Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging. The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: 'semi-structured' and 'unstructured'. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry. The best result of 99.4% accuracy - which included only one semi-structured report predicted as unstructured - was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured. These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Nuclear power plays an important but controversial role in policies to ensure domestic energy security, fuel poverty reduction and the mitigation of climate change. Our article construes the problem of nuclear power in terms of social discourse, language and public choice; specifically examining the role that metaphors play in the policy domain. We empirically analyze metaphors as framing devices in nuclear energy policy debates in the UK between April 2009 and March 2013, thereby capturing the impact of the Fukushima nuclear disaster in 2011. We employ documentary analysis of major UK national broadsheet and tabloid newspapers, using electronic bibliographic tools to extract the metaphors. We then map these metaphors using a Type Hierarchy Analysis, which examines how elements of the target domain (energy technologies and policies) originate from a different source domain. Type hierarchies identify and categorize metaphors, defining the affectual and emotional responses associated with them, providing us with grounded insight into their role in shaping discourse and as a consequence influence public engagement with energy policy. Our analysis highlights three emergent domains of discourse metaphors and discusses the implications of their deployment. Metaphors were found to be classified into three different categories: Rebirth (Renaissance), Devastation (Apocalypse, Inferno, Genie and Bomb) and Sickness (Addiction and Smoking).

Drawing on the literature on framing, we explore the emotional framing differences in radical and reformative NGOs over time. We analyse the sentiment of a sample of 5880 press releases issued by five NGOs positioned differently on the reformative‐radical spectrum and examine how they address large companies. Our findings reveal an increasing polarisation of sentiment in these NGOs' framing, with individual NGOs gravitating towards ideal‐type radical or reformative positions, respectively. In alignment with the differences in their framing, we observe differences in their approaches to cross‐sector partnerships. Policymakers need to note the implications of the observed polarisation for the effectiveness and credibility of cross‐sector partnerships and multi‐stakeholder initiatives more generally, given the risk of co‐optation (for reformative NGOs) as well as the risk of foregoing significant funding and governance opportunities (for radical NGOs).

Since life expectancy has increased significantly over the past century, society is being forced to discover innovative ways to support active aging and elderly care. The e-VITA project, which receives funding from both the European Union and Japan, is built on a cutting edge method of virtual coaching that focuses on the key areas of active and healthy aging. The requirements for the virtual coach were ascertained through a process of participatory design in workshops, focus groups, and living laboratories in Germany, France, Italy, and Japan. Several use cases were then chosen for development utilising the open-source Rasa framework. The system uses common representations such as Knowledge Bases and Knowledge Graphs to enable the integration of context, subject expertise, and multimodal data, and is available in English, German, French, Italian, and Japanese.

With a substantial rise in life expectancy throughout the last century, society faces the imperative of seeking inventive approaches to foster active aging and provide adequate aging care. The e-VITA initiative, jointly funded by the European Union and Japan, centers on an advanced virtual coaching methodology designed to target essential aspects of promoting active and healthy aging. This paper describes the technical framework underlying the e-VITA virtual coaching system platform and presents preliminary feedback on its use. At its core is the e-VITA Manager, a pivotal component responsible for harmonizing the seamless integration of various specialized devices and modules. These modules include the Dialogue Manager, Data Fusion, and Emotional Detection, each making distinct contributions to enhance the platform’s functionalities. The platform’s design incorporates a multitude of devices and software components from Europe and Japan, each built upon diverse technologies and standards. This versatile platform facilitates communication and seamless integration among smart devices such as sensors and robots while efficiently managing data to provide comprehensive coaching functionalities.