Home  << 1 >> 
Affolter, C., Kedzierska, J., Vielma, T., Weisse, B., & Aiyangar, A. (2020). Estimating lumbar passive stiffness behaviour from subjectspecific finite element models and in vivo 6DOF kinematics. J. Biomech., 102, 11 pp.
Abstract: Passive rotational stiffness of the osseoligamentous spine is an important input parameter for estimating invivo spinal loading using musculoskeletal models. These data are typically acquired from cadaveric testing. Increasingly, they are also estimated from subjectspecific imagingbased finite element (FE) models, which are typically built from CT/MR data obtained in supine position and employ pure rotation kinematics. We explored the sensitivity of FEbased lumbar passive rotational stiffness to two aspects of functional invivo kinematics: (a) passive strain changes from supine to upright standing position, and (b) invivo coupled translationrotation kinematics. We developed subjectspecific FE models of four subjects' L4L5 segments from supine CT images. Sagittally symmetric flexion was simulated in two ways: (i) pure flexion up to 12 degrees under a 500 N follower load directly from the supine pose. (ii) First, a displacementbased approach was implemented to attain the upright pose, as measured using Dynamic Stereo Xray (DSX) imaging. We then simulated invivo flexion using DSX imagingderived kinematics. Datasets from weightbearing motion with three different external weights [(4.5 kg), (9.1 kg), (13.6 kg)] were used. Accounting for supineupright motion generated compressive preloads approximate to 468 N (+/ 188 N) and a “pretorque” approximate to 2.5 Nm (+/ 2.2 Nm), corresponding to 25% of the reaction moment at 10 degrees flexion (case (i)). Rotational stiffness estimates from DSXbased coupled translationrotation kinematics were substantially higher compared to pure flexion. Reaction Moments were almost 90% and 60% higher at 5 degrees and 10 degrees of L4L5 flexion, respectively. Withinsubject differences in rotational stiffness based on external weight were small, although betweensubject variations were large. (C) 2020 Elsevier Ltd. All rights reserved.

Canessa, E., & Chaigneau, S. (2015). Calibrating AgentBased Models Using a Genetic Algorithm. Stud. Inform. Control, 24(1), 79–90.
Abstract: We present a Genetic Algorithm (GA)based tool that calibrates Agentbased Models (ABMs). The GA searches through a userdefined set of input parameters of an ABM, delivering values for those parameters so that the output time series of an ABM may match the real system's time series to certain precision. Once that set of possible values has been available, then a domain expert can select among them, the ones that better make sense from a practical point of view and match the explanation of the phenomenon under study. In developing the GA, we have had three main goals in mind. First, the GA should be easily used by nonexpert computer users and allow the seamless integration of the GA with different ABMs. Secondly, the GA should achieve a relatively short convergence time, so that it may be practical to apply it to many situations, even if the corresponding ABMs exhibit complex dynamics. Thirdly, the GA should use a few data points of the real system's time series and even so, achieve a sufficiently good match with the ABM's time series to attaining relational equivalence between the real system under study and the ABM that models it. That feature is important since social science longitudinal studies commonly use few data points. The results show that all of those goals have been accomplished.

Da Silva, C., Astals, S., Peces, M., Campos, J. L., & Guerrero, L. (2018). Biochemical methane potential (BMP) tests: Reducing test time by early parameter estimation. Waste Manage., 71, 19–24.
Abstract: Biochemical methane potential (BMP) test is a key analytical technique to assess the implementation and optimisation of anaerobic biotechnologies. However, this technique is characterised by long testing times (from 20 to > 100 days), which is not suitable for waste utilities, consulting companies or plants operators whose decisionmaking processes cannot be held for such a long time. This study develops a statistically robust mathematical strategy using sensitivity functions for early prediction of BMP firstorder model parameters, i.e. methane yield (B0) and kinetic constant rate (k). The minimum testing time for early parameter estimation showed a potential correlation with the k value, where (i) slowly biodegradable substrates (k <= 0.1 d(1)) have a minimum testing times of >= 15 days, (ii) moderately biodegradable substrates (0.1 < k < 0.2 d(1)) have a minimum testing times between 8 and 15 days, and (iii) rapidly biodegradable substrates (k > 0.2 d(1)) have testing times lower than 7 days. (C) 2017 Elsevier Ltd. All rights reserved.

