Modelling occupants' personal characteristics for thermal comfort prediction

Based on results from a field survey campaign conducted in Switzerand, we show that occupants' variations in clothing choices, which are relatively unconstrained, are best described by the daily mean outdoor temperature and that major clothing adjustments occur rarely during the day. We then develop an ordinal logistic model of the probability distribution of discretised clothing levels, which results in a concise and informative expression of occupants' clothing choices. Results from both cross-validation and independent verification suggest that this model formulation may be used with confidence. Furthermore, the form of the model is readily generalisable, given the requisite calibration data, to environments where dress codes are more specific. We also observe that, for these building occupants, the prevailing metabolic activity levels are mostly constant for the whole range of surveyed environmental conditions, as their activities are relatively constrained by the tasks in hand. Occupants may compensate for this constraint, however, through the consumption of cold and hot drinks, with corresponding impacts on metabolic heat production. Indeed, cold drink consumption was found to be highly correlated with indoor thermal conditions, whilst hot drink consumption is best described by a seasonal variable. These variables can be used for predictive purposes using binary logistic model

The WTO Cotton Case and US Domestic Policy

Crop Production/Industries, International Relations/Trade,

A rapid urban de-carbonization scenario analysis tool

A rapid urban de-carbonization scenario analysis tool has been developed. The tool is able to efficiently and effectively generate and populate spatially resolved large scale building scenes, to generate XML input files for the building energy simulation engine CitySim [1], to quickly modify building thermal attributes and develop and analyze de-carbonization scenarios as snapshot modifications to the building scene. The tool has been developed as a series of plugins to the Quantum Geographical Information System (QGIS) [2] application, whereby it can make use of much of the QGIS existing functionality and software libraries. A tip to tail test of the tool is performed on a basic scenario

Identification of body fat tissues in MRI data

In recent years non-invasive medical diagnostic techniques have been used widely in medical investigations. Among the various imaging modalities available, Magnetic Resonance Imaging is very attractive as it produces multi-slice images where the contrast between various types of body tissues such as muscle, ligaments and fat is well defined. The aim of this paper is to describe the implementation of an unsupervised image analysis algorithm able to identify the body fat tissues from a sequence of MR images encoded in DICOM format. The developed algorithm consists of three main steps. The first step pre-processes the MR images in order to reduce the level of noise. The second step extracts the image areas representing fat tissues by using an unsupervised clustering algorithm. Finally, image refinements are applied to reclassify the pixels adjacent to the initial fat estimate and to eliminate outliers. The experimental data indicates that the proposed implementation returns accurate results and furthermore is robust to noise and to greyscale in-homogeneity

Variability of human behaviour in outdoor public spaces, associated with the thermal environment

This paper presents part of the outcomes of a programme of research into the influence of the thermal environment on human behaviour in an outdoor public seating area. The research was conducted during one month in summer, autumn and winter of 2015 and 2016. The data gathered consists in the conduct of people using a public square in Nottingham city centre, and measurements of the environmental conditions taken at that place. The data of Number of People and the Size of Groups of people, were analysed according with the thermal environment of the place. The results showed a strong significant correlation between Number of People and Globe Temperature_sun [r = .66, p < .001]. A multiple regression analysis found that the Number of People per minute in a public space can be predicted using the Globe Temperature_sun and the Wind Speed data of that place [R-square of .39, p < 0.001]. These prediction models can be used to forecast the occupancy of the place and the grouping of users under different environmental conditions. The results can assist the design of urban spaces by allowing testing their future use with predicted data of human behaviour. In addition, the data obtained will serve as a foundation for further research about the human behaviour in public spaces

A review and critique of UK housing stock energy models, modelling approaches and data sources

