This project attempted to combine digital data sets to define and map geologic features in the Sparta and Montpelier quadrangles of Chickasaw and Clay counties in northeastern Mississippi. LANDSAT TM, digital elevation, and soil permeability data was used in conjunction with reference data for the Sparta quadrangle to build a computer model. Variables used in the model were: geology, slope, soil permeability, vegetation indices, the first three bands of a tasseled cap transformation, and drainage frequency. The data used was LANDSAT TM 30 meter imagery, digital elevation models, also at 30 meter resolution, Penn State STASGO soils data, and the existing map of the Sparta quadrangle. The purpose of this project was to use digital data to remotely map geologic features through heavy vegetation using a computer model. While the results of this project were not completely successful, the methods used show some potential for future application.

A model for estimating travel time on short arterial links of congested urban networks, using currently available technology, is introduced in this thesis. The objective is to estimate travel time, with an acceptable level of accuracy for real-life traffic problems, such as congestion management and emergency evacuation. To achieve this research objective, various travel time estimation methods, including highway trajectories, multiple linear regression (MLR), artificial neural networks (ANN) and K –nearest neighbor (K-NN) were applied and tested on the same dataset. The results demonstrate that ANN and K-NN methods outperform linear methods by a significant margin, also, show particularly good performance in detecting congested intervals. To ensure the quality of the analysis results, set of procedures and algorithms based on traffic flow theory and test field information, were introduced to validate and clean the data used to build, train and test the different models.

No description available.

No description available.

El presente trabajo de investigación tuvo como objetivo determinar la cantidad de alumnos matriculados en los diversos programas para el año 2020 dentro del Centro de Capacitación y Desarrollo Global (CCDG), ubicado en la ciudad de Lima; ello, a través del análisis del historial de la data de los años 2018 y 2019. Se ejecutó una investigación predictiva, dado que dicho tipo de investigación tiene como propósito prever o anticipar situaciones futuras, con el fin de tomar buenas decisiones en cuanto a la apertura de vacantes de los diversos programas y diplomados para cubrir la posible demanda de alumnos matriculados y, por otro lado, determinar las variables que permiten captar un mayor número de clientes. La metodología de ciencia de datos aplicada fue la regresión lineal simple; esta es una técnica que ayuda a proyectar el número de alumnos que podrían matricularse en el periodo 2020. Las variables utilizadas para esta técnica fueron la cantidad de alumnos matriculados y el número de meses correspondientes a los dos últimos años analizados. Como resultado, se obtuvo que, posiblemente, se tendrían 3280 alumnos matriculados para el año 2020 en los programas del CCDG, y que la variable más influyente para la atracción son las clases de modalidad virtual; asimismo, los cursos más demandados son la certificación del Organismo Supervisor de las Contrataciones del Estado (OSCE) y los diplomas del Sistema Integrado de Administración Financiera (SIAF). Los medios de contacto más efectivos, y con los que es posible atraer un mayor número de clientes, son la aplicación de WhatsApp y las llamadas telefónicas. / The present research work aims to determine the number of students enrolled in the various programs for the year 2020 within the Global Training and Development Center, located in the city of Lima. This, through the analysis of 2018 and 2019 data history. A predictive research was carried out, since this type of research is intended to foresee or anticipate future situations, in order to make good decisions regarding the opening of vacancies in the various programs and diplomas to cover the possible demand of enrolled students, and, also, to determine the variables that allow a greater customer acquisition. The applied Data Science methodology is simple linear regression, which is a technique that projects the number of students who would enroll in the 2020 period. The variables used for this technique were the number of enrolled students and the number of months corresponding to the last two years analyzed. As a result, there would possibly be 3280 students enrolled by 2020 in the programs offered at the CCDG, and the most influential variable for customer recruitment are virtual classes; also, the most demanded course is OSCE certification and SIAF diplomas. The most effective means of contact, which can attract more clients, are WhatsApp and phone calls. / Trabajo de investigación

