WorldWideScience

Sample records for risk models based

  1. RISK LOAN PORTFOLIO OPTIMIZATION MODEL BASED ON CVAR RISK MEASURE

    Directory of Open Access Journals (Sweden)

    Ming-Chang LEE

    2015-07-01

    Full Text Available In order to achieve commercial banks liquidity, safety and profitability objective requirements, loan portfolio risk analysis based optimization decisions are rational allocation of assets.  The risk analysis and asset allocation are the key technology of banking and risk management.  The aim of this paper, build a loan portfolio optimization model based on risk analysis.  Loan portfolio rate of return by using Value-at-Risk (VaR and Conditional Value-at-Risk (CVaR constraint optimization decision model reflects the bank's risk tolerance, and the potential loss of direct control of the bank.  In this paper, it analyze a general risk management model applied to portfolio problems with VaR and CVaR risk measures by using Using the Lagrangian Algorithm.  This paper solves the highly difficult problem by matrix operation method.  Therefore, the combination of this paper is easy understanding the portfolio problems with VaR and CVaR risk model is a hyperbola in mean-standard deviation space.  It is easy calculation in proposed method.

  2. Model based risk assessment - the CORAS framework

    Energy Technology Data Exchange (ETDEWEB)

    Gran, Bjoern Axel; Fredriksen, Rune; Thunem, Atoosa P-J.

    2004-04-15

    Traditional risk analysis and assessment is based on failure-oriented models of the system. In contrast to this, model-based risk assessment (MBRA) utilizes success-oriented models describing all intended system aspects, including functional, operational and organizational aspects of the target. The target models are then used as input sources for complementary risk analysis and assessment techniques, as well as a basis for the documentation of the assessment results. The EU-funded CORAS project developed a tool-supported methodology for the application of MBRA in security-critical systems. The methodology has been tested with successful outcome through a series of seven trial within the telemedicine and ecommerce areas. The CORAS project in general and the CORAS application of MBRA in particular have contributed positively to the visibility of model-based risk assessment and thus to the disclosure of several potentials for further exploitation of various aspects within this important research field. In that connection, the CORAS methodology's possibilities for further improvement towards utilization in more complex architectures and also in other application domains such as the nuclear field can be addressed. The latter calls for adapting the framework to address nuclear standards such as IEC 60880 and IEC 61513. For this development we recommend applying a trial driven approach within the nuclear field. The tool supported approach for combining risk analysis and system development also fits well with the HRP proposal for developing an Integrated Design Environment (IDE) providing efficient methods and tools to support control room systems design. (Author)

  3. Model-Based Mitigation of Availability Risks

    NARCIS (Netherlands)

    Zambon, Emmanuele; Bolzoni, D.; Etalle, Sandro; Salvato, Marco

    2007-01-01

    The assessment and mitigation of risks related to the availability of the IT infrastructure is becoming increasingly important in modern organizations. Unfortunately, present standards for Risk Assessment and Mitigation show limitations when evaluating and mitigating availability risks. This is due

  4. Risk Based Milk Pricing Model at Dairy Farmers Level

    Directory of Open Access Journals (Sweden)

    W. Septiani

    2017-12-01

    Full Text Available The milk price from a cooperative institution to farmer does not fully cover the production cost. Though, dairy farmers encounter various risks and uncertainties in conducting their business. The highest risk in milk supply lies in the activities at the farm. This study was designed to formulate a model for calculating milk price at farmer’s level based on risk. Risks that occur on farms include the risk of cow breeding, sanitation, health care, cattle feed management, milking and milk sales. This research used the location of the farm in West Java region. There were five main stages in the preparation of this model, (1 identification and analysis of influential factors, (2 development of a conceptual model, (3 structural analysis and the amount of production costs, (4 model calculation of production cost with risk factors, and (5 risk based milk pricing model. This research built a relationship between risks on smallholder dairy farms with the production costs to be incurred by the farmers. It was also obtained the formulation of risk adjustment factor calculation for the variable costs of production in dairy cattle farm. The difference in production costs with risk and the total production cost without risk was about 8% to 10%. It could be concluded that the basic price of milk proposed based on the research was around IDR 4,250-IDR 4,350/L for 3 to 4 cows ownership. Increasing farmer income was expected to be obtained by entering the value of this risk in the calculation of production costs. 

  5. Development of a GCR Event-based Risk Model

    Science.gov (United States)

    Cucinotta, Francis A.; Ponomarev, Artem L.; Plante, Ianik; Carra, Claudio; Kim, Myung-Hee

    2009-01-01

    A goal at NASA is to develop event-based systems biology models of space radiation risks that will replace the current dose-based empirical models. Complex and varied biochemical signaling processes transmit the initial DNA and oxidative damage from space radiation into cellular and tissue responses. Mis-repaired damage or aberrant signals can lead to genomic instability, persistent oxidative stress or inflammation, which are causative of cancer and CNS risks. Protective signaling through adaptive responses or cell repopulation is also possible. We are developing a computational simulation approach to galactic cosmic ray (GCR) effects that is based on biological events rather than average quantities such as dose, fluence, or dose equivalent. The goal of the GCR Event-based Risk Model (GERMcode) is to provide a simulation tool to describe and integrate physical and biological events into stochastic models of space radiation risks. We used the quantum multiple scattering model of heavy ion fragmentation (QMSFRG) and well known energy loss processes to develop a stochastic Monte-Carlo based model of GCR transport in spacecraft shielding and tissue. We validated the accuracy of the model by comparing to physical data from the NASA Space Radiation Laboratory (NSRL). Our simulation approach allows us to time-tag each GCR proton or heavy ion interaction in tissue including correlated secondary ions often of high multiplicity. Conventional space radiation risk assessment employs average quantities, and assumes linearity and additivity of responses over the complete range of GCR charge and energies. To investigate possible deviations from these assumptions, we studied several biological response pathway models of varying induction and relaxation times including the ATM, TGF -Smad, and WNT signaling pathways. We then considered small volumes of interacting cells and the time-dependent biophysical events that the GCR would produce within these tissue volumes to estimate how

  6. Task-based dermal exposure models for regulatory risk assessment

    NARCIS (Netherlands)

    Warren, N.D.; Marquart, H.; Christopher, Y.; Laitinen, J.; Hemmen, J.J. van

    2006-01-01

    The regulatory risk assessment of chemicals requires the estimation of occupational dermal exposure. Until recently, the models used were either based on limited data or were specific to a particular class of chemical or application. The EU project RISKOFDERM has gathered a considerable number of

  7. Model based climate information on drought risk in Africa

    Science.gov (United States)

    Calmanti, S.; Syroka, J.; Jones, C.; Carfagna, F.; Dell'Aquila, A.; Hoefsloot, P.; Kaffaf, S.; Nikulin, G.

    2012-04-01

    The United Nations World Food Programme (WFP) has embarked upon the endeavor of creating a sustainable Africa-wide natural disaster risk management system. A fundamental building block of this initiative is the setup of a drought impact modeling platform called Africa Risk-View that aims to quantify and monitor weather-related food security risk in Africa. The modeling approach is based the Water Requirement Satisfaction Index (WRSI), as the fundamental indicator of the performances of agriculture and uses historical records of food assistance operation to project future potential needs for livelihood protection. By using climate change scenarios as an input to Africa Risk-View it is possible, in principles, to evaluate the future impact of climate variability on critical issues such as food security and the overall performance of the envisaged risk management system. A necessary preliminary step to this challenging task is the exploration of the sources of uncertainties affecting the assessment based on modeled climate change scenarios. For this purpose, a limited set of climate models have been selected in order verify the relevance of using climate model output data with Africa Risk-View and to explore a minimal range of possible sources of uncertainty. This first evaluation exercise started before the setup of the CORDEX framework and has relied on model output available at the time. In particular only one regional downscaling was available for the entire African continent from the ENSEMBLES project. The analysis shows that current coarse resolution global climate models can not directly feed into the Africa RiskView risk-analysis tool. However, regional downscaling may help correcting the inherent biases observed in the datasets. Further analysis is performed by using the first data available under the CORDEX framework. In particular, we consider a set of simulation driven with boundary conditions from the reanalysis ERA-Interim to evaluate the skill drought

  8. Physics-based Entry, Descent and Landing Risk Model

    Science.gov (United States)

    Gee, Ken; Huynh, Loc C.; Manning, Ted

    2014-01-01

    A physics-based risk model was developed to assess the risk associated with thermal protection system failures during the entry, descent and landing phase of a manned spacecraft mission. In the model, entry trajectories were computed using a three-degree-of-freedom trajectory tool, the aerothermodynamic heating environment was computed using an engineering-level computational tool and the thermal response of the TPS material was modeled using a one-dimensional thermal response tool. The model was capable of modeling the effect of micrometeoroid and orbital debris impact damage on the TPS thermal response. A Monte Carlo analysis was used to determine the effects of uncertainties in the vehicle state at Entry Interface, aerothermodynamic heating and material properties on the performance of the TPS design. The failure criterion was set as a temperature limit at the bondline between the TPS and the underlying structure. Both direct computation and response surface approaches were used to compute the risk. The model was applied to a generic manned space capsule design. The effect of material property uncertainty and MMOD damage on risk of failure were analyzed. A comparison of the direct computation and response surface approach was undertaken.

  9. Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent-Based Model Approach.

    Science.gov (United States)

    Haer, Toon; Botzen, W J Wouter; de Moel, Hans; Aerts, Jeroen C J H

    2017-10-01

    Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent-based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss-reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low-probability/high-impact risks. © 2016 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.

  10. Time-based collision risk modeling for air traffic management

    Science.gov (United States)

    Bell, Alan E.

    Since the emergence of commercial aviation in the early part of last century, economic forces have driven a steadily increasing demand for air transportation. Increasing density of aircraft operating in a finite volume of airspace is accompanied by a corresponding increase in the risk of collision, and in response to a growing number of incidents and accidents involving collisions between aircraft, governments worldwide have developed air traffic control systems and procedures to mitigate this risk. The objective of any collision risk management system is to project conflicts and provide operators with sufficient opportunity to recognize potential collisions and take necessary actions to avoid them. It is therefore the assertion of this research that the currency of collision risk management is time. Future Air Traffic Management Systems are being designed around the foundational principle of four dimensional trajectory based operations, a method that replaces legacy first-come, first-served sequencing priorities with time-based reservations throughout the airspace system. This research will demonstrate that if aircraft are to be sequenced in four dimensions, they must also be separated in four dimensions. In order to separate aircraft in four dimensions, time must emerge as the primary tool by which air traffic is managed. A functional relationship exists between the time-based performance of aircraft, the interval between aircraft scheduled to cross some three dimensional point in space, and the risk of collision. This research models that relationship and presents two key findings. First, a method is developed by which the ability of an aircraft to meet a required time of arrival may be expressed as a robust standard for both industry and operations. Second, a method by which airspace system capacity may be increased while maintaining an acceptable level of collision risk is presented and demonstrated for the purpose of formulating recommendations for procedures

  11. Climate-based risk models for Fasciola hepatica in Colombia.

    Science.gov (United States)

    Valencia-López, Natalia; Malone, John B; Carmona, Catalina Gómez; Velásquez, Luz E

    2012-09-01

    A predictive Fasciola hepatica model, based on the growing degree day-water budget (GDD-WB) concept and the known biological requirements of the parasite, was developed within a geographical information system (GIS) in Colombia. Climate-based forecast index (CFI) values were calculated and represented in a national-scale, climate grid (18 x 18 km) using ArcGIS 9.3. A mask overlay was used to exclude unsuitable areas where mean annual temperature exceeded 25 °C, the upper threshold for development and propagation of the F. hepatica life cycle. The model was then validated and further developed by studies limited to one department in northwest Colombia. F. hepatica prevalence data was obtained from a 2008-2010 survey in 10 municipalities of 6,016 dairy cattle at 673 herd study sites, for which global positioning system coordinates were recorded. The CFI map results were compared to F. hepatica environmental risk models for the survey data points that had over 5% prevalence (231 of the 673 sites) at the 1 km2 scale using two independent approaches: (i) a GIS map query based on satellite data parameters including elevation, enhanced vegetation index and land surface temperature day-night difference; and (ii) an ecological niche model (MaxEnt), for which geographic point coordinates of F. hepatica survey farms were used with BioClim data as environmental variables to develop a probability map. The predicted risk pattern of both approaches was similar to that seen in the forecast index grid. The temporal risk, evaluated by the monthly CFIs and a daily GDD-WB forecast software for 2007 and 2008, revealed a major July-August to January transmission period with considerable inter-annual differences.

  12. Climate-based risk models for Fasciola hepatica in Colombia

    Directory of Open Access Journals (Sweden)

    Natalia Valencia-López

    2012-09-01

    Full Text Available A predictive Fasciola hepatica model, based on the growing degree day-water budget (GDD-WB concept and the known biological requirements of the parasite, was developed within a geographical information system (GIS in Colombia. Climate-based forecast index (CFI values were calculated and represented in a national-scale, climate grid (18 x 18 km using ArcGIS 9.3. A mask overlay was used to exclude unsuitable areas where mean annual temperature exceeded 25 °C, the upper threshold for development and propagation of the F. hepatica life cycle. The model was then validated and further developed by studies limited to one department in northwest Colombia. F. hepatica prevalence data was obtained from a 2008-2010 survey in 10 municipalities of 6,016 dairy cattle at 673 herd study sites, for which global positioning system coordinates were recorded. The CFI map results were compared to F. hepatica environmental risk models for the survey data points that had over 5% prevalence (231 of the 673 sites at the 1 km2 scale using two independent approaches: (i a GIS map query based on satellite data parameters including elevation, enhanced vegetation index and land surface temperature day-night difference; and (ii an ecological niche model (MaxEnt, for which geographic point coordinates of F. hepatica survey farms were used with BioClim data as environmental variables to develop a probability map. The predicted risk pattern of both approaches was similar to that seen in the forecast index grid. The temporal risk, evaluated by the monthly CFIs and a daily GDD-WB forecast software for 2007 and 2008, revealed a major July-August to January transmission period with considerable inter-annual differences.

  13. Quantified Risk Ranking Model for Condition-Based Risk and Reliability Centered Maintenance

    Science.gov (United States)

    Chattopadhyaya, Pradip Kumar; Basu, Sushil Kumar; Majumdar, Manik Chandra

    2017-06-01

    In the recent past, risk and reliability centered maintenance (RRCM) framework is introduced with a shift in the methodological focus from reliability and probabilities (expected values) to reliability, uncertainty and risk. In this paper authors explain a novel methodology for risk quantification and ranking the critical items for prioritizing the maintenance actions on the basis of condition-based risk and reliability centered maintenance (CBRRCM). The critical items are identified through criticality analysis of RPN values of items of a system and the maintenance significant precipitating factors (MSPF) of items are evaluated. The criticality of risk is assessed using three risk coefficients. The likelihood risk coefficient treats the probability as a fuzzy number. The abstract risk coefficient deduces risk influenced by uncertainty, sensitivity besides other factors. The third risk coefficient is called hazardous risk coefficient, which is due to anticipated hazards which may occur in the future and the risk is deduced from criteria of consequences on safety, environment, maintenance and economic risks with corresponding cost for consequences. The characteristic values of all the three risk coefficients are obtained with a particular test. With few more tests on the system, the values may change significantly within controlling range of each coefficient, hence `random number simulation' is resorted to obtain one distinctive value for each coefficient. The risk coefficients are statistically added to obtain final risk coefficient of each critical item and then the final rankings of critical items are estimated. The prioritization in ranking of critical items using the developed mathematical model for risk assessment shall be useful in optimization of financial losses and timing of maintenance actions.

  14. Radiation risk estimation based on measurement error models

    CERN Document Server

    Masiuk, Sergii; Shklyar, Sergiy; Chepurny, Mykola; Likhtarov, Illya

    2017-01-01

    This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies.

  15. Lymphatic filariasis transmission risk map of India, based on a geo-environmental risk model.

    Science.gov (United States)

    Sabesan, Shanmugavelu; Raju, Konuganti Hari Kishan; Subramanian, Swaminathan; Srivastava, Pradeep Kumar; Jambulingam, Purushothaman

    2013-09-01

    The strategy adopted by a global program to interrupt transmission of lymphatic filariasis (LF) is mass drug administration (MDA) using chemotherapy. India also followed this strategy by introducing MDA in the historically known endemic areas. All other areas, which remained unsurveyed, were presumed to be nonendemic and left without any intervention. Therefore, identification of LF transmission risk areas in the entire country has become essential so that they can be targeted for intervention. A geo-environmental risk model (GERM) developed earlier was used to create a filariasis transmission risk map for India. In this model, a Standardized Filariasis Transmission Risk Index (SFTRI, based on geo-environmental risk variables) was used as a predictor of transmission risk. The relationship between SFTRI and endemicity (historically known) of an area was quantified by logistic regression analysis. The quantified relationship was validated by assessing the filarial antigenemia status of children living in the unsurveyed areas through a ground truth study. A significant positive relationship was observed between SFTRI and the endemicity of an area. Overall, the model prediction of filarial endemic status of districts was found to be correct in 92.8% of the total observations. Thus, among the 190 districts hitherto unsurveyed, as many as 113 districts were predicted to be at risk, and the remaining at no risk. The GERM developed on geographic information system (GIS) platform is useful for LF spatial delimitation on a macrogeographic/regional scale. Furthermore, the risk map developed will be useful for the national LF elimination program by identifying areas at risk for intervention and for undertaking surveillance in no-risk areas.

  16. LIFETIME LUNG CANCER RISKS ASSOCIATED WITH INDOOR RADON EXPOSURE BASED ON VARIOUS RADON RISK MODELS FOR CANADIAN POPULATION.

    Science.gov (United States)

    Chen, Jing

    2017-04-01

    This study calculates and compares the lifetime lung cancer risks associated with indoor radon exposure based on well-known risk models in the literature; two risk models are from joint studies among miners and the other three models were developed from pooling studies on residential radon exposure from China, Europe and North America respectively. The aim of this article is to make clear that the various models are mathematical descriptions of epidemiologically observed real risks in different environmental settings. The risk from exposure to indoor radon is real and it is normal that variations could exist among different risk models even when they were applied to the same dataset. The results show that lifetime risk estimates vary significantly between the various risk models considered here: the model based on the European residential data provides the lowest risk estimates, while models based on the European miners and Chinese residential pooling with complete dosimetry give the highest values. The lifetime risk estimates based on the EPA/BEIR-VI model lie within this range and agree reasonably well with the averages of risk estimates from the five risk models considered in this study. © Crown copyright 2016.

  17. Risk assessment and model for community-based construction ...

    African Journals Online (AJOL)

    The consequences of not assessing and managing construction risks are that projects may experience time and cost overruns and lead to poor quality structures. It, therefore, becomes necessary to systematically manage uncertainty in community-based construction in order to increase the likelihood of meeting project ...

  18. Agent-Based Modelling for Security Risk Assessment

    NARCIS (Netherlands)

    Janssen, S.A.M.; Sharpans'kykh, Alexei; Bajo, J.; Vale, Z.; Hallenborg, K.; Rocha, A.P.; Mathieu, P.; Pawlewski, P.; Del Val, E.; Novais, P.; Lopes, F.; Duque Méndez, N.D.; Julián, V.; Holmgren, J.

    2017-01-01

    Security Risk Assessment is commonly performed by using traditional methods based on linear probabilistic tools and informal expert judgements. These methods lack the capability to take the inherent dynamic and intelligent nature of attackers into account. To partially address the limitations,

  19. Model-based risk analysis of coupled process steps.

    Science.gov (United States)

    Westerberg, Karin; Broberg-Hansen, Ernst; Sejergaard, Lars; Nilsson, Bernt

    2013-09-01

    A section of a biopharmaceutical manufacturing process involving the enzymatic coupling of a polymer to a therapeutic protein was characterized with regards to the process parameter sensitivity and design space. To minimize the formation of unwanted by-products in the enzymatic reaction, the substrate was added in small amounts and unreacted protein was separated using size-exclusion chromatography (SEC) and recycled to the reactor. The quality of the final recovered product was thus a result of the conditions in both the reactor and the SEC, and a design space had to be established for both processes together. This was achieved by developing mechanistic models of the reaction and SEC steps, establishing the causal links between process conditions and product quality. Model analysis was used to complement the qualitative risk assessment, and design space and critical process parameters were identified. The simulation results gave an experimental plan focusing on the "worst-case regions" in terms of product quality and yield. In this way, the experiments could be used to verify both the suggested process and the model results. This work demonstrates the necessary steps of model-assisted process analysis, from model development through experimental verification. Copyright © 2013 Wiley Periodicals, Inc.

  20. Physiologically-based kinetic modelling in risk assessment

    Science.gov (United States)

    The European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM) hosted a two-day workshop with an aim to discuss the role and application of Physiologically Based Kinetic (PBK) models in regulatory decision making. The EURL ECVAM strategy document on Toxic...

  1. An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps.

    Science.gov (United States)

    Subramanian, Jayashree; Karmegam, Akila; Papageorgiou, Elpiniki; Papandrianos, Nikolaos; Vasukie, A

    2015-03-01

    There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reported work is on the development of an integrated risk prediction model using a two-level fuzzy cognitive map (FCM) model. The proposed model combines the results of the initial screening mammogram of the given woman with her demographic risk factors to predict the post-screening risk of developing BC. The level-1 FCM models the demographic risk profile. A nonlinear Hebbian learning algorithm is used to train this model and thus to help on predicting the BC risk grade based on demographic risk factors identified by domain experts. The risk grades estimated by the proposed model are validated using two standard BC risk assessment models viz. Gail and Tyrer-Cuzick. The level-2 FCM models the features of the screening mammogram concerning normal, benign and malignant cases. The data driven Hebbian learning algorithm (DDNHL) is used to train this model in order to predict the BC risk grade based on these mammographic image features. An overall risk grade is calculated by combining the outcomes of these two FCMs. The main limitation of the Gail model of underestimating the risk level of women with strong family history is overcome by the proposed model. IBIS is a hard computing tool based on the Tyrer-Cuzick model that is comprehensive enough in covering a wide range of demographic risk factors including family history, but it generates results in terms of numeric risk score based on predefined formulae. Thus the outcome is difficult to interpret by naive users. Besides these models are based only on the demographic details and do not take into account the findings of the screening mammogram. The proposed integrated model overcomes the above described limitations of the existing models and predicts the risk level in terms of qualitative grades. The predictions of the proposed NHL-FCM model comply with the Tyrer-Cuzick model for 36 out of

  2. Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling.

    Science.gov (United States)

    Wang, M; Lindberg, J; Klevebring, D; Nilsson, C; Mer, A S; Rantalainen, M; Lehmann, S; Grönberg, H

    2017-10-01

    Risk stratification of acute myeloid leukemia (AML) patients needs improvement. Several AML risk classification models based on somatic mutations or gene-expression profiling have been proposed. However, systematic and independent validation of these models is required for future clinical implementation. We performed whole-transcriptome RNA-sequencing and panel-based deep DNA sequencing of 23 genes in 274 intensively treated AML patients (Clinseq-AML). We also utilized the The Cancer Genome Atlas (TCGA)-AML study (N=142) as a second validation cohort. We evaluated six previously proposed molecular-based models for AML risk stratification and two revised risk classification systems combining molecular- and clinical data. Risk groups stratified by five out of six models showed different overall survival in cytogenetic normal-AML patients in the Clinseq-AML cohort (P-value0.5). Risk classification systems integrating mutational or gene-expression data were found to add prognostic value to the current European Leukemia Net (ELN) risk classification. The prognostic value varied between models and across cohorts, highlighting the importance of independent validation to establish evidence of efficacy and general applicability. All but one model replicated in the Clinseq-AML cohort, indicating the potential for molecular-based AML risk models. Risk classification based on a combination of molecular and clinical data holds promise for improved AML patient stratification in the future.

  3. Galactic Cosmic Ray Event-Based Risk Model (GERM) Code

    Science.gov (United States)

    Cucinotta, Francis A.; Plante, Ianik; Ponomarev, Artem L.; Kim, Myung-Hee Y.

    2013-01-01

    This software describes the transport and energy deposition of the passage of galactic cosmic rays in astronaut tissues during space travel, or heavy ion beams in patients in cancer therapy. Space radiation risk is a probability distribution, and time-dependent biological events must be accounted for physical description of space radiation transport in tissues and cells. A stochastic model can calculate the probability density directly without unverified assumptions about shape of probability density function. The prior art of transport codes calculates the average flux and dose of particles behind spacecraft and tissue shielding. Because of the signaling times for activation and relaxation in the cell and tissue, transport code must describe temporal and microspatial density of functions to correlate DNA and oxidative damage with non-targeted effects of signals, bystander, etc. These are absolutely ignored or impossible in the prior art. The GERM code provides scientists data interpretation of experiments; modeling of beam line, shielding of target samples, and sample holders; and estimation of basic physical and biological outputs of their experiments. For mono-energetic ion beams, basic physical and biological properties are calculated for a selected ion type, such as kinetic energy, mass, charge number, absorbed dose, or fluence. Evaluated quantities are linear energy transfer (LET), range (R), absorption and fragmentation cross-sections, and the probability of nuclear interactions after 1 or 5 cm of water equivalent material. In addition, a set of biophysical properties is evaluated, such as the Poisson distribution for a specified cellular area, cell survival curves, and DNA damage yields per cell. Also, the GERM code calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle in a selected material. The GERM code makes the numerical estimates of basic

  4. Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

    Directory of Open Access Journals (Sweden)

    Leandro dos Santos Maciel

    Full Text Available ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

  5. Risk assessment and model for community-based construction ...

    African Journals Online (AJOL)

    However, the involvement of local communities introduces a number of risks during the execution of projects as the individuals involved may not be conversant with construction and the procedures involved in the procurement processes of projects. The consequences of not assessing and managing construction risks are ...

