Dissertations / Theses on the topic 'Variability analyzing'

All information about patient and his visits through the data input unit come to database. Information in a database is stored in the related tables. Principles of encapsulation and hiding data should be taken into account. This is necessary to maintain data integrity and consistency which processed in the program. The collection of data and methods of their description should have easy access to them and be structured according to the general algorithm.

For analyzing the effect of climate change on the streamflow at a regional scale, six General Circulation Models (GCMs) were selected from among eighteen GCMs from the Coupled Model Intercomparison Project (CMIP5) for the Pajaro River Watershed in central California. The 1/8° latitude-longitude resolution bias-corrected and downscaled CMIP5 projections were utilized for an ensemble of GCMs under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The twenty-first century is segregated into three time-periods (2016-2039, 2040-2069 and 2070-2099) for comparing the streamflow against changing precipitation and temperature according to the CMIP5 projections. The daily maximum and daily minimum temperature are projected to consistently rise through to the latter part of the century. Csiro-mk3-6 and canesm2 models project an increase of 3.1°C in annual average daily maximum temperature and 3.4°C in annual average daily minimum temperature respectively in 2070-2099 period under RCP8.5 scenarios. Future precipitation is projected to increase in January and February, which means the wet months in the Pajaro River Watershed are likely to get more rainfall. The dry months would continue to receive diminished precipitation throughout the century. The streamflow was increasing on future January, and sporadically, in February months but diminished during the dry months. The range of annual average streamflow for the future years stretched from 0.1 to 29.1 m3/s for the GCM ensemble, mostly close to the lower limit. The results suggest considering multiple climate change scenarios and evaluating alternative setups would provide a robust basis for hydrological assessment.

Over the years there is an increasing evidence of climate change on the available water resources. The interaction of hydrological cycle with climate variability and change may provide information related with several water management issues. The current study analyzes streamflow variability of the United States due to large-scale ocean-atmospheric climate variability. In addition, forecast lead-time is also improved by coupling climate information in a data driven modeling framework. The spatial-temporal correlation between streamflow and oceanic-atmospheric variability represented by sea surface temperature (SST), 500-mbar geopotential height (Z500), 500-mbar specific humidity (SH500), and 500-mbar east-west wind (U500) of the Pacific and the Atlantic Ocean is obtained through singular value decomposition (SVD). For forecasting of streamflow, SVD significant regions are weighted using a non-parametric method and utilized as input in a support vector machine (SVM) framework. The Upper Rio Grande River Basin (URGRB) is selected to test the applicability of the proposed forecasting model for the period of 1965-2014. The April-August streamflow volume is forecasted using previous year climate variability, creating a lagged relationship of 1-13 months. To understand the effect of predefined indices such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) on the regional streamflow, a wavelet analysis is also performed for regions developed by from 2014 National Climate Assessment (NCA). Moreover, different SVD approach is performed for streamflow of each of the six NCA regions named as Great Plains, Midwest, Northeast, Northwest, Southeast, and Southwest. In regional case, SVD is applied initially with streamflow and SST; and that spatial-temporal correlation is later correlated with Z500, SH500, and U500 separately to evaluate the interconnections between climate variables. SVD result showed that the streamflow variability of the URGRB was better explained by SST and U500 as compared to Z500 and SH500. The SVM model showed satisfactory forecasting ability as the observed and forecasted streamflow volume for different selected sites were well correlated. The best results were achieved using a 1-month lead to forecast the following 4-month period. Overall, the SVM results showed excellent predictive ability with average linear correlation coefficient of 0.89 and Nash-Sutcliffe efficiency of 0.79. Whereas regional SVD analysis showed that streamflow variability in the Great Plains, Midwest, and Southwest region is strongly associated with SST of ENSO-like region. However, for Northeast and Southeast region, U500 and SH500 were strongly correlated with streamflow as compared to the SST of the Pacific Ocean. The continuous wavelet analysis of ENSO/PDO/AMO and the regional streamflow patterns revealed different significant timescale bands that affected their variation over the study period. Identification of several teleconnected regions of the climate variables and the association with the streamflow can be helpful to improve long-term prediction of streamflow resulting in better management of water resources in the regional scale.

The South Florida Everglades is a unique ecosystem. Intensive water management in the system has facilitated agricultural, urban, and economic development. The Everglades offers a variety of ecosystem services (ES) to the people living in this region. Nevertheless, the ecosystem is under imminent threat of climate change, which would alter the way water is managed today and ultimately affect the ES offered by the system. On the other hand, substantial restoration is underway that aims to restore the Everglades closer to its historic condition. This research tried to map the public’s preference for Everglades restoration. Using a geocoded discrete-choice survey dataset, the study showed variation in the public’s preference by changing the levels of ES. Additionally, the general public’s attitude toward climate change risk to the Everglades and preference for mitigation were also assessed using the survey data.

