12,600 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
On Monte Carlo methods for the Dirichlet process mixture model, and the selection of its precision parameter prior
Two issues commonly faced by users of Dirichlet process mixture models are: 1) how to appropriately select a hyperprior for its precision parameter alpha, and 2) the typically slow mixing of the MCMC chain produced by conditional Gibbs samplers based on its stick-breaking representation, as opposed to marginal collapsed Gibbs samplers based on the Polya urn, which have smaller integrated autocorrelation times.
In this thesis, we analyse the most common approaches to hyperprior selection for alpha, we identify their limitations, and we propose a new methodology to overcome them.
To address slow mixing, we revisit three label-switching Metropolis moves from the literature (Hastie et al., 2015; Papaspiliopoulos and Roberts, 2008), improve them, and introduce a fourth move. Secondly, we revisit two i.i.d. sequential importance samplers which operate in the collapsed space (Liu, 1996; S. N. MacEachern et al., 1999), and we develop a new sequential importance sampler for the stick-breaking parameters of Dirichlet process mixtures, which operates in the stick-breaking space and which has minimal integrated autocorrelation time. Thirdly, we introduce the i.i.d. transcoding algorithm which, conditional to a partition of the data, can infer back which specific stick in the stick-breaking construction each observation originated from. We use it as a building block to develop the transcoding sampler, which removes the need for label-switching Metropolis moves in the conditional stick-breaking sampler, as it uses the better performing marginal sampler (or any other sampler) to drive the MCMC chain, and augments its exchangeable partition posterior with conditional i.i.d. stick-breaking parameter inferences after the fact, thereby inheriting its shorter autocorrelation times
Early Neanderthal social and behavioural complexity during the Purfleet Interglacial: handaxes in the latest Lower Palaeolithic.
Only a handful of âflagshipâ sites from the Purfleet Interglacial (Marine Isotope Stage 9, c. 350-290,000 years ago) have been properly examined, but the archaeological succession at the proposed type-site at Purfleet suggests a period of complexity and transition, with three techno-cultural groups represented in Britain. The first was a simple toolkit lacking handaxes (the Clactonian), and
the last a more sophisticated technology presaging the coming Middle Palaeolithic (simple prepared core or proto-Levallois technology). Sandwiched between were Acheulean groups, whose handaxes comprise the great majority of the extant archaeological record of the period â these are the focus of this study. It has previously been suggested that some features of the Acheulean in the Purfleet Interglacial were chronologically restricted, particularly the co-occurrence of ficrons and cleavers. These distinctive forms may have exceeded pure functionality and were perhaps imbued with a deeper social and cultural meaning. This study supports both the previously suggested preference for narrow, pointed morphologies, and the chronologically restricted pairing of ficrons and cleavers. By drawing on a wide spatial and temporal range of sites these patterns could be identified beyond the handful of âflagshipâ sites
previously studied. Hypertrophic âgiantsâ have now also been identified as a chronologically restricted form. Greater metrical variability was found than had been anticipated, leading to the creation of two new sub-groups (IA and IB) which are tentatively suggested to represent spatial and
perhaps temporal patterning. The picture in the far west of Britain remains unclear, but the possibility of different Acheulean groups operating in the Solent area, and a late survival of the Acheulean, are both suggested. Handaxes with backing and macroscopic asymmetry may represent prehensile or ergonomic considerations not commonly found on handaxes from earlier interglacial periods. It is argued that these forms anticipate similar developments in the Late Middle Palaeolithic in an example of convergent evolution
From wallet to mobile: exploring how mobile payments create customer value in the service experience
This study explores how mobile proximity payments (MPP) (e.g., Apple Pay) create customer value in the service experience compared to traditional payment methods (e.g. cash and card). The main objectives were firstly to understand how customer value manifests as an outcome in the MPP service experience, and secondly to understand how the customer activities in the process of using MPP create customer value. To achieve these objectives a conceptual framework is built upon the Grönroos-Voima Value Model (Grönroos and Voima, 2013), and uses the Theory of Consumption Value (Sheth et al., 1991) to determine the customer value constructs for MPP, which is complimented with Script theory (Abelson, 1981) to determine the value creating activities the consumer does in the process of paying with MPP.
