Uncertainty In Input Parameters Research Articles

The Impact of Eruption Source Parameter Uncertainties on Ash Dispersion Forecasts During Explosive Volcanic Eruptions

AbstractVolcanic ash in the atmosphere is a hazard to aviation. To predict which areas of airspace are most likely to be affected by the presence of ash, Volcanic Ash Advisory Centers (VAACs) use observations and atmospheric dispersion models. These models are initialized with, among other parameters, a mass eruption rate (MER), which quantifies the emission rate into the atmosphere at the source. This influences the predicted spatial–temporal evolution and concentration of the ash cloud. Different models are available to estimate MER from the volcanic plume height and some models also include the weather conditions (e.g., wind speed). The REFIR software tool uses time‐series of plume height estimated from observations and weather data to provide estimates of MER through time. Here we present an updated version of REFIR that can now be used also to calculate MER for past eruptions and produce output parameters in a format suitable for use with the NAME dispersion model (UK Met Office—London VAAC). We also investigate how uncertainty in input parameters is propagated through to dispersion model output. Our results show that a +/−1 km uncertainty on a 6 km high plume can result in the affected area ranging by a factor of three between the minimum and maximum estimates. Additionally, we show that using wind‐affected plume models results in affected areas that are five times larger than using no‐wind‐affected models. This demonstrates the sensitivity of MER to the type of plume model chosen (no‐wind‐ vs. wind‐affected).

A fuzzy inference based scenario building in two-stage optimization framework for sustainable recycling supply chain redesign

This paper aims to develop a scenario-based optimization framework to deals with several issues related to redesign sustainable reverse supply chain; focused particularly in the epistemic uncertainty of supply and demand input parameters and their relationship with supply chain performance, both important for the redesign problem. The two-step optimization framework starts with a Fuzzy Inference System methodology for scenario generation that faces the lack of information, and the necessity of estimate the expected operation cost and environmental impact. Then we use generated scenarios into the epsilon-constraint method which solves a multi-objective model to obtain a relevant set of solutions. After solving the second optimization model, we propose to analyze the robustness of the achieved redesign solutions considering a customer satisfaction approach. The computational experiments show that our proposed framework supports better the inclusion of scenarios for redesigning the plastic recycling supply chain. Furthermore, we study a real-life plastic recycling problem in Cuba which demonstrates that the framework is able to support the redesign decision making with robust solutions sensitive to the changes of the studied uncertain parameters.

A robust framework for designing blood network in disaster relief: a real-life case

This article proposes a novel design of a disaster relief blood supply chain network with multiple echelons and multiple products. It incorporates: (1) the blood donors’ behavior and preference for the selection of facilities where the blood donation takes place, (2) an estimating the quantity of the injured people under each disaster scenario, (3) the inherent uncertainty in input parameters and (4) the remaining capacity for satisfying the demand affected by the disruption in blood facilities (i.e., both blood collection and production centers). To incorporate the utility of blood collection facilities, a utility assessment approach, called Logit model, is developed, which measures the donors’ preference. The disrupted capacity and the number of injured people under each disaster scenario are estimated based on the disaster severity, the distance from the disaster (particularly earthquake) epicenter and the civil infrastructure. The uncertainty of parameters (e.g., cost parameters as well as blood supply and demand) along with the disruption in blood facilities are two unavoidable concerns of disaster relief blood supply chain planning. Accordingly, to handle the parameters under mixed uncertainty, a new approach which is two-stage stochastic-robust is utilized. Finally, using real data of a case study in Iran, the presented formulation and its solution approach are examined. The obtained results demonstrate that the related managers should be aware of blood donors’ behavior, injured people in disaster, and the effect of disruption in the blood supply chain network design. Moreover, when it comes to the uncertainty of parameters, the obtained results from the robust model overcome those of deterministic one. Finally, based on findings, applicable managerial insights are derived for the decision-makers in blood supply chain network design for disaster relief cases.

