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The UK building stock accounts for about half of all energy consumed in the UK. A large portion of the energy is consumed by nondomestic buildings. Offices and retail are the most energy intensive typologies within the nondomestic building sector, typically accounting for over 50% of the nondomestic buildings’ total energy consumption. Heating, ventilating and air conditioning (HVAC) systems are the largest energy end use in the nondomestic sector, with energy consumption close to 50% of total energy consumption. Different HVAC systems have different energy requirements when responding to the same building heating and cooling demands. On the other hand, building heating and cooling demands depend on various parameters such as building fabrics, glazing ratio, building form, occupancy pattern, and many others. HVAC system energy requirements and building energy demands can be determined by mathematical modelling. A widely accepted approach among building professionals is to use building energy simulation tools such as EnergyPlus, IES, DOE2, etc. which can analyse in detail building energy consumption. However, preparing and running simulations in such tools is usually very complicated, time consuming and costly. Their complexity has been identified as the biggest obstacle. Adequate alternatives to complex building energy simulation tools are regression models which can provide results in an easier and faster way. This research deals with the development of regression models that enable the selection of HVAC systems for office buildings. In addition, the models are able to predict annual heating, cooling and auxiliary energy requirements of different HVAC systems as a function of office building heating and cooling demands. For the first part of the data set development used for the regression analysis, a data set of office building simulation archetypes was developed. The four most typical built forms (open plan sidelit, cellular sidelit, artificially lit open plan and composite sidelit cellular around artificially lit open plan built form) were coupled with five types of building fabric and three levels of glazing ratio. Furthermore, two measures of reducing solar heat gains were considered as well as implementation of daylight control. Also, building orientation was included in the analysis. In total 3840 different office buildings were then further coupled with five different HVAC systems: variable air volume system; constant air volume system; fan coil system with dedicated air; chilled ceiling system with embedded pipes, dedicated air and radiator heating; and chilled ceiling system with exposed aluminium panels, dedicated air and radiator heating. The total number of models simulated in EnergyPlus, in order to develop the input database for regression analysis, was 23,040. The results clearly indicate that it is possible to form a reliable judgement about each different HVAC system’s heating, cooling and auxiliary energy requirements based only on office building heating and cooling demands. High coefficients of determination of the proposed regression models show that HVAC system requirements can be predicted with high accuracy. The lowest coefficient of determination among cooling regression models was 0.94 in the case of the CAV system. HVAC system heating energy requirement regression models had a coefficient of determination above 0.96. The auxiliary energy requirement models had a coefficient of determination above 0.95, except in the case of chilled ceiling systems where the coefficient of determination was around 0.87. This research demonstrates that simplified regression models can be used to provide design decisions for the office building HVAC systems studied. Such models allow more rapid determination of HVAC systems energy requirements without the need for time-consuming (hence expensive) reconfigurations and runs of the simulation program.

