In recent years, sustainable development has become one of the most important issues internationally. Many indicators related to sustainable development have been proposed and implemented, such as Island Taiwan and Urban Taiwan. However the missing values come along with environmental monitoring data pose serious problems when we conducted the study on building a sustainable development indicator for marine environment. Since data is the origin of the summarized information, such as indicators. Given the poor data quality caused by the missing values, there will be some doubts about the result accuracy when using such data set for estimation. It is therefore important to apply suitable data pre-processing, such that reliable information can be acquired by advanced data analysis. Several reasons cause the problem of missing value in environmental monitoring data, for example: breakdown of machines, ruin of samples, forgot recording, mismatch of records when merging data, and lost of records when processing data. The situations of missing data are also diverse, for example: in the same time of sampling, some data records at several sampling sites are partially or completely disappeared. On the contrary, partial or complete time series data are missing at the same sampling site. It is therefore obvious to see that the missing values of environmental monitoring data are both related to spatial and temporal dimensions. Currently the techniques of data imputation have been developed for certain types of data or the interpolation of missing values based on either geographic data distributions or time-series functions. To accommodate both spatial and temporal information in an analysis is rarely seen. The current study has been tried to integrate the related analysis procedures and develop a computing process using both spatial and temporal dimensions inherent in the environmental monitoring data. Such data imputation process can enhance the accuracy of estimated missing values.

Vital signs can indicate patient deterioration prior to adverse events such as cardiac arrest, emergency admission to the intensive care unit (ICU), or death. However, many adverse events occur in wards outside the ICU where the level of care and the frequency of patient monitoring are lower than in the ICU. This thesis describes models for detection of deterioration in acutely ill patients in two environments: a step-down unit in which patients recovering from an ICU stay are continuously monitored, and a general ward where patients are intermittently monitored following upper gastrointestinal cancer surgery. Existing data fusion models for classification of vital signs depend on a threshold which defines a “region of normality”. Bradypnoea (low breathing rate) and bradycardia (low heart rate) are relatively rare, and so these two types of abnormalities tend to be misclassified by existing methods. In this thesis, techniques for selecting a threshold are described, such that the classification of vital-sign data is improved. In particular, the proposed approach reduces the misclassification of bradycardia and bradypnoea events, and indicates the type of abnormality associated with the deterioration in a patient’s vital signs. Patients recovering from upper gastrointestinal (GI) surgery have a high risk of emergency admission to the ICU. At present in the UK, most intermediate and general wards outside the ICU depend on intermittent, manual monitoring using track-and-trigger systems. Both manual and automated patient monitoring systems are reported to have high false alert rates. The models described in this thesis take into account the low monitoring frequency in the upper GI ward, such that the false alert rate is reduced. In addition to accuracy, early detection of deterioration is a highly desirable feature in patient monitoring systems. The models proposed in this thesis generate alerts for patients earlier than the early warning systems which are currently in use in hospitals in the UK. The improvements to existing models proposed in this thesis could be applied to continuous and intermittently acquired vital-sign data from other clinical environments.