  6. Recognition of risk situations based on endoscopic instrument tracking and knowledge based situation modeling

    Science.gov (United States)

    Speidel, Stefanie; Sudra, Gunther; Senemaud, Julien; Drentschew, Maximilian; Müller-Stich, Beat Peter; Gutt, Carsten; Dillmann, Rüdiger

    2008-03-01

    Minimally invasive surgery has gained significantly in importance over the last decade due to the numerous advantages on patient-side. The surgeon has to adapt special operation-techniques and deal with difficulties like the complex hand-eye coordination, limited field of view and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality (AR) techniques. In order to generate a context-aware assistance it is necessary to recognize the current state of the intervention using intraoperatively gained sensor data and a model of the surgical intervention. In this paper we present the recognition of risk situations, the system warns the surgeon if an instrument gets too close to a risk structure. The context-aware assistance system starts with an image-based analysis to retrieve information from the endoscopic images. This information is classified and a semantic description is generated. The description is used to recognize the current state and launch an appropriate AR visualization. In detail we present an automatic vision-based instrument tracking to obtain the positions of the instruments. Situation recognition is performed using a knowledge representation based on a description logic system. Two augmented reality visualization programs are realized to warn the surgeon if a risk situation occurs.

  7. Risk of portfolio with simulated returns based on copula model

    Science.gov (United States)

    Razak, Ruzanna Ab; Ismail, Noriszura

    2015-02-01

    The commonly used tool for measuring risk of a portfolio with equally weighted stocks is variance-covariance method. Under extreme circumstances, this method leads to significant underestimation of actual risk due to its multivariate normality assumption of the joint distribution of stocks. The purpose of this research is to compare the actual risk of portfolio with the simulated risk of portfolio in which the joint distribution of two return series is predetermined. The data used is daily stock prices from the ASEAN market for the period January 2000 to December 2012. The copula approach is applied to capture the time varying dependence among the return series. The results shows that the chosen copula families are not suitable to present the dependence structures of each bivariate returns. Exception for the Philippines-Thailand pair where by t copula distribution appears to be the appropriate choice to depict its dependence. Assuming that the t copula distribution is the joint distribution of each paired series, simulated returns is generated and value-at-risk (VaR) is then applied to evaluate the risk of each portfolio consisting of two simulated return series. The VaR estimates was found to be symmetrical due to the simulation of returns via elliptical copula-GARCH approach. By comparison, it is found that the actual risks are underestimated for all pairs of portfolios except for Philippines-Thailand. This study was able to show that disregard of the non-normal dependence structure of two series will result underestimation of actual risk of the portfolio.

  8. Measuring the coupled risks: A copula-based CVaR model

    Science.gov (United States)

    He, Xubiao; Gong, Pu

    2009-01-01

    Integrated risk management for financial institutions requires an approach for aggregating risk types (such as market and credit) whose distributional shapes vary considerably. The financial institutions often ignore risks' coupling influence so as to underestimate the financial risks. We constructed a copula-based Conditional Value-at-Risk (CVaR) model for market and credit risks. This technique allows us to incorporate realistic marginal distributions that capture essential empirical features of these risks, such as skewness and fat-tails while allowing for a rich dependence structure. Finally, the numerical simulation method is used to implement the model. Our results indicate that the coupled risks for the listed company's stock maybe are undervalued if credit risk is ignored, especially for the listed company with bad credit quality.

  9. Food Safety Risk Assessment in Whole Food Supply Chain Based on Catastrophe Model

    OpenAIRE

    Shangmei Zhao; Xuemei Yang

    2013-01-01

    The objective of this study was to develop a food safety-risk assessment model for the whole food supply chain. In order to assess whole risk of food safety, this study designed the evaluation index system from the perspective of the food chain, which considered agricultural production, food processing and food consumption three stages. Based on catastrophe model and stability theory, the risk of agricultural production, food processing and food consumption is evaluated. This study got the va...

  10. Risk-Based Models for Managing Data Privacy in Healthcare

    Science.gov (United States)

    AL Faresi, Ahmed

    2011-01-01

    Current research in health care lacks a systematic investigation to identify and classify various sources of threats to information privacy when sharing health data. Identifying and classifying such threats would enable the development of effective information security risk monitoring and management policies. In this research I put the first step…

  11. Enterprise Architecture-Based Risk and Security Modelling and Analysis

    NARCIS (Netherlands)

    Jonkers, Henk; Quartel, Dick; Kordy, Barbara; Ekstedt, Mathias; Seong Kim, Deng

    2016-01-01

    The growing complexity of organizations and the increasing number of sophisticated cyber attacks asks for a systematic and integral approach to Enterprise Risk and Security Management (ERSM). As enterprise architecture offers the necessary integral perspective, including the business and IT aspects

  12. Model-based Small Area Estimates of Cancer Risk Factors and Screening Behaviors - Small Area Estimates

    Science.gov (United States)

    These model-based estimates use two surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS). The two surveys are combined using novel statistical methodology.

  13. The Perceived Risk And Value Based Model Of Online Retailing

    OpenAIRE

    Figen YILDIRIM; Özgür ÇENGEL

    2012-01-01

    On the perspective of highly intensive and globally emphasized conceptual approach of online retailing, there dimensions come forward as the emerging competitive factors derived from the literature. Cited as time, speed, and valued-added offerings in terms of products and services, this paper aims to target the consumer’s value and risk perception as an attempt to discuss the overall issue of interest on the ground of online shopping behavior. Briefly, the problem formulation part of this pap...

  14. Model-Based Engineering for Supply Chain Risk Management

    Science.gov (United States)

    2015-09-30

    infrastructure [4]. This is especially true in government where the Clinger-Cohen Act has directed federal agencies to maximize their use of commercial-off...5]. This is especially true when the unique risks faced by government systems are factored in [5]. For instance, the conventional approach in most...to be assured that our adversaries cannot, “destroy power grids, water and sanitary services, induce mass flooding, release toxic/radioactive

  15. Mysterious multiculturalism: the risks of using model-based indices for making meaningful comparisons

    NARCIS (Netherlands)

    Duyvendak, J.W.; van Reekum, R.; El-Hajjari, F.; Bertossi, C.

    2013-01-01

    In this article, we discuss key problems of model-based indices and their indicators used by the students of cross-national differences in the field of immigration, integration and citizenship policies. Model-based indices aggregate scores on a variety of indicators. We scrutinize the risks of

  16. A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

    Science.gov (United States)

    Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

    2017-07-01

    An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

  17. A Risk Assessment Example for Soil Invertebrates Using Spatially Explicit Agent-Based Models

    DEFF Research Database (Denmark)

    Reed, Melissa; Alvarez, Tania; Chelinho, Sonia

    2016-01-01

    Current risk assessment methods for measuring the toxicity of plant protection products (PPPs) on soil invertebrates use standardized laboratory conditions to determine acute effects on mortality and sublethal effects on reproduction. If an unacceptable risk is identified at the lower tier...... population models for ubiquitous soil invertebrates (collembolans and earthworms) as refinement options in current risk assessment. Both are spatially explicit agent-based models (ABMs), incorporating individual and landscape variability. The models were used to provide refined risk assessments for different...... application scenarios of a hypothetical pesticide applied to potato crops (full-field spray onto the soil surface [termed “overall”], in-furrow, and soil-incorporated pesticide applications). In the refined risk assessment, the population models suggest that soil invertebrate populations would likely recover...

  18. Noninvasive Prediction of Erosive Esophagitis Using a Controlled Attenuation Parameter (CAP)-Based Risk Estimation Model.

    Science.gov (United States)

    Chung, Hyunsoo; Chon, Young Eun; Kim, Seung Up; Lee, Sang Kil; Jung, Kyu Sik; Han, Kwang-Hyub; Chon, Chae Yoon

    2016-02-01

    Erosive esophagitis and fatty liver share obesity and visceral fat as common critical pathogenesis. However, the relationship between the amount of hepatic fat and the severity of erosive esophagitis was not well investigated, and there is no risk estimation model for erosive esophagitis. To evaluate the relationship between the amount of hepatic fat and the severity of erosive esophagitis and then develop a risk estimation model for erosive esophagitis. We enrolled 1045 consecutive participants (training cohort, n = 705; validation cohort, n = 340) who underwent esophagogastroduodenoscopy and CAP. The relationship between severity of fatty liver and erosive esophagitis was investigated, and independent predictors for erosive esophagitis that have been investigated through logistic regression analyses were used as components for establishing a risk estimation model. The prevalence of erosive gastritis was 10.7 %, and the severity of erosive esophagitis was positively correlated with the degree of hepatic fatty accumulation (P CAP-based risk estimation model for erosive esophagitis using CAP, Body mass index, and significant alcohol Drinking as constituent variables was established and was dubbed the CBD score (AUROC = 0.819, range 0-11). The high-risk group (CBD score ≥3) showed significantly higher risk of having erosive esophagitis than the low-risk group (CBD score CAP-based risk model for predicting erosive esophagitis.

  19. Erosion risk assessment in the southern Amazon - Data Preprocessing, data base application and process based modelling

    Science.gov (United States)

    Schindewolf, Marcus; Herrmann, Marie-Kristin; Herrmann, Anne-Katrin; Schultze, Nico; Amorim, Ricardo S. S.; Schmidt, Jürgen

    2015-04-01

    The study region along the BR 16 highway belongs to the "Deforestation Arc" at the southern border of the Amazon rainforest. At the same time, it incorporates a land use gradient as colonization started in the 1975-1990 in Central Mato Grosso in 1990 in northern Mato Grosso and most recently in 2004-2005 in southern Pará. Based on present knowledge soil erosion is one of the key driver of soil degradation. Hence, there is a strong need to implement soil erosion control measures in eroding landscapes. Planning and dimensioning of such measures require reliable and detailed information on the temporal and spatial distribution of soil loss, sediment transport and deposition. Soil erosion models are increasingly used, in order to simulate the physical processes involved and to predict the effects of soil erosion control measures. The process based EROSION 3D simulation model is used for surveying soil erosion and deposition on regional catchments. Although EROSION 3D is a widespread, extensively validated model, the application of the model on regional scale remains challenging due to the enormous data requirements and complex data processing operations. In this context the study includes the compilation, validation and generalisation of existing land use and soil data in order to generate a consistent EROSION 3D input datasets. As a part of this process a GIS-linked data base application allows to transfer the original soil and land use data into model specific parameter files. This combined methodology provides different risk assessment maps for certain demands on regional scale. Besides soil loss and sediment transport, sediment pass over points into surface water bodies and particle enrichment can be simulated using the EROSION 3D model. Thus the estimation of particle bound nutrient and pollutant inputs into surface water bodies becomes possible. The study ended up in a user-friendly, timesaving and improved software package for the simulation of soil loss and

  20. Modeling of Ship Collision Risk Index Based on Complex Plane and Its Realization

    Directory of Open Access Journals (Sweden)

    Xiaoqin Xu

    2016-07-01

    Full Text Available Ship collision risk index is the basic and important concept in the domain of ship collision avoidance. In this paper, the advantages and deficiencies of the various calculation methods of ship collision risk index are pointed out. Then the ship collision risk model based on complex plane, which can well make up for the deficiencies of the widely-used evaluation model proposed by Kearon.J and Liu ruru is proposed. On this basis, the calculation method of collision risk index under the encountering situation of multi-ships is constructed, then the three-dimensional image and spatial curve of the risk index are figured out. Finally, single chip microcomputer is used to realize the model. And attaching this single chip microcomputer to ARPA is helpful to the decision-making of the marine navigators.

  1. Framing-based Choice: A Model of Decision-making Under Risk

    OpenAIRE

    Kriesler, Kobi; NITZAN, Shmuel

    2009-01-01

    In this study we propose an axiomatic theory of decision-making under risk that is based on a new approach to the modeling of framing that focuses on the subjective statistical dependence between prizes of compared lotteries. Unlike existing models that allow objective statistical dependence, as in Regret Theory, in our model the emphasis is on alternative subjective statistical dependence patterns that are induced by alternative descriptions of the lotteries, i.e., by alternative framing. A ...

  2. What Role for Biologically Based Dose–Response Models in Estimating Low-Dose Risk?

    Science.gov (United States)

    Crump, Kenny S.; Chen, Chao; Chiu, Weihsueh A.; Louis, Thomas A.; Portier, Christopher J.; Subramaniam, Ravi P.; White, Paul D.

    2010-01-01

    Background Biologically based dose–response (BBDR) models can incorporate data on biological processes at the cellular and molecular level to link external exposure to an adverse effect. Objectives Our goal was to examine the utility of BBDR models in estimating low-dose risk. Methods We reviewed the utility of BBDR models in risk assessment. Results BBDR models have been used profitably to evaluate proposed mechanisms of toxicity and identify data gaps. However, these models have not improved the reliability of quantitative predictions of low-dose human risk. In this commentary we identify serious impediments to developing BBDR models for this purpose. BBDR models do not eliminate the need for empirical modeling of the relationship between dose and effect, but only move it from the whole organism to a lower level of biological organization. However, in doing this, BBDR models introduce significant new sources of uncertainty. Quantitative inferences are limited by inter- and intraindividual heterogeneity that cannot be eliminated with available or reasonably anticipated experimental techniques. BBDR modeling does not avoid uncertainties in the mechanisms of toxicity relevant to low-level human exposures. Although implementation of BBDR models for low-dose risk estimation have thus far been limited mainly to cancer modeled using a two-stage clonal expansion framework, these problems are expected to be present in all attempts at BBDR modeling. Conclusions The problems discussed here appear so intractable that we conclude that BBDR models are unlikely to be fruitful in reducing uncertainty in quantitative estimates of human risk from low-level exposures in the foreseeable future. Use of in vitro data from recent advances in molecular toxicology in BBDR models is not likely to remove these problems and will introduce new issues regarding extrapolation of data from in vitro systems. PMID:20056564

  3. Statistical Assessement on Cancer Risks of Ionizing Radiation and Smoking Based on Poisson Models

    OpenAIRE

    Tomita, Makoto; Otake, Masanori

    2001-01-01

    In many epidemiological and medical studies, a number of cancer motralities in catagorical classification may be considered as having Poisson distribution with person-years at risk depending upon time. The cancer mortalities have been evaluated by additive or multiplicative models with regard to background and excess risks based on several covariances such as sex, age at the time of bombings, time at exposure, or ionizing radiation, cigarette smoking habits, duration of smoking habits, etc. A...

  4. A Two-Account Life Insurance Model for Scenario-Based Valuation Including Event Risk

    DEFF Research Database (Denmark)

    Jensen, Ninna Reitzel; Schomacker, Kristian Juul

    2015-01-01

    model by conducting scenario analysis based on Monte Carlo simulation, but the model applies to scenarios in general and to worst-case and best-estimate scenarios in particular. In addition to easy computations, our model offers a common framework for the valuation of life insurance payments across......Using a two-account model with event risk, we model life insurance contracts taking into account both guaranteed and non-guaranteed payments in participating life insurance as well as in unit-linked insurance. Here, event risk is used as a generic term for life insurance events, such as death...... and unit-linked insurance. By use of a two-account model, we are able to illustrate general concepts without making the model too abstract. To allow for complicated financial markets without dramatically increasing the mathematical complexity, we focus on economic scenarios. We illustrate the use of our...

  5. Early-onset sepsis: a predictive model based on maternal risk factors.

    Science.gov (United States)

    Puopolo, Karen M; Escobar, Gabriel J

    2013-04-01

    Neonatal early-onset sepsis (EOS) is a very low-incidence, but potentially fatal condition among term and late preterm newborns. EOS algorithms based on risk-factor threshold values result in evaluation and empiric antibiotic treatment of large numbers of uninfected newborns, leading to unnecessary antibiotic exposures and maternal/infant separation. Ideally, risk stratification should be quantitative, employ information conserving strategies, and be readily transferable to modern comprehensive electronic medical records. We performed a case-control study of infants born at or above 34 weeks' gestation with blood culture-proven EOS. We defined the relationship of established predictors to the risk of EOS, then used multivariate analyses and split validation to develop a predictive model using objective data. The model provides an estimation of sepsis risk that can identify the same proportion of EOS cases by evaluating fewer infants, as compared with algorithms based on subjective diagnoses and cut-off values for continuous predictors. An alternative approach to EOS risk assessment based only on objective data could decrease the number of infants evaluated and empirically treated for EOS, compared with currently recommended algorithms. Prospective evaluation is needed to determine the accuracy and safety of using the sepsis risk model to guide clinical decision-making.

  6. Addressing dependability by applying an approach for model-based risk assessment

    Energy Technology Data Exchange (ETDEWEB)

    Gran, Bjorn Axel [Institutt for energiteknikk, OECD Halden Reactor Project, NO-1751 Halden (Norway)]. E-mail: bjorn.axel.gran@hrp.no; Fredriksen, Rune [Institutt for energiteknikk, OECD Halden Reactor Project, NO-1751 Halden (Norway)]. E-mail: rune.fredriksen@hrp.no; Thunem, Atoosa P.-J. [Institutt for energiteknikk, OECD Halden Reactor Project, NO-1751 Halden (Norway)]. E-mail: atoosa.p-j.thunem@hrp.no

    2007-11-15

    This paper describes how an approach for model-based risk assessment (MBRA) can be applied for addressing different dependability factors in a critical application. Dependability factors, such as availability, reliability, safety and security, are important when assessing the dependability degree of total systems involving digital instrumentation and control (I and C) sub-systems. In order to identify risk sources their roles with regard to intentional system aspects such as system functions, component behaviours and intercommunications must be clarified. Traditional risk assessment is based on fault or risk models of the system. In contrast to this, MBRA utilizes success-oriented models describing all intended system aspects, including functional, operational and organizational aspects of the target. The EU-funded CORAS project developed a tool-supported methodology for the application of MBRA in security-critical systems. The methodology has been tried out within the telemedicine and e-commerce areas, and provided through a series of seven trials a sound basis for risk assessments. In this paper the results from the CORAS project are presented, and it is discussed how the approach for applying MBRA meets the needs of a risk-informed Man-Technology-Organization (MTO) model, and how methodology can be applied as a part of a trust case development.

  7. A Quantitative Risk Evaluation Model for Network Security Based on Body Temperature

    Directory of Open Access Journals (Sweden)

    Y. P. Jiang

    2016-01-01

    Full Text Available These days, in allusion to the traditional network security risk evaluation model, which have certain limitations for real-time, accuracy, characterization. This paper proposed a quantitative risk evaluation model for network security based on body temperature (QREM-BT, which refers to the mechanism of biological immune system and the imbalance of immune system which can result in body temperature changes, firstly, through the r-contiguous bits nonconstant matching rate algorithm to improve the detection quality of detector and reduce missing rate or false detection rate. Then the dynamic evolution process of the detector was described in detail. And the mechanism of increased antibody concentration, which is made up of activating mature detector and cloning memory detector, is mainly used to assess network risk caused by various species of attacks. Based on these reasons, this paper not only established the equation of antibody concentration increase factor but also put forward the antibody concentration quantitative calculation model. Finally, because the mechanism of antibody concentration change is reasonable and effective, which can effectively reflect the network risk, thus body temperature evaluation model was established in this paper. The simulation results showed that, according to body temperature value, the proposed model has more effective, real time to assess network security risk.

  8. Long-term Failure Prediction based on an ARP Model of Global Risk Network

    Science.gov (United States)

    Lin, Xin; Moussawi, Alaa; Szymanski, Boleslaw; Korniss, Gyorgy

    Risks that threaten modern societies form an intricately interconnected network. Hence, it is important to understand how risk materializations in distinct domains influence each other. In the paper, we study the global risks network defined by World Economic Forum experts in the form of Stochastic Block Model. We model risks as Alternating Renewal Processes with variable intensities driven by hidden values of exogenous and endogenous failure probabilities. Based on the expert assessments and historical status of each risk, we use Maximum Likelihood Evaluation to find the optimal model parameters and demonstrate that the model considering network effects significantly outperforms the others. In the talk, we discuss how the model can be used to provide quantitative means for measuring interdependencies and materialization of risks in the network. We also present recent results of long-term predictions in the form of predicated distributions of materializations over various time periods. Finally we show how the simulation of ARP's enables us to probe limits of the predictability of the system parameters from historical data and ability to recover hidden variable. Supported in part by DTRA, ARL NS-CTA.

  9. The application of Petri nets in a model based risk analysis framework

    Energy Technology Data Exchange (ETDEWEB)

    Braathe, Christian Andre; Kolstad, Kristian; Thunem, Atoosa P-J.

    2005-09-15

    In January 2001 a project called CORAS was started under the Information Society Technologies (IST) Programme. The main objective for the CORAS project was to develop a practical framework for a precise, unambiguous and effective risk analysis of security critical computerised systems. CORAS aimed to adapt, refine, extend and combine methods for risk analysis and methods for object oriented modelling together with a computerized tool to build a framework for model-based risk analysis (MBRA) of security critical systems. In very general terms, the CORAS framework uses system models created by using the Unified Modelling Language (herby known as UML) notation to analyse the systems' functionality. However, UML is not the only modelling language available, and it is not the best language for detailed system modelling, as it can become very large and therefore difficult to manage. Another possibility is to complement UML modelling with modelling by means of Petri nets (herby known as PN), which is a relative powerful modelling language. One of the great advantages of PNs is that it allows the modelling of concurrency, synchronization and resource sharing behaviour of systems. PNs also provide the possibility of a formal verification and it is also useful for detection and performance analysis because the corresponding models can be simulated. Yet, the question with respect to this work is: is it possible to improve the risk analysis process suggested by the CORAS framework when using PNs on the basis of the UML models? Accordingly, the main objective of the work is to investigate whether PNs can help us to improve the risk analysis process by supporting the system modelling activity. (Author)

  10. Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hamdy Abeer

    2016-01-01

    Full Text Available Credit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose. The accuracy of the model is crucial for any financial institution’s profitability. This paper provided a high accuracy credit scoring model that could be utilized with small and large datasets utilizing a principal component analysis (PCA based breakdown to the significance of the attributes commonly used in the credit scoring models. The proposed credit scoring model applied PCA to acquire the main attributes of the credit scoring data then an ANN classifier to determine the credit worthiness of an individual applicant. The performance of the proposed model was compared to other models in terms of accuracy and training time. Results, based on German dataset showed that the proposed model is superior to others and computationally cheaper. Thus it can be a potential candidate for future credit scoring systems.

  11. A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis

    NARCIS (Netherlands)

    Tervonen, Tommi; van Valkenhoef, Gert; Buskens, Erik; Hillege, Hans L.; Postmus, Douwe

    2011-01-01

    Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multicriteria model that fully takes into account the evidence on efficacy and adverse drug

  12. A Risk Prediction Model for Sporadic CRC Based on Routine Lab Results.

    Science.gov (United States)

    Boursi, Ben; Mamtani, Ronac; Hwang, Wei-Ting; Haynes, Kevin; Yang, Yu-Xiao

    2016-07-01

    Current risk scores for colorectal cancer (CRC) are based on demographic and behavioral factors and have limited predictive values. To develop a novel risk prediction model for sporadic CRC using clinical and laboratory data in electronic medical records. We conducted a nested case-control study in a UK primary care database. Cases included those with a diagnostic code of CRC, aged 50-85. Each case was matched with four controls using incidence density sampling. CRC predictors were examined using univariate conditional logistic regression. Variables with p value CRC prediction models which included age, sex, height, obesity, ever smoking, alcohol dependence, and previous screening colonoscopy had an AUC of 0.58 (0.57-0.59) with poor goodness of fit. A laboratory-based model including hematocrit, MCV, lymphocytes, and neutrophil-lymphocyte ratio (NLR) had an AUC of 0.76 (0.76-0.77) and a McFadden's R2 of 0.21 with a NRI of 47.6 %. A combined model including sex, hemoglobin, MCV, white blood cells, platelets, NLR, and oral hypoglycemic use had an AUC of 0.80 (0.79-0.81) with a McFadden's R2 of 0.27 and a NRI of 60.7 %. Similar results were shown in an internal validation set. A laboratory-based risk model had good predictive power for sporadic CRC risk.

  13. Large-scale model-based assessment of deer-vehicle collision risk.

    Science.gov (United States)

    Hothorn, Torsten; Brandl, Roland; Müller, Jörg

    2012-01-01

    Ungulates, in particular the Central European roe deer Capreolus capreolus and the North American white-tailed deer Odocoileus virginianus, are economically and ecologically important. The two species are risk factors for deer-vehicle collisions and as browsers of palatable trees have implications for forest regeneration. However, no large-scale management systems for ungulates have been implemented, mainly because of the high efforts and costs associated with attempts to estimate population sizes of free-living ungulates living in a complex landscape. Attempts to directly estimate population sizes of deer are problematic owing to poor data quality and lack of spatial representation on larger scales. We used data on >74,000 deer-vehicle collisions observed in 2006 and 2009 in Bavaria, Germany, to model the local risk of deer-vehicle collisions and to investigate the relationship between deer-vehicle collisions and both environmental conditions and browsing intensities. An innovative modelling approach for the number of deer-vehicle collisions, which allows nonlinear environment-deer relationships and assessment of spatial heterogeneity, was the basis for estimating the local risk of collisions for specific road types on the scale of Bavarian municipalities. Based on this risk model, we propose a new "deer-vehicle collision index" for deer management. We show that the risk of deer-vehicle collisions is positively correlated to browsing intensity and to harvest numbers. Overall, our results demonstrate that the number of deer-vehicle collisions can be predicted with high precision on the scale of municipalities. In the densely populated and intensively used landscapes of Central Europe and North America, a model-based risk assessment for deer-vehicle collisions provides a cost-efficient instrument for deer management on the landscape scale. The measures derived from our model provide valuable information for planning road protection and defining hunting quota. Open

  14. Trust in risk management: a model-based review of empirical research.

    Science.gov (United States)

    Earle, Timothy C

    2010-04-01

    This review of studies of trust in risk management was designed, in part, to examine the relations between the reviewed research and the consensus model of trust that has recently emerged in other fields of study. The review begins by briefly elaborating the consensus views on the dimensionality and function of trust. It then describes the various models of trust that have been developed in the field of risk management, comparing them with the consensus approach. The findings of previous reviews are outlined, followed by a delineation of the open questions addressed by the present review, the method used, and the results. Finally, the findings of the review are discussed in relation to the important issue of trust asymmetry, the role of trust in risk management, and directions for future research. The consensus model specifies two conceptualizations of trust, each linked to particular types of antecedents. Relational trust, which is called trust in this review, is based on the relations between the trusting person and the other. Calculative trust, which is called confidence, is based on past behavior of the other and/or on constraints on future behavior. Results of this review showed that most studies of trust in risk management, while exploring matters of particular concern to the risk management community, were at least in part consistent with the consensus model. The review concludes by urging greater integration between the concerns of the former and the insights of the latter.

