7,874 research outputs found

    Measuring socioeconomic position in studies of health inequalities

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    There is a consistent finding that the higher the socioeconomic position (SEP), the better the health. The choice of SEP indicator is crucial in explaining these socioeconomic inequalities. However, a poorly motivated use of SEP indicators prevails in the literature on social health inequalities, hampering the transparency and comparability across studies. Its primary aim is to explore different ways of measuring SEP to identify social inequalities in health. The thesis focuses on the most common, objective SEP indicators (education, occupation, and income); subjective SEP; and childhood circumstances. This thesis consists of three papers. Papers I and III apply data from the Tromsø Study, and Paper II is based on an online survey investigating people's views on SEP, conducted in Norway and Australia. Paper I investigates the potential to combine education and income into a composite score for SEP and how it predicts inequalities in health-related quality of life (HRQoL). Paper II assesses the relative importance of objective SEP indicators and childhood circumstances in estimating subjective SEP. Paper III explores the role of circumstances and lifestyle factors in estimating inequalities in HRQoL and self-rated health. While we found that the combination of education and income demonstrated a non-linear relationship with overall SEP, the composite SEP score was not superior as a predictor of HRQoL compared to including education and income separately. Furthermore, we found that childhood circumstances demonstrated a lasting, independent impact on subjective SEP. Paper III revealed that there were inequalities arising from circumstances, with substantial contributions from financial circumstances in childhood and education. This thesis demonstrates the need to motivate the choice of SEP indicator in studies of health inequalities. It also stresses the importance of early-life factors as determinants of adult health, advocating for policies targeting childhood circumstances in equalising early life chances.Et svært vanlig funn på tvers av land, studiepopulasjoner og helseutfall er at desto høyere sosioøkonomisk posisjon (SEP), desto bedre helse. Valg av SEP-indikator som skal reflektere de sosioøkonomiske dimensjonene i helse er avgjørende for å forklare disse helseulikhetene. Likevel er det slik at bruken av SEP-indikatorer i studier om sosial ulikhet i helse ofte preges av svak eller ingen begrunnelse med utgangspunkt i teori og hypoteser, noe som begrenser muligheten til sammenligning mellom studier. Denne avhandlingen bruker ulike tilnærminger for å måle SEP i studier av helseulikhet. Et overordnet formål er å utforske ulike måter å måle sosial posisjon for å identifisere sosiale ulikhet i helse, og hvordan livsstilsfaktorer i tillegg påvirker dette forholdet. Fokuset vil være på de tre vanligste objektive SEP-indikatorene (utdanning, yrke og inntekt); subjektiv SEP; og indikatorer for barndomsforhold. Avhandlingen består av tre artikler. Artikkel I og III er basert på data fra Tromsøundersøkelsen, mens Artikkel II benytter data fra på en nettbasert spørreundersøkelse om folks betraktninger omkring SEP, som har blitt gjennomført i Norge og Australia. Alle de tre artiklene utforsker bruken av ulike SEP-indikatorer i en helseulikhetssammenheng. Artikkel I undersøker potensialet for å kombinere utdanning og inntekt til en samleindikator for SEP, samt hvordan denne samleindikatoren predikerer helse-relatert livskvalitet (HRQoL). Artikkel II måler objektive SEP-indikatorer (utdanning, yrke og inntekt) og barndomsforholds relative betydning i å estimere subjektiv SEP. Artikkel III utforsker hvordan variabler om barndomsforhold på den ene siden og livsstilsfaktorer på den andre estimerer HRQoL og selvrapportert helse, både på et bestemt tidspunkt og over tid. Vi fant at kombinasjonen av utdanning og inntekt viste en sterk ikke-lineær sammenheng med total SEP, mens samleindikatoren for SEP viste seg å ikke være bedre i å predikere HRQoL sammenlignet med å inkludere utdanning og inntekt separat. Videre fant vi at barndomsforhold så ut til å ha en vedvarende påvirkning på subjektiv SEP, som var uavhengig av objektiv SEP. Artikkel III viste at det var ulikheter i helse med røtter i barndomsforhold, med særlig påvirkning fra økonomiske forhold i barndommen og egen utdanning. Denne avhandlingen viser behovet for å gjøre et faglig motivert valg av SEP-indikator i studier av helseulikhet. Den understreker også viktigheten av barndomsforhold som bestemmende faktorer for helseutfall senere i livet, og etterlyser dermed politikk rettet mot tidlige barndomsforhold for å utjevne ulikheter og sikre gode livssjanser

