Political discourse analysis from the point of view of translation studies

Political discourse very often relies on translation. Political Discourse Analysis (PDA), however, has not yet taken full account of the phenomenon of translation. This paper argues that the disciplines of Translation Studies (TS) and PDA can benefit from closer cooperation. It starts by presenting examples of authentic translations of political texts, commenting on them from the point of view of TS. These examples concern political effects caused by specific translation solutions; the processes by which information is transferred via translation to another culture; and the structure and function of equally valid texts in their respective cultures. After a brief survey of the discipline of Translation Studies, the paper concludes with outlining scope for interdisciplinary cooperation between PDA and TS. This is illustrated with reference to an awareness of product features, multilingual texts, process analysis, and the politics of translation.

An investigation into the impact of safety features on the ergonomics of surgical scalpels

In the case of surgical scalpels, blade retraction and disposability have been incorporated into a number of commercial designs to address sharps injury and infection transmission issues. Despite these new designs, the traditional metal reusable scalpel is still extensively used and this paper attempts to determine whether the introduction of safety features has compromised the ergonomics and so potentially the take-up of the newer designs. Examples of scalpels have been analysed to determine the ergonomic impact of these design changes. Trials and questionnaires were carried out using both clinical and non-clinical user groups, with the trials making use of assessment of incision quality, cutting force, electromyography and video monitoring. The results showed that ergonomic performance was altered by the design changes and that while these could be for the worse, the introduction of safety features could act as a catalyst to encourage re-evaluation of the ergonomic demands of a highly traditional product.

State-of-the-art in product-service systems

A Product-Service System (PSS) is an integrated combination of products and services. This Western concept embraces a service-led competitive strategy, environmental sustainability, and the basis to differentiate from competitors who simply offer lower priced products. This paper aims to report the state-of-the-art of PSS research by presenting a clinical review of literature currently available on this topic. The literature is classified and the major outcomes of each study are addressed and analysed. On this basis, this paper defines the PSS concept, reports on its origin and features, gives examples of applications along with potential benefits and barriers to adoption, summarizes available tools and methodologies, and identifies future research challenges.

Product-service systems revisited

There is a growing interest around the potential value of service-led competitive strategies to UK based manufacturers. A Product Service-System (PSS) is one form of such a strategy and is based on integrated combination of products and services. This concept also embraces environmental sustainability. This paper aims to summarise the state-of-the-art of PSS research by presenting a review of literature currently available on this topic. The literature search is described and the major outcomes of the study are presented. On this basis, this paper defines the PSS concept, reports on its origin and features.

Responsive fixture design using dynamic product inspection and monitoring technologies for the precision machining of large-scale aerospace parts

When machining a large-scale aerospace part, the part is normally located and clamped firmly until a set of features are machined. When the part is released, its size and shape may deform beyond the tolerance limits due to stress release. This paper presents the design of a new fixing method and flexible fixtures that would automatically respond to workpiece deformation during machining. Deformation is inspected and monitored on-line, and part location and orientation can be adjusted timely to ensure follow-up operations are carried out under low stress and with respect to the related datum defined in the design models.

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.