Abstract: Modeling productivity entails establishing the relationship between various factors that impact construction productivity, connecting input factors with output productivity. While prefabrication facilities may limit the influence of external project factors on productivity, complexity, and variability in product design, productivity-influencing factors from the internal project environment regulate the production flow and still causes productivity to vary broadly. Such factors are numerous, and it is impossible to account for every relevant detail in the model. In addition, the developed model needs to be logically driven, transparent, easy to use, and practical to secure the trust of the practitioners. Data collection efforts for maintaining the data-driven model must be practically optimized to reduce the overall overhead cost. Therefore, a systematic, transparent, logic-driven, and quantitative approach for modeling productivity would sufficiently transform a particular set of significant input parameters into the output of corresponding productivity. Furthermore, considering those input parameters that describe the fabrication of certain products, productivity variation in connection with each input parameter of the model is attributed to customization of the fabricated product of the same type, which needs to be accounted for to estimate the likely range of the predicted productivity. In other words, besides the point value prediction of productivity, the model should also provide the variance estimate of the prediction to gauge the associated variations. Regression methods like multiple linear regression (MLR) and the model tree (MT) have been mainstream methods for quantitatively modeling labor productivity. Both of these techniques are instrumental in generating productivity prediction models that are transparent and understandable; hence, model predictions can be trusted by decision-makers. It is important to note that even though model tree algorithms are considered as the nonlinear regression model, which combines decision trees and MLR analysis to establish complex-nonlinear relationships between variables. Such regression models are generally established based on analyzing the error terms between the predicted output and the target output without addressing the variance of the predicted output and the impact of individual input parameters on the variance. A model with high accuracy (the mean of the prediction close to the target value) but low precision (too high the variance of the prediction) would be deemed inadequate in the context of cost-estimating applications. An analytical method to account for the impact of the variability associated with each input parameter on the variation of the final output has yet to be formalized. This research critically reviews established methods for variance analysis on commonly applied regression methods, namely MLR and model tree in cost estimating and productivity prediction for fabricated construction, as well as the impact of productivity variance on project cost budgeting. This research first proposes a novel method that integrates the error propagation theory with MLR modeling in an attempt to quantify the variance of the MLR predicted output. A metric based on the resulting variance analysis (i.e., the ratio of standard deviation over mean) for gauging the precision of the MLR model has also been proposed. Next, the variance analysis technique is used to enhance the model tree algorithm to extend its capacity to make predictions along with estimating the variance of the predicted output. A productivity modeling framework, therefore, has been formalized using the enhanced model tree algorithm to connect the unique design features of fabricated products (e.g., structural steel, prestressed concrete elements) as input with productivity as output. The productivity model’s performance has been cross-checked against the models prepared using MLR and artificial neural network (ANN) models. The enhanced model tree outperforms MLR in prediction accuracy and is preferred to ANN because (1) the variance is analytically predicted alongside the point-value output, and (2) the productivity model is explainable in terms of the reasoning logic for productivity prediction. In addition, two additional questions in connection with the variance in productivity prediction are addressed in this research, namely: (1) how the variability encoded in the fabrication productivity at work packages propagates from the work package level to the entire project level through the project network schedule and (2) how variations in project-level labor-hour are accumulated over the course of the project duration. Proposed methodology has been verified using Monte Carlo simulation and validated by conducting case studies based on fabrication projects in the real world.

Hispanic participation in Alzheimer’s disease (AD) research studies has been historically low. With low engagement, there are many nuances which are not understood related to AD care in the Hispanic Community. The primary purpose of this study is to analyze a Hispanic data set of risk factors of Alzheimer's Disease. Three predictors have been identified to be highly correlated with the onset of Alzheimer's disease in other populations and will be analyzed to indicate how predictive they are in a diagnosis of Alzheimer’s disease in a Hispanic population. This dissertation is available in open access at AURA, https://aura.antioch.edu/ and OhioLINK ETD Center, https://etd.ohiolink.edu

The supplies of concrete aggregates from natural sources are rapidly reducing. Recycled concrete aggregates (RCA) are produced by crushing the concrete obtained from demolished concrete structures. The purpose of this study is to determine the durability properties of concrete with 100% recycled concrete coarse aggregates and relate the durability of such concrete to the physical, mechanical, and durability properties of RCA and the RCA parent concrete. The correlations of several properties are determined by developing model equations to predict RCA properties. The parent concrete is crushed and graded at two nominal maximum sizes, 1" and 0.75", to determine the RCA production effects on new concrete. Six varieties of RCA were manufactured for the study. Concrete mixtures with RCA coarse aggregate have water-to-cement ratios of 0.38 and 0.48. Physical properties of aggregates tested in the lab include bulk density, specific gravity, absorption, and residual mortar. The concrete durability is obtained by measurement of resistance to degradation, resistivity test, density, absorption, and voids in the hardened concrete. A regression model for the surface resistivity of RCA showed that the surface resistivity increases when the absorption of RCA, w/c of concrete, and the volume of mortar decrease. The research results contribute to increasing RCA use through an improved understanding of its influence on concrete durability.

