Computer aided conversation for severely physically impaired non-speaking people

Computer aided conversation for severely physically impaired non-speaking peopl

Embedding expert opinion in a Bayesian network model to predict wheat yield from spring-summer weather

Wheat yield is highly dependent on weather, Therefore, predicting its effect can improve crop management decisions. Various modelling approaches have been used to predict wheat yield including process-based modelling, statistical models, and machine learning. However, these models typically require a large data set for training or fitting. They often also have a limited ability in capturing the effects of small-scale variability, time, and duration of extreme weather events. Here, we develop a Bayesian Network (BN) model by interviewing experts including farmers, embedding their knowledge from years of experience within a quantitative model. These experts identified the period from the beginning of anthesis to the end of grain filling stage as a critical period and maximum temperature, mean temperature and precipitation as key weather variables for inclusion in the BN. To keep the time input from experts manageable, the conditional probability table for the BN was constructed based on their anticipated impact on the mean yield of different weather conditions. The model predicted the yield in the same or neighbouring class (very low, low, medium, high and very high) as the reported yield with low error rate ranging from 9.1 to 15.2% and, when used to estimate the median predicted yield, R2 ranging from 41 to 52%. Interestingly, model successfully predicted the yield in years 1998, 2007, 2012 and 2020 which had the most extreme weather events. Additionally, the more recent data, from 2012 to 2022 was predicted more accurately, especially 2022 season which was not sown yet when eliciting information and recently added to the testing data. Little difference was observed between the predictions made using model parameters based only the opinion of the farm manager from which the test data originated, and the predictions made using the average opinion of a group of 9 experts. The inclusion of causal variables in the model also provided insight into the experts’ rationale, allowing unexpected results to be explored. This methodology provides a means to rapidly develop a successful predictive model of wheat yield with limited (or no) data using expert understanding. This model could be tuned and updated with data as it becomes available

Storylines for decision-making: climate and food security in Namibia

Storylines are plausible descriptions of past or future events and can be used to characterise uncertainty through discrete possible futures. They thereby bridge the gap between global-scale future projections and local-scale impacts, providing decision-makers with useful information about potential impacts in multivariate systems despite large swathes of missing data. Here we demonstrate the storyline approach using the case of household food security in the Caprivi region of Namibia, an example of a complex system with multiple interacting drivers. We develop a network characterising influences on household food security, highlighting drivers that are affected by the local weather (with climate understood to constitute the collection of possible weather states). The network is used to understand the storyline leading to household impacts in 2013-14, a consumption year affected by flooding, and the effects of a range of interventions across wealth groups. Counterfactual storylines are also developed to characterise potential impacts under different local and national conditions. Through this we demonstrate how a storyline approach can embed local contextual information to provide decision-makers with comprehensible and assessable information about possible futures and interventions. We also highlight the importance of identifying common drivers, in this case the local weather, in producing plausible impact storylines

The British Army, information management and the First World War revolution in military affairs

Information Management (IM) – the systematic ordering, processing and channelling of information within organisations – forms a critical component of modern military command and control systems. As a subject of scholarly enquiry, however, the history of military IM has been relatively poorly served. Employing new and under-utilised archival sources, this article takes the British Expeditionary Force (BEF) of the First World War as its case study and assesses the extent to which its IM system contributed to the emergence of the modern battlefield in 1918. It argues that the demands of fighting a modern war resulted in a general, but not universal, improvement in the BEF’s IM techniques, which in turn laid the groundwork, albeit in embryonic form, for the IM systems of modern armies. KEY WORDS: British Army, Information Management, First World War, Revolution in Military Affairs, Adaptatio

The RESET project: constructing a European tephra lattice for refined synchronisation of environmental and archaeological events during the last c. 100 ka

