The MapReduce programming model has become widely adopted for large scale analytics on big data. MapReduce systems such as Hadoop have many tuning parameters, many of which have a significant impact on performance. The map and reduce functions that make up a MapReduce job are developed using arbitrary programming constructs, which makes them black-box in nature and prevents users from making good parameter tuning decisions for a submitted MapReduce job. Some research projects, such as the Starfish system, aim to provide automatic tuning decisions for input MapReduce jobs. Starfish and similar systems rely on an execution profile of a MapReduce job being tuned, and this profile is assumed to come from a previous execution of the same job. Managing these execution profiles has not been previously studied. This thesis presents PStorM, a profile store that organizes the collected profiling information in a scalable and extensible data model, and a profile matcher that accurately picks the relevant profiling information even for previously unseen MapReduce jobs. PStorM is currently integrated with the Starfish system, providing the necessary profiles that Starfish needs to tune a job. The thesis presents results that demonstrate the accuracy and efficiency of profile matching. The results also show that the profiles returned by PStorM lead to Starfish tuning decisions that are as good as the decisions made by profiles collected from a previous run of the job.

No description available.

Fluorometric density gradient ultracentrifugation is used to measure the lipoprotein density profile for cardiovascular disease risk assessment. The work presented establishes the effectiveness of using a single-spin separation as both an analytical tool and a preparative tool, while yielding valuable density information. This research expands on the analytical power of density gradient ultracentrifugation (DGU) by combining novel ethylenediaminetetraacetic acid (EDTA) gradient solutions, a fluorescent probe for analysis, and modern statistical methods for classification of heart disease risk. Sub-classes of lipoproteins are analyzed based on their density from the fluorescent lipoprotein density profile. The application of linear discriminant analysis (LDA) and sliced average variance estimation (SAVE) to the fluorometric DGU data yields a powerful classification tool. This method is capable of determining differences between control and cardiovascular disease patients that do not exhibit the traditional risk factors. The combination of these methods has great potential to serve as analytical tools for researchers in understanding the mechanisms of disease development and as a diagnostic tool for clinicians.

With the ever increasing population and economic activities in coastal areas, coastal hazards have become a major concern for coastal management. The fundamental requirement of coastal planning and management is the scientific knowledge about coastal forms and processes. This research aims at developing algorithms for automatically extracting coastal morphological information from LiDAR data. The primary methods developed by this research include automated algorithms for beach profile feature extraction and change analysis, and an object-based approach for spatial pattern analysis of coastal morphologic and volumetric change. Automated algorithms are developed for cross-shore profile feature extraction and change analysis. Important features of the beach profile such as dune crest, dune toe, and beach berm crest are extracted automatically by using a scale-space approach and by incorporating contextual information. The attributes of important feature points and segments are derived to characterize the morphologic properties of each beach profile. Beach profiles from different time periods can be compared for morphologic and volumetric change analysis. An object-oriented approach for volumetric change analysis is developed to identify and delineate individual elevation change patches as discrete objects. A set of two-dimensional and three-dimensional attributes are derived to characterize the objects, which includes planimetric attributes, shape attributes, surface attributes, volumetric attributes, and summary attributes. Both algorithms are implemented as ArcGIS extension modules to perform the feature extraction and attribute derivation for coastal morphological change analysis. To demonstrate the utility and effectiveness of algorithms, the cross-shore profile change analysis method and software tool are applied to a case study area located at southern Monterey Bay, California, and the coastal morphology change analysis method and software tool are applied to a case study area located on Assateague Island, Maryland. The automated algorithms facilitate the efficient beach profile feature analysis over large geographical area and support the analysis of the spatial variations of beach profile changes along the shoreline. The explicit object representation of elevation change patches makes it easy to localize erosion hot spots, to classify the elevation changes caused by various mechanisms, and to analyze spatial pattern of morphologic and volumetric changes.

