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Wind turbine blades must be sufficiently durable to withstand fatigue, strong enough to withstand loading but not excessively heavy so as to impose unnecessary loads on the drive train. Thus, different types of materials such as glass reinforced plastic, wood and steel, are used in its construction. The wide-spread of materials used creates great difficulty in predicting the lifetime health of the blades hence the necessity for condition monitoring (CM). CM systems for blades are not yet widely used. Those which are currently deployed are expensive, contributing to rising operating and maintenance costs especially offshore. The current paper describes a vibration-based in-situ CM system, comprised of low-cost consumer electronics (accelerometers, microcontroller and energy harvester), capable of detecting variations in the dynamic properties of turbine blades.

The problem of predicting accurate wind rose sector populations for sites in complex terrain with standard statistical methods is discussed. A new method for the calculation of sector populations is presented and illustrated with examples of wind data obtained in the UK. Assumptions of this new method are discussed and recommendations for its future use are made.

Molecular characterization studies of a diverse collection of avian influenza viruses (AIVs) have demonstrated that AIVs' greatest genetic variability lies in the HA, NA, and NS genes. The objective here was to quantify the association between geographical locations, periods of time, and host species and pairwise nucleotide variation in the HA, NA, and NS genes of 70 isolates of H5N1 highly pathogenic avian influenza virus (HPAIV) collected from October 2005 to December 2007 from birds in Romania. A mixed-binomial Bayesian regression model was used to quantify the probability of nucleotide variation between isolates and its association with space, time, and host species. As expected for the three target genes, a higher probability of nucleotide differences (odds ratios [ORs] > 1) was found between viruses sampled from places at greater geographical distances from each other, viruses sampled over greater periods of time, and viruses derived from different species. The modeling approach in the present study maybe useful in further understanding the molecular epidemiology of H5N1 HPAI virus in bird populations. The methodology presented here will be useful in predicting the most likely genetic distance for any of the three gene segments of viruses that have not yet been isolated or sequenced based on space, time, and host species during the course of an epidemic.

In September 2012, the World Health Organization reported the first cases of pneumonia caused by the novel Middle East respiratory syndrome coronavirus (MERS-CoV). We describe a cluster of health care-acquired MERS-CoV infections.Medical records were reviewed for clinical and demographic information and determination of potential contacts and exposures. Case patients and contacts were interviewed. The incubation period and serial interval (the time between the successive onset of symptoms in a chain of transmission) were estimated. Viral RNA was sequenced.Between April 1 and May 23, 2013, a total of 23 cases of MERS-CoV infection were reported in the eastern province of Saudi Arabia. Symptoms included fever in 20 patients (87%), cough in 20 (87%), shortness of breath in 11 (48%), and gastrointestinal symptoms in 8 (35%); 20 patients (87%) presented with abnormal chest radiographs. As of June 12, a total of 15 patients (65%) had died, 6 (26%) had recovered, and 2 (9%) remained hospitalized. The median incubation period was 5.2 days (95% confidence interval [CI], 1.9 to 14.7), and the serial interval was 7.6 days (95% CI, 2.5 to 23.1). A total of 21 of the 23 cases were acquired by person-to-person transmission in hemodialysis units, intensive care units, or in-patient units in three different health care facilities. Sequencing data from four isolates revealed a single monophyletic clade. Among 217 household contacts and more than 200 health care worker contacts whom we identified, MERS-CoV infection developed in 5 family members (3 with laboratory-confirmed cases) and in 2 health care workers (both with laboratory-confirmed cases).Person-to-person transmission of MERS-CoV can occur in health care settings and may be associated with considerable morbidity. Surveillance and infection-control measures are critical to a global public health response.

In this paper, a modular SIMULINK implementation of a stall-regulated variable-speed wind turbine (SRVSWT) model is described in a step-by-step approach. SIMULINK is chosen as the numerical environment in this work because it is a powerful modern software package that provides a convenient tool for simulation work instead of compilation of several program codes. With the modular system, each block solves one of the model equations. The preliminary results of the model characteristics for each block are presented. The authenticity of the developed sub-model is verified for each model before the combination of the full model is constructed. Methods for gathering the unavailable modelling data parameters are also discussed. Finally, the use of the model as a stall-regulated variable-speed wind turbine is demonstrated.

