Optimal Input Combinations Research Articles

Simulating the impact of fertiliser strategies and prices on the economics of developing and managing the Cicerone Project farmlets under climatic uncertainty

The application of fertilisers to pastures in the high rainfall regions of southern Australia has contributed to large increases in carrying capacity following the widespread adoption of the practice since the late 1940s. Recently, large shifts in the worldwide demand for fertiliser inputs have lead to large rises in the cost of fertiliser inputs. These increasing costs have significant potential ramifications on the future management of soil fertility and its interaction with the persistence and profitability of sown pastures, especially during periods of climatic uncertainty. A dynamic pasture resource development simulation model was used to investigate the implications of fertiliser rates and costs on the efficient management of soil fertility under climatic uncertainty. The framework also allowed the investigation of how the management of soil fertility interacts with the utilisation of pasture resources through different stocking rates. In the application of this method to the Cicerone Project farmlets case study, fertiliser input costs were found to influence the optimal combination of fertiliser inputs and stocking rate. Analyses of the dynamic interaction between fertiliser application and cost, stocking rate and the persistence of desirable species enabled the identification of the most risk-efficient strategies. The implications for grazing industries in the high rainfall regions of southern Australia are discussed.

Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1h, 24h, 48h and 72h. The used ANN models were trained using three different algorithms, namely, Levenberg–Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg–Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

Development of a support‐vector‐machine‐based model for daily pan evaporation estimation

AbstractEvaporation estimation is an important issue in water resources management. In this article, a four‐season model with optimal input combination is proposed to estimate the daily evaporation. First, the model based on support vector machine (SVM) coupled with an input determination process is used to determine the optimal combination of input variables. Second, a comparison of the SVM‐based model with the model based on back‐propagation network (BPN) is made to demonstrate the superiority of the SVM‐based model. In addition, season data are used to construct the SVM‐based four‐season model to further improve the daily evaporation estimation. An application is conducted to demonstrate the performance of the proposed model. Results show that the SVM‐based model can select the optimal input combination with physical mechanism. The SVM‐based model is more appropriate than the BPN‐based model because of its higher accuracy, robustness and efficiency. Moreover, the improvement due to the use of the four‐season model increases from 3.22% to 15.30% for RMSE and from 4.84% to 91.16% for CE, respectively. In conclusion, the SVM‐based model coupled with the proposed input determination process should be used to select input variables. The proposed four‐season SVM‐based model with optimal input combination is recommended as an alternative to the existing models. The proposed modelling technique is expected to be useful to improve the daily evaporation estimation. Copyright © 2012 John Wiley & Sons, Ltd.

Input-Orientated Data Envelopment Analysis Framework for Measuring and Decomposing Economic, Environmental and Ecological Efficiency: An Application to OECD Agriculture

This paper presents an input-orientated data envelopment analysis (DEA) framework which allows the measurement and decomposition of economic, environmental and ecological efficiency levels in agricultural production across different countries. Economic, environmental and ecological optimisations search for optimal input combinations that minimise total costs, total amount of nutrients, and total amount of cumulative exergy contained in inputs respectively. The application of the framework to an agricultural dataset of 30 OECD countries revealed that (i) there was significant scope to make their agricultural production systems more environmentally and ecologically sustainable; (ii) the improvement in the environmental and ecological sustainability could be achieved by being more technically efficient and, even more significantly, by changing the input combinations; (iii) the rankings of sustainability varied significantly across OECD countries within frontier-based environmental and ecological efficiency measures and between frontier-based measures and indicators.

Resource use efficiency in sole sorghum production in three villages of Kaduna State Nigeria

In explaining the differences in the efficiency of farms, it is necessary to look into the use of resources to show the close links existing between the performance attained from resources on individual farms and efficient allocation of resources between farms of different sizes. This study estimates the productivity of farm resources on small and large scale farms in three villages of Giwa Local Government Area of Kaduna State. Production data were collected using a structure questionnaire from a representative sample of 78 sorghum farmers. The study used the double log production function to determine the relationship between farm resources and the efficiency of resource use. Empirical results showed that seed, fertilizer and labour inputs are statistically significant in explaining the variation in sorghum output in the study area. However the estimates of the ratio of the marginal value productivities (MVP) and marginal factor cost (MFC), point clearly to the non optimal combination of inputs among the farmers, it showed that the farm resources were inefficiently utilized for sole sorghum production by small and large scale farmers, hence, the need for resource adjustment. Implications for regional and national food policies are discussed

Observing and recognizing complicated gravitational signals

An optimal filter receiver is considered for observing and distinguishing quasideterministic gravitational-wave signals against a background of wide-band gaussian noise under conditions of a priori insufficiency. It is shown that optimal input combination of long-baseline laser-interferometer gravitational antennas should lead to a reduction in the probability of erroneous decision.

Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing

A hybrid genetic and neurofuzzy computing algorithm was developed to enhance efficiency of water management for a multipurpose reservoir system. The genetic algorithm was applied to search for the optimal input combination of a neurofuzzy system. The optimal model structure is modified using the selection index (SI) criterion expressed as the weighted combination of normalized values of root mean square error (RMSE) and maximum absolute percentage of error (MAPE). The hybrid learning algorithm combines the gradient descent and the least-square methods to train the genetic-based neurofuzzy network by adjusting the parameters of the neurofuzzy system. The applicability of this modeling approach is demonstrated through an operational study of the Pasak Jolasid Reservoir in Pasak River Basin, Thailand. The optimal reservoir releases are determined based on the reservoir inflow, storage stage, sideflow, diversion flow from the adjoining basin, and the water demand. Reliability, vulnerability and resiliency are used as indicators to evaluate the model performance in meeting objectives of satisfying water demand and maximizing flood prevention. Results of the performance evaluation indicate that the releases predicted by the genetic-based neurofuzzy model gave higher reliability for water supply and flood protection compared to the actual operation, the releases based on simulation following the current rule curve, and the predicted releases based on other approaches such as the fuzzy rule-based model and the neurofuzzy model. Also the predicted releases based on the newly developed approach result in the lowest amount of deficit and spill indicating that the developed modeling approach would assist in improved operation of Pasak Jolasid Reservoir.

Optimal Selection of Control Inputs for Diesel Engines

Diesel engine NOx and PM emissions are characterized by a combination of many system inputs. Since more than one combination of inputs leads to the same output and the high number of degrees of freedom, it is an ill-posed problem to determine the optimal input combination. In this paper the introduction of a two dimensional coordinate system is addressed, with the target of a separate and independent control of NOx and PM. Finally, a validation on testbench data is presented.

Petro‐seismic inversion for sandstone properties

We use empirical relations derived from laboratory and log data for sandstones to estimate unknown parameters from given parameters. The known and unknown parameters may be any of the following: compressional and shear wave velocities (Vp and Vs), density (ρ), compressional and shear wave quality factors (Qp and Qs), effective pressure (P), frequency (f), water saturation (S), porosity (φ), clay content (C), fluid permeability (k), and Vp/Vs and Qs/Qp ratios. The goal is to obtain estimates of all thirteen of these petro‐seismic variables from subsets of these as input; whether this is achievable depends on the particular combination of input variables. The inversion typically proceeds through a hierarchy of three levels of parameter estimation, in order of the expected reliability of the estimates. First, we solve for the parameters that can be obtained directly from the fitted empirical equations. Then, a grid search is performed to simultanously fit more than one unknown parameter by finding values that best predict the known parameters. Finally, various approximations are invoked to predict values when the inputs are insufficient to use the direct equations or a grid search. These three procedures can be initiated in any desired order and any number of times. Even if the complete set of seismic parameters (Vp, and Vs, ρ, Qp, Qs, and f) are given, it is not possible to uniquely obtain any of the remaining petrophysical variables (S, φ, C, P, or k) directly from the fitted equations without constraints on (or assumptions about) some of the other variables. There is a trade‐off between porosity and clay content that can be resolved by simultaneous fitting of multiple seismic parameters (i.e., Vp, Vs, and Vp/Vs). The predictability of the parameters from various combinations of the available data is expressed using correlation coefficients (R2) and standard deviations. With optimal input combinations, parameters with R2 ≥ 0.8 are (in order from best to worst) Vp, 100/Qp, ρ, Vs, and S. Other parameters with 0.63 ≥ R2 ≤, 0.79 are (in order from best to worst) φ, Qs/Qp, Vp/Vs, ln k, 100/Qs, and C. P and f are the least reliably estimated. All of the seismic parameters can be accurately estimated if all of the petrophysical parameters are known.

Nonlinear phase mismatch and optimal input combination in atomic four-wave mixing in Bose-Einstein condensates

This work treats four-wave mixing (4WM) in Bose-Einstein condensates (BEC), focusing on the nonlinear phase mismatch, maximum output, and optimal input combination. We show that the nonlinear phase mismatch decreases the 4WM efficiency. It was found that the 4WM efficiency depends on both the coupling coefficient (i.e., the product of the total number of atoms, the scattering length, and the overlap integral) and the ratios among the three initial input beams. The 4WM efficiency increases with the increase of the coupling coefficient when it is small, then saturates, and finally decreases at high coupling coefficient due to both pump depletion and phase-modulation effects. A maximum output efficiency of about 50% in our case is predicted. In order to get the maximum output, the two pump beams should have equal amplitude and the probe beam should be as small as possible. In addition, a large coupling coefficient (>\ensuremath{\pi}/2), which is determined by the ratio of the probe beam to the total input, is required. On the other hand, when the coupling coefficient is fixed, a maximum output for this case can be obtained by optimizing the input ratios among the three input beams. Other ratio combinations will decrease the 4WM efficiency.