  15. Holistic flood risk assessment using agent-based modelling: the case of Sint Maarten Island

    Science.gov (United States)

    Abayneh Abebe, Yared; Vojinovic, Zoran; Nikolic, Igor; Hammond, Michael; Sanchez, Arlex; Pelling, Mark

    2015-04-01

    Floods in coastal regions are regarded as one of the most dangerous and harmful disasters. Though commonly referred to as natural disasters, coastal floods are also attributable to various social, economic, historical and political issues. Rapid urbanisation in coastal areas combined with climate change and poor governance can lead to a significant increase in the risk of pluvial flooding coinciding with fluvial and coastal flooding posing a greater risk of devastation in coastal communities. Disasters that can be triggered by hydro-meteorological events are interconnected and interrelated with both human activities and natural processes. They, therefore, require holistic approaches to help understand their complexity in order to design and develop adaptive risk management approaches that minimise social and economic losses and environmental impacts, and increase resilience to such events. Being located in the North Atlantic Ocean, Sint Maarten is frequently subjected to hurricanes. In addition, the stormwater catchments and streams on Sint Maarten have several unique characteristics that contribute to the severity of flood-related impacts. Urban environments are usually situated in low-lying areas, with little consideration for stormwater drainage, and as such are subject to flash flooding. Hence, Sint Maarten authorities drafted policies to minimise the risk of flood-related disasters on the island. In this study, an agent-based model is designed and applied to understand the implications of introduced policies and regulations, and to understand how different actors' behaviours influence the formation, propagation and accumulation of flood risk. The agent-based model built for this study is based on the MAIA meta-model, which helps to decompose, structure and conceptualize socio-technical systems with an agent-oriented perspective, and is developed using the NetLogo simulation environment. The agents described in this model are households and businesses, and

  16. An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting

    Science.gov (United States)

    Faramarzi, F.; Ebrahimi Farsangi, M. A.; Mansouri, H.

    2013-07-01

    Most blasting operations are associated with various forms of energy loss, emerging as environmental side effects of rock blasting, such as flyrock, vibration, airblast, and backbreak. Backbreak is an adverse phenomenon in rock blasting operations, which imposes risk and increases operation expenses because of safety reduction due to the instability of walls, poor fragmentation, and uneven burden in subsequent blasts. In this paper, based on the basic concepts of a rock engineering systems (RES) approach, a new model for the prediction of backbreak and the risk associated with a blast is presented. The newly suggested model involves 16 effective parameters on backbreak due to blasting, while retaining simplicity as well. The data for 30 blasts, carried out at Sungun copper mine, western Iran, were used to predict backbreak and the level of risk corresponding to each blast by the RES-based model. The results obtained were compared with the backbreak measured for each blast, which showed that the level of risk achieved is in consistence with the backbreak measured. The maximum level of risk [vulnerability index (VI) = 60] was associated with blast No. 2, for which the corresponding average backbreak was the highest achieved (9.25 m). Also, for blasts with levels of risk under 40, the minimum average backbreaks (<4 m) were observed. Furthermore, to evaluate the model performance for backbreak prediction, the coefficient of correlation ( R 2) and root mean square error (RMSE) of the model were calculated ( R 2 = 0.8; RMSE = 1.07), indicating the good performance of the model.

  17. Physicologically Based Toxicokinetic Models of Tebuconazole and Application in Human Risk Assessment.

    Science.gov (United States)

    Jónsdóttir, Svava Ósk; Reffstrup, Trine Klein; Petersen, Annette; Nielsen, Elsa

    2016-05-16

    A series of physiologically based toxicokinetic (PBTK) models for tebuconazole were developed in four species, rat, rabbit, rhesus monkey, and human. The developed models were analyzed with respect to the application of the models in higher tier human risk assessment, and the prospect of using such models in risk assessment of cumulative and aggregate exposure is discussed. Relatively simple and biologically sound models were developed using available experimental data as parameters for describing the physiology of the species, as well as the absorption, distribution, metabolism, and elimination (ADME) of tebuconazole. The developed models were validated on in vivo half-life data for rabbit with good results, and on plasma and tissue concentration-time course data of tebuconazole after i.v. administration in rabbit. In most cases, the predicted concentration levels were seen to be within a factor of 2 compared to the experimental data, which is the threshold set for the use of PBTK simulation results in risk assessment. An exception to this was seen for one of the target organs, namely, the liver, for which tebuconazole concentration was significantly underestimated, a trend also seen in model simulations for the liver after other nonoral exposure scenarios. Possible reasons for this are discussed in the article. Realistic dietary and dermal exposure scenarios were derived based on available exposure estimates, and the human version of the PBTK model was used to simulate the internal levels of tebuconazole and metabolites in the human body for these scenarios. By a variant of the models where the R(-)- and S(+)-enantiomers were treated as two components in a binary mixture, it was illustrated that the inhibition between the two tebuconazole enantiomers did not affect the simulation results for these realistic exposure scenarios. The developed models have potential as an important tool in risk assessment.

  18. Physics-Based Identification, Modeling and Risk Management for Aeroelastic Flutter and Limit-Cycle Oscillations (LCO) Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed research program will develop a physics-based identification, modeling and risk management infrastructure for aeroelastic transonic flutter and...

  19. Risk factors for addiction and their association with model-based behavioral control

    Directory of Open Access Journals (Sweden)

    Andrea Maria Franziska Reiter

    2016-03-01

    Full Text Available Addiction shows familial aggregation and previous endophenotype research suggests that healthy relatives of addicted individuals share altered behavioral and cognitive characteristics with individuals suffering from addiction. In this study we asked whether impairments in behavioral control proposed for addiction, namely a shift from goal-directed, model-based toward habitual model-free control, extends toward an unaffected sample (n=20 of adult children of alcohol-dependent fathers as compared to a sample without any personal or family history of alcohol addiction (n=17. Using a sequential decision-making task designed to investigate model-free and model-based control combined with a computational modeling analysis, we did not find any evidence for altered behavioral control in individuals with positive family history of alcohol addiction. Independent of family history of alcohol dependence, we however observed that the interaction of two different risk factors of addiction, namely impulsivity and cognitive capacities, predicts the balance of model-free and model-based behavioral control. Post-hoc tests showed an association of model-based behavior with cognitive capacity in the lower, but not in the higher impulsive group of the original sample. In an independent sample of particularly high vs. low impulsive individuals, we confirmed the interaction effect of cognitive capacities and high vs. low impulsivity on model-based control. In the confirmation sample, a positive association of omega with cognitive capacity was observed in high-impulsive individuals. Due to the moderate sample size of the study, further investigation of the association of risk factors for addiction with model-based behavior in larger sample sizes is warranted.

  20. Risk Factors for Addiction and Their Association with Model-Based Behavioral Control

    Science.gov (United States)

    Reiter, Andrea M. F.; Deserno, Lorenz; Wilbertz, Tilmann; Heinze, Hans-Jochen; Schlagenhauf, Florian

    2016-01-01

    Addiction shows familial aggregation and previous endophenotype research suggests that healthy relatives of addicted individuals share altered behavioral and cognitive characteristics with individuals suffering from addiction. In this study we asked whether impairments in behavioral control proposed for addiction, namely a shift from goal-directed, model-based toward habitual, model-free control, extends toward an unaffected sample (n = 20) of adult children of alcohol-dependent fathers as compared to a sample without any personal or family history of alcohol addiction (n = 17). Using a sequential decision-making task designed to investigate model-free and model-based control combined with a computational modeling analysis, we did not find any evidence for altered behavioral control in individuals with a positive family history of alcohol addiction. Independent of family history of alcohol dependence, we however observed that the interaction of two different risk factors of addiction, namely impulsivity and cognitive capacities, predicts the balance of model-free and model-based behavioral control. Post-hoc tests showed a positive association of model-based behavior with cognitive capacity in the lower, but not in the higher impulsive group of the original sample. In an independent sample of particularly high- vs. low-impulsive individuals, we confirmed the interaction effect of cognitive capacities and high vs. low impulsivity on model-based control. In the confirmation sample, a positive association of omega with cognitive capacity was observed in highly impulsive individuals, but not in low impulsive individuals. Due to the moderate sample size of the study, further investigation of the association of risk factors for addiction with model-based behavior in larger sample sizes is warranted. PMID:27013998

  1. The determination of risk areas for muddy floods based on a worst-case erosion modelling

    Science.gov (United States)

    Saathoff, Ulfert; Schindewolf, Marcus; Annika Arévalo, Sarah

    2013-04-01

    Soil erosion and muddy floods are a frequently occurring hazard in the German state of Saxony, because of the topography and the high relief energy together with the high proportion of arable land. Still, the events are rather heterogeneously distributed and we do not know where damage is likely to occur. The goal of this study is to locate hot spots for the risk of muddy floods, with the objective to prevent high economic damage in future. We applied a soil erosion and deposition map of Saxony, calculated with the process based soil erosion model EROSION 3D. This map shows the potential soil erosion and transported sediment for worst case soil conditions and a 10 year rain storm event. Furthermore, a map of the current landuse in the state is used. From the landuse map, we extracted those areas that are especially vulnerable to muddy floods, like residential and industrial areas, infrastructural facilities (e.g. power plants, hospitals) and highways. In combination with the output of the soil erosion model, the amount of sediment, that enters each single landuse entity, is calculated. Based on this data, a state-wide map with classified risks is created. The results are furthermore used to identify the risk of muddy floods for each single municipality in Saxony. The results are evaluated with data of real occurred muddy flood events with documented locations during the period between 2000 and 2010. Additionally, plausibility tests are performed for selected areas (examination of landuse, topography and soil). The results prove to be plausible and most of the documented events can be explained by the modelled risk map. The created map can be used by different institutions like city and traffic planners, to estimate the risk of muddy flood occurrence at specific locations. Furthermore, the risk map can serve insurance companies to evaluate the insurance risk of a building. To make them easily accessible, the risk map will be published online via a web GIS

  2. Data Sources for the Model-based Small Area Estimates of Cancer Risk Factors and Screening Behaviors - Small Area Estimates

    Science.gov (United States)

    The model-based estimates of important cancer risk factors and screening behaviors are obtained by combining the responses to the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS).

  3. Surface water flood risk and management strategies for London: An Agent-Based Model approach

    Directory of Open Access Journals (Sweden)

    Jenkins Katie

    2016-01-01

    Full Text Available Flooding is recognised as one of the most common and costliest natural disasters in England. Flooding in urban areas during heavy rainfall is known as ‘surface water flooding’, considered to be the most likely cause of flood events and one of the greatest short-term climate risks for London. In this paper we present results from a novel Agent-Based Model designed to assess the interplay between different adaptation options, different agents, and the role of flood insurance and the flood insurance pool, Flood Re, in the context of climate change. The model illustrates how investment in adaptation options could reduce London’s surface water flood risk, today and in the future. However, benefits can be outweighed by continued development in high risk areas and the effects of climate change. Flood Re is beneficial in its function to provide affordable insurance, even under climate change. However, it offers no additional benefits in terms of overall risk reduction, and will face increasing pressure due to rising surface water flood risk in the future. The modelling approach and findings are highly relevant for reviewing the proposed Flood Re scheme, as well as for wider discussions on the potential of insurance schemes, and broader multi-sectoral partnerships, to incentivise flood risk management in the UK and internationally.

  4. A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty

    Directory of Open Access Journals (Sweden)

    Ye Xu

    2016-01-01

    Full Text Available Nonpoint source (NPS pollution caused by agricultural activities is main reason that water quality in watershed becomes worse, even leading to deterioration. Moreover, pollution control is accompanied with revenue’s fall for agricultural system. How to design and generate a cost-effective and environmentally friendly agricultural production pattern is a critical issue for local managers. In this study, a risk-based interval two-stage programming model (RBITSP was developed. Compared to general ITSP model, significant contribution made by RBITSP model was that it emphasized importance of financial risk under various probabilistic levels, rather than only being concentrated on expected economic benefit, where risk is expressed as the probability of not meeting target profit under each individual scenario realization. This way effectively avoided solutions’ inaccuracy caused by traditional expected objective function and generated a variety of solutions through adjusting weight coefficients, which reflected trade-off between system economy and reliability. A case study of agricultural production management with the Tai Lake watershed was used to demonstrate superiority of proposed model. Obtained results could be a base for designing land-structure adjustment patterns and farmland retirement schemes and realizing balance of system benefit, system-failure risk, and water-body protection.

  5. Physics-Based Fragment Acceleration Modeling for Pressurized Tank Burst Risk Assessments

    Science.gov (United States)

    Manning, Ted A.; Lawrence, Scott L.

    2014-01-01

    As part of comprehensive efforts to develop physics-based risk assessment techniques for space systems at NASA, coupled computational fluid and rigid body dynamic simulations were carried out to investigate the flow mechanisms that accelerate tank fragments in bursting pressurized vessels. Simulations of several configurations were compared to analyses based on the industry-standard Baker explosion model, and were used to formulate an improved version of the model. The standard model, which neglects an external fluid, was found to agree best with simulation results only in configurations where the internal-to-external pressure ratio is very high and fragment curvature is small. The improved model introduces terms that accommodate an external fluid and better account for variations based on circumferential fragment count. Physics-based analysis was critical in increasing the model's range of applicability. The improved tank burst model can be used to produce more accurate risk assessments of space vehicle failure modes that involve high-speed debris, such as exploding propellant tanks and bursting rocket engines.

  6. Physicologically Based Toxicokinetic Models of Tebuconazole and Application in Human Risk Assessment

    DEFF Research Database (Denmark)

    Jonsdottir, Svava Osk; Reffstrup, Trine Klein; Petersen, Annette

    2016-01-01

    A series of physiologically based toxicokinetic (PBTK) models for tebuconazole were developed in four species, rat, rabbit, rhesus monkey, and human. The developed models were analyzed with respect to the application of the models in higher tier human risk assessment, and the prospect of using...... (ADME) of tebuconazole. The developed models were validated on in vivo half-life data for rabbit with good results, and on plasma and tissue concentration-time course data of tebuconazole after i.v. administration in rabbit. In most cases, the predicted concentration levels were seen to be within...... a factor of 2 compared to the experimental data, which is the threshold set for the use of PBTK simulation results in risk assessment. An exception to this was seen for one of the target organs, namely, the liver, for which tebuconazole concentration was significantly underestimated, a trend also seen...

  7. A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion

    Directory of Open Access Journals (Sweden)

    Xiaoqian Zhu

    2014-01-01

    Full Text Available It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach.

  8. Integrating household risk mitigation behaviour in flood risk analysis : An agent-based model approach

    NARCIS (Netherlands)

    Haer, Toon; Botzen, W.J.W.; Aerts, Jeroen

    2017-01-01

    Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect

  9. Modeling tumor control probability for spatially inhomogeneous risk of failure based on clinical outcome data

    DEFF Research Database (Denmark)

    Lühr, Armin; Löck, Steffen; Jakobi, Annika

    2017-01-01

    PURPOSE: Objectives of this work are (1) to derive a general clinically relevant approach to model tumor control probability (TCP) for spatially variable risk of failure and (2) to demonstrate its applicability by estimating TCP for patients planned for photon and proton irradiation. METHODS...... highest failure rate in the low-risk CTVE) and differing substantially between photon and proton irradiation. CONCLUSIONS: The presented method is of practical value for three reasons: It (a) is based on empirical clinical outcome data; (b) can be applied to non-uniform dose prescriptions as well...... clinical target volume (CTV), and elective CTV (CTVE). The risk of a local failure in each of these sub-volumes was taken from the literature. RESULTS: Convenient expressions for D50,i and γ50,i were provided for the Poisson and the logistic model. Comparable TCP estimates were obtained for photon...

  10. Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India.

    Science.gov (United States)

    Kumar, Rajesh; Dash, Chinmaya; Rani, Khushbu

    2017-09-01

    Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover (p predictive model include the forest cover [IRR-1.033 (1.024-1.042)], maximum humidity [IRR-1.016 (1.013-1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231-1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974-0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.

  11. WRF-based fire risk modelling and evaluation for years 2010 and 2012 in Poland

    Science.gov (United States)

    Stec, Magdalena; Szymanowski, Mariusz; Kryza, Maciej

    2016-04-01

    Wildfires are one of the main ecosystems' disturbances for forested, seminatural and agricultural areas. They generate significant economic loss, especially in forest management and agriculture. Forest fire risk modeling is therefore essential e.g. for forestry administration. In August 2015 a new method of forest fire risk forecasting entered into force in Poland. The method allows to predict a fire risk level in a 4-degree scale (0 - no risk, 3 - highest risk) and consists of a set of linearized regression equations. Meteorological information is used as predictors in regression equations, with air temperature, relative humidity, average wind speed, cloudiness and rainfall. The equations include also pine litter humidity as a measure of potential fuel characteristics. All these parameters are measured routinely in Poland at 42 basic and 94 auxiliary sites. The fire risk level is estimated for a current (basing on morning measurements) or next day (basing on midday measurements). Entire country is divided into 42 prognostic zones, and fire risk level for each zone is taken from the closest measuring site. The first goal of this work is to assess if the measurements needed for fire risk forecasting may be replaced by the data from mesoscale meteorological model. Additionally, the use of a meteorological model would allow to take into account much more realistic spatial differentiation of weather elements determining the fire risk level instead of discrete point-made measurements. Meteorological data have been calculated using the Weather Research and Forecasting model (WRF). For the purpose of this study the WRF model is run in the reanalysis mode allowing to estimate all required meteorological data in a 5-kilometers grid. The only parameter that cannot be directly calculated using WRF is the litter humidity, which has been estimated using empirical formula developed by Sakowska (2007). The experiments are carried out for two selected years: 2010 and 2012. The

  12. Risk Evaluation of Railway Coal Transportation Network Based on Multi Level Grey Evaluation Model

    Science.gov (United States)

    Niu, Wei; Wang, Xifu

    2018-01-01

    The railway transport mode is currently the most important way of coal transportation, and now China’s railway coal transportation network has become increasingly perfect, but there is still insufficient capacity, some lines close to saturation and other issues. In this paper, the theory and method of risk assessment, analytic hierarchy process and multi-level gray evaluation model are applied to the risk evaluation of coal railway transportation network in China. Based on the example analysis of Shanxi railway coal transportation network, to improve the internal structure and the competitiveness of the market.

  13. A risk-return based model to measure the performance of portfolio management

    Directory of Open Access Journals (Sweden)

    Hamid Reza Vakili Fard

    2014-10-01

    Full Text Available The primary concern in all portfolio management systems is to find a good tradeoff between risk and expected return and a good balance between accepted risk and actual return indicates the performance of a particular portfolio. This paper develops “A-Y Model” to measure the performance of a portfolio and analyze it during the bull and the bear market. This paper considers the daily information of one year before and one year after Iran's 2013 precedential election. The proposed model of this paper provides lost profit and unrealized loss to measure the portfolio performance. The proposed study first ranks the resulted data and then uses some non-parametric methods to see whether there is any change because of the changes in markets on the performance of the portfolio. The results indicate that despite increasing profitable opportunities in bull market, the performance of the portfolio did not match the target risk. As a result, using A-Y Model as a risk and return base model to measure portfolio management's performance appears to reduce risks and increases return of portfolio.

  14. Development of genetic programming-based model for predicting oyster norovirus outbreak risks.

    Science.gov (United States)

    Chenar, Shima Shamkhali; Deng, Zhiqiang

    2018-01-01

    Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was utilized to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The

  15. Life cycle cost-based risk model for energy performance contracting retrofits

    Science.gov (United States)

    Berghorn, George H.

    Buildings account for 41% of the primary energy consumption in the United States, nearly half of which is accounted for by commercial buildings. Among the greatest energy users are those in the municipalities, universities, schools, and hospitals (MUSH) market. Correctional facilities are in the upper half of all commercial building types for energy intensity. Public agencies have experienced reduced capital budgets to fund retrofits; this has led to the increased use of energy performance contracts (EPC), which are implemented by energy services companies (ESCOs). These companies guarantee a minimum amount of energy savings resulting from the retrofit activities, which in essence transfers performance risk from the owner to the contractor. Building retrofits in the MUSH market, especially correctional facilities, are well-suited to EPC, yet despite this potential and their high energy intensities, efficiency improvements lag behind that of other public building types. Complexities in project execution, lack of support for data requests and sub-metering, and conflicting project objectives have been cited as reasons for this lag effect. As a result, project-level risks must be understood in order to support wider adoption of retrofits in the public market, in particular the correctional facility sub-market. The goal of this research is to understand risks related to the execution of energy efficiency retrofits delivered via EPC in the MUSH market. To achieve this goal, in-depth analysis and improved understanding was sought with regard to ESCO risks that are unique to EPC in this market. The proposed work contributes to this understanding by developing a life cycle cost-based risk model to improve project decision making with regard to risk control and reduction. The specific objectives of the research are: (1) to perform an exploratory analysis of the EPC retrofit process and identify key areas of performance risk requiring in-depth analysis; (2) to construct a

  16. Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2014-01-01

    Full Text Available Metabolic syndrome is worldwide public health problem and is a serious threat to people's health and lives. Understanding the relationship between metabolic syndrome and the physical symptoms is a difficult and challenging task, and few studies have been performed in this field. It is important to classify adults who are at high risk of metabolic syndrome without having to use a biochemical index and, likewise, it is important to develop technology that has a high economic rate of return to simplify the complexity of this detection. In this paper, an artificial intelligence model was developed to identify adults at risk of metabolic syndrome based on physical signs; this artificial intelligence model achieved more powerful capacity for classification compared to the PCLR (principal component logistic regression model. A case study was performed based on the physical signs data, without using a biochemical index, that was collected from the staff of Lanzhou Grid Company in Gansu province of China. The results show that the developed artificial intelligence model is an effective classification system for identifying individuals at high risk of metabolic syndrome.

  17. A risk-based model for maintenance decision support of civil structures using RAMS

    NARCIS (Netherlands)

    Viana Da Rocha, T. C.; Stipanovic, I.; Hartmann, A.; Bakker, J.

    2017-01-01

    As a cornerstone of transportation asset management, risk-based approaches have been used to support maintenance decisions of civil structures. However, ambiguous and subjective risk criteria and inconsistency on the use of risk-based approaches can lead to a fuzzy understanding of the risks

  18. A Global Climate Model based event set for tropical cyclone risk assessment in the West Pacific

    Science.gov (United States)

    Vitolo, Renato; Strachan, Jane; Vidale, Pier Luigi; Stephenson, David; Cook, Ian; Flay, Shaun; Foote, Matthew

    2010-05-01

    We propose a new approach to the creation of a stochastic event set for tropical cyclone risk assessment in West Pacific, for use in the insurance industry in the catastrophe modelling process. The event set is based on both available observational data and a database of tropical cyclones dynamically simulated by a state-of-the-art Global Climate Model. For an initial proof of concept exercise we focus on the West Pacific region: Japan, China and South-East Asia. A database of tropical cyclone tracks is extracted from over 200 years of current climate simulations by HiGEM1.1, a high resolution, coupled ocean-atmosphere Global Climate Model. A bias correction procedure is applied to model the central pressure of the dynamically HiGEM-simulated tropical cyclones in terms of the observed (IBTrACS) distribution of central pressures. The bias-corrected storm track database is statistically sampled and spatially perturbed to produce a 1000 year database of synthetic storms. The proposed approach has several advantages: 1. it is based on a long-term, globally consistent source of dynamically simulated tropical storms under current state of the atmosphere/climate; this compensates reliance on limited and/or inconsistent historical data and provides a much larger sampling for the distribution of the tropical cyclone landfalls; 2. it allows assessment of how large scale natural climate variability may influence regional tropical cyclone activity on multidecadal time scales, and how this may alter risk; 3. it allows to analyse teleconnections in weather extremes, and hence potential accumulation of seemingly unrelated risk; 4. it can be further developed to assess how climate change may affect tropical cyclone risk in the future. Adopting an integrated approach may begin to change the way that weather related risk is understood and assessed in the insurance industry.

  19. A multiparametric magnetic resonance imaging-based risk model to determine the risk of significant prostate cancer prior to biopsy.

    Science.gov (United States)

    van Leeuwen, Pim J; Hayen, Andrew; Thompson, James E; Moses, Daniel; Shnier, Ron; Böhm, Maret; Abuodha, Magdaline; Haynes, Anne-Maree; Ting, Francis; Barentsz, Jelle; Roobol, Monique; Vass, Justin; Rasiah, Krishan; Delprado, Warick; Stricker, Phillip D

    2017-12-01

    To develop and externally validate a predictive model for detection of significant prostate cancer. Development of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. In all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P prostate cancer. Individualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of over-detection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

  20. Credit Risk Modeling

    DEFF Research Database (Denmark)

    Lando, David

    and students in finance, at quantitative analysts in banks and other financial institutions, and at regulators interested in the modeling aspects of credit risk. David Lando considers the two broad approaches to credit risk analysis: that based on classical option pricing models on the one hand......Credit risk is today one of the most intensely studied topics in quantitative finance. This book provides an introduction and overview for readers who seek an up-to-date reference to the central problems of the field and to the tools currently used to analyze them. The book is aimed at researchers...

