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The influence of blockchains and internet of things on global value chain
Copyright © 2022 The Authors. Despite the increasing proliferation of deploying the internet of things (IoT) in the global value chain (GVC), several challenges might lead to a lack of trust among value chain partners, for example, technical challenges (i.e., confidentiality, authenticity, and privacy); and security challenges (i.e., counterfeiting, physical tampering, and data theft). In this study, we argue that blockchain technology (BT), when combined with the IoT ecosystem, will strengthen GVC and enhance value creation and capture among value chain partners. Therefore, we examine the impact of BT combined with the IoT ecosystem and how it can be utilized to enhance value creation and capture among value chain partners. We collected data through an online survey, and 265 U.K. Agri-food retailers completed the survey. Our data were analyzed using structural equation modeling. Our finding reveals that BT enhances GVC by improving IoT scalability, security, and traceability combined with the IoT ecosystem. Moreover, the combination of BT and IoT strengthens GVC and creates more value for value chain partners, which serves as a competitive advantage. Finally, our research outlines the theoretical and practical contribution of combining BT and the IoT ecosystem
Analysis of reliable deployment of TDOA local positioning architectures
.Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS
Unraveling the effect of sex on human genetic architecture
Sex is arguably the most important differentiating characteristic in most mammalian
species, separating populations into different groups, with varying behaviors, morphologies,
and physiologies based on their complement of sex chromosomes, amongst other factors. In
humans, despite males and females sharing nearly identical genomes, there are differences
between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides
the genome with a distinct hormonal milieu, differential gene expression, and environmental
pressures arising from gender societal roles. This thus poses the possibility of observing
gene by sex (GxS) interactions between the sexes that may contribute to some of the
phenotypic differences observed. In recent years, there has been growing evidence of GxS,
with common genetic variation presenting different effects on males and females. These
studies have however been limited in regards to the number of traits studied and/or
statistical power. Understanding sex differences in genetic architecture is of great
importance as this could lead to improved understanding of potential differences in
underlying biological pathways and disease etiology between the sexes and in turn help
inform personalised treatments and precision medicine.
In this thesis we provide insights into both the scope and mechanism of GxS across the
genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK
Biobank. We found small yet widespread differences in genetic architecture across traits
through the calculation of sex-specific heritability, genetic correlations, and sex-stratified
genome-wide association studies (GWAS). We further investigated whether sex-agnostic
(non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we
studied the potential functional role of sex differences in genetic architecture through sex
biased expression quantitative trait loci (eQTL) and gene-level analyses.
Overall, this study marks a broad examination of the genetics of sex differences. Our findings
parallel previous reports, suggesting the presence of sexual genetic heterogeneity across
complex traits of generally modest magnitude. Furthermore, our results suggest the need to
consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
Structure and adsorption properties of gas-ionic liquid interfaces
Supported ionic liquids are a diverse class of materials that have been considered
as a promising approach to design new surface properties within solids for gas
adsorption and separation applications. In these materials, the surface morphology and
composition of a porous solid are modified by depositing ionic liquid. The resulting
materials exhibit a unique combination of structural and gas adsorption properties
arising from both components, the support, and the liquid. Naturally, theoretical and
experimental studies devoted to understanding the underlying principles of exhibited
interfacial properties have been an intense area of research. However, a complete
understanding of the interplay between interfacial gas-liquid and liquid-solid
interactions as well as molecular details of these processes remains elusive.
The proposed problem is challenging and in this thesis, it is approached from
two different perspectives applying computational and experimental techniques. In
particular, molecular dynamics simulations are used to model gas adsorption in films
of ionic liquids on a molecular level. A detailed description of the modeled systems is
possible if the interfacial and bulk properties of ionic liquid films are separated. In this
study, we use a unique method that recognizes the interfacial and bulk structures of
ionic liquids and distinguishes gas adsorption from gas solubility. By combining
classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous
support.
The developed approach was applied to a range of ionic liquids that feature
different interaction behavior with gas and porous support. Using molecular
simulations with interfacial analysis, it was discovered that gas adsorption capacity
can be directly related to gas solubility data, allowing the development of a predictive
model for the gas adsorption performance of ionic liquid films. Furthermore, it was
found that this CO2 adsorption on the surface of ionic liquid films is determined by the
specific arrangement of cations and anions on the surface. A particularly important
result is that, for the first time, a quantitative relation between these structural and
adsorption properties of different ionic liquid films has been established. This link
between two types of properties determines design principles for supported ionic
liquids.
However, the proposed predictive model and design principles rely on the
assumption that the ionic liquid is uniformly distributed on the surface of the porous
support. To test how ionic liquids behave under confinement, nitrogen physisorption
experiments were conducted for microâ and mesopore analysis of supported ionic
liquid materials. In conjunction with mean-field density functional theory applied to
the lattice gas and pore models, we revealed different scenarios for the pore-filling
mechanism depending on the strength of the liquid-solid interactions.