Bridge monitoring studies indicate that the quasi-static response of a bridge, while dependent on various input forces, is affected predominantly by variations in temperature. In many structures, the quasi-static response can even be approximated as equal to its thermal response. Consequently, interpretation of measurements from quasi-static monitoring requires accounting for the thermal response in measurements. Developing solutions to this challenge, which is critical to relate measurements to decision-making and thereby realize the full potential of SHM for bridge management, is the main focus of this research. This research proposes a data-driven approach referred to as temperature-based measurement interpretation (TB-MI) approach for structural performance evaluation of bridges based on continuous bridge monitoring. The approach characterizes and predicts thermal response of structures by exploiting the relationship between temperature distributions across a bridge and measured bridge response. The TB-MI approach has two components - (i) a regression-based thermal response prediction (RBTRP) methodology and (ii) an anomaly detection methodology. The RBTRP methodology generates models to predict real-time structural response from distributed temperature measurements. The anomaly detection methodology analyses prediction error signals, which are the differences between predicted and real-time response to detect the onset of anomaly events. In order to generate realistic data-sets for evaluating the proposed TB-MI approach, this research has built a small-scale truss structure in the laboratory as a test-bed. The truss is subject to accelerated diurnal temperature cycles using a system of heating lamps. Various damage scenarios are also simulated on this structure. This research further investigates if the underlying concept of using distributed temperature measurements to predict thermal response can be implemented using physics-based models. The case study of Cleddau Bridge is considered. This research also extends the general concept of predicting bridge response from knowledge of input loads to predict structural response due to traffic loads. Starting from the TB-MI approach, it creates an integrated approach for analyzing measured response due to both thermal and vehicular loads. The proposed approaches are evaluated on measurement time-histories from a number of case studies including numerical models, laboratory-scale truss and full-scale bridges. Results illustrate that the approaches accurately predicts thermal response, and that anomaly events are detectable using signal processing techniques such as signal subtraction method and cointegration. The study demonstrates that the proposed TB-MI approach is applicable for interpreting measurements from full-scale bridges, and can be integrated within a measurement interpretation platform for continuous bridge monitoring.

Analysis of reliability data plays an important role in the maintenance decision making process. The accurate estimation of residual life in components and systems can be a great asset when planning the preventive replacement of components on machines. Artificial intelligence is a field that has rapidly developed over the last twenty years and practical applications have been found in many diverse areas. The use of such methods in the maintenance field have however not yet been fully explored. With the common availability of condition monitoring data, another dimension has been added to the analysis of reliability data. Neural networks allow for explanatory variables to be incorporated into the analysis process. This is expected to improve the quality of predictions when compared to the results achieved through the use of methods that rely solely on failure time data. Neural networks can therefore be seen as an alternative to the various regression models, such as the proportional hazards model, which also incorporate such covariates into the analysis. For the purpose of investigating their applicability to the problem of predicting the residual life of machines and components, neural networks were trained and tested with the data of two different reliability related datasets. The first dataset represents the renewal case where repair leads to complete restoration of the system. A typical maintenance situation was simulated in the laboratory by subjecting a series of similar test pieces to different loading conditions. Measurements were taken at regular intervals during testing with a number of sensors which provided an indication of the test piece’s condition at the time of measurement. The dataset was split into a training set and a test set and a number of neural network variations were trained using the first set. The networks’ ability to generalize was then tested by presenting the data from the test set to each of these networks. The second dataset contained data collected from a group of pumps working in a coal mining environment. This dataset therefore represented an example of the situation encountered with a repaired system. The performance of different neural network variations was subsequently compared through the use of cross-validation. It was proved that in most cases the use of condition monitoring data as network inputs improved the accuracy of the neural networks’ predictions. The average prediction error of the various neural networks under comparison varied between 431 and 841 seconds on the renewal dataset, where test pieces had a characteristic life of 8971 seconds. When optimized the multi-layer perceptron neural networks trained with the Levenberg-Marquardt algorithm and the general regression neural network produced a sum of squares error within 11.1% of each other for the data of the repaired system. This result emphasizes the importance of adjusting parameters, network architecture and training targets for optimal performance The advantage of using neural networks for predicting residual life was clearly illustrated when comparing their performance to the results achieved through the use of the traditional statistical methods. The potential of using neural networks for residual life prediction was therefore illustrated in both cases. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007. / Mechanical and Aeronautical Engineering / MEng / unrestricted

