Five hundred and eighteen observed validity coefficients based on correlations between commercially available test data and supervisory ratings of overall job performances were collected in 89 different job titles. Using Dictionary of Occupational Title Codes, Job Component Validity (JUV) estimates based on similar job titles residing in the PAQ Service database were collected and averaged across the General Aptitude Test.

Geochemistry has been used to determine the provenance and diagenetic history of eolian sandstone deposits. However, the grain size, sorting, cementation, and detrital composition of eolian units can change along dune foreset laminae. The purpose of this study was to test for consistent trends of compositional change along dune foresets. Such trends could increase the quality of geochemical sampling of eolian sandstones and possibly aid in estimating the original height of ancient sand dunes. XRF data was gathered for both major and trace elements from the Pennsylvanian to Permian Weber Sandstone, Early Jurassic Navajo Sandstone, and modern Coral Pink Sand Dunes of southern Utah. Data was plotted using both 2-dimensional scatter plots and 3-dimensional principal components analysis (PCA) plots. The PCA plots proved to be the most informative and suggest that there are no consistent, statistically significant geochemical trends within or between the three units sampled. However, this study found that PCA was able to show significant geochemical differences between the three units sampled, even when they are all dominated by a single mineral (>90% quartz). The Weber Sandstone had the most varied composition, and dunes within the unit could be highly dissimilar to each other. The Navajo Sandstone had less overall geochemical variability than the Weber Sandstone, and individual dunes were similar to each other. The modern Coral Pink Sand Dunes had much less compositional variation than either of the other two units, and dunes in this unit were very similar to each other.

In this work on-line discrimination of six bacteria strains by means of the Multivariate discrimination analysis (MVDA) is presented. Principal components analysis (PCA) was selected as most suitable technique. Two analytical methods – Laser-Induced breakdown spectroscopy (LIBS) and Raman spectroscopy were equipped for chemical analysis of bacteria strains. In order to obtain the best possible bacteria strains differentiation, the data from both methods was analyzed in two ways separately and together. The data from both measurements was normalized separately and afterwards combined in one data frame for simultaneous analysis. This data frame contained information from both analytical methods. Moreover an influence of cultivation period for each bacteria strain was studied. It was determined that Raman spectroscopy is able to discriminate two bacteria strains and LIBS even four bacteria strains. Using combination of both methods the complete discrimination was achieved. From information of first principal component it was determined that most valuable information in LIBS data is not included in different elemental composition but rather in influence of matrix effect. Solely the LIBS was utilized for studying the effect of cultivation period. It was possible to observe transformation of four bacteria strains within 24 hours. Based on results of this work it is possible to assume that combination of Raman spectroscopy and LIBS, because of the complementary information, is suitable for fast discrimination of different bacteria species and strains. Moreover it was determined that LIBS is able to observe the transformation caused by cultivation period.

Some methods for generating scenarios from multidimensional distribution assume we are able to generate scenarios from the one-dimensional distribution. We dedicate chapter 3 to this problem. At the end of the chapter, we provide references for applicable algorithms. Chapter 4 is focused on selected methods for generating scenarios from multidimensional distributions. In chapter 4.3, we introduce an algorithm for generating scenarios, which do not use any assumption about the distribution, except the first four moments and correlations to be specified. A method of generating scenarios based on approximation of multivariate normal distribution by the binomial distribution is described in chapter 4.5. Dimension reduction technique using principal components is presented in chapter 4.4. The algorithm is presented under the assumption of normal distribution. In chapter 4.6, we introduce the basics of the copula theory and a method for generating scenarios by C-vine copula. In chapter 5, we implement selected methods for generating scenarios for the estimation of daily value at risk for selected indexes and we discuss the results. Powered by TCPDF (www.tcpdf.org)

Infrastructure monitoring is being transformed by the advancements on remote sensing, unmanned vehicles and information technology. The wide interaction among these fields and the availability of reliable commercial technology are helping pioneer intelligent inspection methods based on digital 3D models. Commercially available Unmanned Aerial Vehicles (UAVs) have been used to create 3D photogrammetric models of industrial equipment. However, the level of automation of these missions remains low. Limited flight time, wireless transfer of large files and the lack of algorithms to guide a UAV through unknown environments are some of the factors that constraint fully automated UAV inspections. This work demonstrates the use of unsupervised Machine Learning methods to develop an algorithm capable of constructing a 3D model of an unknown environment in an autonomous iterative way. The capabilities of this novel approach are tested in a field study, where a municipal water tank is mapped to a level of resolution comparable to that of manual missions by experienced engineers but using $63\%$ . The iterative approach also shows improvements in autonomy and model coverage when compared to reproducible automated flights. Additionally, the use of this algorithm for different terrains is explored through simulation software, exposing the effectiveness of the automated iterative approach in other applications.