In this thesis, a combination of computational and experimental studies provides
a framework for the characterization of complex interfacial gas-liquid and liquid-solid
processes. It is shown that interfacial analysis is a powerful tool for studying
molecular-level interactions between different phases. Finally, nitrogen sorption
experiments were effectively used to obtain information on the structure of supported
ionic liquids
The influence of blockchains and internet of things on global value chain
Despite the increasing proliferation of deploying the Internet of Things (IoT) in global value chain (GVC), several challenges might lead to a lack of trust among value chain partners, e.g., technical challenges (i.e., confidentiality, authenticity, and privacy); and security challenges (i.e., counterfeiting, physical tempering, and data theft). In this study, we argue that Blockchain technology, when combined with the IoT ecosystem, will strengthen GVC and enhance value creation and capture among value chain partners. Thus, we examine the impact of Blockchain technology when combined with the IoT ecosystem and how it can be utilized to enhance value creation and capture among value chain partners. We collected data through an online survey, and 265 UK Agri-food retailers completed the survey. Our data were analyzed using structural equation modelling (SEM). Our finding reveals that Blockchain technology enhances GVC by improving IoT scalability, security, and traceability when combined with the IoT ecosystem. Which, in turn, strengthens GVC and creates more value for value chain partners â which serves as a competitive advantage. Finally, our research outlines the theoretical and practical contribution of combining Blockchain technology and the IoT ecosystem
The labour supply and retirement of older workers: an empirical analysis
This thesis examines the labour supply of older workers, their movement into retirement, and any movement out of retirement and back into work. In particular the labour force participation, labour supply and wage elasticity and other income elasticity of work hours are estimated for older workers and compared to younger workers. The thesis goes on to look at the movement into retirement for older workers as a whole by examining cohorts by gender, wave and age. The thesis also presents a descriptive and quantitative âą examination of the changes in income and happiness that occur as an individual retires. Finally the thesis examines the reasons why an individual may return to work from v . retirement. The results of the findings suggest: that younger workers are significantly more responsive to wage and household income changes than older worker
Gendered spaces in contemporary Irish poetry
The thrust of this thesis is summarized by the following questions: How does contemporary Irish poetry migrate from traditional conceptions of identity drawn on by the cultural nationalism of the Irish Literary Revival, and what effects does this have on understanding gendered and national identity formation? Chapters are on the following: Seamus Heaney, Tom Paulin, Paul Muldoon, MedbhMcGuckian, Eavan Boland and Sara Berkeley. These poets are chosen for discussion since their work most effectively engages with the relationship between woman and nation, the representation of gendered national identity, and the importance of feminist and post-colonial theorization. Focusing on poetry worth and South of the border from the last fifteen years, the thesis asks how a younger generation of poets provide a response to nationality which is significantly different from their predecessors. The thesis is composed of three parts: the first understand how the male poets depart from conventional conceptions of the nation with reference to post-colonial theorization; the second explores how feminist theorization informs readings of how the female poets respond to the nation; the final part investigates migration in the poetry and problematizes this in terms of post-nationalism. Discussing the issue of deterritorialization in Irish poetry, the thesis notice how as the poets attempt to take flight from the mythologies of nationhood, they undermine the monoliths of gendered and national identity inscribed within Irish political discourse, which is typified at a representative level by the figure of Mother Ireland or Cathleen Ni Houlihan. Investigating the ways in which gender and nation, and the body and space are reinscribed by the poets, the thesis argues that their poetry challenges authentic conceptions of Irish identity and the nation-state, so as to loosen the legacy of a colonial and nationalist inheritance
Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer
Many of the mechanisms underpinning cancer risk and tumorigenesis are still not
fully understood. However, the next-generation sequencing revolution and the
rapid advances in big data analytics allow us to study cells
and complex phenotypes at unprecedented depth and breadth. While experimental
and clinical data are still fundamental to validate findings and confirm
hypotheses, computational biology is key for the analysis of system- and
population-level data for detection of hidden patterns and the generation of
testable hypotheses.
In this work, I tackle two main questions regarding cancer risk and tumorigenesis
that require novel computational methods for the analysis of system-level omic
data. First, I focused on how frequent, low-penetrance inherited variants modulate
cancer risk in the broader population. Genome-Wide Association Studies (GWAS)
have shown that Single Nucleotide Polymorphisms (SNP) contribute to cancer risk
with multiple subtle effects, but they are still failing to give further insight
into their synergistic effects. I developed a novel hierarchical Bayesian
regression model, BAGHERA, to estimate heritability at the gene-level from GWAS
summary statistics. I then used BAGHERA to analyse data from 38 malignancies in
the UK Biobank. I showed that genes with high heritable risk are involved in key
processes associated with cancer and are often localised in genes that are
somatically mutated drivers.