  1. Large-scale model-based assessment of deer-vehicle collision risk.

    Directory of Open Access Journals (Sweden)

    Torsten Hothorn

    Full Text Available Ungulates, in particular the Central European roe deer Capreolus capreolus and the North American white-tailed deer Odocoileus virginianus, are economically and ecologically important. The two species are risk factors for deer-vehicle collisions and as browsers of palatable trees have implications for forest regeneration. However, no large-scale management systems for ungulates have been implemented, mainly because of the high efforts and costs associated with attempts to estimate population sizes of free-living ungulates living in a complex landscape. Attempts to directly estimate population sizes of deer are problematic owing to poor data quality and lack of spatial representation on larger scales. We used data on >74,000 deer-vehicle collisions observed in 2006 and 2009 in Bavaria, Germany, to model the local risk of deer-vehicle collisions and to investigate the relationship between deer-vehicle collisions and both environmental conditions and browsing intensities. An innovative modelling approach for the number of deer-vehicle collisions, which allows nonlinear environment-deer relationships and assessment of spatial heterogeneity, was the basis for estimating the local risk of collisions for specific road types on the scale of Bavarian municipalities. Based on this risk model, we propose a new "deer-vehicle collision index" for deer management. We show that the risk of deer-vehicle collisions is positively correlated to browsing intensity and to harvest numbers. Overall, our results demonstrate that the number of deer-vehicle collisions can be predicted with high precision on the scale of municipalities. In the densely populated and intensively used landscapes of Central Europe and North America, a model-based risk assessment for deer-vehicle collisions provides a cost-efficient instrument for deer management on the landscape scale. The measures derived from our model provide valuable information for planning road protection and defining

  2. Home-Based Risk of Falling Assessment Test Using a Closed-Loop Balance Model.

    Science.gov (United States)

    Ayena, Johannes C; Zaibi, Helmi; Otis, Martin J-D; Menelas, Bob-Antoine J

    2016-12-01

    The aim of this study is to improve and facilitate the methods used to assess risk of falling at home among older people through the computation of a risk of falling in real time in daily activities. In order to increase a real time computation of the risk of falling, a closed-loop balance model is proposed and compared with One-Leg Standing Test (OLST). This balance model allows studying the postural response of a person having an unpredictable perturbation. Twenty-nine volunteers participated in this study for evaluating the effectiveness of the proposed system which includes seventeen elder participants: ten healthy elderly ( 68.4 ±5.5 years), seven Parkinson's disease (PD) subjects ( 66.28 ±8.9 years), and twelve healthy young adults ( 28.27 ±3.74 years). Our work suggests that there is a relationship between OLST score and the risk of falling based on center of pressure measurement with four low cost force sensors located inside an instrumented insole, which could be predicted using our suggested closed-loop balance model. For long term monitoring at home, this system could be included in a medical electronic record and could be useful as a diagnostic aid tool.

  3. [Usability first. Model-based approach for the use-oriented risk analysis of medical devices].

    Science.gov (United States)

    Janß, A; Radermacher, K

    2014-12-01

    Due to increasing automation, the number and complexity of technical components have increased in the medical context (e.g., in the clinic or in the home care sector) in recent years. Besides new effective and efficient therapeutic and diagnostic options, these devices entail a wide range of functions and very complex (often computer-based) user interfaces that may lead to human-induced risk potential. A systematic and early human risk analysis and a usability evaluation allow medical device manufacturers to identify and control risks within the human-machine interaction very efficiently. At the Department of Medical Engineering in the Helmholtz Institute for Biomedical Engineering at the RWTH Aachen University, a formal-analytical methodology and a corresponding software tool for prospective human-risk analysis and model-based usability evaluation has been developed. Based on a twofold approach, user interactive process sequences and their potential impacts on the overall process are identified and the resulting use-related risks are assessed. For this, the tasks are categorized (in system and user tasks) and modeled and temporally related within the framework of a high-level task analysis. Within a subsequent cognitive low-level task analysis, potentially critical parallel process sequences are then tested in order to detect a potential resource overload of the user. The subsequent corresponding human-risk analysis is developed according to a knowledge base (checklist) of taxonomies related to human error. The HiFEM (human-function effect modeling) methodology is universally applicable and can be used for the evaluation of human-computer interfaces as well as for the analysis of purely mechanical control interfaces and simple hand-held instruments (such as a scalpel and implant). In a comparative study, the HiFEM method clearly outperforms the classic FMEA (failure modes and effects analysis) process with regard to effectiveness, efficiency, learnability, and

  4. A Simple Model to Rank Shellfish Farming Areas Based on the Risk of Disease Introduction and Spread.

    Science.gov (United States)

    Thrush, M A; Pearce, F M; Gubbins, M J; Oidtmann, B C; Peeler, E J

    2017-08-01

    The European Union Council Directive 2006/88/EC requires that risk-based surveillance (RBS) for listed aquatic animal diseases is applied to all aquaculture production businesses. The principle behind this is the efficient use of resources directed towards high-risk farm categories, animal types and geographic areas. To achieve this requirement, fish and shellfish farms must be ranked according to their risk of disease introduction and spread. We present a method to risk rank shellfish farming areas based on the risk of disease introduction and spread and demonstrate how the approach was applied in 45 shellfish farming areas in England and Wales. Ten parameters were used to inform the risk model, which were grouped into four risk themes based on related pathways for transmission of pathogens: (i) live animal movement, (ii) transmission via water, (iii) short distance mechanical spread (birds) and (iv) long distance mechanical spread (vessels). Weights (informed by expert knowledge) were applied both to individual parameters and to risk themes for introduction and spread to reflect their relative importance. A spreadsheet model was developed to determine quantitative scores for the risk of pathogen introduction and risk of pathogen spread for each shellfish farming area. These scores were used to independently rank areas for risk of introduction and for risk of spread. Thresholds were set to establish risk categories (low, medium and high) for introduction and spread based on risk scores. Risk categories for introduction and spread for each area were combined to provide overall risk categories to inform a risk-based surveillance programme directed at the area level. Applying the combined risk category designation framework for risk of introduction and spread suggested by European Commission guidance for risk-based surveillance, 4, 10 and 31 areas were classified as high, medium and low risk, respectively. © 2016 Crown copyright.

  5. Simulation of Land-Use Development, Using a Risk-Regarding Agent-Based Model

    Directory of Open Access Journals (Sweden)

    F. Hosseinali

    2012-01-01

    Full Text Available The aim of this paper is to study the spatial consequences of applying different Attitude Utility Functions (AUFs, which reflect peoples’ simplified psychological frames, to investment plans in land-use decision making. For this purpose, we considered and implemented an agent-based model with new methods for searching landscapes, for selecting parcels to develop, and for allowing competitions among agents. Besides this, GIS (Geographic Information Systems as a versatile and powerful medium of analyzing and representing spatial data is used. Our model is implemented on an artificial landscape in which land is being developed by agents. The agents are assumed to be mobile developers that are equipped with several land-related objectives. In this paper, agents mimic various risk-bearing attitudes and sometimes compete for developing the same parcel. The results reveal that patterns of land-use development are different in the two cases of regarding and disregarding AUFs. Therefore, it is considered here that using the attitudes of people towards risk helps the model to better simulate the decision making of land-use developers. The different attitudes toward risk used in this study can be attributed to different categories of developers based on sets of characteristics such as income, age, or education.

  6. CT scan-based modelling of anastomotic leak risk after colorectal surgery.

    Science.gov (United States)

    Gervaz, P; Platon, A; Buchs, N C; Rocher, T; Perneger, T; Poletti, P-A

    2013-01-01

    Prolonged ileus, low-grade fever and abdominal discomfort are common during the first week after colonic resection. Undiagnosed anastomotic leak carries a poor outcome and computed tomography (CT) scan is the best imaging tool for assessing postoperative abdominal complications. We used a CT scan-based model to quantify the risk of anastomotic leak after colorectal surgery. A case-control analysis of 74 patients who underwent clinico-radiological evaluation after colorectal surgery for suspicion of anastomotic leak was undertaken and a multivariable analysis of risk factors for leak was performed. A logistic regression model was used to identify determinant variables and construct a predictive score. Out of 74 patients with a clinical suspicion of anastomotic leak, 17 (23%) had this complication confirmed following repeat laparotomy. In multivariate analysis, three variables were associated with anastomotic leak: (1) white blood cells count > 9 × 10(9) /l (OR = 14.8); (2) presence of ≥ 500 cm(3) of intra- abdominal fluid (OR = 13.4); and (3) pneumoperitoneum at the site of anastomosis (OR = 9.9). Each of these three parameters contributed one point to the risk score. The observed risk of leak was 0, 6, 31 and 100%, respectively, for patients with scores of 0, 1, 2 and 3. The area under the receiver operating characteristic curve for the score was 0.83 (0.72-0.94). This CT scan-based model seems clinically promising for objective quantification of the risk of a leak after colorectal surgery. Colorectal Disease © 2013 The Association of Coloproctology of Great Britain and Ireland.

  7. A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis.

    Science.gov (United States)

    Tervonen, Tommi; van Valkenhoef, Gert; Buskens, Erik; Hillege, Hans L; Postmus, Douwe

    2011-05-30

    Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multi-criteria model that fully takes into account the evidence on efficacy and adverse drug reactions. Our approach is based on the stochastic multi-criteria acceptability analysis methodology, which allows us to compute the typical value judgments that support a decision, to quantify decision uncertainty, and to compute a comprehensive BR profile. We construct a multi-criteria model for the therapeutic group of second-generation antidepressants. We assess fluoxetine and venlafaxine together with placebo according to incidence of treatment response and three common adverse drug reactions by using data from a published study. Our model shows that there are clear trade-offs among the treatment alternatives. Copyright © 2011 John Wiley & Sons, Ltd.

  8. Model-Based Estimation of the Attributable Risk: A Loglinear Approach.

    Science.gov (United States)

    Cox, Christopher; Li, Xiuhong

    2012-12-01

    This paper considers model-based methods for estimation of the adjusted attributable risk (AR) in both case-control and cohort studies. An earlier review discussed approaches for both types of studies, using the standard logistic regression model for case-control studies, and for cohort studies proposing the equivalent Poisson model in order to account for the additional variability in estimating the distribution of exposures and covariates from the data. In this paper we revisit case-control studies, arguing for the equivalent Poisson model in this case as well. Using the delta method with the Poisson model, we provide general expressions for the asymptotic variance of the AR for both types of studies. This includes the generalized AR, which extends the original idea of attributable risk to the case where the exposure is not completely eliminated. These variance expressions can be easily programmed in any statistical package that includes Poisson regression and has capabilities for simple matrix algebra. In addition, we discuss computation of standard errors and confidence limits using bootstrap resampling. For cohort studies, use of the bootstrap allows binary regression models with link functions other than the logit.

  9. A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty

    DEFF Research Database (Denmark)

    Zhang, Ning; Kang, Chongqing; Xia, Qing

    2015-01-01

    presents a novel risk-based day-ahead unit commitment (RUC) model that considers the risks of the loss of load, wind curtailment and branch overflow caused by wind power uncertainty. These risks are formulated in detail using the probabilistic distributions of wind power probabilistic forecast...

  10. Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

    Directory of Open Access Journals (Sweden)

    Hujun He

    2017-01-01

    Full Text Available The prediction and risk classification of collapse is an important issue in the process of highway construction in mountainous regions. Based on the principles of information entropy and Mahalanobis distance discriminant analysis, we have produced a collapse hazard prediction model. We used the entropy measure method to reduce the influence indexes of the collapse activity and extracted the nine main indexes affecting collapse activity as the discriminant factors of the distance discriminant analysis model (i.e., slope shape, aspect, gradient, and height, along with exposure of the structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate, and degree of rock weathering. We employ postearthquake collapse data in relation to construction of the Yingxiu-Wolong highway, Hanchuan County, China, as training samples for analysis. The results were analyzed using the back substitution estimation method, showing high accuracy and no errors, and were the same as the prediction result of uncertainty measure. Results show that the classification model based on information entropy and distance discriminant analysis achieves the purpose of index optimization and has excellent performance, high prediction accuracy, and a zero false-positive rate. The model can be used as a tool for future evaluation of collapse risk.

  11. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  12. A Model-based Framework for Risk Assessment in Human-Computer Controlled Systems

    Science.gov (United States)

    Hatanaka, Iwao

    2000-01-01

    The rapid growth of computer technology and innovation has played a significant role in the rise of computer automation of human tasks in modem production systems across all industries. Although the rationale for automation has been to eliminate "human error" or to relieve humans from manual repetitive tasks, various computer-related hazards and accidents have emerged as a direct result of increased system complexity attributed to computer automation. The risk assessment techniques utilized for electromechanical systems are not suitable for today's software-intensive systems or complex human-computer controlled systems. This thesis will propose a new systemic model-based framework for analyzing risk in safety-critical systems where both computers and humans are controlling safety-critical functions. A new systems accident model will be developed based upon modem systems theory and human cognitive processes to better characterize system accidents, the role of human operators, and the influence of software in its direct control of significant system functions. Better risk assessments will then be achievable through the application of this new framework to complex human-computer controlled systems.

  13. Probabilistic ecological risk assessment of effluent toxicity of a wastewater reclamation plant based on process modeling.

    Science.gov (United States)

    Zeng, Siyu; Huang, Yunqing; Sun, Fu; Li, Dan; He, Miao

    2016-09-01

    The growing use of reclaimed wastewater for environmental purposes such as stream flow augmentation requires comprehensive ecological risk assessment and management. This study applied a system analysis approach, regarding a wastewater reclamation plant (WRP) and its recipient water body as a whole system, and assessed the ecological risk of the recipient water body caused by the WRP effluent. Instead of specific contaminants, two toxicity indicators, i.e. genotoxicity and estrogenicity, were selected to directly measure the biological effects of all bio-available contaminants in the reclaimed wastewater, as well as characterize the ecological risk of the recipient water. A series of physically based models were developed to simulate the toxicity indicators in a WRP through a typical reclamation process, including ultrafiltration, ozonation, and chlorination. After being validated against the field monitoring data from a full-scale WRP in Beijing, the models were applied to simulate the probability distribution of effluent toxicity of the WRP through Latin Hypercube Sampling to account for the variability of influent toxicity and operation conditions. The simulated effluent toxicity was then used to derive the predicted environmental concentration (PEC) in the recipient stream, considering the variations of the toxicity and flow of the upstream inflow as well. The ratio of the PEC of each toxicity indicator to its corresponding predicted no-effect concentration was finally used for the probabilistic ecological risk assessment. Regional sensitivity analysis was also performed with the developed models to identify the critical control variables and strategies for ecological risk management. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. IT Operational Risk Measurement Model Based on Internal Loss Data of Banks

    Science.gov (United States)

    Hao, Xiaoling

    Business operation of banks relies increasingly on information technology (IT) and the most important role of IT is to guarantee the operational continuity of business process. Therefore, IT Risk management efforts need to be seen from the perspective of operational continuity. Traditional IT risk studies focused on IT asset-based risk analysis and risk-matrix based qualitative risk evaluation. In practice, IT risk management practices of banking industry are still limited to the IT department and aren't integrated into business risk management, which causes the two departments to work in isolation. This paper presents an improved methodology for dealing with IT operational risk. It adopts quantitative measurement method, based on the internal business loss data about IT events, and uses Monte Carlo simulation to predict the potential losses. We establish the correlation between the IT resources and business processes to make sure risk management of IT and business can work synergistically.

  15. Psychosocial Modeling of Insider Threat Risk Based on Behavioral and Word Use Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Greitzer, Frank L.; Kangas, Lars J.; Noonan, Christine F.; Brown, Christopher R.; Ferryman, Thomas A.

    2013-10-01

    In many insider crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they can be assessed. A psychosocial model was developed to assess an employee’s behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. A complementary Personality Factor modeling approach was developed based on analysis to derive relevant personality characteristics from word use. Several implementations of the psychosocial model were evaluated by comparing their agreement with judgments of human resources and management professionals; the personality factor modeling approach was examined using email samples. If implemented in an operational setting, these models should be part of a set of management tools for employee assessment to identify employees who pose a greater insider threat.

  16. Screening for gestational diabetes mellitus by a model based on risk indicators: a prospective study

    DEFF Research Database (Denmark)

    Jensen, Dorte; Mølsted-Pedersen, Lars; Beck-Nielsen, Henning

    2003-01-01

    This study was performed to prospectively evaluate a screening model for gestational diabetes mellitus on the basis of clinical risk indicators.......This study was performed to prospectively evaluate a screening model for gestational diabetes mellitus on the basis of clinical risk indicators....

  17. Detection performance and risk stratification using a model-based shape index characterizing heart rate turbulence.

    Science.gov (United States)

    Martínez, Juan Pablo; Cygankiewicz, Iwona; Smith, Danny; Bayés de Luna, Antonio; Laguna, Pablo; Sörnmo, Leif

    2010-10-01

    A detection-theoretic approach to quantify heart rate turbulence (HRT) following a ventricular premature beat is proposed and validated using an extended integral pulse frequency modulation (IPFM) model which accounts for HRT. The modulating signal of the extended IPFM model is projected into a three-dimensional subspace spanned by the Karhunen-Loève basis functions, characterizing HRT shape. The presence or absence of HRT is decided by means of a likelihood ratio test, the Neyman-Pearson detector, resulting in a quadratic detection statistic. Using a labeled dataset built from different interbeat interval series, detection performance is assessed and found to outperform the two widely used indices: turbulence onset (TO) and turbulence slope (TS). The ability of the proposed method to predict the risk of cardiac death is evaluated in a population of patients (n = 90) with ischemic cardiomyopathy and mild-to-moderate congestive heart failure. While both TS and the novel HRT index differ significantly in survivors and cardiac death patients, mortality analysis shows that the latter index exhibits much stronger association with risk of cardiac death (hazard ratio = 2.8, CI = 1.32-5.97, p = 0.008). It is also shown that the model-based shape indices, but not TO and TS, remain predictive of cardiac death in our population when computed from 4-h instead of 24-h ambulatory ECGs.

  18. Modeling tumor control probability for spatially inhomogeneous risk of failure based on clinical outcome data.

    Science.gov (United States)

    Lühr, Armin; Löck, Steffen; Jakobi, Annika; Stützer, Kristin; Bandurska-Luque, Anna; Vogelius, Ivan Richter; Enghardt, Wolfgang; Baumann, Michael; Krause, Mechthild

    2017-12-01

    Objectives of this work are (1) to derive a general clinically relevant approach to model tumor control probability (TCP) for spatially variable risk of failure and (2) to demonstrate its applicability by estimating TCP for patients planned for photon and proton irradiation. The approach divides the target volume into sub-volumes according to retrospectively observed spatial failure patterns. The product of all sub-volume TCPi values reproduces the observed TCP for the total tumor. The derived formalism provides for each target sub-volume i the tumor control dose (D50,i) and slope (γ50,i) parameters at 50% TCPi. For a simultaneous integrated boost (SIB) prescription for 45 advanced head and neck cancer patients, TCP values for photon and proton irradiation were calculated and compared. The target volume was divided into gross tumor volume (GTV), surrounding clinical target volume (CTV), and elective CTV (CTVE). The risk of a local failure in each of these sub-volumes was taken from the literature. Convenient expressions for D50,i and γ50,i were provided for the Poisson and the logistic model. Comparable TCP estimates were obtained for photon and proton plans of the 45 patients using the sub-volume model, despite notably higher dose levels (on average +4.9%) in the low-risk CTVE for photon irradiation. In contrast, assuming a homogeneous dose response in the entire target volume resulted in TCP estimates contradicting clinical experience (the highest failure rate in the low-risk CTVE) and differing substantially between photon and proton irradiation. The presented method is of practical value for three reasons: It (a) is based on empirical clinical outcome data; (b) can be applied to non-uniform dose prescriptions as well as different tumor entities and dose-response models; and (c) is provided in a convenient compact form. The approach may be utilized to target spatial patterns of local failures observed in patient cohorts by prescribing different doses to

  19. Road Risk Modeling and Cloud-Aided Safety-Based Route Planning.

    Science.gov (United States)

    Li, Zhaojian; Kolmanovsky, Ilya; Atkins, Ella; Lu, Jianbo; Filev, Dimitar P; Michelini, John

    2016-11-01

    This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the "best" route can be adjusted to favor time versus safety metrics.

  20. Regional risk assessment for point source pollution based on a water quality model of the Taipu River, China.

    Science.gov (United States)

    Yao, Hong; Qian, Xin; Yin, Hong; Gao, Hailong; Wang, Yulei

    2015-02-01

    Point source pollution is one of the main threats to regional environmental health. Based on a water quality model, a methodology to assess the regional risk of point source pollution is proposed. The assessment procedure includes five parts: (1) identifying risk source units and estimating source emissions using Monte Carlo algorithms; (2) observing hydrological and water quality data of the assessed area, and evaluating the selected water quality model; (3) screening out the assessment endpoints and analyzing receptor vulnerability with the Choquet fuzzy integral algorithm; (4) using the water quality model introduced in the second step to predict pollutant concentrations for various source emission scenarios and analyzing hazards of risk sources; and finally, (5) using the source hazard values and receptor vulnerability scores to estimate overall regional risk. The proposed method, based on the Water Quality Analysis Simulation Program (WASP), was applied in the region of the Taipu River, which is in the Taihu Basin, China. Results of source hazard and receptor vulnerability analysis allowed us to describe aquatic ecological, human health, and socioeconomic risks individually, and also integrated risks in the Taipu region, from a series of risk curves. Risk contributions of sources to receptors were ranked, and the spatial distribution of risk levels was presented. By changing the input conditions, we were able to estimate risks for a range of scenarios. Thus, the proposed procedure may also be used by decisionmakers for long-term dynamic risk prediction. © 2014 Society for Risk Analysis.

  1. On Scalable and Efficient Security Risk Modelling of Cloud Computing Infrastructure based on Markov processes

    Directory of Open Access Journals (Sweden)

    Karras Dimitrios A.

    2017-01-01

    Full Text Available While cloud computing infrastructures proliferates in nowadays computing and communications technology there are few reports investigating models for their security. In this paper, new efficient models are developed and evaluated for analyzing the security-related behavior of cloud computing architectures and networks comprising complex interconnected communication systems adapted towards a generalized analysis. These cloud related models, based on Markov processes, allow calculation of critical security factors for the cloud infrastructure, related to intrusion detection, of such interconnected and distributed systems components and the evaluation of the associated security mechanisms. Although, at this step an architecture of at least three interconnected systems is analyzed, the systematic model introduced allows for a generalized model of N interconnected systems in a cloud architecture under reasonable assumptions. We herein show the principles of such an analysis. Security parameters calculation and Security mechanisms evaluation may support the risk analysis and the decision making process in resolving the trade-offs between security and quality of service characteristics corresponding to the complex interconnected computing and communication systems.

  2. Model-based assessment of erosion risks on man-made slopes in recultivation areas

    Science.gov (United States)

    Kunth, F.; Schmidt, J.

    2012-04-01

    The present study deals with non-vegetated slopes of post mining areas which are heavily endangered by soil erosion by water. The prevention of massive on-site damages as well as off-site effects by the emission of acid dump materials is one of the major challenges in the context of recultivation of closed-down open cast mining areas. Hence, the aim of this study is the development of a reproducible methodology to determine erosion risks on slopes in recultivation areas. Moreover, a standardised technique is developed to plan, dimension and test erosion protection measures in recultivation landscapes. The analyses of the study are based on the event-based physical erosion model EROSION 3D. The widely used model is able to predict runoff as well as detachment, transport and deposition of sediments. Its use and validation ranges from erosion prediction from agricultural land to sediment input into water bodies. The required input parameters of EROSION 2D/3D (hydraulic roughness, infiltration rates etc.) were determined under field conditions by simulated rainfall experiments. These field experiments took place on selected non-vegetated plots of the Lusatian mining district in eastern Germany. Due to their huge influence on infiltration and erosion processes special characteristics of coal-containing dump soils (hydrophobicity, air trapping effect) have to be considered and implemented into the model within this survey.

  3. Methodology for the Model-based Small Area Estimates of Cancer Risk Factors and Screening Behaviors - Small Area Estimates

    Science.gov (United States)

    This model-based approach uses data from both the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) to produce estimates of the prevalence rates of cancer risk factors and screening behaviors at the state, health service area, and county levels.

  4. Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

    Directory of Open Access Journals (Sweden)

    Li Jian

    2017-01-01

    Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.

  5. Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

    Science.gov (United States)

    Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and human health effect...

  6. The influence of hazard models on GIS-based regional risk assessments and mitigation policies

    Science.gov (United States)

    Bernknopf, R.L.; Rabinovici, S.J.M.; Wood, N.J.; Dinitz, L.B.

    2006-01-01

    Geographic information systems (GIS) are important tools for understanding and communicating the spatial distribution of risks associated with natural hazards in regional economies. We present a GIS-based decision support system (DSS) for assessing community vulnerability to natural hazards and evaluating potential mitigation policy outcomes. The Land Use Portfolio Modeler (LUPM) integrates earth science and socioeconomic information to predict the economic impacts of loss-reduction strategies. However, the potential use of such systems in decision making may be limited when multiple but conflicting interpretations of the hazard are available. To explore this problem, we conduct a policy comparison using the LUPM to test the sensitivity of three available assessments of earthquake-induced lateral-spread ground failure susceptibility in a coastal California community. We find that the uncertainty regarding the interpretation of the science inputs can influence the development and implementation of natural hazard management policies. Copyright ?? 2006 Inderscience Enterprises Ltd.

  7. A global airport-based risk model for the spread of dengue infection via the air transport network.

    Science.gov (United States)

    Gardner, Lauren; Sarkar, Sahotra

    2013-01-01

    The number of travel-acquired dengue infections has seen a consistent global rise over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue has contributed to a rise in the number of dengue cases in both areas of endemicity and elsewhere. This paper reports results from a network-based risk assessment model which uses international passenger travel volumes, travel routes, travel distances, regional populations, and predictive species distribution models (for the two vector species, Aedes aegypti and Aedes albopictus) to quantify the relative risk posed by each airport in importing passengers with travel-acquired dengue infections. Two risk attributes are evaluated: (i) the risk posed by through traffic at each stopover airport and (ii) the risk posed by incoming travelers to each destination airport. The model results prioritize optimal locations (i.e., airports) for targeted dengue surveillance. The model is easily extendible to other vector-borne diseases.

