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Validity of a Field-Based Critical Velocity Test on Predicting 5,000-Meter Running PerformanceVoth, Nicholas 09 August 2019 (has links)
No description available.
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Predicting the crystal structure of organic molecular materialsChaka, Anne Marie January 1993 (has links)
No description available.
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Predicting transit times for outbound logisticsCochenour, Brooke R. 08 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / On-time delivery of supplies to industry is essential because delays can disrupt
production schedules. The aim of the proposed application is to predict transit times
for outbound logistics thereby allowing suppliers to plan for timely mitigation of
risks during shipment planning. The predictive model consists of a classifier that is
trained for each specific source-destination pair using historical shipment, weather,
and social media data. The model estimates the transit times for future shipments
using Support Vector Machine (SVM). These estimates were validated using four case
study routes of varying distances in the United States. A predictive model is trained
for each route. The results show that the contribution of each input feature to the
predictive ability of the model varies for each route. The mean average error (MAE)
values of the model vary for each route due to the availability of testing and training
historical shipment data as well as the availability of weather and social media data.
In addition, it was found that the inclusion of the historical traffic data provided by
INRIXTM improves the accuracy of the model. Sample INRIXTM data was available
for one of the routes. One of the main limitations of the proposed approach is the
availability of historical shipment data and the quality of social media data. However,
if the data is available, the proposed methodology can be applied to any supplier with
high volume shipments in order to develop a predictive model for outbound transit
time delays over any land route.
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Intersection of Longest Paths in Graph Theory and Predicting Performance in Facial RecognitionYates, Amy 06 January 2017 (has links)
A set of subsets is said to have the Helly property if the condition that each pair of subsets has a non-empty intersection implies that the intersection of all subsets has a non-empty intersection. In 1966, Gallai noticed that the set of all longest paths of a connected graph is pairwise intersecting and asked if the set had the Helly property. While it is not true in general, a number of classes of graphs have been shown to have the property. In this dissertation, we show that K4-minor-free graphs, interval graphs, circular arc graphs, and the intersection graphs of spider graphs are classes that have this property.
The accuracy of facial recognition algorithms on images taken in controlled conditions has improved significantly over the last two decades. As the focus is turning to more unconstrained or relaxed conditions and toward videos, there is a need to better understand what factors influence performance. If these factors were better understood, it would be easier to predict how well an algorithm will perform when new conditions are introduced.
Previous studies have studied the effect of various factors on the verification rate (VR), but less attention has been paid to the false accept rate (FAR). In this dissertation, we study the effect various factors have on the FAR as well as the correlation between marginal FAR and VR. Using these relationships, we propose two models to predict marginal VR and demonstrate that the models predict better than using the previous global VR.
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A Comparison of Opinions of Three Professional Groups with Regard to Various Levels of Deviant Behavior in ChildrenWoodruff, Ralph S. 12 1900 (has links)
The purpose of this study is to examine the extent to which fifth-grade teachers, teachers in special education, and child psychiatrists hold similar views regarding the seriousness of commonly observed student behaviors. In addition, the views of these three groups are compared to research regarding which behaviors in children are predictive of future delinquency. The Wickman Scale, consisting of fifty commonly observed children's behaviors, and a fifteen-pair Semantic Differential Scale, designed for use in this study, were administered to a group of fifth-grade teachers, a group of special education teachers, and a group of child psychiatrists.
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Using Association Rules to Guide a Search for Best Fitting Transfer Models of Student LearningFreyberger, Jonathan E 30 April 2004 (has links)
Transfer models provide a viable means of determining which skills a student needs in order to solve a given problem. However, constructing a good fitting transfer model requires a lot of trial and error. The main goal of this thesis was to develop a procedure for developing better fit transfer models for intelligent tutoring systems. The procedure implements a search method using association rules as a means of guiding the search. The association rules are mined from the instances in the dataset that the transfer model predicts incorrectly. The association rules found in the mining process determines what operation to perform on the current transfer model. Our search algorithm using association rules was compared to a blind search method that finds all possible transfer models for a given set of factors. Our search process was able to find statistically similar models to the ones the blind search method finds in a considerably shorter amount of time. The difference in times between our search process and the blind search method is days to minutes. Being able to find good transfer models quicker will help intelligent tutor system builders as well as cognitive science researchers better assess what makes certain problems hard and other problems easy for students.