Optimization-based method for the calibration of geometrical acoustic models

Uncertainty of the final results of room acoustic modelling is a result of uncertainties of many model input parameters, which have to be discovered and limited if possible. It can be done by model calibration. In this paper, a detailed procedure of room acoustic model calibration is proposed. It is based on detection and limitation of possible sources of discrepancies between measured and simulated selected room acoustic parameters defined in ISO 3382-1 standard. As independent parameters which describe the variability of the acoustic field in the room the most accurately, clarity C80 and early decay time EDT are suggested. Based on uncertainty analysis, sound absorption coefficients of all materials used in the interior and selected sound scattering coefficients as well as positions of the receivers are chosen to be input parameters adjusted in the model calibration process. Correction of these parameters is done using optimization algorithms in order to accelerate the procedure. Methods for choosing key ray-tracing simulation parameters are presented, according to which it is possible to obtain repeatable results in the shortest possible time. All analyses are done for five different interiors (three philharmonics and two churches). Correctness of sound absorption/scattering coefficients calculated in the calibration process is validated in the comparison of simulation and measurement results, using a different sound source position. Obtained results are specific for analysed rooms, but proposed methodology is universal for all kinds of room acoustic models.

Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique.

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006–2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty.

Path following of underactuated surface ships based on model predictive control with neural network

A model predictive control approach is proposed for path following of underactuated surface ships with input saturation, parameters uncertainties, and environmental disturbances. An Euler iterative algorithm is used to reduce the calculation amount of model predictive control. The matter of input saturation is addressed naturally and flexibly by taking advantage of model predictive control. The mathematical model group (MMG) model as the internal model improves the control accuracy. A radial basis function neural network is also applied to compensate the total unknowns including parameters uncertainties and environmental disturbances. The numerical simulation results show that the designed controller can force an underactuated ship to follow the desired path accurately in the case of input saturation and time-varying environmental disturbances including wind, current, and wave.

Research of the metrological model of optic-thermal method of natural gas flow measurement

The article substantiates the importance of research aimed at the development and study of contactless method of natural gas flow measurement.On the basis of the developed mathematical model of contactless optic-thermal method of gas flow measurement and key provisions of the uncertainty theory the metrological model analysis is made. The component of the combined standard uncertainty due to the uncertainty of uncorrelated input parameters of the measurement equation is researched and evaluated. The dominant components and the ways of reducing their impact on the combined standard uncertainty are selected. The greatest contribution to combined standard uncertainty of the method makes the uncertainty of interference fringes number measurement, the uncertainty of the distance between the cross-sections of the pipeline measurement and uncertainty of the coefficient determination, which characterizes the velocity distribution of the gas flow.The components of combined standard uncertainty (thermophysical parameters of the gas and the pipeline material, the geometric characteristics of the pipeline), which are correlated with each other due to the temperature dependence, are identified. The uncertainty budget of correlated measurements is compiled. Quantitative assessment showed that the correlation between certain input parameters does not have large impact on the combined standard uncertainty of the measurement.Analysis of the metrological model of the contactless optic-thermal method of gas flow measurement allowed estimating the relative combined standard uncertainty of the method and substantiating the perspective applications of the method for measuring gas flow in large diameter pipelines.

Budget Impact Analysis of EGFR Mutation Liquid Biopsy for First- and Second-Line Treatment of Metastatic Non-Small Cell Lung Cancer in Greece.

Within the European Union, Greece has the highest incidence of lung cancer among people under 45 years of age. Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors are indicated for the treatment of patients with EGFR mutation-positive metastatic non-small cell lung cancer (mNSCLC). Tumor tissue biopsy is the standard method for EGFR mutation detection but is invasive, is resource-intensive, and has risks of complications. The objective of this analysis was to estimate the financial impact on the Greek National Health System of adopting plasma biopsy and to identify the cost-optimal approach for EGFR mutation testing of patients with mNSCLC. We developed a budget impact model to estimate total costs for three EGFR mutation testing approaches: (1) plasma test, (2) combined testing (tissue and plasma test), and (3) reflex testing, compared to the current scenario of tissue biopsy only. One-way sensitivity and scenario analyses were conducted to evaluate the impact of uncertainty and variance of different input parameters on the results. In the first-line (1L) setting, base-case results showed that adopting plasma testing in a combined testing approach identified more EGFR mutation-positive patients and yielded cost savings (−€17 per correctly classified patient) relative to tissue testing alone. The reflex testing approach was the cost-optimal strategy in the second-line (2L) setting as it identified the most EGFR mutation-positive patients with cost savings of −€42 per correctly classified patient relative to tissue testing alone. This analysis suggests that access to both EGFR mutation tissue and plasma testing are important for optimizing mNSCLC treatment decisions in Greece. Inclusion of plasma testing in either a combined or reflex testing approach may be cost optimal for EGFR mutation plasma test implementation.