Nonlinear structures, when subjected to multiple ground motion records that are scaled to consistent ground motion intensity show significant variation in their response. This effect of ground motion randomness on the variation of structural response is defined as Record-to-Record (RTR) Variability. Ground motion characteristics that contribute to this variability in response includes the variation of signal composition (frequency content) with time (spectral nonstationarity).The phenomenon of moving resonance which occurs when the frequency content of the ground motion shifts in a similar manner as the natural frequencies of the structural response, is likely a contributor to variability. This brings the need to further understand the sources of variability due to moving resonance. The present study was carried out to develop a method to analyze the time-frequency content of a ground motion to assess the occurrence of moving resonance and to quantify its potential in effecting the structural systems. Bilinear elastic and elastoplastic hysteretic behavior was considered. Detailed analysis is done to quantify the effect of moving resonance on structural systems due to 22 far field ground motion records. The wavelet coefficient plots gave very good detail of the characteristics of the ground motions that were not clear from the acceleration time histories and response spectra plots. Instances of moving resonance were found out to be significant. Amplification due to moving resonance was found to be quite large. One instance studied in detail (accelerogram of Northridge earthquake at Beverly Hills) had peak displacement amplified by 6 times compared to the amount of peak displacement expected if the system did not exhibit moving resonance. Based on the analyses results, the characteristics of the ground motion records that donâ t cause significant moving resonance effect on structural systems were observed. Similarly, the characteristics of the ground motions that do cause moving resonance effect on structural systems were examined.
Master of Science