Heritability, like many other omics analysis methods, study the effects of DNA
variants on single genes in isolation. However, we know that most biological
processes require the interplay of multiple genes and we often lack a broad
perspective on them. For the second part of this thesis, I then worked on the
integration of Protein-Protein Interaction (PPI) graphs and omics data, which
bridges this gap and recapitulates these interactions at a system level. First,
I developed a modular and scalable Python package, PyGNA, that enables
robust statistical testing of genesets' topological properties. PyGNA complements
the literature with a tool that can be routinely introduced in bioinformatics
automated pipelines. With PyGNA I processed multiple genesets obtained from
genomics and transcriptomics data. However, topological properties alone have
proven to be insufficient to fully characterise complex phenotypes.
Therefore, I focused on a model that allows to combine topological and functional
data to detect multiple communities associated with a phenotype. Detecting
cancer-specific submodules is still an open problem, but it has the potential to
elucidate mechanisms detectable only by integrating multi-omics data. Building
on the recent advances in Graph Neural Networks (GNN), I present a supervised
geometric deep learning model that combines GNNs and Stochastic Block Models
(SBM). The model is able to learn multiple graph-aware representations, as
multiple joint SBMs, of the attributed network, accounting for nodes
participating in multiple processes. The simultaneous estimation of structure
and function provides an interpretable picture of how genes interact in specific
conditions and it allows to detect novel putative pathways associated with
cancer
Scalable software and models for large-scale extracellular recordings
The brain represents information about the world through the electrical activity of
populations of neurons. By placing an electrode near a neuron that is firing (spiking), it
is possible to detect the resulting extracellular action potential (EAP) that is transmitted
down an axon to other neurons. In this way, it is possible to monitor the communication
of a group of neurons to uncover how they encode and transmit information. As the
number of recorded neurons continues to increase, however, so do the data processing
and analysis challenges. It is crucial that scalable software and analysis tools are developed
and made available to the neuroscience community to keep up with the large
amounts of data that are already being gathered.
This thesis is composed of three pieces of work which I develop in order to better
process and analyze large-scale extracellular recordings. My work spans all stages of extracellular
analysis from the processing of raw electrical recordings to the development
of statistical models to reveal underlying structure in neural population activity.
In the first work, I focus on developing software to improve the comparison and adoption
of different computational approaches for spike sorting. When analyzing neural
recordings, most researchers are interested in the spiking activity of individual neurons,
which must be extracted from the raw electrical traces through a process called
spike sorting. Much development has been directed towards improving the performance
and automation of spike sorting. This continuous development, while essential,
has contributed to an over-saturation of new, incompatible tools that hinders rigorous
benchmarking and complicates reproducible analysis. To address these limitations, I
develop SpikeInterface, an open-source, Python framework designed to unify preexisting
spike sorting technologies into a single toolkit and to facilitate straightforward
benchmarking of different approaches. With this framework, I demonstrate that modern,
automated spike sorters have low agreement when analyzing the same dataset, i.e.
they find different numbers of neurons with different activity profiles; This result holds
true for a variety of simulated and real datasets. Also, I demonstrate that utilizing a
consensus-based approach to spike sorting, where the outputs of multiple spike sorters
are combined, can dramatically reduce the number of falsely detected neurons.
In the second work, I focus on developing an unsupervised machine learning approach
for determining the source location of individually detected spikes that are
recorded by high-density, microelectrode arrays. By localizing the source of individual
spikes, my method is able to determine the approximate position of the recorded neuriii
ons in relation to the microelectrode array. To allow my model to work with large-scale
datasets, I utilize deep neural networks, a family of machine learning algorithms that
can be trained to approximate complicated functions in a scalable fashion. I evaluate
my method on both simulated and real extracellular datasets, demonstrating that it is
more accurate than other commonly used methods. Also, I show that location estimates
for individual spikes can be utilized to improve the efficiency and accuracy of spike
sorting. After training, my method allows for localization of one million spikes in approximately
37 seconds on a TITAN X GPU, enabling real-time analysis of massive
extracellular datasets.
In my third and final presented work, I focus on developing an unsupervised machine
learning model that can uncover patterns of activity from neural populations
associated with a behaviour being performed. Specifically, I introduce Targeted Neural
Dynamical Modelling (TNDM), a statistical model that jointly models the neural activity
and any external behavioural variables. TNDM decomposes neural dynamics (i.e.
temporal activity patterns) into behaviourally relevant and behaviourally irrelevant dynamics;
the behaviourally relevant dynamics constitute all activity patterns required
to generate the behaviour of interest while behaviourally irrelevant dynamics may be
completely unrelated (e.g. other behavioural or brain states), or even related to behaviour
execution (e.g. dynamics that are associated with behaviour generally but are not
task specific). Again, I implement TNDM using a deep neural network to improve its
scalability and expressivity. On synthetic data and on real recordings from the premotor
(PMd) and primary motor cortex (M1) of a monkey performing a center-out reaching
task, I show that TNDM is able to extract low-dimensional neural dynamics that are
highly predictive of behaviour without sacrificing its fit to the neural data
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