Facilitation of consumer loyalty toward branded applications: The dual-route perspective

Although many brands develop mobile applications (apps) to build relationships with consumers, most branded apps fail to retain consumers’ loyalty. This study examines the facilitation of consumer loyalty toward branded apps (continuance intention, in-app purchase intention, and word-of-mouth intention) from the dual-route perspective. One route is the affective (relationship) route, where brand benefits (functional benefits, experiential benefits, symbolic benefits, and monetary benefits) drive parasocial interactions between consumers and the brand, which, in turn, influences branded app loyalty. The other route is the utility route, where system characteristics (system quality and information quality) affect perceived usefulness, which, in turn, facilitates branded app loyalty. An online survey was conducted, and the research model was empirically tested using partial least squares structural equation modeling. The findings support the dual-route perspective according to which both affective and utilitarian paths facilitate branded app loyalty. The key theoretical contribution of this study is that it moves beyond the utilitarian path and finds the affective (relationship) path to give a more complete picture of the facilitation of consumer loyalty in the branded app context. A strategy is provided to suggest to practitioners how to design branded apps to facilitate consumer loyalty.     

不同水分工夫红茶贮藏过程中主要内含成分及感官品质变化研究

目的研究不同水分工夫红茶贮藏品质变化规律,为工夫红茶贮藏的适宜水分条件提供理论参考。方法系统比较不同水分工夫红茶(国家标准范围内)在自然贮藏条件下其主要内含成分及感官品质的变化。结果 1年贮藏期内,初始水分含量越低,含水率增长速度越快,总体上,茶叶初始水分含量高低决定贮藏过程中水分的高低。在短期贮藏(≤6月)时含水量越高的红茶中茶多酚、水浸出物保留量越多;长期贮藏(6月)时水分较低的红茶中茶多酚、水浸出物保留量越多。咖啡碱、可溶性糖在短期贮藏(≤6月)中水分含量适中的红茶中下降速度越慢;长期贮藏(6月)含水量较高的保留量越高。氨基酸含量在含水量越低的红茶中越高。初始水分含量高的红茶L*、a*、b*值均稍高于水分低的红茶。结论从综合品质的变化来看,本实验中以处理3含水率为5.93%的处理最好,处理2含水率为4.1%次之,处理1含水率为2.33%最差。     

Assessment of chemical and microbiological parameters of indoor swimming pool atmosphere using multiple comparisons

The aim of this study was to evaluate the air quality of an indoor swimming pool, analyzing diurnal and seasonal variations in microbiological counts and chemical parameters. The results indicated that yeast and bacteria counts, as well as carbon dioxide (CO₂), nitrogen oxides (NO_x) and O₃ concentrations, showed significant diurnal difference. On the other hand, temperature, relative humidity (R.H.), yeast counts and concentrations of CO₂, particles, O₃, toluene, and benzene showed seasonal differences. In addition, the relationship between indoor and outdoor air and the degree of correlation between the different parameters have been calculated, suggesting that CO₂, fine particles and NO_x would have indoor origin due to the human activity and secondary reactions favored by the chemical and environmental conditions of the swimming pool; while O₃, benzene and toluene, would come from outside, mainly. The overall results indicated that indoor air quality (IAQ) in the swimming pool building was deficient by the high levels of CO₂ and microorganisms, low temperatures, and high R.H., because frequently the limits established by the legislation were exceeded. This fact could be due to the poor ventilation and the inadequate operation of heating, ventilation, and air‐conditioning systems.     

Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. ¹     

基于ELM的英语教学质量评价研究

对英语教学质量实施科学化的评价关系到英语教学质量的提升,为减少主观因素的影响,本文提出一种ELM的教学质量评价方法,该种方法将可以在很大程度上提高教学质量评价的精度。对影响英语教学质量的12个指标进行打分,以此作为ELM的输入,同时将英语教学质量的综合得分作为ELM的输出。评价结果表明,基于EIM的英语教学质量评价结果比SVM、BP具有了很大程度的提升,为实施英语教学质量的评价提供了参考。     

公众素质、外部环境对公众再生水接受态度的影响

公众对再生水的接受态度是再生水推广的关键。构建公众素质、主观规范、信息公开、政策环境、再生水接受态度与再生水接受意愿的关系假设模型,并以356份来自西安市居民问卷作为调研对象,运用结构方程模型进行实证分析。结果表明,接受态度正向影响接受意愿,其中主观规范是影响再生水接受态度的主要因素,信息公开与公众素质是次要因素,政策环境对再生水接受态度的影响不显著。     

Modeling of quantitative relationships between physicochemical properties of active pharmaceutical ingredients and tensile strength of tablets using a boosted tree

Objectives: The aim of this study was to explore the potential of boosted tree (BT) to develop a correlation model between active pharmaceutical ingredient (API) characteristics and a tensile strength (TS) of tablets as critical quality attributes.

Methods: First, we evaluated 81 kinds of API characteristics, such as particle size distribution, bulk density, tapped density, Hausner ratio, moisture content, elastic recovery, molecular weight, and partition coefficient. Next, we prepared tablets containing 50% API, 49% microcrystalline cellulose, and 1% magnesium stearate using direct compression at 6, 8, and 10?kN, and measured TS. Then, we applied BT to our dataset to develop a correlation model. Finally, the constructed BT model was validated using k-fold cross-validation.

Results: Results showed that the BT model achieved high-performance statistics, whereas multiple regression analysis resulted in poor estimations. Sensitivity analysis of the BT model revealed that diameter of powder particles at the 10th percentile of the cumulative percentage size distribution was the most crucial factor for TS. In addition, the influences of moisture content, partition coefficients, and modal diameter were appreciably meaningful factors.

Conclusions: This study demonstrates that BT model could provide comprehensive understanding of the latent structure underlying APIs and TS of tablets.     

Two‐sided matching framework for optimal user association in 5G multi‐RAT UDNs

Recently, academic and industrial research communities are paying more explicit attention to the 5G multiple radio access technology ultra‐dense networks (5G multi‐RAT UDNs) for boosting network capacity, especially in UD urban zones. To this aim, in this paper, we intend to tackle the user association problem in 5G multi‐RAT UDNs. By considering the decoupled uplink/downlink access (DUDA), we divide our user association problem into two distinct subproblems representing, respectively, the uplink and the downlink channels. Next, we formulated each one as a nonlinear optimization problem with binary variables. Then, to solve them, we were restricted by the hard complexity, as well as the hard feasibility of centralized user association schemes. Thus, to resolve our user association problem in a reasonable time and distributed manner, we formulated each subproblem as a many‐to‐one matching game based on matching theory. Next, we provide two fully distributed association algorithms to compute the uplink and downlink stable matching among user equipments (UEs) and base stations (BSs). Simulation results corroborate our theoretical model and show the effectiveness and improvement of our achieved results in terms of the overall network performance, quality of service (QoS), and energy efficiency (EE) of UEs.     

Research on the parallelization of image quality analysis algorithm based on deep learning

Image quality assessment is an indispensable in computer vision applications, such as image classification, image parsing. With the development of Internet, image data acquisition becomes more conveniently. However, image distortion is inevitable due to imperfect image acquisition system, image transmission medium and image recording equipment. Traditional image quality assessment algorithms only focus on low-level visual features such as color or texture, which could not encode high-level features effectively. CNN-based methods have shown satisfactory results in image quality assessment. However, existing methods have problems such as incomplete feature extraction, partial image block distortion, and inability to determine scores. So in this paper, we propose a novel framework for image quality assessment based on deep learning. We incorporate both low-level visual features and high-level semantic features to better describe images. And image quality is analyzed in a parallel processing mode. Experiments are conducted on LIVE and TID2008 datasets demonstrate the proposed model can predict the quality of the distorted image well, and both SROCC and PLCC can reach 0.92 or higher.