The study uses a sequential exploratory mixed methods design, wherein the first qualitative stage uses two methods, self-observations (n=200) and semi-structured interviews (n=18). The subsequent second quantitative stage uses an online survey (n=441) and Structural Equation Modelling analysis to further examine the relationships and effect between the value creating activities and customer value constructs identified in stage one. The academic contributions include the development of a model of mobile payment services value creation in the service experience, introducing the concept of in-use barriers which occur after adoption and constrains the consumers existing use of MPP, and revealing the importance of the mobile in-hand momentary condition as an antecedent state. Additionally, the customer value perspective of this thesis demonstrates an alternative to the dominant Information Technology approaches to researching mobile payments and broadens the view of technology from purely an object a user interacts with to an object that is immersed in consumersâ daily life
Omics measures of ageing and disease susceptibility
While genomics has been a major field of study for decades due to relatively inexpensive genotyping arrays, the recent advancement of technology has also allowed the measure and study of various âomicsâ. There are now numerous methods and platforms available that allow high throughput and high dimensional quantification of many types of biological molecules. Traditional genomics and transcriptomics are now joined by proteomics, metabolomics, glycomics, lipidomics and epigenomics.
I was lucky to have access to a unique resource in the Orkney Complex Disease Study (ORCADES), a cohort of individuals from the Orkney Islands that are extremely deeply annotated. Approximately 1000 individuals in ORCADES have genomics, proteomics, lipidomics, glycomics, metabolomics, epigenomics, clinical risk factors and disease phenotypes, as well as body composition measurements from whole body scans. In addition to these cross-sectional omics and health related measures, these individuals also have linked electronic health records (EHR) available, allowing the assessment of the effect of these omics measures on incident disease over a ~10-year follow up period. In this thesis I use this phenotype rich resource to investigate the relationship between multiple types of omics measures and both ageing and health outcomes.
First, I used the ORCADES data to construct measures of biological age (BA). The idea that there is an underlying rate at which the body deteriorates with age that varies between individuals of the same chronological age, this biological age, would be more indicative of health status, functional capacity and risk of age-related diseases than chronological age. Previous models estimating BA (ageing clocks) have predominantly been built using a single type of omics assay and comparison between different omics ageing clocks has been limited. I performed the most exhaustive comparison of different omics ageing clocks yet, with eleven clocks spanning nine different omics assays. I show that different omics clocks overlap in the information they provide about age, that some omics clocks track more generalised ageing while others track specific disease risk factors and that omics ageing clocks are prognostic of incident disease over and above chronological age.
Second, I assessed whether individually or in multivariable models, omics measures are associated with health-related risk factors or prognostic of incident disease over 10 years post-assessment. I show that 2,686 single omics biomarkers are associated with 10 risk factors and 44 subsequent incident diseases. I also show that models built using multiple biomarkers from whole body scans, metabolomics, proteomics and clinical risk factors are prognostic of subsequent diabetes mellitus and that clinical risk factors are prognostic of incident hypertensive disorders, obesity, ischaemic heart disease and Framingham risk score.
Third, I investigated the genetic architecture of a subset of the proteomics measures available in ORCADES, specifically 184 cardiovascular-related proteins. Combining genome-wide association (GWAS) summary statistics from ORCADES and 17 other cohorts from the SCALLOP Consortium, giving a maximum sample size of 26,494 individuals, I performed 184 genome-wide association meta-analyses (GWAMAs) on the levels of these proteins circulating in plasma. I discovered 592 independent significant loci associated with the levels of at least one protein. I found that between 8-37% of these significant loci colocalise with known expression quantitative trait loci (eQTL). I also find evidence of causal associations between 11 plasma protein levels and disease susceptibility using Mendelian randomisation, highlighting potential candidate drug targets
Stratigraphy, chronology, and correlation of the Plio-Pleistocene (c. 2.2-0.8 Ma) Kauroa Ash sequence, western central North Island, New Zealand
The Kauroa Ash beds (K-beds) comprise a 12-20 m-thick sequence of extremely weathered, clay-rich (40-95% <4 ÎŒm clay) beds of tephra and loess, and associated paleosols. Found in isolated remnants throughout the western central North Island, the sequence comprises 15 defined members, with as many as 44 constituent macroscopic beds. The type site, âWoodstockâ, near Raglan, is the most comprehensive sequence known, but other sites (e.g. Papakura Creek and Tiritirimatangi Peninsula) contain units not found or poorly defined at Woodstock.