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A Comparison of Devices for Measuring Soil Moisture Tension and their Effectiveness in Predicting Irrigation Requirements in the FieldCapiel, Modesto 01 May 1956 (has links)
Many problems of management of irrigated soils require a knowledge of the soil moisture status and its relationship to plant growth before they can be solved.
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The Validity and Use of Wechsler Pre-School and Primary Scale of Intelligence in Predicting School AchievementCampanella, Sam 01 May 1968 (has links)
The purpose of this study was to evaluate the Wechsler Pre-School and Primary Scale of Intelligence for use in predicting school achievement. The Wechsler Pre-School and Primary Scale of Intelligence was administered to 22 children enrolled in the Operation Head Start Program in Logan, Utah. The Wide Range Achievement Test was administered five months later to the same 22 children. The scores on the Wechsler Pre-School and Primary Scale of Intelligence were correlated to the scores on the Wide Range Achievement Test.
On the basis of the .53 correlation between the Wechsler Pre-School and Primary Scale of Intelligence Full Scale IQ and the Wide Range Achievement Test Average Standard Score, the Wechsler Pre-School and Primary Scale of Intelligence was judged to be a useful instrument in predicting school achievement.
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Predicting Stock Market Crises by VAR ModelYang, Han-Chih 23 June 2012 (has links)
There are several methods to predict financial crises. There are also several types of indicators used by financial institutions. These indicators, which are estimated in different ways, often show various developments, although it is not possible to directly assess which is the most suitable. Here, we still try to find what characteristics that industry group has and forecast financial crises
In this paper, our data started from monthly of 1977 January to 2008 December in S&P100. We consider Fama-French and Cluster Analysis to process data to make data with same characteristic within a group. Then, we use GARCH type models and apply it to VaR predicting stock turmoil.
In conclusion, we found that the group which has high kurtosis value is the key factor for predicting stock crises instead of volatility. Moreover, the characteristics of this industry which can predict stock crises is a great scale. On the other hand, we can through this model to double check the reaction for anticipating. Therefore, people can do some actions to control risk to reduce the loss.
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Methodology for Predicting Drilling Performance from Environmental ConditionsDe Almeida, Jose Alejandro 2010 December 1900 (has links)
The use of statistics has been common practice within the petroleum industry for
over a decade. With such a mature subject that includes specialized software and
numerous articles, the challenge of this project was to introduce a duplicable method to
perform deterministic regression while confirming the mathematical and actual
validation of the resulting model. A five-step procedure was introduced using Statistical
Analysis Software (SAS) for necessary computations to obtain a model that describes an
event by analyzing the environmental variables. Since SAS may not be readily available,
the code to perform the five-step methodology in R has been provided.
The deterministic five-step procedure methodology may be applied to new fields
with a limited amount of data. As an example case, 17 wells drilled in north central
Texas were used to illustrate how to apply the methodology to obtain a deterministic
model. The objective was to predict the number of days required to drill a well using
environmental conditions and technical variables. Ideally, the predicted number of days
would be within +/- 10% of the observed time of the drilled wells. The database created
contained 58 observations from 17 wells with the descriptive variables, technical limit
(referred to as estimated days), depth, bottomhole temperature (BHT), inclination (inc),
mud weight (MW), fracture pressure (FP), pore pressure (PP), and the average,
maximum, and minimum difference between fracture pressure minus mud weight and
mud weight minus pore pressure. Step 1 created a database. Step 2 performed initial statistical regression on the
original dataset. Step 3 ensured that the models were valid by performing univariate
analysis. Step 4 history matched the models-response to actual observed data. Step 5
repeated the procedure until the best model had been found. Four main regression
techniques were used: stepwise regression, forward selection, backward elimination, and
least squares regression. Using these four regression techniques and best engineering
judgment, a model was found that improved time prediction accuracy, but did not
constantly result in values that were +/- 10% of the observed times.
The five-step methodology to determine a model using deterministic statistics
has applications in many different areas within the petroleum field. Unlike examples
found in literature, emphasis has been given to the validation of the model by analysis of
the model error. By focusing on the five-step procedure, the methodology may be
applied within different software programs, allowing for greater usage. These two key
parameters allow companies to obtain their time prediction models without the need to
outsource the work and test the certainty of any chosen model.
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