O desenvolvimento infantil é um processo complexo que começa na concepção e se estende por longo período, envolvendo vários aspectos como crescimento físico, maturação neurológica, comportamental, cognitiva, social e afetiva. De acordo com a Organização Mundial da Saúde - OMS, a presença de fatores negativos que intervém no desenvolvimento infantil afeta cerca de 10% da população infantil mundial. Existem evidências suficientes de que quanto mais precoce for a identificação de possíveis problemas de atraso no desenvolvimento com consequente intervenção adequada, menor será o impacto desses problemas na vida futura da criança. Entretanto, para que seja realizada futura intervenção precoce, os profissionais de atenção básica necessitam de instrumentos que possam de maneira rápida, alertar sobre sinais que signifiquem possíveis problemas no desenvolvimento da criança. Sendo assim, o objetivo deste estudo foi validar a Ficha de Acompanhamento do Desenvolvimento Infantil, proposta pelo Ministério da Saúde em 2002, em uso em muitos serviços de saúde no Brasil. Foi realizada a aplicação do instrumento em 269 crianças de 0 a 6 anos, no Ambulatório de Pediatria do Hospital Universitário da Universidade de São Paulo e realizado as análises estatísticas Análise AC1 e Alfa de Cronbach para verificar a validade e confiabilidade do instrumento.O instrumento apresentou boa validez e confiabilidade através das duas análises, necessitando revisão para o segundo, nono e décimo primeiro marco do desenvolvimento.Concluímos que o Instrumento Ficha de Acompanhamento do Desenvolvimento Infantil é um instrumento válido a ser recomendado para uso dos profissionais de Saúde Básica como instrumento de triagem para possíveis atrasos no desenvolvimento infantil de crianças de zero a seis anos / Child development is a complex process that begins at conception moment and extends for a long period, which involves a lot of points such as physical growth, maturation neurological, behavior/ cognitive, social and emotional. According to World Health Organization - OMS, there is a presence of negative factors that have affected around 10% of worldwide children population. There are a lot of enough evidences that as previous we identify these possible delays issues in the development, with appropriated intervention, lower will be the impact on child future life. In order we can held this previous future intervention, the primary care professional need tools can quickly warning them that potential problems in child development. For this reason, the goal of this study is to validate the Child Development Monitoring Chart, proposed by Ministry of Health in 2002, using in Brazil Health services. This instrument applications was performed in 269 children 0 - 6 years age at the Clinic of Pediatrics, University Hospital, São Paulo University and performed statistical analyses AC1 and Cronbach´s Alpha to check the validity and reliability of this instrument. The instrument presented good performance and reliability through two analyses process, but a review will be necessary for second, ninth and eleventh mark of development. We concluded that Child Development Monitoring Instrument Data Sheet is a valid instrument to be recommended to be used by Basic Health professional as a screening tool for potential delays in child development of children from 0 to 6 years age

The increasing adoption of electronic health records (EHRs) has led to an unprecedented amount of patient health information stored in an electronic format. The ability to comb through this information is imperative, both for patient care and computational modeling. Creating a system to minimize unnecessary EHR data, automatically distill longitudinal patient information, and highlight salient parts of a patient’s record is currently an unmet need. However, summarization of EHR data is not a trivial task, as there exist many challenges with reasoning over this data. EHR data elements are most often obtained at irregular intervals as patients are more likely to receive medical care when they are ill, than when they are healthy. The presence of narrative documentation adds another layer of complexity as the notes are riddled with over-sampled text, often caused by the frequent copy-and-pasting during the documentation process. This dissertation synthesizes a set of challenges for automated EHR summarization identified in the literature and presents an array of methods for dealing with some of these challenges. We used hybrid data-driven and knowledge-based approaches to examine abundant redundancy in clinical narrative text, a data-driven approach to identify and mitigate biases in laboratory testing patterns with implications for using clinical data for research, and a probabilistic modeling approach to automatically summarize patient records and learn computational models of disease with heterogeneous data types. The dissertation also demonstrates two applications of the developed methods to important clinical questions: the questions of laboratory test overutilization and cohort selection from EHR data.