Cette étude propose d’analyser l’influence de l’architecture du palmier à huile sur sa capacité à intercepter la lumière, en se basant sur des reconstructions 3D de palmiers et en établissant un bilan radiatif sur ses structures végétales reconstruites in silico. Le premier objectif de l’étude était de caractériser et modéliser la variabilité génétique de l’architecture du palmier à huile et de son interception lumineuse. Dans un deuxième objectif l’amélioration potentielle de l’interception de la lumière et de l’assimilation carbonée a été évaluée en modifiant les traits morphologiques et géométriques des feuilles et des idéotypes architecturaux de palmiers à huile ont été proposés.Des relations allométriques ont été utilisées pour modéliser les traits architecturaux en fonction de gradients ontogénétique et de topologie des feuilles dans la couronne. La méthode permet de reconstruire des palmiers à huile virtuels à différents âges au cours du développement. De plus, l’approche allométrique a été couplée à des modèles à effets mixtes pour intégrer au travers de paramètres la variabilité entre et au sein des cinq progénies. Le modèle permet ainsi de simuler les spécificités architecturales des cinq progenies en incluant les variabilités entre individus observés. Le modèle architectural, paramétré pour les différentes progénies, a ensuite été implémenté dans AMAPstudio pour générer des maquettes 3D de palmiers et ainsi estimer leur interception lumineuse, de l’individu à la parcelle entière.Les résultats de ces analyses ont révélé des différences significatives entre et au sein des progenies, dans la géométrie des feuilles (longueur du pétiole, densité de folioles sur le rachis, et courbure du rachis) et dans la morphologie des folioles (gradients de longueurs et largeurs le long du rachis). La comparaison virtuelle des différentes progénies ont aussi montré des efficacités distinctes de l’interception lumineuse.Des analyses de sensibilité ont ensuite été réalisées pour identifier les traits architecturaux influençant l’interception lumineuse et l’assimilation potentielle à différents âges de la plante. Les paramètres les plus sensibles au cours du développement furent ceux reliés à la surface totale foliaire (longueur des rachis, nombre de folioles, morphologie des folioles), mais les attributs géométriques plus fins de la feuille ont montré un effet croissant avec la fermeture de la canopée. Sur un couvert adulte, l’optimum en assimilation carbonée est atteint pour des indices de surfaces foliaires (LAI) entre 3,2 et 5,5 m2.m−2, avec des feuilles érigées, de courts pétioles et rachis et un nombre important de folioles sur le rachis. Quatre idéotypes architecturaux pour l’assimilation carbonée ont été proposés et présentent des combinaisons spécifiques de traits géométriques, limitant l’ombrage mutuel des plantes et optimisant la distribution de la lumière dans la couronne.En conclusion, le modèle 3D de palmiers à huile, dans sa conception et son application, a permis de détecter les traits architecturaux génétiquement déterminés et influençant l’interception lumineuse. Ainsi, le nombre limité de traits dégagés par l’analyse de sensibilité ainsi que les combinaisons de traits révélées au travers des idéotypes pourraient être pris en compte dans de futurs programmes de sélection. En perspective, des travaux dédiés à intégrer dans ce modèle d’autres processus physiologiques, tels que la régulation de la conductance stomatique et le partitionnement du carbone dans la plante, sont à envisager. Ce nouvel FSPM pourrait alors être utilisé pour tester différents scénarii, comme par exemple dans un contexte de changement climatique avec de faibles radiations et des périodes de sécheresse fréquentes. De même, ce modèle pourrait être utilisé pour étudier différentes configurations de plantation et des systèmes de cultures intercalaires, et ainsi proposer de nouveaux idéotypes multicritères
In this study we proposed to investigate the influence of oil palm architecture on the capacity of the plant to intercept light, by using 3D reconstructions and model-assisted evaluation of radiation-use efficiency. The first objective of this study was to analyse and model oil palm architecture and light interception taking into account genetic variability. A second objective was to explore the potential improvements in light capture and carbon assimilation by manipulating oil palm leaf traits and propose architectural ideotypes.Allometric relationships were applied to model these traits according to ontogenetic gradients and leaf position within the crown. The methodology allowed reconstructing virtual oil palms at different stages over plant development. Additionally, the allometric-based approach was coupled to mixed-effect models in order to integrate inter and intra progeny variability through progeny-specific parameters. The model thus allowed simulating the specificity of plant architecture for a given progeny while including observed inter-individual variability. The architectural model, parameterized for the different progenies, was then implemented in AMAPstudio to generate 3D mock-ups and estimate light interception efficiency, from individual to stand scales.Significant differences in leaf geometry (petiole length, density of leaflets and rachis curvature) and leaflets morphology (gradients of leaflets length and width) were detected between and within progenies, and were accurately simulated by the modelling approach. Besides, light interception estimated from the validated 3D mock-ups showed significant variations among the five progenies.Sensitivity analyses were then performed on a subset of architectural parameters to identify the architectural traits impacting on light interception efficiency and potential carbon assimilation over plant development. The most sensitive parameters over plant development were those related to leaf area (rachis length, number of leaflets, leaflets morphology), but fine attribute related to leaf geometry showed increasing influence when canopy got closed. In adult stand, optimized carbon assimilation was estimated on plants presenting a leaf area index (LAI) between 3.2 and 5.5 m2.m−2, with erected leaves, short rachis and petiole and high number of leaflet on rachis. Four architectural ideotypes for carbon assimilation were proposed based on specific combinations of organs geometry, limiting mutual shading and optimizing light distribution within plant crown.In conclusion, this study highlighted how a functional-structural plant model (FSPM) can be used to virtually explore plant biology. In our case of study, the 3D model of oil palm, in its conception and its application, permitted to detect the architectural traits genetically determined and influencing light interception. The limited number of traits revealed in the sensitivity analysis and the combination of traits proposed through ideotypes could guide further breeding programs. Forthcoming work will be dedicated to integrate in the modeling approach other physiological processes such as stomatal conductance and carbon partitioning. The improved FSPM could then be used to test different scenarios, for instance in climate change context with low radiations or frequent drought events. Similarly, the model could be used to investigate different planting patterns and intercropping systems, and proposed new multi-criteria ideotypes of oil palm

Climate related problems such as droughts, heat waves, increased levels of precipitation and storms threaten the functionality of several infrastructural systems. This thesis focus on infrastructure that provides for water and sanitation services because it has been identified as being particular at risk when the climate is changing. The identification and mapping of the vulnerability of a system can improve the prerequisites to choose more appropriate measures to facilitate the situation at hand. In this study a set of GIS based methodologies using indicators (simple and composite) of vulnerability are proposed and assessed. “Physical” vulnerability is used as a measure combining the intrinsic characteristics of a system and the climate related hazard resulting in a measure for physical vulnerability. GIS software is used to manage the spatial data sets and to combine the indicators into indexes of physical vulnerability. The assessed systems and related climate hazards are: - Water and sewage pipe network and an increased risk of pipe breakage due to increased frequencies of landslides and – An increased risk for ground and surface water supplies related to pollution from the point sources sewage infiltration and polluted ground”. The resulting GIS applications are tested on pilot areas located in the Stockholm region and GIS based sensitivity analyses are performed. The availability and accessibility of relevant digital spatial data is also assessed and discussed.