  8. A global airport-based risk model for the spread of dengue infection via the air transport network.

    Directory of Open Access Journals (Sweden)

    Lauren Gardner

    Full Text Available The number of travel-acquired dengue infections has seen a consistent global rise over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue has contributed to a rise in the number of dengue cases in both areas of endemicity and elsewhere. This paper reports results from a network-based risk assessment model which uses international passenger travel volumes, travel routes, travel distances, regional populations, and predictive species distribution models (for the two vector species, Aedes aegypti and Aedes albopictus to quantify the relative risk posed by each airport in importing passengers with travel-acquired dengue infections. Two risk attributes are evaluated: (i the risk posed by through traffic at each stopover airport and (ii the risk posed by incoming travelers to each destination airport. The model results prioritize optimal locations (i.e., airports for targeted dengue surveillance. The model is easily extendible to other vector-borne diseases.

  9. Mental Models of Security Risks

    Science.gov (United States)

    Asgharpour, Farzaneh; Liu, Debin; Camp, L. Jean

    In computer security, risk communication refers to informing computer users about the likelihood and magnitude of a threat. Efficacy of risk communication depends not only on the nature of the risk, but also on the alignment between the conceptual model embedded in the risk communication and the user's mental model of the risk. The gap between the mental models of security experts and non-experts could lead to ineffective risk communication. Our research shows that for a variety of the security risks self-identified security experts and non-experts have different mental models. We propose that the design of the risk communication methods should be based on the non-expert mental models.

  10. A process-based model for the definition of hydrological alert systems in landslide risk mitigation

    Directory of Open Access Journals (Sweden)

    M. Floris

    2012-11-01

    Full Text Available The definition of hydrological alert systems for rainfall-induced landslides is strongly related to a deep knowledge of the geological and geomorphological features of the territory. Climatic conditions, spatial and temporal evolution of the phenomena and characterization of landslide triggering, together with propagation mechanisms, are the key elements to be considered. Critical steps for the development of the systems consist of the identification of the hydrological variable related to landslide triggering and of the minimum rainfall threshold for landslide occurrence.

    In this paper we report the results from a process-based model to define a hydrological alert system for the Val di Maso Landslide, located in the northeastern Italian Alps and included in the Vicenza Province (Veneto region, NE Italy. The instability occurred in November 2010, due to an exceptional rainfall event that hit the Vicenza Province and the entire NE Italy. Up to 500 mm in 3-day cumulated rainfall generated large flood conditions and triggered hundreds of landslides. During the flood, the Soil Protection Division of the Vicenza Province received more than 500 warnings of instability phenomena. The complexity of the event and the high level of risk to infrastructure and private buildings are the main reasons for deepening the specific phenomenon occurred at Val di Maso.

    Empirical and physically-based models have been used to identify the minimum rainfall threshold for the occurrence of instability phenomena in the crown area of Val di Maso landslide, where a retrogressive evolution by multiple rotational slides is expected. Empirical models helped in the identification and in the evaluation of recurrence of critical rainfall events, while physically-based modelling was essential to verify the effects on the slope stability of determined rainfall depths. Empirical relationships between rainfall and landslide consist of the calculation of rainfall

  11. Risk forewarning model for rice grain Cd pollution based on Bayes theory.

    Science.gov (United States)

    Wu, Bo; Guo, Shuhai; Zhang, Lingyan; Li, Fengmei

    2017-10-17

    Cadmium (Cd) pollution of rice grain caused by Cd-contaminated soils is a common problem in southwest and central south China. In this study, utilizing the advantages of the Bayes classification statistical method, we established a risk forewarning model for rice grain Cd pollution, and put forward two parameters (the prior probability factor and data variability factor). The sensitivity analysis of the model parameters illustrated that sample size and standard deviation influenced the accuracy and applicable range of the model. The accuracy of the model was improved by the self-renewal of the model through adding the posterior data into the priori data. Furthermore, this method can be used to predict the risk probability of rice grain Cd pollution under similar soil environment, tillage and rice varietal conditions. The Bayes approach thus represents a feasible method for risk forewarning of heavy metals pollution of agricultural products caused by contaminated soils. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. A risk-based model for predicting the impact of using condoms on the spread of sexually transmitted infections

    Directory of Open Access Journals (Sweden)

    Asma Azizi

    2017-02-01

    Full Text Available We create and analyze a mathematical model to understand the impact of condom-use and sexual behavior on the prevalence and spread of Sexually Transmitted Infections (STIs. STIs remain significant public health challenges globally with a high burden of some Sexually Transmitted Diseases (STDs in both developed and undeveloped countries. Although condom-use is known to reduce the transmission of STIs, there are a few quantitative population-based studies on the protective role of condom-use in reducing the incidence of STIs. The number of concurrent partners is correlated with their risk of being infectious by an STI such as chlamydia, gonorrhea, or syphilis. We develop a Susceptible-Infectious-Susceptible (SIS model that stratifies the population based on the number of concurrent partners. The model captures the multi-level heterogeneous mixing through a combination of biased (preferential and random (proportional mixing processes between individuals with distinct risk levels, and accounts for differences in condom-use in the low- and high-risk populations. We use sensitivity analysis to assess the relative impact of high-risk people using condom as a prophylactic intervention to reduce their chance of being infectious, or infecting others. The model predicts the STI prevalence as a function of the number of partners of an individual, and quantifies how this distribution of effective partners changes as a function of condom-use. Our results show that when the mixing is random, then increasing the condom-use in the high-risk population is more effective in reducing the prevalence than when many of the partners of high-risk people have high risk. The model quantifies how the risk of being infected increases for people who have more partners, and the need for high-risk people to consistently use condoms to reduce their risk of infection. Keywords: Mathematical modeling, Sexually transmitted infection (STI, Biased (preferential mixing, Random

  13. A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

    Science.gov (United States)

    Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

    2015-01-01

    This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. PMID:26633446

  14. A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013-2014.

    Science.gov (United States)

    Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

    2015-12-01

    This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections.

  15. An example of population-level risk assessments for small mammals using individual-based population models

    DEFF Research Database (Denmark)

    Schmitt, Walter; Auteri, Domenica; Bastiansen, Finn

    2016-01-01

    This article presents a case study demonstrating the application of 3 individual-based, spatially explicit population models (IBMs, also known as agent-based models) in ecological risk assessments to predict long-term effects of a pesticide to populations of small mammals. The 3 IBMs each used...... and structural complexity. The toxicological profile of FungicideX was defined so that the deterministic long-term first tier risk assessment would result in high risk to small mammals, thus providing the opportunity to use the IBMs for risk assessment refinement (i.e., higher tier risk assessment). Despite...... assessments for small mammals, including consistent and transparent direct links to specific protection goals, and the consideration of more realistic scenarios....

  16. Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks.

    Science.gov (United States)

    Hartemink, Nienke; Vanwambeke, Sophie O; Purse, Bethan V; Gilbert, Marius; Van Dyck, Hans

    2015-11-01

    Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional habitat of vectors or hosts, and hence of the pathogen. Empirical-statistical methods do not explicitly incorporate biological mechanisms, whereas current mechanistic models are rarely spatially explicit; both methods ignore the way animals use the landscape (i.e. movement ecology). We argue that applying a functional concept for habitat, i.e. the resource-based habitat concept (RBHC), can solve these issues. The RBHC offers a framework to identify systematically the different ecological resources that are necessary for the completion of the transmission cycle and to relate these resources to (combinations of) landscape features and other environmental factors. The potential of the RBHC as a framework for identifying suitable habitats for vector-borne pathogens is explored and illustrated with the case of bluetongue virus, a midge-transmitted virus affecting ruminants. The concept facilitates the study of functional habitats of the interacting species (vectors as well as hosts) and provides new insight into spatial and temporal variation in transmission opportunities and exposure that ultimately determine disease risks. It may help to identify knowledge gaps and control options arising from changes in the spatial configuration of key resources across the landscape. The RBHC framework may act as a bridge between existing mechanistic and statistical modelling approaches. © 2014 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.

  17. Risk analysis of urban gas pipeline network based on improved bow-tie model

    Science.gov (United States)

    Hao, M. J.; You, Q. J.; Yue, Z.

    2017-11-01

    Gas pipeline network is a major hazard source in urban areas. In the event of an accident, there could be grave consequences. In order to understand more clearly the causes and consequences of gas pipeline network accidents, and to develop prevention and mitigation measures, the author puts forward the application of improved bow-tie model to analyze risks of urban gas pipeline network. The improved bow-tie model analyzes accident causes from four aspects: human, materials, environment and management; it also analyzes the consequences from four aspects: casualty, property loss, environment and society. Then it quantifies the causes and consequences. Risk identification, risk analysis, risk assessment, risk control, and risk management will be clearly shown in the model figures. Then it can suggest prevention and mitigation measures accordingly to help reduce accident rate of gas pipeline network. The results show that the whole process of an accident can be visually investigated using the bow-tie model. It can also provide reasons for and predict consequences of an unfortunate event. It is of great significance in order to analyze leakage failure of gas pipeline network.

  18. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  19. A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

    Science.gov (United States)

    Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming

    2015-01-01

    Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

  20. An integrated model-based approach to the risk assessment of pesticide drift from vineyards

    Science.gov (United States)

    Pivato, Alberto; Barausse, Alberto; Zecchinato, Francesco; Palmeri, Luca; Raga, Roberto; Lavagnolo, Maria Cristina; Cossu, Raffaello

    2015-06-01

    The inhalation of pesticide in air is of particular concern for people living in close contact with intensive agricultural activities. This study aims to develop an integrated modelling methodology to assess whether pesticides pose a risk to the health of people living near vineyards, and apply this methodology in the world-renowned Prosecco DOCG (Italian label for protection of origin and geographical indication of wines) region. A sample field in Bigolino di Valdobbiadene (North-Eastern Italy) was selected to perform the pesticide fate modellization and the consequent inhalation risk assessment for people living in the area. The modellization accounts for the direct pesticide loss during the treatment of vineyards and for the volatilization from soil after the end of the treatment. A fugacity model was used to assess the volatilization flux from soil. The Gaussian puff air dispersion model CALPUFF was employed to assess the airborne concentration of the emitted pesticide over the simulation domain. The subsequent risk assessment integrates the HArmonised environmental Indicators for pesticide Risk (HAIR) and US-EPA guidelines. In this case study the modelled situation turned to be safe from the point of view of human health in the case of non-carcinogenic compounds, and additional improvements were suggested to further mitigate the effect of the most critical compound.

  1. Decision tree-based modeling of androgen pathway genes and prostate cancer risk.

    Science.gov (United States)

    Barnholtz-Sloan, Jill S; Guan, Xiaowei; Zeigler-Johnson, Charnita; Meropol, Neal J; Rebbeck, Timothy R

    2011-06-01

    Inherited variability in genes that influence androgen metabolism has been associated with risk of prostate cancer. The objective of this analysis was to evaluate interactions for prostate cancer risk by using classification and regression tree (CART) models (i.e., decision trees), and to evaluate whether these interactive effects add information about prostate cancer risk prediction beyond that of "traditional" risk factors. We compared CART models with traditional logistic regression (LR) models for associations of factors with prostate cancer risk using 1,084 prostate cancer cases and 941 controls. All analyses were stratified by race. We used unconditional LR to complement and compare with the race-stratified CART results using the area under curve (AUC) for the receiver operating characteristic curves. The CART modeling of prostate cancer risk showed different interaction profiles by race. For European Americans, interactions among CYP3A43 genotype, history of benign prostate hypertrophy, family history of prostate cancer, and age at consent revealed a distinct hierarchy of gene-environment and gene-gene interactions, whereas for African Americans, interactions among family history of prostate cancer, individual proportion of European ancestry, number of GGC androgen receptor repeats, and CYP3A4/CYP3A5 haplotype revealed distinct interaction effects from those found in European Americans. For European Americans, the CART model had the highest AUC whereas for African Americans, the LR model with the CART discovered factors had the largest AUC. These results provide new insight into underlying prostate cancer biology for European Americans and African Americans. ©2011 AACR.

  2. 3-Self behavior modification programs base on the PROMISE Model for clients at metabolic risk.

    Science.gov (United States)

    Intarakamhang, Ungsinun

    2011-12-29

    The objectives of this mixed methods research were 1) to study effects of the health behavior modification program (HBMP) conducted under the principles of the PROMISE Model and the CIPP Model and 2) to compare the 3-self health behaviors and the biomedical indicators before with after the program completion. During the program, three sample groups including 30 program leaders, 30 commanders and 120 clients were assessed, and there were assessments taken on 4,649 volunteers who were at risk of metabolic syndrome before and after the program conducted in 17 hospitals. The collected data were analyzed by the t-test and the path analysis. The research instruments were questionnaires used for program evaluation, structuralized interview forms, and questionnaires used for 3-self health behavior assessment. The findings were as follows: 1) During the program, the assessment result deriving from comparing the overall opinions toward the program among the three sample groups showed no difference (F=2.219), 2) The program management factors based on the PROMISE Model (positive reinforcement, optimism, context, and process or activity provision) had an overall influence on the product or success of the HBMP (p< 0.05) with size effects at 0.37, 0.13, 0.31 and 0.88 respectively. All of the factors could predict the product of the program by 69%. 3) After participating in the program, the clients' 3-self health behaviors (self-efficacy, self-regulation, and self-care) were significantly higher than those appeared before the participation (p< 0.05), and their biomedical indicators (BMI, blood pressure, waistline, blood glucose, lipid profiles, cholesterol, and HbA1c) were significantly lower than those measured before the program (p< 0.05).

  3. Validation of a model for ranking aquaculture facilities for risk-based disease surveillance.

    Science.gov (United States)

    Diserens, Nicolas; Falzon, Laura Cristina; von Siebenthal, Beat; Schüpbach-Regula, Gertraud; Wahli, Thomas

    2017-09-15

    A semi-quantitative model for risk ranking of aquaculture facilities in Switzerland with regard to the introduction and spread of Viral Haemorrhagic Septicaemia (VHS) and Infectious Haematopoietic Necrosis (IHN) was developed in a previous study (Diserens et al., 2013). The objective of the present study was to validate this model using data collected during field visits on aquaculture sites in four Swiss cantons compared to data collected through a questionnaire in the previous study. A discrepancy between the values obtained with the two different methods was found in 32.8% of the parameters, resulting in a significant difference (psystem could be advantageous for the factors which were identified as being more likely to vary over time, in particular for factors considering fish movements, which showed a marginally significant difference in their risk scores (p≥0.1) within a six- month period. Nevertheless, the model proved to be stable over the considered period of time as no substantial fluctuations in the risk categorisation were observed (Kappa agreement of 0.77).Finally, the model proved to be suitable to deliver a reliable risk ranking of Swiss aquaculture facilities according to their risk of getting infected with or spreading of VHS and IHN, as the five facilities that tested positive for these diseases in the last ten years were ranked as medium or high risk. Moreover, because the seven fish farms that were infected with Infectious Pancreatic Necrosis (IPN) during the same period also belonged to the risk categories medium and high, the classification appeared to correlate with the occurrence of this third viral fish disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Development of Rock Engineering Systems-Based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting

    Science.gov (United States)

    Faramarzi, Farhad; Mansouri, Hamid; Farsangi, Mohammad Ali Ebrahimi

    2014-07-01

    The environmental effects of blasting must be controlled in order to comply with regulatory limits. Because of safety concerns and risk of damage to infrastructures, equipment, and property, and also having a good fragmentation, flyrock control is crucial in blasting operations. If measures to decrease flyrock are taken, then the flyrock distance would be limited, and, in return, the risk of damage can be reduced or eliminated. This paper deals with modeling the level of risk associated with flyrock and, also, flyrock distance prediction based on the rock engineering systems (RES) methodology. In the proposed models, 13 effective parameters on flyrock due to blasting are considered as inputs, and the flyrock distance and associated level of risks as outputs. In selecting input data, the simplicity of measuring input data was taken into account as well. The data for 47 blasts, carried out at the Sungun copper mine, western Iran, were used to predict the level of risk and flyrock distance corresponding to each blast. The obtained results showed that, for the 47 blasts carried out at the Sungun copper mine, the level of estimated risks are mostly in accordance with the measured flyrock distances. Furthermore, a comparison was made between the results of the flyrock distance predictive RES-based model, the multivariate regression analysis model (MVRM), and, also, the dimensional analysis model. For the RES-based model, R 2 and root mean square error (RMSE) are equal to 0.86 and 10.01, respectively, whereas for the MVRM and dimensional analysis, R 2 and RMSE are equal to (0.84 and 12.20) and (0.76 and 13.75), respectively. These achievements confirm the better performance of the RES-based model over the other proposed models.

  5. Integration of an Evidence Base into a Probabilistic Risk Assessment Model. The Integrated Medical Model Database: An Organized Evidence Base for Assessing In-Flight Crew Health Risk and System Design

    Science.gov (United States)

    Saile, Lynn; Lopez, Vilma; Bickham, Grandin; FreiredeCarvalho, Mary; Kerstman, Eric; Byrne, Vicky; Butler, Douglas; Myers, Jerry; Walton, Marlei

    2011-01-01

    This slide presentation reviews the Integrated Medical Model (IMM) database, which is an organized evidence base for assessing in-flight crew health risk. The database is a relational database accessible to many people. The database quantifies the model inputs by a ranking based on the highest value of the data as Level of Evidence (LOE) and the quality of evidence (QOE) score that provides an assessment of the evidence base for each medical condition. The IMM evidence base has already been able to provide invaluable information for designers, and for other uses.

  6. Overview of the Graphical User Interface for the GERM Code (GCR Event-Based Risk Model

    Science.gov (United States)

    Kim, Myung-Hee; Cucinotta, Francis A.

    2010-01-01

    The descriptions of biophysical events from heavy ions are of interest in radiobiology, cancer therapy, and space exploration. The biophysical description of the passage of heavy ions in tissue and shielding materials is best described by a stochastic approach that includes both ion track structure and nuclear interactions. A new computer model called the GCR Event-based Risk Model (GERM) code was developed for the description of biophysical events from heavy ion beams at the NASA Space Radiation Laboratory (NSRL). The GERM code calculates basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at NSRL for the purpose of simulating space radiobiological effects. For mono-energetic beams, the code evaluates the linear-energy transfer (LET), range (R), and absorption in tissue equivalent material for a given Charge (Z), Mass Number (A) and kinetic energy (E) of an ion. In addition, a set of biophysical properties are evaluated such as the Poisson distribution of ion or delta-ray hits for a specified cellular area, cell survival curves, and mutation and tumor probabilities. The GERM code also calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle. The contributions from primary ion and nuclear secondaries are evaluated. The GERM code accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections, and has been used by the GERM code for application to thick target experiments. The GERM code provides scientists participating in NSRL experiments with the data needed for the interpretation of their

  7. Overview of the Graphical User Interface for the GERMcode (GCR Event-Based Risk Model)

    Science.gov (United States)

    Kim, Myung-Hee Y.; Cucinotta, Francis A.

    2010-01-01

    The descriptions of biophysical events from heavy ions are of interest in radiobiology, cancer therapy, and space exploration. The biophysical description of the passage of heavy ions in tissue and shielding materials is best described by a stochastic approach that includes both ion track structure and nuclear interactions. A new computer model called the GCR Event-based Risk Model (GERM) code was developed for the description of biophysical events from heavy ion beams at the NASA Space Radiation Laboratory (NSRL). The GERMcode calculates basic physical and biophysical quantities of high-energy protons and heavy ions that have been studied at NSRL for the purpose of simulating space radiobiological effects. For mono-energetic beams, the code evaluates the linear-energy transfer (LET), range (R), and absorption in tissue equivalent material for a given Charge (Z), Mass Number (A) and kinetic energy (E) of an ion. In addition, a set of biophysical properties are evaluated such as the Poisson distribution of ion or delta-ray hits for a specified cellular area, cell survival curves, and mutation and tumor probabilities. The GERMcode also calculates the radiation transport of the beam line for either a fixed number of user-specified depths or at multiple positions along the Bragg curve of the particle. The contributions from primary ion and nuclear secondaries are evaluated. The GERMcode accounts for the major nuclear interaction processes of importance for describing heavy ion beams, including nuclear fragmentation, elastic scattering, and knockout-cascade processes by using the quantum multiple scattering fragmentation (QMSFRG) model. The QMSFRG model has been shown to be in excellent agreement with available experimental data for nuclear fragmentation cross sections, and has been used by the GERMcode for application to thick target experiments. The GERMcode provides scientists participating in NSRL experiments with the data needed for the interpretation of their

  8. Model of personalised risk assessment of phytoestrogen intake based on 11 SNP in ESR1 and ESR2 genes

    Directory of Open Access Journals (Sweden)

    Radoslav Zidek

    2016-12-01

    Full Text Available Phytoestrogens can induce biological responses in vertebrates by mimicking or modulating the action or production of endogenous hormones, and because of their structural similarity with estradiol they have the ability to cause estrogenic or anti-estrogenic effects. Risk assessment of phytoestrogens intake may therefore provide important information useful in the adjustment of nutrients composition, as one of nutrigenomics approaches. Proper risk assessment is an essential part of good nutrient composition. The current risk assessment procedures does use an additive effect of genes, but the accumulation of relevant factors do not count with the distribution of risk in the European population. A combination of approaches based on genetic score, along with the use of the data bases like 1000 genomes and dbSNP is a powerful tool for population risk modelling that would provide reasonable results without needs of as testing a representative number of individual genetic profiles.

  9. Using physiologically based pharmacokinetic models to estimate the health risk of mixtures of trihalomethanes from reclaimed water.

    Science.gov (United States)

    Niu, Zhiguang; Zang, Xue; Zhang, Ying

    2015-03-21

    To estimate the health risk of mixture of trihalomethanes (THMs) from reclaimed water during toilet flushing, the interaction-based Hazard Index (HI(interaction-based)) and the mixture carcinogenic risk (CRM) according to tissue dose were conducted through the integrated use of both the exposure concentrations model and the physiologically based pharmacokinetic (PBPK) model of THMs. Monte Carlo simulations were employed to implement the probabilistic risk analysis and sensitivity analysis. Nine samples were analyzed, which were collected from J Water Reclamation Plant (JWRP) in Tianjin of China. The results indicated that the mean HI(interaction-based) (=0.85) was lower than the acceptable risk level (=1). The probability that the HI(interaction-based) exceeded the acceptable risk level is 22.97%. For carcinogenic risk, the CRM ranges from 9.41×10(-7) to 3.54×10(-5), with a mean of 5.49×10(-6). Moreover, the probability of exceeding the acceptable risk level (1×10(-6)) is near 100%. And the values of HI(interaction-based) from sample no. 1, 5, and 7 exceeded 1, while the values of CRM for all samples exceeded 1×10(-6). Consequently, the reclaimed water used for flushing toilets should be paid more attention, though non-carcinogenic effect is relatively small. Furthermore, the concentrations of DBCM had greater impact on both the carcinogenic and non-carcinogenic risk based on sensitivity analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

  10. Modeling infection transmission in primate networks to predict centrality-based risk.

    Science.gov (United States)

    Romano, Valéria; Duboscq, Julie; Sarabian, Cécile; Thomas, Elodie; Sueur, Cédric; MacIntosh, Andrew J J

    2016-07-01

    Social structure can theoretically regulate disease risk by mediating exposure to pathogens via social proximity and contact. Investigating the role of central individuals within a network may help predict infectious agent transmission as well as implement disease control strategies, but little is known about such dynamics in real primate networks. We combined social network analysis and a modeling approach to better understand transmission of a theoretical infectious agent in wild Japanese macaques, highly social animals which form extended but highly differentiated social networks. We collected focal data from adult females living on the islands of Koshima and Yakushima, Japan. Individual identities as well as grooming networks were included in a Markov graph-based simulation. In this model, the probability that an individual will transmit an infectious agent depends on the strength of its relationships with other group members. Similarly, its probability of being infected depends on its relationships with already infected group members. We correlated: (i) the percentage of subjects infected during a latency-constrained epidemic; (ii) the mean latency to complete transmission; (iii) the probability that an individual is infected first among all group members; and (iv) each individual's mean rank in the chain of transmission with different individual network centralities (eigenvector, strength, betweenness). Our results support the hypothesis that more central individuals transmit infections in a shorter amount of time and to more subjects but also become infected more quickly than less central individuals. However, we also observed that the spread of infectious agents on the Yakushima network did not always differ from expectations of spread on random networks. Generalizations about the importance of observed social networks in pathogen flow should thus be made with caution, since individual characteristics in some real world networks appear less relevant than

  11. Risk Evaluation of Debris Flow Hazard Based on Asymmetric Connection Cloud Model

    Directory of Open Access Journals (Sweden)

    Xinyu Xu

    2017-01-01

    Full Text Available Risk assessment of debris flow is a complex problem involving various uncertainty factors. Herein, a novel asymmetric cloud model coupled with connection number was described here to take into account the fuzziness and conversion situation of classification boundary and interval nature of evaluation indicators for risk assessment of debris flow hazard. In the model, according to the classification standard, the interval lengths of each indicator were first specified to determine the digital characteristic of connection cloud at different levels. Then the asymmetric connection clouds in finite intervals were simulated to analyze the certainty degree of measured indicator to each evaluation standard. Next, the integrated certainty degree to each grade was calculated with corresponding indicator weight, and the risk grade of debris flow was determined by the maximum integrated certainty degree. Finally, a case study and comparison with other methods were conducted to confirm the reliability and validity of the proposed model. The result shows that this model overcomes the defect of the conventional cloud model and also converts the infinite interval of indicators distribution into finite interval, which makes the evaluation result more reasonable.

  12. Screening for gestational diabetes mellitus by a model based on risk indicators

    DEFF Research Database (Denmark)

    Jensen, Dorte Møller; Mølsted-Pedersen, Lars; Beck-Nielsen, Henning

    2003-01-01

    OBJECTIVE: This study was performed to prospectively evaluate a screening model for gestational diabetes mellitus on the basis of clinical risk indicators. STUDY DESIGN: In a prospective multicenter study with 5235 consecutive pregnant women, diagnostic testing with a 2-hour 75-g oral glucose...... of the results from tested women to the whole group in question, a 2.4% prevalence of gestational diabetes mellitus was calculated. Sensitivity and specificity of the model was 80.6 (73.7-87.6) and 64.8 (63.5-66.1), respectively (95% CIs). CONCLUSION: Under ideal conditions, sensitivity of the model...