    Parámetros genéticos de los caracteres morfológicos lineales de la raza caprina murciano-granadina y sus relaciones con otros caracteres funcionales

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    Linear appraisal systems (LAS) are effective strategies for systematically collecting zoometric information from animal populations. Traditionally applied LAS in goats was developed considering the variability and scales found in highly selected breeds. Implementing LAS may reduce time, personnel, and resource needs when performing zoometric large-scale collection. Moreover, selection for zoometrics defines individuals’ productive longevity, endurance, enhanced productive abilities, and consequently, long-term profitability. As a result, traditional LAS may no longer cover the different contexts of goat breeds widespread throughout the world, and departures from normality may be indicative of the different stages of selection at which a certain population can be found. In the first study, an evaluation of the distribution and symmetry properties of twenty-eight zoometric traits was developed. After symmetry analysis was performed, the scale readjustment proposal suggested specific strategies should be implemented such as scale reduction of lower or upper levels, determination of a setup moment to evaluate and collect information from young (up to 2 years) and adult bucks (over 2 years), the addition of upper categories in males due to upper values in the scale being incorrectly clustered together. Thus, the particular analysis of each variable permits determining specific strategies for each trait and serve as a model for other breeds, either selected or in terms of selection. The aim of the second study was to propose a method to optimize and validate LAS in opposition to traditional measuring protocols routinely implemented in Murciano-Granadina goats. The data sample consisted of 41323 LAS and traditional measuring records, belonging to 22727 herdbook registered primipara does, 17111 multipara does, and 1485 bucks. Each record comprised information on 17 linear traits for primipara and multipara does, and 10 traits for bucks. All zoometric parameters were scored on a 9-points scale. Cronbach’s alpha values suggested a high internal consistency of the optimized variable panel. Model fit, variability explanation power, and predictive power (MSE, AIC/AICc, and BIC, respectively) suggested a model comprising zoometric LAS scores performed better than traditional zoometry. Optimization procedures result in reduced models able to capture variability for dairy-related zoometric traits without noticeable detrimental effects on model validity properties. The third study aimed to perform a particular analysis of each variable that permits determining specific strategies for each trait and serves as a model for other breeds. Among the strategies proposed are the reduction/readjustment of the levels in the scale as it happens for limb-related traits, the extension of the scale as it occurs in the stature of males, or the subdivision of the scale used in males into two categories, bucks younger than two years and bucks of two years old and older. Murciano- Granadina goat breed has drifted towards better dairy-linked conformation traits but without losing the grounds of the zoometric basis which confers it with enhanced adaptability to the environment. Hence, such strategies can help to achieve a better understanding of the momentum of selection for dairy-linked zoometric traits in Murciano-Granadina population and their future evolution to enhance the profitability and efficiency of breeding plans. The objective of the fourth study was to evaluate the progress of heritabilities of the traits comprising the linear appraisal system in the Murciano-Granadina breed during the complete decade from December 2011 to December 2021. The estimated values for heritability were obtained from multivariate analyzes using the BLUP methodology and MTDFREML software. For 2021 heritabilities, a simple animal model was applied to records collected from 22727 primiparous goats and 17111 multiparous goats belonging to 85 herds. The model included the linear and quadratic and linear components of the covariates age and days in milk, respectively. The fixed effects considered in the model were herd, reproductive status, calving month, and herd/year interaction. The animal was considered as a random effect. The variables studied included five characteristics related to structure and capacity, two traits related to dairy structure, six related to the mammary system, and three related to legs and feet. The heritabilities for structure and capacity characters progressed from 0.22 to 0.28 including non-convergent variables in June 2012 to values between 0.10 and 0.41 with all variables converging in June 