During the first few centuries AD, Rome and the imperial frontiers were supplied with olive oil from the province of Hispania Baetica (southern Spain). Vast quantities of oil were exported in Dressel 20 amphorae. But how did the agricultural economy of Baetica relate to global demand and how did it change over time? The author focuses on relative changes in agricultural output, using a new method to model fluctuations in amphora production based on more than 1000 waster sherds collected from 23 amphora workshops in the Guadalquivir Valley. The chrono-proportional representation method indicates variation in production between individual workshops and wider production districts, contributing to assessments of the scale and organisation of the Roman economy.

Some human settlements endure for millennia, while others are founded and abandoned within a few decades or centuries. The reasons for variation in the duration of site occupation, however, are rarely addressed. Here, the authors introduce a new approach for the analysis of settlement longevity or persistence. Using seven regional case studies comprising both survey and excavation data, they demonstrate how the median persistence of individual settlements varies widely within and among regions. In turn, this variability is linked to the effects of environmental potential. In seeking to identify the drivers of settlement persistence in the past, it is suggested that archaeologists can contribute to understanding of the sustainability and resilience of contemporary cities.

This study investigates how Japanese-speaking children learn interactional dependencies in conversations that determine the use of un, a token typically used as a positive response for yes-no questions, backchannel, and acknowledgement. We hypothesise that children learn to produce un appropriately by recognising different types of cues occurring in the immediately preceding turns. We built a set of generalised linear models on the longitudinal conversation data from seven children aged 1 to 5 years and their caregivers. Our models revealed that children not only increased their un production, but also learned to attend relevant cues in the preceding turns to understand when to respond by producing un. Children increasingly produced un when their interlocutors asked a yes-no question or signalled the continuation of their own speech. These results illustrate how children learn the probabilistic dependency between adjacent turns, and become able to participate in conversational interactions.

This article explores the anglicisation of the Scots language between the sixteenth and eighteenth centuries, focusing on the variation between the orthographic clusters <quh-> and <wh-> found in relative and interrogative clause markers. Using modern statistical techniques, we provide the most comprehensive empirical analysis of this variation so far in the Helsinki Corpus of Older Scots (Meurman-Solin 1995). By combining the techniques of Variability-Based Neighbour Clustering (Gries & Hilpert 2008, 2010, 2012) with mixed-effects logistic regression modelling (Baayen et al.2008), we uncover a different trajectory of change than that which has previously been reported for this feature (Meurman-Solin 1993, 1997). We argue that by using modern methods of data reduction and statistical modelling, we can present a picture of language change in Scots that is more fine-grained than previous studies which use only descriptive statistics.

Due to differences in the circulation of influenza viruses, distribution and antigenic drift of A subtypes and B lineages, and susceptibility to infection in the population, the incidence of symptomatic influenza infection can vary widely between seasons and age-groups. Our goal was to estimate the symptomatic infection incidence in the Netherlands for the six seasons 2011/2012 through 2016/2017, using Bayesian evidence synthesis methodology to combine season-specific sentinel surveillance data on influenza-like illness (ILI), virus detections in sampled ILI cases and data on healthcare-seeking behaviour. Estimated age-aggregated incidence was 6.5 per 1000 persons (95% uncertainty interval (UI): 4.7–9.0) for season 2011/2012, 36.7 (95% UI: 31.2–42.8) for 2012/2013, 9.1 (95% UI: 6.3–12.9) for 2013/2014, 41.1 (95% UI: 35.0–47.7) for 2014/2015, 39.4 (95% UI: 33.4–46.1) for 2015/2016 and 27.8 (95% UI: 22.7–33.7) for season 2016/2017. Incidence varied substantially between age-groups (highest for the age-group <5 years: 23 to 47/1000, but relatively low for 65+ years: 2 to 34/1000 over the six seasons). Integration of all relevant data sources within an evidence synthesis framework has allowed the estimation – with appropriately quantified uncertainty – of the incidence of symptomatic influenza virus infection. These estimates provide valuable insight into the variation in influenza epidemics across seasons, by virus subtype and lineage, and between age-groups.

We present a statistical-modeling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.