This paper presents the power optimization for a stall-regulated wind turbine with a variable-speed concept, using scalar control approach. To optimize the power under low wind speed velocity, speed regulation was adapted. In this regulation, speed needs to be controlled at the optimum points by keeping the power coefficient at the peak curve and by maintaining the tip speed ratio at optimal value. To achieve this, the conventional method of closed-loop scalar control was developed and then compared with the cascade-loop scalar control. The complete proposed system was developed by using Matlab/Simulink Software. The results showed that both scalar control methods could regulate the speeds, hence able to optimize the power at each wind speed under low wind speed velocity. However, cascade-loop scalar control method performed a better result compared to the conventional one.

Context Passive infrared cameras have become a widely utilised method for surveying mammals, providing substantial benefits over conventional trapping methods. Cameras have only recently been tested for their ability to survey terrestrial reptiles, and have not yet been tested against other reptile survey methods for their comparative effectiveness. Aims To investigate the reliability of passive infrared cameras as a reptile survey method, compared with pitfall trapping. In addition, to test a refinement of a current protocol for using cameras to survey reptiles. Methods The study was carried out in the herpetologically diverse, semiarid Mallee region of Victoria, Australia. Paired camera and pitfall lines were set up at 10 sites within Murray Sunset National Park and results from the two methods were compared. A comparison of results from cameras with and without the use of a cork tile substrate was also made. Key results Cameras were just as effective as pitfall traps for detecting some common diurnal species – detecting additional species that pitfalls did not – but were significantly less effective overall. Cameras provided lower estimates of species richness and failed to detect nocturnal species. We also discovered that cork tiles, required in other environments for the cameras to be effective in detecting diurnal reptiles, were not needed here. Conclusions Cameras can be an effective, efficient non-invasive alternative to conventional trapping methods, such as pitfall trapping, for surveying some terrestrial diurnal reptile species. However, further investigation into using cameras for surveying nocturnal reptile species is still required. Implications If the methodological issues identified during this study can be overcome, passive infrared cameras have the potential to be a valuable tool for future herpetological research.

RNA viruses within infected individuals exist as a population of evolutionary-related variants. Owing to evolutionary change affecting the constitution of this population, the frequency and/or occurrence of individual viral variants can show marked or subtle fluctuations. Since the development of massively parallel sequencing platforms, such viral populations can now be investigated to unprecedented resolution. A critical problem with such analyses is the presence of sequencing-related errors that obscure the identification of true biological variants present at low frequency. Here, we report the development and assessment of the Quality Assessment of Short Read (QUASR) Pipeline ( http://sourceforge.net/projects/quasr ) specific for virus genome short read analysis that minimizes sequencing errors from multiple deep-sequencing platforms, and enables post-mapping analysis of the minority variants within the viral population. QUASR significantly reduces the error-related noise in deep-sequencing datasets, resulting in increased mapping accuracy and reduction of erroneous mutations. Using QUASR, we have determined influenza virus genome dynamics in sequential samples from an in vitro evolution of 2009 pandemic H1N1 (A/H1N1/09) influenza from samples sequenced on both the Roche 454 GSFLX and Illumina GAIIx platforms. Importantly, concordance between the 454 and Illumina sequencing allowed unambiguous minority-variant detection and accurate determination of virus population turnover in vitro .

In the UK, there is an interest in the expected offshore wind resource given ambitious national plans to expand offshore capacity. There is also an increasing interest in alternative datasets to evaluate wind seasonal and inter-annual cycles which can be very useful in the initial stages of the design of wind farms in order to identify prospective areas where local measurements can then be applied to determine small-scale variations in the marine wind climate. In this paper we analyse both MERRA2 reanalysis data and measured offshore mast data to determine patterns in wind speed variation and how they change as a function of the distance from the coast. We also identify an empirical expression to estimate wind speed based on the distance from the coast. From the analysis, it was found that the variations of the seasonal cycles seem to be almost independent of the distance to the nearest shore and that they are an order of magnitude larger than the variations of the diurnal cycles. It was concluded that the diurnal variations decreased to less than a half for places located more than 100km from the nearest shore and that the data from the MERRA2 reanalysis grid points give an under-prediction of the average values of wind speed for both the diurnal and seasonal cycles. Finally, even though the two offshore masts were almost the same nearest distance from the coast and were geographically relatively close, they exhibited significantly different behaviour in terms of the strength of their diurnal and seasonal cycles which may be due to the distance from the coast for the prevailing wind direction being quite different for the two sites.

