165 research outputs found

    Customers' intention towards O2O food delivery service under the different characteristic of customer group – a case study of Suzhou Industrial Park

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    Two research questions are identified and discussed. Relevant factors that influence the customer preferences were selected as research objects, data collection was based on 348 valid questionnaires, SPSS software was used for data analysis by the means of the multivariate logistic research model. Customer intentions include delivery payment, delivery time, food quality and brand trust. Customer reviews are related to customer preferences. The delivery payment is the most important factor when customers use food delivery service, but different groups of people have different tendency compared with their counterparts. All variables are designed based on baseline categories; the outcome of the model is only significant while comparing two groups of variables. Multivariate logistic research model is used to find customer preferences under the different characteristic of customer groups based on questionnaires and tries to forecast the possibility of the tendency of one targeting customer group in Suzhou Industrial Park. This research conduct a questionnaire on the Suzhou industry park, the respondents are mainly students and white collars customers, the characteristics of respondents are typical in this area

    Single-ended Recovery of Optical fiber Transmission Matrices using Neural Networks

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    Ultra-thin multimode optical fiber imaging technology promises next-generation medical endoscopes that provide high image resolution deep in the body (e.g. blood vessels, brain). However, this technology suffers from severe optical distortion. The fiber's transmission matrix (TM) calibrates for this distortion but is sensitive to bending and temperature so must be measured immediately prior to imaging, i.e. \emph{in vivo} and thus with access to a single end only. We present a neural network (NN)-based approach that quickly reconstructs transmission matrices based on multi-wavelength reflection-mode measurements. We introduce a custom loss function insensitive to global phase-degeneracy that enables effective NN training. We then train two different NN architectures, a fully connected NN and convolutional U-Net, to reconstruct 64×6464\times64 complex-valued fiber TMs through a simulated single-ended optical fiber with ≤4%\leq 4\% error. This enables image reconstruction with ≤8%\leq 8\% error. This TM recovery approach shows advantages compared to conventional TM recovery methods: 4500 times faster; robustness to 6\% fiber perturbation during characterization; operation with non-square TMs and no requirement for prior characterization of reflectors.Comment: 13 pages, 9 figure

    Global phase insensitive loss function for deep learning in holographic imaging and projection applications

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    Holographic imaging and projection are increasingly used for important applications such as augmented reality, 1 3D microscopy 2 and imaging through optical fibres. 3 However, there are emerging applications that require control or detection of phase, where deep learning techniques are used as faster alternatives to conventional hologram generation algorithms or phase-retrieval algorithms. 4 Although conventional mean absolute error (MAE) loss function or mean squared error (MSE) can directly compare complex values for absolute control of phase, there is a class of problems whose solutions are degenerate within a global phase factor, but whose relative phase between pixels must be preserved. In such cases, MAE is not suitable because it is sensitive to global phase differences. We therefore develop a ‘global phase insensitive’ loss function that estimates the global phase factor between predicted and target outputs and normalises the predicted output to remove this factor before calculating MAE. As a case study we demonstrate ≤ 0.1% error in the recovery of complex-valued optical fibre transmission matrices via a neural network. This global phase insensitive loss function will offer new opportunities for deep learning-based holographic image reconstruction, 3D holographic projection for augmented reality and coherent imaging through optical fibres

    CCN1 Secretion Induced by Cigarette Smoking Extracts Augments IL-8 Release from Bronchial Epithelial Cells

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    Inflammation involves in many cigarette smoke (CS) related diseases including the chronic obstructive pulmonary disease (COPD). Lung epithelial cell released IL-8 plays a crucial role in CS induced lung inflammation. CS and cigarette smoke extracts (CSE) both induce IL-8 secretion and subsequently, IL-8 recruits inflammatory cells into the lung parenchyma. However, the molecular and cellular mechanisms by which CSE triggers IL-8 release remain not completely understood. In this study, we identified a novel extracellular matrix (ECM) molecule, CCN1, which mediated CSE induced IL-8 secretion by lung epithelial cells. We first found that CS and CSE up-regulated CCN1 expression and secretion in lung epithelial cells in vivo and in vitro. CSE up-regulated CCN1 via induction of reactive oxygen spices (ROS) and endoplasmic reticulum (ER) stress. p38 MAPK and JNK activation were also found to mediate the signal pathways in CSE induced CCN1. CCN1 was secreted into ECM via Golgi and membrane channel receptor aquaporin4. After CSE exposure, elevated ECM CCN1 functioned via an autocrine or paracrine manner. Importantly, CCN1 activated Wnt pathway receptor LRP6, subsequently stimulated Wnt pathway component Dvl2 and triggered beta-catenin translocation from cell membrane to cytosol and nucleus. Treatment of Wnt pathway inhibitor suppressed CCN1 induced IL-8 secretion from lung epithelial cells. Taken together, CSE increased CCN1 expression and secretion in lung epithelial cells via induction of ROS and ER stress. Increased ECM CCN1 resulted in augmented IL-8 release through the activation of Wnt pathway

    Proposers’ Economic Status Affects Behavioral and Neural Responses to Unfairness

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    Economic status played an important role in the modulation of economic decision making. The present fMRI study aimed at investigating how economic status modulated behavioral and neural responses to unfairness in a modified Ultimatum Game (UG). During scanning, participants played as responders in the UG, and they were informed of the economic status of proposers before receiving offers. At the behavioral level, higher rejection rates and lower fairness ratings were revealed when proposers were in high economic status than in low economic status. Besides, the most time-consuming decisions tended to occur at lower unfairness level when the proposers were in high (relative to low) economic status. At the neural level, stronger activation of left thalamus was revealed when fair offers were proposed by proposers in high rather than in low economic status. Greater activation of right medial prefrontal cortex was revealed during acceptance to unfair offers in high economic status condition rather than in low economic status condition. Taken together, these findings shed light on the significance of proposers’ economic status in responders’ social decision making in UG

    The coordinated roles of miR-26a and miR-30c in regulating TGFβ1-induced epithelial-to-mesenchymal transition in diabetic nephropathy

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    MicroRNAs (miRNAs) play vital roles in the development of diabetic nephropathy. Here, we compared the protective efficacies of miR-26a and miR-30c in renal tubular epithelial cells (NRK-52E) and determined whether they demonstrated additive effects in the attenuation of renal fibrosis. TGFβ1 suppressed miR-26a and miR-30c expression but up-regulated pro-fibrotic markers in NRK-52E cells, and these changes were also found in the kidney cortex of 40-week-old diabetic Otsuka Long-Evans Tokushima fatty (OLETF) rats. Bioinformatic analyses and luciferase assays further demonstrated that both miR-26a and miR-30c targeted connective tissue growth factor (CTGF); additionally, Snail family zinc finger 1 (Snail1), a potent epithelial-to-mesenchymal transition (EMT) inducer, was targeted by miR-30c. Overexpression of miR-26a and miR-30c coordinately decreased CTGF protein levels and subsequently ameliorated TGFβ1-induced EMT in NRK-52E cells. Co-silencing of miR-26a and miR-30c exhibited the opposite effect. Moreover, miR-26a and miR-30c co-silenced CTGF to decrease ERK1/2 and p38 MAPK activation. Furthermore, miR-26a was up-regulated in urinary extracellular vesicles of diabetic nephropathy patients. Our study provides evidence for the cooperative roles of miR-26a and miR-30c in the pathogenesis of diabetic nephropathy, and the co-targeting of miR-26a and miR-30c could provide a new direction for diabetic nephropathy treatment
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