Sensitivity Analysis Of Inputs Research Articles

Modeling and sensitivity analysis of energy inputs for apple production in Iran

Abstract This study was conducted to determine the energy balance between the energy inputs and yield for apple production in Tehran, Iran. For this purpose the data were collected from 56 apple orchards. The following results were obtained from this study: The total energy input of 42819.25 MJ ha −1 was required for apple production. The share of diesel fuel by 21.88% of the total energy inputs was the highest energy input. This was followed by farmyard manure (17.66%) and electricity (13.09%), respectively. The energy use efficiency, energy productivity, Specific energy, and net energy were found as 1.16, 0.49 kg MJ −1 , 2.06 MJ kg −1 and 7038.18 MJ ha −1 , respectively. According to the research results, the contribution of direct energy was higher than that of indirect energy; also the share of non-renewable energy was more than that of renewable energy. The results of econometric model estimation revealed that the impact of farmyard manure, water for irrigation, electricity, chemical fertilizer and human labour energy inputs were significantly positive on yield. The results of sensitivity analysis of the energy inputs showed that the MPP value of water for irrigation was the highest, followed by human labour and chemicals energy inputs, respectively.

Sensitivity analysis of energy inputs for barley production in Hamedan Province of Iran

The objectives of this study were to determine the energy consumption and evaluation of inputs sensitivity for barley production in Hamedan Province, Iran. The sensitivity of energy inputs was estimated using the marginal physical productivity (MPP) method and partial regression coefficients on barley yield. The results revealed that total energy input for barley production was ∼25,027 MJ ha −1; the non-renewable energy shared about 66% while the renewable energy did 34%. Energy use efficiency, energy productivity, and net energy were 2.86, 0.19 kg MJ −1, and ∼46,498 MJ ha −1, respectively. Econometric model evaluation showed that machinery energy was the most significant input which affects the output level. Sensitivity analysis indicates that with an additional use of 1 MJ of each of the human labour, machinery and electricity energy would lead to an increase in yield by 7.37, 1.66 and 0.33 kg, respectively. Also, the MPP of biocides energy was calculated to be −1.97 implying that the use of biocides energy is in excess for barley production, causing an environmental risk problem in the region.

Joint metamodeling for sensitivity analysis of continuous and stochastic inputs of a computer code

To perform the global sensitivity analysis of a complex and cpu time expensive code, a mathematical function built from a small number of simulations referred to as a metamodel can be used to approximate the code. In some applications like oil reservoir simulations, the code output can depend on complex stochastic inputs such as random permeability fields. This paper proposes a new metamodeling approach to perform global sensitivity analysis of both scalar and such type of stochastic input. A joint metamodeling based on two Gaussian process metamodels is proposed to model the mean and the variance. Then, the sensitivity indices of scalar and stochastic inputs are estimated from this joint metamodeling. An application on a reservoir simulator illustrates the overall methodology.

Computation of Sensitivity Indices with different routines for Latin Hypercube Designs

Complex mathematical simulations are performed to understand the various phenomena in physics, chemistry, etc. Usually these models consist of several input variables and sensitivity analysis explains the variation of computer code output due to the changes in the input values. We have used the software package, Gaussian Emulation Machine for sensitivity analysis (GEM-SA), (Kennedy and O’Hagan, 2006) for performing sensitivity analysis and chosen Rosenbrock function as a test problem. In this paper we have studied the effect of different available software routines for Latin hypercube designs (LHD) on values of the variance-based sensitive indices, total sensitive indices and roughness parameters for the test problem. We have used MATLAB, R software and GEM-SA modules for LHD.

Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model-building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base-consumption were found low compared to the large uncertainty observed in the antibiotic and off-gas CO(2) predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases - meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass-transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes.

Queuing simulation of roadside survey station: Blocked traffic lane

External station travel surveys provide critical inputs to travel demand models. The results from these models are frequently used for statewide planning purposes. Although a roadside survey is very effective in obtaining useful information from road users, its major drawback is the excessive delay that is imposed onto road users particularly on high-volume facilities. In this paper, we used a discrete event simulation to model a blocked traffic lane survey, which is usually conducted for two-lane undivided highways. This type of survey station requires a complete stop of all oncoming traffic. Non-surveyed traffic has no ability to go around and thus has to wait in a queue in order to proceed through the survey station. Road users’ impacts are quantified in terms of delay and queue length while the performance of surveyors is measured by the number of surveys completed per unit time. Sensitivity analyses of simulation inputs reveal that simulation results are fairly insensitive to selected parameters. The results in this study provide a quick and useful guideline that roadside surveyors can use to estimate the road user impacts prior to the survey and to plan the survey procedure accordingly.

