Experimental and Theoretical Analysis of Pressure Coupled Infusion Gyration for Fibre Production

In this work, we uncover the science of the combined application of external pressure, controlled infusion of polymer solution and gyration in the field of nanofiber preparation. This novel application takes gyration-based method into another new arena through enabling the mass production of exceedingly fine (few nanometres upwards) nanofibres in a single step. Polyethylene oxide (PEO) was used as a model polymer in the experimental study, which shows the use of this novel method to fabricate polymeric nanofibres and nanofibrous mats under different combinations of operating parameters, including working pressure, rotational speed, infusion rate and collection distance. The morphologies of the nanofibres were characterised using scanning electron microscopy, and the anisotropy of alignment of fibre was studied using two dimensional fast Fourier transform analysis. A correlation between the product morphology and the processing parameters is established. The response surface models of the experimental process were developed using the least squares fitting. A systematic description of the PCIG spinning was developed to help us obtain a clear understanding of the fibre formation process of this novel application. The input data we used are the conventional mean of fibre diameter measurements obtained from our experimental works. In this part, both linear and nonlinear fitting formats were applied, and the successes of the fitted models were mainly evaluated using Adjusted R2 and Akaike Information Criterion (AIC). The correlations and effects of individual parameters and their interactions were explicitly studied. The modelling results indicated the polymer concentration has the most significant impact on fibre diameters. A self-defined objective function was studied with the best-fitted model to optimise the experimental process for achieving the desired nanofibre diameters and narrow standard deviations. The experimental parameters were optimised by several algorithms, and the most favoured sets of parameters recommended by the non-linear interior point methods were further validated through a set of additional experiments. The results of validation indicated that pressure coupled infusion gyration offers a facile way for forming nanofibres and nanofibre assemblies, and the developed model has a good prediction power of experimental parameters that are possible to be useful for achieving the desirable PEO nanofibres

Flexural Mechanical Properties of Hybrid Epoxy Composites Reinforced with Nonwoven Made of Flax Fibres and Recycled Carbon Fibres

Can a hybrid composite made of recycled carbon fibres and natural fibres improve the flexural mechanical properties of epoxy composites compared to pure natural fibre reinforced polymers (NFRP)? Growing environmental concerns have led to an increased interest in the application of bio-based materials such as natural fibres in composites. Despite their good specific properties based on their low fibre density, the application of NFRP in load bearing applications such as aviation secondary structures is still limited. Low strength NFRP, compared to composites such as carbon fibre reinforced polymers (CFRP), have significant drawbacks. At the same time, the constantly growing demand for CFRP in aviation and other transport sectors inevitably leads to an increasing amount of waste from manufacturing processes and end-of-life products. Recovering valuable carbon fibres by means of recycling and their corresponding re-application is an important task. However, such recycled carbon fibres (rCF) are usually available in a deteriorated (downcycled) form compared to virgin carbon fibres (vCF), which is limiting their use for high performance applications. Therefore, in this study the combination of natural fibres and rCF in a hybrid composite was assessed for the effect on flexural mechanical properties. Monolithic laminates made of hybrid nonwoven containing flax fibres and recycled carbon fibres were manufactured with a fibre volume fraction of 30% and compared to references with pure flax and rCF reinforcement. Three-point bending tests show a potential increase in flexural mechanical properties by combining rCF and flax fibre in a hybrid nonwoven

Understanding the measurement of forests with waveform lidar

The measurement of forests is essential for monitoring and predicting the role and response of the land surface to global climate change. Globally consistent and frequent measurements can only be made by satellites; unfortunately many current system’s measurements saturate at moderate canopy densities and are not directly related to forest properties, requiring tenuous empirical relationships that are insensitive to many of the Earth’s most important, Carbon rich forests. Lidar (laser radar) is a relatively new technology that offers the potential to make direct measurements of forest height, vertical density and, when ground based, explicit measurements of structure. In addition measurements do not saturate until much higher forest densities. In recent years there has been much interest in the measurement of forests by lidar, with a number of airborne and terrestrial and one spaceborne lidar developed. Measuring a forest leaf by leaf is impractical and very tedious, so more rapid ground based methods are needed to collect data to validate satellite derived estimates. These rapid methods are themselves not directly related to forest properties causing uncertainty in any validation of remotely sensed estimates. This thesis uses Monte Carlo ray tracing to simulate the measurement of forests by full waveform lidars over explicit geometric forest models for both above and below canopy instruments. Existing methods for deriving forest properties from measurements are tested against the known truth of these simulated forests, a process impossible in reality. Causes of disagreements are explored and new methods developed to attempt to overcome any shortcomings. These new methods include dual wavelength lidar for correcting satellite based measurements for topography and a voxel based method for more directly relating terrestrial lidar signals to forest properties

Fungal Endophytes in a Seed-Free Host: New Species that Demonstrate Unique Community Dynamics