Excessive algal growth in freshwater lakes can negatively impact ecosystems, recreation, and human health. Though algae are a natural part of freshwater ecosystems, elevated nutrient loading from anthropogenic and natural sources can lead to algal blooms. Both algae and blue-green algae (BGA) are responsible for algal blooms; however, BGA (cyanobacteria) is more dangerous. The first objective of this research was to prepare a conceptual model to understand how various environmental variables affect algae. This conceptual model was used to choose the environmental variables that help increase or decrease algae in the water environment. The second objective was to develop empirical equations to identify how the environmental variables are helping algal increase or decrease. Lake Mitchell, near Mitchell, SD, was chosen as a case study to collect the data of the environmental variables. Along with the total algae (Total algae = Chlorophyll-a + Phycocyanin), five variables: (1) conductivity, (2) temperature, (3) fluorescent dissolved organic matter, (4) ammonium, and (5) dissolved oxygen, were collected. Algae concentrations can change temporally, vertically within the water column, and spatially across lakes and thus, a four-dimensional approach was used to accurately quantify algal

With the development of algorithms, models and data-driven efforts in other areas, machine learning is beginning to make impacts in materials science and engineering. In this work, we review the basic steps of using machine learning in materials science. We also develop several machine learning methods to predict the two physically-distinct properties of transparent conductors: formation enthalpy, which is an indication of stability, and bandgap energy, which is an indication of optical transparency. These include regression-based models such as the ordinary least squares (OLS) regression model, stepwise selection model, Ridge model and Lasso model, and tree-based models such as the random forest model and gradient boosted model (GBM). We discuss the advantages and potential problems of each model and provide suggestions for possible applications.

This paper extends recent research into the long term pricing impacts of the American Airlines and US Airways merger in 2013. Zhang and Nozick (2018), using the 10% ticket sample, demonstrated that low cost carriers’ post-merger lowered prices for non-stop service post-merger and those reductions in fare were the largest where American Airlines and US Airways were more prominent pre-merger. The paper extends their analysis to include multi-hop service, the impact of hubs and focus cities, and to address the feedback between prices and market concentration by introducing population and income into the statistical models. The estimated models again show that (1) legacy carriers were able to raise prices after the merger and the amount of the increase was larger where American and US Airways played a smaller role in the market pre-merger; and (2) low cost carriers reduced their prices and the amount of the reduction was also heavily influenced by the role that US Airways and American Airlines played in the market pre-merger. For the legacy carriers these trends have generally increased across 2015, 2016 and 2017. For the low cost carriers, the reductions in price over these three years have generally decreased. Finally, we find that where service is provided using airports that are hubs or focus cities, prices are generally higher and that impact is more pronounced in 2016 and 2017 in contrast to 2015.

A forecasting model, associated with predictive analysis, is an elementary requirement for academic leaders to plan course requirements. The M.S. in Operations Management (MSOM) program at the University of Arkansas desires to understand future student enrollment more accurately. The available literature shows that there is an absence of forecasting models based on quantitative, qualitative and predictive analysis. This study develops a combined forecasting model focusing on three admission stages. The research uses simple regression, Delphi analysis, Analysis of Variance (ANOVA), and classification tree system to develop the models. It predicts that 272, 173, and 136 new students will apply, matriculate and enroll in the MSOM program during Fall 2017, respectively. In addition, the predictive analysis reveals that 45% of applicants do not enroll in the program. The tuition fee of the program is negatively associated with the student enrollment and significantly influences individuals’ decision. Moreover, the students’ enrollment in the program is distributed over 6 semesters after matriculation. The classification tree classifies that 61% of applicants with non-military status will join the program. Based on the outcomes, this study proposes a set of recommendations to improve the admission process.

Air transportation demand forecasting is a core element in aviation planning and policy decision making. NASA Langley Research Center addressed the need of a global forecast model to be integrated into the Transportation Systems Analysis Model (TSAM) to fulfil the vision of the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters to develop a picture of future demand worldwide. Future forecasts can be performed using a range of techniques depending on the data available and the scope of the forecast. Causal models are widely used as a forecasting tool by looking for relationships between historical demand and variables such as economic and population growth. The Global Demand Model is an econometric regression model that predicts the number of air passenger seats worldwide using the Gross Domestic Product (GDP), population, and airlines market share as the explanatory variables. GDP and Population are converted to 2.5 arc minute individual cell resolution and calculated at the airport level in the geographic area 60 nautical miles around the airport. The global demand model consists of a family of models, each airport is assigned the model that best fits the historical data. The assignment of the model is conducted through an algorithm that uses the R2 as the measure of Goodness-of-Fit in addition to a sanity check for the generated forecasts. The output of the model is the projection of the number of seats offered at each airport for every year up to the year 2040.

