The prediction of seasonal and inter-annual climate variations and their impacts on the water resources in the eastern seaboard of Thailand

The research of this thesis dissertation covers developments and applications of short-and long-term climate predictions. The short-term prediction emphasizes monthly and seasonal climate, i.e. forecasting from up to the next month over a season to up to a year or so. The long-term predictions pertain to the analysis of inter-annual- and decadal climate variations over the whole 21st century. These two climate prediction methods are validated and applied in the study area, namely, Khlong Yai (KY) water basin located in the eastern seaboard of Thailand which is a major industrial zone of the country and which has been suffering from severe drought and water shortage in recent years. Since water resources are essential for the further industrial development in this region, a thorough analysis of the potential climate change with its subsequent impact on the water supply in the area is at the heart of this thesis research. The short-term forecast of the next-season climate, such as temperatures and rainfall, offers a potential general guideline for water management and reservoir operation. To that avail, statistical models based on autoregressive techniques, i.e., AR-, ARIMA- and ARIMAex-, which includes additional external regressors, and multiple linear regression- (MLR) models, are developed and applied in the study region. Teleconnections between ocean states and the local climate are investigated and used as extra external predictors in the ARIMAex- and the MLR-model and shown to enhance the accuracy of the short-term predictions significantly. However, as the ocean state – local climate teleconnective relationships provide only a one- to four-month ahead lead time, the ocean state indices can support only a one-season-ahead forecast. Hence, GCM- climate predictors are also suggested as an additional predictor-set for a more reliable and somewhat longer short-term forecast. For the preparation of “pre-warning” information for up-coming possible future climate change with potential adverse hydrological impacts in the study region, the long-term climate prediction methodology is applied. The latter is based on the downscaling of climate predictions from several single- and multi-domain GCMs, using the two well-known downscaling methods SDSM and LARS-WG and a newly developed MLR-downscaling technique that allows the incorporation of a multitude of monthly or daily climate predictors from one- or several (multi-domain) parent GCMs. The numerous downscaling experiments indicate that the MLR- method is more accurate than SDSM and LARS-WG in predicting the recent past 20th-century (1971-2000) long-term monthly climate in the region. The MLR-model is, consequently, then employed to downscale 21st-century GCM- climate predictions under SRES-scenarios A1B, A2 and B1. However, since the hydrological watershed model requires daily-scale climate input data, a new stochastic daily climate generator is developed to rescale monthly observed or predicted climate series to daily series, while adhering to the statistical and geospatial distributional attributes of observed (past) daily climate series in the calibration phase. Employing this daily climate generator, 30 realizations of future daily climate series from downscaled monthly GCM-climate predictor sets are produced and used as input in the SWAT- distributed watershed model, to simulate future streamflow and other hydrological water budget components in the study region in a multi-realization manner. In addition to a general examination of the future changes of the hydrological regime in the KY-basin, potential future changes of the water budgets of three main reservoirs in the basin are analysed, as these are a major source of water supply in the study region. The results of the long-term 21st-century downscaled climate predictions provide evidence that, compared with the past 20th-reference period, the future climate in the study area will be more extreme, particularly, for SRES A1B. Thus, the temperatures will be higher and exhibit larger fluctuations. Although the future intensity of the rainfall is nearly constant, its spatial distribution across the region is partially changing. There is further evidence that the sequential rainfall occurrence will be decreased, so that short periods of high intensities will be followed by longer dry spells. This change in the sequential rainfall pattern will also lead to seasonal reductions of the streamflow and seasonal changes (decreases) of the water storage in the reservoirs. In any case, these predicted future climate changes with their hydrological impacts should encourage water planner and policy makers to develop adaptation strategies to properly handle the future water supply in this area, following the guidelines suggested in this study.

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.