Whole HIV-1 genome sequences are pivotal for large-scale studies of inter- and intrahost evolution, including the acquisition of drug resistance mutations. The ability to rapidly and cost-effectively generate large numbers of HIV-1 genome sequences from different populations and geographical locations and determine the effect of minority genetic variants is, however, a limiting factor. Next-generation sequencing promises to bridge this gap but is hindered by the lack of methods for the enrichment of virus genomes across the phylogenetic breadth of HIV-1 and methods for the robust assembly of the virus genomes from short-read data. Here we report a method for the amplification, next-generation sequencing, and unbiased de novo assembly of HIV-1 genomes of groups M, N, and O, as well as recombinants, that does not require prior knowledge of the sequence or subtype. A sensitivity of at least 3,000 copies/ml was determined by using plasma virus samples of known copy numbers. We applied our novel method to compare the genome diversities of HIV-1 groups, subtypes, and genes. The highest level of diversity was found in the env , nef , vpr , tat , and rev genes and parts of the gag gene. Furthermore, we used our method to investigate mutations associated with HIV-1 drug resistance in clinical samples at the level of the complete genome. Drug resistance mutations were detected as both major variant and minor species. In conclusion, we demonstrate the feasibility of our method for large-scale HIV-1 genome sequencing. This will enable the phylogenetic and phylodynamic resolution of the ongoing pandemic and efficient monitoring of complex HIV-1 drug resistance genotypes.

There was an error in the accession number in Materials and Methods. EBV and KSHV dataset can be found under the Study Accession number: ERP001026. Individual Sample Accessions are as follows: 1208_JSC1_SN_KS - ERS074823 1212_HBL6_SN_KS - ERS074824 1210_JSC1_SN_EB - ERS074825 1215_HBL6_SN_EB - ERS074833 Whole genome sequencing of viruses directly from clinical samples is integral for understanding the genetics of host-virus interactions. Here, we report the use of sample sparing target enrichment (by hybridisation) for viral nucleic acid separation and deep-sequencing of herpesvirus genomes directly from a range of clinical samples including saliva, blood, virus vesicles, cerebrospinal fluid, and tumour cell lines. We demonstrate the effectiveness of the method by deep-sequencing 13 highly cell-associated human herpesvirus genomes and generating full length genome alignments at high read depth. Moreover, we show the specificity of the method enables the study of viral population structures and their diversity within a range of clinical samples types.

Highly pathogenic avian influenza virus (HPAIV) H5N1 can infect mammals via the intestine; this is unusual since influenza viruses typically infect mammals via the respiratory tract. The dissemination of HPAIV H5N1 following intestinal entry and associated pathogenesis are largely unknown. To assess the route of spread of HPAIV H5N1 to other organs and to determine its associated pathogenesis, we inoculated infected chicken liver homogenate directly into the intestine of cats by use of enteric-coated capsules. Intestinal inoculation of HPAIV H5N1 resulted in fatal systemic disease. The spread of HPAIV H5N1 from the lumen of the intestine to other organs took place via the blood and lymphatic vascular systems but not via neuronal transmission. Remarkably, the systemic spread of the virus via the vascular system was associated with massive infection of endothelial and lymphendothelial cells, resulting in widespread hemorrhages. This is unique for influenza in mammals and resembles the pathogenesis of HPAIV infection in terrestrial poultry. It contrasts with the pathogenesis of systemic disease from the same virus following entry via the respiratory tract, where lesions are characterized mainly by necrosis and inflammation and are associated with the presence of influenza virus antigen in parenchymal, not endothelial cells. The marked endotheliotropism of the virus following intestinal inoculation indicates that the pathogenesis of systemic influenza virus infection in mammals may differ according to the portal of entry.

