7,802 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
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Production networks in the cultural and creative sector: case studies from the publishing industry
The CICERONE project investigates cultural and creative industries through case study research, with a focus on production networks. This report, part of WP2, examines the publishing industry within this framework. It aims to understand the industryâs hidden aspects, address statistical issues in measurement, and explore the industryâs transformation and integration of cultural and economic values. The report provides an overview of the production network, explores statistical challenges, and presents qualitative analyses of two case studies. It concludes by highlighting the potential of the Global Production Network (GPN) approach for analyzing, researching, policymaking, and intervening in the European publishing network.
The CICERONE projectâs case study research delves into the publishing industry, investigating its production networks and examining key aspects often unseen by the public. The report addresses statistical challenges in measuring the industry and sheds light on its ongoing transformations and integration of cultural and economic values. It presents an overview of the production network, explores statistical issues, and provides qualitative analyses of two case studies. The report emphasizes the potential of the GPN approach for analyzing and intervening in the European publishing network, ultimately contributing to research, policymaking, and understanding within the industry
Analyzing Eurocitiesâ role in the Urban Agenda for the EU. Empowering cities in sustainable urban governance
MĂ ster en DiplomĂ cia i Organitzacions Internacionals, Centre d'Estudis Internacionals. Universitat de Barcelona. Curs: 2022-2023. Tutora: Andrea NoferiniThis investigation aims to analyze the role of Eurocitiesâ network in the UAEU. Unveiling
the implications this framework has for empowering cities and local entities within the urban
governance structure at the EU level
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Rare-Event Estimation and Calibration for Large-Scale Stochastic Simulation Models
Stochastic simulation has been widely applied in many domains. More recently, however, the rapid surge of sophisticated problems such as safety evaluation of intelligent systems has posed various challenges to conventional statistical methods. Motivated by these challenges, in this thesis, we develop novel methodologies with theoretical guarantees and numerical applications to tackle them from different perspectives.
In particular, our works can be categorized into two areas: (1) rare-event estimation (Chapters 2 to 5) where we develop approaches to estimating the probabilities of rare events via simulation; (2) model calibration (Chapters 6 and 7) where we aim at calibrating the simulation model so that it is close to reality.
In Chapter 2, we study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests. We investigate an importance sampling scheme that integrates the dominating point machinery in large deviations and sequential mixed integer programming to locate the underlying dominating points. We provide efficiency guarantees and numerical demonstration of our approach.
In Chapter 3, we propose a new efficiency criterion for importance sampling, which we call probabilistic efficiency. Conventionally, an estimator is regarded as efficient if its relative error is sufficiently controlled. It is widely known that when a rare-event set contains multiple "important regions" encoded by the dominating points, importance sampling needs to account for all of them via mixing to achieve efficiency. We argue that the traditional analysis recipe could suffer from intrinsic looseness by using relative error as an efficiency criterion. Thus, we propose the new efficiency notion to tighten this gap. In particular, we show that under the standard Gartner-Ellis large deviations regime, an importance sampling that uses only the most significant dominating points is sufficient to attain this efficiency notion.
In Chapter 4, we consider the estimation of rare-event probabilities using sample proportions output by crude Monte Carlo. Due to the recent surge of sophisticated rare-event problems, efficiency-guaranteed variance reduction may face implementation challenges, which motivate one to look at naive estimators. In this chapter we construct confidence intervals for the target probability using this naive estimator from various techniques, and then analyze their validity as well as tightness respectively quantified by the coverage probability and relative half-width.
In Chapter 5, we propose the use of extreme value analysis, in particular the peak-over-threshold method which is popularly employed for extremal estimation of real datasets, in the simulation setting. More specifically, we view crude Monte Carlo samples as data to fit on a generalized Pareto distribution. We test this idea on several numerical examples. The results show that in the absence of efficient variance reduction schemes, it appears to offer potential benefits to enhance crude Monte Carlo estimates.
In Chapter 6, we investigate a framework to develop calibration schemes in parametric settings, which satisfies rigorous frequentist statistical guarantees via a basic notion that we call eligibility set designed to bypass non-identifiability via a set-based estimation. We investigate a feature extraction-then-aggregation approach to construct these sets that target at multivariate outputs. We demonstrate our methodology on several numerical examples, including an application to calibration of a limit order book market simulator.
