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

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

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 $64\times64$ complex-valued fiber TMs through a simulated single-ended optical fiber with $\leq 4\%$ error. This enables image reconstruction with $\leq 8\%$ 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

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

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

Flotillin-2 Modulates Fas Signaling Mediated Apoptosis after Hyperoxia in Lung Epithelial Cells

Lipid rafts are subdomains of the cell membrane with distinct protein composition and high concentrations of cholesterol and glycosphingolipids. Raft proteins are thought to mediate diverse cellular processes including signal transduction. However, its cellular mechanisms remain unclear. Caveolin-1 (cav-1, marker protein of caveolae) has been thought as a switchboard between extracellular matrix (ECM) stimuli and intracellular signals. Flotillin-2/reggie-1(Flot-2) is another ubiquitously expressed raft protein which defines non-caveolar raft microdomains (planar raft). Its cellular function is largely uncharacterized. Our novel studies demonstrated that Flot-2, in conjunction with cav-1, played important functions on controlling cell death via regulating Fas pathways. Using Beas2B epithelial cells, we found that in contrast to cav-1, Flot-2 conferred cytoprotection via preventing Fas mediated death-inducing signaling complex (DISC) formation, subsequently suppressed caspase-8 mediated extrinsic apoptosis. Moreover, Flot-2 reduced the mitochondria mediated intrinsic apoptosis by regulating the Bcl-2 family and suppressing cytochrome C release from mitochondria to cytosol. Flot-2 further modulated the common apoptosis pathway and inhibited caspase-3 activation via up-regulating the members in the inhibitor of apoptosis (IAP) family. Last, Flot-2 interacted with cav-1 and limited its expression. Taken together, we found that Flot-2 protected cells from Fas induced apoptosis and counterbalanced the pro-apoptotic effects of cav-1. Thus, Flot-2 played crucial functions in cellular homeostasis and cell survival, suggesting a differential role of individual raft proteins

Interpretable cancer cell detection with phonon microscopy using multi-task conditional neural networks for inter-batch calibration

Advances in artificial intelligence (AI) show great potential in revealing underlying information from phonon microscopy (high-frequency ultrasound) data to identify cancerous cells. However, this technology suffers from the 'batch effect' that comes from unavoidable technical variations between each experiment, creating confounding variables that the AI model may inadvertently learn. We therefore present a multi-task conditional neural network framework to simultaneously achieve inter-batch calibration, by removing confounding variables, and accurate cell classification of time-resolved phonon-derived signals. We validate our approach by training and validating on different experimental batches, achieving a balanced precision of 89.22% and an average cross-validated precision of 89.07% for classifying background, healthy and cancerous regions. Classification can be performed in 0.5 seconds with only simple prior batch information required for multiple batch corrections. Further, we extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state including sound velocity, sound attenuation and cell-adhesion to substrate

Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode

We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation. This DRL approach has the potential to lead to a large management model based on certain transformer neural network structures, resulting in an artificial general intelligence paradigm for various management tasks. Traditional methods have limitations for solving complex real-world problems, and we demonstrate how DRL can surpass existing heuristic approaches for solving management tasks. We aim to solve the problems in a unified framework, considering the interconnections between different tasks. Central to our methodology is the development of a foundational decision model coordinating decisions across the different domains through generative decision-making. Our experimental results affirm the effectiveness of our DRL-based framework in complex and dynamic business environments. This work opens new pathways for the application of DRL in management problems, highlighting its potential to revolutionize traditional business management