Phonological processes tend to involve local dependencies, an observation that has been expressed explicitly or implicitly in many phonological theories, such as the use of minimal symbols in SPE and the inclusion of primarily strictly local constraints in Optimality Theory. I propose a learning-based account of local phonological processes, providing an explicit computational model. The model is grounded in experimental results that suggest children are initially insensitive to long-distance dependencies and that as their ability to track non-adjacent dependencies grows, learners still prefer local generalisations to non-local ones. The model encodes these results by constructing phonological processes starting around an alternating segment and expanding outward to incorporate more phonological context only when surface forms cannot be predicted with sufficient accuracy. The model successfully constructs local phonological generalisations and exhibits the same preference for local patterns that humans do, suggesting that locality can emerge as a computational consequence of a simple learning procedure.

This article presents a broad overview of the fluid mechanics of the human cardiovascular system. Beginning in the heart, we travel through the main features of our circulation to highlight important functions and diseases where fluid mechanics plays a central role. Of particular focus is the role of computational modelling in uncovering the dynamic flow phenomenon throughout our body, its association with cardiovascular disease mechanisms and progression and its importance in clinical treatment planning. We also emphasize the multiscale nature of the cardiovascular system, and associated challenges. The main aim of this review is to highlight progress and ongoing challenges in our understanding of cardiovascular haemodynamics, as well as the future outlook for translating the current state-of-the-art to widespread clinical application and improved patient outcomes.

There is a current ‘theory crisis’ in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic input and multiple levels of language, have the potential to contribute significantly to our understanding of language acquisition. We then review recent results obtained through such language learning simulations. Finally, we propose some guidelines for the community to build better simulations.

Computational models of reading have tended to focus on the cognitive requirements of mapping among written, spoken, and meaning representations of individual words in adult readers. Consequently, the alignment of these computational models with behavioural studies of reading development has to date been limited. Models of reading have provided us with insights into the architecture of the reading system, and these have recently been extended to investigate literacy development, and the early language skills that influence children’s reading. These models show us: how learning to read builds on early language skills, why various reading interventions might be more or less effective for different children, and how reading develops across different languages and writing systems. Though there is growing alignment between descriptive models of reading behaviour and computational models, there remains a gap, and I lay out the groundwork for how translation may become increasingly effective through future modelling work.

In the design of long fibre reinforced thermoplastic (LFT) structures, there is a direct dependency on the manufacturing. Therefore, it is indispensable to integrate the manufacturing influences into the design process. This not only offers new opportunities for material- and load-adapted designs, but also reduces cost-intensive modifications in later stages. The goal of this contribution is to make the complexity manageable by presenting a method which couples LFT manufacturing and structural simulations in an automated optimization loop. Herein, the influence of linear-elastic, local anisotropic material properties as well as residual stresses resulting from the compression molding of LFT on the stiffness-optimized design of beaded plates is investigated. Based on the simulation studies in this contribution, it can be summarized that the resulting bead height and flank angle, considering anisotropies and residual stresses, are smaller compared to isotropic modelling. As a conclusion, the strength constraint limits the maximum bead height and the flank angle needs to be additionally chosen as a consequence of the local fibre orientations and residual stresses resulting from manufacturing. Optimized bead cross sections are only valid for a specific system under investigation, as they depend on the defined boundary conditions (load case, initial charge geometry and position, fibre orientations, etc.).

Formal models of past human societies informed by archaeological research have a high potential for shaping some of the most topical current debates. Agent-based models, which emphasize how actions by individuals combine to produce global patterns, provide a convenient framework for developing quantitative models of historical social processes. However, being derived from computer science, the method remains largely specialized in archaeology. In this paper and the associated tutorial, we provide a jargon-free introduction to the technique, its potential and limits as well as its diverse applications in archaeology and beyond. We discuss the epistemological rationale of using computational modeling and simulation, classify types of models, and give an overview of the main concepts behind agent-based modeling.

The Virtual Personalities Model is a motive-based neural network model that provides both a psychological model and a computational implementation that explicates the dynamics and often large within-person variability in behavior that arises over time. At the same time the same model can produce—across many virtual personalities—between-subject variability in behavior that when factor analyzed yields familiar personality structure (e.g., the Big Five). First, we describe our personality model and its implementation as a neural network model. Second, we focus on detailing the neurobiological underpinnings of this model. Third, we examine the learning mechanisms, and their biological substrates, as ways that the model gets “wired up,” discussing Pavlovian and Instrumental conditioning, Pavlovian to Instrumental transfer, and habits. Finally, we describe the dynamics of how initial differences in propensities (e.g., dopamine functioning), wiring differences due to experience, and other factors could operate together to develop and change personality over time, and how this might be empirically examined. Thus, our goal is to contribute to the rising chorus of voices seeking a more precise neurobiologically based science of the complex dynamics underlying personality.