  13. Calculation of lifetime lung cancer risks associated with radon exposure, based on various models and exposure scenarios.

    Science.gov (United States)

    Hunter, Nezahat; Muirhead, Colin R; Bochicchio, Francesco; Haylock, Richard G E

    2015-09-01

    The risk of lung cancer mortality up to 75 years of age due to radon exposure has been estimated for both male and female continuing, ex- and never-smokers, based on various radon risk models and exposure scenarios. We used risk models derived from (i) the BEIR VI analysis of cohorts of radon-exposed miners, (ii) cohort and nested case-control analyses of a European cohort of uranium miners and (iii) the joint analysis of European residential radon case-control studies. Estimates of the lifetime lung cancer risk due to radon varied between these models by just over a factor of 2 and risk estimates based on models from analyses of European uranium miners exposed at comparatively low rates and of people exposed to radon in homes were broadly compatible. For a given smoking category, there was not much difference in lifetime lung cancer risk between males and females. The estimated lifetime risk of radon-induced lung cancer for exposure to a concentration of 200 Bq m(-3) was in the range 2.98-6.55% for male continuing smokers and 0.19-0.42% for male never-smokers, depending on the model used and assuming a multiplicative relationship for the joint effect of radon and smoking. Stopping smoking at age 50 years decreases the lifetime risk due to radon by around a half relative to continuing smoking, but the risk for ex-smokers remains about a factor of 5-7 higher than that for never-smokers. Under a sub-multiplicative model for the joint effect of radon and smoking, the lifetime risk of radon-induced lung cancer was still estimated to be substantially higher for continuing smokers than for never smokers. Radon mitigation-used to reduce radon concentrations at homes-can also have a substantial impact on lung cancer risk, even for persons in their 50 s; for each of continuing smokers, ex-smokers and never-smokers, radon mitigation at age 50 would lower the lifetime risk of radon-induced lung cancer by about one-third. To maximise risk reductions, smokers in high

  14. Modelling and Simulating of Risk Behaviours in Virtual Environments Based on Multi-Agent and Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Linqin Cai

    2013-11-01

    Full Text Available Due to safety and ethical issues, traditional experimental approaches to modelling underground risk behaviours can be costly, dangerous and even impossible to realize. Based on multi-agent technology, a virtual coalmine platform for risk behaviour simulation is presented to model and simulate the human-machine-environment related risk factors in underground coalmines. To reveal mine workers' risk behaviours, a fuzzy emotional behaviour model is proposed to simulate underground miners' responding behaviours to potential hazardous events based on cognitive appraisal theories and fuzzy logic techniques. The proposed emotion model can generate more believable behaviours for virtual miners according to personalized emotion states, internal motivation needs and behaviour selection thresholds. Finally, typical accident cases of underground hazard spotting and locomotive transport were implemented. The behaviour believability of virtual miners was evaluated with a user assessment method. Experimental results show that the proposed models can create more realistic and reasonable behaviours in virtual coalmine environments, which can improve miners' risk awareness and further train miners' emergent decision-making ability when facing unexpected underground situations.

  15. Evaluating Risk Measures and Capital Allocations Based on Multi-Losses Driven by a Heavy-Tailed Background Risk: The Multivariate Pareto-II Model

    Directory of Open Access Journals (Sweden)

    Alexandru V. Asimit

    2013-03-01

    Full Text Available Evaluating risk measures, premiums, and capital allocation based on dependent multi-losses is a notoriously difficult task. In this paper, we demonstrate how this can be successfully accomplished when losses follow the multivariate Pareto distribution of the second kind, which is an attractive model for multi-losses whose dependence and tail heaviness are influenced by a heavy-tailed background risk. A particular attention is given to the distortion and weighted risk measures and allocations, as well as their special cases such as the conditional layer expectation, tail value at risk, and the truncated tail value at risk. We derive formulas that are either of closed form or follow well-defined recursive procedures. In either case, their computational use is straightforward.

  16. Bayesian operational risk models

    OpenAIRE

    Silvia Figini; Lijun Gao; Paolo Giudici

    2013-01-01

    Operational risk is hard to quantify, for the presence of heavy tailed loss distributions. Extreme value distributions, used in this context, are very sensitive to the data, and this is a problem in the presence of rare loss data. Self risk assessment questionnaires, if properly modelled, may provide the missing piece of information that is necessary to adequately estimate op- erational risks. In this paper we propose to embody self risk assessment data into suitable prior distributions, and ...

  17. Risk Level Based Management System: a control banding model for occupational health and safety risk management in a highly regulated environment

    Energy Technology Data Exchange (ETDEWEB)

    Zalk, D; Kamerzell, R; Paik, S; Kapp, J; Harrington, D; Swuste, P

    2009-05-27

    The Risk Level Based Management System (RLBMS) is an occupational risk management (ORM) model that focuses occupational safety, hygeiene, and health (OSHH) resources on the highest risk procedures at work. This article demonstrates the model's simplicity through an implementation within a heavily regulated research institution. The model utilizes control banding strategies with a stratification of four risk levels (RLs) for many commonly performed maintenance and support activities, characterizing risk consistently for comparable tasks. RLBMS creates an auditable tracking of activities, maximizes OSHH professional field time, and standardizes documentation and control commensurate to a given task's RL. Validation of RLs and their exposure control effectiveness is collected in a traditional quantitative collection regime for regulatory auditing. However, qualitative risk assessment methods are also used within this validation process. Participatory approaches are used throughout the RLBMS process. Workers are involved in all phases of building, maintaining, and improving this model. This work participation also improves the implementation of established controls.

  18. Quantitative Microbial Risk Assessment for Spray Irrigation of Dairy Manure Based on an Empirical Fate and Transport Model.

    Science.gov (United States)

    Burch, Tucker R; Spencer, Susan K; Stokdyk, Joel P; Kieke, Burney A; Larson, Rebecca A; Firnstahl, Aaron D; Rule, Ana M; Borchardt, Mark A

    2017-08-16

    Spray irrigation for land-applying livestock manure is increasing in the United States as farms become larger and economies of scale make manure irrigation affordable. Human health risks from exposure to zoonotic pathogens aerosolized during manure irrigation are not well understood. We aimed to a) estimate human health risks due to aerosolized zoonotic pathogens downwind of spray-irrigated dairy manure; and b) determine which factors (e.g., distance, weather conditions) have the greatest influence on risk estimates. We sampled downwind air concentrations of manure-borne fecal indicators and zoonotic pathogens during 21 full-scale dairy manure irrigation events at three farms. We fit these data to hierarchical empirical models and used model outputs in a quantitative microbial risk assessment (QMRA) to estimate risk [probability of acute gastrointestinal illness (AGI)] for individuals exposed to spray-irrigated dairy manure containing Campylobacter jejuni, enterohemorrhagic Escherichia coli (EHEC), or Salmonella spp. Median risk estimates from Monte Carlo simulations ranged from 10-5 to 10-2 and decreased with distance from the source. Risk estimates for Salmonella or EHEC-related AGI were most sensitive to the assumed level of pathogen prevalence in dairy manure, while risk estimates for C. jejuni were not sensitive to any single variable. Airborne microbe concentrations were negatively associated with distance and positively associated with wind speed, both of which were retained in models as a significant predictor more often than relative humidity, solar irradiation, or temperature. Our model-based estimates suggest that reducing pathogen prevalence and concentration in source manure would reduce the risk of AGI from exposure to manure irrigation, and that increasing the distance from irrigated manure (i.e., setbacks) and limiting irrigation to times of low wind speed may also reduce risk. https://doi.org/10.1289/EHP283.

  19. Quantitative microbial risk assessment for spray irrigation of dairy manure based on an empirical fate and transport model

    Science.gov (United States)

    Burch, Tucker R; Spencer, Susan K.; Stokdyk, Joel; Kieke, Burney A; Larson, Rebecca A; Firnstahl, Aaron; Rule, Ana M; Borchardt, Mark A.

    2017-01-01

    BACKGROUND: Spray irrigation for land-applying livestock manure is increasing in the United States as farms become larger and economies of scale make manure irrigation affordable. Human health risks from exposure to zoonotic pathogens aerosolized during manure irrigation are not well understood. OBJECTIVES: We aimed to a) estimate human health risks due to aerosolized zoonotic pathogens downwind of spray-irrigated dairy manure; and b) determine which factors (e.g., distance, weather conditions) have the greatest influence on risk estimates. METHODS: We sampled downwind air concentrations of manure-borne fecal indicators and zoonotic pathogens during 21 full-scale dairy manure irri- gation events at three farms. We fit these data to hierarchical empirical models and used model outputs in a quantitative microbial risk assessment (QMRA) to estimate risk [probability of acute gastrointestinal illness (AGI)] for individuals exposed to spray-irrigated dairy manure containing Campylobacter jejuni, enterohemorrhagic Escherichia coli (EHEC), or Salmonella spp. RESULTS: Median risk estimates from Monte Carlo simulations ranged from 10−5 to 10−2 and decreased with distance from the source. Risk estimates for Salmonella or EHEC-related AGI were most sensitive to the assumed level of pathogen prevalence in dairy manure, while risk estimates for C. jejuni were not sensitive to any single variable. Airborne microbe concentrations were negatively associated with distance and positively associated with wind speed, both of which were retained in models as a significant predictor more often than relative humidity, solar irradiation, or temperature. CONCLUSIONS: Our model-based estimates suggest that reducing pathogen prevalence and concentration in source manure would reduce the risk of AGI from exposure to manure irrigation, and that increasing the distance from irrigated manure (i.e., setbacks) and limiting irrigation to times of low wind speed may also reduce risk.

  20. Assessing surface water flood risk and management strategies under future climate change: Insights from an Agent-Based Model.

    Science.gov (United States)

    Jenkins, K; Surminski, S; Hall, J; Crick, F

    2017-10-01

    Climate change and increasing urbanization are projected to result in an increase in surface water flooding and consequential damages in the future. In this paper, we present insights from a novel Agent Based Model (ABM), applied to a London case study of surface water flood risk, designed to assess the interplay between different adaptation options; how risk reduction could be achieved by homeowners and government; and the role of flood insurance and the new flood insurance pool, Flood Re, in the context of climate change. The analysis highlights that while combined investment in property-level flood protection and sustainable urban drainage systems reduce surface water flood risk, the benefits can be outweighed by continued development in high risk areas and the effects of climate change. In our simulations, Flood Re is beneficial in its function to provide affordable insurance, even under climate change. However, the scheme does face increasing financial pressure due to rising surface water flood damages. If the intended transition to risk-based pricing is to take place then a determined and coordinated strategy will be needed to manage flood risk, which utilises insurance incentives, limits new development, and supports resilience measures. Our modelling approach and findings are highly relevant for the ongoing regulatory and political approval process for Flood Re as well as for wider discussions on the potential of insurance schemes to incentivise flood risk management and climate adaptation in the UK and internationally. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies.

    Science.gov (United States)

    Mehralian, Gholamhossein; Rajabzadeh Gatari, Ali; Morakabati, Mohadese; Vatanpour, Hossein

    2012-01-01

    The supply chain represents the critical link between the development of new product and the market in pharmaceutical industry. Over the years, improvements made in supply chain operations have focused largely on ways to reduce cost and gain efficiencies in scale. In addition, powerful regulatory and market forces have provided new incentives for pharmaceutical firms to basically rethink the way they produce and distribute products, and also to re-imagine the role of the supply chain in driving strategic growth, brand differentiation and economic value in the health continuum. The purpose of this paper is to formulate basic factors involved in risk analysis of pharmaceutical industry, and also determine the effective factors involved in suppliers selection and their priorities. This paper is based on the results of literature review, experts' opinion acquisition, statistical analysis and also using MADM models on data gathered from distributed questionnaires. The model consists of the following steps and components: first factors involved in to supply chain risks are determined. Based on them a framework is considered. According the result of statistical analysis and MADM models the risk factors are formulated. The paper determines the main components and influenceial factors involving in the supply chain risks. Results showed that delivery risk can make an important contribution to mitigate the risk of pharmaceutical industry.

  2. Multiple imputation was a valid approach to estimate absolute risk from a prediction model based on case-cohort data.

    Science.gov (United States)

    Mühlenbruch, Kristin; Kuxhaus, Olga; di Giuseppe, Romina; Boeing, Heiner; Weikert, Cornelia; Schulze, Matthias B

    2017-04-01

    To compare weighting methods for Cox regression and multiple imputation (MI) in a case-cohort study in the context of risk prediction modeling. Based on the European Prospective Investigation into Cancer and Nutrition Potsdam study, we estimated risk scores to predict incident type-2 diabetes using full cohort data and case-cohort data assuming missing information on waist circumference outside the case-cohort (∼90%). Varying weighting approaches and MI were compared with regard to the calculation of relative risks, absolute risks, and predictive abilities including C-index, the net reclassification improvement, and calibration. The full cohort comprised 21,845 participants, and the case-cohort comprised 2,703 participants. Relative risks were similar across all methods and compatible with full cohort estimates. Absolute risk estimates showed stronger disagreement mainly for Prentice and Self & Prentice weighting. Barlow and Langholz & Jiao weighting methods and MI were in good agreement with full cohort analysis. Predictive abilities were closest to full cohort estimates for MI or for Barlow and Langholz & Jiao weighting. MI seems to be a valid method for deriving or extending a risk prediction model from case-cohort data and might be superior for absolute risk calculation when compared to weighted approaches. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  3. Using an Agent-Based Model to Simulate the Development of Risk Behaviors During Adolescence

    OpenAIRE

    Nils Schuhmacher; Laura Ballato; Paul van Geert

    2014-01-01

    Adolescents tend to adopt behaviors that are similar to those of their friends, and also tend to become friends with peers that have similar interests and behaviors. This tendency towards homogeneity applies not only to conventional behaviors such as working for school and participating in sports activities, but also to risk behaviors such as drug use, oppositional behavior or unsafe sex. The current study aims at building an agent model to answer the following related questions: how do frien...

  4. A geographical information system-based web model of arbovirus transmission risk in the continental United States of America

    Directory of Open Access Journals (Sweden)

    Sarah K. Konrad

    2012-11-01

    Full Text Available A degree-day (DD model of West Nile virus capable of forecasting real-time transmission risk in the continental United States of America up to one week in advance using a 50-km grid is available online at https://sites. google.com/site/arbovirusmap/. Daily averages of historical risk based on temperatures for 1994-2003 are available at 10- km resolution. Transmission risk maps can be downloaded from 2010 to the present. The model can be adapted to work with any arbovirus for which the temperature-related parameters are known, e.g. Rift Valley fever virus. To more effectively assess virus establishment and transmission, the model incorporates “compound risk” maps and forecasts, which includes livestock density as a parameter.

  5. A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

    Science.gov (United States)

    Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

    2017-02-01

    Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

  6. [Non-linear System Dynamics Simulation Modeling of Adolescent Obesity: Using Korea Youth Risk Behavior Web-based Survey].

    Science.gov (United States)

    Lee, Hanna; Park, Eun Suk; Yu, Jae Kook; Yun, Eun Kyoung

    2015-10-01

    The purpose of this study was to develop a system dynamics model for adolescent obesity in Korea that could be used for obesity policy analysis. On the basis of the casual loop diagram, a model was developed by converting to stock and flow diagram. The Vensim DSS 5.0 program was used in the model development. We simulated method of moments to the calibration of this model with data from The Korea Youth Risk Behavior Web-based Survey 2005 to 2013. We ran the scenario simulation. This model can be used to understand the current adolescent obesity rate, predict the future obesity rate, and be utilized as a tool for controlling the risk factors. The results of the model simulation match well with the data. It was identified that a proper model, able to predict obesity probability, was established. These results of stock and flow diagram modeling in adolescent obesity can be helpful in development of obesity by policy planners and other stakeholders to better anticipate the multiple effects of interventions in both the short and the long term. In the future we suggest the development of an expanded model based on this adolescent obesity model.

  7. Probabilistic Modeling of Seismic Risk Based Design for a Dual System Structure

    Directory of Open Access Journals (Sweden)

    Indra Djati Sidi

    2017-04-01

    Full Text Available The dual system structure concept has gained popularity in the construction of high-rise buildings over the last decades. Meanwhile, earthquake engineering design provisions for buildings have moved from the uniform hazard concept to the uniform risk concept upon recognizing the uncertainties involved in the earthquake resistance of concrete structures. In this study, a probabilistic model for the evaluation of such risk is proposed for a dual system structure consisting of shear walls or core walls and a moment frame structure as earthquake resistant structure. Uncertainties in the earthquake resistance of the dual system structure due to record-to-record variability, limited amount of data, material variability and structure modeling are included in the formulation by means of the first-order second-moment method. The statistics of resistance against earthquake forces are estimated by making use of incremental nonlinear time history analysis using 10 recorded earthquake histories. Then, adopting the total probability theorem, the reliability of the structure is evaluated through a risk integral scheme by combining the earthquake resistance of the structure with the annual probability of exceedance for a given location where the building is being constructed.

  8. A Risk Explicit Interval Linear Programming Model for Uncertainty-Based Environmental Economic Optimization in the Lake Fuxian Watershed, China

    Directory of Open Access Journals (Sweden)

    Xiaoling Zhang

    2013-01-01

    Full Text Available The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers’ preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of “low risk and high return efficiency” in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

  9. A risk explicit interval linear programming model for uncertainty-based environmental economic optimization in the Lake Fuxian watershed, China.

    Science.gov (United States)

    Zhang, Xiaoling; Huang, Kai; Zou, Rui; Liu, Yong; Yu, Yajuan

    2013-01-01

    The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP) method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP) and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers' preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of "low risk and high return efficiency" in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

  10. Assessment of Hip Fracture Risk Using Cross-Section Strain Energy Determined by QCT-Based Finite Element Modeling

    Science.gov (United States)

    Kheirollahi, Hossein

    2015-01-01

    Accurate assessment of hip fracture risk is very important to prevent hip fracture and to monitor the effect of a treatment. A subject-specific QCT-based finite element model was constructed to assess hip fracture risk at the critical locations of femur during the single-leg stance and the sideways fall. The aim of this study was to improve the prediction of hip fracture risk by introducing a novel failure criterion to more accurately describe bone failure mechanism. Hip fracture risk index was defined using cross-section strain energy, which is able to integrate information of stresses, strains, and material properties affecting bone failure. It was found that the femoral neck and the intertrochanteric region have higher fracture risk than other parts of the femur, probably owing to the larger content of cancellous bone in these regions. The study results also suggested that women are more prone to hip fracture than men. The findings in this study have a good agreement with those clinical observations reported in the literature. The proposed hip fracture risk index based on strain energy has the potential of more accurate assessment of hip fracture risk. However, experimental validation should be conducted before its clinical applications. PMID:26601105

  11. GIS-based flood risk model evaluated by Fuzzy Analytic Hierarchy Process (FAHP)

    Science.gov (United States)

    Sukcharoen, Tharapong; Weng, Jingnong; Teetat, Charoenkalunyuta

    2016-10-01

    Over the last 2-3 decades, the economy of many countries around the world has been developed rapidly but it was unbalanced development because of expecting on economic growth only. Meanwhile it lacked of effective planning in the use of natural resources. This can significantly induce climate change which is major cause of natural disaster. Hereby, Thailand has also suffered from natural disaster for ages. Especially, the flood which is most hazardous disaster in Thailand can annually result in the great loss of life and property, environment and economy. Since the flood management of country is inadequate efficiency. It is unable to support the flood analysis comprehensively. This paper applied Geographic Information System and Multi-Criteria Decision Making to create flood risk model at regional scale. Angthong province in Thailand was used as the study area. In practical process, Fuzzy logic technique has been used to improve specialist's assessment by implementing with Fuzzy membership because human decision is flawed under uncertainty then AHP technique was processed orderly. The hierarchy structure in this paper was categorized the spatial flood factors into two levels as following: 6 criteria (Meteorology, Geology, Topography, Hydrology, Human and Flood history) and 8 factors (Average Rainfall, Distance from Stream, Soil drainage capability, Slope, Elevation, Land use, Distance from road and Flooded area in the past). The validity of the pair-wise comparison in AHP was shown as C.R. value which indicated that the specialist judgment was reasonably consistent. FAHP computation result has shown that the first priority of criteria was Meteorology. In addition, the Rainfall was the most influencing factor for flooding. Finally, the output was displayed in thematic map of Angthong province with flood risk level processed by GIS tools. The map was classified into: High Risk, Moderate Risk and Low Risk (13.20%, 75.58%, and 11.22% of total area).

  12. MORM--a Petri net based model for assessing OH&S risks in industrial processes: modeling qualitative aspects.

    Science.gov (United States)

    Vernez, David; Buchs, Didier R; Pierrehumbert, Guillaume E; Besrour, Adel

    2004-12-01

    Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets

  13. Providing a theoretical basis for nanotoxicity risk analysis departing from traditional physiologically-based pharmacokinetic (PBPK) modeling

    Science.gov (United States)

    Yamamoto, Dirk P.

    The same novel properties of engineered nanoparticles that make them attractive may also present unique exposure risks. But, the traditional physiologically-based pharmacokinetic (PBPK) modeling assumption of instantaneous equilibration likely does not apply to nanoparticles. This simulation-based research begins with development of a model that includes diffusion, active transport, and carrier mediated transport. An eigenvalue analysis methodology was developed to examine model behavior to focus future research. Simulations using the physico-chemical properties of size, shape, surface coating, and surface charge were performed and an equation was determined which estimates area under the curve for arterial blood concentration, which is a surrogate of nanoparticle dose. Results show that the cellular transport processes modeled in this research greatly affect the biokinetics of nanoparticles. Evidence suggests that the equation used to estimate area under the curve for arterial blood concentration can be written in terms of nanoparticle size only. The new paradigm established by this research leverages traditional in vitro, in vivo, and PBPK modeling, but includes area under the curve to bridge animal testing results to humans. This new paradigm allows toxicologists and policymakers to then assess risk to a given exposure and assist in setting appropriate exposure limits for nanoparticles. This research provides critical understanding of nanoparticle biokinetics and allows estimation of total exposure at any toxicological endpoint in the body. This effort is a significant contribution as it highlights future research needs and demonstrates how modeling can be used as a tool to advance nanoparticle risk assessment.

  14. Study of sex differences in the association between hip fracture risk and body parameters by DXA-based biomechanical modeling.

    Science.gov (United States)

    Nasiri, Masoud; Luo, Yunhua

    2016-09-01

    There is controversy about whether or not body parameters affect hip fracture in men and women in the same way. In addition, although bone mineral density (BMD) is currently the most important single discriminator of hip fracture, it is unclear if BMD alone is equally effective for men and women. The objective of this study was to quantify and compare the associations of hip fracture risk with BMD and body parameters in men and women using our recently developed two-level biomechanical model that combines a whole-body dynamics model with a proximal-femur finite element model. Sideways fall induced impact force of 130 Chinese clinical cases, including 50 males and 80 females, were determined by subject-specific dynamics modeling. Then, a DXA-based finite element model was used to simulate the femur bone under the fall-induced loading conditions and calculate the hip fracture risk. Body weight, body height, body mass index, trochanteric soft tissue thickness, and hip bone mineral density were determined for each subject and their associations with impact force and hip fracture risk were quantified. Results showed that the association between impact force and hip fracture risk was not strong enough in both men (r=-0.31,phip fracture risk. The correlation between hip BMD and hip fracture risk in men (r=-0.83,phip fracture in men (r=-0.13,p>0.05), but it can be considered as a protective factor among women (r=-0.28,phip fracture in women (r=-0.50,phip fracture are sex-specific. Therefore, the effect of body parameters should be considered differently for men and women in hip fracture risk assessment tools. These findings support further exploration of sex-specific preventive and protective measurements to reduce the incidence of hip fractures. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Multilevel joint competing risk models

    Science.gov (United States)

    Karunarathna, G. H. S.; Sooriyarachchi, M. R.

    2017-09-01

    Joint modeling approaches are often encountered for different outcomes of competing risk time to event and count in many biomedical and epidemiology studies in the presence of cluster effect. Hospital length of stay (LOS) has been the widely used outcome measure in hospital utilization due to the benchmark measurement for measuring multiple terminations such as discharge, transferred, dead and patients who have not completed the event of interest at the follow up period (censored) during hospitalizations. Competing risk models provide a method of addressing such multiple destinations since classical time to event models yield biased results when there are multiple events. In this study, the concept of joint modeling has been applied to the dengue epidemiology in Sri Lanka, 2006-2008 to assess the relationship between different outcomes of LOS and platelet count of dengue patients with the district cluster effect. Two key approaches have been applied to build up the joint scenario. In the first approach, modeling each competing risk separately using the binary logistic model, treating all other events as censored under the multilevel discrete time to event model, while the platelet counts are assumed to follow a lognormal regression model. The second approach is based on the endogeneity effect in the multilevel competing risks and count model. Model parameters were estimated using maximum likelihood based on the Laplace approximation. Moreover, the study reveals that joint modeling approach yield more precise results compared to fitting two separate univariate models, in terms of AIC (Akaike Information Criterion).

