Este trabalho apresenta os resultados obtidos no desenvolvimento da pesquisa \"Estudo do potencial expansivo dos sedimentos argilosos da Formação Guabirotuba na região do Alto Iguaçu- PR\". Foram realizados ensaios laboratoriais para a caracterização e previsão do comportamento expansivo dos materiais, tais como: Análises granulométricas, Limites de Consistência e demais Índices Físicos, Difração de Raios - X, Análise Térmico Diferencial, Capacidade de Troca de Cátions, além de Expansão Livre e Pressão de Expansão. Para estas investigações foram utilizadas 51 amostras deformadas e 11 blocos de material indeformado. A análise desses ensaios e de informações básicas do meio físico, permitiram a avaliação de áreas que apresentam diferentes potenciais à expansão, permitindo a elaboração da Carta de Predisposição à Ocorrência de Problemas Expansivos. / This work presents the results obtained in the development of the research \"Study of the swell potential of Guabirotuba Formation clayey sediments in the area of Alto Iguaçu - PR\". Laboratorial tests, such as: Grain Size Analysis, Liquid and Plastic Limits and others Index Tests; X-Ray Diffractometry, Differential Thermal Analysis, Cation Exchange Capacity, besides Free Swelling and Axial Swelling Stress, were executed for the characterization and prediction of the studied material swelling behavior. Fifty - one disturbed samples and eleven blocks of undisturbed material were used for these investigations. The analysis of the test results and the basic information of the environment allowed the evaluation of are as that present different swell potentials, and the elaboration of the Chart of Predisposition to the Occurrence of Swell Problems.

The dynamic response of the demand is defined as the time-domain real and reactive power response to a voltage disturbance, and it represents the dynamic load characteristics. This thesis develops a methodology for probabilistic estimation and prediction of dynamic responses of the demand at bulk supply points. The main outcome of the research is being able to predict the contribution of different categories of loads to the total demand mix and their controllability without conducting detailed customer surveys or collecting smart meter data, and to predict the dynamic response of the demand without performing field tests. The prediction of the contributions of different load categories and their controllability and load characteristics in the near future (e.g., day ahead) plays an important role in system analysis and planning, especially in the short-term dispatch and control. However, the research related to this topic is missing in the publically available literature, and an approach needs to be developed to enable the prediction of the participation of different loads in total load mix, their controllability and the dynamic response of the demand. This research contributes to a number of areas, such as load forecasting, load disaggregation and load modelling. First, two load forecasting methodologies which have not been compared before are compared; and based on the results of comparison and considering the actual requirements in this research, a methodology is selected and used to predict both the real and reactive power. Second, a unique methodology for load disaggregation is developed. This methodology enables the estimation of the contributions of different load categories to the total demand mix and their controllability based on RMS measured voltage and real and reactive power. The confidence level of the estimation is also assessed. The methodology for disaggregation is integrated with the load forecasting tool to enable prediction of load compositions and dynamic responses of the demand. The prediction is validated with data collected from real UK power network. Finally, based on the prediction, an example of load shifting is used to demonstrate that different dynamic responses can be obtained based on the availability and redistribution of controllable devices and that load shifting decisions, i.e., demand side management actions, should be made based not only on the amount of demand to be shifted, but also on predicted responses before and after load shifting.

This study investigates whether the current lack of structure of internal control weakness disclosures (a narrative about the reliability of the financial reporting system) leads nonprofessional investors to make differential investment decisions. Using the non-accelerated filer (smaller public company) setting, where nonprofessional investors are likely to consume unaudited internal control reports in their investing judgments and decisions, I examine two facets of internal control disclosure formats: presentation salience and disaggregation of material weaknesses. A 2 x 2 between-participants behavioral experiment was conducted with internal control presentation salience (bulleted vs. in-text) and disaggregation level (a single material weakness vs. a combination of multiple control deficiencies that is a material weakness). I find that nonprofessional investors reward companies that disclose internal control weaknesses more saliently. The results also indicate that disaggregation interacts with salience in that it increases the effect of salience on investing judgments such that salient (stealth) disclosure of a combination of control deficiencies is viewed more positively (negatively) than salient (stealth) disclosure of a material weakness. These findings are contrary to Rennekamp (2012) who finds that processing fluency in bad news leads to more negative investment judgements. Additional analyses indicated that the results related to management trust and credibility are consistent with prior literature. The findings contribute to academia and practice by shedding light on the importance that needs to be placed on the presentation format of internal control disclosures.