Evaluation and field-scale application of an analytical method to quantify groundwater discharge using mapped streambed temperatures

A method for calculating groundwater discharge through a streambed on a sub-reach to a reach scale has been developed using data from plan-view mapping of streambed temperatures at a uniform depth along a reach of a river or stream. An analytical solution of the one-dimensional steady-state heat-diffusion–advection equation was used to determine fluxes from observed temperature data. The method was applied to point measurements of streambed temperatures used to map a 60 m long reach of a river by Conant Jr. [Conant Jr. B., 2004. Delineating and quantifying ground water discharge zones using streambed temperatures. Ground Water 42(2), 243–257] and relies on the underlying assumption that streambed temperatures are in a quasi-steady-state during the period of mapping. The analytical method was able to match the values and pattern of flux previously obtained using an empirical relationship that related streambed temperatures to fluxes obtained from piezometers and using Darcy’s law. A second independent test of the analytical method using temperature mapping and seepage meter fluxes along a first-order stream confirmed the validity of the approach. The USGS numerical heat transport model VS2DH was also used to evaluate the thermal response of the streambed sediments to transient variations in surface water temperatures and showed that quasi-steady-state conditions occurred for most, but not all, conditions. During mapping events in the winter, quasi-steady-state conditions were typically observed for both high and low groundwater discharge conditions, but during summer mapping events quasi-steady-state conditions were typically not achieved at low flux areas or where measurements were made at shallow depths. Major advantages of using this analytical method include: it can be implemented using a spreadsheet; it does not require the installation or testing of piezometers or seepage meters (although they would help to confirm the results); and it needs only a minimal amount of input data related to water temperatures and the thermal properties of water and the sediments. The field results showed the analytical solution tends to underestimate high fluxes. However, a sensitivity analysis of possible model inputs shows the solution is relatively robust and not particularly sensitive to small uncertainties in input data and can produce reasonable flux estimates without the need for calibration.

First- and Second-Trimester Evaluation of Risk for Down Syndrome

To investigate the differences in costs and outcomes of Down syndrome screening using data from the First and Second Trimester Evaluation of Risk (FASTER) Trial. Seven possible screening options for Down syndrome were compared: 1) Triple Screen-maternal serum alpha fetoprotein, estriol, and hCG; 2) Quad-maternal serum alpha fetoprotein, estriol, hCG, and Inhibin A; 3) Combined First-nuchal translucency, pregnancy-associated plasma protein A (PAPP-A), free beta-hCG; 4) Integrated-nuchal translucency, PAPP-A, plus Quad; 5) Serum Integrated-PAPP-A, plus Quad; 6) Stepwise Sequential-Combined First plus Quad with results given after each test; and 7) Contingent Sequential-Combined First and only those with risk between 1:30 and 1:1,500 have Quad screen. The detection rates for each option were used given a 5% false-positive rate except for Contingent Sequential with a 4.3% false-positive rate. Outcomes included societal costs of each screening regimen (screening tests, amniocentesis, management of complications, and cost of care of Down syndrome live births), Down syndrome fetuses identified and born, the associated quality-adjusted life years, and the incremental cost-utility ratio. Based on the screening results derived from the 38,033 women evaluated in the FASTER trial, the Contingent Sequential screen dominated (lower costs with better outcomes) all other screens. For example, the Contingent Sequential cost 32.3 million dollars whereas the other screens ranged from 32.8 to 37.5 million dollars. The Sequential strategy led to the identification of the most Down syndrome fetuses of all of the screens, but at a higher cost per Down syndrome case diagnosed ($719,675 compared with $690,427) as compared with the Contingent Sequential. Because of the lower overall false-positive rate leading to fewer procedure-related miscarriages, the Contingent Sequential resulted in the highest quality-adjusted life years as well. The Contingent Sequential remained the most cost-effective option throughout sensitivity analysis of inputs, including amniocentesis rate after positive screen, rate of therapeutic abortion after Down syndrome diagnosis, and rate of procedure-related miscarriages. Analysis of this actual data from the FASTER Trial demonstrates that the Contingent Sequential test is the most cost-effective. This information can help shape future policy regarding Down syndrome screening.

