Consumer Stated Preference for Acer Laptop from Online Reviews

Consumer preference is a hot topic in the domain of marking management and e-commerce. Many previous studies have been conducted in this field. Whereas, there are rarely studies building on the particular commodity such as laptop. Therefore, this study explores comprehensive features that affect consumer preference for laptops by mining the online reviews. Firstly, we collect 6531 online reviews for Acer laptop from Amazon.cn and code these reviews with Nvivo10. Secondly, we develop a feature-based consumer preference model named MCPL based on the review text analysis. Considering the data imbalance of the collected 6531 product reviews, we adopt a random cluster sampling method to extract 50 groups with 100 samples per group. Then the correspondent regression analyses are conducted for the 50 groups of reviews. Finally, the meta-analysis is creatively conducted to integrate the multiple liner regression results of different groups. According to the result of meta-analysis, we demonstrate dominant features on behalf of the consumer preference of laptop and draw practical implications for enterprise competition strategies to facilitate product design or improvement

Target profiling analyses of bile acids in the evaluation of hepatoprotective effect of gentiopicroside on ANIT-induced cholestatic liver injury in mice

AbstractEthnopharmacological relevanceGentiopicroside (GPS), one of iridoid glucoside representatives, is the most potential active component in Gentiana rigescens Franch. ex Hemsl and Gentiana macrophylla Pall. These two herbs have been used to treat jaundice and other hepatic and billiary diseases in traditional Chinese medicine for thousands of years.Aim of the studyThis study aimed to investigate the protective effects and mechanisms of GPS on α-naphthylisothiocyanate (ANIT) induced cholestatic liver injury in mice.Materials and methodsMice were treated with GPS (130mg/kg, ig) for 5 consecutive days. On the third day, mice were given a single dose of Alpha-naphthylisothiocyanate (75mg/kg, ig). Serum biochemical markers and individual bile acids in serum, liver, urine and feces were measured at different time points after ANIT administration. The expression of hepatic bile acid synthesis, uptake and transporter genes as well as ileum bile acid transporter genes were assayed.ResultsIn this study, ANIT exposure resulted in serious cholestasis with liver injury, which was demonstrated by dramatically increased serum levels of ALT, ALP, TBA and TBIL along with TCA CA, MCAs and TMCAs accumulation in both liver and serum. Furthermore, ANIT significantly decreased bile acid synthesis related gene expressions, and increased expression of bile acid transporters in liver. Continuous treatment with GPS attenuated ANIT-induced acute cholestasis as well as liver injury and correct the dyshomeostasis of bile acids induced by ANIT. Our data showed that GPS significantly upregulated the hepatic mRNA levels of synthesis enzymes (Cyp8b1 and Cyp27a1) and transporters (Mrp4 Mdr1 and Ost-β) as well as ileal bile acid circulation mediators (Asbt and Fgf15), accompanied by serum and hepatic bile acid levels decrease and further urinary and fecal bile acid levels increase.ConclusionGPS can change bile acids metabolism which highlights its importance in mitigating cholestasis, resulting in the marked decrease of intracellular bile acid pool back toward basal levels. And the protective mechanism was associated with regulation of bile acids-related transporters, but the potential mechanism warrants further investigation

Ozonation of trace organic compounds in different municipal and industrial wastewaters : kinetic-based prediction of removal efficiency and ozone dose requirements

For the wide application of ozonation in (industrial and municipal) wastewater treatment, prediction of trace organic compounds (TrOCs) removal and evaluation of energy requirements are essential for its design and operation. In this study, a kinetics approach, based on the correlation between the second order reaction rate constants of TrOCs with ozone and hydroxyl radicals ((OH)-O-center dot) and the ozone and (OH)-O-center dot exposure (i.e., integral (sic)O-3(sic)dt and integral [(OH)-O-center dot]dt, which are defined as the time integral concentration of O-3 and (OH)-O-center dot for a given reaction time), was validated to predict the elimination efficiency in not only municipal wastewaters but also industrial wastewaters. Two municipal wastewater treatment plant effluents from Belgium (HB-effluent) and China (QG-effluent) and two industrial wastewater treatment plant effluents respectively from a China printing and dyeing factory (PD-effluent) and a China lithium-ion battery factory (LZ-effluent) were used for this purpose. The (OH)-O-center dot scavenging rate from the major scavengers (namely alkalinity, effluent organic matter (EfOM) and NO2-) and the total (OH)-O-center dot scavenging rate of each effluent were calculated. The various water matrices and the (OH)-O-center dot scavenging rates resulted in a difference in the requirement for ozone dose and energy for the same level of TrOCs elimination. For example, for more than 90% atrazine (ATZ) abatement in HB-effluent (with a total (OH)-O-center dot scavenging rate of 1.9 x 10(5) s(-1)) the energy requirement was 12.3 x 10(-2) kWh/m(3), which was lower than 30.1 x 10(-2) kWh/m(3) for PD-effluent (with the highest total (OH)-O-center dot scavenging rate of 4.7 x 10(5) s(-1)). Even though the water characteristics of selected wastewater effluents are quite different, the results of measured and predicted TrOCs abatement efficiency demonstrate that the kinetics approach is applicability for the prediction of target TrOCs elimination by ozonation in both municipal and industrial wastewater treatment plant effluents

MaxMin-L2-SVC-NCH: A New Method to Train Support Vector Classifier with the Selection of Model's Parameters

