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

GRASS: Unified Generation Model for Speech Semantic Understanding

This paper explores the instruction fine-tuning technique for speech semantic understanding by introducing a unified end-to-end (E2E) framework that generates semantic labels conditioned on a task-related prompt for audio data. We pre-train the model using large and diverse data, where instruction-speech pairs are constructed via a text-to-speech (TTS) system. Extensive experiments demonstrate that our proposed model significantly outperforms state-of-the-art (SOTA) models after fine-tuning downstream tasks. Furthermore, the proposed model achieves competitive performance in zero-shot and few-shot scenarios. To facilitate future work on instruction fine-tuning for speech-to-semantic tasks, we release our instruction dataset and code

Intelligent Natural Gas and Hydrogen Pipeline Dispatching Using the Coupled Thermodynamics-Informed Neural Network and Compressor Boolean Neural Network

Natural gas pipelines have attracted increasing attention in the energy industry thanks to the current demand for green energy and the advantages of pipeline transportation. A novel deep learning method is proposed in this paper, using a coupled network structure incorporating the thermodynamics-informed neural network and the compressor Boolean neural network, to incorporate both functions of pipeline transportation safety check and energy supply predictions. The deep learning model is uniformed for the coupled network structure, and the prediction efficiency and accuracy are validated by a number of numerical tests simulating various engineering scenarios, including hydrogen gas pipelines. The trained model can provide dispatchers with suggestions about the number of phases existing during the transportation as an index showing safety, while the effects of operation temperature, pressure and compositional purity are investigated to suggest the optimized productions

Growth of noble metal nanoparticles on single-layer TiS2 and TaS2 nanosheets for hydrogen evolution reaction

The preparation of single-layer TiS2 and TaS2 nanosheets is realized by optimizing the electrochemical lithium interaction and exfoliation method. As a proof of concept, Pt and Au nanoparticles are grown on the aforementioned ultra-thin nanosheets to form functional composites. Notably, the Pt–TiS2 hybrid presents good electrocatalytic activity in the hydrogen evolution reaction

Association between Knowledge-Attitude-Practices and Control of Blood Glucose, Blood Pressure, and Blood Lipids in Patients with Type 2 Diabetes in Shanghai, China: A Cross-Sectional Study

Knowledge-attitude-practices (KAP) significantly impact the outcome of self-management in patients with diabetes, yet the association between KAP and the combined control of the levels of blood glucose, blood pressure, and blood lipids in these patients remains uncertain. This community-based cross-sectional study was conducted from December 2014 to December 2016 on 3977 patients with type 2 diabetes in Shanghai. KAP were evaluated using the modified Chinese version of the Diabetes, Hypertension and Hyperlipidemia (DHL) Knowledge Instrument, Diabetes Empowerment Scale–Short Form (DES-SF), and Summary of Diabetes Self-Care Activities (SDSCA). Clinical and biochemical measurements were performed at each sampling site. The association between KAP scores and achieving the combined target goal was assessed by multiple logistic regression. Patients having a higher score of knowledge were more likely to achieve the combined target goal. Furthermore, a turning point of knowledge score was found that the possibility of achieving the combined target goal presented a sharp increase when the knowledge score was more than 70. However, the scores of attitude and practices had no significant relations with achieving the combined target goal. Health intervention strategies, especially increasing integrated diabetes knowledge, should be targeted to patients with type 2 diabetes in communities

A new antifibrotic target of Ac-SDKP: inhibition of myofibroblast differentiation in rat lung with silicosis.

BACKGROUND: Myofibroblast differentiation, characterized by α-smooth muscle actin (α-SMA) expression, is a key process in organ fibrosis, and is induced by TGF-β. Here we examined whether an anti-fibrotic agent, N-acetyl-seryl-aspartyl-lysylproline (Ac-SDKP), can regulate induction of TGF-β signaling and myofibroblast differentiation as a potential key component of its anti-fibrotic mechanism in vivo and in vitro. METHODOLOGY/PRINCIPAL FINDINGS: Rat pulmonary fibroblasts were cultured in vitro and divided to 4 groups 1) control; 2) TGF-β1; 3) TGF-β1+ LY364947; 4) TGF-β1+Ac-SDKP. For in vivo studies, six groups of animals were utilized 1) control 4w; 2) silicotic 4w; 3) control 8w; 4) silicotic 8w; 5) Ac-SDKP post-treatment; 6)Ac-SDKP pre-treatment. SiO(2) powders were douched in the trachea of rat to make the silicotic model. Myofibroblast differentiation was measured by examining expression of α-SMA, as well as expression of serum response factor (SRF), a key regulator of myofibroblast differentiation. The expressions of collagen, TGF-β1 and RAS signaling were also assessed. The results revealed that TGF-β1 strongly induced myofibroblast differentiation and collagen synthesis in vitro, and that pre-treatment with Ac-SDKP markedly attenuated myofibroblast activation, as well as induction of TGF-β1 and its receptor. Similar results were observed in vivo in the pathologically relevant rat model of silicosis. Ac-SDKP treatment in vivo strongly attenuated 1) silicosis-induced increased expressions of TGF-β1 and RAS signaling, 2) myofibroblast differentiation as indicated by a robust decrease of SRF and α-SMA-positive myofibroblast localization in siliconic nodules in the lung, 3) collagen deposition. CONCLUSION/SIGNIFICANCE: The results of the present study suggest a novel mechanism of action for Ac-SDKP's beneficial effect in silicosis, which involves attenuation of TGF-β1 and its receptors, SRF and Ang II type 1 receptor (AT(1)) expression, collagen deposition and myofibroblast differentiation. The results further suggest that therapies targeting myofibroblast differentiation may have therapeutic efficacy in treatment of silicosis of the lung

MoS2 nanoflower-decorated reduced graphene oxide paper for high-performance hydrogen evolution reaction

A facile, one-pot solvothermal method is developed to synthesize MoS2 nanoflowers (MoS2NFs) coated on reduced graphene oxide (rGO) paper. The resulting MoS2NF/rGO paper serves as a freestanding, flexible and durable working electrode for hydrogen evolution reaction (HER), exhibiting an overpotential lowered to −0.19 V with a Tafel slope of [similar]95 mV per decade

Growth of high-density single-wall carbon nanotubes with a uniform structure using a CoRu catalyst

The inefficient production of structurally uniform single-wall carbon nanotubes (SWCNTs) is an obstacle to their practical use in high-performance electronic devices. We have synthesized SWCNTs with a narrow diameter distribution (1.35 +/- 0.25 nm) using a CoRu catalyst. Monodispersed nanoparticles with a narrow size dis-tribution (2.4 +/- 0.6 nm) and different compositions were prepared and used as catalysts for SWCNT growth. A furnace with an 80 cm-long uniform temperature zone (+/- 10 degrees C) was designed and used to study the effect of catalyst composition on the growth of SWCNTs under the same conditions. By optimizing the composition of the bimetallic CoRu catalyst, SWCNTs with a uniform structure were efficiently synthesized. In addition, the effect of the growth conditions of temperature and carbon feed rate was investigated, and it was found that with an increase in yield, the structural uniformity of SWCNTs usually became worse. Both catalysts with elements in the suitable proportions and appropriate growth conditions are critical to achieving the high-efficiency structure-controlled growth of SWCNTs