Decision making under uncertainty in environmental projects using mathematical simulation modeling

The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-016-6135-yIn decision-making processes, reliability and risk aversion play a decisive role. The aim of this study is to perform an uncertainty assessment of the effects of future scenarios of sustainable groundwater pumping strategies on the quantitative and chemical status of an aquifer. The good status of the aquifer is defined according to the terms established by the EU Water Framework Directive (WFD). A decision support systems (DSS) is presented, which makes use of a stochastic inverse model (GC method) and geostatistical approaches to calibrate equally likely realizations of hydraulic conductivity (K) fields for a particular case study. These K fields are conditional to available field data, including hard and soft information. Then, different future scenarios of groundwater pumping strategies are generated, based on historical information and WFD standards, and simulated for each one of the equally likely K fields. The future scenarios lead to different environmental impacts and levels of socioeconomic development of the region and, hence, to a different degree of acceptance among stakeholders. We have identified the different stakeholders implied in the decision-making process, the objectives pursued and the alternative actions that should be considered by stakeholders in a public participation project (PPP). The MonteCarlo simulation provides a highly effective way for uncertainty assessment and allows presenting the results in a simple and understandable way even for non-experts stakeholders. The methodology has been successfully applied to a real case study and lays the foundations to performa PPP and stakeholders' involvement in a decisionmaking process as required by the WFD. The results of the methodology can help the decision-making process to come up with the best policies and regulations for a groundwater system under uncertainty in groundwater parameters and management strategies and involving stakeholders with conflicting interests.Llopis Albert, C.; Palacios Marqués, D.; Merigó -Lindahl, JM. (2016). Decision making under uncertainty in environmental projects using mathematical simulation modeling. Environmental Earth Sciences. 75(19):1-11. doi:10.1007/s12665-016-6135-yS1117519Arhonditsis GB, Perhar G, Zhang W, Massos E, Shi M, Das A (2008) Addressing equifinality and uncertainty in eutrophication models. Water Resour Res 44:W01420. doi: 10.1029/2007WR005862Capilla JE, Llopis-Albert C (2009) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. 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Comput Geosci 16(4):395–440Harbaugh AW, Banta ER, Hill MC and McDonald MG (2000) MODFLOW- 2000, The US geological survey modular groundwater model-user guide to modularization concepts and the groundwater flow process. US Geol. Surv. Open-File Rep 00–92, 12Hu LY (2000) Gradual deformation and iterative calibration of Gaussian related stochastic models. Math Geol 32(1):87–108Jagelke J, Barthel R (2005) Conceptualization and implementation of a regional groundwater model for the Neckar catchment in the framework of an integrated regional model. Adv Geosci 5:105–111Llopis-Albert C (2008) Stochastic inverse modeling conditional to flow, mass transport and secondary information. Universitat Politècnica de València, València. ISBN 978-84-691-9796-7Llopis-Albert C, Capilla JE (2009a) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Demonstration on a synthetic aquifer. J Hydrol 371:53–55. doi: 10.1016/j.jhydrol.2009.03.014Llopis-Albert C, Capilla JE (2009b) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Application to the macrodispersion experiment (MADE-2) site, on Columbus air force base in Mississippi (USA). J Hydrol 371:75–84. doi: 10.1016/j.jhydrol.2009.03.016Llopis-Albert C, Capilla JE (2010a) Stochastic simulation of non-gaussian 3D conductivity fields in a fractured medium with multiple statistical populations: a case study. J Hydrol Eng 15(7):554–566. doi: 10.1061/(ASCE)HE.1943-5584.0000214Llopis-Albert C, Capilla JE (2010b) Stochastic inverse modeling of hydraulic conductivity fields taking into account independent stochastic structures: a 3D case study. J Hydrol 391:277–288. doi: 10.1016/j.jhydrol.2010.07.028Llopis-Albert C, Pulido-Velazquez D (2014) Discussion about the validity of sharp-interface models to deal with seawater intrusion in coastal aquifers. 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Prediction of lethal and synthetically lethal knock-outs in regulatory networks

The complex interactions involved in regulation of a cell's function are captured by its interaction graph. More often than not, detailed knowledge about enhancing or suppressive regulatory influences and cooperative effects is lacking and merely the presence or absence of directed interactions is known. Here we investigate to which extent such reduced information allows to forecast the effect of a knock-out or a combination of knock-outs. Specifically we ask in how far the lethality of eliminating nodes may be predicted by their network centrality, such as degree and betweenness, without knowing the function of the system. The function is taken as the ability to reproduce a fixed point under a discrete Boolean dynamics. We investigate two types of stochastically generated networks: fully random networks and structures grown with a mechanism of node duplication and subsequent divergence of interactions. On all networks we find that the out-degree is a good predictor of the lethality of a single node knock-out. For knock-outs of node pairs, the fraction of successors shared between the two knocked-out nodes (out-overlap) is a good predictor of synthetic lethality. Out-degree and out-overlap are locally defined and computationally simple centrality measures that provide a predictive power close to the optimal predictor.Comment: published version, 10 pages, 6 figures, 2 tables; supplement at http://www.bioinf.uni-leipzig.de/publications/supplements/11-01