An inversion method for estimating strong point carbon dioxide emissions using a differential absorption Lidar

Anthropogenic carbon emissions increase the atmospheric concentration of carbon dioxide, resulting in global warming and climate change. To complement the existing carbon emission verification system, it is crucial to develop a novel measurement-based estimation method for determining the carbon emissions produced by strong point sources. This study determines the feasibility of measuring point source carbon emissions using differential absorption Lidar (DIAL). First, for measuring carbon emissions, an estimation method is introduced that utilizes a Gaussian dispersion model with range-resolved carbon dioxide concentrations for the inversion of carbon emissions produced at specific localized point sources. On this basis, a sensitivity analysis is conducted to study the quantitative effect of different parameters on the performance of the proposed method. The inversion method yields less than 0.01% error in ideal conditions. Considering the uncertainties in the relevant input parameters, we found that the errors in the estimates of point source carbon emissions can be maintained below 5.8% using hypothetical equipment with a configuration identical to that of the planned DIAL. Such a novel framework for estimating point source carbon emissions is important for monitoring, reporting, and verifying anthropogenic carbon emissions.

Cost-effectiveness of dental antibiotic prophylaxis in total knee arthroplasty recipients with type II diabetes mellitus

ObjectiveType II diabetes mellitus (T2DM) is prevalent in knee osteoarthritis (OA) patients undergoing total knee arthroplasty (TKA) and increases risk for prosthetic joint infection (PJI). We examined the cost-effectiveness of antibiotic prophylaxis (AP) before dental procedures to reduce PJI in TKA recipients with T2DM. DesignWe used the Osteoarthritis Policy Model, a validated computer simulation of knee OA, to compare two strategies among TKA recipients with T2DM (mean age 68 years, mean BMI 35.4 kg/m2): 1) AP before dental procedures and 2) no AP. Outcomes included quality-adjusted life expectancy (QALE) and lifetime medical costs. We used published efficacy of AP. We report incremental cost-effectiveness ratios (ICERs) and considered strategies with ICERs below well-accepted willingness-to-pay (WTP) thresholds cost-effective. We conducted sensitivity analyses to examine the robustness of findings to uncertainty in model input parameters. We used a lifetime horizon and healthcare sector perspective. ResultsWe found that AP added 1.0 quality-adjusted life-year (QALY) and $66,000 for every 1000 TKA recipients with T2DM, resulting in an ICER of $66,000/QALY. In sensitivity analyses, reduction of the probability of PJI, T2DM-associated risk of infection, or attribution of infections to dental procedures by 50% resulted in ICERs exceeding $100,000/QALY. Probabilistic sensitivity analyses showed that AP was cost-effective in 32% and 58% of scenarios at WTP of $50,000/QALY and $100,000/QALY, respectively. ConclusionsAP prior to dental procedures is cost-effective for TKA recipients with T2DM. However, the cost-effectiveness of AP depends on the risk of PJI and efficacy of AP in this population.

Quantifying operating uncertainties of a PEMFC – Monte Carlo-machine learning based approach

Abstract To enhance efficiency, maintain performance and prolong the lifetime of a PEMFC functioning under diverse operating conditions, development of robust control strategies, which encompasses not only the ohmic loss region but also the activation and concentration loss regions, is indispensable. However, a reliable control strategy can be developed only with the information on the uncertainties in extrinsic and intrinsic input parameters in all the three regions of operation. To achieve this, we perform Monte Carlo simulation to correlate the simultaneous variation of 33 input parameters to the cell performance, water, and thermal management in all three regions of operation. Then, the critical parameters in each region of operation are ranked using sensitivity analysis. The catalyst parameters and operating cell potential have a significant influence in activation loss and ohmic loss regions, while the operating pressure and physical parameters affect the concentration loss region significantly. Subsequently, based on these sensitivities we develop reduced regression models to predict the cell performance, without having to solve the full set of equations, with at least 90% accuracy using machine learning techniques. Finally, we also statistically discuss the distribution of cell performance in all three regions of operation.