Valid estimation of usual dietary intake in epidemiological studies is a topic of present interest. The aim of the present paper is to review recent literature on innovative approaches focussing on: (1) the requirements to assess usual intake and (2) the application in large-scale settings. Recently, a number of technology-based self-administered tools have been developed, including short-term instruments such as web-based 24-h recalls, mobile food records or simple closed-ended questionnaires that assess the food intake of the previous 24 h. Due to their advantages in terms of feasibility and cost-effectiveness these tools may be superior to conventional assessment methods in large-scale settings. New statistical methods have been developed to combine dietary information from repeated 24-h dietary recalls and FFQ. Conceptually, these statistical methods presume that the usual food intake of a subject equals the probability of consuming a food on a given day, multiplied by the average amount of intake of that food on a typical consumption day. Repeated 24-h recalls from the same individual provide information on consumption probability and amount. In addition, the FFQ can add information on intake frequency of rarely consumed foods. It has been suggested that this combined approach may provide high-quality dietary information. A promising direction for estimation of usual intake in large-scale settings is the integration of both statistical methods and new technologies. Studies are warranted to assess the validity of estimated usual intake in comparison with biomarkers.

Next to the information on frequency of food consumption, information on consumption-day amounts is important to estimate usual dietary intake in epidemiological studies. Our objective was to identify determinants of consumption-day amounts to derive person-specific standard consumption-day amounts applicable for the estimation of usual dietary intake using separate sources to assesss information on consumption probability and amount consumed. 24-h Dietary recall data from the German National Nutrition Survey II (n = 8522; aged 20–80 years) conducted between 2005 and 2007 were analysed for determinants of consumption-day amounts of thirty-eight food and beverage groups using LASSO variable selection for linear mixed-effects models. Determinants included sex, age, BMI, smoking status, years of education, household net income, living status and employment status. Most often, sex, age and smoking status were selected as predictors for consumption-day amounts across thirty-eight food groups. In contrast, living with a partner, employment status and household net income were less frequently chosen. Overall, different determinants were of relevance for different food groups. The number of selected determinants ranged from eight for coffee and juice to zero for cabbage, tea, root vegetables, leafy vegetables, fruit vegetables, legumes, offal, vegetable oils, and other fats. For the estimation of usual dietary intake in a combined approach with a 24-h food list, person-specific standard consumption-day amounts could be used. Sex, age and smoking status were shown to be the most relevant predictors in our analysis. Their impact on the estimation of usual dietary intake needs to be evaluated in future studies.

In this paper we calculate and compare diagnosis and net premium rates for critical illness insurance using different models for the claim delay distribution (CDD). The choice of CDD affects the diagnosis rates and hence the net premium rates in two ways: through the estimation of missing dates of diagnosis and through the adjustment of the exposure to allow for claims diagnosed but not settled in the observation period. We consider two CDDs: a three-parameter Burr distribution and a lognormal distribution. Our conclusion, based on a single, but extensive, data set, is that net premium rates are not significantly affected by the choice of CDD.

The force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data.

Astronomy as an observational science is technology driven both from the point of view of data acquisition and of data processing and visualisation. Astronomy exploits a very wide base of technologies which are developed, enhanced and extended by users. Consequently, astronomy can return new and enhanced technologies to areas well outside of astronomy itself. My own hi-tech company, Astraguard, a video imaging company, is a small but significant example of that technology return. Astronomy can provide both know-how and people for a diverse variety of areas: security, industrial process control, medical and biological imaging, petrochemicals, databases, and the financial industries to name but a few. It is unfortunate that those who teach astronomy are generally not aware of these possibilities.

In this lecture I hope to take a first step towards showing what is possible. I hope to convince the reader that astronomy education, at all levels, can play a significant role in career development outside of astronomy and in higher education in developing countries.

Developmental rates for Wuchereria bancrofti larvae maturing in the vector Aedes polynesiensis were estimated by analysing stage-frequency data consisting of counts of larval stages in mosquitoes reared at 20, 22·5, 25, 27·5, 30 and 32 °C. Base temperatures (i.e. low temperature thresholds) for W. bancrofti development were estimated by the x-intercept method and the model of Lactin et al. (1995). Resulting values were similar with both methods and were ≈12·5 °C for microfilariae (mf) in thorax, ≈17 °C for L1, 15·5 °C for L2 and 16·5 °C for L3. Upper thresholds estimated by the Lactin et al. model were 29·3 °C for mf, 29·1 °C for L1, 32·2 °C for L2 and 31·5 °C for L3. In addition, an original method was devised for computing the L3 upper threshold, by modelling L3 length shrinkage with temperature. It gave a value of 31·4 °C. At 32 °C, L2 and L3 stages exhibited altered morphology, larvae being shorter and wider than expected. The model of Lactin et al. described adequately the non-linear relationship between developmental rate and temperature, but a linear degree-day approach may be applied for temperatures below 27–28 °C.