Non-residential users contribute to a significant portion of the total water delivered by water supplying agencies. However, a very limited number of studies have attempted to investigate the water use behavior of non-residential users. With the emergence of newer “smart” meters, water use now can be measured and recorded at a very high temporal frequency. Smart meters can help determine total water use, timing, and component end uses to better understand water use practices by non-residential users. Water end use disaggregation is the process of separating the water used by each fixture or process within a facility. This is useful because having a breakdown of the consumption of all end uses may encourage users to consume less water and gives them indications on how to do so. This project involved collecting and working with three different datasets with three different temporal scales (monthly billing data, 5-minute water use data, and 5-second water use data). We analyzed monthly billing data to solicit potential participating facilities for the study. For each participating facility, new smart devices were installed on their existing water meters, including an advanced water meter register and a pulse counting data logger. The newer registers logged and transmitted data to a web-accessible data portal at 5-minute intervals, while the pulse counters recorded water use at 5- second intervals. These devices enabled us to measure the timing and volume of different water uses (e.g., indoor versus outdoor versus industrial processes uses). In this project, we identified different water use events, average water used by each end use (from plumbing fixtures to industrial machinery), variability in end uses (faucets/toilets versus showers), variability in use by the type of user (manufacturing facilities versus assisted living homes), and the impact of the business type on the water use.

Ces travaux ont pour objectif général d'améliorer la représentation spatio-temporelle des processus hydrologiques de surface à partir de modèles dont la complexité est adaptée aux informations disponibles par la télédétection multi-capteur/multi-résolution. Nous avons poursuivi des développements méthodologiques (désagrégation, assimilation, modélisation du bilan d'énergie) autour de l'estimation de l'humidité du sol dans le contexte de la gestion des ressources en eau dans les régions semi-arides. Récemment, des missions spatiales permettent d'observer l'humidité des sols en surface; notamment avec le capteur AMSR-E (Advanced Microwave Scanning Radiometer-EOS) et la mission SMOS (Soil Moisture Ocean Salinity). Toutefois la résolution spatiale de ces capteurs est trop large (> 40 km) pour des applications hydrologiques. Afin de résoudre le problème d'échelle, l'algorithme de désagrégation DisPATCh (Disaggregation based on Physical and Theoretical Scale Change) a été développé en se basant sur un modèle d'évapotranspiration. Dans la première partie de thèse, l'algorithme est appliqué et validé sur le bassin du Murrumbidgee (sud-est de l'Australie) avec une résolution spatiale cible de 1 km à partir des données de LST (Température de surface) et NDVI (indice de végétation) issues de MODIS (MODerate resolution Imaging Spectroradiometer) et de deux produits d'humidité du sol basse résolution : SMOS et AMSR-E. Les résultats montrent que la désagrégation est plus efficace en été, où la performance du modèle d'évapotranspiration est optimale. L'étude précédente a notamment mis en évidence que la résolution temporelle des données DisPATCh est limitée par la couverture nuageuse visible sur les images MODIS et la résolution temporelle des radiomètres micro-ondes (3 jours pour SMOS). Dans la deuxième partie, une nouvelle approche est donc développée pour assurer la continuité temporelle des données d'humidité de surface en assimilant les données DisPATCh dans un modèle dynamique de type force-restore, forcé par des données météorologiques issus de ré-analyses, dont les précipitations. La méthode combine de manière originale un système variationnel (2D-VAR) pour estimer l'humidité du sol en zone racinaire et une approche séquentielle (filtre de Kalman simplifié) pour analyser l'humidité du sol en surface. La performance de l'approche est évaluée sur deux zones: la région Tensift-Haouz au Maroc et la région de Yanco en Australie. Les résultats montrent que le couplage désagrégation/assimilation de