ABSTRACT PREDICTION OF LITIGATION PROBABILITY FOR INTERNATIONAL CONSTRUCTION PROJECTS DURING BIDDING STAGE Ayten, ilkay M.S., Department of Civil Engineering Supervisor: Assoc. Prof. Dr. Rifat S&ouml / nmez February 2010, 102 pages Over the years many researchers agreed that between the parties involved in construction projects such as / owner, contractor, engineer and suppliers trying to perform different scopes in different timetables. Therefore, disputes are inevitable due to the complexity of the work. Occurrence of litigation is the most terrifying process to deal with during any construction project for both owner and the contractor because of the time and money consuming nature of the process. Hence, contractors should try to eliminate any potential risk factors that will lead to litigation. The aim of this study is to investigate the factors that influence court action between parties in international construction projects and develop a statistical model that will predict the litigation probability of an international construction project during bidding stage. The final prediction model revealed that contractual awareness and consciousness of risk factors is the key to predict litigation probability. Considering awareness of the factors affecting litigation probability are displayed in this thesis. Companies may have the opportunity to develop risk assessment and management strategies while reconsidering their contingency estimates.

Panel data, also known as longitudinal data, are composed of repeated measurements taken from the same subject over different time points. Although it is generally used in time series applications, forecasting can also be used in panel data due to its time dimension. However, there is limited number of studies in this area in the literature. In this thesis, forecasting is studied for panel data with binary response because of its increasing importance and increasing fundamental roles. A simulation study is held to compare the efficiency of different methods and to find the one that gives the optimal forecast values. In this simulation, 21 different methods, including na&iuml / ve and complex ones, are used by the help of R software. It is concluded that transition models and random effects models with no lag of response can be chosen for getting the most accurate forecasts, especially for the first two years of forecasting.

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Man-made farm ponds are unique geographic features of the Taoyuan Tableland. Besides irrigation, they provide refuges for wintering birds. The issue at hand is that these features are disappearing and bring with it the loss of this refuge function. It is ecologically significant because one fifth of all the bird species in Taiwan find a home on these ponds. This study aims at characterizing the diversity of bird species associated with these ponds whose likelihood of survival was assessed along the gradient of land development intensities. Such characterization helps establish decision criteria needed for designating certain ponds for habitat preservation and developing their protection strategies. A holistic model was developed by incorporating logistic regression with error back-propagation into the paradigm of artificial neural networks (ANN). The model considers pond shape, size, neighboring farmlands, and developed areas in calculating parameters pertaining to their respective and interactive influences on avian diversity, among them the Shannon-Wiener diversity index (H’). Results indicate that ponds with regular shape or the ones with larger size possess a strong positive correlation with H’. Farm ponds adjacent to farmland benefited waterside bird diversity. On the other hand, urban development was shown to cause the reduction of farmland and pond numbers, which in turn reduced waterside bird diversity. By running the ANN model with four neurons, the resulting H’ index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0.72, in contrast to the results from a linear regression model (r < 0.28). Analysis of historical pond occurrence to the present showed that ponds with larger size and a long perimeter were less likely to disappear. Smaller (< 0.1 ha) and more curvilinear ponds had a more drastic rate of disappearance. Based on this finding, a logistic regression was constructed to predict pond-loss likelihood in the future and to help identify ponds that should be protected. Overlaying results from ANN and form logistic regression enabled the creation of pond-diversity maps for these simulated scenarios of development intensities with respective to pond-loss trends and the corresponding dynamics of bird diversity.