Fungal endophytes are highly diverse, cryptic plant endosymbionts that form asymptomatic infections within host tissue. They represent a large fraction of the millions of undescribed fungal taxa on our planet with some demonstrating mutualistic benefits to their hosts including herbivore and pathogen defense and abiotic stress tolerance. Other endophytes are latent saprotrophs or pathogens, awaiting host plant senescence to begin alternative stages of their life cycles. Most, however, are likely plant commensals with no observable benefits to their hosts while under study. Yet, when considering the context-dependence that may determine plant resistance to pathogen attack, the consortium of endophytes present in the host could alter these outcomes, thereby affecting plant evolution. Despite the excitement of exploiting endophytes for their potential to produce bioactive compounds that are useful to medicine and agriculture, fungal endophyte community ecology is a lagging field. Much remains unknown regarding their colonization patterns within hosts, their spatial and temporal turnover and their diversity. Further, a severe deficiency exists in work on endophytes in seed-free plants, particularly ferns. Since ferns exist in free-living forms in both the haploid and diploid stages, are the second largest group of vascular plants, occupy important ecological niches and represent an older lineage of land plants, this is a major shortcoming in our understanding of plant-fungal ecology and evolution. For these reasons, I have examined endophyte community ecology in a widespread fern host in the Pacific Northwest, Polystichum munitum, addressing several gaps in the endophyte literature. Since an understanding of the degree of early endophyte colonization, temporal turnover and the relative contribution of priority effects to community composition are unknown, I conducted a temporal survey of fern endophyte communities utilizing culture-independent, next-generation sequencing on a monthly basis for an entire growing season. A high degree of temporal turnover was observed early in the growing season, where a late colonist rapidly took over the fern population and persisted throughout the year. This colonist, which was isolated from several of the same plants, appears to support growth rates of the host plant when in the gametophytic stage, but is not highly competitive against other endophytes in vitro. Additionally, in an effort to examine host and habitat specificity I examined the spatial turnover of endophytes across four distinct habitat types: a coastal site, a coniferous understory site, a montane site near Mount Saint Helens but not impacted by the 1980 eruption, and a secondary succession site at Mount Saint Helens, spanning 150-km at a single point in time. A high degree of host specificity was found when compared to endophyte communities in neighboring plant taxa and a lack of distance decay was also observed contrasting with other work examining endophyte biogeographic patterns. We conclude that a high degree of host filtering, combined with an abundance of senescent fern material around the base of the plant--which potentially serves as a reservoir of endophyte inoculum--is likely responsible for the observed results. In the process of the ecological studies described above, I isolated over 500 strains of endophytes that corresponded to ca. 100 operational taxonomic units (OTUs). Four of these OTUs are previously undescribed and form a new family and genus, Catenosporaceae and Catenospora, respectively. One of these taxa is responsible for the strong spatial and temporal signals found in the ecological studies. We emphasize that future work should examine if the same phenomena are observed in other fern systems and further encourage endophyte researchers to expand the scope of their investigations into non-traditional plant lineages, as exciting ecological interactions that contribute to our understanding endophyte ecology--and community ecology as a whole--are waiting to be discovered

Candidate genes for stress response in silver fir (Abies alba Mill.)

