Lemongrass is a plant that contains aromatic compounds (myrcene and limonene), powerful deodorants, and antimicrobial compounds (citral and geraniol). Identifying a suitable drying model for the material is crucial for establishing an initial step for the development of dried products. Convection drying is a commonly used drying method that could extend the shelf life of the product. In this study, a suitable kinetic model for the drying process was determined by fitting moisture data corresponding to four different temperature levels: 50, 55, 60 and 65 °C. In addition, the effect of drying temperature on the moisture removal rate, the effective diffusion coefficient and activation energy were also estimated. The results showed that time for moisture removal increases proportionally with the air-drying temperature, and that the Weibull model is the most suitable model for describing the drying process. The effective diffusion coefficient ranges from 7.64 × 10−11 m2/s to 1.48 × 10−10 m2/s and the activation energy was 38.34 kJ/mol. The activation energy for lemongrass evaporation is relatively high, suggesting that more energy is needed to separate moisture from the material by drying.
A Zr(IV)-based metal-organic framework (MOF), termed reo-MOF-1 [ZrO(HO)(SNDC)], composed of 4-sulfonaphthalene-2,6-dicarboxylate (HSNDC) linkers and ZrO(HO)(CO) clusters was synthesized by solvothermal synthesis. Structural analysis revealed that reo-MOF-1 adopts the reo topology highlighted with large cuboctahedral cages (23 Å). This structure is similar to that found in DUT-52 (fcu topology), however, reo-MOF-1 lacks the body-centered packing of the 12-connected ZrO(OH)(CO) clusters, which is attributed to the subtle, but crucial influence in the bulkiness of functional groups on the linkers. The control experiments, where the ratio of HSNDC/naphthalene-2,6-dicarboxylate linkers was varied, also support our finding that the bulky functionalities play a key role for defect-controlled synthesis. The reo-MOF-1 framework was obtained by linker exchange to yield a chemically and thermally stable material despite its large pores. Remarkably, reo-MOF-1 exhibits permanent porosity (Brunauer-Emmett-Teller and Langmuir surface areas of 2104 and 2203 m g, respectively). Owing to these remarkable structural features, reo-MOF-1 significantly enhances the yield in Brønsted acid-catalyzed reactions.
Segmentation is one of the most common methods for analyzing and processing medical images, assisting doctors in making accurate diagnoses by providing detailed information about the required body part. However, segmenting medical images presents a number of challenges, including the need for medical professionals to be trained, the fact that it is time-consuming and prone to errors. As a result, it appears that an automated medical image segmentation system is required. Deep learning algorithms have recently demonstrated superior performance for segmentation tasks, particularly semantic segmentation networks that provide a pixel-level understanding of images. U- Net for image segmentation is one of the modern complex networks in the field of medical imaging; several segmentation networks have been built on its foundation with the advancements of Recurrent Residual convolutional units and the construction of recurrent residual convolutional neural network based on U-Net (R2U-Net). R2U-Net is used to perform trachea and bronchial segmentation on a dataset of 36,000 images. With a variety of experiments, the proposed segmentation resulted in a dice-coefficient of 0.8394 on the test dataset. Finally, a number of research issues are raised, indicating the need for future improvements.
Electrical Impedance Tomography (EIT) is a technology that uses recorded voltage data to reconstruct thedistribution of electrical conductivity in a turbid medium and to illustrate the structures and abnormalities within that medium. The advantages of this imaging technology include non-ionization, non-invasive, impact and continuous monitoring, optimum design, and lower production costs as compared to current techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography Scan (CT- Scan), and Ultrasonic... EIT has shown useful in a variety of medical domains, including identifying pneumothorax, assessing pulmonary edema, and evaluating ventilation distribution between different breathing modes. TheEIT is also being studied for application for imaging reconstruction used to obtain images for medical imaging, geological exploration, industrial application and environmental sciences. In this research, a system of 16 electrodes is manufactured for simulate biological tissue using ground pork to simulate the internal environment of the human body. The confirmed experiments are carried out using a phantom while the frequencies between 10kHz to 100 kHz are changed. An Arduino and a PC are used to collect and process the measured data. Electrical Impedance Tomography and Diffusion-based Optical Tomography (EIDORS) software is used to recreate the cross-sectional image. The image reproduced at 100 kHz gives high accuracy to the reconstructed subject.
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