11,251 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
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Quantum Mechanics Lecture Notes. Selected Chapters
These are extended lecture notes of the quantum mechanics course which I am
teaching in the Weizmann Institute of Science graduate physics program. They
cover the topics listed below. The first four chapter are posted here. Their
content is detailed on the next page. The other chapters are planned to be
added in the coming months.
1. Motion in External Electromagnetic Field. Gauge Fields in Quantum
Mechanics.
2. Quantum Mechanics of Electromagnetic Field
3. Photon-Matter Interactions
4. Quantization of the Schr\"odinger Field (The Second Quantization)
5. Open Systems. Density Matrix
6. Adiabatic Theory. The Berry Phase. The Born-Oppenheimer Approximation
7. Mean Field Approaches for Many Body Systems -- Fermions and Boson
Minimum income support systems as elements of crisis resilience in Europe: Final Report
Mindestsicherungssysteme dienen in den meisten entwickelten Wohlfahrtsstaaten als Sicherheitsnetz letzter Instanz. Dementsprechend spielen sie gerade in wirtschaftlichen Krisenzeiten eine besondere Rolle. Inwieweit Mindestsicherungssysteme in Zeiten der Krise beansprucht werden, hängt auch von der Ausprägung vorgelagerter Sozialschutzsysteme ab. Diese Studie untersucht die Bedeutung von Systemen der Mindestsicherung sowie vorgelagerter Systeme wie Arbeitslosenversicherung, Kurzarbeit und arbeitsrechtlichem Bestandsschutz fĂźr die Krisenfestigkeit in Europa. Im Kontext der Finanzkrise von 2008/2009 und der Corona-Krise wird die Fähigkeit sozialpolitischer MaĂnahmen untersucht, Armut und EinkommensÂverluste einzudämmen und gesellschaftliche Ausgrenzung zu vermeiden. Die Studie setzt dabei auf quantitative und qualitative Methoden, etwa multivariate Analysen, Mikrosimulationsmethoden sowie eingehende Fallstudien der Länder Dänemark, Frankreich, Irland, Polen und Spanien, die fĂźr unterschiedliche Typen von Wohlfahrtsstaaten stehen.The aim of this study is to analyse the role of social policies in different European welfare states regarding minimum income protection and active inclusion. The core focus lies on crisis resilience, i.e. the capacity of social policy arrangements to contain poverty and inequality and avoid exclusion before, during and after periods of economic shocks. To achieve this goal, the study expands its analytical focus to include other tiers of social protection, in particular upstream systems such as unemployment insurance, job retention and employment protection, as they play an additional and potentially prominent role in providing income and job protection in situations of crisis. A mixed-method approach is used that combines quantitative and qualitative research, such as descriptive and multivariate quantitative analyses, microsimulation methods and in-depth case studies. The study finds consistent differences in terms of crisis resilience across countries and welfare state types. In general, Nordic and Continental European welfare states with strong upstream systems and minimum income support (MIS) show better outcomes in core socio-economic outcomes such as poverty and exclusion risks. However, labour market integration shows some dualisms in Continental Europe. The study shows that MIS holds particular importance if there are gaps in upstream systems or cases of severe and lasting crises
Mixed volumes of networks with binomial steady-states
The steady-state degree of a chemical reaction network is the number of
complex steady-states for generic rate constants and initial conditions. One
way to bound the steady-state degree is through the mixed volume of the
steady-state system or an equivalent system. In this work, we show that for
partionable binomial networks, whose resulting steady-state systems are given
by a set of binomials and a set of linear (not necessarily binomial)
conservation equations, computing the mixed volume is equivalent to finding the
volume of a single mixed cell that is the translate of a parallelotope. We then
turn our attention to identifying cycles with binomial steady-state ideals. To
this end, we give a coloring condition on directed cycles that guarantees the
network has a binomial steady-state ideal. We highlight both of these theorems
using a class of networks referred to as species-overlapping networks and give
a formula for the mixed volume of these networks.Comment: 17 page
Model Diagnostics meets Forecast Evaluation: Goodness-of-Fit, Calibration, and Related Topics
Principled forecast evaluation and model diagnostics are vital in fitting probabilistic models and forecasting outcomes of interest. A common principle is that fitted or predicted distributions ought to be calibrated, ideally in the sense that the outcome is indistinguishable from a random draw from the posited distribution. Much of this thesis is centered on calibration properties of various types of forecasts.
In the first part of the thesis, a simple algorithm for exact multinomial goodness-of-fit tests is proposed. The algorithm computes exact -values based on various test statistics, such as the log-likelihood ratio and Pearson\u27s chi-square. A thorough analysis shows improvement on extant methods. However, the runtime of the algorithm grows exponentially in the number of categories and hence its use is limited.
