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The presented research introduces a novel hybrid deep learning approach for the dynamic prediction of the attitude and position of super-large diameter shields - a critical consideration for construction safety and tunnel lining quality. This study proposes a hybrid deep learning approach for predicting dynamic attitude and position prediction of super-large diameter shield. The approach consists of principal component analysis (PCA) and temporal convolutional network (TCN). The former is used for employing feature level fusion based on features of the shield data to reduce uncertainty, improve accuracy and the data effect, and 9 sets of required principal component characteristic data are obtained. The latter is adopted to process sequence data in predicting the dynamic attitude and position for the advantages and potential of convolution network. The approach's effectiveness is exemplified using data from a tunnel construction project in China. The obtained results show remarkable accuracy in predicting the global attitude and position, with an average error ratio of less than 2 mm on four shield outputs in 97.30% of cases. Moreover, the approach displays strong performance in accurately predicting sudden fluctuations in shield attitude and position, with an average prediction accuracy of 89.68%. The proposed hybrid model demonstrates superiority over TCN, long short-term memory (LSTM), and recurrent neural network (RNN) in multiple indexes. Shapley additive exPlanations (SHAP) analysis is also performed to investigate the significance of different data features in the prediction process. This study provides a real-time warning for the shield driver to adjust the attitude and position of super-large diameter shields. [ABSTRACT FROM AUTHOR]

Rhododendron, one of the most famous ornamental plants in the world, is traditionally a medicinal plant. However, the potential bioactivities of native Rhododendron in Taiwan have not been completely studied. In this study, the results revealed that Rhododendron pseudochrysanthum exhibited the best antioxidant activities among 10 native Rhododendron species in Taiwan. Furthermore, based on a bioactivity-guided isolation principle, nine specific phytochemicals were isolated and identified as (2R,3S)-catechin (1), (2R,3R)-epicatechin (1'), (2R,3R)-dihydromyricetin 3-O-β-L-arabinopyranoside (2), (2S,3S)-taxifolin 3-O-β-L-arabinopyranoside (2'), (2R,3R)-taxifolin 3-O-β-L-arabinopyranoside (3), myricetin 3-O-β-d-glucopyranoside (3'), rutin (4), hyperoside (5), and quercitrin (6). Of these compounds, 2 and 3 were found to be major bioactive compounds, and their concentrations in the n-butanol (BuOH) fraction were determined to be 52.0 and 67.3 mg per gram, respectively. These results demonstrated that methanolic extracts of Rhododendron pseudochrysanthum leaves have excellent antioxidant activities and great potential as a source for natural health products. [ABSTRACT FROM AUTHOR]

We use a great deal of wireless sensor nodes to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and then it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rule (C-CR) have the characteristics on high energy consumption and low detection precision. Consequently, this paper proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor network. It first introduces the clustering method based on tree structure to establish the precursor-successor relationships among the clusters in the region of interests and then fuses the decision data along the direction from the precursor clusters to the successor clusters gradually, and DFC (i.e., tree root) makes final determination by overall judgment values from subclusters and ordinary nodes. Simulation experiments show that the fusion rule can obtain more satisfactory system level performance at the environment of low signal to noise compared with CR and C-CR methods. [ABSTRACT FROM AUTHOR]

Substantial evidence implicates 𝛽-amyloid (A𝛽) peptides in the etiology of Alzheimer’s disease (AD). A𝛽 is produced by the proteolytic cleavage of the amyloid precursor protein by 𝛽- and 𝛾-secretase suggesting that 𝛾-secretase inhibition may provide therapeutic benefit for AD. Although many 𝛾-secretase inhibitors have been shown to be potent at lowering A𝛽, some have also been shown to have side effects following repeated administration. All of these side effects can be attributed to altered Notch signaling, another 𝛾-secretase substrate. Here we describe the in vivo characterization of the novel 𝛾-secretase inhibitor SCH 697466 in rodents. Although SCH 697466 was effective at lowering A𝛽, Notch-related side effects in the intestine and thymus were observed following subchronic administration at doses that provided sustained and complete lowering of A𝛽. However, additional studies revealed that both partial but sustained lowering of A𝛽 and complete but less sustained lowering of A𝛽 were successful approaches for managing Notch-related side effects. Further, changes in several Notch-related biomarkers paralleled the side effect observations. Taken together, these studies demonstrated that, by carefully varying the extent and duration of A𝛽 lowering by 𝛾-secretase inhibitors, it is possible to obtain robust and sustained lowering of A𝛽 without evidence of Notch-related side effects. [ABSTRACT FROM AUTHOR]