l'humidité du sol est un outil performant pour estimer l'humidité en surface à l'échelle journalière, même lorsque les données météorologiques sont incertaines. Dans la troisième partie, une méthode de correction des effets topographiques sur la LST est développée dans le but d'étendre l'applicabilité de DisPATCh aux zones vallonnées ou montagneuses qui jouent souvent le rôle de château d'eau sur les régions semi-arides. Cette approche, basée sur un modèle de bilan d'énergie à base physique, est testée avec les données ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) et Landsat sur la vallée d'Imlil dans le Haut Atlas Marocain. Les résultats indiquent que les effets topographiques ont été fortement réduits sur les images de LST à ~100 m de résolution et que la LST corrigée pourrait être utilisée comme une signature de l'état hydrique en montagne. Les perspectives ouvertes par ces travaux concernent la correction/désagrégation des données de précipitations et l'estimation des apports par l'irrigation pour une gestion optimisée de l'eau. / This thesis aims to improve the spatio-temporal resolution of surface water fluxes at the land surface-atmosphere interface based on appropriate models that rely on readily available multi-sensor remote sensing data. This work has been set up to further develop (disaggregation, assimilation, energy balance modeling) approaches related to soil moisture monitoring in order to optimize water management over semi-arid areas. Currently, the near surface soil moisture data sets available at global scale have a spatial resolution that is too coarse for hydrological applications. Especially, the near surface soil moisture retrieved from passive microwave observations such as AMSR-E (Advanced Microwave Scanning Radiometer-EOS) and SMOS (Soil Moisture and Ocean Salinity) data have a spatial resolution of about 60 km and 40 km, respectively. In this context, the downscaling algorithm "DISaggregation based on Physical And Theoretical scale Change" (or DisPATCh) has been developed. The near surface soil moisture variability is estimate within a low resolution pixel at the targeted 1 km resolution based on an evapotranspiration model using LST (Land surface temperature) and NDVI (vegetation index) derived from MODIS (MODerate resolution Imaging Spectroradiometer) data. Within a first step, DisPATCh is applied to SMOS and AMSR-E soil moisture products over the Murrumbidgee river catchment in Southeastern Australia and is evaluated during a one-year period. It is found that the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. However, the temporal resolution of DisPATCh data is limited by the gaps in MODIS images due to cloud cover, and by the temporal resolution of passive microwave observations (global coverage every 3 days for SMOS). The second step proposes an approach to overcome these limitations by assimilating the 1 km resolution DisPATCh data into a simple dynamic soil model forced by reanalysis meteorological data including precipitation. The original approach combines a variational scheme for root-zone soil moisture analysis and a sequential approach for the update of surface soil moisture. The performance is assessed using ground measurements of soil moisture in the Tensift-Haouz region in Morocco and the Yanco area in Australia during 2014. It is found that the downscaling/assimilation scheme is an efficient approach to estimate the dynamics of the 1 km resolution surface soil moisture at daily time scale, even when coarse scale and inaccurate meteorological data including rainfall are used. The third step presents a physically-based method to correct LST data for topographic effects in order to offer the opportunity for applying DisPATCh over mountainous areas. The approach is tested using ASTER (Advanced Spaceborne Thermal Emission Reflection Radiometer) and Landsat data over a 6 km by 6 km steep-sided area in the Moroccan Atlas. It is found that the strong correlations between LST and illumination over rugged terrain before correction are greatly reduced at ~100 m resolution after the topographic correction. Such a correction method could potentially be used as a proxy of the surface water status over mountainous terrain. This thesis opens the path for developing new remote sensing-based methods in order to retrieve water inputs -including both precipitation and irrigation- at high spatial resolution for water management.