In the second part, a framework rooted in probability theory is developed, which gives rise to hierarchies of calibration, and applies to both predictive distributions and stand-alone point forecasts. Based on a general notion of conditional T-calibration, the thesis introduces population versions of T-reliability diagrams and revisits a score decomposition into measures of miscalibration, discrimination, and uncertainty. Stable and efficient estimators of T-reliability diagrams and score components arise via nonparametric isotonic regression and the pool-adjacent-violators algorithm. For in-sample model diagnostics, a universal coefficient of determination is introduced that nests and reinterprets the classical in least squares regression.
In the third part, probabilistic top lists are proposed as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicited by strictly consistent evaluation metrics, based on symmetric proper scoring rules, which admit comparison of various types of predictions
Mathematical models to evaluate the impact of increasing serotype coverage in pneumococcal conjugate vaccines
Of over 100 serotypes of Streptococcus pneumoniae, only 7 were included in the first pneumo- coccal conjugate vaccine (PCV). While PCV reduced the disease incidence, in part because of a herd immunity effect, a replacement effect was observed whereby disease was increasingly caused by serotypes not included in the vaccine. Dynamic transmission models can account for these effects to describe post-vaccination scenarios, whereas economic evaluations can enable decision-makers to compare vaccines of increasing valency for implementation. This thesis has four aims. First, to explore the limitations and assumptions of published pneu- mococcal models and the implications for future vaccine formulation and policy. Second, to conduct a trend analysis assembling all the available evidence for serotype replacement in Europe, North America and Australia to characterise invasive pneumococcal disease (IPD) caused by vaccine-type (VT) and non-vaccine-types (NVT) serotypes. The motivation behind this is to assess the patterns of relative abundance in IPD cases pre- and post-vaccination, to examine country-level differences in relation to the vaccines employed over time since introduction, and to assess the growth of the replacement serotypes in comparison with the serotypes targeted by the vaccine. The third aim is to use a Bayesian framework to estimate serotype-specific invasiveness, i.e. the rate of invasive disease given carriage. This is useful for dynamic transmission modelling, as transmission is through carriage but a majority of serotype-specific pneumococcal data lies in active disease surveillance. This is also helpful to address whether serotype replacement reflects serotypes that are more invasive or whether serotypes in a specific location are equally more invasive than in other locations. Finally, the last aim of this thesis is to estimate the epidemiological and economic impact of increas- ing serotype coverage in PCVs using a dynamic transmission model. Together, the results highlight that though there are key parameter uncertainties that merit further exploration, divergence in serotype replacement and inconsistencies in invasiveness on a country-level may make a universal PCV suboptimal.Open Acces
GENDERED EMBODIMENT, STABILITY AND CHANGE: WOMENâS WEIGHTLIFTING AS A TOOL FOR RECOVERY FROM EATING DISORDERS
This thesis explores the everyday embodied experiences of women who use amateur weightlifting as a vehicle for recovery from eating disorders. Within online spaces and on social media, women frequently share their experiences of using weightlifting to overcome issues relating to disordered eating, body image, and mental health. In particular, women with a history of eating disorders credit weightlifting to be integral to their recovery journey. However, there is a dearth of research on womenâs experiences with exercise during eating disorder recovery and no research that identifies weightlifting as beneficial to this process. To the contrary, discursive links are drawn between the practices of self-surveillance exercised by both eating disorder sufferers and weightlifters alike. In this regard, engagement with weightlifting during eating disorder recovery may signal the transferal of pathology from one set of behaviours to another. That is, from disordered eating to rigid and self-regulatory exercise routines. This thesis examines how women subjectively navigate and make sense of this pathologisation.
The data for this research comes from longitudinal semi-structured interviews and photo elicitation with 19 women, living in the United Kingdom, who engaged in weightlifting during their eating disorder recovery. In addition, to build up a holistic picture and to explore how this phenomenon also âtakes placeâ online, I conducted a netnography of the overlapping subcultures of female weightlifting and eating disorder recovery on Instagram. Womenâs standpoint theory and interpretative phenomenological analysis are combined to form the underpinning theoretical and analytical tools used to engage with these three rich data sets. Moreover, throughout I draw on an eclectic range of disciplinary perspectives, in order to bring together multiple fields of research and develop novel theoretical frameworks.
In the findings, I argue that womenâs experiences using weightlifting as a tool for recovery from eating disorders manifests in an embodied sense of multiplicity. In this sense, understandings of the body that are often viewed as ontologically distinct (muscularity/thinness/fatness) hang-together at once in the lived experience of a single individual.
I argue that women, particularly those who have previously struggled with an eating disorder, are too readily positioned as vulnerable to media and representation. To theoretically combat these ideas regarding womenâs assumed passivity, I develop the concept of âdigital pruningâ to account for womenâs agency in relation to new media.
I contend that weightlifting offers women in recovery from eating disorders a new framework for approaching eating and exercise. Specifically, weightliftingâs norms and values legitimate occupying a larger body, which gives women in recovery permission to eat and gain-weight in a way that is both culturally sanctioned and health-promoting.
