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Mortensen Polat posted an update 6 months, 3 weeks ago
Moisture content is an important index to evaluate the water content in substrate. Near-infrared (NIR) spectroscopy was used for rapid quantitative detection of moisture content of coco-peat substrate. The different spectral pretreatment methods were adopted to pre-process the spectral data. Successive projection algorithm (SPA), elimination of uninformative variables algorithm (UVE) and synergy interval partial least squares algorithm (Si-PLS) were used to screen characteristic variables of coco-peat substrate original spectral data and different pretreatment spectral data. The partial least squares (PLSR) and multiple linear regression (MLR) were used to establish the relationship model between the spectral data and reference measurement value of moisture content. In comparison, the best and simplest spectral prediction model was established when SPA was used to screen the characteristic variables of Savitzky-Golay (S-G) smoothing spectral data and MLR was used to establish the model. And the corresponding correlation coefficient and root mean square error of calibration set were 0.9976 and 1.0989%, respectively; the correlation coefficient and root mean square error of prediction set were 0.9963 and 1.4029%, respectively, and RPD was 11.28. The results of this study provided a feasible method for the rapid detection of moisture content of coco-peat substrate.In this paper, we exploit an innovative strategy to reuse waste rubber tires as a low-cost source for the fabrication of a high-value material, fluorescent carbon dots (CDs). In the hydrothermal condition, ammonium persulphate is utilized to oxidize the tires and offer nitrogen atom for doping, to produce CDs with a high quantum yield (QY) of up to 23.8%. Such a QY is outstanding among the reported waste-derived CDs. It is found that the fluorescence of CDs can be remarkably quenched by Sudan I-IV with negligible interference from other substances. selleck chemicals llc The corresponding linear ranges are 0.5-60, 0.5-60, 1-70, and 1-70 μM, and limits of detection are 0.17, 0.21, 0.53, and 0.62 μM for Sudan I, II, III, and IV, respectively. Systematic investigations reveal that the fluorescence quenching mainly stems from the inner filter effect. Moreover, the CD-based sensor shows an excellent applicability for the assay of Sudan dyes in chili powder sample.The development of functional foods based on medicinal food ingredients has become a hot topic in China. Di Wu Yang Gan (DWYG) is a Chinese medicinal food that contains five dietary plants. Various health benefits, including anti-inflammation, liver regeneration regulation, have been reported, though the mechanism is not clear. This study aimed to investigate the protective effect of DWYG on carbon tetrachloride-induced acute liver injury (ALI) in embryonic liver L-02 cells and mice model. DWYG-medicated serum protected L-02 cells from carbon tetrachloride-induced damage, reduced the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in the culture medium, decreased the expression of Bax and increased the expression of Bcl-2. Mice study suggested that DWYG decreased the levels of malondialdehyde, ALT and AST. Together, these results suggest the hepatoprotective effects of DWYG against ALI and provide an experimental basis for the utilization of DWYG to treat liver damage.In this study, the chemical characterization and bioactive properties of S. minor cultivated under different fertilization rates (control, half rate and full rate) were evaluated. Twenty-two phenolic compounds were identified, including five phenolic acids, seven flavonoids and ten tannins. Hydrolysable tannins were prevalent, namely Sanguiin H-10, especially in leaves without fertilization (control). Roots of full-rate fertilizer (660 Kg/ha) presented the highest flavonoid content, mainly catechin and its isomers, whereas half-rate fertilizer (330 Kg/ha), presented the highest content of total phenolic compounds, due to the higher amount of ellagitannins (lambertianin C 84 ± 1 mg/g of dry extract). Antimicrobial activities were also promising, especially against Salmonella typhimurium (MBC = 0.44 mg/mL). Moreover, root samples revealed activity against all tested cell lines regardless of fertilization rate, whereas leaves were effective only against HeLa cell line. In conclusion, S. minor could be a source of natural bioactive compounds, while fertilization could increase phenolic compounds content.Continual learning is the ability of a learning system to solve new tasks by utilizing previously acquired knowledge from learning and performing prior tasks without having significant adverse effects on the acquired prior knowledge. Continual learning is key to advancing machine learning and artificial intelligence. Progressive learning is a deep learning framework for continual learning that comprises three procedures curriculum, progression, and pruning. The curriculum procedure is used to actively select a task to learn from a set of candidate tasks. The progression procedure is used to grow the capacity of the model by adding new parameters that leverage parameters learned in prior tasks, while learning from data available for the new task at hand, without being susceptible to catastrophic forgetting. The pruning procedure is used to counteract the growth in the number of parameters as further tasks are learned, as well as to mitigate negative forward transfer, in which prior knowledge unrelated to the task at hand may interfere and worsen performance. Progressive learning is evaluated on a number of supervised classification tasks in the image recognition and speech recognition domains to demonstrate its advantages compared with baseline methods. It is shown that, when tasks are related, progressive learning leads to faster learning that converges to better generalization performance using a smaller number of dedicated parameters.Detecting the locations of multiple actions in videos and classifying them in real-time are challenging problems termed “action localization and prediction” problem. Convolutional neural networks (ConvNets) have achieved great success for action localization and prediction in still images. A major advance occurred when the AlexNet architecture was introduced in the ImageNet competition. ConvNets have since achieved state-of-the-art performances across a wide variety of machine vision tasks, including object detection, image segmentation, image classification, facial recognition, human pose estimation, and tracking. However, few works exist that address action localization and prediction in videos. The current action localization research primarily focuses on the classification of temporally trimmed videos in which only one action occurs per frame. Moreover, nearly all the current approaches work only offline and are too slow to be useful in real-world environments. In this work, we propose a fast and accurate deep-learning approach to perform real-time action localization and prediction.
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