The impacts of climate change on the environment at the global scale can be determined through the use of large-scale circulation models; however, the results from these models are difficult to interpret at the regional or local levels. Regional vulnerability analyses consider the knowledge of locals, which may provide insight into the effects of climate variability on the environment at smaller scales, and most importantly, the effects that these developments are having on society. The objective of this research was to analyze the vulnerability to climate variability of the beef cattle ranching communities of the municipalities of Acatic and of Tepatitlán de Morelos, Jalisco, Mexico. These municipalities are found in a region of the state referred to as "Los Altos". The economy of Los Altos largely relies on agricultural and farming practices; these sectors provide the largest source of employment in the area. In the two municipalities that comprise the study area, the beef cattle industry is one of the strongest economic activities. Climate variability poses great threat on these communities because the main economic activities of the region are highly dependent on natural resources. To have a better understanding of the human-environment interactions in this region, remote sensing methods were applied. Three Landsat Thematic Mapper TM images (years: 1985, 1993 and 2000) were employed to generate land use and land cover classification maps of the study area; these maps were then subjected to a change detections analysis. Some of the land use and land cover categories experienced more change than others; among those was the category of water, shrub land and crop land. The area covered by water nearly doubled from 1985 to 1993 and then nearly decreased by half by the year 2000. From 1985 to 1993, here was a decrease in the shrub land of about 1200 ha and concurrently an increase in the crop land of about 1400 ha. From 1993 to 2000 there was an increase in the shrub land category of about 430 ha and a decrease in the crop land category of about 690 ha. To gain insight into the effects of climate variability on the livelihoods of these communities, nine local beef cattle ranchers were interviewed on a one-on-one basis. All participants believe that the local beef cattle industry is highly valuable to the economy and culture of the region. All participants also mentioned that notable variations in to the climate have been occurring in recent decades; mainly precipitation scarcity and higher temperatures. The locals believe that these changes are the result of extensive deforestation. In past decades, deforestation of native vegetation has been intensely performed in order to make land available for agricultural purposes. Therefore, among the various adaptation measures to the changes presented in the climate, the cattle ranchers talked about planting trees. People believe that the "vision" of the region is changing and that reforestation has become a priority in this land. To determine the exact causes of the climate changes experienced in this region, further investigations have to be done. However, it is certain that these changes are having implications on the economic activities of the region; the people of these communities will continue facing difficulties if the present changes in the regional climate continue to develop. The employment of proper adaptation measures has the potential to reduce climate-related losses within the livestock and agricultural sectors. Therefore, it is crucial that preventive measures are taken by the members of these communities before the effects of climate change worsen in the region.

碩士
國立陽明大學
生物醫學工程學系
105
According to the investigation done by the Ministry of Health and Welfare, heart attack is a major cause of death of elders in Taiwan. The sudden cardiac death onset leads to short rescue time, thus causes extremely high mortality rate. If we can detect the signs of heart attack beforehand, it may offer time to take a prompt medication action. The aim of this study is to investigate the relationship between heart rate variability (HRV) and sudden cardiac death by means of electrocardiography (ECG) analysis. The heart rate variability analysis used in this study includes time domain and frequency domain analysis, and the analysis in the frequency domain is performed by the Hilbert-Huang transform and Fourier transform. This study analyzed the ECG data measured minutes before sudden cardiac death onset, and the result shows that there were statistically significant differences. For the five minutes HRV analysis, nine of eleven features extracted from the HRV showed statistically significant differences between healthy adults and the adults who are on the verge of suffering sudden cardiac death. Furthermore, we trained those features by using the k-nearest neighbor algorithm as a classification tool. The accuracy rate of this classification model used to distinguish between the healthy adults and cardiac patients is 94.7%. And the accuracy rates analyzed using the ECG data measured more than five minutes, including ten minutes, thirty minutes and an hour, before onset is about 70-80%. This study reports our proposed method may detect sudden cardiac death minutes before its onset.

