Zhang Peng

Inventory prediction and management is a key issue in a retail store. There are a number of inventory prediction techniques. However, it is difficult to identify a time series prediction model for inventory forecasting that provides uniformly good results for all the products in a store. This paper uses data from a small retail store to demonstrate the variability of results for different modeling techniques and different products. We demonstrate inadequacy of a generic inventory model. Stability and seasonality analysis of the time series is used to identify groups of products exhibiting similar sales patterns. Different clustering techniques are applied to determine reasonable local groups. With the help of Mean absolute percentage error, the effectiveness of dataset partitioning for better inventory management is demonstrated. Appropriate inventory management strategies are proposed based on the stability and seasonality analysis.

The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron, recurrent neural network, and autoregressive integrated moving average on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA-MLP and ARIMA-RNN. Because of the complicated and noisy nature of financial data, we combine novel machine-learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA-MLP and ARIMA-RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root-mean-square error and mean absolute error. Apart from this, ARIMA-MLP outperformed the ARIMA-RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA-RNN beat the ARIMA-MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time-series forecasting.

Cognitive and neural processes underlying visual creativity have attracted substantial attention. The current research uses a critical time point analysis to examine how spontaneous activity in the primary visual area is related to visual creativity. We acquired the functional magnetic resonance imaging data of 16 participants at the resting state and during performing a visual creative synthesis task. According to the CTPA, we then classified spontaneous activity in the PVA into critical time points, which reflect the most useful and important functional meaning of the entire resting-state condition, and the remaining time points. We constructed functional brain networks based on the brain activity at two different time points and then subsequently based on the brain activity at the task state in a separate manner. We explore the relationship between resting-state and task-fMRI functional brain networks. Our results found that: the pattern of spontaneous activity in the PVA may associate with mental imagery, which plays an important role in visual creativity; in comparison with the RTPs-based brain network, the CTP-network showed an increase in global efficiency and a decrease in local efficiency; the regional integrated properties of the CTP-network could predict the integrated properties of the creative-network while the RTP-network could not. Thus, our findings indicated that spontaneous activity in the PVA at CTPs was associated with a visual creative task-evoked brain response. Our findings may provide an insight into how the visual cortex is related to visual creativity.

Objective: Pulmonary complications could badly affect the recovery of neurological function and neurological prognosis of neurological critically ill patients. This study evaluated the effect of high-flow nasal cannula therapy on decreasing pulmonary complications in neurologically critically ill patients.Patients and Methods: The patients admitted to the intensive care unit with serious neurological disease and receiving oxygen therapy were retrospectively reviewed. Patients were divided into the HFNC group and the conventional oxygen therapy group. We analyzed the data within these two groups, including patients’ baseline data, short-term outcomes of respiratory complications, general outcomes including hospital stay, ICU stay and mortality, and neurological functions. To analyze the relevant factors, we performed multivariable logistic regression analysis.Results: A total of 283 patients met the criteria, including 164 cases in the HFNC group and 119 cases in the COT group. The HFNC group had remarkably less mechanical ventilation requirement with lower phlegm viscosity. Even more, ICU stay and total hospital stay were significantly shortened in the HNFC group.Conclusion: HFNC decreased pulmonary complications in neurologically critically ill patients and improved recovery of neurological function and neurological prognosis.

© 2016 by the American Association for the Study of Liver Diseases.Integrin αvβ6 is rapidly up-regulated on cells of epithelial lineage during tissue injury, where one of its primary functions is activation of latent transforming growth factor beta 1. In human liver cirrhosis, αvβ6 is overexpressed by cells comprising the ductular reaction, and its inhibition suppresses experimental biliary fibrosis in rodents. Here, we show that αvβ6 is expressed on the actively proliferating subset of hepatic progenitor cells and is required for their progenitor function in vivo and in vitro through integrin αvβ6-dependent TGFβ1 activation. Freshly isolated αvβ6+ liver cells demonstrate clonogenic potential and differentiate into cholangiocytes and functional hepatocytes in vitro, whereas colony formation by epithelial cell adhesion molecule-positive progenitor cells is blocked by αvβ6-neutralizing antibody and in integrin beta 6-deficient cells. Inhibition of progenitors by anti-αvβ6 antibody is recapitulated by TGFβ1 neutralization and rescued by addition of bioactive TGFβ1. Genetic disruption or selective targeting of αvβ6 with 3G9 antibody potently inhibits progenitor cell responses in mouse models of chronic biliary injury and protects from liver fibrosis and tumorigenesis, two conditions clinically associated with exacerbated ductular reaction. Conclusion: These results suggest that αvβ6 is a promising target for chronic fibrotic liver diseases and associated cancers.

Land is the process of economic and social development of China's important strategic resources, land resources, the optimal allocation is to achieve economical use of land resources and a prerequisite for sustainable development. To achieve optimal allocation of land resources in the process, in terms of national policy-making point of view, or from the local government and the interests of farmers perspective, there is a series of dilemmas. We must follow the laws of science, by adjusting the distribution of benefits related to land issues to resolve these dilemmas, in order to achieve rational allocation of resources and land use. Land is one of the most important strategic resources during the social and economic developing process of China, and it is the prior condition of land saving and sustainable development to allocate rationally land resource though there are many factors which effect the realization of the land resource allocation . This paper aims to analyze these problems from policies' making, local government's interests and farmer's interests, and tries to find out the solution