Using petro-elastic proxy model to integrate 4D seismic in ensemble based data assimilation

4D seismic data provide valuable information for petroleum engineers to update simulation models using different data assimilation algorithms. To assimilate 4D seismic data, the conventional approach is to use a petro-elastic model (PEM) to bring simulation models to the same domain as the 4D seismic data, or vice-versa. PEMs are constructed using rock and fluid models with uncertainty in both input parameters and models. Apart from their uncertainty, PEMs’ inclusion in data assimilation algorithms requires an interdisciplinary team of rock physicists and petroleum engineers to develop and implement them. A method was developed in this research to replace the PEM with petro-elastic proxy model (PEM-Proxy) for 4D seismic data assimilation in ensemble based algorithms. PEM-Proxy used here was a linear equation with two coefficients linking water saturation and pore pressure changes to the 4D signal, in our case 4D acoustic impedance (4DAI). Two versions of the PEM-Proxy were developed namely PFC and PUC with fixed and uncertain coefficients, respectively. Eventually, PEM was replaced by the PEM-Proxies in Ensemble Smoother with Multiple Data Assimilation (ES-MDA) algorithm to integrate 4D seismic data. Our method was applied to a benchmark case study, using various data assimilation cases to compare the PEM-Proxies application with the PEM. Results showed that the implementation of the PEM-Proxies did not change the behavior of well and 4D seismic objective functions through data assimilation iterations. Interestingly the PUC implementation was satisfactory in terms of well objective functions and even provided better data assimilation cases for 4D seismic objective function. Production forecast reliability for various cases were analyzed and wells forecast showed similar behavior comparing PEM and PEM-Proxies cases. Our method helps petroleum engineers avoid demanding rock and fluid physics equations and replace them with a straightforward and computationally less expensive model for 4D seismic data assimilation.

Active chatter control in turning processes with input constraint

Regenerative chatter is an annoying dynamic phenomenon in machining processes. In this paper, an adaptive sliding mode controller (SMC) is developed for turning chatter mitigation in the presence of control input constraint, parameter uncertainties, and external disturbances. Adaptive laws are employed to estimate parameter uncertainties and disturbances, while a SMC, fulfilling the control input constraint, is designed to mitigate chatter. One unique feature of the developed adaptive approach is that it guarantees chatter suppression even in the presence of control input constraint. Theoretical analyses and experimental investigations are performed, and results demonstrate that the developed SMC with chosen controller parameters guarantees chatter mitigation in the tested examples.

A non-stationary Markov model for economic evaluation of grass pollen allergoid immunotherapy.

Allergic rhino-conjunctivitis (ARC) is an IgE-mediated disease that occurs after exposure to indoor or outdoor allergens, or to non-specific triggers. Effective treatment options for seasonal ARC are available, but the economic aspects and burden of these therapies are not of secondary importance, also considered that the prevalence of ARC has been estimated at 23% in Europe. For these reasons, we propose a novel flexible cost-effectiveness analysis (CEA) model, intended to provide healthcare professionals and policymakers with useful information aimed at cost-effective interventions for grass-pollen induced allergic rhino-conjunctivitis (ARC). Treatments compared are: 1. no AIT, first-line symptomatic drug-therapy with no allergoid immunotherapy (AIT). 2. SCIT, subcutaneous immunotherapy. 3. SLIT, sublingual immunotherapy. The proposed model is a non-stationary Markovian model, that is flexible enough to reflect those treatment-related problems often encountered in real-life and clinical practice, but that cannot be adequately represented in randomized clinical trials (RCTs). At the same time, we described in detail all the structural elements of the model as well as its input parameters, in order to minimize any issue of transparency and facilitate the reproducibility and circulation of the results among researchers. Using the no AIT strategy as a comparator, and the Incremental Cost Effectiveness Ratio (ICER) as a statistic to summarize the cost-effectiveness of a health care intervention, we could conclude that: SCIT systematically outperforms SLIT, except when a full societal perspective is considered. For example, for T = 9 and a pollen season of 60 days, we have ICER = €16,729 for SCIT vs. ICER = €15,116 for SLIT (in the full societal perspective).For longer pollen seasons or longer follow-up duration the ICER decreases, because each patient experiences a greater clinical benefit over a larger time span, and Quality-adjusted Life Year (QALYs) gained per cycle increase accordingly.Assuming that no clinical benefit is achieved after premature discontinuation, and that at least three years of immunotherapy are required to improve clinical manifestations and perceiving a better quality of life, ICERs become far greater than €30,000.If the immunotherapy is effective only at the peak of the pollen season, the relative ICERs rise sharply. For example, in the scenario where no clinical benefit is present after premature discontinuation of immunotherapy, we have ICER = €74,770 for SCIT vs. ICER = €152,110 for SLIT.The distance between SCIT and SLIT strongly depends on under which model the interventions are meta-analyzed. Even though there is a considerable evidence that SCIT outperforms SLIT, we could not state that both SCIT and SLIT (or only one of these two) can be considered cost-effective for ARC, as a reliable threshold value for cost-effectiveness set by national regulatory agencies for pharmaceutical products is missing. Moreover, the impact of model input parameters uncertainty on the reliability of our conclusions needs to be investigated further.