Abstract: This thesis introduces a new class of robust estimators for regression mod- els. Specifically, a class of weighted least square estimators under linear re- gression models is introduced in Chapter 2, with a continuous adaptive weight function computed using the Kolmogorov-Smirnov statistic. Asymptotic prop- erties, such as consistency and asymptotic normality, of the proposed estimator are established under the model. Simulation studies show that the proposed estimator attains almost full e�ciency and have a better robustness proper- ties than the initial estimators for finite sample sizes. An application to a real contaminated dataset shows that it’s comparable to other robust estimators in practice. In Chapter 3, a class of weighted maximum likelihood estimators under logistic regression models is introduced, again with a continuous adaptive weight function computed using Mahalanobis distances of exploratory vari- ables. Asymptotic consistency of the proposed estimator is proved under the model, and finite-sample properties are also studied by simulation. In simu- lation studies, it is observed that the proposed estimator is almost as e�cient as the maximum likelihood estimator under the model, and under point-mass contamination models, the proposed estimator shows a comparable robustness. This is also verified in an application to a real data set. Chapter 4 contains some concluding remarks and future directions.

This research focuses on spatial analysis of Chinese air transportation from 2001 to 2010. First, we examine how Chinese air transportation has changed during the past decade in terms of the hierarchy of airports based on the number of air passengers. Second, as suggested by previous research, we explore what kinds of socio-economic, demographic, and geographical factors led to changes in the number of air passengers. All these facts have led to what appears to be an evolving hub and spoke network in China. Third, we construct optimal hub and spoke networks using p-hub problem and compare them to the current hub and spoke network structure in China.

Parking cost is an important and sensitive factor in understanding travel behavior and is typically utilized in the mode choice model of regional demand forecasting models. There are various socio-economics variables that can affect the value of parking cost by employment type, time periods, and trip purposes. In this study, a set of parking cost forecasting models are developed using survey data and local socio-economic data with the objective of identifying parking cost patterns and forecasting future parking costs. This study first summarizes methods applied in previous parking cost forecasting models. Two categories of models were estimated. The first category does not consider parking space supply as a factor in forecasting TAZ parking; the second category considers both parking space supply and parking demand as explanatory variables. For each category, using current off-street parking cost survey data, linear regression models are built for hourly, daily and monthly pricing for SCAG Tier 2 Transportation Analysis Zones (TAZ) using R and Matlab. Daily parking rates are set as the base rates to generate the hourly and monthly parking cost models. The consideration of parking demand is a major contribution of this study, with demand generated based on home-based-work trip attractions for commuters by income groups in all models. This study found that daily parking rates can be explained by total employment, the proportion of office to total jobs, and the proportion of multiple to total households. Hourly parking cost can be explained based on daily parking rates and travel behavior associated with education, hospital, finance, entertainment and other employment types. The monthly parking cost model is built base on both daily and hourly parking rates as independent variables. Future work includes, integration of on-street parking costs with the current models for off-street parking.

Abstract: This thesis describes a methodology for process improvement of panelized home building production lines through increasing productivity and reducing production cycle time based on lean production and simulation tools. In panelized home building, panels are produced in a factory and then moved to the construction site for assembly. The challenge here is the high customization level in home models and also the wide range of panel types with different design properties which lead to highly variant processing times. In this thesis, a regression-based methodology is proposed which formulates the task times of different activities based on the unique design properties of a panel. The regression models are used as input in the simulation model to validate the process improvement plans such as line balancing and panel sequencing. This methodology is implemented in a home building manufacturer in Edmonton in order to reduce the overall home completion time.

