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With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort. Author summary: In recent years, there is an increased interest of estimating heritability from genome-wide SNP data in large scale cohort studies. Here, we propose the PredLMM, a computationally rapid and memory-efficient linear mixed model for heritability estimation. The proposed approach can estimate SNP heritability on Biobank-scale datasets in a fraction of time compared to the existing mixed model based approaches. Along with the extensive simulations illustrating the precision and robustness of the PredLMM, we have also estimated heritability of several anthropometric traits from the UK Biobank cohort. [ABSTRACT FROM AUTHOR]

Operating reserves (OR), alongside other ancillary services, are essential for mitigating the increasing uncertainty in generation from Intermittent Renewable Energy Sources (IRES). Traditional OR are determined based on rules of thumb, such as reliability considerations (N − 1 or N − k), and they neglect the uncertainty between dispatch intervals. Load–generation imbalances lead to excessive reliance on regulation reserves and out-of-merit dispatch. Highlighting this, recent literature has focused on developing models for dynamic reserve quantification. While the models significantly enhance the traditional static reserve quantification, they heavily rely on probabilistic approaches. Despite their effectiveness, probabilistic approaches are computationally complex and necessitate precise historical data. Furthermore, most studies do not consider the correlation between load and IRES when determining reserves, leading to inaccurate estimation and impacting the system's economics. Affine arithmetic-based models emerge as a promising solution, offering the ability to estimate correlated uncertainties with reduced computational complexity. This paper contributes to developing affine arithmetic models for OR quantification, considering IRES and load correlated uncertainties. Two distinct approaches to reserve quantification are explored: (1) deterministic scheduling with exogenous OR quantification, and (2) affine arithmetic-based scheduling framework to quantify and allocate OR endogenously. A comparative analysis is conducted with probabilistic scenario-based method and interval arithmetic scheduling. The effectiveness of the models is analyzed on the Great Britain test system with 40% renewable integration. Numerical results highlight that around 99% of the probabilistic net-load scenarios are within the net-load bounds generated by the proposed methodology. The reserve requirement is minimized by approximately 8% during peak hours and 35% during off-peak hours with correlated uncertainty. Furthermore, AA-based approach achieved a 15% reduction in total operating cost during the considered operational time-frame, compared to interval arithmetic optimization. • A multi-objective optimization model for reserve quantification and allocation. • Affine arithmetic based correlated uncertainties of renewable power and load. • Exogenous and endogenous methods for reserve assessment are presented. • Trade-offs between computational time and cost are deciding factors. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights: [•] Global models in the theory and practice of public relations tend toward hegemony and simplification. [•] The UK and Singapore are exemplars where hegemony has arguably succeeded and where it needs to be resisted, respectively. [•] Theoretical diversity is required to reflect the complexity of globalization. [•] Epistemological and practical subsidiarity can and should lead the way. [ABSTRACT FROM AUTHOR]

AimThe aim of this paper is to identify and descriptively map the key characteristics of the model of service delivery in operation, and to explore the user, carer and professional experience of service provision. This included an exploration of congruity and mismatch between the different stakeholder groups.BackgroundIn the United Kingdom (UK), 15% of the children under five years of age and 20% of the 5 to 15-year age group are reported to have a complex long-term condition, with the likelihood of having a condition increasing according to socio-economic circumstances. An increasing number of young people with complex needs are now surviving into late adolescence and early adulthood. However, service provision for children with complex needs is an area that, nationally, has been underdeveloped.MethodsAn exploratory single-site case study was undertaken across one Primary Care Trust in the UK. Documentary and policy review were undertaken along with in-depth qualitative exploration. Eighteen in-depth interviews were undertaken with relevant stakeholders and professionals across the multidisciplinary teams. Families with children between 12 months and 16 years of age who have continuing complex care needs were invited to take part in an interview to give their views about the care they receive. Interviews focused on the family experience and understanding of the child's condition, transition between secondary and primary care, effectiveness of admission and discharge planning and the overall contribution of different professionals. Professionals were also asked about their experiences of delivering care.FindingsThis study highlighted issues of communication between professionals and with parents and children as a major factor in determining the quality of service provision. Key aspects relating to the model of service provision, namely, paucity of communication, interagency collaboration and the parent as health worker, are highlighted.ConclusionsParents experienced both health and social service communication challenges when seeking care for their child. These challenges can be located within a general systems theory and hierarchy approaches to understand the complexity of service provision. [ABSTRACT FROM AUTHOR]

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states ('rumination', 'eating' and 'other') using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F 1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years. [ABSTRACT FROM AUTHOR]

Understanding the energy demand of a city's housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity. [ABSTRACT FROM AUTHOR]