The aim of this thesis was the identification and analysis of candidate genes for stress response in silver fir (Abies alba Mill.). This ecologically and economically important forest tree species is native to many mountainous regions of Europe but little is known about its ecological characteristics. Silver fir populations were heavily transformed by human activity, which results in a mismatch between past and current distribution. Recent studies suggest that silver fir can occupy warmer and dryer climates than it currently does. However, the species also suffered considerably during the 1970s and 1980s, including foliar damage, radial growth depression and local diebacks in Germany. This is attributed mainly to the peak in air pollution during this period, especially sulfur dioxide (SO2), which seems to heavily increase drought sensitivity in silver fir. The combination of both stressors, SO2 and drought events, negatively affected silver fir even in regions where drought is usually not a problem. In the context of anthropogenic global climate change that will very likely lead to an increase in temperature in Europe and to more extreme events such as severe drought periods, the question arises, how silver fir will cope with these environmental changes. Given the speed of the predicted changes and the increasing landscape fragmentation, silver fir might not be able to evade it via seed dispersal. As a sessile organism, the only other option is adaptation, which will likely draw from standing genetic variation. To successfully predict the fate of silver fir, especially in the face of global climate change, and to potentially manage populations based on such predictions, the genetic architecture of silver fir in the context of such important stressors as drought and air pollution has to be understood. There exist, however, little genomic resources for silver fir and conifers in general. This is due to their large and complex genomes and the long generational cycle, which makes conifers typical nonmodel species. As such, methods for the identification of the genetic basis of stress response are effectively limited to a candidate gene approach. The candidate gene approach includes the identification of functional candidate genes by measuring differential gene expression between a stressed and a control group. In the context of this thesis, the water content of silver fir seedlings was monitored in a laboratory using a novel terahertz spectroscopy setup. One group of seedlings was regularly irrigated while the other group was drought stressed. Continually measuring the water content allowed to harvest needles from both groups at a time when the water status was comparable between the individuals within each group. A differential expression analysis between the needles from both groups then revealed 296 genes that were significantly up- or down-regulated in response to drought stress. Of those genes, approximately 45% have not been previously described in any organism and are potentially unique to silver fir or conifers in general. However, since only needles of seedlings were analyzed at a specific level of drought stress, the results are limited in scope to the source material and stress intensity and cannot be directly applied to silver fir or drought stress in general. Also, this approach implies a cause-effect relationship between gene expression and a specific level of drought stress. Thus, it is very important that confounding factors are excluded from the experiment. Chlorophyll content in the needles, for example, might change over the course of the monitoring period due to the drought treatment. To test if the chlorophyll content could potentially influence the terahertz signal, chlorophyll was extracted from silver fir needles, in the course of this thesis, and different concentrations were measured using terahertz spectroscopy, showing that chlorophyll content does not influence terahertz monitoring. Another aspect of the candidate gene approach involves the variation within a polymorphic gene and its potential association with the variation in a phenotypic trait. Since the growth depression period of silver fir in the 1970s and 1980s was mostly influenced by the combination of air pollution and drought, in the context of this thesis, genetic variation, in the form of single nucleotide polymorphims (SNPs) in pre-selected genes, was associated with tree-ring derived phenotypes for individual trees in the Bavarian Forest National Park. These so called ’dendrophenotypes’ were measures for resistance, resilience and recovery during the depression period, as well as the drought year 1976. Using general linear models and feature selection techniques based on the machine learning algorithm random forest, 15 out of 103 polymorphic candidate genes for trait variation could be identified. Since the associated dendrophenotyes are potentially adaptively relevant, the variation in this candidate genes could influence the stress coping capability of individual trees. However, this approach is of an observational nature and thus, cause-effect relationships cannot be derived from this type of experiment. The identified SNPs might be the causal variant or physically close to the true causal variant or it might just be a spurious correlation. Further, reliance on advanced statistical techniques can be troublesome, as could be demonstrated in the course of this thesis for a random forest based feature selection technique, developed for genetic association studies in conifers. Replicating this study and evaluating the algorithm, non-uniqueness of the results could be demonstrated, which not only hinders biological interpretation but can severely negatively influence downstream analyses, such as tests for interaction between SNPs. In conclusion, this thesis presents new techniques to add to the current methodology for candidate gene selection and analysis in the stress response of the non-model organism silver fir and other conifer species. Both approaches should be combined, for example by drawing polymorphic candidate genes for trait variation from the pool of functional candidate genes to ensure the involvement of the studied genes in the variation of the trait of interest. Further, the results of this thesis add to the growing molecular resources in silver fir and thereby, hopefully, contribute to the successful prediction and management of this important forest tree species in the face of rapidly changing environmental conditions

Searching for Needles in the Cosmic Haystack

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best practice. This dissertation is focused on identifying and classifying radio pulsar candidates from single pulse searches. First, to identify and classify Dispersed Pulse Groups (DPGs), we developed a supervised machine learning approach that consists of RAPID (a novel peak identification algorithm), feature extraction, and supervised machine learning classification. We tested six algorithms for classification with four imbalance treatments. Results showed that classifiers with imbalance treatments had higher recall values. Overall, classifiers using multiclass RandomForests combined with Synthetic Majority Oversampling TEchnique (SMOTE) were the most efficient; they identified additional known pulsars not in the benchmark, with less false positives than other classifiers. Second, we developed a parallel single pulse identification method, D-RAPID, and introduced a novel automated multiclass labeling (ALM) technique that we combined with feature selection to improve execution performance. D-RAPID improved execution performance over RAPID by a factor of 5. We also showed that the combination of ALM and feature selection sped up the execution performance of RandomForest by 54% on average with less than a 2% average reduction in classification performance. Finally, we proposed CoDRIFt, a novel classification algorithm that is distributed for scalability and employs semi-supervised learning to leverage unlabeled data to inform classification. We evaluated and compared CoDRIFt to eleven other classifiers. The results showed that CoDRIFt excelled at classifying candidates in imbalanced benchmarks with a majority of non-pulsar signals (\u3e95%). Furthermore, CoDRIFt models created with very limited sets of labeled data (as few as 22 labeled minority class instances) were able to achieve high recall (mean = 0.98). In comparison to the other algorithms trained on similar sets, CoDRIFt outperformed them all, with recall 2.9% higher than the next best classifier and a 35% average improvement over all eleven classifiers. CoDRIFt is customizable for other problem domains with very large, imbalanced data sets, such as fraud detection and cyber attack detection