Field properties as well as magnetic susceptibility measurements and particle-size analysis characterise the facies in the sequence. Field properties (in particular colour, consistence, macrofabric) describe the lithostratigraphy. The sequence contains five interpretive (i.e. genetic) âfaciesâ: paleosols, primary tephra, very weathered tephra (possibly composite beds), loess and âtephric loessâ beds. At least seven loess beds are (newly) identified in the sequence: K4a, K5, K6ai, K8ai, K8bi, K10a and K14ai.
Mass-specific susceptibility and frequency-dependent susceptibility results partly conform to established models (developed mostly on Chinese loess-paleosol deposits) of susceptibility enhancement in paleosols and depletion in loess. Many parts of the sequence do not appear to conform to this model and the results more closely resemble the inverse relationship found on Alaskan loess-paleosol beds. Frequency-dependent susceptibility is reliable in delineating paleosols by their âultrafineâ ferrimagnetic mineral content, and citrate-bicarbonate-dithionite treatments successfully remove all iron oxides so that remeasurement of susceptibility isolates a strictly âpedogenicâ, rather than lithogenic, fraction.
Laser diffraction particle-size analysis shows that the Kauroa Ash beds are texturally reasonably homogenous. They have bimodal particle-size distributions with the most dominant mode at around 11.25 Éž inferred to be the product of intense and prolonged weathering. Other modes are variously centred on 7-8.5 Éž and, despite weathering and pedogenesis, have some relationship to the original depositional particle-size distributions because trends between facies (i.e. genetic units) are delineated. Principal components analysis objectively characterises these modes as (Wentworth size classes) âvery fine clayâ and âcoarse siltâ, although there is no proportional relationship between them, supporting a post-depositional origin for the former mode.
The chronology of the sequence, previously poorly defined, is greatly improved by a combination of tephrochronologic correlations, fission-track dating, and paleomagnetism. Five zircon fission-track dates provide independent age âspikesâ and range from 2.24 ± 0.29 Ma in the basal member, K1, to 1.28 ± 0.11 Ma for the distal ignimbrite unit K12a. Paleomagnetism is invaluable in providing additional age information. The top of the sequence, member K15, is dated as >0.78 Ma (Brunhes-Matuyama boundary) because of its reversed polarity; two episodes of normal polarity are found in beds K14b and K2b and are inferred to represent the Jaramillo (1.07-0.99 Ma) and Olduvai (1.95-1.79 Ma) subchrons, respectively. Beds underlying the Kauroa Ash sequence are also of normal polarity, indicating that they were deposited in the Gauss Chron (>2.6 Ma).
Identification and correlation of tephras by conventional means (fingerprinting by their lithological or geochemical properties) is impossible in the Kauroa Ash sequence because the beds have no remaining volcanic glass, which has instead been altered to an assemblage of authigenic phases (clays) by weathering and pedogenesis. However, a new technique analysing fresh glass found as melt inclusions in quartz grains is successful in circumventing this problem. Inclusions represent samples of non-degassed magma that became entrapped during phenocryst growth prior to eruption. The glass has remained unaltered because it is hermetically sealed in a chemically resistant phenocryst, which has protected it from weathering processes. Electron microprobe analysis of the glass inclusions yield results which are wholly reasonable for glass (totals ranging from 93-97%; low standard deviations of <1 %), and a number of provisional correlations are established by comparing the major element composition of Kauroa Ash tephra beds with those of proximal deposits. The Kauroa Ash sequence may contain deposits correlated with at least seven major TVZ eruptions, in many cases expanding the known extent of the (distal) deposit and, for the first time answering the question as to the origin of the Kauroa Ash beds.
