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  • Resultat 1478411-1478420 av 1637197
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1478411.
  • Ulak, Manjeswori, et al. (författare)
  • Infant feeding practices in Bhaktapur, Nepal: A cross-sectional, health facility based survey.
  • 2012
  • Ingår i: International breastfeeding journal. - : Springer Science and Business Media LLC. - 1746-4358. ; 7:1, s. 1-8
  • Tidskriftsartikel (refereegranskat)abstract
    • ABSTRACT: BACKGROUND: Promotion of proper breastfeeding practices for the first six months of life is the most cost-effective intervention for reducing childhood morbidity and mortality. However, the adherence to breastfeeding recommendations in many developing countries is not satisfactory. The aims of the study were to determine breastfeeding and infant feeding patterns at nine months of age and to assess factors influencing exclusive breastfeeding practices. METHODS: In Bhaktapur, Nepal, we carried out a cross-sectional survey of 325 infants who came for measles vaccination at the age of nine months. Mothers were interviewed on details regarding feeding of their child and health since birth. RESULTS: Three quarters of all mothers reported that they did not receive any information on breastfeeding during the antenatal visit. Two hundred and ninety five (91%) mothers gave colostrum and 185 (57%) initiated breastfeeding within one hour of delivery. The prevalence of exclusively breastfeeding at 1, 3 and 6 months were 240 (74%), 78 (24%) and 29 (9%), and partial feeding was initiated in 49 (15%), 124 (38%) and 257 (79%) babies, respectively. The main reason, according to the mother, for introducing other foods before six months of age was insufficient breast milk. In logistic regression analyses, mother's knowledge on how long child should be given only breast milk and not living in joint families were associated positively with exclusive or predominant breastfeeding for four months or beyond. CONCLUSIONS: Despite the high proportion of mothers who initiated breastfeeding immediately after birth, continuation of exclusive breastfeeding for up to six months was not common. Very few mothers received any information on breastfeeding during the antenatal visit, indicating a need for counseling on exclusive breastfeeding. Possible options for this counseling could be during antenatal visits and at regular clinic visits for vaccination.
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1478412.
  • Ulama, Jeanette (författare)
  • A Waterborne Colloidal Model System Consisting of Fluorinated Spheres Bearing Grafted PEG: Synthesis, Characterization and Properties
  • 2016
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Model systems have expanded our knowledge of numerous phenomena in Colloid Science, such as the appearance of glasses, order-disorder transitions involving crystals and attraction induced formation of gels. So far, the existing colloidal model systems have been limited mainly to nonaqueous media. Given that water is such an important solvent, an aqueous colloidal model system is called for. Here we present such an aqueous colloidal model system with core-shell particle morphology, where the interior is composed of spherical fluorinated cores and the exterior of a poly(ethylene glycol) (PEG) polymer graft. To synthesize these colloids, we have adopted a semi-batch emulsion polymerization, in which the initiator is slowly fed into the reaction mixture. Using this approach not only can monodisperse, low refractive index and sterically stabilized colloids be produced, but also various lengths of the PEG polymer could be successfully grafted onto the particles. Throughout this thesis, several different instrumental techniques have been used to gain an insight into the collective phenomena of these particles and how particle interactions contribute to the observed phase behavior. Although steric stabilization is very robust way of stabilizing colloidal particles against aggrega- tion, attractions between particles can nevertheless appear, e.g. through the addition of certain salts or addition of a non-solvent. The origin of these attractions is not fully understood. Our results show that colloidal stability increases with decreasing length of the steric stabilizer and that the polymer graft contracts as the solvent quality is worsened. The contraction is accompanied by moderately strong attractions even though the van der Waals force due to core-core interactions is essentially absent. It follows that the attractions are caused by purely polymer-mediated interactions.
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1478413.