  17. Diagnosis Of The Risk For Carotid Artery Stenos Based On Thermal Model In Infrared Images

    Directory of Open Access Journals (Sweden)

    Fatemeh Valipoori Goodarzi

    2017-02-01

    Full Text Available Background and purpose: Ischemic stroke is the third leading cause of death and a common cause of hospitalization in the United States of America and is also an important factor for Inability of patients and carotid stenos is one of the most important factors in creating it. Now, Imaging studies include: Angiography, MRI, CT scan and Doppler ultrasonography , are used to detect carotid artery stenos that is one of the most important causes of ischemic stroke. However, each method has unique advantages and disadvantages, that many of them will have a compromise between performance and accuracy versus easy usage and cost considerations. In contrast, in this paper, thermography is used as a non-invasive and cost effective to detect carotid artery Stenos and thus the risk of stroke. Materials and methods: This study is done on a series of thermal images obtained from the Clinical Center in California. In this imaging, the automatic detection of carotid artery stenos and thus Risk for stroke was done, based on: (1 the difference of average temperature between the right and left carotid arteries in the neck (2 The presence or absence of internal and external carotid arteries. Results: In this study, with the survey conducted by a specialist brain of patients had been previously, the accuracy of this work is confirmed. the techniques and points that are Experimental and  scientifically based  and obtained in this study, can help to doctors for Early detection of Artery disease, based on analysis of thermal images . Conclusion: The method presented in this paper is considered as a non-invasive and cost-effective method that automatically operates to detect the carotid arteries and prevent the Risk for stroke.

  18. Optimal Allocation of Tunnel Safety Provisions Based on a Quantitative Risk Assessment Model

    Directory of Open Access Journals (Sweden)

    Pan Li

    2016-01-01

    Full Text Available This paper addresses the issue of optimally selecting the tunnel safety provisions. Tunnel safety provisions are the assets of urban road tunnels which are installed and implemented to reduce the tunnel risks, which are basically selected by expert judgment in practice. An optimization model is proposed to obtain the optimal solution for the selection of tunnel safety provisions. The objective function is to minimize the life cycle costs of tunnel safety provisions, which is subject to the requirements for tunnel safety provisions and the safety targets. Finally, by taking advantage of the special structure of the optimization model, a Bi-Section Search and Bound Algorithm (BSSBA is designed to efficiently solve the problem.

  19. An Empirical Agent-Based Model to Simulate the Adoption of Water Reuse Using the Social Amplification of Risk Framework.

    Science.gov (United States)

    Kandiah, Venu; Binder, Andrew R; Berglund, Emily Z

    2017-10-01

    Water reuse can serve as a sustainable alternative water source for urban areas. However, the successful implementation of large-scale water reuse projects depends on community acceptance. Because of the negative perceptions that are traditionally associated with reclaimed water, water reuse is often not considered in the development of urban water management plans. This study develops a simulation model for understanding community opinion dynamics surrounding the issue of water reuse, and how individual perceptions evolve within that context, which can help in the planning and decision-making process. Based on the social amplification of risk framework, our agent-based model simulates consumer perceptions, discussion patterns, and their adoption or rejection of water reuse. The model is based on the "risk publics" model, an empirical approach that uses the concept of belief clusters to explain the adoption of new technology. Each household is represented as an agent, and parameters that define their behavior and attributes are defined from survey data. Community-level parameters-including social groups, relationships, and communication variables, also from survey data-are encoded to simulate the social processes that influence community opinion. The model demonstrates its capabilities to simulate opinion dynamics and consumer adoption of water reuse. In addition, based on empirical data, the model is applied to investigate water reuse behavior in different regions of the United States. Importantly, our results reveal that public opinion dynamics emerge differently based on membership in opinion clusters, frequency of discussion, and the structure of social networks. © 2017 Society for Risk Analysis.

  20. Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials.

    Science.gov (United States)

    Hristozov, Danail; Zabeo, Alex; Alstrup Jensen, Keld; Gottardo, Stefania; Isigonis, Panagiotis; Maccalman, Laura; Critto, Andrea; Marcomini, Antonio

    2016-11-01

    Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.

  1. Model-based measurement of latent risk in time series with applications.

    NARCIS (Netherlands)

    Bijleveld, F.D. Commandeur, J.J.F. Gould, P. & Koopman, S.J.

    2006-01-01

    Risk is at the center of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning counter- measures, the risk and severity of counterparty default concerns bank risk managers on a daily basis and the risk of infection

  2. Model-based measurement of latent risk in time series with applications.

    NARCIS (Netherlands)

    Bijleveld, F.D. Commandeur, J.J.F. Gould, P. & Koopman, S.J.

    2008-01-01

    Risk is at the centre of many policy decisions in companies, governments and other institutions.The risk of road fatalities concerns local governments in planning countermeasures, the risk and severity of counterparty default concerns bank risk managers daily and the risk of infection has actuarial

  3. Industry practices in credit risk modeling and internal capital allocations: implications for a models-based regulatory capital standard

    OpenAIRE

    Jones, David M.; Mingo, John J

    1998-01-01

    This paper was presented at the conference "Financial services at the crossroads: capital regulation in the twenty-first century" as part of session 2, "Credit risk modeling." The conference, held at the Federal Reserve Bank of New York on February 26-27, 1998, was designed to encourage a consensus between the public and private sectors on an agenda for capital regulation in the new century.

  4. Collision risk in white-tailed eagles. Modelling kernel-based collision risk using satellite telemetry data in Smoela wind-power plant

    Energy Technology Data Exchange (ETDEWEB)

    May, Roel; Nygaard, Torgeir; Dahl, Espen Lie; Reitan, Ole; Bevanger, Kjetil

    2011-05-15

    Large soaring birds of prey, such as the white-tailed eagle, are recognized to be perhaps the most vulnerable bird group regarding risk of collisions with turbines in wind-power plants. Their mortalities have called for methods capable of modelling collision risks in connection with the planning of new wind-power developments. The so-called 'Band model' estimates collision risk based on the number of birds flying through the rotor swept zone and the probability of being hit by the passing rotor blades. In the calculations for the expected collision mortality a correction factor for avoidance behaviour is included. The overarching objective of this study was to use satellite telemetry data and recorded mortality to back-calculate the correction factor for white-tailed eagles. The Smoela wind-power plant consists of 68 turbines, over an area of approximately 18 km2. Since autumn 2006 the number of collisions has been recorded on a weekly basis. The analyses were based on satellite telemetry data from 28 white-tailed eagles equipped with backpack transmitters since 2005. The correction factor (i.e. 'avoidance rate') including uncertainty levels used within the Band collision risk model for white-tailed eagles was 99% (94-100%) for spring and 100% for the other seasons. The year-round estimate, irrespective of season, was 98% (95-99%). Although the year-round estimate was similar, the correction factor for spring was higher than the correction factor of 95% derived earlier from vantage point data. The satellite telemetry data may provide an alternative way to provide insight into relative risk among seasons, and help identify periods or areas with increased risk either in a pre- or post construction situation. (Author)

  5. Credit Risk Modelling and Implementation of Credit Risk Models in China

    OpenAIRE

    Yu, Mengxiao

    2007-01-01

    Credit risk, or the risk of counterparty default, is an important factor in the valuation and risk management of financial assets. It has become increasingly important to financial institutions. A variety of credit risk models have been developed to measure credit risk. They are J.P. Morgan's CreditMetrics; KMV's PortfolioManager based on Merton (1974) option pricing model; macroeconomic model CreditPortfolio View developed by McKinsey; CSFB's Credit Risk+ Model based on actuarial science fra...

  6. Development and validation of a risk stratification-based screening model for predicting colorectal advanced neoplasia in Korea.

    Science.gov (United States)

    Kim, Dong Hyun; Cha, Jae Myung; Shin, Hyun Phil; Joo, Kwang Ro; Lee, Joung Il; Park, Dong Il

    2015-01-01

    To develop and validate a risk stratification-based screening model for predicting colorectal advanced neoplasia in Korea. Colorectal advanced neoplasia is the relevant finding of screening colonoscopy. Risk estimation for advanced neoplasia may be helpful to improve compliance and to develop more cost-effective approaches toward screening. We developed Korean Colorectal Screening (KCS) score by optimizing and adjusting Asia-Pacific Colorectal Screening (APCS) score to predict advanced neoplasia in an asymptomatic Korean population who received screening colonoscopies from September 2006 to September 2009. Moreover, we validated the KCS score in another Korean cohort who received screening colonoscopies from October 2009 to February 2011. We also assessed the predictive power and diagnostic performance of both KCS and APCS scores. There were 3561 subjects in the derivation cohort and 1316 subjects in the validation cohort, with a prevalence of advanced neoplasia of 4.7% and 4.3%, respectively. After a multivariate analysis, KCS was developed as 0 to 8 points comprising of age, sex, body mass index, smoking, and family history of CRC. Using KCS scores to stratify the validation cohort, the prevalences of advanced neoplasia in the 3 risk tiers (average, moderate, and high) were 2.0%, 3.7%, and 10.9%, respectively. Moderate-risk and high-risk tiers showed 2.1- and 6.5-fold increased prevalences, respectively, of advanced neoplasia compared with average risk tier. In addition, KCS score showed relatively good discriminative power (ROC=0.681) and higher sensitivity compared with APCS score for the high-risk tier. KCS score may be clinically simple and useful for assessing advanced neoplasia risk in Korea. However, racial disparity should be considered in risk stratification-based screening in each country.

  7. Evaluation of three physiologically based pharmacokinetic (PBPK) modeling tools for emergency risk assessment after acute dichloromethane exposure.

    Science.gov (United States)

    Boerleider, R Z; Olie, J D N; van Eijkeren, J C H; Bos, P M J; Hof, B G H; de Vries, I; Bessems, J G M; Meulenbelt, J; Hunault, C C

    2015-01-05

    Physiologically based pharmacokinetic (PBPK) models may be useful in emergency risk assessment, after acute exposure to chemicals, such as dichloromethane (DCM). We evaluated the applicability of three PBPK models for human risk assessment following a single exposure to DCM: one model is specifically developed for DCM (Bos) and the two others are semi-generic ones (Mumtaz and Jongeneelen). We assessed the accuracy of the models' predictions by simulating exposure data from a previous healthy volunteer study, in which six subjects had been exposed to DCM for 1h. The time-course of both the blood DCM concentration and percentage of carboxyhemoglobin (HbCO) were simulated. With all models, the shape of the simulated time course resembled the shape of the experimental data. For the end of the exposure, the predicted DCM blood concentration ranged between 1.52-4.19mg/L with the Bos model, 1.42-4.04mg/L with the Mumtaz model, and 1.81-4.31mg/L with the Jongeneelen model compared to 0.27-5.44mg/L in the experimental data. % HbCO could be predicted only with the Bos model. The maximum predicted % HbCO ranged between 3.1 and 4.2% compared to 0.4-2.3% in the experimental data. The % HbCO predictions were more in line with the experimental data after adjustment of the Bos model for the endogenous HbCO levels. The Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. The Bos model appears to be useful for calculating HbCO concentrations in emergency risk assessment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  8. Evaluation of caries risk in a young adult population using a computer-based risk assessment model (Cariogram

    Directory of Open Access Journals (Sweden)

    Ilkay Peker

    2012-06-01

    Conclusions: According to the results of this study, the most important factors for caries risk were the past caries experience, fluoride programs, and S. mutans and Lactobacillus counts in saliva. Cariogram is a helpful method for dentists in clinical practice to assess caries risk, and it can be used as a didactic tool for patient education and motivation.

  9. A risk-based monitoring model for health care service institutions as a tool to protect health rights in Peru

    OpenAIRE

    Benites-Zapata, Vicente A.; Intendencia de Investigación y Desarrollo, Superintendencia Nacional de Salud (SUSALUD). Lima, Perú. Centro de investigación en Salud Pública, Instituto de Investigación, Facultad de Medicina, Universidad de San Martín de Porres. Lima, Perú. Médico cirujano maestro en Ciencias en Investigación Epidemiológica;; Saravia-Chong, Héctor A.; Intendencia de Supervisión de Instituciones Prestadoras de Servicios de Salud, Superintendencia Nacional de Salud (SUSALUD). Lima, Perú. ingeniero estadístico e informático; Mezones-Holguin, Edward; Intendencia de Investigación y Desarrollo, Superintendencia Nacional de Salud (SUSALUD), Lima, Perú. Escuela de Medicina, Universidad Peruana de Ciencias Aplicadas, Lima, Perú.; Aquije-Díaz, Allen J.; Intendencia de Supervisión de Instituciones Prestadoras de Servicios de Salud, Superintendencia Nacional de Salud (SUSALUD). Lima, Perú. Médico cirujano especialista en Administración de Salud y Auditoría Médica; Villegas-Ortega, José; Intendencia de Investigación y Desarrollo, Superintendencia Nacional de Salud. Lima, Perú. Facultad de Ingeniería de Sistemas, Universidad Nacional Mayor de San Marcos. Lima, Perú Licenciado en Computación, magíster en Gestión de Tecnologías de Información.; Rossel-de-Almeida, Gustavo; Superintendencia Adjunta de Supervisión, Superintendencia Nacional de Salud (SUSALUD). Lima, Perú. Médico cirujano magíster en Salud Pública; Acosta-Saal, Carlos; Intendencia de Supervisión de Instituciones Prestadoras de Servicios de Salud, Superintendencia Nacional de Salud (SUSALUD). Lima, Perú. Médico cirujano; Philipps-Cuba, Flor; Superintendencia Nacional de Salud (SUSALUD). Lima, Perú. Escuela de Posgrado, Universidad Peruana de Ciencias Aplicadas. Lima, Perú. Médico cirujano maestría en Administración de Negocios

    2016-01-01

    Objectives. To describe the monitoring model of the Health Care Service Institutions (HCSI) of the National Health Authority (NHA) and assess the factors associated with risk-adjusted normative compliance (%RANC) within the Peruvian Health System (PHS). Materials and Methods. We carried out a case study of the experience of the NHA in the development and implementation of a monitoring program based on the ISO 31000-2009. With HCSI as the units of analysis, we calculated the %RANC (a score in ...

  10. A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

    Science.gov (United States)

    Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

    2016-11-23

    A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P < 0.001). Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.

  11. Finding Groups Using Model-based Cluster Analysis: Heterogeneous Emotional Self-regulatory Processes and Heavy Alcohol Use Risk

    Science.gov (United States)

    Mun, Eun-Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

    2010-01-01

    Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of non-nested models using the Bayesian Information Criterion (BIC) to compare multiple models and identify the optimum number of clusters. The current study clustered 36 young men and women based on their baseline heart rate (HR) and HR variability (HRV), chronic alcohol use, and reasons for drinking. Two cluster groups were identified and labeled High Alcohol Risk and Normative groups. Compared to the Normative group, individuals in the High Alcohol Risk group had higher levels of alcohol use and more strongly endorsed disinhibition and suppression reasons for use. The High Alcohol Risk group showed significant HRV changes in response to positive and negative emotional and appetitive picture cues, compared to neutral cues. In contrast, the Normative group showed a significant HRV change only to negative cues. Findings suggest that the individuals with autonomic self-regulatory difficulties may be more susceptible to heavy alcohol use and use alcohol for emotional regulation. PMID:18331138

  12. Development and external validation of new ultrasound-based mathematical models for preoperative prediction of high-risk endometrial cancer.

    Science.gov (United States)

    Van Holsbeke, C; Ameye, L; Testa, A C; Mascilini, F; Lindqvist, P; Fischerova, D; Frühauf, F; Fransis, S; de Jonge, E; Timmerman, D; Epstein, E

    2014-05-01

    To develop and validate strategies, using new ultrasound-based mathematical models, for the prediction of high-risk endometrial cancer and compare them with strategies using previously developed models or the use of preoperative grading only. Women with endometrial cancer were prospectively examined using two-dimensional (2D) and three-dimensional (3D) gray-scale and color Doppler ultrasound imaging. More than 25 ultrasound, demographic and histological variables were analyzed. Two logistic regression models were developed: one 'objective' model using mainly objective variables; and one 'subjective' model including subjective variables (i.e. subjective impression of myometrial and cervical invasion, preoperative grade and demographic variables). The following strategies were validated: a one-step strategy using only preoperative grading and two-step strategies using preoperative grading as the first step and one of the new models, subjective assessment or previously developed models as a second step. One-hundred and twenty-five patients were included in the development set and 211 were included in the validation set. The 'objective' model retained preoperative grade and minimal tumor-free myometrium as variables. The 'subjective' model retained preoperative grade and subjective assessment of myometrial invasion. On external validation, the performance of the new models was similar to that on the development set. Sensitivity for the two-step strategy with the 'objective' model was 78% (95% CI, 69-84%) at a cut-off of 0.50, 82% (95% CI, 74-88%) for the strategy with the 'subjective' model and 83% (95% CI, 75-88%) for that with subjective assessment. Specificity was 68% (95% CI, 58-77%), 72% (95% CI, 62-80%) and 71% (95% CI, 61-79%) respectively. The two-step strategies detected up to twice as many high-risk cases as preoperative grading only. The new models had a significantly higher sensitivity than did previously developed models, at the same specificity. Two

  13. New approaches to infection prevention and control: implementing a risk-based model regionally.

    Science.gov (United States)

    Wale, Martin; Kibsey, Pamela; Young, Lisa; Dobbyn, Beverly; Archer, Jana

    2016-06-01

    Infectious disease outbreaks result in substantial inconvenience to patients and disruption of clinical activity. Between 1 April 2008 and 31 March 2009, the Vancouver Island Health Authority (Island Health) declared 16 outbreaks of Vancomycin Resistant Enterococci and Clostridium difficile in acute care facilities. As a result, infection prevention and control became one of Island Health's highest priorities. Quality improvement methodology, which promotes a culture of co-production between front-line staff, physicians and Infection Control Practitioners, was used to develop and test a bundle of changes in practices. A series of rapid Plan-Do-Study-Act cycles, specific to decreasing hospital-acquired infections, were undertaken by a community hospital, selected for its size, clinical specialty representation, and enthusiasm amongst staff and physicians for innovation and change. Positive results were incorporated into practice at the test site, and then introduced throughout the rest of the Health Authority. The changes implemented as a result of this study have enabled better control of antibiotic resistant organisms and have minimized disruption to routine activity, as well as saving an estimated $6.5 million per annum. When outbreaks do occur, they are now controlled much more promptly, even in existing older facilities. Through this process, we have changed our approach in Infection Prevention and Control (IPAC) from a rules-based approach to one that is risk-based, focusing attention on identifying and managing high-risk situations. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

  14. Methodology for setting risk-based concentrations of contaminants in soil and groundwater and application to a model contaminated site.

    Science.gov (United States)

    Fujinaga, Aiichiro; Uchiyama, Iwao; Morisawa, Shinsuke; Yoneda, Minoru; Sasamoto, Yuzuru

    2012-01-01

    In Japan, environmental standards for contaminants in groundwater and in leachate from soil are set with the assumption that they are used for drinking water over a human lifetime. Where there is neither a well nor groundwater used for drinking, the standard is thus too severe. Therefore, remediation based on these standards incurs excessive effort and cost. In contrast, the environmental-assessment procedure used in the United States and the Netherlands considers the site conditions (land use, existing wells, etc.); however, a risk assessment is required for each site. Therefore, this study proposes a new framework for judging contamination in Japan by considering the merits of the environmental standards used and a method for risk assessment. The framework involves setting risk-based concentrations that are attainable remediation goals for contaminants in soil and groundwater. The framework was then applied to a model contaminated site for risk management, and the results are discussed regarding the effectiveness and applicability of the new methodology. © 2011 Society for Risk Analysis.

  15. Micro-scale flood risk analysis based on detailed 2D hydraulic modelling and high resolution geographic data

    OpenAIRE

    Ernst, Julien; Dewals, Benjamin; Detrembleur, Sylvain; Archambeau, Pierre; Erpicum, Sébastien; Pirotton, Michel

    2010-01-01

    The paper presents a consistent micro-scale flood risk analysis procedure, relying on detailed 2D inundation modelling as well as on high resolution topographic and land use database. The flow model is based on the shallow-water equations, solved by means of a finite volume scheme on multiblock structured grids. Using highly accurate laser altimetry, the simulations are performed with a typical grid spacing of 2m, which is fine enough to represent the flow at the scale of individual buildi...

  16. Risk analysis for rumor propagation in metropolises based on improved 8-state ICSAR model and dynamic personal activity trajectories

    Science.gov (United States)

    Zhang, N.; Huang, H.; Duarte, M.; Zhang, J.

    2016-06-01

    Social media has developed extremely fast in metropolises in recent years resulting in more and more rumors disturbing our daily lives. Knowing the characteristics of rumor propagation in metropolises can help the government make efficient rumor refutation plans. In this paper, we established a dynamic spatio-temporal comprehensive risk assessment model for rumor propagation based on an improved 8-state ICSAR model (Ignorant, Information Carrier, Information Spreader, Advocate, Removal), large personal activity trajectory data, and governmental rumor refutation (anti-rumor) scenarios. Combining these relevant data with the 'big' traffic data on the use of subways, buses, and taxis, we simulated daily oral communications among inhabitants in Beijing. In order to analyze rumor and anti-rumor competition in the actual social network, personal resistance, personal preference, conformity, rumor intensity, government rumor refutation and other influencing factors were considered. Based on the developed risk assessment model, a long-term dynamic rumor propagation simulation for a seven day period was conducted and a comprehensive rumor propagation risk distribution map was obtained. A set of the sensitivity analyses were conducted for different social media and propagation routes. We assessed different anti-rumor coverage ratios and the rumor-spreading thresholds at which the government started to launch anti-rumor actions. The results we obtained provide worthwhile references useful for governmental decision making towards control of social-disrupting rumors.

  17. Choosing where to work at work - towards a theoretical model of benefits and risks of activity-based flexible offices.

    Science.gov (United States)

    Wohlers, Christina; Hertel, Guido

    2017-04-01

    Although there is a trend in today's organisations to implement activity-based flexible offices (A-FOs), only a few studies examine consequences of this new office type. Moreover, the underlying mechanisms why A-FOs might lead to different consequences as compared to cellular and open-plan offices are still unclear. This paper introduces a theoretical framework explaining benefits and risks of A-FOs based on theories from work and organisational psychology. After deriving working conditions specific for A-FOs (territoriality, autonomy, privacy, proximity and visibility), differences in working conditions between A-FOs and alternative office types are proposed. Further, we suggest how these differences in working conditions might affect work-related consequences such as well-being, satisfaction, motivation and performance on the individual, the team and the organisational level. Finally, we consider task-related (e.g. task variety), person-related (e.g. personality) and organisational (e.g. leadership) moderators. Based on this model, future research directions as well as practical implications are discussed. Practitioner Summary: Activity-based flexible offices (A-FOs) are popular in today's organisations. This article presents a theoretical model explaining why and when working in an A-FO evokes benefits and risks for individuals, teams and organisations. According to the model, A-FOs are beneficial when management encourages employees to use the environment appropriately and supports teams.

  18. A new information diffusion modelling technique based on vibrating string equation and its application in natural disaster risk assessment

    Science.gov (United States)

    Bai, Cheng-Zu; Zhang, Ren; Hong, Mei; Qian, Long-xia; Wang, Zhengxin

    2015-07-01

    In this paper, to naturally fill the gap in incomplete data, a new algorithm is proposed for estimating the risk of natural disasters based on the information diffusion theory and the equation of the vibrating string. Two experiments are performed with small samples to investigate its effectiveness. Furthermore, to demonstrate the practicality of the new algorithm, it is applied to study the relationship between epicentral intensity and earthquake magnitude, with strong-motion earthquake observations measured in Yunnan Province in China. The regression model, the back-propagation neural network and the conventional information diffusion model are also involved for comparison. All results show that the new algorithm, which can unravel fuzzy information in incomplete data, is better than the main existing methods for risk estimation.

  19. Research on Investment Risk Management of Chinese Prefabricated Construction Projects Based on a System Dynamics Model

    Directory of Open Access Journals (Sweden)

    Ming Li

    2017-09-01

    Full Text Available Prefabricated construction, a new direction for the future development of the Chinese construction industry, can maximize the requirements of “green”. As a new form of green building, prefabricated construction is of particular interest. On account of the immature development of the green building market in China, the investment risk for prefabricated construction is higher than for traditional architecture. Hence, it is especially important to improve its investment risk identification and management. This study adopts system dynamics and builds a risk identification feedback chart and risk flow chart, to comprehensively identify investment risks that projects in China may face and to process quantitative estimation of investment risk factors. Key factors influencing project investment risks are found, and corresponding measures are pointedly proposed. This paper may provide guidance and a reference for promoting the sound development of prefabricated construction in China.

  20. Risk Modelling of Agricultural Products

    Science.gov (United States)

    Nugrahani, E. H.

    2017-03-01

    In the real world market, agricultural commodity are imposed with fluctuating prices. This means that the price of agricultural products are relatively volatile, which means that agricultural business is a quite risky business for farmers. This paper presents some mathematical models to model such risks in the form of its volatility, based on certain assumptions. The proposed models are time varying volatility model, as well as time varying volatility with mean reversion and with seasonal mean equation models. Implementation on empirical data show that agricultural products are indeed risky.

  1. Pathways from parental AIDS to child psychological, educational and sexual risk: developing an empirically-based interactive theoretical model.

    Science.gov (United States)

    Cluver, Lucie; Orkin, Mark; Boyes, Mark E; Sherr, Lorraine; Makasi, Daphne; Nikelo, Joy

    2013-06-01

    Increasing evidence demonstrates negative psychological, health, and developmental outcomes for children associated with parental HIV/AIDS illness and death. However, little is known about how parental AIDS leads to negative child outcomes. This study used a structural equation modelling approach to develop an empirically-based theoretical model of interactive relationships between parental or primary caregiver AIDS-illness, AIDS-orphanhood and predicted intervening factors associated with children's psychological distress, educational access and sexual health. Cross-sectional data were collected in 2009-2011, from 6002 children aged 10-17 years in three provinces of South Africa using stratified random sampling. Comparison groups included children orphaned by AIDS, orphaned by other causes and non-orphans, and children whose parents or primary caregivers were unwell with AIDS, unwell with other causes or healthy. Participants reported on psychological symptoms, educational access, and sexual health risks, as well as hypothesized sociodemographic and intervening factors. In order to build an interactive theoretical model of multiple child outcomes, multivariate regression and structural equation models were developed for each individual outcome, and then combined into an overall model. Neither AIDS-orphanhood nor parental AIDS-illness were directly associated with psychological distress, educational access, or sexual health. Instead, significant indirect effects of AIDS-orphanhood and parental AIDS-illness were obtained on all measured outcomes. Child psychological, educational and sexual health risks share a common set of intervening variables including parental disability, poverty, community violence, stigma, and child abuse that together comprise chain effects. In all models, parental AIDS-illness had stronger effects and more risk pathways than AIDS-orphanhood, especially via poverty and parental disability. AIDS-orphanhood and parental AIDS-illness impact

  2. Modelling allergenic risk

    DEFF Research Database (Denmark)

    Birot, Sophie

    for all the methods using uncertainty analysis [11]. The recommended approach for the allergen risk assessment was implemented in a Shiny application with the R software. Thus, allergen risk assessment can be performed easily by non-statisticians with the interactive application....... Allergen and Allergy Management) aims at developing strategies for food allergies based on evidences. Especially, food allergen risk assessment helps food producers or authorities to make decisions on withdrawing a food product from the market or adding more information on the label when allergen presence...... is unintended. The risk assessment method has three different kinds of input. The exposure is calculated from the product consumption and the allergen contamination in the food product. The exposure is then compared to the thresholds to which allergic individuals react in order to calculate the chance...

