The Analysis of Dynamic Interaction in Legume Binary Mixture Under Controlled Conditions of Irrigation and Clipping

The objective of this study was to analyse the type of interference that occurred between the annual legume species, purple clover (Trifolium purpureum L.) and narrow leaved crimson clover (Trifolium angustifolium Loisel. ), growing in mixed conditions under two different watering regimes and two different clipping treatments. A replacement series experiment was conducted in pots placed in the field. The above ground biomass (gr/plant) were measured. The recently proposed Inverse Linear Model was implied in order to analyse the competitive interaction between the above species. The results suggest that Tr. purpureum was the superior competitor to Tr. angustifolium and a remarkable niche differentiation was occurred after the clipping treatment

Wind turbine gearbox planet bearing failure prediction using vibration data

This paper presents a methodology for predicting planet bearing failures utilising vibration data acquired through accelerometers installed on the gearbox surface. The proposed methodology applies certain signal pre-processing techniques in order to remove the speed variations of the turbine and separate the stochastic bearing components from the deterministic gear ones. Then, spectral kurtosis is used to enhance the impulsiveness of the bearing fault signatures and envelope analysis is used to demodulate the signal. Features are extracted from the envelope spectrum and are used as an input to a classification model. The classification labelling is performed based on the time before failure. The methodology is tested on real offshore wind turbine vibration data collected at various times before failure. The performance of the classifier is assessed using k-fold cross validation. The results are compared with methods of classic envelope analysis that uses a constant demodulation band

Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications

This work presents a comprehensive review and discussion of the identification of critical components of the currently installed and next generation of offshore wind turbines. A systematic review on the reliability, availability, and maintainability data of both offshore and onshore wind turbines is initially performed, collecting the results from 24 initiatives, at system and subsystem level. Due to the scarcity of data from the offshore wind industry, the analysis is complemented with the extensive experience from onshore structures. Trends based on the deployment parameters for the influence of design characteristics and environmental conditions on the onshore wind turbines' reliability and availability are first investigated. The estimation of the operational availability for a set of offshore wind farm scenarios allowed a comparison with the recently published performance statistics and the discussion of the integrity of the data available to date. The failure statistics of the systems deployed offshore are then discussed and compared to the onshore ones, with regard to their normalised results. The availability calculations supported the hypothesis of the negative impact of the offshore environmental conditions on the reliability figures. Nonetheless, similarities in the reliability figures of the blade adjustment system and the maintainability of the power generation and the control systems are outlined. Finally, to improve the performance prediction of future offshore projects, recommendations on the effort worth putting into research and data collection are provided

Prediction of wind turbine generator bearing failure through analysis of high frequency vibration data and the application of support vector machine algorithms

Reducing wind turbine downtime through innovations surrounding asset management has the potential to greatly influence the overall levelised cost of energy of large onshore and offshore developments. This research paper uses multiple examples of the same generator bearing failure to provide insight into how condition monitoring systems can be used in to train machine learning algorithms with the ultimate goal of predicting failure and remaining useful life. Results show that by analysing high frequency vibration data and extracting key features to train support vector machine algorithms, an accuracy of 67% can be achieved in successfully predicting failure 1-2 months before occurrence. This paper reflects on the limitations surrounding a generalised training approach, taking advantage of all available data, showing that if too many different examples are considered of different wind turbines and operating conditions the overall accuracy can be diminished

Effect of weather forecast uncertainty on offshore wind farm availability assessment

With the growing demand for offshore wind energy and the continued drive for reduced levelised cost of energy, it is necessary to make operation and maintenance activities more effective and reduce related costs. A key factor in achieving this aim is to more representatively model operation and maintenance activities, and to do this, simulation models should include more accurate weather forecasting algorithms. In this paper, three weather forecast modelling methods are used to generate projections of wind and wave values which are then used as inputs in an operation and maintenance simulation model. These methods include Markov Chains, gradient boosting and a novel hybrid regression/statistical approach which has been developed and is presented herein. The change in key performance indicators after the wind farm lifespan is simulated using the forecasting methods and then compared to one another. It is shown that the Markov Chain and hybrid models numerically perform similarly, although the hybrid method has some additional desirable features. Finally, it is shown that the effect of this type of modelling uncertainty leads to significantly differing performance estimates through the operation and maintenance model

Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks

Predicting car ownership patterns at high spatial resolution is key to understanding pathways for decarbonisation—via electrification and demand reduction—of the private vehicle fleet. As the factors widely understood to influence car ownership are highly interdependent, linearised regression models, which dominate previous work on spatially explicit car ownership modelling in the UK, have shortcomings in accurately predicting the relationship. This paper presents predictions of spatially disaggregated car ownership—and change in car ownership over time—in Great Britain (GB) using deep neural networks (NNs) with hyperparameter tuning. The inputs to the models are demographic, socio-economic and geographic datasets compiled at the level of Census Lower Super Output Areas (LSOAs)—areas covering between 300 and 600 households. It was found that when optimal hyperparameters are selected, these neural networks can predict car ownership with a mean absolute error of up to 29% lower than when formulating the same problem as a linear regression; the results from NN regression are also shown to outperform three other artificial intelligence (AI)-based methods: random forest, stochastic gradient descent and support vector regression. The methods presented in this paper could enhance the capability of transport/energy modelling frameworks in predicting the spatial distribution of vehicle fleets, particularly as demographics, socio-economics and the built environment—such as public transport availability and the provision of local amenities—evolve over time. A particularly relevant contribution of this method is that by coupling it with a technology dissipation model, it could be used to explore the possible effects of changing policy, behaviour and socio-economics on uptake pathways for electric vehicles —cited as a vital technology for meeting Net Zero greenhouse gas emissions by 2050

