[pt] Os estudos de modelos de previsão de falência tiveram seu início há quase 90 anos, sempre com o intuito de ser uma ferramenta de gestão útil para analistas e gestores das empresas. Embora as primeiras pesquisas sejam antigas, o assunto continua atual. Diversos setores da economia passaram, ou passam, por crises ao longo do tempo e não foi diferente para a indústria de aviação. Nesse contexto, o presente trabalho usou dados históricos de indicadores financeiros das empresas aéreas americanas de um período de três décadas para elaborar quatro modelos de previsão de falência e comparar suas performances preditivas com o Modelo Z-Score. Todas as elaborações foram calibragens do Modelo Z-Score, usando técnicas de simulação e estatística. Duas usaram Análise Discriminante Múltipla (MDA) e duas utilizaram Bootstrap junto com MDA. Um par de cada método utilizou as variáveis originais do Modelo Z-Score e o outro par apresentou sugestão de novo conjunto de variáveis. Os resultados mostraram que o modelo de previsão mais preciso, com 75,0 porcento de acerto na amostra In-Sample e 79,2 porcento na Out-of-Sample, utilizou o conjunto original de variáveis e as técnicas Bootstrap e MDA. / [en] Studies of bankruptcy prediction models started almost 90 years ago, with the intention of being a useful management tool for analysts and managers. Although the first researches are ancient, the subject remains current. Several sectors of the economy have experienced, or are experiencing, crises over time and the aviation industry is no exception. In this context, the present work used historical data of financial indicators of American airlines over a period of three decades to develop four models of bankruptcy forecast and compared their predictive performances with the Z-Score Model. All proposed models were calibrations of the Z-Score model, using simulation and statistical techniques. Two models were generated using Discriminant Analyzes Multiple (MDA) and two using Bootstrap along with MDA. A pair of each method used the original variables of the model s Z-Score and the other pair presented a novel set of variables. Results showed that the most accurate forecasting model, with 75.0 percent accuracy in-sample and 79.2 percent out-of-sample, used the original variables of the model s Z-Score and the Bootstrap e MDA techniques.

Indiana University-Purdue University Indianapolis (IUPUI) / The link between volatile organic compounds (VOCs) from breath and various diseases and specific conditions has been identified since long by the researchers. Canine studies and breath sample analysis on Gas chromatography/ Mass Spectroscopy has proven that there are VOCs in the breath that can detect and potentially predict hypoglycemia. This project aims at developing a smart nanosensor system to detect hypoglycemia from human breath. The sensor system comprises of 1-Mercapto-(triethylene glycol) methyl ether functionalized goldnanoparticle (EGNPs) sensors coated with polyetherimide (PEI) and poly(vinylidene fluoride -hexafluoropropylene) (PVDF-HFP) and polymer composite sensor made from PVDF-HFP-Carbon Black (PVDF-HFP/CB), an interface circuit that performs signal conditioning and amplification, and a microcontroller with Bluetooth Low Energy (BLE) to control the interface circuit and communicate with an external personal digital assistant. The sensors were fabricated and tested with 5 VOCs in dry air and simulated breath (a mixture of air, small portion of acetone, ethanol at high humidity) to investigate sensitivity and selectivity. The name of the VOCs is not disclosed herein but these VOCs have been identified in-breath and are identified as potential biomarkers for other diseases as well. The sensor hydrophobicity has been studied using contact angle measurement. The GNPs size was verified using Ultra-Violent-Visible (UV-VIS) Spectroscopy. Field Emission Scanning Electron Microscope (FESEM) image is used to show GNPs embedded in the polymer film. The sensors sensitivity increases by more than 400\% in an environment with relative humidity (RH) of 93\% and the sensors show selectivity towards VOCs of interest. The interface circuit was designed on Eagle PCB and was fabricated using a two-layer PCB. The fabricated interface circuit was simulated with variable resistance and was verified with experiments. The system is also tested at different power source voltages and it was found that the system performance is optimum at more than 5 volts. The sensor fabrication, testing methods, and results are presented and discussed along with interface circuit design, fabrication, and characterization. / 2022-05-8