  • Ulama, Jeanette, et al. (författare)
  • Monodisperse PEGylated Spheres: An Aqueous Colloidal Model System
  • 2014
  • Ingår i: Journal of Physical Chemistry B. - : American Chemical Society (ACS). - 1520-6106 .- 1520-5207. ; 118:9
  • Tidskriftsartikel (refereegranskat)abstract
    • Fluorinated core–shell spheres have been synthesized using a novel semibatch emulsion polymerization protocol employing slow feeding of the initiator. The synthesis results in aqueous dispersions of highly monodisperse spheres bearing a well-defined poly(ethylene glycol) graft (PEGylation). Measurements are consistent with the synthesis achieving a high grafting density that moreover consists of a single PEG layer with the polymer significantly elongated beyond its radius of gyration in bulk. The fluorination of the core of the particles confers a low index of refraction such that the particles can be refractive index matched in water through addition of relatively small amounts of a cosolvent, which enables the use of optical and laser-based methods for studies of concentrated systems. The systems exhibit an extreme stability in NaCl solutions, but attractions among particles can be introduced by addition of other salts, in which case aggregation is shown to be reversible. The PEGylated sphere dispersions are expected to be ideally suited as model systems for studies of the effect of PEG-mediated interactions on, for instance, structure, dynamics, phase behavior, and rheology.
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1478414.
  • Ulama, Jeanette, et al. (författare)
  • Polymer-Graft-Mediated Interactions between Colloidal Spheres
  • 2016
  • Ingår i: Langmuir. - : American Chemical Society (ACS). - 0743-7463 .- 1520-5827. ; 32:12, s. 2882-2890
  • Tidskriftsartikel (refereegranskat)abstract
    • Aqueous dispersions of fluorinated colloidal spheres bearing grafted poly(ethylene glycol) (PEG) are studied as a function of salt and particle concentration with the aim of improving the understanding of interactions among polymer-grafted particles. These dispersions can sustain large concentrations of salt, but crystals nucleate in dilute dispersions when a sufficient Na2CO3 concentration is reached, which is attributed to the presence of attractions between particles. On further increasing the Na2CO3 concentration, the solvent is rapidly cleared of particles. Small-angle X-ray scattering and cryogenic transmission electron microscopy are employed in order to quantify the attractions. The former is used to extract a second virial coefficient, and the latter shows that the PEG-graft contracts as a function of increasing salt concentration. The contraction not only leads to a reduction in excluded volume but also is accompanied by attractions of moderate magnitude. In contrast, dispersion of the particles in ethanol, in which bulk PEG solutions crystallize, lead to fractal structures caused by strong attractions.
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1478415.
  • Ulan, Maria, et al. (författare)
  • Aggregation as Unsupervised Learning and its Evaluation
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Regression uses supervised machine learning to find a model that combines several independent variables to predict a dependent variable based on ground truth (labeled) data, i.e., tuples of independent and dependent variables (labels). Similarly, aggregation also combines several independent variables to a dependent variable. The dependent variable should preserve properties of the independent variables, e.g., the ranking or relative distance of the independent variable tuples, and/or represent a latent ground truth that is a function of these independent variables. However, ground truth data is not available for finding the aggregation model. Consequently, aggregation models are data agnostic or can only be derived with unsupervised machine learning approaches.We introduce a novel unsupervised aggregation approach based on intrinsic properties of unlabeled training data, such as the cumulative probability distributions of the single independent variables and their mutual dependencies.For assessing this against other aggregation approaches, two perspectives are relevant: (i) how well the aggregation output represents properties of the input tuples, and (ii) how well can aggregated output predict a latent ground truth. We present an empirical evaluation framework that allows to evaluate aggregation approaches from both perspectives. To this end, we use data sets for assessing supervised regression approaches that contain explicit ground truth labels. However, the ground truth is not used for deriving the aggregation models, but it allows for the assessment from a perspective (ii). More specifically, we use regression data sets from the UCI machine learning repository and benchmark several data-agnostic and unsupervised approaches for aggregation against ours.The benchmark results indicate that our approach outperforms the other data-agnostic and unsupervised aggregation approaches. It is almost on par with linear regression.