In Chapter 7, we study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge, a model calibration problem under both aleatory and epistemic uncertainties. Our methodology is based on an integration of distributionally robust optimization and importance sampling. The main computation machinery in this integrated methodology amounts to solving sampled linear programs. We present theoretical statistical guarantees of our approach via connections to nonparametric hypothesis testing, and numerical performances including parameter calibration and downstream decision and risk evaluation tasks
Therapeutic/Expressive Writing and Resilience Promotion for Nurses to Reduce Burnout Syndrome
COVID-19 accelerated the rate in which nurses were unable to maintain resilience and reduce burnout. This evidence-based DNP project obtained data from a therapeutic/expressive writing intervention and group resilience discussion with Womenâs Care Center (WCC) nurses to improve resilience acuity and reduce symptoms associated with burnout syndrome. A review of previous studies indicated therapeutic/expressive writing and group resilience discussions have been beneficial in improving resilience and reducing burnout. A demographic and two preintervention surveys were completed by WCC nurses in the hospital relaxation room or skills lab. The Connor-Davidson RISC-25© was used to determine resilience scores for morning and evening shift nurses. The Maslach Burnout Inventory© (MBI) Survey for Medical Personnel was used to assess emotional exhaustion (EE), depersonalization (DP), and personal achievement (PA) in the same groups. Participants (n =20) nurses completed the pre-intervention resilience and burnout surveys. Day and night shift nurses (n= 19) resilience mean average increased after the intervention 2.65 mean score (M = 83.31, SD =9.86) for the Connor-Davidson RISC-25© survey which is in the intermediate range: 50% of the population. The burnout mean for the morning shift nurses, EE (M = 21.88), DP (M = 4.88), and PA (M = 39.27) which indicated moderate burnout for all categories. The burnout means for evening shift nurses, EE (M = 17.33), DP (M = 7.22), and PA (M = 40.00), which fell within the moderate burnout range
What issues do school staff describe as important when introducing a whole school attachment-based approach? A Reflexive Thematic Analysis
Research demonstrates that supporting childrenâs emotional needs promotes better learning outcomes (Geddes, 2018). In the United Kingdom, hundreds of schools are trained in whole school approaches that have a basis in attachment theory. These approaches emphasise the relational needs of pupils and prioritise their sense of safety. They are often referred to by schools and in the limited literature as âattachment awareâ approaches. The current study took place in a deprived inner East London borough. It has one of the highest proportions of children with social, emotional, and mental health needs in the country. Reflexive Thematic Analysis (Braun & Clarke, 2019) was used to provide an answer to the following research question: âWhat issues do school staff describe as important when introducing a whole school attachment-based approach?â Eight semi-structured interviews were conducted in three schools with a range of staff including senior leaders, teachers and support staff. The researcher constructed five overarching themes to organise 13 themes that reflected patterns in participant experience. These five overarching themes were âContext Affects Deliveryâ, âTraining Must Resonateâ, âScope and Remit of School and School Staff Widensâ, âPermission to Feelâ and âNot Running Alone with Themâ. In the current climate, emotionally focused âapproaches could be referred to as an addon to the real business of educationâ (Parker & Levinson, 2018: 9). This research argues that emotionally focused approaches such as whole school attachment-based approaches are well placed to meet the needs of the entire school community and promote increased pupil engagement. This study adds to the exponentially growing body of research on whole school attachment-based approaches. The research has implications for local and national practice due to the priority given to trauma-based approaches in recent government guidance
The nexus between e-marketing, e-service quality, e-satisfaction and e-loyalty: a cross-sectional study within the context of online SMEs in Ghana
The spread of the Internet, the proliferation of mobile devices, and the onset of the COVID-19
pandemic have given impetus to online shopping in Ghana and the subregion. This situation
has also created opportunities for SMEs to take advantage of online marketing technologies.
However, there is a dearth of studies on the link between e-marketing and e-loyalty in terms
of online shopping, thereby creating a policy gap on the prospects for business success for
online SMEs in Ghana. Therefore, the purpose of the study was to examine the relationship
between the main independent variable, e-marketing and the main dependent variable, e-loyalty, as well as the mediating roles of e-service quality and e-satisfaction in the link between
e-marketing and e-loyalty. The study adopted a positivist stance with a quantitative method.
The study was cross-sectional in nature with the adoption of a descriptive correlational design.
A Structural Equation Modelling approach was employed to examine the nature of the
associations between the independent, mediating and dependent variables. A sensitivity
analysis was also conducted to control for the potential confounding effects of the
demographic factors. A sample size of 1,293 residents in Accra, Ghana, who had previously
shopped online, responded to structured questionnaire in an online survey via Google Docs.
The IBM SPSS Amos 24 software was used to analyse the data collected. Positive
associations were found between the key constructs in the study: e-marketing, e-service
quality, e-satisfaction and e-Loyalty. The findings from the study gave further backing to the
diffusion innovation theory, resource-based view theory, and technology acceptance model.
In addition, e-service quality and e-satisfaction individually and jointly mediated the
relationship between e-marketing and e-loyalty. However, these mediations were partial,
instead of an originally anticipated full mediation. In terms of value and contribution, this is the
first study in a developing economy context to undertake a holistic examination of the key
marketing performance variables within an online shopping context. The study uniquely tested
the mediation roles of both e-service quality and e-satisfaction in the link between e-marketing
and e-loyalty. The findings of the study are novel in the e-marketing literature as they
unearthed the key antecedents of e-loyalty for online SMEs in a developing economy context.
The study suggested areas for further related studies and also highlighted the limitations
Data Tiling for Sparse Computation
Many real-world data contain internal relationships. Efficient analysis of these relationship data is crucial for important problems including genome alignment, network vulnerability analysis, ranking web pages, among others. Such relationship data is frequently sparse and analysis on it is called sparse computation. We demonstrate that the important technique of data tiling is more powerful than previously known by broadening its application space. We focus on three important sparse computation areas: graph analysis, linear algebra, and bioinformatics. We demonstrate data tiling's power by addressing key issues and providing significant improvements---to both runtime and solution quality---in each area. For graph analysis, we focus on fast data tiling techniques that can produce well-structured tiles and demonstrate theoretical hardness results. These tiles are suitable for graph problems as they reduce data movement and ultimately improve end-to-end runtime performance. For linear algebra, we introduce a new cache-aware tiling technique and apply it to the key kernel of sparse matrix by sparse matrix multiplication. This technique tiles the second input matrix and then uses a small, summary matrix to guide access to the tiles during computation. Our approach results in the fastest known implementation across three distinct CPU architectures. In bioinformatics, we develop a tiling based de novo genome assembly pipeline. We start with reads and develop either a graph or hypergraph that captures internal relationships between reads. This is then tiled to minimize connections while maintaining balance. We then treat each resulting tile independently as the input to an existing, shared-memory assembler. Our pipeline improves existing state-of-the-art de novo genome assemblers and brings both runtime and quality improvements to them on both real-world and simulated datasets.Ph.D
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