The future of electric aircraft is closing in, with several companies racing to develop the first electric aircraft for commercial use. Researchers believe that electric aircraft have many benefits compared to conventional aircraft: the decrease of emissions, cheaper costs in the chain of development from manufacturing to ticket prices and a decrease in both fuel costs and overall operating costs. Amidst the race for the development of electric aircraft for commercial purposes between new and existing manufacturers, there is missing information which is needed to understand the profitability of electric aircraft. The question remains of how much the cost will change between conventional aircraft and electric aircraft. The aim of this study is to compare the operating cost of an electric aircraft (with 19 seats) to similar conventional aircraft on regional routes. The comparison would inform the decision of airlines interested in investing in electric aviation. To help achieve the aim of the study, a comparison analysis, a cost calculation, and analysis, as well as a SWOT analysis were performed using data gathered through literature search, a case study, and interviews. The study was performed on two conventional aircraft, the Beechcraft 1900D and the Jetstream JS31, both turboprop aircraft powered by jet fuel, and an electric aircraft, Heart Aerospace’s ES-19. Common for all aircraft is the capacity of 19 seats and their utilization for short-range flights. The total cost of electric aircraft is between 15 to 22% cheaper than the operating cost for conventional aircraft. The most expensive category for electric aircraft is the ownership cost, which can be reduced through mass production of electric aircraft, as the ownership cost of aircraft is generally higher when fewer aircraft are made. The electric aircraft is currently at a disadvantage due to the weight of the batteries and overall aircraft weight, which increases the weight-related airport charges. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Yes / Reducing the cost of fresh water has always been a major concern in the desalination industry. A solar powered hybrid multi-effect distillation and reverse osmosis desalination plant (MED+RO) has been designed and optimised from an economical point of view in a previous work by the same authors. In the present study, the possibility of coupling the desalination plant with a photovoltaic (PV) solar farm is investigated, with the aim of generating electricity at low cost and in a sustainable way. A detailed mathematical model for the PV system has been implemented from the literature. Interestingly, the model can predict the cost of the PV system in terms of capital cost and electricity cost per kWh considering the input data of solar irradiation, duration of daylight and technical specification of a real solar module. Consequently, the solar PV model has been combined with the desalination model, which enables to estimate the cost of fresh water per cubic meter. Data about four locations, namely Isola di Pantelleria (IT), Las Palmas (ES), Abu Dhabi (UAE), and Perth (AUS), have been used to economically test the feasibility of installing the proposed plant, and especially of the PV solar farm.

The purpose of a cost model is to provide designers and decision-makers with accurate cost information to assess and compare multiple alternatives for obtaining the optimal solution and controlling cost. The cost models developed in the design phases are the most important and the most difficult to develop. Therefore it is necessary to identify appropriate cost drivers and employ appropriate modeling techniques to accurately estimate cost for directing designers. The objective of this study is to provide higher predictive accuracy of cost estimation for directing designer in the early design phases of complex products. After a generic cost estimation model is presented and the existing methods for identification of cost drivers and different cost modeling techniques are reviewed, the dissertation first proposes new methodologies to identify and select the cost drivers: Causal-Associated (CA) method and Tabu-Stepwise selection approach. The CA method increases understanding and explanation of the cost analysis and helps avoid missing some cost drivers. The Tabu-Stepwise selection approach is used to select significant cost drivers and eliminate irrelevant cost drivers under nonlinear situation. A case study is created to illustrate their procedure and benefits. The test data show they can improve predictive capacity. Second, this dissertation introduces Tabu-SVR, a nonparametric approach based on support vector regression (SVR) for cost estimation for complex products in the early design phases. Tabu-SVR determines the parameters of SVR via a tabu search algorithm improved by the author. For verification and validation of performance on Tabu-SVR, the five common basic cost characteristics are summarized: accumulation, linear function, power function, step function, and exponential function. Based on these five characteristics and the Flight Optimization Systems (FLOPS) cost module (engine part), seven test data sets are generated to test Tabu-SVR and are used to compare it with other traditional methods (parametric modeling, neural networking and case-based reasoning). The results show Tabu-SVR significantly improves the performance compared to SVR based on empirical study. The radial basis function (RBF) kernel, which is much more robust, often has better performance over linear and polynomial kernel functions. Compared with other traditional cost estimating approaches, Tabu-SVR with RBF kernel function has strong predicable capability and is able to capture nonlinearities and discontinuities along with interactions among cost drivers. The third part of this dissertation focuses on semiparametric cost estimating approaches. Extensive studies are conducted on three semiparametric algorithms based on SVR. Three data sets are produced by combining the aforementioned five common basic cost characteristics. The experiments show Semiparametric Algorithm 1 is the best approach under most situations. It has better cost estimating accuracy over the pure nonparametric approach and the pure parametric approach. The model complexity influences the estimating accuracy for Semiparametric Algorithm 2 and Algorithm 3. If the inexact function forms are used as the parametric component of semiparametric algorithm, they often do not bring any improvement of cost estimating accuracy over the pure nonparametric approach and even worsen the performance. The last part of this dissertation introduces two existing methods for sensitivity analysis to improve the explanation capability of the cost estimating approach based on SVR. These methods are able to show the contribution of cost drivers, to determine the effect of cost drivers, to establish the profiles of cost drivers, and to conduct monotonic analysis. They finally can help designers make trade-off study and answer “what-i” questions. / Ph. D.