This paper introduces the aims and scope of the RESET project (. RESponse of humans to abrupt Environmental Transitions), a programme of research funded by the Natural Environment Research Council (UK) between 2008 and 2013; it also provides the context and rationale for papers included in a special volume of Quaternary Science Reviews that report some of the project's findings. RESET examined the chronological and correlation methods employed to establish causal links between the timing of abrupt environmental transitions (AETs) on the one hand, and of human dispersal and development on the other, with a focus on the Middle and Upper Palaeolithic periods. The period of interest is the Last Glacial cycle and the early Holocene (c. 100-8 ka), during which time a number of pronounced AETs occurred. A long-running topic of debate is the degree to which human history in Europe and the Mediterranean region during the Palaeolithic was shaped by these AETs, but this has proved difficult to assess because of poor dating control. In an attempt to move the science forward, RESET examined the potential that tephra isochrons, and in particular non-visible ash layers (cryptotephras), might offer for synchronising palaeo-records with a greater degree of finesse. New tephrostratigraphical data generated by the project augment previously-established tephra frameworks for the region, and underpin a more evolved tephra 'lattice' that links palaeo-records between Greenland, the European mainland, sub-marine sequences in the Mediterranean and North Africa. The paper also outlines the significance of other contributions to this special volume: collectively, these illustrate how the lattice was constructed, how it links with cognate tephra research in Europe and elsewhere, and how the evidence of tephra isochrons is beginning to challenge long-held views about the impacts of environmental change on humans during the Palaeolithic. © 2015 Elsevier Ltd.RESET was funded through Consortium Grants awarded by the Natural Environment Research Council, UK, to a collaborating team drawn from four institutions: Royal Holloway University of London (grant reference NE/E015905/1), the Natural History Museum, London (NE/E015913/1), Oxford University (NE/E015670/1) and the University of Southampton, including the National Oceanography Centre (NE/01531X/1). The authors also wish to record their deep gratitude to four members of the scientific community who formed a consultative advisory panel during the lifetime of the RESET project: Professor Barbara Wohlfarth (Stockholm University), Professor Jørgen Peder Steffensen (Niels Bohr Institute, Copenhagen), Dr. Martin Street (Romisch-Germanisches Zentralmuseum, Neuwied) and Professor Clive Oppenheimer (Cambridge University). They provided excellent advice at key stages of the work, which we greatly valued. We also thank Jenny Kynaston (Geography Department, Royal Holloway) for construction of several of the figures in this paper, and Debbie Barrett (Elsevier) and Colin Murray Wallace (Editor-in-Chief, QSR) for their considerable assistance in the production of this special volume.Peer Reviewe

Multilocus ISSR Markers Reveal Two Major Genetic Groups in Spanish and South African Populations of the Grapevine Fungal Pathogen Cadophora luteo-olivacea

Cadophora luteo-olivacea is a lesser-known fungal trunk pathogen of grapevine which has been recently isolated from vines showing decline symptoms in grape growing regions worldwide. In this study, 80 C. luteo-olivacea isolates (65 from Spain and 15 from South Africa) were studied. Inter-simple-sequence repeat-polymerase chain reaction (ISSR-PCR) generated 55 polymorphic loci from four ISSR primers selected from an initial screen of 13 ISSR primers. The ISSR markers revealed 40 multilocus genotypes (MLGs) in the global population. Minimum spanning network analysis showed that the MLGs from South Africa clustered around the most frequent genotype, while the genotypes from Spain were distributed all across the network. Principal component analysis and dendrograms based on genetic distance and bootstrapping identified two highly differentiated genetic clusters in the Spanish and South African C. luteo-olivacea populations, with no intermediate genotypes between these clusters. Movement within the Spanish provinces may have occurred repeatedly given the frequent retrieval of the same genotype in distant locations. The results obtained in this study provide new insights into the population genetic structure of C. luteo-olivacea in Spain and highlights the need to produce healthy and quality planting material in grapevine nurseries to avoid the spread of this fungus throughout different grape growing regions

A customisable framework for hardware compilation

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