The Effect Prediction of Acquiring New Customers Based on Gongtianxia\u27s Dutch Auction

With the development of the Mobile Internet, many E-commerce sites are using mobile applications to promote marketing and to acquire new customers, mobile marketing activities has become one of the best ways to expand market share. Therefore, it’s very concerned to study how to acquire new customers effectively in the early stage of entering the market. Gongtianxia’s WeChat public platform is committed to attract new customers through Mobile Internet. Gongtianxia adopted two kinds of Dutch auctions, ‘7-day auction’ and ‘15-minute auction’ respectively, which can effectively acquire new customers. This study collected more than 80000 of records, 738 pieces of auction data from June 2015 to December 2015 in Gongtianxia’s Dutch auctions, by collecting, sorting and analyzing the auction data, and established a BPNN simulation and prediction model. The prediction model for each auction data can be used to predict the customer number, cost and blowout price in advance of the auction. This study can improve customer-attracting effect of mobile application and make a theoretical complement for Dutch auction as Mobile Internet sale, and enriches the research for acquiring new customers through Mobile Internet

Storage Fit Learning with Feature Evolvable Streams

Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no label will be given at most time steps. A good solution is to leverage the technique of manifold regularization to utilize the previous similar data to assist the refinement of the online model. Nevertheless, this approach needs to store all previous data which is impossible in learning with streams that arrive sequentially in large volume. Thus we need a buffer to store part of them. Considering that different devices may have different storage budgets, the learning approaches should be flexible subject to the storage budget limit. In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF$^2$EL) which incorporates the issue of rarely-provided labels into feature evolution. Our framework is able to fit its behavior to different storage budgets when learning with feature evolvable streams with unlabeled data. Besides, both theoretical and empirical results validate that our approach can preserve the merit of the original feature evolvable learning i.e., can always track the best baseline and thus perform well at any time step

Synchronization of fractional chaotic complex networks with delays

summary:The synchronization of fractional-order complex networks with delay is investigated in this paper. By constructing a novel Lyapunov-Krasovskii function $V$ and taking integer derivative instead of fractional derivative of the function, a sufficient criterion is obtained in the form of linear matrix inequalities to realize synchronizing complex dynamical networks. Finally, a numerical example is shown to illustrate the feasibility and effectiveness of the proposed method

A role of corazonin receptor in larval-pupal transition and pupariation in the oriental fruit fly Bactrocera dorsalis (Hendel) (Diptera: Tephritidae)

Corazonin (Crz) is a neuropeptide hormone, but also a neuropeptide modulator that is internally released within the CNS, and it has a widespread distribution in insects with diverse physiological functions. Here, we identified and cloned the cDNAs of Bactrocera dorsalis that encode Crz and its receptor CrzR. Mature BdCrz has 11 residues with a unique Ser11 substitution (instead of the typical Asn) and a His in the evolutionary variable position 7. The BdCrzR cDNA encodes a putative protein of 608 amino acids with 7 putative transmembrane domains, typical for the structure of G-protein-coupled receptors. When expressed in Chinese hamster ovary (CHO) cells, the BdCrzR exhibited a high sensitivity and selectivity for Crz (EC50 approximate to 52.5 nM). With qPCR, the developmental stage and tissue-specific expression profiles in B. dorsalis demonstrated that both BdCrz and BdCrzR were highly expressed in the larval stage, and BdCrzR peaked in 2-day-old 3rd-instar larvae, suggesting that the BdCrzR may play an important role in the larval-pupal transition behavior. Immunochemical localization confirmed the production of Crz in the central nervous system (CNS), specifically by a group of three neurons in the dorso-lateral protocerebrum and eight pairs of lateral neurons in the ventral nerve cord. qPCR analysis located the BdCrzR in both the CNS and epitracheal gland, containing the Inka cells. Importantly, dsRNA-BdCrzR-mediated gene-silencing caused a delay in larval-pupal transition and pupariation, and this phenomenon agreed with a delayed expression of tyrosine hydroxylase and dopa-decarboxylase genes. We speculate that CrzR-silencing blocked dopamine synthesis, resulting in the inhibition of pupariation and cuticular melanization. Finally, injection of Crz in head-ligated larvae could rescue the effects. These findings provide a new insight into the roles of Crz signaling pathway components in B. dorsalis and support an important role of CrzR in larval-pupal transition and pupariation behavior

Current Understanding of Gut Microbiota in Mood Disorders: An Update of Human Studies

Gut microbiota plays an important role in the bidirectional communication between the gut and the central nervous system. Mounting evidence suggests that gut microbiota can influence the brain function via neuroimmune and neuroendocrine pathways as well as the nervous system. Advances in gene sequencing techniques further facilitate investigating the underlying relationship between gut microbiota and psychiatric disorders. In recent years, researchers have preliminarily explored the gut microbiota in patients with mood disorders. The current review aims to summarize the published human studies of gut microbiota in mood disorders. The findings showed that microbial diversity and taxonomic compositions were significantly changed compared with healthy individuals. Most of these findings revealed that short-chain fatty acids-producing bacterial genera were decreased, while pro-inflammatory genera and those involved in lipid metabolism were increased in patients with depressive episodes. Interestingly, the abundance of Actinobacteria, Enterobacteriaceae was increased and Faecalibacterium was decreased consistently in patients with either bipolar disorder or major depressive disorder. Some studies further indicated that specific bacteria were associated with clinical characteristics, inflammatory profiles, metabolic markers, and pharmacological treatment. These studies present preliminary evidence of the important role of gut microbiota in mood disorders, through the brain-gut-microbiota axis, which emerges as a promising target for disease diagnosis and therapeutic interventions in the future