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

An Efficient Anonymous Authentication Scheme Using Registration List in VANETs

Nowadays, Vehicular Ad hoc Networks (VANETs) are popularly known as they can reduce traffic and road accidents. These networks need several security requirements, such as anonymity, data authentication, confidentiality, traceability and cancellation of offending users, unlinkability, integrity, undeniability and access control. Authentication is one of the most important security requirements in these networks. So many authentication schemes have been proposed up to now. One of the well-known techniques to provide users authentication in these networks is the authentication based on the smartcard (ASC). In this paper, we propose an ASC scheme that not only provides necessary security requirements such as anonymity, traceability and unlinkability in the VANETs but also is more efficient than the other schemes in the literatures.Comment: 5 pages, 4 figures, Accepted for ICEE202

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

Immunological and histopathological changes in Penaeus semisulcatus challenged with Vibrio harveyi

Two-hundred and sixty five green tiger shrimp juveniles (Penaeus semisulcatus) with the average weight of 7-12g were collected from Helleh farms in Bushehr province and transported to Iran Shrimp Research Center of Bushehr in October, 2009. The juveniles were acclimated for two weeks. The experiment was designed in three treatments (named 3, 4 and 5) and two controls (named 1 and 2) in triplicate with 15 shrimp in each repetition prepared of glass aquarium. All the treatments and repetitions were exposed to Vibrio harveyi (NCBI: GU974342.1). The concentrations of the treatments were 10^8,10^6 and 10^4 CFU ml^-1 in individual containers dedicated for each mentioned treatment (3, 4 and 5, respectively). The controls prepared with no any bacteria and fully filled with chlorinated and UV treated sea water were named 1 and 2 respectively. The hemolymph were withdrawn from abdominal segments of samples for measuring THC and TPC evaluation at designed hours (2, 6, 12, 24, 48, 96, 144, 192 and 240). The shrimp samples were also fixed in Davidson fixative for histopathological studies. The results showed that the difference of THC value between controls and group 3 during 12 till 96 hours of experiment was significant (P 0.05) between group 5 and control groups of THC. The data showed that differences of TPC value between control groups during 24 to 96 hours were significant (P< 0.05), whereas the differences between controls with groups 4 and 5 during 48 to 144 hours and 192 hours, were significant (P< 0.05) respectively. TPC and THC were observed with an increase in the concentration of bacteria and passing the time as inverse bell shape procedure. In histopathology, gills showed melanization and color changed to brown and black. The hepatopancreas cells revealed necrosis and vacuolization of B, E, R and F cells. The bolitas ball and bacterial colonization was observed in the intestine. Our results showed that Vibrio harveyi with 10^8 and 10^6 cell/ml decreased immunity factors such as THC and TPC. The histopathological changes increased with increasing the concentration of bacterial level. This finding can be used for assessing the health of shrimp culture and prevention of vibriosis

Morphometric Characteristics and Time to Hatch as Efficacious Indicators for Potential Nanotoxicity Assay in Zebrafish

Although the effects of nano-sized titania (nTiO2) on hatching events (change in hatching time and total hatching) in zebrafish have been reported, additional consequences of nTiO2 exposure (i.e., the effects of nTiO2-induced changes in hatching events and morphometric parameters on embryo-larvae development and survivability) have not been reported. To address this knowledge gap, embryos 4 h postfertilization were exposed to nTiO2 (0, 0.01, 10, and 1000 μg/mL) for 220 h. Hatching rate (58, 82, and 106 h postexposure [hpe]), survival rate (8 times from 34 to 202 hpe), and 21 morphometric characteristics (8 times from 34 to 202 hpe) were recorded. Total hatching (rate at 106 hpe) was significantly and positively correlated to survival rate, but there was no direct association between nTiO2-induced change in hatching time (hatching rate at 58 and 82 hpe) and survival rate. At 58, 82, and 106 hpe, morphometric characteristics were significantly correlated to hatching rate, suggesting that the nTiO2-induced change in hatching time can affect larval development. The morphometric characteristics that were associated with change in hatching time were also significantly correlated to survival rate, suggesting an indirect significant influence of the nTiO2-induced change in hatching time on survivability. These results show a significant influence of nTiO2-induced change in hatching events on zebrafish embryo-larvae development and survivability. They also show that morphometric maldevelopments can predict later-in-life consequences (survivability) of an embryonic exposure to nTiO2. This suggests that zebrafish can be sensitive biological predictors of nTiO2 acute toxicity

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

The structure of gravel-bed flow with intermediate submergence: a laboratory study

The paper reports an experimental study of the flow structure over an immobile gravel bed in open channel at intermediate submergence, with particular focus on the near-bed region. The experiments consisted of velocity measurements using three-component (stereoscopic) Particle Image Velocimetry (PIV) in near-bed horizontal plane and two-component PIV in three vertical planes that covered three distinctly different hydraulic scenarios where the ratio of flow depth to roughness height (i.e., relative submergence) changes from 7.5 to 10.8. Detailed velocity measurements were supplemented with fine-scale bed elevation data obtained with a laser scanner. The data revealed longitudinal low-momentum and high-momentum "strips'' in the time-averaged velocity field, likely induced by secondary currents. This depth-scale pattern was superimposed with particle-scale patches of flow heterogeneity induced by gravel particle protrusions. A similar picture emerged when considering second-order velocity moments. The interaction between the flow field and gravel-bed protrusions is assessed using cross correlations of velocity components and bed elevations in a horizontal plane just above gravel particle crests. The cross correlations suggest that upward and downward fluid motions are mainly associated with upstream-facing and lee sides of particles, respectively. Results also show that the relative submergence affects the turbulence intensity profiles for vertical velocity over the whole flow depth, while only a weak effect, limited to the near-bed region, is noticed for streamwise velocity component. The approximation of mean velocity profiles with a logarithmic formula reveals that log-profile parameters depend on relative submergence, highlighting inapplicability of a conventional "universal'' logarithmic law for gravel-bed flows with intermediate submergence

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

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