This dissertation describes the work conducted on error analysis for Brillouin Optical Time Domain Reflectometry (BOTDR), a distributed strain sensing technology used for monitoring the structural performance of infrastructures. Although BOTDR has been recently applied to many infrastructure monitoring applications, its measurement error has not yet been thoroughly investigated. The challenge to accurately monitor structures using BOTDR sensors lies in the fact that the measurement error is dependent on the noise and the spatial resolution of the sensor as well as the non-uniformity of the monitored infrastructure strain conditions. To improve the reliability of this technology, measurement errors (including precision error and systematic error) need to be carefully investigated through fundamental analysis, lab testing, numerical modelling, and real site monitoring verification. The relationship between measurement error and sensor characteristics is firstly studied experimentally and theoretically. In the lab, different types of sensing cables are compared with regard to their measurement errors. Influences of factors including fibre diameters, polarization and cable jacket on measurement error are characterized. Based on experimental characterization results, an optics model is constructed to simulate the Brillouin back scattering process. The basic principle behind this model is the convolution between the injected pulse and the intrinsic Brillouin spectrum. Using this model, parametric studies are conducted to theoretically investigate the impacts of noise, frequency step and spectrum bandwidth on final strain measurement error. The measurement precision and systematic error are then investigated numerically and experimentally. Measurement results of field sites with installed optical fibres displayed that a more complicated strain profile leads to a larger measurement error. Through extensive experimental and numerical verifications using a Brillouin Optical Time Domain Reflectometry (BOTDR), the dependence of precision error and systematic error on input strain were then characterized in the laboratory and the results indicated that a) the measurement precision error can be predicted using analyzer frequency resolution and the location determination error and b) the characteristics of the measurement systematic error can be described using the error to strain gradient curve. This is significant because for current data interpretation process, data quality is supposed to be constant along the fibre although the monitored strain for most of the site cases is non-uniformly distributed, which is verified in this thesis leading to a varying data quality. A novel data quality quantification method is therefore proposed as a function of the measured strain shape. Although BOTDR has been extensively applied in infrastructure monitoring in the past decade, their data interpretation has been proven to be nontrivial, due to the nature of field monitoring. Based on the measurement precision and systematic error characterization results, a novel data interpretation methodology is constructed using the regularization decomposing method, taking advantages of the measured data quality. Experimental results indicate that this algorithm can be applied to various strain shapes and levels, and the accuracy of the reconstructed strain can be greatly improved. The developed algorithm is finally applied to real site applications where BOTDR sensing cables were implemented in two load bearing piles to monitor the construction loading and ground heaving processes.

O desenvolvimento infantil é um processo complexo que começa na concepção e se estende por longo período, envolvendo vários aspectos como crescimento físico, maturação neurológica, comportamental, cognitiva, social e afetiva. De acordo com a Organização Mundial da Saúde - OMS, a presença de fatores negativos que intervém no desenvolvimento infantil afeta cerca de 10% da população infantil mundial. Existem evidências suficientes de que quanto mais precoce for a identificação de possíveis problemas de atraso no desenvolvimento com consequente intervenção adequada, menor será o impacto desses problemas na vida futura da criança. Entretanto, para que seja realizada futura intervenção precoce, os profissionais de atenção básica necessitam de instrumentos que possam de maneira rápida, alertar sobre sinais que signifiquem possíveis problemas no desenvolvimento da criança. Sendo assim, o objetivo deste estudo foi validar a Ficha de Acompanhamento do Desenvolvimento Infantil, proposta pelo Ministério da Saúde em 2002, em uso em muitos serviços de saúde no Brasil. Foi realizada a aplicação do instrumento em 269 crianças de 0 a 6 anos, no Ambulatório de Pediatria do Hospital Universitário da Universidade de São Paulo e realizado as análises estatísticas Análise AC1 e Alfa de Cronbach para verificar a validade e confiabilidade do instrumento.O instrumento apresentou boa validez e confiabilidade através das duas análises, necessitando revisão para o segundo, nono e décimo primeiro marco do desenvolvimento.Concluímos que o Instrumento Ficha de Acompanhamento do Desenvolvimento Infantil é um instrumento válido a ser recomendado para uso dos profissionais de Saúde Básica como instrumento de triagem para possíveis atrasos no desenvolvimento infantil de crianças de zero a seis anos / Child development is a complex process that begins at conception moment and extends for a long period, which involves a lot of points such as physical growth, maturation neurological, behavior/ cognitive, social and emotional. According to World Health Organization - OMS, there is a presence of negative factors that have affected around 10% of worldwide children population. There are a lot of enough evidences that as previous we identify these possible delays issues in the development, with appropriated intervention, lower will be the impact on child future life. In order we can held this previous future intervention, the primary care professional need tools can quickly warning them that potential problems in child development. For this reason, the goal of this study is to validate the Child Development Monitoring Chart, proposed by Ministry of Health in 2002, using in Brazil Health services. This instrument applications was performed in 269 children 0 - 6 years age at the Clinic of Pediatrics, University Hospital, São Paulo University and performed statistical analyses AC1 and Cronbach´s Alpha to check the validity and reliability of this instrument. The instrument presented good performance and reliability through two analyses process, but a review will be necessary for second, ninth and eleventh mark of development. We concluded that Child Development Monitoring Instrument Data Sheet is a valid instrument to be recommended to be used by Basic Health professional as a screening tool for potential delays in child development of children from 0 to 6 years age

