95 research outputs found

    A Family of Interleaved High Step-Up DC-DC Converters by Integrating a Voltage Multiplier and an Active Clamp Circuits

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    A family of interleaved current-fed high step-up dc-dc converters are introduced and analyzed here by combining a voltage multiplier (VM) and an active clamp circuit for high-voltage high-power applications. Low input currents and output voltages ripples values and high voltage-gains characteristics of these converters make them suitable for lots of dc-dc applications. All power devices operate entirely under soft switching conditions, even when wide load and input voltage variations are applied. Thus, they can be designed at high switching frequencies to reduce passive components sizes to achieve high-power density, one of the main targets of the power electronics researches. Also, their input and output ports common ground simplifies the gate-drives and control circuits. To verify the given analyses and simulations, a 120-320 V to 1 kV, 50-1300 W three-stage two-leg prototype converter has been implemented at 100 kHz. Based on the experimental results, maximum efficiency of 96.5% is achieved.Comment: 14 pages, 15 figure

    Growth Performance, Eating Behavior, Digestibility, Blood Metabolites, and Carcass Traits in Growing-Finishing Fat-Tailed Lambs Fed Different Levels of Dietary Neutral Detergent Fiber with High Rumen Undegradable Protein

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    This study was conducted to investigate the effect of decreasing concentrations of dietary neutral detergent fiber (NDF) at high rumen undegradable protein (RUP) on performance, digestibility, chewing activity, blood attributes, and carcass characteristics in 32 weaned male Afshari lambs (90 days of age; 26 kg initial body weight; BW). Dietary metabolic energy (ME) was increased from 10.6–11.5 and 11.8 MJ/kg dry matter (DM) by replacing alfalfa hay with grain to achieve NDF concentrations of 270, 245, and 220 g/kg DM, respectively, at 66.6 g/kg DM of RUP. The control (CON) diet contained 10.9 MJ/kg ME, 270 g/kg NDF and 59.6 g/kg RUP on DM basis. Rations containedsimilar concentrations of crude protein (CP, 160 g/kg DM). Lambs were slaughtered after a 56-d feeding period. The increase in dietary RUP had no effect on BW and average daily gain (ADG) but tended to decrease apparent digestibility of CP and DM, significantlydecreasedplasma urea concentration, and increased carcass CP content. Other body or carcass characteristics were unchanged. Decreasing dietary fiber at high RUP did not result in adverse effects on BW, ADG, body length, withers height, apparent digestibility of DM and CP, and BFT, but decreased DM intake (1539 vs. 1706 g/d) and feed conversion ratio (FCR; 4.33 vs. 5.39) compared with CON. Gradual reduction in NDF and physically effective NDF did not affecteating, ruminating or chewing times. Plasma glucose concentration was greater for NDF220 than for the three other treatments (p = 0.015).Slaughtering traits were not affected by dietary treatment except for hot carcass weight, which increased in NDF220 and NDF245 compared with NDF270 (p = 0.021). The concentration of meat CP increased in NDF270 versus CON (167 vs. 152 g/kg). Quadratic effects occurred for meat ether extract concentration (highest in NDF220) and fat-tail weight (highest in NDF245). In conclusion, the results showed that increasing the proportion of RUP within dietary CP improves carcass protein accretion. Decreasing dietary NDF to 220 g/kg DM at high RUP does not impair eating behavior and improves FCR in 3-month-old fat-tailed lambs

    A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

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    Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.</jats:p

    The emerging role of regulatory cell-based therapy in autoimmune disease

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    Autoimmune disease, caused by unwanted immune responses to self-antigens, affects millions of people each year and poses a great social and economic burden to individuals and communities. In the course of autoimmune disorders, including rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes mellitus, and multiple sclerosis, disturbances in the balance between the immune response against harmful agents and tolerance towards self-antigens lead to an immune response against self-tissues. In recent years, various regulatory immune cells have been identified. Disruptions in the quality, quantity, and function of these cells have been implicated in autoimmune disease development. Therefore, targeting or engineering these cells is a promising therapeutic for different autoimmune diseases. Regulatory T cells, regulatory B cells, regulatory dendritic cells, myeloid suppressor cells, and some subsets of innate lymphoid cells are arising as important players among this class of cells. Here, we review the roles of each suppressive cell type in the immune system during homeostasis and in the development of autoimmunity. Moreover, we discuss the current and future therapeutic potential of each one of these cell types for autoimmune diseases
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