Maximizing the Simulation Output: A Competition

The following competition was organized by the Business Section of the Netherlands Society for Statistics and Operations Research (VVS): maxi mize the output of a given simulation model by selecting the best combination of six inputs; only 32 runs are permitted. Twelve teams competed; these teams came from industry and academia. This paper is written by the winning team, ex plaining its design and analysis. That design pro ceeded in stages. First, a special design was used to estimate all main effects and two-factor inter actions (namely, Rechtschaffner's saturated de sign). Then quadratic effects were estimated by changing factors one at a time. Finally, the re sulting estimated second-order polynomial was used to estimate the optimal input combination. The paper presents a combination of design of ex periment techniques and common sense that may have more applications in solving real problems.

Biological and economic optima in a tropical grazing ecosystem in Australia

Our aim in this study was to identify the economically optimal level of use of resources for a grazing system in tropical Australia and to compare it with the biological optimum. Grazing management trials were conducted in the Ord River irrigation area of north-western Australia. Biologists at the Commonwealth Scientific and Industrial Research Organization provided the biological data. The liveweight gains of Kimberley shorthorn steers were recorded, after a 1-year fattening period, for different grazing management strategies. Five different stocking rates were used on irrigated pangola grass ( Digitaria decumbens) fertilized with nitrogen at five different rates. All other were applied at fixed, non-limiting levels. The results of the trials were modelled by a liveweight gain production function. This production function was used in an income simulation model to identify economically optimal (profit maximizing) stocking rates. This was done in two stages. First, a base analysis was undertaken on the basis o the economic conditions prevailing in September 1987. Second, sensitivity analysis was applied to the results of the base analysis. The parameters varied were the rate of interest, store cattle prices, finished cattle prices, transport costs and the length of the planning horizon. The biological optimum, when defined as maximum liveweight gain ha −1, corresponds to a much higher intensity of use of resources than the economic optimum, and yields substantially lower profits or higher losses. These discrepancies increase when economic conditions worsen, or when the planning horizon of graziers is decreased from 50 to 10 years. Therefore, the adoption of the biological optimum cannot result in an econonomically stable grazing system in the medium to long term, when it is highly likely that economic parameters will fluctuate. The economic stability of the system is increased, but is still quite weak, if the economically optimal input combination is utilized. It was not possible to determine whether this economic optimum would result in long-term ecological stability because the relevant data were not collected during the trials. The relevance of management recommendations made to graziers and land administrators would be increased if these recommendations were based on an analysis of both the economic and the ecological stability of grazing systems over the medium to long term. We show in the last part of this study how this could be accomplished.

Cost Savings, Wages and the Growth of the Firm

The conventional theory regards the firm as a production unit in which optimal price and input combinations are chosen. Decisions are taken on the basis of demand and cost; and the expansion of a firm is constrained primarily by the exogenous variables. Penrose (I959) brought a new dimension into the neoclassical theory of the firm. She argued that the general rules governing the growth of the firm involve managerial limitations since management must operate as a team not as individuals. Since then, considerable research has been done in this area, notably by Baumol (1 962), Marris (i 964), Gould (i 968), and Slater (i 980). While this research has focused mainly on the costs of adjustment arising from expansion, Penrose (I959) and Marris (I97I) gave limited attention to some of the benefits of expansion.' No attention, however, has been given to the cost-saving aspect of growth as it relates to managerial wages, an omission that appears to result from a tendency to ignore both the career decisions managers face and the existence of firmspecific skills required on the job. For example, the models of Baumol and Slater assume constant managerial compensation. This assumption implies that a nongrowing firm can obtain managerial services at a given price without paying adjustment costs (e.g. training costs and losses of coordination), if vacancies need to be filled due to managers' retirement. Such an assumption does not capture the firm-specific nature of management. Promotions and the increased salaries that result from promotion are determined internally, often independent of the bids of manager's services in the market.2 A firm's wage structure may depend on such factors as the promotion opportunities it makes available, its employment policies, etc. Compensation in the managerial market is far more complex than assumed by Baumol and Slater. Thus, instead of treating managerial salary structures as given, we shall view them as endogenous variables. In the process of growth, costs of adjustment and cost savings are two sides of the same coin. Both are equally important. This paper, however, focuses on the cost savings or gains the employer may derive from growth. Firm growth, other

Measuring Efficiency in Educational Production

An attempt is made to explore the production set for educational achievement for both “efficient” and “inefficient” schools. The inefficient or average production relationship is obtained by estimating a reduced-form equation for all schools among a sample drawn from a large Eastern city. The efficient set is derived by using a linear programming approach to yield coefficients for those schools that show the largest student achievement output relative to their resource inputs. A comparison of the two sets of technical coefficients suggests that the relative marginal products are probably different. Because of such differences, the optimal combination of inputs for producing educational achievement relative to a given budget constraint will probably vary between achievement-efficient and inefficient schools, and may even vary from school to school. The result is that the use of such production-function estimates for attempting to improve the efficiency of the educational sector may have far less utility than its advocates imply.