기후, 지하수 취수 및 토지이용 변화의 건기 총유출량에 대한 영향

본 연구는 SWAT모형을 이용하여 기상, 지하수 취수량, 토지이용 변화에 대한 건기 총유출량의 민감도를 제시하였으며 더 나아가 건기의 총 유출량을 추정하기 위해 건기 총강우량, 전 우기 총강우량, 평균 일 최대온도, 일평균 태양복사량과 같은 기상 변수들과 지하수 취수량 및 도시면적 비율을 이용하여 회귀식을 도출하였다. 도출된 식을 이용하여 기후변화에 대한 건기 총유출량 변화를 살펴보기 위하여 온도와 강우량의 변화에 대한 건기 총유출량의 변화율을 제시하였는데 기후변화로 인해 온도가 상승할 경우 건기의 총 유출량은 감소하는 것으로 나타났다. 지하수 취수량은 총 유출량과 관계가 높은데 반해 토지이용 변화는 산간유역인 대상유역의 경우 크게 영향을 미치지 않음을 알 수 있었다. 본 연구에서 제안된 식은 기저유출에 영향을 크게 미치는 강우와 기온 및 태양복사량을 포함하는 기상상태, 지하수 취수량, 도시면적 비율을 변수로 갖는 식이므로 기후변화를 비롯한 유역의 다양한 수문학적 변화에 대해 대상유역의 미래 건기의 수자원 확보량을 예측하는데 유용하게 사용될 수 있다. In this study, the effects of variability in climate, groundwater withdrawal, and landuse on dry-weather streamflows were investigated by input sensitivity analysis using SWAT (Soil and Water Assessment Tool). Since only dry-period precipitation and daily average solar radiation among climate variables have high correlation coefficients to total flow (TF), sensitivity analyses of those were conducted. Furthermore, an equation was derived from simulation results for 30 years by multiple regression analysis. It may be used to estimate effects of various climatic variations (precipitation during the dry period, precipitation during the previous wet period, solar radiation, and maximum temperature). If daily average maximum temperatures increase, TFs during the dry period will decrease. Sensitivities of groundwater withdrawal and landuse were also conducted. Similarly, groundwater withdrawals strongly affect streamflow during the dry period. However, landuse changes (increasing urbanization) within the forested watershed do not appear to significantly affect TF during the dry period. Finally, a combined equation was derived that describes the relationship between the total runoff during the dry period and the climate, groundwater withdrawal and urban area proportion. The proposed equation will be useful to predict the water availability during the dry period in the future since it is dependent upon changes of temperature, precipitation, solar radiation, urban area ratio, and groundwater withdrawal.

Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach

Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition.

An LMI approach to minimum sensitivity analysis with application to fault detection

This paper systematically studies the minimum input sensitivity analysis problem. The lowest level of sensitivity of system outputs to system inputs is defined as an H - index. A full characterization of the H - index is given, first, in terms of matrix equalities and inequalities, and then in terms of linear matrix inequalities (LMIs), as a dual of the Bounded Real Lemma. A related problem of input observability is also studied, with new necessary and sufficient conditions given, which are necessary for a fault detection system to have a nonzero worst-case fault sensitivity. The above results are applied to the problem of fault detection filter analysis, with numerical examples given to show the effectiveness of the proposed approaches.

Artificial neural networks in reorganization outcome and investment of distressed firms: The Taiwanese case

Using a sample of 59 Taiwanese firms in distress, we investigate investor gains or losses around the filing date and the confirmation date of the reorganization plan and propose ex ante trading strategies that can generate potentially substantial returns from these stocks. We further experiment with a novel approach to sensitivity analysis of neural network inputs for refining and rating the most important input variables and then construct artificial neural network (ANN) models to classify and predict the post-reorganization filing resolutions in a three- (Model 1) and two-group (Models 2 and 3) resolution setting. We are unaware of any other paper that has used artificial intelligence techniques in analyzing post-bankruptcy resolutions. The results show that our five-variable models produce a consistent estimate between the training and testing sets; that is, both three- and two-group models are of a high degree of accuracy and stability, demonstrating the robustness of the ANN models.