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1478416.
  • Ulan, Maria (författare)
  • Aggregation as Unsupervised Learning in Software Engineering and Beyond
  • 2021
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Ranking alternatives is fundamental to effective decision making. However, creating an overall ranking is difficult if there are multiple criteria, and no single alternative performs best across all criteria. Software engineering is no exception.Software quality is usually decomposed hierarchically into characteristics, and their quality can be assessed by various direct and indirect metrics. Although such quality models provide a basic understanding of what data to collect and which metrics to use, it is not clear how the metrics should be combined to assess the overall quality. Due to different approaches for aggregation of metrics, the same quality model and the same metrics for assessing the same software artifact could still lead to different assessment results and even to different interpretations.The proposed aggregation approach in this thesis is well-defined, interpretable, and applicable under realistic conditions. This approach can turn the quality- model- and metric-based assessment of (software) quality into a reliable and reproducible process. We express quality as the probability of detecting something with equal or worse quality, based on all software artifacts observed; good and bad quality is expressed in terms of lower and higher probabilities. We validated our approach theoretically and empirically. We conducted empirical studies on Bug prediction, Maintainability assessment, and Information Quality.We used Software Visualization to analyze the usability of aggregation for analyzing multivariate data in general and the effect of different alternative aggregation approaches, i.e., we designed and implemented an exploratory multivariate data visualization tool.Finally, we applied our approach to Multi-criteria Ranking to evaluate its transferability to other domains. We evaluated it on a real-world decision-making problem for assessment and ranking of alternatives. Moreover, we applied our approach to the context of Machine Learning. We created a benchmark from a collection of regression problems, and evaluated how well the aggregation output agrees with a ground truth, and how well it represents the properties of the input variables.The results showed that our approach is not only theoretically sound, it is also accurate, sensitive, identifies anomalies, scales in performance, and can support multi-criteria decision making. Furthermore, our approach is transferable to other domains that require aggregation in hierarchically structured models, and it can be used as an agnostic unsupervised predictor in the absence of a ground truth.
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1478417.
  • Ulan, Maria, et al. (författare)
  • AI-baserad säkerhet på byggarbetsplatser
  • 2023
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • AI-baserad säkerhet på byggarbetsplatser har varit ett ett-årigt projekt som genomförts för att förbättra säkerheten på anläggnings- och byggarbetsplatser genom användning av AI-teknologi. Projektet har varit ett samarbete mellan RISE Research Institutes of Sweden, NCC, Ramirent, Skanska och Viscando. Målet var att identifiera och analysera risker på arbetsplatserna med hjälp av sensorer och AI-modeller för att kunna förutsäga farliga situationer och utveckla lösningar för att förbättra säkerheten. Genom observationer, intervjuer och datainsamling med 3D-sensorer analyserades beteendemönster och riskområden på utvalda byggarbetsplatser. Resultaten användes sedan för att  kommunicera och utveckla konkreta lösningar för att minska riskerna. Rapporten ger en översikt av metodologin, datainsamlings- och analysprocessen samt betonar vikten av att förstå både teknologins möjligheter och begränsningar för att skapa en säkrare arbetsmiljö på byggarbetsplatser. 
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1478418.
  • Ulan, Maria, et al. (författare)
  • Artifact: Quality Models Inside Out : Interactive Visualization of Software Metrics by Means of Joint Probabilities
  • 2018
  • Annan publikation (mjukvara/multimedium) (refereegranskat)abstract
    • Assessing software quality, in general, is hard; each metric has a different interpretation, scale, range of values, or measurement method. Combining these metrics automatically is especially difficult, because they measure different aspects of software quality, and creating a single global final quality score limits the evaluation of the specific quality aspects and trade-offs that exist when looking at different metrics. We present a way to visualize multiple aspects of software quality. In general, software quality can be decomposed hierarchically into characteristics, which can be assessed by various direct and indirect metrics. These characteristics are then combined and aggregated to assess the quality of the software system as a whole. We introduce an approach for quality assessment based on joint distributions of metrics values. Visualizations of these distributions allow users to explore and compare the quality metrics of software systems and their artifacts, and to detect patterns, correlations, and anomalies. Furthermore, it is possible to identify common properties and flaws, as our visualization approach provides rich interactions for visual queries to the quality models’ multivariate data. We evaluate our approach in two use cases based on: 30 real-world technical documentation projects with 20,000 XML documents, and an open source project written in Java with 1000 classes. Our results show that the proposed approach allows an analyst to detect possible causes of bad or good quality.