This bachelor thesis examines factors affecting the academic performance in Swedish schools. Specifically, the average qualification point among ninth grade students in schools in Stockholm municipality during the academic year 2021-2022 are studied. Multiple linear regression is used to identify individual, social, and school specific factors which have a significant impact on the average qualification point in schools. The purpose is to identify factors affecting the academic performance, and by that contribute to the knowledge base constituting the foundation for the work to improve the academic performance and provide equal opportunities for all students. The Swedish grading system, previous research on factors affecting students' performance, and the Swedish school in a societal perspective are also discussed. The findings indicate that the background of the students, the parents' level of education, and the number of students per teacher are good predictors for academic performance. / Denna kandidatuppsats undersöker faktorer som påverkar studieresultatet i svenska skolor. Specifikt studeras det genomsnittliga betyget bland elever i årskurs nio i Stockholms kommuns skolor under läsåret 2021-2022. Multipel linjär regression används för att identifiera individfaktorer, sociala faktorer och skolspecifika faktorer som har en signifikant inverkan på skolors genomsnittliga betyg. Syftet är att identifiera faktorer som påverkar studieresultatet och därmed bidra till kunskapsbasen som utgör grunden för arbetet med att förbättra studieresultat och tillhandahålla lika möjligheter för alla elever. Det svenska betygssystemet, tidigare forskning kring faktorer som påverkar elevers studieprestation samt den svenska skolan i ett samhälleligt perspektiv diskuteras också. Resultatet tyder på att elevernas bakgrund, föräldrarnas utbildningsnivå och antalet elever per lärare är bra prediktorer för akademisk prestation.

This study investigated the relationship between Sweden’s CO2e (Carbon Dioxide Equivalent) emissions and key macroeconomic factors, for the period 2008Q1- 2022Q3. The aim was to enhance the understanding of the link between macroeconomic factors and greenhouse gas emissions in a post-industrial economy, using multiple regression analysis. The study identified several significant macroeconomic factors affecting CO2e emissions and examined the extent to which these variables explain the fluctuations in Sweden’s emissions. Additionally, the study assessed the validity of the Environmental Kuznets Curve and Porter Hypothesis within Sweden’s environmental context. In the study, two multiple regression models were developed. Model 1 had an R^2 of 0.90, using the macroeconomic variables Industry Fuel Consumption, Population, Net Export, and Oil Prices. However, since the first model displayed moderate autocorrelation, a second model was also built by introducing a lagged dependent variable which yielded an R^2 of 0.92. / Denna studie undersökte förhållandet mellan Sveriges CO2e (koldioxidekvivalent) utsläpp och centrala makroekonomiska faktorer för perioden 2008K1-2022K3. Syftet var att öka förståelsen för sambandet mellan makroekonomiska faktorer och växthusgasutsläpp i en postindustriell ekonomi, med användning av multipel regressionsanalys. Studien identifierade flera betydande makroekonomiska faktorer som påverkar CO2e-utsläpp och undersökte i vilken utsträckning dessa variabler förklarar fluktuationerna i Sveriges utsläpp. Dessutom utvärderade studien giltigheten av Miljökuznetskurvan och Porters hypotes inom ramen för Sveriges miljökontext. I studien skapades två multipel regressionsmodeller. Modell 1 hade ett R^2 på 0,90, med de makroekonomiska variablerna Industriell Bränsleförbrukning, Befolkning, Nettoexport och Oljepriser. Eftersom den första modellen visade måttlig autokorrelation byggdes dock även en andra modell genom att införa en fördröjd beroende variabel, vilket resulterade i ett R^2 på 0,92.