These correlations, together with an improved chronology, enable the Kauroa Ash sequence to be placed in a regional stratigraphic framework alongside other New Zealand Plio-Pleistocene sequences such as those in the Wanganui Basin, Wairarapa, Cape Kidnappers and Port Waikato. Using paleosols as chrono- and climatostratigraphic entities (correlated to warm periods in global climate), the sequence can also be placed alongside a global reference, the marine oxygen isotope stratigraphy. A further correlation to the Chinese loess-paleosol record suggests that large parts of the Kauroa Ash sequence were deposited in an incremental manner akin to deposition of loess, so that the sequence is not only a record of TVZ volcanism, but also of Plio-Pleistocene paleoclimate
Basis expansion approaches for functional analysis of variance with repeated measures
The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several independent samples of subjects walk or run under different conditions (repeated measures). Classic kinematic studies often analyse discrete summaries of the sample curves discarding important information and providing biased results. As the sample data are obviously curves, a Functional Data Analysis approach is proposed to solve the problem of testing the equality of the mean curves of a functional variable observed on several independent groups under different treatments or time periods. A novel approach for Functional Analysis of Variance (FANOVA) for repeated measures that takes into account the complete curves is introduced. By assuming a basis expansion for each sample curve, two-way FANOVA problem is reduced to Multivariate ANOVA for the multivariate response of basis coefficients. Then, two different approaches for MANOVA with repeated measures are considered. Besides, an extensive simulation study is developed to check their performance. Finally, two applications with gait data are developed
Optimizing transcriptomics to study the evolutionary effect of FOXP2
The field of genomics was established with the sequencing of the human genome, a pivotal achievement that has allowed us to address various questions in biology from a unique perspective. One question in particular, that of the evolution of human speech, has gripped philosophers, evolutionary biologists, and now genomicists. However, little is known of the genetic basis that allowed humans to evolve the ability to speak. Of the few genes implicated in human speech, one of the most studied is FOXP2, which encodes for the transcription factor Forkhead box protein P2 (FOXP2). FOXP2 is essential for proper speech development and two mutations in the human lineage are believed to have contributed to the evolution of human speech. To address the effect of FOXP2 and investigate its evolutionary contribution to human speech, one can utilize the power of genomics, more specifically gene expression analysis via ribonucleic acid sequencing (RNA-seq).
To this end, I first contributed in developing mcSCRB-seq, a highly sensitive, powerful, and efficient single cell RNA-seq (scRNA-seq) protocol. Previously having emerged as a central method for studying cellular heterogeneity and identifying cellular processes, scRNA-seq was a powerful genomic tool but lacked the sensitivity and cost-efficiency of more established protocols. By systematically evaluating each step of the process, I helped find that the addition of polyethylene glycol increased sensitivity by enhancing the cDNA synthesis reaction. This, along with other optimizations resulted in developing a sensitive and flexible protocol that is cost-efficient and ideal in many research settings.
A primary motivation driving the extensive optimizations surrounding single cell transcriptomics has been the generation of cellular atlases, which aim to identify and characterize all of the cells in an organism. As such efforts are carried out in a variety of research groups using a number of different RNA-seq protocols, I contributed in an effort to benchmark and standardize scRNA-seq methods. This not only identified methods which may be ideal for the purpose of cell atlas creation, but also highlighted optimizations that could be integrated into existing protocols.
Using mcSCRB-seq as a foundation as well as the findings from the scRNA-seq benchmarking, I helped develop prime-seq, a sensitive, robust, and most importantly, affordable bulk RNA-seq protocol. Bulk RNA-seq was frequently overlooked during the efforts to optimize and establish single-cell techniques, even though the method is still extensively used in analyzing gene expression. Introducing early barcoding and reducing library generation costs kept prime-seq cost-efficient, but basing it off of single-cell methods ensured that it would be a sensitive and powerful technique. I helped verify this by benchmarking it against TruSeq generated data and then helped test the robustness by generating prime-seq libraries from over seventeen species. These optimizations resulted in a final protocol that is well suited for investigating gene expression in comprehensive and high-throughput studies.
Finally, I utilized prime-seq in order to develop a comprehensive gene expression atlas to study the function of FOXP2 and its role in speech evolution. I used previously generated mouse models: a knockout model containing one non-functional Foxp2 allele and a humanized model, which has a variant Foxp2 allele with two human-specific mutations. To study the effect globally across the mouse, I helped harvest eighteen tissues which were previously identified to express FOXP2. By then comparing the mouse models to wild-type mice, I helped highlight the importance of FOXP2 within lung development and the importance of the human variant allele in the brain.
Both mcSCRB-seq and prime-seq have already been used and published in numerous studies to address a variety of biological and biomedical questions. Additionally, my work on FOXP2 not only provides a thorough expression atlas, but also provides a detailed and cost-efficient plan for undertaking a similar study on other genes of interest. Lastly, the studies on FOXP2 done within this work, lay the foundation for future studies investigating the role of FOXP2 in modulating learning behavior, and thereby affecting human speech
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
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