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1478419.
  • Ulan, Maria, et al. (författare)
  • Copula-based software metrics aggregation
  • 2021
  • Ingår i: Software quality journal. - : Springer. - 0963-9314 .- 1573-1367. ; 29, s. 863-899
  • Tidskriftsartikel (refereegranskat)abstract
    • A quality model is a conceptual decomposition of an abstract notion of quality into relevant, possibly conflicting characteristics and further into measurable metrics. For quality assessment and decision making, metrics values are aggregated to characteristics and ultimately to quality scores. Aggregation has often been problematic as quality models do not provide the semantics of aggregation. This makes it hard to formally reason about metrics, characteristics, and quality. We argue that aggregation needs to be interpretable and mathematically well defined in order to assess, to compare, and to improve quality. To address this challenge, we propose a probabilistic approach to aggregation and define quality scores based on joint distributions of absolute metrics values. To evaluate the proposed approach and its implementation under realistic conditions, we conduct empirical studies on bug prediction of ca. 5000 software classes, maintainability of ca. 15000 open-source software systems, and on the information quality of ca. 100000 real-world technical documents. We found that our approach is feasible, accurate, and scalable in performance.
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1478420.
  • Ulan, Maria (författare)
  • Foundation of Multi-Criteria Quality Scoring
  • 2019
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Software quality becomes more critical as our dependence on software increases. We need better quality assessment than ever. Comparison and ranking of software artifacts, detection of bad or good quality are important tasks for quality assessment.Software quality models are widely used to support quality assessment. In general, they have a hierarchical structure and defines quality in terms of sub-qualities and metrics in a tree-like structure. Different metrics evaluate different quality criteria, and several metrics often needs to be assessed and aggregated to obtain a total quality score. The quality models standards of today do not enable numerical metrics aggregation. They leave aggregation to decision makers, and different methods of aggregation lead to different assessment results and interpretations. Hence, there is a need to define metrics aggregation formally based on well-known theories.We propose to consider the probabilistic nature of quality as a solution. We consider metrics as random variables and define quality scores based on joint probabilities. The aggregation, and the quality model in extension, express quality as the probability of detecting something with equal or worse quality, based on all software projects observed; good and bad quality is expressed in terms of lower and higher probabilities. We analyze metrics dependencies using Bayesian networks and define quality models as directed acyclic graphs. Nodes correspond to metrics, and edges indicate dependencies. We propose an implementation using multi-threading to improve the efficiency of joint probabilities computations.We validate our approach theoretically and in an empirical study on software quality assessment of approximately 100\,000 real-world software artifacts with approximately 4\,000\,000 measurements in total. The results show that our approach gives likely results and scales in performance to large projects.We also applied our approach to a multi-criteria decision-making task to propose a ranking method to aid evaluation processes. We use a real-world funding allocation problem for a call that attracted approximately 600 applications to evaluate our approach. We compared our approach with the traditional weighted sum aggregation model and found that ranks are similar between the two methods, but our approach provides a more sound basis for a fair assessment.Further, we implemented an exploratory multivariate data visualization tool, which visualizes the similarities between software artifacts based on joint distributions. We illustrate the usability of our tool with two case studies of real-world examples: a set of technical documents and an open source project written in Java.Our overall results show that our approach for multi-criteria quality scoring is well-defined, has a clear interpretation, and is applicable under realistic conditions, generalizable, and transferable to other domains.
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