Probabilistic abstract interpretation: From trace semantics to DTMC’s and linear regression

In order to perform probabilistic program analysis we need to consider probabilistic languages or languages with a probabilistic semantics, as well as a corresponding framework for the analysis which is able to accommodate probabilistic properties and properties of probabilistic computations. To this purpose we investigate the relationship between three different types of probabilistic semantics for a core imperative language, namely Kozen’s Fixpoint Semantics, our Linear Operator Semantics and probabilistic versions of Maximal Trace Semantics. We also discuss the relationship between Probabilistic Abstract Interpretation (PAI) and statistical or linear regression analysis. While classical Abstract Interpretation, based on Galois connection, allows only for worst-case analyses, the use of the Moore-Penrose pseudo inverse in PAI opens the possibility of exploiting statistical and noisy observations in order to analyse and identify various system properties

Health-related quality of life in Huntington’s Disease patients: a comparison of proxy assessment and patient self-rating using the disease-specific Huntington’s Disease health-related quality of life questionnaire (HDQoL)

Huntington’s disease (HD) is a fatal, neurodegenerative disease for which there is no known cure. Proxy evaluation is relevant for HD as its manifestation might limit the ability of persons to report their health-related quality of life (HrQoL). This study explored patient–proxy ratings of HrQoL of persons at different stages of HD, and examined factors that may affect proxy ratings. A total of 105 patient–proxy pairs completed the Huntington’s disease health-related quality of life questionnaire (HDQoL) and other established HrQoL measures (EQ-5D and SF-12v2). Proxy–patient agreement was assessed in terms of absolute level (mean ratings) and intraclass correlation. Proxies’ ratings were at a similar level to patients’ self-ratings on an overall Summary Score and on most of the six Specific Scales of the HDQoL. On the Specific Hopes and Worries Scale, proxies on average rated HrQoL as better than patients’ self-ratings, while on both the Specific Cognitive Scale and Specific Physical and Functional Scale proxies tended to rate HrQoL more poorly than patients themselves. The patient’s disease stage and mental wellbeing (SF-12 Mental Component scale) were the two factors that primarily affected proxy assessment. Proxy scores were strongly correlated with patients’ self-ratings of HrQoL, on the Summary Scale and all Specific Scales. The patient–proxy correlation was lower for patients at moderate stages of HD compared to patients at early and advanced stages. The proxy report version of the HDQoL is a useful complementary tool to self-assessment, and a promising alternative when individual patients with advanced HD are unable to self-report

Structural efficiency of percolation landscapes in flow networks

Complex networks characterized by global transport processes rely on the presence of directed paths from input to output nodes and edges, which organize in characteristic linked components. The analysis of such network-spanning structures in the framework of percolation theory, and in particular the key role of edge interfaces bridging the communication between core and periphery, allow us to shed light on the structural properties of real and theoretical flow networks, and to define criteria and quantities to characterize their efficiency at the interplay between structure and functionality. In particular, it is possible to assess that an optimal flow network should look like a "hairy ball", so to minimize bottleneck effects and the sensitivity to failures. Moreover, the thorough analysis of two real networks, the Internet customer-provider set of relationships at the autonomous system level and the nervous system of the worm Caenorhabditis elegans --that have been shaped by very different dynamics and in very different time-scales--, reveals that whereas biological evolution has selected a structure close to the optimal layout, market competition does not necessarily tend toward the most customer efficient architecture.Comment: 8 pages, 5 figure

Research collaboration in Tehran University of Medical Sciences: two decades after integration

Background: In 1985 medical schools were integrated into the Ministry of Health, and the Ministry of Health and Medical Education was created in Iran. Under this infrastructure education, research and service provision are unified, and it is expected that collaboration between researchers and decision makers become easier in such an integrated context. The question here is how the researchers behavior in the biggest medical university of the country towards collaboration is, i.e. how much do decision makers participate in different stages of research? Which factors affect it? Methodology: The samples under study were all Tehran University of Medical Sciences (TUMS) completed research projects that had gotten grants in 2004 and were over by the time this study was done. Two questionnaires were designed for this study: i) the research checklist which was filled for 301 projects, ii) the researcher's questionnaire, which was sent to principle investigators, 208 of which were collected. Multiple linear regression analysis was used for evaluating the potential factors affecting individuals 'collaboration score'. Results: Only 2.2 percent of TUMS' projects initiated in 2004 have had collaboration as a joint PI or co-investigator from non-academic organizations. The principle investigators mean collaboration score was 2.09, where 6 was the total score. So the collaboration score obtained was 35%. The 'type of research' had significant association with the collaboration score which is shown in the linear regression; collaboration was seen more in clinical (p = 0.007) and health system researches (p = 0.001) as compared to basic research. Conclusion: The present study shows that not many individuals collaborated as co-investigators from outside the university. This finding shows that research policy makers need to introduce interventions in this field. And assessment of barriers to collaboration and its facilitating factors should be considered in order to make it actually happen. © 2009 Majdzadeh et al; licensee BioMed Central Ltd