The UK housing stock is responsible for some 27% of national energy demand and associated carbon dioxide emissions. 80% of this energy demand is due to heating (60%) and domestic hot water (20%), the former reflecting the poor average thermal integrity of the envelope of the homes comprising this stock. To support the formulation of policies and strategies to decarbonise the UK housing stock, a large number of increasingly sophisticated Housing Stock Energy Models (HSEMs) have been developed throughout the past 25 years. After describing the sources of data and the spatio-temporal granularity with which these data are available to represent this stock, as well as the physical and social phenomena that are modelled and the range of strategies employed to do so, this paper evaluates the 29 HSEMs that have been developed and deployed in the UK. In this we consider the models' predictive accuracy, predictive sensitivity to design parameters, versatility, computational efficiency, the reproducibility of predictions and software usability as well as the models' transparency (how open they are) and modularity. We also discuss their comprehensiveness. From this evaluation, we conclude that current HSEMs are lacking in transparency and modularity, they are limited in their scope and employ simplistic models that limit their utility; in particular, relating to the modelling of heat flow and in the modelling of household behaviours relating to investment decisions and energy using practices. There is a need for an open-source and modular dynamic housing stock energy modelling platform that addresses current limitations, can be readily updated as new (e.g. housing survey) calibration data is released and be readily extended by the modelling community at large: improving upon the utilisation of scarce developmental resources. This would represent a considerable step forward in the formulation of housing stock decarbonisation policy that is informed by sound evidence

Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms

Estimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate models of building stock from which appropriate parameters may be extracted. For example, the form of a building, such as whether it is detached, semi-detached, terraced etc and its shape may be used as part of a typology for defining its likely energy use. When these details are combined with information on building construction materials or glazing ratio, it can be used to infer the heat transfer characteristics of different properties. However, these data are not readily available for energy modelling or urban simulation. Although this is not a problem when the geographic scope corresponds to a small area and can be hand-collected, such manual approaches cannot be easily applied at the city or national scale. In this paper, we demonstrate an approach that can automatically extract this information at the city scale using off-the-shelf products supplied by a National Mapping Agency. We present two novel techniques to create this knowledge directly from input geometry. The first technique is used to identify built form based upon the physical relationships between buildings. The second technique is used to determine a more refined internal/external wall measurement and ratio. The second technique has greater metric accuracy and can also be used to address problems identified in extracting the built form. A case study is presented for the City of Nottingham in the United Kingdom using two data products provided by the Ordnance Survey of Great Britain (OSGB): MasterMap and AddressBase. This is followed by a discussion of a new categorisation approach for housing form for urban energy assessment

Rapid automated measurement of body fat distribution from whole-body MRI

The accurate determination of a person’s total body fat is an important issue in medical analysis because obesity is a significant contributing factor to a variety of serious health problems. The medical literature identifies a wide range of diseases that are closely linked to obesity. Current methods of fat assessment are largely inaccurate, and most current methods of fat determination cannot show regional fat distribution, which is important in defining disease risk. We introduce a method that combines computer-aided techniques with whole-body MRI techniques and enables accurate quantification and visualization of total body fat burden and regional fat distribution. This technique may be important in identifying and treating at-risk populations

Модель похибки вимірювального трансформатора напруги

QC 20160524</p

Evolution of the Mass Function of Dark Matter Haloes

We use a high resolution $\Lambda$CDM numerical simulation to calculate the mass function of dark matter haloes down to the scale of dwarf galaxies, back to a redshift of fifteen, in a 50 $h^{-1}$Mpc volume containing 80 million particles. Our low redshift results allow us to probe low $\sigma$ density fluctuations significantly beyond the range of previous cosmological simulations. The Sheth and Tormen mass function provides an excellent match to all of our data except for redshifts of ten and higher, where it overpredicts halo numbers increasingly with redshift, reaching roughly 50 percent for the 10^{10}-10^{11} \msun haloes sampled at redshift 15. Our results confirm previous findings that the simulated halo mass function can be described solely by the variance of the mass distribution, and thus has no explicit redshift dependence. We provide an empirical fit to our data that corrects for the overprediction of extremely rare objects by the Sheth and Tormen mass function. This overprediction has implications for studies that use the number densities of similarly rare objects as cosmological probes. For example, the number density of high redshift (z $\simeq$ 6) QSOs, which are thought to be hosted by haloes at 5$\sigma$ peaks in the fluctuation field, are likely to be overpredicted by at least a factor of 50%. We test the sensitivity of our results to force accuracy, starting redshift, and halo finding algorithm.Comment: v2: 9 pages, 11 figures, accepted by MNRAS with revisions. Includes additional numerical tests and error discussion, clarifications, and referee suggestion