No description available.

The goal of this research is to build an experimental environment for the Simulation Interoperability Laboratory (SIL) of the University of Central Florida (UCF). The Simulation Interoperability Laboratory (SIL) is researching about multi-resolution modeling(MRM), with a focus on military field uses. This thesis proposes steps to develop an MRM federation system and build two different MRM systems using COTS simulations (SIMBox, VR-Forces, and MASA Sword). This report is written to provide the basis for a time-based MRM federation study in the Simulation Interoperability Laboratory. The report describes many definitions and notions related to Multi-Resolution Modeling(MRM) and discusses examples to make better understanding for further research. MRM is relatively new research, and there are high demands for integrating simulators running in military field purposes. Most military-related research is based on simulators currently being used in the military; this poses a problem for research because the data is classified, resulting in many limitations for outside researchers to see the military's process for building an MRM system or the results of the research. Therefore, development of the MRM federation using COTS simulations can provide many examples of MRM issues for future research.

Protein disulfide isomerase (PDI) is an endoplasmic reticulum (ER)-resident chaperone with oxidoreductase and isomerase activity. Unique to its normal function, PDI also appears to disassemble the A1 subunits of cholera toxin (CT) and heat-labile enterotoxin (LT). It does so using an unfolding mechanism that knocks the catalytic A1 subunit away from the rest of the holotoxin. Release of the A1 subunit is linked to the diarrheal diseases caused by V. cholerae and enterotoxicogenic E. coli (ETEC). Due to the previously established difference in disease potency between CT and LT, we investigated and established a distinction between the two toxins in their efficacy of disassembly by PDI. We further identified four amino acid differences between the CTA2 and LTA2 linkers, which connect the A1 and cell-binding B subunits of both toxins, as the basis for this difference. We believe these four amino acids result in changes to holotoxin architecture that lead to antiparallel binding of PDI to LT as opposed to CT, which translates to a loss of momentum for the physical disassembly of LT. We have shown this through algorithmic simulations of the binding event between PDI and either CT or LT. We hypothesized the unfolding mechanism of PDI, which dislodges the A1 subunit of both CT and LT, can also break down neurotoxic aggregates of β-Amyloid (AB) and α-Synuclein (AS). PDI is known to inhibit the aggregation of the amyloid proteins. We demonstrated here that PDI could also reverse oligomeric and post-oligomeric aggregates of AB and AS, respectively. Our work sheds light on the specifics of PDI's novel physical mechanism as well as introduce it as a possible therapeutic for both Alzheimer's and Parkinson's disease due to its unique ability to disaggregate early fibrillar structures of AS and AB proteins.

Building energy models are used to forecast building energy use to design and control efficient building energy systems. Building energy use can generally be decomposed into heating, ventilation and air conditioning, refrigeration, appliance and lighting loads. These loads will depend on multiple factors such as outdoor weather conditions, occupants, building type, controls and scheduling. Data-driven models have become more popular with the increase in smart meter data available that can be used to train and fit the models. Additionally, buildings with high refrigeration loads have greater heat harvesting potential, however, few data-driven models have been developed for buildings such as supermarkets and ice rinks. In this work, linear regression models are used to predict the disaggregated space cooling, heating, baseload and refrigeration components of building energy use. In most cases, measured aggregate electricity use is available, however individual appliances or component loads require submetering equipment which can be expensive. Therefore the proposed models use time-based and weather features to separate the thermal and baseload portions of the electrical load. A generalized approach is also used to predict new buildings with data from existing buildings. Furthermore, a simplified model is used to predict hourly space heating from monthly natural gas measurements and hourly weather measurements. The models were evaluated on real data from buildings in Ontario and the disaggregated loads were verified with synthetic data. The results found that aggregate use was predicted reasonably well using linear regression methods, with most building types having a median normalized root mean squared error between 0.2 and 0.3, depending on the forecasting period. The model is flexible as it does not require detailed information related to the building such as lighting or setpoint schedules, however, it can be adapted in the future to include additional information and improve predictive capability. / Thesis / Master of Applied Science (MASc)

The cloud is evolving due to additional demands introduced by new technological advancements and the wide movement toward digitalization. Therefore, next-generation data centers (DCs) and clouds are expected (and need) to become cheaper, more efficient, and capable of offering more predictable services. Aligned with this, we examine the concept of software-defined “hardware” infrastructures (SDHI) based on hardware resource disaggregation as one possible way of realizing next-generation DCs. We start with an overview of the functional architecture of a cloud based on SDHI. Following this, we discuss a series of use-cases and deployment scenarios enabled by SDHI and explore the role of each functional block of SDHI’s architecture, i.e., cloud infrastructure, cloud platforms, cloud execution environments, and applications. Next, we propose a framework to evaluate the impact of SDHI on techno-economic efficiency of DCs, specifically focusing on application profiling, hardware dimensioning, and total cost of ownership (TCO). Our study shows that combining resource disaggregation and software-defined capabilities makes DCs less expensive and easier to expand; hence they can rapidly follow the exponential demand growth. Additionally, we elaborate on technologies behind SDHI, its challenges, and its potential future directions. Finally, to identify a suitable memory management scheme for SDHI and show its advantages, we focus on the management of Last Level Cache (LLC) in currently available Intel processors. Aligned with this, we investigate how better management of LLC can provide higher performance, more predictable response time, and improved isolation between threads. More specifically, we take advantage of LLC’s non-uniform cache architecture (NUCA) in which the LLC is divided into “slices,” where access by the core to which it closer is faster than access to other slices. Based upon this, we introduce a new memory management scheme, called slice-aware memory management, which carefully maps the allocated memory to LLC slices based on their access time latency rather than the de facto scheme that maps them uniformly. Many applications can benefit from our memory management scheme with relatively small changes. As an example, we show the potential benefits that Key-Value Store (KVS) applications gain by utilizing our memory management scheme. Moreover, we discuss how this scheme could be used to provide explicit CPU slicing – which is one of the expectations of SDHI  and hardware resource disaggregation. / <p>QC 20190415</p>