Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).

Assessing local food systems in China for building healthy mega-cities

Despite the long-lasting concern for food security in China at the national level, policy attempts to cope with this issue have often resulted to be ineffective. More importantly, they have rarely addressed the question from a local perspective. International experiences of urban food strategies proved to be quite efficacious in enhancing the local provision of food and improving the overall city sustainability by shortening the supply chain, preserving peri-urban areas and improving the nutrition of citizens. By reviewing existing practices of city farming in China, mainly ascribable to urban agriculture experiences, the intention of this paper is to reflect upon the challenges of implementing more comprehensive local food systems. In the conclusion the paper argues that, given the current institutional, socio-economic, and environmental constrains of Chinese cities there is a need of introducing holistic planning tool to assess local food systems in order to ensure the building of real healthy cities.

Organisational and systems factors impacting on patient safety in acute care organisations: lessons from four multi-site research studies

Background Patient safety is concerned with preventable harm in healthcare, a subject that became a focus for study in the UK in the late 1990s. How to improve patient safety, presented both a practical and a research challenge in the early 2000s, leading to the eleven publications presented in this thesis. Research question The overarching research question was: What are the key organisational and systems factors that impact on patient safety, and how can these best be researched? Methods Research was conducted in over 40 acute care organisations in the UK and Europe between 2006 and 2013. The approaches included surveys, interviews, documentary analysis and non-participant observation. Two studies were longitudinal. Results The findings reveal the nature and extent of poor systems reliability and its effect on patient safety; the factors underpinning cases of patient harm; the cultural issues impacting on safety and quality; and the importance of a common language for quality and safety across an organisation. Across the publications, nine key organisational and systems factors emerged as important for patient safety improvement. These include leadership stability; data infrastructure; measurement capability; standardisation of clinical systems; and creating an open and fair collective culture where poor safety is challenged. Conclusions and contribution to knowledge The research presented in the publications has provided a more complete understanding of the organisation and systems factors underpinning safer healthcare. Lessons are drawn to inform methods for future research, including: how to define success in patient safety improvement studies; how to take into account external influences during longitudinal studies; and how to confirm meaning in multi-language research. Finally, recommendations for future research include assessing the support required to maintain a patient safety focus during periods of major change or austerity; the skills needed by healthcare leaders; and the implications of poor data infrastructure.