A Two-level Prediction Model for Deep Reactive Ion Etch (DRIE)

We contribute a quantitative and systematic model to capture etch non-uniformity in deep reactive ion etch of microelectromechanical systems (MEMS) devices. Deep reactive ion etch is commonly used in MEMS fabrication where high-aspect ratio features are to be produced in silicon. It is typical for many supposedly identical devices, perhaps of diameter 10 mm, to be etched simultaneously into one silicon wafer of diameter 150 mm. Etch non-uniformity depends on uneven distributions of ion and neutral species at the wafer level, and on local consumption of those species at the device, or die, level. An ion–neutral synergism model is constructed from data obtained from etching several layouts of differing pattern opening densities. Such a model is used to predict wafer-level variation with an r.m.s. error below 3%. This model is combined with a die-level model, which we have reported previously, on a MEMS layout. The two-level model is shown to enable prediction of both within-die and wafer-scale etch rate variation for arbitrary wafer loadings.

The Influence of Adjacent Segment on the Reliability of Cu Dual Damascene Interconnects

Three terminal ‘dotted-I’ interconnect structures, with vias at both ends and an additional via in the middle, were tested under various test conditions. Mortalities (failures) were found in right segments with jL value as low as 1250 A/cm, and the mortality of a dotted-I segment is dependent on the direction and magnitude of the current in the adjacent segment. Some mortalities were also found in the right segments under a test condition where no failure was expected. Cu extrusion along the delaminated Cu/Si₃N₄ interface near the central via region was believed to cause the unexpected failures. From the time-to-failure (TTF), it is possible to quantify the Cu/Si₃N₄ interfacial strength and bonding energy. Hence, the demonstrated test methodology can be used to investigate the integrity of the Cu dual damascene processes. As conventionally determined critical jL values in two-terminal via-terminated lines cannot be directly applied to interconnects with branched segments, this also serves as a good methodology to identify the critical effective jL values for immortality.

Effects of the heterogeneity of the variances on reliability generalization : An example with the beck depression inventory

Resumen tomado de la publicaci??n

Extension of the Job Demands-Resources model in the prediction of burnout and engagement among teachers over time. 'Una ampliaci??n del modelo de demandas-recursos laborales en la predicci??n del burnout y del engagement en profesores'

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculated

Factores asociados a calidad de vida en pacientes de hemodiálisis incidentes y prevalentes

Introducción: la insuficiencia renal crónica IRC ha aumentado su prevalencia en los últimos años pasando de 44.7 pacientes por millón en 1993 a 538.46 pacientes por millón en 2010, los pacientes quienes reciben terapia de remplazo renal hemodiálisis en Colombia cada vez tienen una mayor sobrevida. El incremento de los pacientes y el incremento de la sobrevida nos enfocan a mejorar la calidad de vida de los años de diálisis. Metodología: se comparó la calidad de vida por medio del SF-36 en 154 pacientes con IRC estadio terminal en manejo con hemodiálisis, 77 pacientes incidentes y 77 pacientes prevalentes, pertenecientes a una unidad renal en Bogotá, Colombia. Resultados: se encontró una disminución de la calidad de vida en los componentes físicos (PCS) y metales (MCS) de los pacientes de hemodiálisis en ambos grupos. En el modelo de regresión logística la incapacidad laboral (p=0.05), el uso de catéter (p= 0,000), el bajo índice de masa corporal (p=0.021), la hipoalbuminemia (p=0,033) y la anemia (p=0,001) fueron factores determinantes en un 78,9% de baja calidad de vida de PCS en los pacientes incidentes con respecto a los prevalentes. En el MCS de los pacientes incidentes vs. Prevalentes se encontró la hipoalbuminemia (p=0.007), la anemia (p=0.001) y el acceso por catéter (p=0.001) como factores determinantes en un 70.6% de bajo MCS Conclusiones: la calidad de vida de los pacientes de diálisis se encuentra afectada con mayor repercusión en el grupo de los pacientes incidentes, se debe mejorar los aspectos nutricionales, hematológicos y de acceso vascular en este grupo.

Validity and reliability pie

Validity and reliability pie animation for display in RM&EBP

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.

