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Drawing on critical realism, complexity theory, and emergence, this chapter supports the call to re-imagine doctoral writing by arguing that academic writing in general is a complex open and emergent social system that can change. Several reasons to re-imagine doctoral writing are discussed. The first reason is that academic writings already exhibit considerable diversity. This suggests that the conditions of possibility for re-imagining them are already in place and provide a conceptual space from which to further imagine. Second, there are\ud epistemic reasons for re-thinking how we write, as evidenced by research on socio-semiotics. Several examples of doctoral writers\ud who have re-imagined their writing for epistemic reasons are given. To explain how change in social phenomena is possible and how it can continue to be justified, I draw on the theory of complex permeable open systems. These systems are emergent and, as such, allow us to think of social phenomena, such as writing, as non-reductive organic unities whose characteristics emerge from but cannot be reduced to any single constituent feature (such as grammar or lexis). By re-thinking academic writings in this way, we can provide a rationale to explain how they can continue to change. The chapter concludes by sharing the work of scholars engaged in re-imagining doctoral writings. The significance for writing studies is that critical realism offers a systematic and critical space within which to explain change\ud in social phenomena and provides a theoretical foundation for continuing to re-imagine conditions of possibility.

Part 12: Workshops; International audience; The manipulation of information and the dissemination of “fake news” are practices that trace back to the early records of human history. Significant changes in the technological environment enabling ubiquity, immediacy and considerable anonymity, have facilitated the spreading of misinformation in unforeseen ways, raising concerns around people’s (mis)perception of social issues worldwide. As a wicked problem, limiting the harm caused by misinformation goes beyond technical solutions, requiring also regulatory and behavioural changes. This workshop proposes to unpack the challenge at hand by bringing together diverse perspectives to the problem. Based on participatory design principles, it will challenge participants to critically reflect the limits of existing socio-technical approaches and co-create scenarios in which digital platforms support misinformation resilience.

Martian gullies were initially hypothesized to be carved by liquid water, due to their resemblance to gullies on Earth. Recent observations have highlighted significant sediment transport events occurring in Martian gullies at times and places where CO2 ice should be actively sublimating. Here we explore the role of CO2 sublimation in mobilizing sediment through laboratory simulation. In our previous experimental work, we reported the first observations of sediment slope movement triggered by the sublimation of CO2 frost. We used a Mars regolith simulant near the angle of repose. The current study extends our previous work by including two additional substrates, fine and coarse sand, and by testing slope angles down to 10°. We find that the Mars regolith simulant is active down to 17°, the fine sand is active only near the angle of repose and the coarse sand shows negligible movement. Using an analytical model, we show that under Martian gravity motion should be possible at even lower slope angles. We conclude that these mass-wasting processes could be involved in shaping Martian gullies at the present day and intriguingly the newly reported CO2-creep process could provide an alternative explanation for putative solifluction lobes on Mars.

In this chapter we explore the future for innovation in two related, but distinct, sectors. We consider the linkages between medical technology(MedTech) and agricultural technology (Agri-Tech) innovation in the UK. We ask and discuss questions: Who are the key actors in the innovation systems of Medtech and Agri-Tech in the UK? What are the core technologies driving the current waves of innovation in these two sectors? Can one industry learn from the other? Where is the scope for cooperation and synergies? We notice that both sectors are technologically linked through foundational technologies underpinning the majority of the observed innovation e.g. big data, AI, IoT and robotics. The outputs of these technologies rely crucially on digital data for insight and decision support. However, Agri-Tech benefits from less complex stakeholder issues regarding data security and privacy. Both sectors are important to the UK going forwards, and both will be exposed to Brexit and the consequences of the COVID pandemic. Our discussion on the future of innovation should be of particular interest to start-up leaders, entrepreneurs, investors, managers and policy-makers in MedTech, Agri-Tech and cognate sectors.

This chapter presents the research rationale underpinning the book. It addresses the intertwining challenges of food security and surplus food management, discussing recent data and literature. It also presents how social innovation is conceptualized in the book as the theoretical framework to analyse partnerships between business and non-profit organisations in managing food surplus. The methodology of the research is also detailed, along with the book structure.

This chapter is based on the talk that I gave in August 2018 at the ICM in Rio de Janeiro at the panel on "The Gender Gap in Mathematical and Natural Sciences from a Historical Perspective". It provides some examples of the challenges and prejudices faced by women mathematicians during last two hundred and fifty years. I make no claim for completeness but hope that the examples will help to shed light on some of the problems many women mathematicians still face today.

Social Media is commonly used by policing organisations to spread the word on crime, weather, missing person, etc. In this work we aim to understand what attracts citizens to engage with social media policing content. To study these engagement dynamics we propose a combination of machine learning and semantic analysis techniques. Our initial research, performed over 3,200 posts from @dorsetpolice Twitter account, shows that writing longer posts, with positive sentiment, and sending them out before 4pm, was found to increase the probability of attracting attention. Additionally, posts about weather, roads and infrastructures, mentioning places, are also more likely to \ud attract attention.

Inspired by insights from theories of human reasoning and language, we propose additions to the Manchester OWL Syntax to improve comprehensibility. These additions cover: functional and inverse functional properties, negated conjunction, the definition of exceptions, and existential and universal restrictions. By means of an empirical study, we demonstrate the effectiveness of a number of these additions, in particular: the use of solely to clarify the uniqueness of the object in a functional property; the replacement of and with intersection in conjunction, which was particularly beneficial in negated conjunction; the use of except as a substitute for and not; and the replacement of some with including and only with noneOrOnly, which helped in certain situations to clarify the nature of these restrictions.

We investigate the problem of adaptive targeting for real-time bidding in\ud online advertisement using independent advertisement exchanges. This is a problem\ud of making decisions based on information extracted from large data sets related\ud to previous experience. We describe an adaptive strategy for optimizing the click\ud through rate which is a key criterion used by advertising platforms to measure the\ud efficiency of an advertisement campaign.We also provide some results of statistical\ud analysis of real data.

We propose a novel approach capable of embedding the unsupervised objective into hidden layers of the deep neural network (DNN) for preserving important unsupervised information. To this end, we exploit a very simple yet effective unsupervised method, i.e. principal component analysis (PCA), to generate the unsupervised “label" for the latent layers of DNN. Each latent layer of DNN can then be supervised not just by the class label, but also by the unsupervised “label" so that the intrinsic structure information of data can be learned and embedded. Compared with traditional methods which combine supervised and unsupervised learning, our proposed model avoids the needs for layer-wise pre-training and complicated model learning e.g. in deep autoencoder. We show that the resulting model achieves state-of-the-art performance in both face and handwriting data simply with learning of unsupervised “labels".