A review of the available research indicates that relatively little is known about how districts employ Academic Achievement Teams or similar mechanisms to reduce declines in student achievement and sustain increased student achievement at the elementary school level (Kutash, Nico, Gorin, Rahmatullah, & Tallant, 2010).   Turning around chronically low-performing schools is challenging work requiring a systemic rather than school-by-school approach (Robinson & Buntrock, 2011). The most successful turnaround efforts have both high-impact leaders and the district capacity to initiate, support, and enhance transformational change through the use of data. Educational leaders on all levels are realizing meaningful information can only be acquired through a proper analysis of data and good decisions are based on this thoughtful process of inquiry and analysis (Creighton, 2007).  The intent of this study was to identify practices of Academic Achievement Teams that facilitated student achievement.  Interviews were conducted with principals, directors of elementary education, a teacher, and district liaison representing the Virginia Department of Education\'s Office of School Improvement to gain insight into the operational and organizational structures of the Academic Achievement Teams. A qualitative design was selected to conduct this descriptive cross case study. In addition to the one-on-one interviews, observations of the Academic Achievement Team meetings and review of documents from each of the two study schools were examined to gain additional perspective regarding how the Academic Achievement Team operated to increase student achievement.  The interviews, observations, and document reviews were analyzed using the Constant Comparative Method to understand the specific practices employed by Academic Achievement Teams that increased student achievement at two elementary schools (Maykut & Morehouse, 1994). / Ed. D.

With the increasing elderly population and longer life expectancies, smart wearable technologies are playing an important role in facilitating caregivers to monitor elderly people remotely. Aifloo’s wristband is one smart wristband which can collect various data, predict activities and detect abnormalities to enable elderly people to live independently at home. However, too much information and poor visualizations will cause huge difficulties for caregivers to interpret the data. Six caregivers were interviewed in this study to investigate what data is relevant to monitor elderly people and how they interpret the different designed displays. The main results show that alarms, fall incidents and medication compliance are the most important. Besides, caregivers place a greater emphasis on holistic views of data and they want to highlight abnormal behaviors and alerts. In the end, design guidelines for the information system to present data meaningfully and intuitively are generated. / Med ett ökande antal äldre och en ökande medellivslängd kommer smart, bärbar teknologi att spela en större roll i äldrevården för att övervaka de äldre. Aifloos armband är en smart teknologi som kan samla in olika former av data, förutsäga aktiviteter och upptäcka avvikande och onormala beteenden, vilket kan användas av äldre som bor självständiga i sena egna hem. Stora mängder data, och dåliga visualiseringar av dem, orsakar svårigheter för vårdgivare att tolka datan. I den här studien har sex vårdgivare intervjuats för att utforska vilken data som är relevant för dem, och hur de kan tolka information ifrån en grupp olika gränssnitt. Studiens resultat visar att alarm, fallolyckor och översikt över hur de äldre efterföljer sina medicinska recept är viktigast. Vårdgivarna lägger en större vikt vid att förstå datan holistiskt, och de vill synliggöra avvikande beteendemönster och varningar. Slutgiltligen presenteras riktlinjer för hur IT-system kan designas för att presentera data på ett meningsfullt och intuitivt vis.