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    Wyświetlanie 1-4 z 4
    Tytuł:
    ANTI-INFLAMMATORY ACTIVITY AND CYTOTOXICITY OF DIOSGENIN ON LIPOPOLYSACCHARIDES INDUCED RAW 264.7 CELLS
    Autorzy:
    ning, jing
    chang, yu
    wang, ruxia
    lan, zhiwei
    ru, qing
    liu, mingchun
    tian, chunlian
    Powiązania:
    https://bibliotekanauki.pl/articles/895615.pdf
    Data publikacji:
    2020-04-29
    Wydawca:
    Polskie Towarzystwo Farmaceutyczne
    Tematy:
    Diosgenin
    Anti-inflammatory
    lipopolysaccharides
    cytotoxicity
    phagocytosis ability  
    Opis:
    Diosgenin is a steroidal sapogenin compound, and possesses multiple biological activities including anti-inflammatory, anticancer, immunological regulation, and anti-aging. The current study focused on its anti-inflammatory activities and cytotoxicity by analysis of NO production, phagocytosis activity, secretion of TNF-α and IL-6 and cell viability in LPS-induced RAW 264.7 cells. An IC50 value of diosgenin of 2.8 μM was calculated for diosgenin by regression of cell viability from concentrations ranging from 0.01 to 25 μM; this indicated that 0.01, 0.02, 0.04 μM diosgenin could reduce phagocytic activity very significantly (p<0.01) in a dose-dependent manner with no cytotoxic effect on the LPS-induced RAW 264.7 cells. However, there was no significant effect on NO content and secretion of TNF-α and IL-6 after diosgenin treatment. The research revealed that low concentrations diosgenin can directly inhibit cells phagocytosis, with no effect on the release of inflammatory mediators and cytokines. This lays a foundation for screening for a safe dose in research and developent of derivatives and new formulation of diosgenin for its anti-inflammatory effect.
    Źródło:
    Acta Poloniae Pharmaceutica - Drug Research; 2020, 77, 2; 313-317
    0001-6837
    2353-5288
    Pojawia się w:
    Acta Poloniae Pharmaceutica - Drug Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
    Autorzy:
    Chen, Xuan
    Przystupa, Krzysztof
    Ye, Zhiwei
    Chen, Feng
    Wang, Chunzhi
    Liu, Jinhang
    Gao, Rong
    Wei, Ming
    Kochan, Orest
    Powiązania:
    https://bibliotekanauki.pl/articles/2087016.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    short-term electric load forecast
    extreme learning machine
    Lévy flight
    tree-seed algorithm
    Kernel principal component analysis
    Opis:
    In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.
    Źródło:
    Eksploatacja i Niezawodność; 2022, 24, 2; 153--162
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fault diagnosis model of rolling bearing based on parameter adaptive VMD algorithm and Sparrow Search Algorithm-Based PNN
    Autorzy:
    Li, Junxing
    Liu, Zhiwei
    Qiu, Ming
    Niu, Kaicen
    Powiązania:
    https://bibliotekanauki.pl/articles/24200836.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    rolling bearing
    failure diagnosis
    adaptive variational mode decomposition
    sparrow probabilistic neural network
    Opis:
    Fault diagnosis of rolling bearings is essential to ensure the proper functioning of the entire machinery and equipment. Variational mode decomposition (VMD) and neural networks have gained widespread attention in the field of bearing fault diagnosis due to their powerful feature extraction and feature learning capacity. However, past methods usually utilize experiential knowledge to determine the key parameters in the VMD and neural networks, such as the penalty factor, the smooth factor, and so on, so that generates a poor diagnostic result. To address this problem, an Adaptive Variational Mode Decomposition (AVMD) is proposed to obtain better features to construct the fault feature matrix and Sparrow probabilistic neural network (SPNN) is constructed for rolling bearing fault diagnosis. Firstly, the unknown parameters of VMD are estimated by using the genetic algorithm (GA), then the suitable features such as kurtosis and singular value entropy are extracted by automatically adjusting the parameters of VMD. Furthermore, a probabilistic neural network (PNN) is used for bearing fault diagnosis. Meanwhile, embedding the sparrow search algorithm (SSA) into PNN to obtain the optimal smoothing factor. Finally, the proposed method is tested and evaluated on a public bearing dataset and bearing tests. The results demonstrate that the proposed method can extract suitable features and achieve high diagnostic accuracy.
    Źródło:
    Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 163547
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The influence of air inlet layout on the inner flow field for a vertical turbo air classifier
    Autorzy:
    Yu, Yuan
    Li, Xingshuai
    Zhang, Yu
    Jiao, Zhiwei
    Liu, Jiaxiang
    Powiązania:
    https://bibliotekanauki.pl/articles/27323654.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    turbo air classifier
    powder technology
    air inlet layout
    numerical simulation
    classification performance
    Opis:
    In this study, the influence of air inlet layout on the flow field distribution and particle movement trajectory for the vertical turbo air classifier are analyzed comparatively using the numerical simulation method. The air inlet layout adjustment can increase the axial velocity and turbulent dissipation rate at the feeding inlet and do not generate the axial negative velocity, which improves powder material pneumatic transportation and dispersion capacity; the air inlet layout adjustment can match the airflow rotation direction with the rotation direction of the rotor cage, which can eliminate the vortices in the rotor cage channel effectively. Moreover, the particle movement time is shortened and fast classification is completed, which can decrease the particle agglomeration probability and weaken the ‘fish-hook’ effect. The optimization scheme of the air inlet layout is Type-BC. In accordance with the numerical simulation results, the calcium carbonate classification experimental results indicate that the classification performance of the classifier is improved using Type-BC.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2023, 59, 6; art. no. 175859
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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