Using Numerical Weather Prediction (NWP) models it has been shown that they, combined with models (either physical or statistical) taking local effects into account, can be used to predict the wind locally better than the models commonly used today (as eg persistence). By “local” is meant at one distinct spot, as eg the location of a meteorological mast. The physical model of local effects takes the following into account: shelter from near-by obstacles, the effect of roughness changes and the effect of the local orography. The large-scale flow is linked to the surface flow by the geostrophic drag law, and the logarithmic wind profile. The predictions are made up to 36 hours ahead. The model is tested on data from 50 meteorological stations scattered all over Europe.

This paper presents an analysis of the data collected during a test of the 36 kW alkaline electrolyser at West Bacon Farm (WBF), Loughborough, UK. This data is then used to verify a software model of an electrolyser. The test consisted of collecting data under different operating conditions, in particular controlling the power supplied to the electrolyser. The experiment was divided in four phases. In the first two phases, the electrolyser was operated at full power, in the third phase it was operated at 60% of maximum power and in the forth it was operated at 20% of maximum power, which is the minimum permitted level. Each phase lasted approximately an hour. During phase 1, the initial warm up period, the voltage remained almost constant, while the current increased to a maximum of 428 A. After about an hour, the current suddenly dropped to 310 A, the voltage decreased from 92 V to 88 V and hydrogen production decreased from 7.3 Nm 3 /h to 6.6 Nm 3 /h, even though no change had been made to the control parameters, which were still set to maximum power input. The temperature continued upwards with only a slight reduction in its rate of increase and the pressure stopped rising, remaining at 22 bar(g). This point marks the transition from phase 1 to phase 2 and the reasons for the sudden discontinuity are investigated in this study. Once the optimum operating temperature was reached, during phase 2, it was maintained within a limited range by the cooling system. The initial stack temperature, at the beginning of phase 1, had been 17 °C. The net power draw increased until the stack temperature reached its maximum at 76 °C after about 1 h 20 min.

1. Patterns of species richness often correlate strongly with measures of energy. The more individuals hypothesis (MIH) proposes that this relationship is facilitated by greater resources supporting larger populations, which are less likely to become extinct. Hence, the MIH predicts that community abundance and species richness will be positively related. 2. Recently, BuckleyJetz (2010, Journal of Animal Ecology, 79, 358-365) documented a decoupling of community abundance and species richness in lizard communities in south-west United States, such that richer communities did not contain more individuals. They predicted, as a consequence of the mechanisms driving the decoupling, a more even distribution of species abundances in species-rich communities, evidenced by a positive relationship between species evenness and species richness. 3. We found a similar decoupling of the relationship between abundance and species richness for lizard communities in semi-arid south-eastern Australia. However, we note that a positive relationship between evenness and richness is expected because of the nature of the indices used. We illustrate this mathematically and empirically using data from both sets of lizard communities. When we used a measure of evenness, which is robust to species richness, there was no relationship between evenness and richness in either data set. 4. For lizard communities in both Australia and the United States, species dominance decreased as species richness increased. Further, with the iterative removal of the first, second and third most dominant species from each community, the relationship between abundance and species richness became increasingly more positive. 5. Our data support the contention that species richness in lizard communities is not directly related to the number of individuals an environment can support. We propose an alternative hypothesis regarding how the decoupling of abundance and richness is accommodated; namely, an inverse relationship between species dominance and species richness, possibly because of ecological release.

This paper presents a study of the variability of future planned wind and marine power output in the UK by analysing historic resource data and developing power output time series for each resource individually and in combination. For wind and wave power the hourly power outputs were calculated using power curves and matrices, while for tidal power, annual generation and installed capacities were taken from previous studies and only the relative shapes of the time series were calculated. The findings in this paper suggest a simple relationship between installed capacities of the wind and marine renewable plant for minimal variability. Finally, the impacts of allowing renewable generation to be spilled or used to charge electrical vehicles off-peak in a 'smart grid' scenario are assessed. (6 pages)

This paper presents a time series analysis of historical observations of wind speed in order to project future wind speed trends. For this study, 52 years of data have been used from seven suitable stations across the UK. Four parsimonious models have been employed, and the data were split into two different segments: the training and the validation data sets. During the fitting process, the optimum parameters for each model were determined in order to minimize the mean square error in the predictions. The results suggest that the seasonal pattern in wind speeds is the most important factor but that there is some monthly autocorrelation in the data, which can improve forecasts. This is confirmed by testing the four models with the model having considered both autocorrelation and seasonality achieving the smallest errors. The approach proposed for forecasting wind speeds a month ahead may be deemed useful to suppliers for purchasing base load in advance and to system operators for power system maintenance scheduling up to a month ahead.

