Optimized Microstrip Antennas with Metamaterial Superstrates Using Particle Swarm Optimization

Two new designs of compact microstrip antennas, where metamaterials are placed on structure as superstrate, are proposed. The newly designed metamaterial unit cell and antenna feed position optimized by particle swarm optimization. It was found that the characteristics of novel microstrip antennas with designed metamaterials placed on the superstrate are comparable to the conventional patch antennas, while their gain, directivity and radiating efficiency are noticeably improved. Gain of microstrip antenna is increased 3dB to 4dB and level of back lobe is decresed

Improving indoor thermal comfort, air quality and the health of older adults through environmental policies in London

In this work we evaluate the potential of selected environmental strategies in reducing air pollution and summertime indoor overheating. Associated changes in mortality rates are also calculated for older adults in London. Reducing these risks for vulnerable groups is an immediate priority and given that seniors spend most of their time indoors, our focus is on strategies that prioritize the transformation of residential environments for indoor thermal comfort and air quality improvements. For each strategy, we develop specific scenarios related to building adaptations and test potential reductions on indoor overheating and pollutant exposures from outdoor sources (PM2.5), as well as on senior mortality through the CRAFT tool (Cities Rapid Assessment Framework for Transformation). We then pick the scenarios with highest impacts on mortality, aiming to formulate effective policy recommendations for Greater London. Preliminary results suggest that environmental policies related to the installation of shading could have the highest reduction in heat and pollution-related senior mortality, followed by moderate effects due to building insulation retrofits and the greening of roofs. With an increasing ageing population in the UK and beyond, our work highlights the need for city-level policies to address building modifications, considering the importance of indoor spaces for older adults

Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential

With wind power providing an increasing amount of electricity worldwide, the quantification of its spatio-temporal variations and the related uncertainty is crucial for energy planners and policy-makers. Here, we propose a methodological framework which (1) uses machine learning to reconstruct a spatio-temporal field of wind speed on a regular grid from spatially irregularly distributed measurements and (2) transforms the wind speed to wind power estimates. Estimates of both model and prediction uncertainties, and of their propagation after transforming wind speed to power, are provided without any assumptions on data distributions. The methodology is applied to study hourly wind power potential on a grid of 250×250 m2 for turbines of 100 m hub height in Switzerland, generating the first dataset of its type for the country. We show that the average annual power generation per turbine is 4.4 GWh. Results suggest that around 12,000 wind turbines could be installed on all 19,617 km2 of available area in Switzerland resulting in a maximum technical wind potential of 53 TWh. To achieve the Swiss expansion goals of wind power for 2050, around 1000 turbines would be sufficient, corresponding to only 8% of the maximum estimated potential

Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference

The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface. In this paper, we develop a method using Machine Learning to estimate the technical potential of shallow GSHPs, which enables such an estimation for Switzerland with limited data and computational resources. A training dataset is constructed based on meteorological and geological data across Switzerland. We analyse correlations and the importance of each of the input data for estimating the GSHP potential and compare different input feature sets and Machine Learning models. The Random Forest algorithm, trained on the full dataset, provides the best performance to estimate the GSHP potential. The resulting model yields an R2 score of 0.95 for the annual energy potential, 0.86 for the heat extraction rate, and 0.82 for the potential number of boreholes per GSHP system

Evolution and entropy in the organization of urban street patterns

The street patterns of cities are the result of long-term evolution and interaction between various internal, social and economic, and external, environmental and landscape, processes and factors. In this article, we use entropy as a measure of dispersion to study the effects of landscapes on the evolution and associated street patterns of two cities: Dundee in Eastern Scotland and Khorramabad in Western Iran, cities which have strong similarities in terms of the size of their street systems and populations but considerable differences in terms of their evolution within the landscape. Landscape features have strong effects on the city shape and street patterns of Dundee, which is primarily a shoreline city, while Khorramabad is primarily located within mountainous and valley terrain. We show how cumulative distributions of street lengths when graphed as log-log plots show abrupt changes in their straight-line slopes at lengths of about 120 m, indicating a change in street functionality across scale: streets shorter than 120 m are primarily local streets, whereas longer streets are mainly collectors and arterials. The entropy of a street-length population varies positively over its average length and length range which is the difference between the longest and the shortest streets in a population. Similarly, the entropies of the power law tails of the street populations of both cities have increased during their growth, indicating that the distribution of street lengths has gradually become more dispersed as these cities have expanded. © 2013 Copyright Taylor and Francis Group, LLC

Shallow geothermal energy potential for heating and cooling of buildings with regeneration under climate change scenarios

Shallow ground-source heat pumps (GSHPs) are a promising technology for contributing to the decarbonisation of the energy sector. In heating-dominated climates, the combined use of GSHPs for both heating and cooling increases their technical potential, defined as the maximum energy that can be exchanged with the ground, as the re-injection of excess heat from space cooling leads to a seasonal regeneration of the ground. This paper proposes a new approach to quantify the technical potential of GSHPs, accounting for effects of seasonal regeneration, and to estimate the useful energy to supply building energy demands at regional scale. The useful energy is obtained for direct heat exchange and for district heating and cooling (DHC) under several scenarios for climate change and market penetration levels of cooling systems. The case study in western Switzerland suggests that seasonal regeneration allows for annual maximum heat extraction densities above 300 kWh/m2 at heat injection densities above 330 kWh/m2. Results also show that GSHPs may cover up to 55% of heating demand while covering 57% of service-sector cooling demand for individual GSHPs in 2050, which increases to around 85% with DHC. The regional-scale results may serve to inform decision making on strategic areas for installing GSHPs