This paper analyzes the current operating situation of financial holding companies in Taiwan. After referring to the operation of financial early warning systems of various countries, the study chooses appropriate financial ratios to establish a financial early warning model for quantitative analysis, evaluate the management efficiency of financial holding companies, discriminate the correct classification rate of prediction probability and rating system, and seek an optimal early warning model as the basis for supervision and governance of financial holding companies. In 2008, the financial tsunami that swept over the global economy resulted in a disastrous loss to the financial industry. To cope with the impact of financial crisis, most countries in the world have developed their early warning models. In Taiwan, the CAMELS framework was adopted for the establishment of Taiwan¡¦s financial early warning system and a risk-oriented auditing system. With the financial liberalization, the government of Taiwan lifted the ban on the business operation of financial holding companies step by step in order to enhance the operating efficiency of financial holding companies and activate the financial market. However, the competitive ability of Taiwan¡¦s financial industry was not significantly improved. Instead, a series of problems with the financial sector erupted one after another. The reasons for such a condition were due to more risks faced by the financial holding companies after financial deregulation. Therefore, this study used 14 financial holding companies in Taiwan as of 2006 as subjects, and constructed a financial early warning system for the original samples by using the following two kinds of models. After factor analysis¡Athe remaining financial variables ¡Alike capital adequary ratio(C2) ¡Atotal debt/equit capital (C3) ¡A total deposit/equit capital(C4), ratio of non-performing loans(A2) the operational expense ratio(M3), efficiency ratio (M4), earnings before taxes/sales(E1) and so on, have more influence on the performances of the financial holding companies in Taiwan. As to the whole efficiency of the self-examination, CAMELS still has good prediction ability and can enable predicting ability increases after joining the risk parameters¡D Predictive sample enters two models and obtains¡Gthe predictive efficiency, type error and type error of Model Two work better than Model One¡Aso in predicting samples, think CAMELS still has good predicition ability and can enable predicting ability increases after joining the risk parameters.

<p>Background: AA Logistics Sweden is having logistic efficiency problems, and at this point they do not have performance measurement in terms of KPI´s. Due to constant development and demand on their products, there have not been enough resources available to perform these measurements.</p><p>Purpose: Our purpose is, on the basis of service level and turnover speed, to measure efficiency in terms of KPI’s at AA. It is also to design record sheets that can assist AA to increase the efficiency over time.</p><p>Methodology: Interviews with managers and employees, in order to have a solid foundation for what to look for and analyse in the ERP system. The empirical material that was received was analysed on the basis of different theories.</p><p>Result, conclusions: We have developed values for each KPI and also suggested SMART goals that in the long run will contribute to increasing the logistic efficiency.</p>

Abstract Sinkholes and sinkhole-related features in West-Central Florida (WCF) are commonly identified using geotechnical investigations such as standard penetration test (SPT) borings and geophysical methods such as ground penetrating radar (GPR) and electrical resistivity tomography (ERT). Geophysical investigation results can be used to locate drilling and field testing sites while geotechnical investigation can be used to ground truth geophysical results. Both methods can yield complementary information. Geotechnical investigations give important information about the type of soil, groundwater level and presence of low-density soils or voids at the test location, while geophysical investigations like GPR surveys have better spatial coverage and can resolve shallow stratigraphic indicators of subsidence. In GPR profiles collected at 103 residential sites in covered-karst terrain in WCF, sinkhole-related anomalies are identified using GPR and SPT methods. We analyze the degree to which the shallow features imaged in GPR correlate spatially with the N-values (blow counts) derived from SPTs at the 103 residential sites. GPR anomalies indicating sinkhole activity are defined as zones where subsurface layers show local downwarping, discontinuities, or sudden increases in amplitude or penetration of the GPR signal. "Low SPT values" indicating sinkhole activity are defined using an optimization code that searched for threshold SPT value showing optimum correlation between GPR and SPT for different optimal depth ranges. We also compared these criteria with other commonly used geotechnical criteria such as weight of rod and weight of hammer conditions. Geotechnical results were also used to filter the data based on site characteristics such as presence of shallow clay layers to study the effectiveness of GPR at different zones. Subsets of the dataset are further analyzed based on geotechnical results such as clay thickness, bedrock depth, groundwater conditions and other geological factors such as geomorphology, lithology, engineering soil type, soil thickness and prevalent sinkhole type. Results are used to examine (1) which SPT indicators show the strongest correlations with GPR anomalies, (2) the degree to which GPR surveys improve the placement of SPT borings, and (3) what these results indicate about the structure of sinkholes at these sites. For the entire data set, we find a statistically significant correlation between GPR anomalies and low SPT N-values with a confidence level of 90%. Logistic regression analysis shows that the strongest correlations are between GPR anomalies and SPT values measured in the depth range of 0-4.5 m. The probability of observing a GPR anomaly on a site will decrease by up to 84% as the minimum SPT value increases from 0 to 20 in the general study area. Boreholes drilled on GPR anomalies are statistically significantly more likely to show zones of anomalously low SPT values than boreholes drilled off GPR anomalies. We also find that the optimum SPT criteria result in better correlation with GPR than other simple commonly used geotechnical criteria such as weight of rod and weight of hammer. Better correlations were found when sites with poor GPR penetrations are filtered out from the dataset. The odds ratio showed similar result while the result varied with the depth range, statistics and threshold SPT value (low N- value with optimum correlation), with a maximum observed odds ratio of 3. Several statistical results suggest that raveling zones that connect voids to the surface may be inclined, so that shallow GPR anomalies are laterally offset from deeper zones of low N-values. Compared to the general study area, we found locally stronger correlation in some sub-regions. For example, the odds ratio found for tertiary hawthorn subgroup were 25 times higher than the odds ratio found for the general study area (WCF).