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Shrimp is the most valued shellfish product in the United States, and is highly perishable with post mortem metabolic changes that are deteriorative to its shelf-life. The objective of this research was to utilize GC-MS Gas Chromatography-Mass Spectrometry (GC-MS), Gas Chromatography Olfactometry (GCO), and sensory analysis to determine if the relationships exist between consumer acceptability, sensory descriptors and shelf-life of ready-to-eat shrimp. Three different cooking and packaging treatments were utilized: 63°C/15 s, 85°C/5 min, and 93°C/5 min for vacuum, MAP and aerobic packaging treatments, and stored at 2°±1 for 1, 3, 5, 6, 10, 15, 25, and 50 days or until the product was found unacceptable by a trained sensory panel. For Consumer acceptability (Day 3), the 85°C vacuum and 85°C MAP (Modified Atmosphere Packaging) shrimp were preferred (P < 0.05) over other treatments that were evaluated. The aroma active compounds that were identified using GC-MS and GCO consisted of one amine (trimethylamine), five aldehydes (3-methyl butanal, pentanal, hexanal, heptenal, geranial), one organic acid (butyric acid), two sulfur containing compounds (methional, dimethyltrisulfide), one pyrazine (methyl pyrazine) (amine), two alcohols (2-nonen-1-ol, 4 ethyl guaiacol), and one hydrocarbon (camphene). Results showed that for most of the packaging-temperature combinations, even beyond Day 25, the MAP product had fewer compounds and odors associated with spoilage than the aerobic and vacuum packaged products. This was consistent with the descriptive analysis data for which both the 85°C MAP and 93°C MAP treatment products had a longer shelf-life than vacuum treatment products, which had shelf-lives of 15 to 21 Days. The shelf-life of the cooked RTE shrimp that was MAP packaged and cooked at either 85°C or 93°C was between 39 and 42 Days at 2°C. Research indicates that processors of medium gulf brown shrimp could utilize 85°C/5min with MAP due to the shelf-life of the product and the elevated pleasantness scores when compared to the 93°C/5min treatment throughout the shelf-life of the product.

Thesis advisor: Christopher Maxwell / This paper examines neighborhood socioeconomic ascent in both Boston and the Greater Boston metropolitan statistical area. Using random forests, a supervised machine learning algorithm, and a collection of physical and demographic neighborhood characteristics gathered from the American Community Survey, I model changes in neighborhood socioeconomic status and identify neighborhoods in my study area that experienced relative socioeconomic ascent or relative socioeconomic decline between 2010 and 2019. In order to gain a better understanding of future socioeconomic change throughout my study area, I use a random forests model to predict neighborhood socioeconomic status in 2028. I find that my best random forests model offers an improvement over traditional linear modeling techniques and, through mapping results for Boston specifically, that change in Boston is occurring in minority, working class neighborhoods, especially along the city’s waterfront. These findings, in combination with qualitative community data, can be used to inform policy concerning matters ranging from housing to transportation in the years to come. / Thesis (BA) — Boston College, 2022. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Economics.

The Hermit Thrush is a new breeding bird in the Southern Appalachian high-elevation mountains, having expanded its range southward over the last few decades. Here it is sympatric with the Veery, a congeneric breeding resident. In order to more fully understand why the range expansion took place and to understand more about the local ecology of the newly arrived bird, I measured several habitat variables in 30 Hermit Thrush and 24 Veery territories. Principal Components Analysis and Cluster Analysis brought to light several patterns of habitat preferences for these 2 species. Hermit Thrushes prefer territories with more leaf litter on the ground and less shrub density than Veery territories. Competition between these species should remain low, as their niche differences brought to light in this study should enable them to breed in close proximity to each other in these high-elevation mountains.

Geochemistry plays a vital role in our understanding mechanisms behind major geological systems such as the Earth's crust and its oceans (Albarède, F. 2003). More recently, geo-chemistry has played a vital role in the field of forensic investigation and in period dating. Forensic soil samples have been traditionally analysed via examinations of colour, texture and mineral content by physical or chemical methods. However, these methods leave any organic or water-soluble fractions unexamined. Tephrochronology (the dating of sedimentary sequences using volcanic ash layers) is an important tool for the dating and correlation of sedimentary sequences containing archives and proxies of past environmental change. Its importance in this area has increased since the increased free carbon in out atmosphere has made radio-carbon dating unreliable. Tephrochronology requires successful geo-chemical identification of the tephras, a method reliant on electron probe micro-analysis (EPMA) to analyse major element composition. However, it is often impossible to differentiate key tephra layers using EPMA alone. Raman spectroscopy is commonly used in chemistry, since vibrational information is specific to the chemical bonds and symmetry of molecules, and can provide a fingerprint by which these can be identified. Here, we demonstrate how Raman spectroscopy can be used for the successful discrimination of mineral species in tephra through the analysis of individual glass shards. We further demonstrate how, with the use of oxidative preparation methods, Raman spectroscopy can be used to successfully discriminate between soil types using mineralogy as well as the organic and water-soluble fractions of soils.