Forests are an important component in the earth system. They cover nearly one third of the land surface, store about as much carbon as the entire atmosphere and host more than half of the planet’s biodiversity. Forests provide ecosystem services such as climate regulation and water cycling and they supply resources. However, forests are increasingly at risk worldwide, due to anthropogenic deforestation, degradation and climate change. Concepts for counteracting this development require abilities to monitor forests and predict possible future developments. Given the vast size of forest cover along with the variety of forest types, field measurements and experiments alone cannot provide the solution for this task. Remote sensing and forest modeling enable a broader and deeper understanding of the processes that shape our planet’s forests. Remote sensing from airborne and spaceborne platforms can provide detailed measurements of forest attributes ranging from landscape to global scale. The challenge is to interpret the measurements in an appropriate way and derive biophysical properties. This requires a good understanding of the interaction between radiation and the vegetation. Forest models are tools that synthesize our knowledge about processes, such as tree growth, competition, disturbances and mortality. They allow simulation experiments which go beyond the spatial and temporal scales of field experiments. In this thesis, several major challenges in forest ecology and remote sensing were addressed. The main variable of interest was forest biomass, as it is the most important variable for forest carbon mapping and for understanding the role of vegetation in the global carbon cycle. For the purpose of biomass estimation, remote sensing derived canopy height and structure measurements were combined with field data, forest simulations and remote sensing simulations. The goals were: 1) to integrate remote sensing measurements into a forest model; 2) to understand the effects of spatial scale and disturbances on biomass estimation using a variety of remote sensing metrics; 3) to develop approaches for quantifying biomass changes over time with remote sensing and 4) to overcome differences among forest types by considering several structural aspects in the biomass estimation function. In the first study, a light detection and ranging (lidar) simulator was developed and integrated in the forest model FORMIND. The model was parameterized for the tropical rainforest on Barro Colorado Island (BCI, Panama). The output of the lidar simulator was validated against real airborne lidar data from BCI. Undisturbed and disturbed forests were simulated with FORMIND to identify the most well suited lidar metric for biomass estimation. The objective hereby was to achieve a low normalized root mean squared error (nRMSE) over the entire range of forest structures caused by disturbances and succession. Results identified the mean top-of-canopy height (TCH) as the best lidar-derived predictor. The accuracy strongly depended on spatial scale and relative errors < 10% could be achieved if the spatial resolution of the produced biomass map was ≥ 100 m and the spatial resolution of the remote sensing input was ≤ 10 m. These results could provide guidance for biomass mapping efforts. In the second study, forest simulations were used to explore approaches for estimating changes in forest biomass over time based on observed changes in canopy height. In an ideal situation, remote sensing provides measurements of canopy height above ground which allows the estimation of biomass stocks and changes. However, this requires sensors which are able to detect canopy surface and terrain elevation, and some sensors can only detect the surface (e.g., X-band radar). In such cases, biomass change has to be estimated from height change using a direct relationship. Unfortunately, such a relationship is not constant for forests in different successional stages, which can lead to considerable biases in the estimates of biomass change. A solution to this problem was found, where missing information of canopy height was compensated by integrating metrics of canopy texture. Applying this improved approach enables estimations of biomass losses and gains after disturbances at 1-ha resolution. In mature forests with very small changes in height and biomass all tested approaches have limited capabilities, as was revealed by an application using TanDEM-X derived canopy height from BCI. In the third study, a general biomass estimation function, which links remote sensing-derived structure metrics to forest biomass, was developed. General in this context means that it can be applied in different forest types and different biomes. For this purpose a set of predictor metrics was explored, with each predictor representing one of the following structural aspects: mean canopy height, maximal possible canopy height, maximal possible stand density, vertical canopy structure and wood density. The derived general equation resulted in almost equally accurate biomass estimates across the five considered sites (nRMSE = 12.4%, R² = 0.74) as site-specific equations (nRMSE = 11.7%, R²= 0.77). The contributions of the predictors provide a better understanding of the variability in the height-to-biomass relationship observed across forest types. The thesis has laid foundations for a close link between remote sensing, forest modeling and forest inventories. Several ongoing projects carry this further, by 1) disentangling and quantifying the uncertainty in biomass remote sensing, 2) trying to predict forest productivity based on structure and 3) detecting single trees from lidar to be used as forest model input. These methods can in the future lead to an integrated forest monitoring and information system, which assimilates remote sensing measurements and produces predictions about forest development. Such tools are urgently needed to reduce the risks forests are facing worldwide.

碩士
國立中央大學
資訊工程學系
103
Arrhythmia,one of common heart diseases, is defined by slow, rapid or irregular heartbeats. It would have serious results in patients with severe arrhythmia. Analyzing electrocardiogram (ECG) and heart rate variability (HRV) is one of the approaches to estimate arrhythmia and heart status.Through wearable devices, ECG signals can be obtain in convenient form. This study is designed to perform HRV analysis and arrhythmia detection through wearable device. The results of this study are evaluated with MIT-BIH Arrhythmia Database and MIT-BIH Normal Sinus Rhythm Database. The proposed system could help understand the condition of heart and benefit health care.