Reliability of the Brief Neuropsychological Assessment Instrument Neupsilin

This study aimed to evaluate the reliability of Neupsilin Brief Neuropsychological Assessment Instrument, a brief battery developed in Brazil. Hundred two Brazilian man and women participated, from 18 to 40 years of age. It was evaluated the test-retest reliability of the Neupsilin tasks and the reliability of the correction of the constructional praxis task by different evaluators. The data were analyzed by Spearman’s correlation, intraclass correlation and Cronbach’s alpha. Language, memory, praxis and executive functions presented the highest correlations in the test-retest analyses. The agreement in the correction of the constructional praxis task was moderate to high. The results indicate temporal reliability of Neupsilin tasks and inter-rater agreement in the correction of the constructional praxis task. Suggestions to improve the tasks, the validity and reliability of Neupsilin were presented.

Assessment of construct validity and reliability of the inventory of masculinity and femininity in adolescents and young adults Colombian

This study evaluated the construct validity and reliability of Femininity and Masculinity Inventory-Imafe (Lara, 1993), and calculated standardized punctuations for Colombian teenagers and young adults. Participants were 1527 male and female, between 15 and 42. The general and scale reliability was evaluated through Alfa and Guttmann coefficients, and the factor analysis was used to estimate the construct validity. Standardized punctuations were obtained for gender, since there were statistically significant differences for sex in the six scales of the instrument. The inventory presented reliability values that oscillated between .76 and .88, and the factorial analysis showed three factors coherent with the factors found in its original validation. Correlacional inter-scales analysis and sex-differences were theoretically consistent.

A prediction model for establishing the disability degree in adults with spinal cord injury: results based on WHO-DAS II perspective

Objective: To establish a prediction model of the degree of disability in adults with Spinal CordInjury (SCI ) based on the use of the WHO-DAS II . Methods: The disability degree was correlatedwith three variable groups: clinical, sociodemographic and those related with rehabilitation services.A model of multiple linear regression was built to predict disability. 45 people with sci exhibitingdiverse etiology, neurological level and completeness participated. Patients were older than 18 andthey had more than a six-month post-injury. The WHO-DAS II and the ASIA impairment scale(AIS ) were used. Results: Variables that evidenced a significant relationship with disability were thefollowing: occupational situation, type of affiliation to the public health care system, injury evolutiontime, neurological level, partial preservation zone, ais motor and sensory scores and number ofclinical complications during the last year. Complications significantly associated to disability werejoint pain, urinary infections, intestinal problems and autonomic disreflexia. None of the variablesrelated to rehabilitation services showed significant association with disability. The disability degreeexhibited significant differences in favor of the groups that received the following services: assistivedevices supply and vocational, job or educational counseling. Conclusions: The best predictiondisability model in adults with sci with more than six months post-injury was built with variablesof injury evolution time, AIS sensory score and injury-related unemployment.

Clinical prediction of occupational and non-specific low back pain

Non-specific Occupational Low Back Pain (NOLBP) is a health condition that generates a high absenteeism and disability. Due to multifactorial causes is difficult to determine accurate diagnosis and prognosis. The clinical prediction of NOLBP is identified as a series of models that integrate a multivariate analysis to determine early diagnosis, course, and occupational impact of this health condition. Objective: to identify predictor factors of NOLBP, and the type of material referred to in the scientific evidence and establish the scopes of the prediction. Materials and method: the title search was conducted in the databases PubMed, Science Direct, and Ebsco Springer, between1985 and 2012. The selected articles were classified through a bibliometric analysis allowing to define the most relevant ones. Results: 101 titles met the established criteria, but only 43 metthe purpose of the review. As for NOLBP prediction, the studies varied in relation to the factors for example: diagnosis, transition of lumbar pain from acute to chronic, absenteeism from work, disability and return to work. Conclusion: clinical prediction is considered as a strategic to determine course and prognostic of NOLBP, and to determine the characteristics that increase the risk of chronicity in workers with this health condition. Likewise, clinical prediction rules are tools that aim to facilitate decision making about the evaluation, diagnosis, prognosis and intervention for low back pain, which should incorporate risk factors of physical, psychological and social.