[pt] Neste estudo, tentamos estimar o impacto da medida intervencionista mais recente e amplamente adotada pelo Banco Central do Brasil no mercado de câmbio sobre a Curva de Cupom Cambial: a emissão de Swaps Cambiais. O objetivo do BCB com essa intervenção era prover o setor privado de proteção contra a volatilidade cambial à época. O trabalho foca no efeito dessas medidas na curva de Cupom Cambial por conta da importância do funcionamento dessa curva para a correta precificação do mercado de dólar futuro, já que, no Brasil, a formação da taxa de câmbio se dá no preço futuro de dólar e não no preço à vista, como é comum nos outros países. Através de Análise de Componentes Principais sobre a Curva de Cupom Cambial, encontramos seus três primeiros componentes (nível, inclinação e curvatura) e os utilizamos para regredi-los em variáveis independentes que representam a série de emissões de Swap por parte do BC. Os resultados indicam que os Swaps Cambiais geram mudanças significativas no nível geral da Curva de Cupom Cambial. Já os Swaps Reversos não apresentam impacto estatisticamente significante no nível, mas sim na inclinação da curva. / [en] In this study, we try to estimate the impact of the most recent currency intervention measure widely adopted by the Central Bank of Brazil and how it affects the Cupom Cambial Curve: the issue of Foreign Exchange Swaps. The BCB s objective with this intervention was to provide the private sector with hedge against exchange rate volatility. This paper focus on the effect of these measures on the Cupom Curve due to the importance of the comprehension of this curve for the correct pricing of the future dollar market, given that, in Brazil, the formation of the foreign exchange rate occurs with the future dollar price and not in the spot price, as is more common in other countries. Through Principal Component Analysis on the Foreign Exchange Coupon Curve, we find its three components (level, slope and curvature) and use it as an explained variable to regress it with independent variables that represent the series of Swap issued by the Central Bank. The results indicate that the Foreign Exchange Swaps generate significant changes in the overall level of the Cupom Cambial Curve. Otherwise, Reverse Swaps don t represent a statistically significant impact on the level but do impact the slope of the curve.

Prior evidence suggests that sensorimotor regions play a crucial role in habit formation. Yet, whether and how their global functional network properties might contribute to a more comprehensive characterization of habit formation still remains unclear. Capitalizing on advances in Elastic Net regression and predictive modeling, we examined whether learning-related functional connectivity alterations distributed across the whole brain could predict individual habit strength. Using the leave-one-subject-out cross-validation strategy, we found that the habit strength score of the novel unseen subjects could be successfully predicted. We further characterized the contribution of both, individual large-scale networks and individual brain regions by calculating their predictive weights. This highlighted the pivotal role of functional connectivity changes involving the sensorimotor network and the cingulo–opercular network in subject-specific habit strength prediction. These results contribute to the understanding the neural basis of human habit formation by demonstrating the importance of global functional network properties especially also for predicting the observable behavioral expression of habits.

This degree project undertakes a detailed examination of various algorithms used in Very Short-Term Load Forecasting (VSTLF) within network control systems, prioritizing forecasting accuracy and computational efficiency as critical evaluation criteria. The research comprehensively assesses a range of forecasting methods, including statistical models, machine learning algorithms, and advanced deep learning techniques, aiming to highlight their respective advantages, limitations, and suitability for different operational contexts. The study conducts a detailed analysis by comparing essential performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and execution time, before and after implementing adjustments to the formulations. This approach highlights how optimization strategies enhance the effectiveness of the models. Notably, the study identifies Support Vector Machine (SVM) and Multiple Linear Regression as frontrunners in terms of balancing accuracy with computational demand, making them particularly suitable for real-time forecasting needs. Meanwhile, Long Short-Term Memory (LSTM) networks demonstrate a commendable ability to capture complex, non-linear data patterns, albeit at a higher computational cost. The degree project further explores the sensitivity of these forecasting models to parameter adjustments, revealing a nuanced landscape where strategic modifications can significantly enhance model performance. This degree project not only contributes to the ongoing discourse on optimizing VSTLF algorithms but also provides actionable insights for stakeholders in the energy sector, aiming to facilitate the development of more reliable, efficient, and sustainable power system operations.