The evolution of multivariate maternal effects.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tThere is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.This work was supported by EPSRC grant EP/H031928/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

STABILIZATION BY ADAPTIVE FEEDBACK CONTROL FOR POSITIVE DIFFERENCE EQUATIONS WITH APPLICATIONS IN PEST MANAGEMENT

An adaptive feedback control scheme is proposed for stabilizing a class of forced nonlinear positive difference equations. The adaptive scheme is based on so-called high-gain adaptive controllers and contains substantial robustness with respect to model uncertainty as well as with respect to persistent forcing signals, including measurement errors. Our results take advantage of the underlying positive systems structure and ideas from input-to-state stability from nonlinear control theory. Our motivating application is to pest or weed control, and in this context the present work substantially strengthens previous work by the authors. The theory is illustrated with examples

Hyperparameter optimized classification pipeline for handling unbalanced urban and rural energy consumption patterns

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Data availability: Data will be made available on request.Energy consumer locations are required for framing effective energy policies. However, due to privacy concerns, it is becoming increasingly difficult to obtain the locational data of the consumers. Machine learning (ML) based classification strategies can be used to find the locational information of the consumers based on their historical energy consumption patterns. The ML methods in this paper are applied to the Residential Energy Consumption Survey 2009 dataset. In this dataset, the number of consumers in the urban area is higher than the rural area, thus making the classification problem unbalanced. The unbalanced classification problem has been solved in original and transformed or reduced feature space using Monte Carlo based under-sampling of the majority class datapoints. The hyperparameters for each classification algorithm family is represented as an optimized pipeline, obtained using the genetic programming (GP) optimizer. The classification performance metrics are then obtained for different algorithm families on the original and transformed feature spaces. Performance comparisons have been reported using univariate and bivariate distributions of the classification metrics viz. accuracy, geometric mean score (GMS), F1 score, precision, area under the curve (AUC) of receiver operator characteristics (ROC). The energy policy aspects for the urban and rural residential consumers based on the classification results have also been discussed.European Regional Development Fund (ERDF

Swarm Intelligence Based Drone Flocking Model

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordFlocking, shoaling and swarming in animal groups serve a number of functions, including improving information transmission and reducing predation risks. Individuals in biological populations tend to make limited and simple responses to each other and also to stimuli in the environment. But by acting together they can accomplish collective tasks, which is referred to as swarm intelligence. Insights from natural systems have inspired work in numerous areas, such as meta-heuristic optimization, machine learning and image processing. However, the limitations of information sharing, and transfer make it difficult to solve real-world engineering problems in physical world using the swarm intelligence mechanism. This contrasts with natural systems where, for example, birds use social information to improve sensing of environmental cues and make decisions without lag during flight. Thus, behavioural modelling of animal swarming may provide new insights into this problem. Here, we show comparison of two data-driven deep neural network models for drone flocking

A cluster analysis approach to sampling domestic properties for sensor deployment

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Sensors are an increasingly widespread tool for monitoring utility usage (e.g., electricity) and environmental data (e.g., temperature). In large-scale projects, it is often impractical and sometimes impossible to place sensors at all sites of interest, for example due to limited sensor numbers or access. We test whether cluster analysis can be used to address this problem. We create clusters of potential sensor sites using factors that may influence sensor measurements. The clusters provide groups of sites that are similar to each other, and that differ between groups. Sampling a few sites from each group provides a subset that captures the diversity of sites. We test the approach with two types of sensors: utility usage (gas and water) and outdoor environment. Using a separate analysis for each sensor type, we create clusters using characteristics from up to 298 potential sites. We sample across these clusters to provide representative coverage for sensor installations. We verify the approach using data from the sensors installed as a result of the sampling, as well as using other sensor measures from all available sites over one year. Results show that sensor data vary across clusters, and vary with the factors used to create the clusters, thereby providing evidence that this cluster-based approach captures differences across sensor sites. This novel methodology provides representative sampling across potential sensor sites. It is generalisable to other sensor types and to any situation in which influencing factors at potential sites are known. We also discuss recommendations for future sensor-based large-scale projects.European Regional Development Fund (ERDF)Southwest Academic Health Science NetworkCornwall Counci

AI-based healthcare: a new dawn or apartheid revisited?