The selection of model's parameters plays an important role in the application of support vector classification (SVC). The commonly used method of selecting model's parameters is the k-fold cross validation with grid search (CV). It is extremely time-consuming because it needs to train a large number of SVC models. In this paper, a new method is proposed to train SVC with the selection of model's parameters. Firstly, training SVC with the selection of model's parameters is modeled as a minimax optimization problem (MaxMin-L2-SVC-NCH), in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal model's parameters. A lower time complexity can be expected in MaxMin-L2-SVC-NCH because CV is abandoned. A gradient-based algorithm is then proposed to solve MaxMin-L2-SVC-NCH, in which L2-SVC-NCH is solved by a projected gradient algorithm (PGA) while the maximization problem is solved by a gradient ascent algorithm with dynamic learning rate. To demonstrate the advantages of the PGA in solving L2-SVC-NCH, we carry out a comparison of the PGA and the famous sequential minimal optimization (SMO) algorithm after a SMO algorithm and some KKT conditions for L2-SVC-NCH are provided. It is revealed that the SMO algorithm is a special case of the PGA. Thus, the PGA can provide more flexibility. The comparative experiments between MaxMin-L2-SVC-NCH and the classical parameter selection models on public datasets show that MaxMin-L2-SVC-NCH greatly reduces the number of models to be trained and the test accuracy is not lost to the classical models. It indicates that MaxMin-L2-SVC-NCH performs better than the other models. We strongly recommend MaxMin-L2-SVC-NCH as a preferred model for SVC task

SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture

Support vector machine (SVM) and neural networks (NN) have strong complementarity. SVM focuses on the inner operation among samples while NN focuses on the operation among the features within samples. Thus, it is promising and attractive to combine SVM and NN, as it may provide a more powerful function than SVM or NN alone. However, current work on combining them lacks true integration. To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN. SVM can be viewed as a sample attention machine. It allows us to add a sample attention module to NN to implement the main function of SVM. Class prototypes are representatives of all classes, which can be viewed as alternatives to support vectors. The memory block is used for the storage and update of class prototypes. Class prototypes and memory block effectively reduce the computational cost of sample attention and make SAMN suitable for multi-classification tasks. Extensive experiments show that SAMN achieves better classification performance than single SVM or single NN with similar parameter sizes, as well as the previous best model for combining SVM and NN. The sample attention mechanism is a flexible module that can be easily deepened and incorporated into neural networks that require it

Impacts of climate change on TN load and its control in a River Basin with complex pollution sources

It is increasingly recognized that climate change could affect the quality of water through complex natural and anthropogenic mechanisms. Previous studies on climate change and water quality have mostly focused on assessing its impact on pollutant loads from agricultural runoff. A sub-daily SWAT model was developed to simulate the discharge, transport, and transformation of nitrogen from all known anthropogenic sources including industries, municipal sewage treatment plants, concentrated and scattered feedlot operations, rural households, and crop production in the Upper Huai River Basin. This is a highly polluted basin with total nitrogen (TN) concentrations frequently exceeding Class V of the Chinese Surface Water Quality Standard (GB3838-2002). Climate change projections produced by 16 Global Circulation Models (GCMs) under the RCP 4.5 and RCP 8.5 scenarios in the mid (2040–2060) and late (2070–2090) century were used to drive the SWAT model to evaluate the impacts of climate change on both the TN loads and the effectiveness of three water pollution control measures (reducing fertilizer use, constructing vegetative filter strips, and improving septic tank performance) in the basin. SWAT simulation results have indicated that climate change is likely to cause an increase in both monthly average and extreme TN loads in February, May, and November. The projected impact of climate change on TN loads in August is more varied between GCMs. In addition, climate change is projected to have a negative impact on the effectiveness of septic tanks in reducing TN loads, while its impacts on the other two measures are more uncertain. Despite the uncertainty, reducing fertilizer use remains the most effective measure for reducing TN loads under different climate change scenarios. Meanwhile, improving septic tank performance is relatively more effective in reducing annual TN loads, while constructing vegetative filter strips is more effective in reducing annual maximum monthly TN loads

Design of Lead-Free Inorganic Halide Perovskites for Solar Cells via Cation-Transmutation

Hybrid organic-inorganic halide perovskites with the prototype material of CH$_{3}$NH$_{3}$PbI$_{3}$ have recently attracted intense interest as low-cost and high-performance photovoltaic absorbers. Despite the high power conversion efficiency exceeding 20% achieved by their solar cells, two key issues -- the poor device stabilities associated with their intrinsic material instability and the toxicity due to water soluble Pb$^{2+}$ -- need to be resolved before large-scale commercialization. Here, we address these issues by exploiting the strategy of cation-transmutation to design stable inorganic Pb-free halide perovskites for solar cells. The idea is to convert two divalent Pb$^{2+}$ ions into one monovalent M$^{+}$ and one trivalent M$^{3+}$ ions, forming a rich class of quaternary halides in double-perovskite structure. We find through first-principles calculations this class of materials have good phase stability against decomposition and wide-range tunable optoelectronic properties. With photovoltaic-functionality-directed materials screening, we identify eleven optimal materials with intrinsic thermodynamic stability, suitable band gaps, small carrier effective masses, and low excitons binding energies as promising candidates to replace Pb-based photovoltaic absorbers in perovskite solar cells. The chemical trends of phase stabilities and electronic properties are also established for this class of materials, offering useful guidance for the development of perovskite solar cells fabricated with them.Comment: pages 19, 4 figures in main tex