Betygsättning upptar idag en stor del av lärares arbetstid och det finns en betydande inkonsekvens vid bedömning utförd av olika lärare. Denna studie ämnar undersöka vilken träffsäkerhet som en automtiserad bedömningsmodell kan uppnå. Tre maskininlärningsmodeller för klassifikation i form av Linear Discriminant Analysis, K-Nearest Neighbor och Random Forest tränas och testas med femfaldig korsvalidering på uppsatser från nationella prov i svenska. Klassificeringen baseras på språk och formrelaterade attribut inkluderande ord och teckenvisa längdmått, likhet med texter av olika formalitetsgrad och grammatikrelaterade mått. Detta utmynnar i ett maximalt quadratic weighted kappa-värde på 0,4829 och identisk överensstämmelse med expertgivna betyg i 57,53 % av fallen. Dessa resultat uppnåddes av en modell baserad på Linear Discriminant Analysis och uppvisar en högre korrelation med expertgivna betyg än en ordinarie lärare. Trots pågående digitalisering inom skolväsendet kvarstår ett antal hinder innan fullständigt maskininlärningsbaserad bedömning kan realiseras, såsom användarnas inställning till tekniken, etiska dilemman och teknikens svårigheter med förståelse av semantik. En delvis integrerad automatisk betygssättning har dock potential att identifiera uppsatser där behov av dubbelrättning föreligger, vilket kan öka överensstämmelsen vid storskaliga prov till en låg kostnad. / Today, a large amount of a teacher’s workload is comprised of essay scoring and there is a large variability between teachers’ gradings. This report aims to examine what accuracy can be acceived with an automated essay scoring system for Swedish. Three following machine learning models for classification are trained and tested with 5-fold cross-validation on essays from Swedish national tests: Linear Discriminant Analysis, K-Nearest Neighbour and Random Forest. Essays are classified based on 31 language structure related attributes such as token-based length measures, similarity to texts with different formal levels and use of grammar. The results show a maximal quadratic weighted kappa value of 0.4829 and a grading identical to expert’s assessment in 57.53% of all tests. These results were achieved by a model based on Linear Discriminant Analysis and showed higher inter-rater reliability with expert grading than a local teacher. Despite an ongoing digitilization within the Swedish educational system, there are a number of obstacles preventing a complete automization of essay scoring such as users’ attitude, ethical issues and the current techniques difficulties in understanding semantics. Nevertheless, a partial integration of automatic essay scoring has potential to effectively identify essays suitable for double grading which can increase the consistency of large-scale tests to a low cost.

Fecal pollution of surface water is a significant environmental health issue. Indicator organisms are used to monitor microbial water quality, but often their presence does not coincide with the presence of pathogens. Bacterial source tracking is a term describing methods to determine the origin of fecal pollution based on bacterial traits. The objective of this research is to evaluate the use of 2 bacterial source tracking techniques, antibiotic resistance analysis (ARA) and ribotyping, to determine the sources of bacteria isolated from Sinking Creek. Based on the results of this study, ARA and ribotyping are not useful techniques for identifying sources of fecal pollution in Sinking Creek. ARA classification rates were low, and ribotype pattern generation success was 37%. The results of this study bring into question the reliability and reproducibility of these 2 source tracking methods for routine use in small watersheds.

In order to understand Mio-Pliocene floristic characteristics of the southern Appalachian Mountains, 47 palynological samples from six different testing-pits across the Gray Fossil Site (GFS) were analyzed. The site exhibits a low pollen yield resulting from basic pH levels, drought, and fire events occurring during deposition. The palynofloral assemblage has a low to moderate diversity, and it is largely dominated by Quercus-Carya-Pinus (~90% of the palynoflora). The reported presence of Pterocarya grains supports a Late Neogene age for the lacustrine sediments. Comparison with modern pollen-based floras suggests that: (1) the Mio-Pliocene Oak-Hickory-Pine association varied in structure between a woodland to woodland/savanna, depending on the intensity and frequency of drought, fire events and herbivory, (2) pits show different structure of the co-dominant genera, which indicate alternating composition of the vegetation, and (3) in term of modern vegetation, the GFS flora corresponds well with the Mesophytic Forest region.