Da Silva, C., Peces, M., Faundez, M., Hansen, H., Campos, J. L., Dosta, J., et al. (2022). Gamma distribution function to understand anaerobic digestion kinetics: Kinetic constants are not constant. Chemosphere, 306, 135579.
Abstract: The Gamma model is a novel approach to characterise the complex degradation dynamics taking place during anaerobic digestion. This three parameters model results from combining the firstorder kinetic model and the Gamma distribution function. In contrast to conventional models, where the kinetic constant is considered invariant, the Gamma model allows analysing the variability of the kinetic constant using a probability density function. The kinetic constant of monodigestion and codigestion batch tests of different wastes were modelled using the Gamma model and two common firstorder models: onestep onefraction model and onestep twofraction model. The Gamma distribution function approximates three distinct probability density functions, i.e. exponential, lognormal, and delta Dirac. Specifically, (i) cattle paunch and pig manure approximated a lognormal distribution; (ii) cattle manure and microalgae approximated an exponential distribution, and (iii) primary sludge and cellulose approximated a delta Dirac distribution. The Gamma model was able to characterise two distinct waste activated sludge, one approximated to a lognormal distribution and the other to an exponential distribution. The same cellulose was tested with two different inocula; in both tests, the Gamma distribution function approximated a delta Dirac function but with a different kinetic value. The potential and consistency of Gamma model were also evident when analysing pig manure and microalgae codigestion batch tests since (i) the mean k of the codigestion tests were within the values of the monodigestion tests, and (ii) the profile of the density function transitioned from lognormal to exponential distribution as the percentage of microalgae in the mixture increased.

O' Ryan, R., Benavides, C., Diaz, M., San Martin, J. P., & Mallea, J. (2019). Using probabilistic analysis to improve greenhouse gas baseline forecasts in developing country contexts: the case of Chile. Clim. Policy, 19(3), 299–314.
Abstract: In this paper, initial steps are presented toward characterizing, quantifying, incorporating and communicating uncertainty applying a probabilistic analysis to countrywide emission baseline forecasts, using Chile as a case study. Most GHG emission forecasts used by regulators are based on bottomup deterministic approaches. Uncertainty is usually incorporated through sensitivity analysis and/or use of different scenarios. However, much of the available information on uncertainty is not systematically included. The deterministic approach also gives a wide range of variation in values without a clear sense of probability of the expected emissions, making it difficult to establish both the mitigation contributions and the subsequent policy prescriptions for the future. To improve on this practice, we have systematically included uncertainty into a bottomup approach, incorporating it in key variables that affect expected GHG emissions, using readily available information, and establishing expected baseline emissions trajectories rather than scenarios. The resulting emission trajectories make explicit the probability percentiles, reflecting uncertainties as well as possible using readily available information in a manner that is relevant to the decision making process. Additionally, for the case of Chile, contradictory deterministic results are eliminated, and it is shown that, whereas under a deterministic approach Chile's mitigation ambition does not seem high, the probabilistic approach suggests this is not necessarily the case. It is concluded that using a probabilistic approach allows a better characterization of uncertainty using existing data and modelling capacities that are usually weak in developing country contexts. Key policy insights Probabilistic analysis allows incorporating uncertainty systematically into key variables for baseline greenhouse gas emission scenario projections. By using probabilistic analysis, the policymaker can be better informed as to future emission trajectories. Probabilistic analysis can be done with readily available data and expertise, using the usual models preferred by policymakers, even in developing country contexts.

Simon, F., Ordonez, J., Girard, A., & Parrado, C. (2019). Modelling energy use in residential buildings: How design decisions influence final energy performance in various Chilean climates. Indoor Built Environ., 28(4), 533–551.
Abstract: To reduce the energy consumption in buildings, there is a demand for tools that identify significant parameters of energy performance. The work presents the development and validation of a simulation model, called MEEDI, and graphical figures for the parametric sensitivity investigation of energy performance in different climates in Chile. The MEEDI is based on the ISO 13790 monthly calculation method of building energy use with two improved procedures for the calculation of the heat transfer through the floor and the solar heat gains. The graphical figures illustrate the effects of climate conditions, envelope components and window size and orientation on the energy consumption. The MEEDI program can contribute to find the best solution to increase energy efficiency in residential buildings. It can be adapted for various parameters, making it useful for future projects. The economic viability of specific measures for building envelope materials was analysed. Payback periods range from 5 to 27 years depending on the location and energy scenario. The study illustrates how building design decisions can have a significant impact on final energy performance. With simple envelope components modification, valuable energy gains and carbon emission reductions can be achieved in a costeffective manner in Chile.