This dissertation presents a model of translator satisfaction along with an instrument and empirical data that can be used for the assessment of translator satisfaction in the language industry (LI). The model has been adapted to address specific trends widely prevalent in the industry. These trends have emerged over the last two decades due to the widespread adoption of internet technologies and other technological advancements. The subcontracting model has become a very common mode of operation among large corporations and language service providers, leading to the replacement of the traditional translation processes by mass-production methods. Division of labor, higher specialization of skills and higher process standardization are some of the inevitable consequences of mass-production. Subcontracting has radically reshaped the work environment, with increasing virtual teamwork, outsourcing and telecommuting becoming widespread. Subject matter and technical expertise, terminology management and project management skills have become essential in the new professional profile that has emerged due to the trends in the current LI.The model proposed in this dissertation implements Herzberg’s framework of job satisfaction in order to investigate the intrinsic and extrinsic predictors that influence three facets of satisfaction among active professionals in the LI—task, job and professional. A survey has been designed to collect data from active translation professionals, and the survey results are comprehensively post-processed and statistically analyzed in order to comprehend the factors and variables for each facet of satisfaction.Study of the work behavior of a highly skilled labor force is important because translators remain a crucial component of the LI despite all the technological advancements. The findings suggest that most predictors come from the intrinsic sources of satisfaction found in individuals, tasks or the profession. Thus, new retention strategies that reinforce intrinsic satisfiers are recommended. The findings could be used to establish best practices, and further used by human resources management, employers and recruiters to review their approaches for retention as well as training and mentoring. The findings of this study may also be used to improve the academic curriculum in Translation Studies by incorporating modules that are deemed as important by the industry professionals.

Environmental pressures brought about by climate change have increased the urgency for biomass assessment to measure the potential of forests to be carbon sinks and carbon sources. The researcher suggests that improving the quality and precision of models used for measuring carbon stock in forests is thus important. This study aims to investigate the relationships between independent factors, such as dry weight biomass (B), and dependent factors, such as diameter at breast height (D), height (H), and wood specific gravity (ρ), to formulate biomass equations for four common tree species: Sterculia pexa; Millettia brandisiana; Grewia eriocarpa; and Bridelia ovata. Regression models, each with different independent variables (D, ρD, D2H, and ρD2H), were studied. The results showed a strong correlation between B, D, and H, but not ρ. However, ρ showed a significant variation between the four species which indicated that proper species identification is required for accurate modelling. The best regression models for estimating biomass had two forms: ln(B) = c + αln(D) and ln(B) = c + αln(ρD2H). The dry weight of individual trees using the regression model with ρD2H had an average estimated error of 0.09–2.66%. The dry weight using D had an average estimated error of 0.28– 1.77%. Thus, it was most appropriate to use ρD2H as the independent variable in the model. Furthermore, linear regression indicated a significant statistical difference between the four species. In conclusion, the researcher found that formulating species-specific regression models is essential in assessing biomass and carbon, particularly for the mixed-deciduous degraded forest areas in this study.

This research aims to present an empirical investigation of the relative and incremental predictive ability of alternative accrual- and cash-based accounting measures to forecast future cash flows in an Australian context. To achieve this goal, eight ordinary least squares (OLS) regression models on pooled time-series of cross-sectional data are developed. To evaluate the forecasting performance of the models, both within-sample and out-of-sample forecasting tests are employed. A number of contextual factors, including the length of the operating cash cycle, industry membership, firm profitability and firm size, were taken into account in the main OLS regression analysis of this study to examine whether the predictive ability of accrual- and cash-based data to forecast future cash flows were influenced by these factors. In addition, a more sophisticated approach used for estimating regression models, namely fixed-effects, is undertaken to control for unobseved differences across firms. Results from various regression model analyses suggest that cash flow from operations is more important than earnings in predicting future cash flows. Disaggregating earnings into cash flow from operations and accruals enhances the forecast of future cash flows, compared with aggregate earnings. The accrual component of earnings provides incremental information content for future cash flows beyond that provided by the cash flow component of earnings. Various decompositions of total accruals (i.e., current and non-current accruals, accrual components, and discretionary and nondiscretionary accruals) have incremental information content beyond that contained in total accruals. Disaggregating cash flow from operations into operating cash receipts and payments, as reported under the direct method of cash flow presentation, improves the predictive ability of aggregate cash flow from operations. Indirect cash flow information incrementally enhances the ability of direct cash flow components in forecasting future cash flows. This study also provides evidence that the model based on both accrual and cash flow components outperforms other existing cash flow prediction models. Various categorisations of the sample confirm that the findings are not conditional on the selected control factors. However, the results reveal new knowledge on the characteristics of accrual- and cash-based accounting data relating to future cash flows. For instance, this study documents that cash flow and its components (earnings and accruals) are more (less) informative to market participants in the forecast of a firm’s future cash flows when: (i) the firm is small; (ii) their operating cash cycle is short; and (iii) the firm makes a loss. Furthermore, the level of the predictive ability of both accrual- and cash-based accounting variables varies across industries.