With an increasing number of wind turbines being erected offshore, there is a need for cost-effective, predictive, and proactive maintenance. A large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox. One way of assessing impending problems is to install vibration sensors in key positions on these subassemblies. Such equipment can be costly and requires sophisticated software for analysis of the data. An alternative approach, which does not require extra sensors, is investigated in this paper. This involves monitoring the power output of a variable-speed wind turbine generator and processing the data using a wavelet in order to extract the strength of particular frequency components, characteristic of faults. This has been done for doubly fed induction generators (DFIGs), commonly used in modern variable-speed wind turbines. The technique is first validated on a test rig under controlled fault conditions and then is applied to two operational wind turbine DFIGs where generator shaft misalignment was detected. For one of these turbines, the technique detected a problem 3 months before a bearing failure was recorded.

Following a lipophilicity-based hypothesis, an 8-hydroxyquinolinone 2-aminoindan derived series of beta(2)-adrenoceptor agonists have been prepared and evaluated for their potential as inhaled ultralong-acting bronchodilators. Determination of their activities at the human beta(2)-adrenoceptor receptor showed symmetrical substitution of the 2-aminoindan moiety at the 5- and 6-positions delivered the targeted intermediate potency and intrinsic-efficacy profiles relative to a series of clinical reference beta(2)-adrenoceptor agonists. Further assessment with an in vitro superfused electrically stimulated guinea-pig tracheal-strip assay established the onset and duration of action time courses, which could be rationalized by considering the lipophilicity, potency, and intrinsic efficacy of the compounds. From these studies the 5,6-diethylindan analogue indacaterol 1c was shown to possess a unique profile of combining a rapid onset of action with a long duration of action. Further in vivo profiling of 1c supported the long duration of action and a wide therapeutic index following administration to the lung, which led to the compound being selected as a development candidate.

Measurements are presented of the photofission and photoneutron cross sections of 241 Am and of the mean number v of neutrons per fission between 5 and 10 MeV. The measurements were carried out using a bremsstrahlung beam generated by the Harwell electron linear accelerator HELIOS and a 1.5 g metallic 241 Am target. Neutron multiplicity distributions were recorded using a high-efficiency neutron detector. Yield measurements made in steps of 50 keV show possible evidence for a resonance at 6.1 MeV in the photofission cross section, but the statistical significance is not high.

High throughput screening identified a 7-azaindole-3-acetic acid scaffold as a novel CRTh2 receptor antagonist chemotype, which could be optimised to furnish a highly selective compound with good functional potency for inhibition of human eosinophil shape change in whole blood and oral bioavailability in the rat.

In this paper, we present a neural networks model-based method to monitor the condition of a generator bearing in a wind turbine. The temperature of the generator bearing is modelled as a function of the generator speed in the wind turbine. The difference between the temperature measurement and the model is calculated and compared to logbook records to verify the method. Other methods are also given for comparison.

Supervisory Control and Data Acquisition (SCADA) systems are fundamental tools for quick fault diagnosis and efficient restoration of power systems. When multiple faults, or malfunctions of protection devices occur in the system, the SCADA system issues many alarm signals rapidly and relays these to the control center. The original cause and location of the fault can be difficult to determine for operators under stress without assistance from a computer aided decision support system. In cases of power system disturbances, network operators in the control center must use their judgement and experience to determine the possible faulty elements as the first step in the restoration procedures. If a breaker or its associated relays fail to operate, the fault is removed by backup protection. In such cases, the outage area can be large and it is then difficult for the network operators to estimate the fault location. Multiple faults, events and actions may eventually take place with many breakers being tripped within a short time. In these circumstances, many alarms need to be analysed by the operators to ensure that the most appropriate actions are taken [1]. Therefore, it is essential to develop software tools to assist in these situations.