Comparison of anomaly detection techniques for wind turbine gearbox SCADA data

This analysis looks at the use of anomaly detection to assess the condition of wind turbine gearboxes based on data from a number of operational turbines. A comparison is made between various methods of anomaly detection, these being one class support vector machine (OCSVM), random forests, and nonlinear autoregressive neural networks with exogenous inputs (NARX)

Fat dads must not be blamed for their children's health problems

The relationship between the parental genomes in terms of the future growth and development of their offspring is not critical. For the majority of the genome the tissue-specific gene expression and epigenetic status is shared between the parents equally, with both alleles contributing without parental bias. For a very small number of genes the rules change and control of expression is restricted to a specific, parentally derived allele, a phenomenon known as genomic imprinting. The insulin-like growth factor 2 (Igf2/IGF2) is a robustly imprinted gene, important for fetal growth in both mice and humans. In utero IGF2 exhibits paternal expression, which is controlled by several mechanisms, including the maternally expressing untranslated H19 gene. In the study by Soubry et al., a correlation is drawn between the IGF2 methylation status in fetal cord blood leucocytes, and the obesity status of the father from whom the active IGF2 allele is derived through his sperm. These data imply that paternal obesity affects the normal IGF2 methylation in the sperm and this in turn alters the expression of IGF2 in the baby

Neue linguistische Methoden und arbeitstechnische Verfahren in der Erschliessung der ägyptischen Grammatik

15 páginas, 1 tabla, 6 figuras.Does diversity beget diversity? Diversity includes a diversity of concepts because it is linked to variability in and of life and can be applied to multiple levels. The connections between multiple levels of diversity are poorly understood. Here, we investigated the relationships between genetic, bacterial, and chemical diversity of the endangered Atlanto-Mediterranean sponge Spongia lamella. These levels of diversity are intrinsically related to sponge evolution and could have strong conservation implications. We used microsatellite markers, denaturing gel gradient electrophoresis and quantitative polymerase chain reaction, and high performance liquid chromatography to quantify genetic, bacterial, and chemical diversity of nine sponge populations. We then used correlations to test whether these diversity levels covaried. We found that sponge populations differed significantly in genetic, bacterial, and chemical diversity. We also found a strong geographic pattern of increasing genetic, bacterial, and chemical dissimilarity with increasing geographic distance between populations. However, we failed to detect significant correlations between the three levels of diversity investigated in our study. Our results suggest that diversity fails to beget diversity within a single species and indicates that a diversity of factors regulates a diversity of diversities, which highlights the complex nature of the mechanisms behind diversityResearch funded by grants from the Agence Nationale de la Recherche (ECIMAR), from the Spanish Ministry of Science and Technology SOLID (CTM2010-17755) and Benthomics (CTM2010-22218-C02-01) and the BIOCAPITAL project (MRTN-CT-2004-512301) of the European Union. This is a contribution of the Consolidated Research Group ‘‘Grupo de Ecologı´a Bento´nica,’’ SGR2009-655.Peer reviewe

Genetically defined elevated homocysteine levels do not result in widespread changes of DNA methylation in leukocytes

BACKGROUND:DNA methylation is affected by the activities of the key enzymes and intermediate metabolites of the one-carbon pathway, one of which involves homocysteine. We investigated the effect of the well-known genetic variant associated with mildly elevated homocysteine: MTHFR 677C>T independently and in combination with other homocysteine-associated variants, on genome-wide leukocyte DNA-methylation. METHODS:Methylation levels were assessed using Illumina 450k arrays on 9,894 individuals of European ancestry from 12 cohort studies. Linear-mixed-models were used to study the association of additive MTHFR 677C>T and genetic-risk score (GRS) based on 18 homocysteine-associated SNPs, with genome-wide methylation. RESULTS:Meta-analysis revealed that the MTHFR 677C>T variant was associated with 35 CpG sites in cis, and the GRS showed association with 113 CpG sites near the homocysteine-associated variants. Genome-wide analysis revealed that the MTHFR 677C>T variant was associated with 1 trans-CpG (nearest gene ZNF184), while the GRS model showed association with 5 significant trans-CpGs annotated to nearest genes PTF1A, MRPL55, CTDSP2, CRYM and FKBP5. CONCLUSIONS:Our results do not show widespread changes in DNA-methylation across the genome, and therefore do not support the hypothesis that mildly elevated homocysteine is associated with widespread methylation changes in leukocytes