The large volume of water produced during the extraction of oil presents a significant problem due to the high cost of disposal in an environmentally friendly manner. On average, an estimated seven barrels of water is produced per barrel of oil in the US alone and the associated treatment and disposal cost is an estimated $5-10 billion. Besides making oil-water separation more complex, produced water also causes problems such as corrosion in the wellbore, decline in production rate and ultimate recovery of hydrocarbons and premature well or field abandonment. Water production can be more problematic during waterflooding in a highly heterogeneous reservoir with vertical communication between layers leading to unevenness in the flood front, cross-flow between high and low permeability layers and early water breakthrough from high permeability layers. Some of the different technologies that can be used to counteract this involve reducing the mobility of water or using a permeability block in the higher permeability, swept zones. This research was initiated to evaluate the potential effectiveness of the latter method, known as deep diverting gels (DDG) to plug thief zones deep within the reservoir and far from the injection well. To evaluate the performance of DDG, its injection was modeled, sensitivities run for a range of reservoir characteristics and conditions and an economic analysis was also performed. The performance of the DDG was then compared to other recovery methods, specifically waterflooding and polymer flooding from a technical and economic perspective. A literature review was performed on the background of injection profile control methods, their respective designs and technical capabilities. For the methods selected, Schlumberger's Eclipse software was used to simulate their behavior in a reservoir using realistic and simplified assumptions of reservoir characteristics and fluid properties. The simulation results obtained were then used to carry out economic analyses upon which conclusions and recommendations are based. These results show that the factor with the largest impact on the economic success of this method versus a polymer flood was the amount of incremental oil produced. By comparing net present values of the different methods, it was found that the polymer flood was the most successful with the highest NPV for each configuration followed by DDG.

Tremendous interest to the study of guided waves in pipe inspection in the oil, chemical, and power generating industries has peaked during the last decade. Since the advantages are inspecting long lengths of pipe quickly and without removing insulation. Recent researches in defects inspection are determined by reflection coefficients from the cracks. However, the purpose of this thesis is to excite at a single probe position and to receive the signals of guided waves with the form of loops. For the variations of wave profiles of guided wave, this thesis aimed at the largest energy distribution of wave profiles to proceed with the researches of mode conversion phenomena caused by defects. This thesis utilizes the partial loading source, and excites the non-axisymmetric and axisymmetric guided waves individually along the carbon steel pipes with circumferential defects and without defects to contrast and analyze. According to the change of wave profiles, we can find the variables that change wave profiles for different guided waves modes include propagating distance and frequency, and these cause that the circumferential energy distribution will change. For the non-axisymmetric guided waves in this thesis are non-dispersive, and its variations of phase velocity and group velocity are small, so the variations of wave profiles are also small. Moreover we study the mode conversion phenomena caused by defects from its position which the circumferential energy is largest. It also investigates new modes from mode conversion phenomena produced by defects more completely. Finally, we can predict the types and the number of new modes from mode conversion phenomena by phase velocity dispersion curve, and compare with the experiments well.

As computer technologies are developed rapidly in recent years, the ways to diagnose diseases also alter in clinical practice. Picture Archiving and Communication System (PACS) is an example that makes the diagnostic way for medical images change from view box to monitor. All types of medical images tend to be digitized and this makes it practical for helping doctor diagnose medical images via computer technologies. In this thesis, we propose a systemic approach to screen abnormalities in chest radiographs. First, a preprocess step identifying the view of chest radiographs is introduced. Second, a method is proposed for automated detection of gross abnormal opacity in chest radiographs. Third, computation time reduction is performed by subsampling. Finally, a computer-aided diagnosis system is implemented and evaluated in a clinical environment. Major technique used in this thesis is to analyze the projection profile obtained by projecting a chest image on to the mediolateral axis. The discriminant performance for each method is evaluated by using receiver operating characteristic (ROC) analysis. The results indicate that the proposed methods are potentially useful for screening abnormalities in chest radiographs.