2021. Heritabilities for dairy structure progressed from 0.18 with nonconvergent variables in 2011 to 0.17 to 0.25 in 2021. Heritabilities for mammary system traits progressed from 0.12 to 0, 27 with non-convergent variables in 2012 to between 0.10 and 0.41 in 2021. For legs and feet, heritabilities progressed from 0.16 to 0.17 with non-convergent variables to 0.09 a 0.22. Genetic progress is not only evident in heritability values, but there has been a notable reduction in the standard error of heritabilities from 0.1000 (0.080-0.120) to 0.000 (0.000-0.001) from 2011 to 2021. These results provide evidence of the enhancement in the effectiveness and precision of the linear qualification system applied during the past decade and its successful integration into the breeding program of the Murciano- Granadina breed. The fifth study estimates genetic and phenotypic parameters for zoometric/LAS traits in Murciano-Granadina goats, estimate genetic and phenotypic correlations among all traits, and to determine whether major area selection would be appropriate or if adaptability strategies may need to be followed. Heritability estimates for the zoometric/LAS traits were low to high, ranging from 0.09 to 0.43 and the accuracy of estimation has improved after decades rendering standard errors negligible. Scale inversion of specific traits may need to be performed before major areas selection strategies are implemented. Genetic and phenotypic correlations suggest that negative selection against thicker bones and higher rear insertion heights, indirectly results in the optimization of selection practices in the rest of the traits, especially of those in the structure and capacity and mammary system major areas. The integration and implementation of the strategies proposed within Murciano-Granadina breeding program maximize selection opportunities and the sustainable international competitiveness of the Murciano- Granadina goat in the dairy goat breed panorama. The objective of the sixth study was to develop a discriminant canonical analysis (DCA) tool that permits outlining the role of the individual haplotypes of each component of the casein complex (αS1, β, αS2, and κ-casein) on zoometrics/linear appraisal breeding values. The relationship of the predicted breeding value for 17 zoometric/Linear appraisal traits and αS1, β, αS2, and κ-casein genes haplotypic sequences was assessed. Results suggest that, although a lack of significant differences (P>0.05) was reported across the predictive breeding values of zoometric/linear appraisal traits for αS1, αS2 and κ casein, significant differences were found for β Casein (P0,05) en los valores de cría predichos de los rasgos de zoometría/calificación lineal para la αS1, αS2 y κ-caseína, se encontraron diferencias significativas para la β-caseína (P<0,05), respectivamente. La presencia de secuencias haplotípicas de β-caseína GAGACCCC, GGAACCCC, GGAACCTC, GGAATCTC, GGGACCCC, GGGATCTC y GGGGCCCC, vinculadas a combinaciones diferenciales de mayores cantidades de leche de mayor calidad en términos de su composición, también puede estar relacionada con una mayor valoración zoométrica/lineal de la predicción de los valores de cría. La selección debe realizarse con cuidado, dado que la consideración de animales aparentemente deseables que presentan la secuencia haplotípica GGGATCCC en el gen de la β- caseína, debido a sus valores genéticos predichos positivos para ciertos rasgos de zoometría/calificación lineal, como la altura de la inserción trasera, la calidad ósea , la inserción anterior, la profundidad de ubre, la vista lateral de patas traseras y la vista trasera de patas traseras pueden conducir a una selección indirecta frente al resto de rasgos de zoometría/calificación lineal y a su vez conducir a una selección ineficiente hacia un tipo morfotipo lechero óptimo en cabras Murciano-Granadina. Por el contrario, la consideración de animales que presentan la secuencia haplotípica GGAACCCC implica también considerar animales que aumentan el potencial genético para todos los rasgos de zoometría/calificación lineal, haciéndolos así recomendables como reproductores. La información derivada de los presentes análisis mejorará la selección de individuos reproductores que busquen un tipo lechero bastante deseable, a través de la determinación de las secuencias haplotípicas que presentan en el locus β-caseína. Todos estos estudios persiguen la obtención de un conocimiento más profundo de los caracteres morfológicos lineales de la raza caprina Murciano-Granadina y sus relaciones con otras características funcionales. Esto sienta las bases para estrategias de normalización y mejora de la capacidad productiva y el morfotipo lechero de la cabra Murciano-Granadina y ayudará a alcanzar su consolidación competitiva en el panorama caprino lechero internacional