The successful application of high throughput molecular simulations to determine biochemical properties would be of great importance to the biomedical community if such simulations could be turned around in a clinically relevant timescale. An important example is the determination of antiretroviral inhibitor efficacy against varying strains of HIV through calculation of drug-protein binding affinities. We describe the Binding Affinity Calculator (BAC), a tool for the automated calculation of HIV-1 protease-ligand binding affinities. The tool employs fully atomistic molecular simulations alongside the well established molecular mechanics Poisson-Boltzmann solvent accessible surface area (MMPBSA) free energy methodology to enable the calculation of the binding free energy of several ligand-protease complexes, including all nine FDA approved inhibitors of HIV-1 protease and seven of the natural substrates cleaved by the protease. This enables the efficacy of these inhibitors to be ranked across several mutant strains of the protease relative to the wildtype. BAC is a tool that utilizes the power provided by a computational grid to automate all of the stages required to compute free energies of binding: model preparation, equilibration, simulation, postprocessing, and data-marshaling around the generally widely distributed compute resources utilized. Such automation enables the molecular dynamics methodology to be used in a high throughput manner not achievable by manual methods. This paper describes the architecture and workflow management of BAC and the function of each of its components. Given adequate compute resources, BAC can yield quantitative information regarding drug resistance at the molecular level within 96 h. Such a timescale is of direct clinical relevance and can assist in decision support for the assessment of patient-specific optimal drug treatment and the subsequent response to therapy for any given genotype.

This paper discusses the results of an extensive investigation to assess the added value of various techniques of health monitoring to optimize the maintenance procedures of offshore wind farms. This investigation was done within the framework of the EU funded Condition Monitoring for Offshore Wind Farms (CONMOW) project, which was carried out from 2002 to 2007. A small wind farm of five turbines has been instrumented with several condition monitoring systems and also with the “traditional” measurement systems for measuring mechanical loads and power performance. Data from vibration and traditional measurements, together with data collected by the turbine’s system control and data acquisition (SCADA) systems, have been analyzed to assess (1) if failures can be determined from the different data sets; (2) if so, if they can be detected at an early stage and if their progress over time can be monitored; and (3) if criteria are available to assess the component’s health. Several data analysis methods and measurement configurations have been developed, applied, and tested. This paper first describes the use of condition monitoring if condition based maintenance is going to be applied instead of only scheduled and corrective maintenance. Second, the paper describes the CONMOW project and its major results, viz., the assessment of the usefulness and capabilities of condition monitoring systems, including algorithms for identifying early failures. Finally, the economic consequences of applying condition monitoring systems have been quantified and assessed.

High throughput screening identified a phenoxyacetic acid scaffold as a novel CRTh2 receptor antagonist chemotype, which could be optimised to furnish a compound with functional potency for inhibition of human eosinophil shape change and oral bioavailability in the rat.

Climate change has been widely blamed to have caused an increase in unstable weather events such as storm, temperature extremes, severe precipitation and unseasonal weather. In the last decade, some notable weather events have occurred, e.g. high temperature in 1995 and 2003 created significantly increased electricity demand compared with the long term average. Such events pose a very challenging task for energy forecasters who must provide high quality predictions to utility decision makers. This paper outlines a methodology using autoregressive integrated moving average (ARIMA) model to predict daily load patterns. The purpose of this work serves as an initial step to investigate the impacts of climate change and weather extremes on electricity demand patterns and the electricity network. It is necessary for us not just to develop a robust methodology to predict the daily load accurately for planning purposes like most conventional models, but more importantly, to provide a reliable long range forecast. Our model takes account of four future climate change scenarios and socioeconomic scenarios to project 90 years (2011-2100) of future load demand patterns. The forecasted load will be used as an input to our transmission network model to study security and grid reinforcement of the power network as the result of climate change. As extreme weather occurs on short time scale, as such the model also incorporates the concept of generalised autoregressive conditional heteroscedasticity GARCH to model the residual in the student-t distribution and to estimate the maximum load demand that would be likely to occur within a finite time series with each estimated demand level corresponding to accepted levels of risk. The model has fitted to an in sample training data from 1970-1998 and the out of sample results were then verified with actual electricity data from 1999-2003. The mean absolute percentage error (MAPE) for each month generally lies within 1-3%

A series of novel corticosteroid derivatives featuring C-17 furoate ester functionality have been synthesised. Profiling in vitro and in vivo has resulted in the identification of a compound with a longer duration of action and a lower oral side effect profile in rodents compared to budesonide.