During emerging disease outbreaks, public health, emergency management officials and decision-makers increasingly rely on epidemiological models to forecast outbreak progression and determine the best response to health crisis needs. Outbreak response strategies derived from such modelling may include pharmaceutical distribution, immunisation campaigns, social distancing, prophylactic pharmaceuticals, medical care, bed surge, security and other requirements. Infectious disease modelling estimates are unavoidably subject to multiple interpretations, and full understanding of a model's limitations may be lost when provided from the disease modeller to public health practitioner to government policymaker. We review epidemiological models created for diseases which are of greatest concern for public health protection. Such diseases, whether transmitted from person-to-person (Ebola, influenza, smallpox), via direct exposure (anthrax), or food and waterborne exposure (cholera, typhoid) may cause severe illness and death in a large population. We examine disease-specific models to determine best practices characterising infectious disease outbreaks and facilitating emergency response and implementation of public health policy and disease control measures.

CVD accounted for 27 % of all deaths in the UK in 2014, and was responsible for 1·7 million hospital admissions in 2013/2014. This condition becomes increasingly prevalent with age, affecting 34·1 and 29·8 % of males and females over 75 years of age respectively in 2011. The dysregulation of cholesterol metabolism with age, often observed as a rise in LDL-cholesterol, has been associated with the pathogenesis of CVD. To compound this problem, it is estimated by 2050, 22 % of the world's population will be over 60 years of age, in culmination with a growing resistance and intolerance to pre-existing cholesterol regulating drugs such as statins. Therefore, it is apparent research into additional therapies for hypercholesterolaemia and CVD prevention is a growing necessity. However, it is also imperative to recognise this complex biological system cannot be studied using a reductionist approach; rather its biological uniqueness necessitates a more integrated methodology, such as that offered by systems biology. In this review, we firstly discuss cholesterol metabolism and how it is affected by diet and the ageing process. Next, we describe therapeutic strategies for hypercholesterolaemia, and finally how the systems biology paradigm can be utilised to investigate how ageing interacts with complex systems such as cholesterol metabolism. We conclude by emphasising the need for nutritionists to work in parallel with the systems biology community, to develop novel approaches to studying cholesterol metabolism and its interaction with ageing.

One key issue in bilingualism is how bilinguals control production, particularly to produce words in the less dominant language. Language switching is one method to investigate control processes. Language switching has been much studied in comprehension, e.g., in lexical decision task, but less so in production. Here we first present a study of language switching in Italian–English adult bilinguals in a naming task for visually presented words. We demonstrate an asymmetric pattern of time costs to switch language, where participants incurred a greater time cost to switch into naming in their dominant language (Italian). In addition, costs were greater where the stimuli were interlingual cognates or homographs than words existing in only one language, implicating lexical competition as a source of the cost. To clarify the operation of control processes, we then present two connectionist models of bilingual naming, based on the previous models of Seidenberg and McClelland (1989), Cohen, Dunbar and McClelland (1990), Gilbert and Shallice (2002), and Karaminis and Thomas (2010). Crucially, both models acquired their differential language dominance via an experience-dependent learning process. The models embody different assumptions about the language control processes that produce the switch cost. We consider which processing assumptions are sufficient to explain asymmetric language switch costs and word class effects on language switching in individual word reading, as well as generating novel predictions for future testing.

A computational framework for testing the effects of cytotoxic molecules, specific to agiven phase of the cell cycle, and vascular disrupting agents (VDAs) is presented. Themodel is based on a cellular automaton to describe tumour cell states transitions fromproliferation to death. It is coupled with a model describing the tumour vasculature andits adaptation to the blood rheological constraints when alterations are induced by VDAstreatment. Several therapeutic protocols in two structurally different vascular networkswere tested by varying the duration of cytotoxic drug perfusion and the periodicity oftreatment cycles. The impact of VDAs were also tested both experimentally from intravitalmicroscopy through a dorsal skinfold chamber on a mouse and numerically. Simulationresults show that combining cytotoxic treatment with a post treatment of VDA through ajudicious timing could favour the rapid eradication of the tumour. The computationalframework thus gives some insights into the outcome of cytotoxic and VDAs treatments on aqualitative basis. Future validation from our experimental setup could open up newperspectives towards Computer-Assisted Therapeutic Strategies.