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

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    Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

    Novel Neural Network Applications to Mode Choice in Transportation: Estimating Value of Travel Time and Modelling Psycho-Attitudinal Factors

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    Whenever researchers wish to study the behaviour of individuals choosing among a set of alternatives, they usually rely on models based on the random utility theory, which postulates that the single individuals modify their behaviour so that they can maximise of their utility. These models, often identified as discrete choice models (DCMs), usually require the definition of the utilities for each alternative, by first identifying the variables influencing the decisions. Traditionally, DCMs focused on observable variables and treated users as optimizing tools with predetermined needs. However, such an approach is in contrast with the results from studies in social sciences which show that choice behaviour can be influenced by psychological factors such as attitudes and preferences. Recently there have been formulations of DCMs which include latent constructs for capturing the impact of subjective factors. These are called hybrid choice models or integrated choice and latent variable models (ICLV). However, DCMs are not exempt from issues, like, the fact that researchers have to choose the variables to include and their relations to define the utilities. This is probably one of the reasons which has recently lead to an influx of numerous studies using machine learning (ML) methods to study mode choice, in which researchers tried to find alternative methods to analyse travellers’ choice behaviour. A ML algorithm is any generic method that uses the data itself to understand and build a model, improving its performance the more it is allowed to learn. This means they do not require any a priori input or hypotheses on the structure and nature of the relationships between the several variables used as its inputs. ML models are usually considered black-box methods, but whenever researchers felt the need for interpretability of ML results, they tried to find alternative ways to use ML methods, like building them by using some a priori knowledge to induce specific constrains. Some researchers also transformed the outputs of ML algorithms so that they could be interpreted from an economic point of view, or built hybrid ML-DCM models. The object of this thesis is that of investigating the benefits and the disadvantages deriving from adopting either DCMs or ML methods to study the phenomenon of mode choice in transportation. The strongest feature of DCMs is the fact that they produce very precise and descriptive results, allowing for a thorough interpretation of their outputs. On the other hand, ML models offer a substantial benefit by being truly data-driven methods and thus learning most relations from the data itself. As a first contribution, we tested an alternative method for calculating the value of travel time (VTT) through the results of ML algorithms. VTT is a very informative parameter to consider, since the time consumed by individuals whenever they need to travel normally represents an undesirable factor, thus they are usually willing to exchange their money to reduce travel times. The method proposed is independent from the mode-choice functions, so it can be applied to econometric models and ML methods equally, if they allow the estimation of individual level probabilities. Another contribution of this thesis is a neural network (NN) for the estimation of choice models with latent variables as an alternative to DCMs. This issue arose from wanting to include in ML models not only level of service variables of the alternatives, and socio-economic attributes of the individuals, but also psycho-attitudinal indicators, to better describe the influence of psychological factors on choice behaviour. The results were estimated by using two different datasets. Since NN results are dependent on the values of their hyper-parameters and on their initialization, several NNs were estimated by using different hyper-parameters to find the optimal values, which were used to verify the stability of the results with different initializations
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