17 research outputs found

    The Key Algorithm of the Sterilization Effectiveness of Pulsating Vacuum Sterilizer

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    Part 1: Digital ServicesInternational audienceThe relationship between the physical parameters of pulsating vacuum sterilizer and the piping system states is analyzed based on cluster analysis and interpolation approximation algorithm. The cluster analysis is performed first, followed by the interpolation approximation of the cluster data. The algorithm provides a comprehensive sterilization efficacy evaluation, as well a feasible method to analyze the states of the system

    An Improved Weighting Method of Time-Lag-Ensemble Averaging for Hourly Precipitation Forecasts and Its Application in a Typhoon-Induced Heavy Rainfall Event

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    Heavy rainfall events often cause great societal and economic impacts. The prediction ability of traditional extrapolation techniques decreases rapidly with the increase in the lead time. Moreover, deficiencies of high-resolution numerical models and high-frequency data assimilation will increase the prediction uncertainty. To address these shortcomings, based on the hourly precipitation prediction of Global/Regional Assimilation and Prediction System-Cycle of Hourly Assimilation and Forecast (GRAPES-CHAF) and Shanghai Meteorological Service-WRF ADAS Rapid Refresh System (SMS-WARR), we present an improved weighting method of time-lag-ensemble averaging for hourly precipitation forecast which gives more weight to heavy rainfall and can quickly select the optimal ensemble members for forecasting. In addition, by using the cross-magnitude weight (CMW) method, mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (CC), the verification results of hourly precipitation forecast for next six hours in Hunan Province during the 2019 typhoon Bailu case and heavy rainfall events from April to September in 2020 show that the revised forecast method can more accurately capture the characteristics of the hourly short-range precipitation forecast and improve the forecast accuracy and the probability of detection of heavy rainfall

    Methionine Augments Antioxidant Activity of Rice Protein during Gastrointestinal Digestion

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    To elucidate the influence of methionine, which is an essential sulfur-containing amino acid, on the antioxidant activity of rice protein (RP), methionine was added to RP (RM). The addition of methionine to RM0.5, RM1.0, RM1.5, RM2.0, and RM2.5 was 0.5-, 1.0-, 1.5-, 2.0-, and 2.5-fold of methionine of RP, respectively. Using the in vitro digestive system, the antioxidant capacities of scavenging free radicals (superoxide; nitric oxide; 2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt, ABTS), chelating metal (iron), and reducing power were investigated in the hydrolysates of RP and RMs. Upon pepsin-pancreatin digestion, the weakest antioxidant capacity was produced by RP. With the addition of methionine, RMs exhibited more excellent responses to free radical scavenging activities and reducing power than RP, whereas RMs did not produce the marked enhancements in iron chelating activity as compared to RP. The present study demonstrated that RMs differently exerted the free radical scavenging activities that emerged in the protein hydrolysates, in which the strongest scavenging capacities for ABTS, superoxide, and nitric oxide were RM1.5, RM2.0, and RM2.5, respectively. Results suggested that the availability of methionine is a critical factor to augment antioxidant ability of RP in the in vitro gastrointestinal tract

    Rice Protein Exerts Anti-Inflammatory Effect in Growing and Adult Rats via Suppressing NF-κB Pathway

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    To elucidate the effect of rice protein (RP) on the depression of inflammation, growing and adult rats were fed with caseins and RP for 2 weeks. Compared with casein, RP reduced hepatic accumulations of reactive oxygen species (ROS) and nitro oxide (NO), and plasma activities of alanine transaminase (ALT) and aspartate transaminase (AST) in growing and adult rats. Intake of RP led to increased mRNA levels, and protein expressions of phosphoinositide 3 kinase (PI3K), protein kinase B (Akt), nuclear factor-κB 1 (NF-αB1), reticuloendotheliosis viral oncogene homolog A (RelA), tumor necrotic factor α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and monocyte chemoattractant protein-1 (MCP-1) were decreased, whereas hepatic expressions of interleukin-10 (IL-10) and heme oxygenase 1 (HO-1) were increased by RP. The activation of NF-κB was suppressed by RP through upregulation of inhibitory κB α (IκBα), resulting in decreased translocation of nuclear factor-κB 1 (p50) and RelA (p65) to the nucleus in RP groups. The present study demonstrates that RP exerts an anti-inflammatory effect to inhibit ROS-derived inflammation through suppression of the NF-κB pathway in growing and adult rats. Results suggest that the anti-inflammatory capacity of RP is independent of age

    Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

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    The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system

    Impact of Low-Temperature Storage on the Microstructure, Digestibility, and Absorption Capacity of Cooked Rice

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    This study examined the effects of low-temperature storage on the microstructural, absorptive, and digestive properties of cooked rice. Cooked rice was refrigerated and stored at 4 °C for 0.5, 1, 3, 5, and 7 days, as well as frozen and preserved at −20, −40, and −80 °C for 0.5, 1, 3, 5, 7, 14, 21, and 28 days. The results indicated that the stored rice samples generally exhibited a higher absorption capacity for oil, cholesterol, and glucose than the freshly cooked rice. In addition, after storage, the digestibility of the cooked rice declined, namely, the rapidly digestible starch (RDS) content and estimated glycemic index (eGI) decreased, whereas the slowly digestible starch (SDS) and resistant starch (RS) content increased. Moreover, the increment of the storage temperatures or the extension of storage periods led to a lower amylolysis efficiency. Scanning electron microscopy (SEM) analysis indicated that storage temperature and duration could effectively modify the micromorphology of the stored rice samples and their digestion. Moreover, microstructural differences after storage and during simulated intestinal digestion could be correlated to the variations in the absorption capacity and digestibility. The findings from this study will be useful in providing alternative storage procedures to prepare rice products with improved nutritional qualities and functional properties

    Exploring best-matched embedding model and classifier for charging-pile fault diagnosis

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    Abstract The continuous increase of electric vehicles is being facilitating the large-scale distributed charging-pile deployment. It is crucial to guarantee normal operation of charging piles, resulting in the importance of diagnosing charging-pile faults. The existing fault-diagnosis approaches were based on physical fault data like mechanical log data and sensor data streams. However, there are other types of fault data, which cannot be used for diagnosis by these existing approaches. This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault, charging-pile program fault, user personal fault, signal fault (offline), pile compatibility fault, charging platform fault, and other faults. We aim to find out how to combine existing feature-extraction and machine learning techniques to make the better diagnosis by conducting experiments on realistic dataset. 4 word embedding models are investigated for feature extraction of fault data, including N-gram, GloVe, Word2vec, and BERT. Moreover, we classify the word embedding results using 10 machine learning classifiers, including Random Forest (RF), Support Vector Machine, K-Nearest Neighbor, Multilayer Perceptron, Recurrent Neural Network, AdaBoost, Gradient Boosted Decision Tree, Decision Tree, Extra Tree, and VOTE. Compared with original fault record dataset, we utilize paraphrasing-based data augmentation method to improve the classification accuracy up to 10.40%. Our extensive experiment results reveal that RF classifier combining the GloVe embedding model achieves the best accuracy with acceptable training time. In addition, we discuss the interpretability of RF and GloVe

    Multi-omics analysis of a drug-induced model of bipolar disorder in zebrafish

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    Summary: Emerging studies demonstrate that inflammation plays a crucial role in the pathogenesis of bipolar disorder (BD), but the underlying mechanism remains largely unclear. Given the complexity of BD pathogenesis, we performed high-throughput multi-omic profiling (metabolomics, lipidomics, and transcriptomics) of the BD zebrafish brain to comprehensively unravel the molecular mechanism. Our research proved that in BD zebrafish, JNK-mediated neuroinflammation altered metabolic pathways involved in neurotransmission. On one hand, disturbed metabolism of tryptophan and tyrosine limited the participation of the monoamine neurotransmitters serotonin and dopamine in synaptic vesicle recycling. On the other hand, dysregulated metabolism of the membrane lipids sphingomyelin and glycerophospholipids altered the synaptic membrane structure and neurotransmitter receptors (chrnα7, htr1b, drd5b, and gabra1) activity. Our findings revealed that disturbance of serotonergic and dopaminergic synaptic transmission mediated by the JNK inflammatory cascade was the key pathogenic mechanism in a zebrafish model of BD, provides critical biological insights into the pathogenesis of BD
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