This report presents a top-down methodology to forecast annual international flight operations at sixty-six U.S. airports, whose combined operations accounted for 99.8% of the total international passenger flight operations in National Airspace System (NAS) in 2004. The forecast of international flight operations at each airport is derived from the combination of passenger flight operations at the airport to ten World Regions. The regions include: Europe, Asia, Africa, South America, Mexico, Canada, Caribbean and Central America, Middle East, Oceania and U.S. International. In the forecast, a "top-down" methodology is applied in three steps. In the fist step, individual linear regression models are developed to forecast the total annual international passenger enplanements from the U.S. to each of nine World Regions. The resulting regression models are statistically valid and have parameters that are credible in terms of signs and magnitude. In the second step, the forecasted passenger enplanements are distributed among international airports in the U.S. using individual airport market share factors. The airport market share analysis conducted in this step concludes that the airline business is the critical factor explaining the changes associated with airport market share. In the third and final step, the international passenger enplanements at each airport are converted to flight operations required for transporting the passengers. In this process, average load factor and average seats per aircraft are used. The model has been integrated into the Transportation Systems Analysis Model (TSAM), a comprehensive intercity transportation planning tool. Through a simple graphic user interface implemented in the TSAM model, the user can test different future scenarios by defining a series of scaling factors for GDP, load factor and average seats per aircraft. The default values for the latter two variables are predefined in the model using 2004 historical data derived from Department of Transportation T100 international segment data.

Modernizing the agricultural sector is one of the main thrusts of the Philippine government. Partnership with private and government financial institutions has been a major mechanism of the government in developing the agricultural sector through credit and financing. Agricultural producers rely on credit facilities to help them raise the capital needed in their endeavors. They require investments and inputs in agriculture-related activities that will initiate production and increase returns. Total loans granted to small farmers and fisherfolk remains low despite government efforts agricultural credit and insurance programs. Banks are reluctant to provide them financial assistance due to the high risk involved in agricultural lending. This research examines the accessibility to credit of small farmers and fisherfolk in the Philippines. It also examines if differential pricing exists in agricultural loans. This research gathered primary data from 1028 sample respondents receiving credit from both formal and informal sectors as well as non-borrowers. The sample respondents consist of small-scale farmers and fisherfolk in selected regions in the Philippines. They were interviewed using a pre-tested structured questionnaire. Selection of sample respondents was done using stratified random sampling. Logit and general regression analyses were undertaken for this research and a number of specification tests were performed to determine the robustness of the models. The findings showed that the explanatory variables such as age, household income, household size, loan duration, loan processing, bank distance, interest rates, loan size and regional dummy variable for Southern Tagalog have significant influence on the small-scale farmers and fisherfolk probability of borrowing from formal lenders and their accessibility to formal credit. Except for the household size, loan processing, loan duration and loan size, all the statistically significant independent variables had signs consistent with the hypotheses. In the regression model, the main determinants of interest rates are loan purpose, loan duration and source of loan. Regions in Central Luzon and Southern Tagalog were found to have lower interest rates relative to Western Visayas. In addition, interest charged on loans to farmers tends to be lower than the interest charged on loans to fisherfolk.

With the view of stressing the importance of forecasting and discovering underlying patterns in Tourism data series -for the purpose of assisting management and policy makers in the Tourism Industry - a multiple regression model and one classical decomposition model were constructed in order to investigate Outbound Tourism from Australia to Greece.