Residential density classification for sustainable housing development using a machine learning approach

Using Machine Learning (ML) algorithms for classification of the existing residential neighbourhoods and their spatial characteristics (e.g. density) so as to provide plausible scenarios for designing future sustainable housing is a novel application. Here we develop a methodology using a Random Forests algorithm (in combination with GIS spatial data processing) to detect and classify the residential neighbourhoods and their spatial characteristics within the region between Oxford and Cambridge, that is, the 'Oxford-Cambridge Arc'. The classification model is based on four pre-defined urban classes, that is, Centre, Urban, Suburban, and Rural for the entire region. The resolution is a grid of 500 m × 500 m. The features for classification include (1) dwelling geometric attributes (e.g. garden size, building footprint area, building perimeter), (2) street networks (e.g. street length, street density, street connectivity), (3) dwelling density (number of housing units per hectare), (4) building residential types (detached, semi-detached, terraced, and flats), and (5) characteristics of the surrounding neighbourhoods. The classification results, with overall average accuracy of 80% (accuracy per class: Centre: 38%, Urban 91%, Suburban 83%, and Rural 77%), for the Arc region show that the most important variables were three characteristics of the surrounding area: residential footprint area, dwelling density, and number of private gardens. The results of the classification are used to establish a baseline for the current status of the residential neighbourhoods in the Arc region. The results bring data-driven decision-making processes to the level of local authority and policy makers in order to support sustainable housing development at the regional scale

Covid-19 mobility restrictions: impacts on urban air quality and health

In 2020, Covid-19-related mobility restrictions resulted in the most extensive human-made air-quality changes ever recorded. The changes in mobility are quantified in terms of outdoor air pollution (concentrations of PM2.5 and NO2) and the associated health impacts in four UK cities (Greater London, Cardiff, Edinburgh and Belfast). After applying a weather-corrected machine learning (ML) technique, all four cities show NO2 and PM2.5 concentration anomalies in 2020 when compared with the ML-predicted values for that year. The NO2 anomalies are –21% for Greater London, –19% for Cardiff, –27% for Belfast and –41% for Edinburgh. The PM2.5 anomalies are 7% for Greater London, –1% for Cardiff, –15% for Edinburgh, –14% for Belfast. All the negative anomalies, which indicate air pollution at a lower level than expected from the weather conditions, are attributable to the mobility restrictions imposed by the Covid-19 lockdowns. Spearman rank-order correlations show a significant correlation between the lowering of NO2 levels and reduction in public transport (p < 0.05) and driving (p < 0.05), which is associated with a decline in NO2-attributable mortality. These positive effects of the mobility restrictions on public health can be used to evaluate policies for improved outdoor air quality

An open-source automatic survey of green roofs in London using segmentation of aerial imagery

Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate-resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine learning algorithm based on U-Net (Ronneberger et al., 2015) to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset (https://doi.org/10.5281/zenodo.7603123, Simpson et al., 2023). We estimate that there was 0.23 km2 of green roof in the Central Activities Zone (CAZ) of London, 1.07 km2 in Inner London, and 1.89 km2 in Greater London in the year 2021. This corresponds to 2.0 % of the total building footprint area in the CAZ and 1.3 % in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9 % of the total building footprint area. Test set accuracy was 0.99, with an F score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.</p

Prevalence and genotyping identification of Cryptosporidium in adult ruminants in central Iran

Background Apicomplexan parasites of the genus Cryptosporidium infect a wide range of animal species as well as humans. Cryptosporidium spp. can cause life threatening diarrhea especially in young animals, children, immunocompromised patients and malnourished individuals. Asymptomatic cryptosporidial infections in animals can also occur, making these animals potential reservoirs of infection. Methods In the present study, a molecular survey of Cryptosporidium spp. in ruminants that were slaughtered for human consumption in Yazd Province, located in central Iran was conducted. Faeces were collected per-rectum from 484 animals including 192 cattle, 192 sheep and 100 goats. DNA was extracted from all samples and screened for Cryptosporidium by PCR amplification of the 18S rRNA gene. Positives were Sanger sequenced and further subtyped by sequence analysis of the 60 kDa glycoprotein (gp60) locus. Results In total, Cryptosporidium spp. were detected in 22 animals: C. andersoni and C. bovis in seven and two cattle faecal samples, respectively, C. ubiquitum in five sheep, and C. xiaoi in six sheep and two goat samples, respectively. To our knowledge, this study provides for the first time, molecular information concerning Cryptosporidium species infecting goats in Iran, and is also the first report of C. ubiquitum and C. xiaoi from ruminants in Iran. Conclusion The presence of potentially zoonotic species of Cryptosporidium in ruminants in this region may suggest that livestock could potentially contribute to human cryptosporidiosis, in particular among farmers and slaughterhouse workers, in the area. Further molecular studies on local human populations are required to more accurately understand the epidemiology and transmission dynamics of Cryptosporidium spp. in this region