Quantification of the effect of Cr-coated-Zircaloy cladding during a short term station black out

Accident Tolerant Fuel (ATF) has the potential to improve the inherent safety of current reactors. In particular, an ATF Cr-coated Zirconium alloy (Cr-coated-Zr) cladding can be an effective short-term approach to improve the cladding oxidation resistance without changing the cladding base materials. In order to appropriately assess the effect of such an ATF concept it is important to quantify the performance of such a cladding improvement not only during normal operation but also during postulated accidents. In this work, we examine the effect of Cr-coated-Zr cladding (Cr-Zr) during a short-term station blackout (STSBO) accident as well as quantify the uncertainty in the use of ATF Cr-coated-Zr cladding. This is accomplished using a coated cladding model developed for the MELCOR severe accident systems code; i.e., MELCOR-1.8.6-UDGC model. First, we quantify the effect of the coated cladding using a set of base case simulations that show the improved oxidation resistance of the Cr-coated-Zr cladding with the UDGC model parameters. Then, we quantify the uncertainty in these simulations by a Monte-Carlo analysis that examines the effect of the ATF clad input parameters (e.g., oxidation rate and failure temperature) on key output parameters; i.e., timing of rapid hydrogen generation, clad temperatures and hot leg creep rupture. Finally, we use regression analysis methods to estimate the importance of these input parameters. These analyses show that the Cr-coated-Zr cladding can delay rapid clad oxidation and generation of hydrogen by 3600 s to 7200 s with associated delays in rapid clad temperature increases. This additional time can be of benefit for compensatory operator actions. In contrast there is little effect on the timing of hot leg creep rupture since this failure is mainly controlled by core decay heat. We also find that these results are not significantly affected by the uncertainty in input parameters, and based on the regression analysis, the high temperature Zr oxidation rate has the major influence on the selected output parameters for the Cr-coated-Zr cladding.

Sensitivity of macroscopic transport calculations to uncertain microscale relationships during metal alloy solidification

The formation of grains during metallic solidification results in a multiphase system consisting of many moving interfaces. Models have been formulated in terms of variables describing the microscopic details of interface motion during solidification, but the computational cost prohibits their extension to the length scale of industrial castings. For this reason, macroscopic transport equations are mathematically formulated using averaging methods to replace the instantaneous description of interfaces. The most common approach is the volume-averaged, Eulerian approach relying on both the proper derivation of macroscopic field equations as well as uncertain interfacial conditions used to account for the average behavior of the interphase transfer occurring on the sub-grid length scale. In this work, common models used to describe the microsegregation occurring during solidification are evaluated in terms of the uncertainty of parametric inputs invoked in each model and their effect on macroscale predictions. We show that the qualitative macroscale predictions are not significantly changed by using more sophisticated microsegregation models, however significant uncertainty in input parameters used to describe the unresolved sub-grid interface is accumulated. Therefore, the use of simplified models is preferred until the uncertainty in model inputs can be reduced.

Uncertainty and influence of input parameters and assumptions on the design and analysis of thermochemical waste conversion processes: A stochastic approach

Process design is a challenging task for researchers and engineers. Incomplete information and variation in input data affect the outputs and reliability of key performance indicators (KPIs) of the designed process. The efficient utilization of waste is becoming increasingly important, and researchers use simulation and modelling tools for design and assessment of waste conversion processes. The complex nature of modelling of waste conversion processes and uncertainty of technical and financial data result in substantial variation in the KPIs of the designed process. In this study, we identified the critical parameters and assumptions that cause uncertainty in the process design analysis of waste-to-biofuels conversions. We used a stochastic modelling approach to address these methodological challenges and performed Monte Carlo simulations on waste-to-biofuel processes. The identified uncertain parameters and inputs were varied for a whole year with a one-minute time step. Different thermochemical conversion pathways were modelled by varying uncertain inputs and assumptions over the year by applying Monte Carlo simulations. Variations in the system’s technical and economic KPIs were observed and compared. The results show that the heterogeneous nature of waste is a highly sensitive parameter, and a small change in its elemental analysis varies the technical performance significantly. Similarly, operating hours, plant size, capital investment, waste, and biofuel price are also very influential parameters on process design. Furthermore, the feasibility of waste-to-biofuel systems depends largely on how researchers and engineers select these parameters. Overall, the results reveal that by including the uncertainty of input parameters and assumptions in process design, the biases in results could be addressed transparently, making the overall assessment more reliable.