Data from four well instrumented met masts located in heavily forested European sites in different locations and terrain types are examined. Seven stability metrics are applied to the data sets and a novel method is used to identify the metric which most consistently identifies stability events of importance for wind energy generation. It was found that the Obukhov length, as calculated by fast response sonic anemometer, provides the most reliable results in these highly complex sites. It was also found that non-neutral stabilities can be expected a significant portion of the time for wind speeds of less than 10 m/s at the considered sites.

Genetic diversity of influenza A viruses (IAV) acquired through the error-prone RNA-dependent RNA polymerase (RdRP) or through genetic reassortment enables perpetuation of IAV in humans through epidemics or pandemics. Here, to assess the biological significance of genetic diversity acquired through RdRP, we characterize an IAV fidelity variant derived from passaging a seasonal H3N2 virus in the presence of ​ribavirin, a purine analogue that increases ​guanosine-to-​adenosine mutations. We demonstrate that a single ​PB1-V43I mutation increases selectivity to ​guanosine in A/Wuhan/359/95 (H3N2) and A/Vietnam/1203/04 (H5N1) viruses. The H5N1 ​PB1-V43I-recombinant virus replicates to comparable titres as the wild-type virus in vitro or in the mouse lungs. However, a decrease in viral population diversity at day 3 post inoculation is associated with a tenfold reduced lethality and neurotropism in mice. Applying a fidelity variant with reduced mutational frequency, we provide direct experimental evidence for the role of genetic diversity in IAV pathogenesis.

In this paper computational uid dynamics (CFD) simulations are performed using ANSYS CFX to compare wake interaction results obtained from two rotor modelling methodologies: the standard actuator disc and the blade element momentum model (BEM). The unsteady simulations embed Coriolis forces and neutral stability conditions in the surface layer and stable conditions in the free stream. The BEM method is implemented in the CFD code through a pre-processing set of les that employs look-up tables. The control system for the wind turbines is considered through look-up tables that are constructed based on operational wind farm data. Simulations using the actuator disc and BEM methodologies have been performed using a number of dierent turbulence models in order to compare the wind turbine wake structure results. The use of URANS and LES numerical methods, coupled with the two dierent methodologies of representing the turbine, enables an assessment to be made of the details required for varying degrees of accuracy in computing the wake structures. The ndings stress the importance of including the rotation of the wake and the non-uniform load on the rotor in LES simulations to account for more accurate turbulence intensity levels in the near wake.

Gout is a common problem in renal transplant recipients but often difficult to treat. Allopurinol can be combined with azathioprine but clinicians should be aware of the need for dose reduction, the potential to measure azathioprine breakdown products and the possible side effects of this combination. Leucopenia is a known side effect but this case report shows that haemolytic anaemia can also occur.

Fire is a widespread disturbance and an important ecological process in semi-arid mallee ecosystems of southern Australia. Understanding the effects of fire on plants and animals is a key challenge for the conservation and management of biodiversity in this ecosystem. Commencing in 2006, the Mallee Fire and Biodiversity Project is investigating the effects of fire on a range of taxa (vascular plants, invertebrates, reptiles, birds and mammals), with a focus on the influence of the properties of ‘fire mosaics’ on biota. A ‘whole of landscape’ design was employed, in which the flora and fauna were sampled in 28 study landscapes, each 4 km in diameter (12.5 km2) across a 104, 000 km2 area of the Murray Mallee region of Victoria, SA and NSW. Here, we summarise some key results and outputs from this project to date. These include: detailed maps of fire history and major vegetation types; a method for predicting the age of mallee vegetation; novel information about the distribution of fire age-classes in the region; and changes to vegetation structure and in the occurrence of reptile, bird and mammal species over a century-long post-fire time-frame. We also present an overview of the effects of fire mosaics (extent of particular age classes, diversity of fire age-classes) on the richness of some mallee fauna. A wealth of knowledge has been developed through the Mallee Fire and Biodiversity Project that will assist the management of mallee ecosystems in southern Australia for the future.

