20,190 research outputs found
Modelling uncertainties for measurements of the H â γγ Channel with the ATLAS Detector at the LHC
The Higgs boson to diphoton (H â γγ) branching ratio is only 0.227 %, but this
final state has yielded some of the most precise measurements of the particle. As
measurements of the Higgs boson become increasingly precise, greater import is
placed on the factors that constitute the uncertainty. Reducing the effects of these
uncertainties requires an understanding of their causes. The research presented
in this thesis aims to illuminate how uncertainties on simulation modelling are
determined and proffers novel techniques in deriving them.
The upgrade of the FastCaloSim tool is described, used for simulating events in
the ATLAS calorimeter at a rate far exceeding the nominal detector simulation,
Geant4. The integration of a method that allows the toolbox to emulate the
accordion geometry of the liquid argon calorimeters is detailed. This tool allows
for the production of larger samples while using significantly fewer computing
resources.
A measurement of the total Higgs boson production cross-section multiplied
by the diphoton branching ratio (Ï Ă Bγγ) is presented, where this value was
determined to be (Ï Ă Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement
with the Standard Model prediction. The signal and background shape modelling
is described, and the contribution of the background modelling uncertainty to the
total uncertainty ranges from 18â2.4 %, depending on the Higgs boson production
mechanism.
A method for estimating the number of events in a Monte Carlo background
sample required to model the shape is detailed. It was found that the size of
the nominal γγ background events sample required a multiplicative increase by
a factor of 3.60 to adequately model the background with a confidence level of
68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate,
0.5 billion additional simulated events were produced, substantially reducing the
background modelling uncertainty.
A technique is detailed for emulating the effects of Monte Carlo event generator
differences using multivariate reweighting. The technique is used to estimate the
event generator uncertainty on the signal modelling of tHqb events, improving the
reliability of estimating the tHqb production cross-section. Then this multivariate
reweighting technique is used to estimate the generator modelling uncertainties
on background V γγ samples for the first time. The estimated uncertainties were
found to be covered by the currently assumed background modelling uncertainty
Comparative study of classification algorithms for quality assessment of resistance spot welding joints from preand post-welding inputs
Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-single-output methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter
Enhancing Parkinsonâs Disease Prediction Using Machine Learning and Feature Selection Methods
Several millions of people suffer from Parkinsonâs disease globally. Parkinsonâs affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic problems, such as Parkinsonâs Disease (PD). In addition, to improve the performance of classification, feature selection methods have been extensively used in many fields. This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based. The dataset includes 240 recodes with 46 acoustic features extracted from 3 voice recording replications for 80 patients. The experimental results showed improvements when wrapper-based features selection method was used with KNN classifier with accuracy of 88.33%. The best obtained results were compared with other studies and it was found that this study provides comparable and superior results
The association between neurodegeneration and local complement activation in the thalamus to progressive multiple sclerosis outcome
The extent of grey matter demyelination and neurodegeneration in the progressive multiple sclerosis (PMS) brains at postâmortem associates with more severe disease. Regional tissue atrophy, especially affecting the cortical and deep grey matter, including the thalamus, is prognostic for poor outcomes. Microglial and complement activation are important in the pathogenesis and contribute to damaging processes that underlie tissue atrophy in PMS. We investigated the extent of pathology and innate immune activation in the thalamus in comparison to cortical grey and white matter in blocks from 21 cases of PMS and 10 matched controls. Using a digital pathology workflow, we show that the thalamus is invariably affected by demyelination and had a far higher proportion of active inflammatory lesions than forebrain cortical tissue blocks from the same cases. Lesions were larger and more frequent in the medial nuclei near the ventricular margin, whilst neuronal loss was greatest in the lateral thalamic nuclei. The extent of thalamic neuron loss was not associated with thalamic demyelination but correlated with the burden of white matter pathology in other forebrain areas (Spearman r = 0.79, p < 0.0001). Only thalamic neuronal loss, and not that seen in other forebrain cortical areas, correlated with disease duration (Spearman r = â0.58, p = 0.009) and age of death (Spearman r = â0.47, p = 0.045). Immunoreactivity for the complement pattern recognition molecule C1q, and products of complement activation (C4d, Bb and C3b) were elevated in thalamic lesions with an active inflammatory pathology. Complement regulatory protein, C1 inhibitor, was unchanged in expression. We conclude that active inflammatory demyelination, neuronal loss and local complement synthesis and activation in the thalamus, are important to the pathological and clinical disease outcomes of PMS
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 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
Differences in external match load metrics between professional and semi-professional football players
This study aimed to investigate the differences in external match load between professional and semi-professional footballers, and also aimed to investigate whether periods of fixture congestion throughout the season had an effect on the external match load of players at either the professional or semi-professional level. This study consisted of data from 51 football players, 21 professional and 30 semi-professional footballers, playing in the 2019/2020 football season. The data collected was obtained via MEMS (microelectromechanical systems) devices, which measured the playersâ total distance, high-speed distance, accelerations, decelerations and player load. Once the external match load data was quantified, a comparison between playing levels took place using a univariate ANOVA. A two-way repeated measures ANOVA was used to examine if significant differences existed in external match load variables across player performance level (2 levels) and time of the season (3 levels) during periods of time when teams experienced fixture congestion. This study found that professional players travelled significantly greater distances in a 90 minute match (10.93 ± 2.46 vs 9.02 ± 1.56 km respectively; P<0.001). No differences in high-speed distance were observed between playing level (P=0.70), whereas semiprofessional players recorded significantly greater player load value than the professional players (88.6 ± 12.2 vs 68.8 ± 18.9% respectively; P<0.001). Periods of fixture congestion were not found to significantly affect any of the match load variables at either playing level despite the time of the season. In conclusion, neither playing level was found to exhibit a superior level of external match load. The other major finding of this thesis was that fixture congestion did not affect match load. Further research is required to quantify and compare the external match load at the non-elite professional and semi-professional level of football, as these levels of football are largely ignored in this field of literature
Studies of strategic performance management for classical organizations theory & practice
Nowadays, the activities of "Performance Management" have spread very broadly in actually every part of business and management. There are numerous practitioners and researchers from very different disciplines, who are involved in exploring the different contents of performance management. In this thesis, some relevant historic developments in performance management are first reviewed. This includes various theories and frameworks of performance management. Then several management science techniques are developed for assessing performance management, including new methods in Data Envelopment Analysis (DEA) and Soft System Methodology (SSM). A theoretical framework for performance management and its practical procedures (five phases) are developed for "classic" organizations using soft system thinking, and the relationship with the existing theories are explored. Eventually these results are applied in three case studies to verify our theoretical development. One of the main contributions of this work is to point out, and to systematically explore the basic idea that the effective forms and structures of performance management for an organization are likely to depend greatly on the organizational configuration, in order to coordinate well with other management activities in the organization, which has seemingly been neglected in the existing literature of performance management research in the sense that there exists little known research that associated particular forms of performance management with the explicit assumptions of organizational configuration. By applying SSM, this thesis logically derives some main functional blocks of performance management in 'classic' organizations and clarifies the relationships between performance management and other management activities. Furthermore, it develops some new tools and procedures, which can hierarchically decompose organizational strategies and produce a practical model of specific implementation steps for "classic" organizations. Our approach integrates popular types of performance management models. Last but not least, this thesis presents findings from three major cases, which are quite different organizations in terms of management styles, ownership, and operating environment, to illustrate the fliexbility of the developed theoretical framework
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