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

Research on cemented artificial pillars to replace protective inter-block coal pillars and stope failure laws

Replacing protective inter-block coal pillars (PICPs) with cemented artificial pillars is proposed here to address low coal recovery rates. The use of cemented artificial pillars also reduces resource waste when PICPs are used in the short-wall block mining (SBM) process. A coal mine test area in northern Shaanxi, China, was employed as the study site. Artificial pillar replacement techniques were developed based on the layout characteristics of a typical SBM workface. High-strength cemented backfill materials for artificial pillars were manufactured using innovative material ratio testing, and the optimum ratios for backfill materials are discussed. A cusp catastrophe model of an artificial pillar was then developed and used to deduce the conditions and critical widths necessary to generate catastrophic instability of an artificial pillar. This theoretical analysis was validated using FLAC3D simulations. Using the test site conditions, the simulations revealed that when an artificial pillar had a width of 14 m, the destruction of the pillar and associated stope was gradual and would not cause catastrophic instability. Field monitoring performed at the test site verified the theoretical analysis and numerical simulation results, confirming that it was feasible to replace PICPs with cemented artificial pillars

Regulation of IL-2 gene expression by Siva and FOXP3 in human T cells

<p>Abstract</p> <p>Background</p> <p>Severe autoinflammatory diseases are associated with mutations in the <it>Foxp3 </it>locus in both mice and humans. <it>Foxp3 </it>is required for the development, function, and maintenance of regulatory T cells (T_regs), a subset of CD4 cells that suppress T cell activation and inflammatory processes. <it>Siva </it>is a pro-apoptotic gene that is expressed across a range of tissues, including CD4 T cells. Siva interacts with three tumor necrosis factor receptor (TNFR) family members that are constitutively expressed on T_regcells: CD27, GITR, and OX40.</p> <p>Results</p> <p>Here we report a biophysical interaction between FOXP3 and Siva. We mapped the interaction domains to Siva's C-terminus and to a central region of FOXP3. We showed that <it>Siva </it>repressed IL-2 induction by suppressing <it>IL-2 </it>promoter activity during T cell activation. Siva-1's repressive effect on <it>IL-2 </it>gene expression appears to be mediated by inhibition of NFkappaB, whereas FOXP3 repressed both NFkappaB and NFAT activity.</p> <p>Conclusions</p> <p>In summary, our data suggest that both <it>FOXP3 </it>and <it>Siva </it>function as negative regulators of IL-2 gene expression in T_regcells, via suppression of NFAT by <it>FOXP3 </it>and of NFkappaB by both <it>FOXP3 </it>and <it>Siva</it>. Our work contributes evidence for <it>Siva's </it>role as a T cell signalling mediator in addition to its known pro-apoptotic function. Though further investigations are needed, evidence for the biophysical interaction between FOXP3 and Siva invites the possibility that Siva may be important for proper T_regcell function.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Research on User Information Behavior and Hotspot Prediction of WeChat Official Accounts Based on Sootoo Network

With the rapid development of mobile Internet, more and more attention has been paid to the new form of online social media represented by WeChat official accounts. However, these operators do not have clear ideas for enhancing WeChat heat, and clear and effective guidance on how to positively influence users\u27 information behavior. Therefore, this paper combines the psychology neutral Stimulus-Response theory with the user information behavior research to construct a second-order user information behavior model. Then, through the official accounts data provided by the Sootoo Network, we collected a total of more than 10,000 push data of nine accounts from January to December, 2017. Based on the empirical research, we find the main influencing factors of user information behavior are identified, and construct a push hotspot prediction model. Also, this paper provides account operator with practical guidance significance in enhancing user information behavior

Alteration of vaginal microbiota in patients with recurrent miscarriage

The aim of this study was to characterise the structure of vaginal microbiota in unexplained recurrent miscarriage (RM). The vaginal bacterial communities of 16 patients with RM and 20 healthy volunteers were sampled. Then, the microbiomes of bacterial profiles of RM patients and healthy volunteers were compared by sequencing the V3–V4 regions of the bacterial 16S ribosomal RNA gene using the Illumina MiSeq platform (Illumina, San Diego, CA). Taxonomic analysis demonstrated that abundance of Lactobacillus and Gardnerella were significantly different between the RM and control groups. Furthermore, at the genus level, Lactobacillus was the most dominant genus in the two groups. Statistically significant differences were observed in three genera between RM and control groups. In the control group, two bacterial taxa were significantly more abundant (Lactobacillus and Gardnerella), while only one taxon was overrepresented in the RM group (Atopobium). These present findings provide experimental evidence supporting vaginal microbiota dysbiosis in women with RM.Impact statement What is already known on this subject? Currently, bacterial vaginosis is thought to be mainly due to the vaginal dysbacteriosis, which can induce unexplained recurrent miscarriage, premature rupture of membranes, low birth weight premature birth, premature birth, chorioamnionitis and series of diseases. What do the results of this study add? The current study demonstrated that Lactobacillus and Gardnerella were significantly decreased in RM patients compared to healthy control, while Atopobium was overrepresented in the RM group. What are the implications of these findings for clinical practice and/or further research? Clinically, women with RM might benefit from vaginal microbiota treatment, adjuvant therapy with Lactobacillus-based live biotherapeutics

Investigation on Microstructures and High-Temperature Oxidation Resistance of Cr Coatings on Zircaloy-4 by Multi-Arc Ion Plating Technology

Introducing the oxidation-resistant coating on Zr alloy is considered to be one of the potential solutions for accident-tolerant fuel (ATF) materials. In this study, pure Cr coatings were prepared on a Zircaloy-4 (Zry-4) alloy surface by multi-arc ion plating under different process parameters. The ability of Cr coating on Zry-4 alloy cladding to improve the oxidation resistance to prevent a loss-of-coolant accident (LOCA) was studied. The microstructure of Cr coating was analyzed using the EBSD technique, and the high-temperature steam oxidation was tested at 800, 1000 and 1200 &deg;C. Compared with the original Zry-4 alloy, the samples with Cr coatings exhibited much better oxidation resistance under different high-temperature steam oxidation conditions. However, the Cr coating exhibited columnar grain, strong preferred orientation and (001) fiber texture. The columnar grain boundaries provided paths for the diffusion of oxygen atoms to the Zry-4 alloy matrix at high temperatures. The results showed that the oxidation film of Cr coating with relatively random grain orientation was compact and uniform and exhibited the best oxidation resistance at high temperatures

Explainable machine learning models for early gastric cancer diagnosis

Abstract Gastric cancer remains a significant global health concern, with a notably high incidence in East Asia. This paper explores the potential of explainable machine learning models in enhancing the early diagnosis of gastric cancer. Through comprehensive evaluations, various machine learning models, including WeightedEnsemble, CatBoost, and RandomForest, demonstrated high potential in accurately diagnosing early gastric cancer. The study emphasizes the importance of model explainability in medical diagnostics, showing how transparent, explainable models can increase trust and clinical acceptance, thereby improving diagnostic accuracy and patient outcomes. This research not only highlights key biomarkers and clinical features critical for early detection but also presents a versatile approach that could be applied to other medical diagnostics, promoting broader adoption of machine learning in clinical settings