A critical requirement in the ecological management of fire is knowledge of the age-class distribution of the vegetation. Such knowledge is important because it underpins the distribution of ecological features important to plants and animals including retreat sites, food sources and foraging microhabitats. However, in many regions, knowledge of the age-class distribution of vegetation is severely constrained by the limited data available on fire history. Much fire-history mapping is restricted to post-1972 fires, following satellite imagery becoming widely available. To investigate fire history in the semi-arid Murray Mallee region in southern Australia, we developed regression models for six species of mallee eucalypt (Eucalyptus oleosa F.Muell. ex. Miq. subsp. oleosa, E. leptophylla F.Muell. ex. Miq., E. dumosa J. Oxley, E. costata subsp. murrayana L. A. S. Johnson & K. D. Hill, E. gracilis F.Muell. and E. socialis F.Muell. ex. Miq.) to quantify the relationship between mean stem diameter and stem age (indicated by fire-year) at sites of known time since fire. We then used these models to predict mean stem age, and thus infer fire-year, for sites where the time since fire was not known. Validation of the models with independent data revealed a highly significant correlation between the actual and predicted time since fire (r = 0.71, P 35 years since fire). Nevertheless, this approach enables examination of post-fire chronosequences in semi-arid mallee ecosystems to be extended from 35 years post-fire to over 100 years. The predicted ages identified for mallee stands imply a need for redefining what is meant by ‘old-growth’ mallee, and challenges current perceptions of an over-abundance of ‘long-unburnt’ mallee vegetation. Given the strong influence of fire on semi-arid mallee vegetation, this approach offers the potential for a better understanding of long-term successional dynamics and the status of biota in an ecosystem that encompasses more than 250 000 km2 of southern Australia.

This paper presents computational fluid dynamics (CFD) modelling of the performance of a building-mounted ducted wind turbine. A resistive volume within the duct is used to represent a cross-flow turbine and different diffuser geometries have been investigated. A comparison is made between the power performance ratio of such a building-mounted ducted wind turbine rotor predicted by CFD calculations and those predicted on the basis of one-dimensional (1D) theory. Good agreement is seen between the two approaches for a freestanding duct but deviations are seen for the building-mounted case for the calculated power performance ratio apparently due to asymmetry in the flow profile entering the duct and the flow geometry around the combination of building and duct.

The use of numerical weather prediction (NWP) model as used by national meteorological agencies is assessed in conjunction with a physical flow model and a statistical model as a means of predicting wind power that could be generated by a large number of wind turbines spread throughout the UK. Economic benefits have been demonstrated, and are evaluated in the form of fossil fuel savings using a numerical simulation model which emulates the power plant scheduling of the England and Wales National Grid. The results highlight the importance of effective planning of on-line reserve capacity to cover sudden deficits in wind power generation.

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.

Wind energy science : WES 7(1), 387-411 (2022). doi:10.5194/wes-7-387-2022, Published by Copernicus Publications, Go��ttingen

Effective condition monitoring techniques for wind turbines are needed to improve maintenance processes and reduce operational costs. Normal behaviour modelling of temperatures with information from other sensors can help to detect wear processes in drive trains. In a case study, modelling of bearing and generator temperatures is investigated with operational data from the SCADA systems of more than 100 turbines. The focus is here on automated training and testing on a farm level to enable an on-line system, which will detect failures without human interpretation. Modelling based on linear combinations, artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines and Gaussian process regression is compared. The selection of suitable modelling inputs is discussed with cross-correlation analyses and a sensitivity study, which reveals that the investigated modelling techniques react in different ways to an increased number of inputs. The case study highlights advantages of modelling with linear combinations and artificial neural networks in a feedforward configuration.