Partially Connected Feedforward Neural Networks Structured by Input Types

This paper proposes a new method to model partially connected feedforward neural networks (PCFNNs) from the identified input type (IT) which refers to whether each input is coupled with or uncoupled from other inputs in generating output. The identification is done by analyzing input sensitivity changes as amplifying the magnitude of inputs. The sensitivity changes of the uncoupled inputs are not correlated with the variation on any other input, while those of the coupled inputs are correlated with the variation on any one of the coupled inputs. According to the identified ITs, a PCFNN can be structured. Each uncoupled input does not share the neurons in the hidden layer with other inputs in order to contribute to output in an independent manner, while the coupled inputs share the neurons with one another. After deriving the mathematical input sensitivity analysis for each IT, several experiments, as well as a real example (blood pressure (BP) estimation), are described to demonstrate how well our method works.

Neural network-based prediction and optimization of estradiol release from ethylene–vinyl acetate membranes

Drug-delivery systems, with predictable delivery rates, were designed using an artificial neural network-based optimization algorithm. A two-chamber diffusion cell was used to study the permeation of estradiol through ethylene–vinyl acetate copolymer membrane. The explanatory variables were the vinyl acetate (VA) content of the membrane, poly(ethylene glycol) (PEG)—solvent-composition, and membrane thickness. After deriving a neural network model to predict estradiol delivery rates as a function of these input variables, a constrained optimization procedure was applied to estimate the membrane/vehicle properties necessary to achieve a prescribed dosage. The results compared adequately well with experimental data with 71% of the data agreeing within one standard deviation. Input sensitivity analysis showed that at specific VA levels, drug delivery was more sensitive to changes in PEG compositions. The non-uniqueness of the inversion method and the accuracy of the procedure were investigated using neural network-based two-dimensional contour plots. The methodology proposed could be used to design customized polymer-based drug-delivery systems that meet specific end-user requirements.

Updated emissions from ocean shipping

Marine vessel inventories demonstrate that ship emissions cannot be neglected in assessing environmental impacts of air pollution, although significant uncertainty in these inventories remains. We address this uncertainty by employing a bottom‐up estimate of fuel consumption and vessel activity for internationally registered fleets, including cargo vessels, other commercial vessels, and military vessels. We identify model bias in previous work, which assumed internationally registered ships primarily consume international marine fuels. Updated results suggest fuel consumption is ∼289 million metric tons per year, more than twice the quantity reported as international fuel. According to our analysis, fuel used by internationally registered fleets is apparently allocated to both international and domestic fuel statistics; this implies either that ships operate along domestic routes much of the time or that marine fuel sales to these ships may be misassigned. If the former is true, then allocation of emissions to international shipping routes may underestimate near‐coastal emissions from ships. Our updated inventories increases previous ship emissions inventories for all pollutants; for example, global NOx emissions (∼6.87 Tg N) are more than doubled. This work also produces detailed sensitivity analyses of inputs to these estimates, identifying uncertainty in vessel duty‐cycle as critical to overall emissions estimates. We discuss implications for assessing ship emissions impacts.

A proposal for input-sensitivity analysis of profile-driven optimizations on embedded applications

The ever-increasing gap between processor and memory speed is an issue also in embedded systems, because of the increased complexity of multimedia elaborations and the strict resource constraints of these devices.Profile-driven code optimization techniques can be effectively employed for tuning application-cache interaction and performances of cache system itself. In fact, applications running on such systems are usually known in advance and do not change over time. In a previous paper, we presented a profile-based code restructuring technique (CAT) that was able to dramatically increase cache exploitation of embedded applications.However, it is well known that profile-driven optimizations can suffer from input-sensitivity problems: an application that is optimized for a particular input can perform even worse than the original one, when subjected other inputs.In this paper we take into account jpeg and mpeg compressor/decompressor applications and analyze the input-sensitivity of CAT improved layouts over a wide range of inputs. The input sets were accurately determined through both black-box and white-box analysis of applications.We propose two metrics for measuring the input-sensitivity of application layouts, and show how our profile-driven code transformation technique is able to reduce the input-sensitivity of the considered applications up to 48% on caches ranging from 1 KByte to 8KByte.

Predicting regional emissions and near-field air concentrations of soil fumigants using modest numerical algorithms: a case study using 1,3-dichloropropene.