The placement of an unconformable surface within a stratal succession affects the interpreted thickness of units and sequences in contact with that surface. Unit thickness influences the interpretation of basin subsidence, paleotopography, diagenesis, and depositional style. Accurate placement of an unconformity results in true formational thicknesses for formations associated with that unconformity. True thicknesses aid in producing more precise surface to subsurface correlations, isopach maps, and paleogeographic maps. An unconformity may be difficult to identify in the stratal succession due to similar rocks above and below the unconformity and the presence of multiple candidate surfaces. Using statistical discriminant analysis of XRF data, formations bounding an unconformity can be discriminated by elemental composition which results in delineation of the associated unconformity. This discrimination is possible even for rocks that do not have significant differences in provenance if they have experienced distinct diagenetic histories. Elemental differences can be explained by quantity and type of cement. Three discriminant models were created. These models were tested with samples from three formations of similar facies, appearance, and provenance that are all associated with the same regional unconformity. All data, regardless of location, facies, or tectonic feature were used to create the first model. This model achieved moderate success by correctly classifying 80% of known samples. In a second model, data were grouped by facies trends. Separating the data by facies resulted in 94% of known samples being correctly classified. This model was most useful for delineation of an unconformity and discrimination of formations. A third model based solely on location or local tectonic feature produced the best results statistically. 96% of known samples were classified correctly. This third model does not compare locations to each other, thus making it less robust. This last model contributes by adding detail to interpretations made with the facies trend model.

The National Academy of Sciences recently published a report which calls for improvements to the field of forensic science. Their report criticized many forensic disciplines for failure to establish rigorously-tested methods of comparison, and encouraged more research in these areas to establish limitations and assess error rates. This study applies chemometric and statistical methods to current and developing analytical techniques in fiber analysis. In addition to analysis of commercially available dyed textile fibers, two pairs of dyes are selected based for custom fabric dyeing on the similarities of their absorbance spectra and dye molecular structures. Visible absorption spectra for all fiber samples are collected using microspectrophotometry (MSP) and mass spectra are collected using electrospray ionization (ESI) mass spectrometry. Statistical calculations are performed using commercial software packages and software written in-house. Levels of Type I and Type II error are examined for fiber discrimination based on hypothesis testing of visible absorbance spectra using a nonparametric permutation method. This work also explores evaluation of known and questioned fiber populations based on an assessment of p-value distributions from questioned-known fiber comparisons with those of known fiber self-comparisons. Results from the hypothesis testing are compared with principal components analysis (PCA) and discriminant analysis (DA) of visible absorption spectra, as well as PCA and DA of ESI mass spectra. The sensitivity of a statistical approach will also be discussed in terms of how instrumental parameters and sampling methods may influence error rates.

This study is concerned with developing a model to identify small-medium U.K. companies at risk of financial failure up to five years in advance. The importance of small companies in an economy, the impact of their failures, and the lack of failure research with respect to . this population, provided justification for this study. The research was undertaken in two stages. The first stage included a detailed description and discussion of the nature and role of small business in the UK economy, heir relevance, problems and Government involvement in this sector, together with literature review and assessment of past research relevant to this study. The second stage was involved with construction of the models using multiple discriminant analysis, applied to published accountancy data for two groups of failed and nonfailed companies. The later stage was performed in three parts : (1) evaluating five discriminant models for each of five years prior to failure; (2) testing the performance of each of the .five models over time on data not used . in their construction; (3) testing the discriminant models on a validation sample. The purpose was to establish the "best" discriminant model. "Best" was determined according to classification ability of the model and interpretation of variables. Finally a model comprising seven financial ratios measuring four aspects of a company's financial profile, such as profitability, gearing, capital turnover and liquidity was chosen. The model has shown to be a valid tool for predicting companies' health up to five years in advance. / Ministry of Higher Education and Scientific Research of the Iraqi Government.

Classification of data from repeated measurements is useful in various disciplines, for example that of medicine. This thesis explores how classification trees (CART) can be used for classifying repeated measures data. The reader is introduced to variations of the CART algorithm which can be used for classifying the data set and tests the performance of these algorithms on a data set that can be modelled using bilinear regression. The performance is compared with that of a classification rule based on linear discriminant analysis. It is found that while the performance of the CART algorithm can be satisfactory, using linear discriminant analysis is more reliable for achieving good results. / Klassificering av data från upprepade mätningar är användbart inom olika discipliner, till exempel medicin. Denna uppsats undersöker hur klassificeringsträd (CART) kan användas för att klassificera upprepade mätningar. Läsaren introduceras till varianter av CART-algoritmen som kan användas för att klassificera datamängden och testar prestandan för dessa algoritmer på en datamängd som kan modelleras med hjälp av bilinjär regression. Prestandan jämförs med en klassificeringsregel baserad på linjär diskriminantanalys. Det har visar sig att även om prestandan för CART-algoritmen kan vara tillfredsställande, är användning av linjär diskriminantanalys mer tillförlitlig för att uppnå goda resultat.

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