠ΅ с Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΊ этим Π΄Π°Π½Π½Ρ‹ΠΌ аэродинамичСских характСристик ΡΡƒΡˆΠΈΠ»ΡŒΠ½ΠΎΠ³ΠΎ оборудования, Π²ΠΊΠ»ΡŽΡ‡Π°Ρ ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠΉ РСйнольдса, ΠΌΠΎΠΆΠ½ΠΎ ΡΠΎΡΡ‚Π°Π²ΠΈΡ‚ΡŒ Ρ„ΠΈΠ·ΠΈΠΊΠΎ-ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ модСль процСсса ΡΡƒΡˆΠΊΠΈ ΠΏΠΈΠ»ΠΎΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»ΠΎΠ² ΠΈ Π·Π°Π³ΠΎΡ‚ΠΎΠ²ΠΎΠΊ. Π­Ρ‚Π° модСль опрСдСляСт влияниС Π½Π° ΠΊΠΈΠ½Π΅Ρ‚ΠΈΠΊΡƒ процСсса ΡΡƒΡˆΠΊΠΈ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² срСды тСплофизичСских свойств дрСвСсины ΠΈ аэродинамичСских характСристик ΡΡƒΡˆΠΈΠ»ΡŒΠ½Ρ‹Ρ… ΠΊΠ°ΠΌΠ΅Ρ€.It is known that the complexity of the heat-and-mass exchange processes in woodworking consists in the complexity of the phenomena of heat-and-moisture transfer both within the material and at the phase interface environment-solid body, as well as the complexity of changing physical-and-mechanical characteristics of wood during heat treatment. The test material was pine lumber with a thickness of 40Β mm, which had been dried by a soft drying schedule. The objective of the study is the construction of the drying curve which indicates the change in the average moisture and the moisture content of the surface and middle layers of wood during drying. By this data, the values were found for drying rate, drying coefficients, moisture conductivity and moisture-yielding ability, as well as mass-exchange numbers of Nusselt, Fourier, and Kirpichov. By the values of the drying coefficient, we can determine the drying time for 40Β mm thick pine-wood lumber of any initial and final moisture content, and by the magnitude of the conductivity coefficient we can determine drying time for any pine wood of various thicknesses and various initial and final moisture and other characteristics of the drying process. Determination of mass exchange number of Kirpichov (criterion of crack formation) shows how the selected mode is safe in terms of the occurrence of excessive internal stresses in wood. Thus, a physical-mathematical model of the process of drying of lumber and blanks can be constructed using the obtained data on the kinetics of the drying process as well as Nusselt's and Fourier's mass exchange criteria, with the addition to this of the aerodynamic characteristics of the drying equipment, including the Reynolds criterion. This physical-mathematical model identifies the influence of the drying environment parameters, the thermophysical properties of the wood and the aerodynamic characteristics of the drying chambers on the kinetics of the drying process
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