Knowledge of fire risk is crucial for manufacturers and regulators to make correct choices in prescribing fire protection systems, especially flame retardants. Methods of determining fire risk are bogged down by a multitude of confounding factors, such as population demographics and overlapping fire protection systems. Teasing out the impacts of one particular choice or regulatory change in such an environment is crucial. Teasing out such detail requires statistical techniques, and knowledge of the field is important for verifying potential methods. Comparing the fire problems between two states might be one way to identify successful approaches to fire safety. California, a state with progressive fire prevention policies, is compared to Texas using logistic regression modeling to account for various common factors such as percentage of rural population and percentage of population in ‘risky’ age brackets. Results indicate that living room fires, fires in which the first item ignited is a flammable liquid, piping, or filter, and fires started by cigarettes, pipes, and cigars have significantly higher odds of resulting in a casualty or fatality than fires started by other areas of origin, items first ignited, or heat sources. Additionally, fires in Texas have roughly 1.5 times higher odds of resulting in casualties than fires in California for certain areas of origin, items first ignited, and heat sources. Methods of estimating fire losses are also examined. The potential of using Ramachandran’s power-law relationship to estimate fire losses in residential home fires in Texas is examined, and determined to be viable but not discriminating. CFAST is likewise explored as a means to model fire losses. Initial results are inconclusive, but Monte Carlo simulation of home geometries might render the approach viable. / text

The study examined school district expenditures in Texas and their correlations with student achievement. The following research question guided this study: Which resource allocations produce statistically significant correlations between the resource allocation variances among school district and student achievement? An ordinal logistic regression analysis included 1009 school districts in the State of Texas, 18 of 26 possible finance function codes provided per-pupil dollar amounts, and 9 of 11 possible demographic categories were utilized for the study. The study held the school district as the unit of analysis. The statistical model was used to regress the dollar amounts categorized by financial function codes and percent student demographics to determine if a relationship existed with the dependent variable of the Texas Education Agency’s defined accountability rating during the 5-year time period—2004-2008. At the national level, there is a long-standing debate over whether the amount of money allocated to education affects student achievement. The literature review presents two sides of the debate concerning whether financial resources make a difference with regard to student achievement as represented through district-level accountability ratings. The research revealed that specific school district resource allocations by function code are statistically significant with regard to district level accountability measures through the Texas Education Agency (TEA) accountability system. However, the odds ratios temper the impact of the significance. The research also revealed that demographics are statistically significant in the State of Texas accountability system. / text

The time spent searching for a parking space increases air pollution, driver frustration, and safety problems impacting among other issues, traffic congestion and as consequence the environment. In the United States, parking represents a $20 billion industry (National Parking Association, 2005), and research shows that a car is parked on average 90 percent of the time. To alleviate this problem, more parking facilities should be built or intelligent models to better utilize current facilities should be explored. In this thesis, a general methodology is proposed to provide solutions to the parking problem. First, stated preference data is used to study drivers' choice/behavior. Parking choices are modeled as functions of arrival time, parking price, age, income and gender. The estimated values show that choice is relatively inelastic with respect to distance and more elastic with respect to price. The data is used to estimate the price elasticity that induces drivers to change their behavior. Second, neural networks are used to predict space availability using data provided by a parking facility. The model is compared with traditional forecasting models used in revenue management. Results show that neural networks are an effective tool to predict parking demand and perform better than traditional forecasting models. Third, the price elasticity that induces drivers to change their choice or behavior is determined. Finally, taking as an input the forecasting results obtained from the neural network and the price elasticity, parking spaces are optimally allocated at different price levels to optimize facility utilization and increase revenue. This research considers a parking facility network consisting of multiple parking lots with two, three and four fare classes and utilizes revenue management techniques as a mean to maximize revenue and to stimulate and diversify demand. The output indicates the number of parking spaces that should be made available for early booking to ensure full utilization of the parking lot, while at the same time attempting to secure as many full price parking spaces to ensure maximization of revenue.