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Digitized by Kansas State University Libraries

Digitized by Kansas State University Libraries

This study investigated the performance and cost of scarification with a tractormounted excavator. Due to variations in terrain conditions the performance variedbetween 0.08 and 0.11 ha/E15h and, with a machine cost of 366 SEK/h, the cost ofscarification varied between 3 330 and 4 750 SEK/ha.

Master of Science / Department of Agricultural Economics / Kevin C. Dhuyvetter / A Microsoft Excel based budget was developed to find the cost of becoming National Animal Identification Systems (NAIS) compliant in the U.S for beef cow-calf producers. This budget was turned into a stochastic budget by using different distributions for five key variables. From these distributions 10,000 observations were simulated using Latin Hypercube sampling. From the comprehensive budget, a second, more simple budget was constructed for obtaining NAIS cost. This Microsoft Excel based model gives beef cow-calf producers an estimate and a prediction interval associated with the estimated cost of adopting a cattle ID system that is compliant with the National Animal Identification Systems quickly and conveniently, requiring only six inputs. Both the comprehensive and the quick budget are available online. An Ordinary Least Squares regression was estimated using the simulated observations to find marginal effects associated with key variables. The driving factor of total cost per head was eID tag price for operations that tag and eID tag price and chute costs for non-tagging operations. For producers with five or less animals, it was cheaper to hire third parties to tag animals. From the sample data generated, smaller operations pay significantly more than larger operations on a per head basis, as the minimum cost was $2.08 for the larger operations and the maximum cost to small operations was $17.56. The estimated overall average cost per head for the cow/calf industry was $6.26, with a standard deviation of $4.12. Costs were on a per breeding female basis. The Excel spreadsheet budget and model can be downloaded at http://www.agmanager.info/ for producers who wish to estimate NAIS costs specific to their operations.

Research report to the SBL, Unisa, Midrand. / The research project aimed to establish whether corporate governance is important to investors from a value perspective. The implications and recommendations for further research were provided.

Cost behavior analysis was used to operationalize higher education cost factors in the analysis of instructional costs of three baccalaureate nursing programs in one state university system. Higher education cost factors were conceptualized by Robinson, Ray and Turk (1977) to be three; volume, decision, and environmental. The 30 variables derived from the three factors were the independent variables used in this study to explore their influence on the dependent variables of cost per student credit hour and cost per student contact hour. Several data sources were used, including a questionnaire which elicited program administrator's perceptions of the relative importance of the three factors to their instructional costs. Instructional costs were partitioned into direct and indirect costs. The data were analyzed in three phases to describe the relative influence of the independent variables on instructional costs within programs, among programs, relative to their respective institutions, and to their respective classification of institutions. The findings suggest that volume and decision factors more strongly influence costs than do environmental factors. The mix of volume and decision factors influencing costs differed for the three programs. In general, the most influential volume factors on costs were the number and frequency of course and class offerings. The most influential of the decision variables was student-faculty ratio and faculty rank mix. Higher full-time-equivalent enrollments and higher student credit hour production did not correspond to lower instructional costs, largely because of intervening decision factor variables. All three nursing programs were more costly per student credit hour when indexed to the average instructional expenditures for their respective universities. However, when the nursing program costs were indexed to the average instructional expenditures per full-time-equivalent enrollments of their respective institutional classifications, all three nursing programs were lower than the average. The model of instructional cost analysis used in this modified case study proved to be effective in identifying sources of higher costs within programs and variations among the programs. For student contact hour intensive programs such as nursing, student contact hour, as opposed to the student credit hour is recommended as the more sensitive measure of direct instructional costs.