Economic evaluation of health education programs: The case of adolescent reproductive health in Vietnam

ABSTRACT Objectives: To examine the cost-effectiveness of three mutually exclusive reproductive health interventions (named level A, B and C) implemented in Chi Linh for adolescents. Methods: This research involved the standard techniques of cost-utility analysis. A Markov state-transition model was used to show how a hypothetical cohort of 100,000 adolescents moved between 11 different health states over time, beginning at the year the intervention commenced (2011) and continuing in 3-month increments. The uncertainty of model input parameters was examined in probabilistic sensitivity analyses. Results: The deterministic analysis and uncertainty analysis revealed that implementing intervention level B compared to level A was highly cost effective. The ICER of intervention level B in relation to level A was USD 915/QALY gained. Implementing intervention level C compared to level B was cost-effective. The ICER of intervention level C over level B was USD 1911/QALY gained. Conclusions: If the decision makers including the intervention team, Ministry of Health and Ministry of Education and Training, change their current practice of educating adolescents on reproductive health to the best option, costs would be incurred at the lowest amount and health benefits would be brought back to the highest level.

Variation in the carbon footprint of milk production on smallholder dairy farms in central Kenya

Milk production by smallholders in Africa has a high carbon footprint (CF) and is predicted to increase significantly in the coming decades. This study, based on data from a sample of 382 farms in central Kenya, is the first assessment of the CF of milk production in Sub-Saharan Africa based on a large dataset of actual farm management practices. The aims of the study were (1) to determine whether there are significant differences in the CF of farms with different feeding systems (i.e., zero-grazing, grazing and mixed systems), and (2) to identify factors associated with variability in CF between farms. This analysis is used to identify options for mitigating GHG emissions from Kenya’s growing dairy production. Average CF ranged between 2.19 and 3.13 kg CO2e/kg fat and protein corrected milk (FPCM), depending on the GWPs and allocation method used. Analysis based on variability in farm management showed that CF was similar between farms with zero-grazing and mixed feeding systems, and significantly higher on farms with grazing only feeding systems, but no difference was detected when input parameter uncertainty was considered. At individual cow level, variation in milk yields explained more than 70% of the variation in GHG intensity. At farm level, milk yield explained less than half of variation in CF. CF was correlated with feed characteristics, manure management practices and herd size and composition. In particular, the level of concentrate use was positively correlated with CF, and was the most important factor explaining variation in CF not attributable to variation in milk yield. Our findings suggest that promoting balanced feed rations and feeding concentrate according to cows’ needs across the lactation cycle could provide opportunities to both increase milk production and reduce the CF of milk production on smallholder farms in central Kenya. Supporting smallholder farmers to implement these mitigation options will require interventions at several levels in feed supply chains in the dairy sector.

Cost-effectiveness analysis of two attachment systems for mandibular overdenture.

ObjectivesThis study analysed the cost‐effectiveness of two different attachments for the 2‐implant overdenture (2IOD) in edentulous mandibles.Materials and methodsWhen considering alternative treatments, cost‐effectiveness analysis is an important factor for stakeholders (patient, clinician, social security, insurance company, etc.). A general practice population (n = 116) was treated between 2003 and 2013 with a mandibular 2IOD with 2 different ball/stud attachment systems, one spherical (Group D) and one cylindrical (Group L). Patient well‐being was assessed with OHIP‐14‐Total (OHIP‐14‐T), at intake and annually up to 5 years, to calculate the health effect. Initial and maintenance costs of both treatments were inventoried. The cost‐effectiveness was compared. Annual discount rates of 4% and 1.5% were applied to future costs and health outcomes, following Dutch guidelines. Prices were adjusted to the year 2003. To offset the uncertainty in relevant input parameters, a sensitivity analysis was performed using bootstrap analysis. Significance was set at p < .05.ResultsThe health effect was 6.36 (SD 5.32) for Group D and 8.54 (SD 5.63) for Group L. The sum of the discounted costs up to 5 years was EUR 4,210.98 (SD 634.75) for the D and EUR 3,840.62 (SD 302.63) for the Group L (p = .005). The bootstrapping reports that L abutment clearly dominates the D abutment in terms of cost‐effectiveness.ConclusionsThe 2IOD on the L abutment is dominant compared to the 2IOD on D abutment, in a 5‐year perspective.