16,875 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
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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
CO2 electroreduction: sustainability analysis of the renewable synthetic natural gas
Capture and utilization of industrial CO2 emissions into low-carbon fuels is a promising alternative to store renewable electricity into chemical vectors while decarbonizing the economy. This work evaluates the viability pathways of producing synthetic natural gas (SNG) by direct CO2 electroreduction (ER) in Power-To-Synthetic Natural Gas electrolyzers (PtSNG). We perform an ex-ante techno-economic (TEA) and life cycle analysis (LCA) for a 2030 framework in Europe. ER performance is varied in defined scenarios and assessed using a built-in process model of the PtSNG system, revealing uncharted limitations and benchmarks to achieve. Results show that substitution of fossil natural gas with renewable SNG could avoid more than 1 kg CO2e/kg SNG under moderate ER conditions when using low-carbon electricity (< 60 kg CO2e/MWh). SNG profitability for 2030 would rely on: i) higher CH4 current densities (800â1000 mA/cm2), ii) improvements in energy efficiency (higher than 60%), and iii) valorization of the anodic product or additional carbon incentives. Our study proves that if market and technology evolve appropriately in the coming years, the SNG by CO2 ER may be a mid-term climate change mitigation technology, among others.The authors thank the Spanish Ministry of Economy and Competitiveness for the financial support through the project PID2020â114,787-RB-I00. Javier FernĂĄndez-GonzĂĄlez and Marta Rumayor would also like to thank the financial support of the Spanish Ministry of Science and Innovation for the concession of a FPU grant (FPU19/05483) and a Juan de la Cierva postdoctoral contract (IJCI-2017-32621), respectively
The Importance of Soft Skills for Strengthening Agency in Female Entrepreneurship Programmes
This paper is part of the MUVA Paper Series on female entrepreneurship. It focuses on how soft skills in female entrepreneurship programmes strengthen agency and impact economic empowerment of women entrepreneurs in low- and middle-income countries (LMICs). It draws on both the literature and lessons learned from Mozambique-based social incubator MUVA. By exploring MUVAâs entrepreneurship experience, this paper contributes to debates in the literature about the importance of soft skills in female entrepreneurship programmes for enhanced self-esteem, self-confidence and self-efficacy to strengthen agency
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
How to Be a God
When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers.
Philosophers have the answers that canât be proven right. Theologians have the answers that canât be proven wrong.
Todayâs designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They canât spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice.
Thatâs todayâs designers. Tomorrowâs will have a whole new set of questions to answer.
The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves?
How should we be gods
Balancing the urban stomach: public health, food selling and consumption in London, c. 1558-1640
Until recently, public health histories have been predominantly shaped by medical and scientific perspectives, to the neglect of their wider social, economic and political contexts. These medically-minded studies have tended to present broad, sweeping narratives of health policy's explicit successes or failures, often focusing on extraordinary periods of epidemic disease viewed from a national context. This approach is problematic, particularly in studies of public health practice prior to 1800. Before the rise of modern scientific medicine, public health policies were more often influenced by shared social, cultural, economic and religious values which favoured maintaining hierarchy, stability and concern for 'the common good'. These values have frequently been overlooked by modern researchers. This has yielded pessimistic assessments of contemporary sanitation, implying that local authorities did not care about or prioritise the health of populations. Overly medicalised perspectives have further restricted historians' investigation and use of source material, their interpretation of multifaceted and sometimes contested cultural practices such as fasting, and their examination of habitual - and not just extraordinary - health actions. These perspectives have encouraged a focus on reactive - rather than preventative - measures.
This thesis contributes to a growing body of research that expands our restrictive understandings of pre-modern public health. It focuses on how public health practices were regulated, monitored and expanded in later Tudor and early Stuart London, with a particular focus on consumption and food-selling. Acknowledging the fundamental public health value of maintaining urban foodways, it investigates how contemporaries sought to manage consumption, food production waste, and vending practices in the early modern City's wards and parishes. It delineates the practical and political distinctions between food and medicine, broadly investigates the activities, reputations of and correlations between London's guild and itinerant food vendors and licensed and irregular medical practitioners, traces the directions in which different kinds of public health policy filtered up or down, and explores how policies were enacted at a national and local level. Finally, it compares and contrasts habitual and extraordinary public health regulations, with a particular focus on how perceptions of and actual food shortages, paired with the omnipresent threat of disease, impacted broader aspects of civic life
Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting
Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statistical models that are often too costly, both computationally and budgetary, or are not applied to downstream applications. Therefore, approaches that use Machine Learning algorithms in conjunction with time-series data are being explored as an alternative to overcome these drawbacks. To this end, this study presents a comparative analysis using simplified rainfall estimation models based on conventional Machine Learning algorithms and Deep Learning architectures that are efficient for these downstream applications. Models based on LSTM, Stacked-LSTM, Bidirectional-LSTM Networks, XGBoost, and an ensemble of Gradient Boosting Regressor, Linear Support Vector Regression, and an Extra-trees Regressor were compared in the task of forecasting hourly rainfall volumes using time-series data. Climate data from 2000 to 2020 from five major cities in the United Kingdom were used. The evaluation metrics of Loss, Root Mean Squared Error, Mean Absolute Error, and Root Mean Squared Logarithmic Error were used to evaluate the models' performance. Results show that a Bidirectional-LSTM Network can be used as a rainfall forecast model with comparable performance to Stacked-LSTM Networks. Among all the models tested, the Stacked-LSTM Network with two hidden layers and the Bidirectional-LSTM Network performed best. This suggests that models based on LSTM-Networks with fewer hidden layers perform better for this approach; denoting its ability to be applied as an approach for budget-wise rainfall forecast applications
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