Soil fumigants, used to control nematodes and crop disease, can volatilize from the soil application zone and into the atmosphere to create the potential for human inhalation exposure. An objective for this work is to illustrate the ability of simple numerical models to correctly predict pesticide volatilization rates from agricultural fields and to expand emission predictions to nearby air concentrations for use in the exposure component of a risk assessment. This work focuses on a numerical system using two U.S. EPA models (PRZM3 and ISCST3) to predict regional volatilization and nearby air concentrations for the soil fumigant 1,3-dichloropropene. New approaches deal with links to regional databases, seamless coupling of emission and dispersion models, incorporation of Monte Carlo sampling techniques to account for parametric uncertainty, and model input sensitivity analysis. Predicted volatility flux profiles of 1,3-dichloropropene (1,3-D) from soil for tarped and untarped fields were compared against field data and used as source terms for ISCST3. PRZM3 can successfully estimate correct order of magnitude regional soil volatilization losses of 1,3-D when representative regional input parameters are used (soil, weather, chemical, and management practices). Estimated 1,3-D emission losses and resulting air concentrations were investigated for five geographically diverse regions. Air concentrations (15-day averages) are compared with the current U.S. EPA's criteria for human exposure and risk assessment to determine appropriate setback distances from treated fields. Sensitive input parameters for volatility losses were functions of the region being simulated.

MD1: COST-EFFECTIVENESS OF AIRLINE DEFIBRILLATORS: IS PEACE OF MIND MORE IMPORTANT THAN SAVING LIVES?

OBJECTIVES: Airline passengers are particularly vulnerable to the effects of cardiac arrest due to a lack of access to emergency medical services. To offset this isolation, airlines are installing automated external defibrillators (AEDs) on aircraft. Our objective was to measure the cost-effectiveness of airline AED programs and estimate their value to the flying public. METHODS: A decision analytic model was constructed to estimate the clinical and economic effects of airline AEDs. Inputs were obtained from published data and the FAA. Utility estimates were derived from cardiac arrest survivors. Sensitivity analyses evaluated changes in AED cost and probability of cardiac arrest. Since AEDs may provide utility gains through “peace of mind” for passengers not experiencing a medical event, the impact of this added passenger confidence was also evaluated. RESULTS: AEDs on commercial aircraft cost an incremental $5.16 per flight. AED deployment resulted in an estimated additional $162,000 per QALY gained (16 quality-adjusted minutes per flight). Sensitivity analysis of event probabilities and cost inputs did not substantially change the results. However, the cost-effectiveness of AEDs was significantly enhanced by the inclusion of utility gain experienced by passengers from increased peace of mind. While the magnitude of this benefit is unknown, an incremental increase of .003 in utility over the flight duration would reduce the incremental cost-effectiveness of AEDs to less than $50,000 per QALY gained. CONCLUSIONS: Our model estimated that when the benefits of on-board AEDs are limited to patients experiencing medical events, the incremental cost-effectiveness is inferior to most recommended medical interventions. However, if passengers gain utility from knowing an AED is on the aircraft, then these incremental expenditures may be justified. Utility gains from “peace of mind” may have significant implications in determining the value of health care interventions. Further research should be conducted into this potentially important area.

Modeling hydrology and sediment transport in vegetative filter strips

The performance of vegetative filter strips is governed by complex mechanisms. Models can help simulate the field conditions and predict the buffer effectiveness. A single event model for simulating the hydrology and sediment filtration in buffer strips is developed and field tested. Input parameters, sensitivity analysis, calibration and field testing of the model are presented. The model was developed by linking three submodels to describe the principal mechanisms found in natural buffers: a Petrov–Galerkin finite element kinematic wave overland flow submodel, a modified Green–Ampt infiltration submodel and the University of Kentucky sediment filtration model for grass areas. The new formulation effectively handles complex sets of inputs similar to those found in natural events. Major outputs of the model are water outflow and sediment trapping on the strip. The strength of the model is a good description of the hydrology within the filter area, which is essential for achieving good sediment outflow predictions or trapping efficiency. The sensitivity analysis indicates that the most sensitive parameters for the hydrology component are initial soil water content and vertical saturated hydraulic conductivity, and sediment characteristics (particle size, fall velocity and sediment density) and grass spacing for the sediment component. A set of 27 natural runoff events (rainfall amounts from 0.003 to 0.03 m) from a North Carolina Piedmont site was used to test the hydrology component, and a subset of nine events for the sediment component. Good predictions are obtained with the model if shallow uniform sheet flow (no channelization) occurs within the filter.