Finally, I explore identity transformation as a specific tenet of recovery from eating disorders. I argue that, on social media, recovery identities are characterised by personal empowerment, resilience, and independence. While offline, quieter and less culturally glorified aspects of recovery (such as relationships of care) are central to womenâs accounts of developing a new sense of self as they transition away from an eating disorder identity.
In summary, this thesis is an examination of the ways in which women strategically navigate pathology in relation to their bodies, social media, food/exercise practices, and identity. I argue that women develop a set of âDIYâ recovery practices that allow them to consciously channel and draw on their negative experiences with eating disorders, to develop new ways of living that serve their overall wellbeing. Weightlifting is integral to this process, as it provides women transitioning out of this difficult phase in their lives with new ways of relating to their bodies and of being in the world. I situate this phenomenon within a neoliberal socio-political climate in which individuals are required to take personal responsibility for their mental health and wellbeing, despite living within conditions which are not conducive to recovery
Contemporary, decadal, and millennial-scale permafrost- and vegetation dynamics and carbon release in an alpine region of Jotunheimen, Norway
Climatic warming in northern alpine regions facilitates the thawing of permafrost, the associated release of soil carbon into the atmosphere, and the altitudinal shifts in vegetation patterns. Here, a multi-disciplinary approach is adopted to investigate the response of an alpine permafrost landscape (Jotunheimen, Norway, with focus on Galdhøpiggen) to climatic changes over long- to medium timescales. First, a gas analyser is used to explore how ecosystem respiration is affected by ecosystem (soil and vegetation) and geomorphological (cryogenic disturbance) factors during the peak growing season. A palaeoecological record is then analysed to infer the past dynamics of the alpine tree lines and the lower limit of permafrost on Galdhøpiggen over the millennial- and centennial scales. Finally, remotely sensed satellite imagery is combined with observed air temperatures to create a model that provides an estimation of land surface temperatures over the past six decades. The model is then used to predict surface âgreennessâ over the same period. Palynological evidence from Galdhøpiggen indicates that the altitudinal limits of alpine tree lines have shifted by hundreds of metres in response to climatic changes over the millennial scale. Since 1957, the model predictions indicate substantial increases in land surface temperatures and growing season surface âgreennessâ (i.e., vegetation abundance) in Jotunheimen, but the change has not been spatially uniform. The highest increases were recorded over the low- and mid-alpine heaths above the tree line (1050-1500 m a.s.l.), which was attributed to increased shrub cover. This trend could facilitate carbon release from the ground, as peak growing season ecosystem respiration was found to be most strongly controlled by soil microclimate and plant growth forms. The likely future scenario in response to warming in Jotunheimen will be continued permafrost degradation, with higher altitudes (âĽ1500 m a.s.l.) experiencing decreased cryoturbation, increased shrub encroachment and higher surface CO2 emissions
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Mixture Models in Machine Learning
Modeling with mixtures is a powerful method in the statistical toolkit that can be used for representing the presence of sub-populations within an overall population. In many applications ranging from financial models to genetics, a mixture model is used to fit the data. The primary difficulty in learning mixture models is that the observed data set does not identify the sub-population to which an individual observation belongs. Despite being studied for more than a century, the theoretical guarantees of mixture models remain unknown for several important settings.
In this thesis, we look at three groups of problems. The first part is aimed at estimating the parameters of a mixture of simple distributions. We ask the following question: How many samples are necessary and sufficient to learn the latent parameters? We propose several approaches for this problem that include complex analytic tools to connect statistical distances between pairs of mixtures with the characteristic function. We show sufficient sample complexity guarantees for mixtures of popular distributions (including Gaussian, Poisson and Geometric). For many distributions, our results provide the first sample complexity guarantees for parameter estimation in the corresponding mixture. Using these techniques, we also provide improved lower bounds on the Total Variation distance between Gaussian mixtures with two components and demonstrate new results in some sequence reconstruction problems.
In the second part, we study Mixtures of Sparse Linear Regressions where the goal is to learn the best set of linear relationships between the scalar responses (i.e., labels) and the explanatory variables (i.e., features). We focus on a scenario where a learner is able to choose the features to get the labels. To tackle the high dimensionality of data, we further assume that the linear maps are also sparse , i.e., have only few prominent features among many. For this setting, we devise algorithms with sub-linear (as a function of the dimension) sample complexity guarantees that are also robust to noise.
In the final part, we study Mixtures of Sparse Linear Classifiers in the same setting as above. Given a set of features and the binary labels, the objective of this task is to find a set of hyperplanes in the space of features such that for any (feature, label) pair, there exists a hyperplane in the set that justifies the mapping. We devise efficient algorithms with sub-linear sample complexity guarantees for learning the unknown hyperplanes under similar sparsity assumptions as above. To that end, we propose several novel techniques that include tensor decomposition methods and combinatorial designs
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