This is the final version. Available on open access from Springer via the DOI in this recordThe Bubonic Plague outbreak that wormed its way through San Francisco’s Chinatown in 1900 tells a story of prejudice guiding health policy, resulting in enormous suffering for much of its Chinese population. This article seeks to discuss the potential for hidden “prejudice” should Artificial Intelligence (AI) gain a dominant foothold in healthcare systems. Using a toy model, this piece explores potential future outcomes, should AI continue to develop without bound. Where potential dangers may lurk will be discussed, so that the full benefits AI has to offer can be reaped whilst avoiding the pitfalls. The model is produced using the computer programming language MATLAB and offers visual representations of potential outcomes. Interwoven with these potential outcomes are numerous historical models for problems caused by prejudice and recent issues in AI systems, from police prediction and facial recognition software to recruitment tools. Therefore, this research’s novel angle, of using historical precedents to model and discuss potential futures, offers a unique contribution

Fertilizer application and deep leaching of nitrate under long term crop rotation

Non-Peer ReviewedIt is commonly believed that the use of nitrogen fertilizers in agriculture will lead eventually to the loss of nitrate via leaching. The nitrate leached below the root zone has the potential to contaminate underground water. The results obtained from various long term crop rotation studies in Saskatchewan suggest that this common belief may not hold in general. This is especially true where nitrogen fertilizers were applied based on soil test recommendation and the land was continuously cropped. Under long term crop rotation studies in the Black Soil Zone at Melfort, the application of nitrogen fertilizer in recent years were based on the general recommendation for wheat. The deep core sample revealed that more nitrate was present in the soil profile under fertilized continuous wheat compared to the unfertilized plots. However, in the Black Soil at Indian Head, where fertilizer application was based on soil test values, similar amounts of nitrate were found below the root zone of fertilized and unfertilized plots after 34 years of continuous wheat. This was in spite of applying 1584 kg of N ha-1 to the fertilized plot over 34 years. A result similar to that at Indian Head was obtained from the crop rotation experiment in the Brown Soil Zone at Swift Current. In the Brown Soil Zone, the inclusion of a fallow phase in the rotation, increased the amount of nitrate found below the root zone although this system had received less fertilizer over the years than the continuously cropped plots. The fallow phase appeared to provide a window for the leakage of nitrate accumulated within the root zone. This was attributed to a better moisture (antecedent moisture) regime and higher amount of mineralized nitrate during the fallow phase. On the other hand, frequent summerfallow can deplete the soil of its N supplying power and this may eventually result in less nitrate leached as was found for the 2-yr rotation at Indian Head after 34 yr

Modelling mould growth in domestic environments using relative humidity and temperature

This is the final version. Available from Elsevier via the DOI in this record. Damp and high levels of relative humidity (RH), typically above 70–80%, are known to provide mould-favourable conditions. Exposure to indoor mould contamination has been associated with an increased risk of developing and/or exacerbating a range of allergic and non-allergic diseases. The VTT model is a mathematical model of indoor mould growth that was developed based on surface readings of RH and temperature on wood in a controlled laboratory chamber. The model provides a mould index based on the environmental readings. We test the generalisability of this laboratory-based model to less-controlled domestic environments across different values of model parameters. Mould indices were generated using objective measurements of RH and temperature in the air, taken from sensors in a domestic setting every 3–5 min over 1 year in the living room and bedroom across 219 homes. Mould indices were assessed against self-reports from occupants regarding the presence of visible mould growth and mouldy odour in the home. Logistic regression provided evidence for relationships between mould indices and occupant responses. Mould indices were most successful at predicting occupant responses when the model parameters encouraged higher vulnerability to mould growth compared with the original VTT model. A lower critical RH level, above which mould grows, a higher sensitivity, and larger increases in the mould index all consistently increased performance. Using moment-to-moment time-series data for temperature and RH, the model and its developments could help inform smart monitoring or control of RH, for example to counter risks associated with reduced ventilation in energy efficient homes.European Regional Development Fund (ERDF)European Regional Development Fund (ERDF