Hydroxytyrosol (HT) has been found to be a potent antioxidant and hypocholesterolemic agent in various animal models of disease including dyslipidemia, atherosclerosis, and diabetes. Therefore, the purpose of this study was to examine the effects of hydroxytyrosol (HT) supplementation on the lipid profile and metabolic risk markers in recreationally active men. Sixty-one (n = 61) subjects (21.46 ± 0.22 yrs, 179.46 ± 0.79 cm, 78.91 ± 1.19 kg) consumed HT in either a high dose (HI, 150 mg HT; n = 22), a low dose (LO, 50 mg HT; n = 20), or a placebo (PLA; n = 19) every day for 6 weeks. Blood draws were obtained at baseline, 14, 28, and 39 days under fasting conditions. Analyzed were the components of the plasma lipid profile: total cholesterol (TC), high density lipoprotein cholesterol (HDLc), the TC:HDLc fraction, low density lipoprotein cholesterol (LDLc), very low density lipoprotein cholesterol (VLDLc), and triglycerides (Tg); and markers of metabolic risk: uric acid, lipase, hemoglobin (Hb), glycated hemoglobin (HbA1c), and blood glucose (BG). The primary finding was that HT, in either HI or LO dosages did not cause clinically meaningful changes in the blood lipid profile or markers of metabolic risk. Subjects in the HI group experienced a small big significant increase in fasting blood glucose, while those in the PLA group experienced a significant increase in VLDLc concentration. In both cases, however, the mean values remained within their respective healthy reference ranges. Whether these changes would persist beyond the 6-week course of this study is not known. While no improvements were seen in any of our selected measures, these results indicate that HT supplementation, ranging from 50 to 150 mg/day, is safe to consume for durations up to 6 weeks in healthy young men. By maintaining the lipid profile and metabolic risk markers within a healthy range, it is possible that HT may impart a degree of protection against cardiovascular and metabolic disease risk, but such an effect may only be apparent when the plasma lipid and/or metabolic risk profile is abnormal. / text

For many applications, such as cryptography and digital communications, binary sequences with certain specific properties are required. These include a balance of 0's and 1's in a period, ideal runs frequencies, good auto- and cross-correlation spectra, and high linear complexity. Perfect Linear Complexity Profile sequences (PLCPs) have the linear complexity of all subsequences (starting with the first bit) equal to half the length of the subsequence (this is the expected value for a random sequence). We investigate the density - proportion of ones - of finite length PLCPs, both in general and for specific examples. We gain results on the average, maximal and minimal densities, as well as their limits as the length tends to infinity. We also study whether the PLCP property is preserved under various decimations. PLCPs are characterised by a simple linear recurrence modulo 2. We look at similar "nearly" perfect profiles and characterise sequences with these profiles in terms of similar recurrences. Also sequences with a PLCP up to a point and then constant complexity are characterised in terms of the convergents in the continued fraction expansion of the generating function of PLCPs, and we look briefly at their corresponding periods. Sequences with bounded jumps in their linear complexity are discussed and a method of generating them is suggested. The interleaving of shifts of a sequence with out-of-phase auto-correlation equal to -1 and balance, in a specific order, seems to be a fundamental method of generating longer sequences with this auto-correlation property. It is shown that two pairs of families of these sequences, derived in different ways, are in fact equivalent. The analysis highlights the general method mentioned above, and so provides examples of families of sequences with 2-valued auto-correlation by changing the ingredients in the interleaving pattern. We also look at the cross-correlation of sequences with this interleaved structure.

Clusters of galaxies are particularly interesting astrophysical systems, are the largest bound structures in the Universe, and contain fair sample of cosmic ingredients. Studies of cluster abundance as a function of mass and redshift were critical in establishing the standard model of cosmology. This dissertation presents results from X-ray imaging of massive distant (M > 10^14 M; 0:3 < z < 1.2) clusters, found via X-ray emission or Sunyaev-Zeldovich eff ect. This is the world's largest sample of massive galaxy clusters. We explore the radial and azimuthal profi les of the X-ray emitting gas and show that clusters are self-similar objects: their internal structure is largely independent of the cluster's mass or redshift, and the fractions of di fferent types of clusters does not change with redshift. We also present a new statistical technique for measuring a cluster's deviations from a perfect axisymmetric shape, which is especially useful in the case of low photon count observations of distant clusters. / Physics