水电站厂房渗水原因分析及处理

文章介绍了纪村水电站厂房地面陆续出现向外渗水现象的具体情况,分析了造成这一现象的原因,提出了解决方法及防范措施。希望对水电站厂房的安全、稳定运行起到一定的借鉴作用。     

Making an exciting brand big: Brand personality,logo size and brand evaluation

This research examines how brand personality (excitement) and logo size used in product design (big versus small) interacts in affecting consumer brand evaluation. We first demonstrate that combining a big logo and a high-excitement brand (versus a small logo combined with a high-excitement brand) leads to high processing fluency, further resulting in more favourable brand evaluation (Study 1). We then determine the consumption situation as a moderator of such an interaction effect: the influence of the big logo and high-excitement-brand combination on brand evaluation is significantly weakened in a private consumption situation (a T-shirt worn at home) compared to a public consumption situation (a T-shirt worn outside, Study 2a), and becomes insignificant for a private product category (slippers, Study 2b).     

Racism in tourism reviews

Racism is increasingly recognised as a key driver of unfair inequalities in power, resources and opportunities across racial groups. A comprehensive understanding of racism is beneficial to activist groups, policymakers and governments. Traditional approaches, such as surveys and interviews, are usually time-consuming and inefficient in capturing the occurrence of large-scale racism. In this study, we utilise routinely collected data available on tourism websites to assess self-reported racism in the tourism domain. We present a data acquisition procedure that collects racism-related reviews from the Internet at the global scale and then utilise statistics and natural language processing techniques to analyse and explore racism in terms of its tendency, distribution, semantics and characteristics. The effectiveness of the proposed method is demonstrated in a case study, in which we acquire racism-related data at the global scale and validate the impact of racial discrimination on tourists’ experience.     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

液态奶加工中的“过度加热”倾向分析

近年来,我国牛奶加工产业有了迅猛发展,但也存在一些问题,如液态奶加工中的“过度加热”问题。本文通过对过度加热液态奶的判定模式进行探究,分析“过度加热”的危害,总结在液态奶加工中出现“过度加热”的原因,以促进液态奶合理加工,推动我国乳业健康发展。     

利用大数据分析技术的智能化网络故障诊断系统

随着网络技术的快速发展以及各种新网络业务的出现，网络的规模日益扩大和复杂。同时，各种网络服务对于网络可靠性的要求也逐渐提高，这就对网络的维护提出了更高的要求。在这种情况下，传统的网络维护模式逐渐不能适应现代网络发展的需求。针对现代大型网络的故障诊断问题，利用大数据分析技术，设计并完成了一套整体的智能化故障诊断系统。在此系统中，针对不同的故障类型，利用能够获取到的数据类型，针对性地设计了相关的故障诊断模块和算法。系统整体基于开源的数据处理平台构建，具备良好的可扩展性。通过实际案例数据的验证，结果表明此系统具备良好的处理实际网络故障的能力，将大数据分析技术应用于网络故障诊断领域也具备较强的可行性。     

Sustainable supply chain management and partner engagement to manage climate change information

Climate change poses significant new risks and challenges for businesses and their supply chains. Additionally, in many sectors, Scope 3 indirect greenhouse gas emissions resulting from the sourcing and distribution of goods and services are larger than firms' own carbon footprints. Here we study how firms engage their key stakeholders in their supply chains in obtaining, processing, and transferring relevant climate change‐related information designed to overcome information asymmetry and drive sustainable development. Grounded in organisational information‐processing theory (OIPT), we draw on data from the Carbon Disclosure Project's Climate Change Supply Chain initiative for a qualitative content analysis of a large sample of global firms. Consistent with OIPT, we find that although firms primarily engage their supply chain partners in a variety of ways to reduce information uncertainty around indirect emissions data, effectively interpreting and managing broader sustainability information equivocality becomes a growing priority. Our findings further suggest that firms engage suppliers, customers, and other supply chain partners through basic, transactional, and collaborative types of engagement. We contribute to literatures on interorganisational information processing and sustainable supply chain management by providing a more detailed understanding of how firms engage supply chain partners in the context of climate change.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Lost and found: Individual differences in propensity to process visual elements of persuasion

Visual processing style, defined as the relative propensity to engage in visual processing rather than verbal processing, is an individual difference variable that has been frequently investigated in the consumer psychology literature. Surprisingly, numerous studies have reported no relationship between visual processing style and viewer responses to visual elements of persuasion. We argue that this accumulation of null results is due to untenable historical theoretical assumptions that underlie the construct, along with methodological problems that are inevitably brought about by those theoretical assumptions. We reconceptualize visual processing style and test an alternative empirical operationalization of it. Using both new data and a reanalysis of data published in the Journal of Consumer Research, we find the old approach yields null results, but we find the new approach yields the expected results. The new approach reinstates the utility of incorporating propensity to engage in visual processing as an individual difference variable into consumer psychology models of visual persuasion, and it reinstates a powerful individual difference variable that can help push forward the investigation of the unique aspects of visual persuasion.