  3. Information risk and security modeling

    Science.gov (United States)

    Zivic, Predrag

    2005-03-01

    This research paper presentation will feature current frameworks to addressing risk and security modeling and metrics. The paper will analyze technical level risk and security metrics of Common Criteria/ISO15408, Centre for Internet Security guidelines, NSA configuration guidelines and metrics used at this level. Information IT operational standards view on security metrics such as GMITS/ISO13335, ITIL/ITMS and architectural guidelines such as ISO7498-2 will be explained. Business process level standards such as ISO17799, COSO and CobiT will be presented with their control approach to security metrics. Top level, the maturity standards such as SSE-CMM/ISO21827, NSA Infosec Assessment and CobiT will be explored and reviewed. For each defined level of security metrics the research presentation will explore the appropriate usage of these standards. The paper will discuss standards approaches to conducting the risk and security metrics. The research findings will demonstrate the need for common baseline for both risk and security metrics. This paper will show the relation between the attribute based common baseline and corporate assets and controls for risk and security metrics. IT will be shown that such approach spans over all mentioned standards. The proposed approach 3D visual presentation and development of the Information Security Model will be analyzed and postulated. Presentation will clearly demonstrate the benefits of proposed attributes based approach and defined risk and security space for modeling and measuring.

  4. A Risk Prediction Model Based on Lymph-Node Metastasis in Poorly Differentiated-Type Intramucosal Gastric Cancer.

    Directory of Open Access Journals (Sweden)

    Jeung Hui Pyo

    Full Text Available Endoscopic submucosal dissection (ESD for undifferentiated type early gastric cancer is regarded as an investigational treatment. Few studies have tried to identify the risk factors that predict lymph-node metastasis (LNM in intramucosal poorly differentiated adenocarcinomas (PDC. This study was designed to develop a risk scoring system (RSS for predicting LNM in intramucosal PDC.From January 2002 to July 2015, patients diagnosed with mucosa-confined PDC, among those who underwent curative gastrectomy with lymph node dissection were reviewed. A risk model based on independent predicting factors of LNM was developed, and its performance was internally validated using a split sample approach.Overall, LNM was observed in 5.2% (61 of 1169 patients. Four risk factors [Female sex, tumor size ≥ 3.2 cm, muscularis mucosa (M3 invasion, and lymphatic-vascular involvement] were significantly associated with LNM, which were incorporated into the RSS. The area under the receiver operating characteristic curve for predicting LNM after internal validation was 0.69 [95% confidence interval (CI, 0.59-0.79]. A total score of 2 points corresponded to the optimal RSS threshold with a discrimination of 0.75 (95% CI 0.69-0.81. The LNM rates were 1.6% for low risk (<2 points and 8.9% for high-risk (≥2 points patients, with a negative predictive value of 98.6% (95% CI 0.98-1.00.A RSS could be useful in clinical practice to determine which patients with intramucosal PDC have low risk of LNM.

  5. Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System

    Directory of Open Access Journals (Sweden)

    Yun-Tao Shi

    2018-01-01

    Full Text Available Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs, how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant control problem of a WECS with a probable actuator fault. A new stochastic model predictive control (SMPC fault-tolerant controller with the Conditional Value at Risk (CVaR objective function is proposed in this paper. First, the Markov jump linear model is used to describe the WECS dynamics, which are affected by many stochastic factors, like the wind. The Markov jump linear model can precisely model the random WECS properties. Second, the scenario-based SMPC is used as the controller to address the control problem of the WECS. With this controller, all the possible realizations of the disturbance in prediction horizon are enumerated by scenario trees so that an uncertain SMPC problem can be transformed into a deterministic model predictive control (MPC problem. Finally, the CVaR object function is adopted to improve the fault-tolerant control performance of the SMPC controller. CVaR can provide a balance between the performance and random failure risks of the system. The Min-Max performance index is introduced to compare the fault-tolerant control performance with the proposed controller. The comparison results show that the proposed method has better fault-tolerant control performance.

  6. Modeling Research Project Risks with Fuzzy Maps

    Science.gov (United States)

    Bodea, Constanta Nicoleta; Dascalu, Mariana Iuliana

    2009-01-01

    The authors propose a risks evaluation model for research projects. The model is based on fuzzy inference. The knowledge base for fuzzy process is built with a causal and cognitive map of risks. The map was especially developed for research projects, taken into account their typical lifecycle. The model was applied to an e-testing research…

  7. Development and Sensitivity Analysis of a Frost Risk model based primarily on freely distributed Earth Observation data

    Science.gov (United States)

    Louka, Panagiota; Petropoulos, George; Papanikolaou, Ioannis

    2015-04-01

    The ability to map the spatiotemporal distribution of extreme climatic conditions, such as frost, is a significant tool in successful agricultural management and decision making. Nowadays, with the development of Earth Observation (EO) technology, it is possible to obtain accurately, timely and in a cost-effective way information on the spatiotemporal distribution of frost conditions, particularly over large and otherwise inaccessible areas. The present study aimed at developing and evaluating a frost risk prediction model, exploiting primarily EO data from MODIS and ASTER sensors and ancillary ground observation data. For the evaluation of our model, a region in north-western Greece was selected as test site and a detailed sensitivity analysis was implemented. The agreement between the model predictions and the observed (remotely sensed) frost frequency obtained by MODIS sensor was evaluated thoroughly. Also, detailed comparisons of the model predictions were performed against reference frost ground observations acquired from the Greek Agricultural Insurance Organization (ELGA) over a period of 10-years (2000-2010). Overall, results evidenced the ability of the model to produce reasonably well the frost conditions, following largely explainable patterns in respect to the study site and local weather conditions characteristics. Implementation of our proposed frost risk model is based primarily on satellite imagery analysis provided nowadays globally at no cost. It is also straightforward and computationally inexpensive, requiring much less effort in comparison for example to field surveying. Finally, the method is adjustable to be potentially integrated with other high resolution data available from both commercial and non-commercial vendors. Keywords: Sensitivity analysis, frost risk mapping, GIS, remote sensing, MODIS, Greece

  8. Models of Credit Risk Measurement

    OpenAIRE

    Hagiu Alina

    2011-01-01

    Credit risk is defined as that risk of financial loss caused by failure by the counterparty. According to statistics, for financial institutions, credit risk is much important than market risk, reduced diversification of the credit risk is the main cause of bank failures. Just recently, the banking industry began to measure credit risk in the context of a portfolio along with the development of risk management started with models value at risk (VAR). Once measured, credit risk can be diversif...

  9. Reducing software security risk through an integrated approach research initiative model based verification of the Secure Socket Layer (SSL) Protocol

    Science.gov (United States)

    Powell, John D.

    2003-01-01

    This document discusses the verification of the Secure Socket Layer (SSL) communication protocol as a demonstration of the Model Based Verification (MBV) portion of the verification instrument set being developed under the Reducing Software Security Risk (RSSR) Trough an Integrated Approach research initiative. Code Q of the National Aeronautics and Space Administration (NASA) funds this project. The NASA Goddard Independent Verification and Validation (IV&V) facility manages this research program at the NASA agency level and the Assurance Technology Program Office (ATPO) manages the research locally at the Jet Propulsion Laboratory (California institute of Technology) where the research is being carried out.

  10. Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function

    DEFF Research Database (Denmark)

    Klein, John P.; Andersen, Per Kragh

    2005-01-01

    Bone marrow transplantation; Generalized estimating equations; Jackknife statistics; Regression models......Bone marrow transplantation; Generalized estimating equations; Jackknife statistics; Regression models...

  11. Estimating the value of a Country's built assets: investment-based exposure modelling for global risk assessment

    Science.gov (United States)

    Daniell, James; Pomonis, Antonios; Gunasekera, Rashmin; Ishizawa, Oscar; Gaspari, Maria; Lu, Xijie; Aubrecht, Christoph; Ungar, Joachim

    2017-04-01

    In order to quantify disaster risk, there is a demand and need for determining consistent and reliable economic value of built assets at national or sub national level exposed to natural hazards. The value of the built stock in the context of a city or a country is critical for risk modelling applications as it allows for the upper bound in potential losses to be established. Under the World Bank probabilistic disaster risk assessment - Country Disaster Risk Profiles (CDRP) Program and rapid post-disaster loss analyses in CATDAT, key methodologies have been developed that quantify the asset exposure of a country. In this study, we assess the complementary methods determining value of building stock through capital investment data vs aggregated ground up values based on built area and unit cost of construction analyses. Different approaches to modelling exposure around the world, have resulted in estimated values of built assets of some countries differing by order(s) of magnitude. Using the aforementioned methodology of comparing investment data based capital stock and bottom-up unit cost of construction values per square meter of assets; a suitable range of capital stock estimates for built assets have been created. A blind test format was undertaken to compare the two types of approaches from top-down (investment) and bottom-up (construction cost per unit), In many cases, census data, demographic, engineering and construction cost data are key for bottom-up calculations from previous years. Similarly for the top-down investment approach, distributed GFCF (Gross Fixed Capital Formation) data is also required. Over the past few years, numerous studies have been undertaken through the World Bank Caribbean and Central America disaster risk assessment program adopting this methodology initially developed by Gunasekera et al. (2015). The range of values of the building stock is tested for around 15 countries. In addition, three types of costs - Reconstruction cost

  12. Escherichia coli pollution in a Baltic Sea lagoon: a model-based source and spatial risk assessment.

    Science.gov (United States)

    Schippmann, Bianca; Schernewski, Gerald; Gräwe, Ulf

    2013-07-01

    Tourism around the Oder (Szczecin) Lagoon, at the southern Baltic coast, has a long tradition, is an important source of income and shall be further developed. Insufficient bathing water quality and frequent beach closings, especially in the Oder river mouth, hamper tourism development. Monitoring data gives only an incomplete picture of Escherichia coli (E. coli) bacteria sources, spatial transport patterns, risks and does neither support an efficient bathing water quality management nor decision making. We apply a 3D ocean model and a Lagrangian particle tracking model to analyse pollution events and to obtain spatial E. coli pollution maps based on scenario simulations. Model results suggests that insufficient sewage treatment in the city of Szczecin is the major source of faecal pollution, even for beaches 20km downstream. E. coli mortality rate and emission intensity are key parameters for concentration levels downstream. Wind and river discharge play a modifying role. Prevailing southwestern wind conditions cause E. coli transport along the eastern coast and favour high concentration levels at the beaches. Our simulations indicate that beach closings in 2006 would not have been necessary according to the new EU-Bathing Water Quality Directive (2006/7/EC). The implementation of the new directive will, very likely, reduce the number of beach closings, but not the risk for summer tourists. Model results suggest, that a full sewage treatment in Szczecin would allow the establishment of new beaches closer to the city (north of Dabie lake). Copyright © 2013 Elsevier GmbH. All rights reserved.

  13. Experiment design for nonparametric models based on minimizing Bayes Risk: application to voriconazole[Formula: see text].

    Science.gov (United States)

    Bayard, David S; Neely, Michael

    2017-04-01

    An experimental design approach is presented for individualized therapy in the special case where the prior information is specified by a nonparametric (NP) population model. Here, a NP model refers to a discrete probability model characterized by a finite set of support points and their associated weights. An important question arises as to how to best design experiments for this type of model. Many experimental design methods are based on Fisher information or other approaches originally developed for parametric models. While such approaches have been used with some success across various applications, it is interesting to note that they largely fail to address the fundamentally discrete nature of the NP model. Specifically, the problem of identifying an individual from a NP prior is more naturally treated as a problem of classification, i.e., to find a support point that best matches the patient's behavior. This paper studies the discrete nature of the NP experiment design problem from a classification point of view. Several new insights are provided including the use of Bayes Risk as an information measure, and new alternative methods for experiment design. One particular method, denoted as MMopt (multiple-model optimal), will be examined in detail and shown to require minimal computation while having distinct advantages compared to existing approaches. Several simulated examples, including a case study involving oral voriconazole in children, are given to demonstrate the usefulness of MMopt in pharmacokinetics applications.

  14. A risk-based multi-objective model for optimal placement of sensors in water distribution system

    Science.gov (United States)

    Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein

    2018-02-01

    In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value

  15. Fuzzy audit risk modeling algorithm

    Directory of Open Access Journals (Sweden)

    Zohreh Hajihaa

    2011-07-01

    Full Text Available Fuzzy logic has created suitable mathematics for making decisions in uncertain environments including professional judgments. One of the situations is to assess auditee risks. During recent years, risk based audit (RBA has been regarded as one of the main tools to fight against fraud. The main issue in RBA is to determine the overall audit risk an auditor accepts, which impact the efficiency of an audit. The primary objective of this research is to redesign the audit risk model (ARM proposed by auditing standards. The proposed model of this paper uses fuzzy inference systems (FIS based on the judgments of audit experts. The implementation of proposed fuzzy technique uses triangular fuzzy numbers to express the inputs and Mamdani method along with center of gravity are incorporated for defuzzification. The proposed model uses three FISs for audit, inherent and control risks, and there are five levels of linguistic variables for outputs. FISs include 25, 25 and 81 rules of if-then respectively and officials of Iranian audit experts confirm all the rules.

  16. Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

    DEFF Research Database (Denmark)

    Winkler Wille, Mathilde M.; van Riel, Sarah J.; Saghir, Zaigham

    2015-01-01

    Objectives: Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. Methods: From...... the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were...... used to evaluate risk discrimination. Results: AUCs of 0.826–0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer...

  17. Driving risk assessment using near-crash database through data mining of tree-based model.

    Science.gov (United States)

    Wang, Jianqiang; Zheng, Yang; Li, Xiaofei; Yu, Chenfei; Kodaka, Kenji; Li, Keqiang

    2015-11-01

    This paper considers a comprehensive naturalistic driving experiment to collect driving data under potential threats on actual Chinese roads. Using acquired real-world naturalistic driving data, a near-crash database is built, which contains vehicle status, potential crash objects, driving environment and road types, weather condition, and driver information and actions. The aims of this study are summarized into two aspects: (1) to cluster different driving-risk levels involved in near-crashes, and (2) to unveil the factors that greatly influence the driving-risk level. A novel method to quantify the driving-risk level of a near-crash scenario is proposed by clustering the braking process characteristics, namely maximum deceleration, average deceleration, and percentage reduction in vehicle kinetic energy. A classification and regression tree (CART) is employed to unveil the relationship among driving risk, driver/vehicle characteristics, and road environment. The results indicate that the velocity when braking, triggering factors, potential object type, and potential crash type exerted the greatest influence on the driving-risk levels in near-crashes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks

    NARCIS (Netherlands)

    Hartemink, N.; Vanwambeke, S.O.; Purse, B.V.; Gilbert, M.; Van Dyck, H.

    2015-01-01

    Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional

  19. Risk Estimation for Lung Cancer in Libya: Analysis Based on Standardized Morbidity Ratio, Poisson-Gamma Model, BYM Model and Mixture Model

    Science.gov (United States)

    Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley

    2017-03-01

    Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models. Creative Commons Attribution License

  20. Model-based Qualitative Risk Assessment for Availability of IT Infrastructures

    OpenAIRE

    Zambon, Emmanuele; Etalle, Sandro; Wieringa, Roelf J.; Hartel, Pieter H.

    2011-01-01

    For today’s organisations, having a reliable information system is crucial to safeguard enterprise revenues (think of on-line banking, reservations for e-tickets etc.). Such a system must often offer high guarantees in terms of its availability; in other words, to guarantee business continuity, IT systems can afford very little downtime. Unfortunately, making an assessment of IT availability risks is difficult: incidents affecting the availability of a marginal component of the system may pro...

  1. Lunar Landing Operational Risk Model

    Science.gov (United States)

    Mattenberger, Chris; Putney, Blake; Rust, Randy; Derkowski, Brian

    2010-01-01

    Characterizing the risk of spacecraft goes beyond simply modeling equipment reliability. Some portions of the mission require complex interactions between system elements that can lead to failure without an actual hardware fault. Landing risk is currently the least characterized aspect of the Altair lunar lander and appears to result from complex temporal interactions between pilot, sensors, surface characteristics and vehicle capabilities rather than hardware failures. The Lunar Landing Operational Risk Model (LLORM) seeks to provide rapid and flexible quantitative insight into the risks driving the landing event and to gauge sensitivities of the vehicle to changes in system configuration and mission operations. The LLORM takes a Monte Carlo based approach to estimate the operational risk of the Lunar Landing Event and calculates estimates of the risk of Loss of Mission (LOM) - Abort Required and is Successful, Loss of Crew (LOC) - Vehicle Crashes or Cannot Reach Orbit, and Success. The LLORM is meant to be used during the conceptual design phase to inform decision makers transparently of the reliability impacts of design decisions, to identify areas of the design which may require additional robustness, and to aid in the development and flow-down of requirements.

  2. Malaria Risk Assessment for the Republic of Korea Based on Models of Mosquito Distribution

    Science.gov (United States)

    2008-06-01

    Yam;lda All. kleilli Rueda All. belellme Rueda VPH 0.8 • 0.6• ~ ~ 0.’ 0.2 0 H P V VPH Figure I, Illustration of the concept of the mal-area as it...the percentage of the sampled area that these parameters cover. The value for VPH could be used as a simplified index of malaria risk to compare...combinations of the VPH variables. These statistics will consist of the percentage of cells that contain a certain value for the user defined area

  3. A scenario-based modeling approach for emergency evacuation management and risk analysis under multiple uncertainties.

    Science.gov (United States)

    Lv, Y; Huang, G H; Guo, L; Li, Y P; Dai, C; Wang, X W; Sun, W

    2013-02-15

    Nuclear emergency evacuation is important to prevent radioactive harms by hazardous materials and to limit the accidents' consequences; however, uncertainties are involved in the components and processes of such a management system. In the study, an interval-parameter joint-probabilistic integer programming (IJIP) method is developed for emergency evacuation management under uncertainties. Optimization techniques of interval-parameter programming (IPP) and joint-probabilistic constrained (JPC) programming are incorporated into an integer linear programming framework, so that the approach can deal with uncertainties expressed as joint probability and interval values. The IJIP method can schedule the optimal routes to guarantee the maximum population evacuated away from the effected zone during a finite time. Furthermore, it can also facilitate post optimization analysis to enhance robustness in controlling system violation risk imposed on the joint-probabilistic constraints. The developed method has been applied to a case study of nuclear emergency management; meanwhile, a number of scenarios under different system conditions have been analyzed. It is indicated that the solutions are useful for evacuation management practices. The result of the IJIP method can not only help to raise the capability of disaster responses in a systematic manner, but also provide an insight into complex relationships among evacuation planning, resources utilizations, policy requirements and system risks. Copyright © 2012 Elsevier B.V. All rights reserved.

  4. The Risk Assessment Study for Electric Power Marketing Competitiveness Based on Cloud Model and TOPSIS

    Science.gov (United States)

    Li, Cunbin; Wang, Yi; Lin, Shuaishuai

    2017-09-01

    With the rapid development of the energy internet and the deepening of the electric power reform, the traditional marketing mode of electric power does not apply to most of electric power enterprises, so must seek a breakthrough, however, in the face of increasingly complex marketing information, how to make a quick, reasonable transformation, makes the electric power marketing competitiveness assessment more accurate and objective becomes a big problem. In this paper, cloud model and TOPSIS method is proposed. Firstly, build the electric power marketing competitiveness evaluation index system. Then utilize the cloud model to transform the qualitative evaluation of the marketing data into quantitative values and use the entropy weight method to weaken the subjective factors of evaluation index weight. Finally, by TOPSIS method the closeness degrees of alternatives are obtained. This method provides a novel solution for the electric power marketing competitiveness evaluation. Through the case analysis the effectiveness and feasibility of this model are verified.

  5. Modelling soil erosion risk based on RUSLE-3D using GIS in a ...

    Indian Academy of Sciences (India)

    The RUSLE-3D (Revised Universal Soil Loss Equation-3D) model was implemented in geographic infor- mation system (GIS) for predicting ... High resolution remote sensing data (IKONOS and IRS LISS-IV) were used to prepare land use/land .... 3.1 Materials. IKONOS 1 m resolution digital satellite data of. November 2004 ...

  6. Agent Based Modeling and Simulation Framework for Supply Chain Risk Management

    Science.gov (United States)

    2012-03-01

    management metric,” International Journal of Physical Distribution & Logistics Management 32(4), 288-298. Foroughi, A., M. Albin and M. Kocakulah...Application Model Active Agents ............................................................................. 86 Figure 13 - Initialization Period...managed; tens of thousands of different interleaved discrete business processes; thousands of different organizations with their own physical plants

  7. Crash Risk Prediction Modeling Based on the Traffic Conflict Technique and a Microscopic Simulation for Freeway Interchange Merging Areas

    Directory of Open Access Journals (Sweden)

    Shen Li

    2016-11-01

    Full Text Available This paper evaluates the traffic safety of freeway interchange merging areas based on the traffic conflict technique. The hourly composite risk indexes (HCRI was defined. By the use of unmanned aerial vehicle (UAV photography and video processing techniques, the conflict type and severity was judged. Time to collision (TTC was determined with the traffic conflict evaluation index. Then, the TTC severity threshold was determined. Quantizing the weight of the conflict by direct losses of different severities of freeway traffic accidents, the calculated weight of the HCRI can be obtained. Calibration of the relevant parameters of the micro-simulation simulator VISSIM is conducted by the travel time according to the field data. Variables are placed into orthogonal tables at different levels. On the basis of this table, the trajectory file of every traffic condition is simulated, and then submitted into a surrogate safety assessment model (SSAM, identifying the number of hourly traffic conflicts in the merging area, a statistic of HCRI. Moreover, the multivariate linear regression model was presented and validated to study the relationship between HCRI and the influencing variables. A comparison between the HCRI model and the hourly conflicts ratio (HCR, without weight, shows that the HCRI model fitting degree was obviously higher than the HCR. This will be a reference to design and implement operational planners.

  8. Overview of Dioxin Kinetics and Application of Dioxin Physiologically Based Phannacokinetic (PBPK) Models to Risk Assessment

    Science.gov (United States)

    The available data on the pharmacokinetics of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in animals and humans have been thoroughly reviewed in literature. It is evident based on these reviews and other analyses that three distinctive features of TCDD play important roles in dete...

  9. A Practical Probabilistic Graphical Modeling Tool for Weighing Ecological Risk-Based Evidence

    Science.gov (United States)

    Past weight-of-evidence frameworks for adverse ecological effects have provided soft-scoring procedures for judgments based on the quality and measured attributes of evidence. Here, we provide a flexible probabilistic structure for weighing and integrating lines of evidence for e...

  10. Gis-Based Method in Developing Wildfire Risk Model (Case Study in Sasamba, East Kalimantan, Indonesia

    Directory of Open Access Journals (Sweden)

    Jarunton Boonyanuphap

    2001-12-01

    Full Text Available Analisis pemetaan lengkap (Cemplete Mapping Analysis yang berbasis sistem informasi geografis (SIG digunakan untuk melakukan pembobotan terhadap nilai “vulnerability” dari faktor-faktor resiko dalam rangka membangun suatu model dan memetakan kelas-kelas resiko kebakaran liar. Ada dua faktor utama, yaitu faktor lingkungan fisik dan aktifitas manusia yang sangat mempengaruhi terjadinya kebakaran hutan. Model yang ditemukan pada saat ini memperlihatkan bahwa kelembaban relatif adalah faktor terpenting diantara faktor lingkungan fisik, sementara jarak terhadap pusat-pusat pemukiman merupakan faktor terpenting diantara faktor aktifitas manusia. Diketahui juga bahwa, terjadinya kebakaran liar lebih banyak dipengaruhi oleh faktor aktifitas manusia daripada faktor lingkungan fisik. Pada studi ini, wilayah resiko kebakaran liar dibagi atas 3 kelas, dimana ditemukan bahwa kelas resiko kebakaran tertinggi mendominasi lokasi penelitian, selanjutnya diikuti dengan kelas resiko sedang dan rendah. Berdasarkan hasil verifikasi, model hanya berhasil menduga kelas resiko tinggi yaitu sebesar 76,05%, sementara gagal dalam menduga resiko kebakaran sedang dan rendah (lebih rendah dari 40%.

  11. A risk factor-based model for upper aerodigestive tract cancers in India: predicting and validating the receiver operating characteristic curve.

    Science.gov (United States)

    Gupta, Bhawna; Kumar, Narinder; Johnson, Newell W

    2017-07-01

    A study was conducted to develop and validate a screening model using risk scores to identify individuals at high risk for developing upper aerodigestive tract (UADT) cancers in an Indian population. A hospital-based case-control study (n = 480) was conducted in Pune, India. We assessed risk factors for UADT cancers by administering a questionnaire through face-to-face interviews. We developed a risk factor model based on the statistically significant risk factors in multiple logistic regression. A total, single risk score was calculated per individual based on the adjusted odds ratio for each of their risk factors. Standard receiver operator characteristic curve was plotted for the total score and the presence of UADT cancers. The stratification ability of the model was determined using the c-statistic. The optimal criterion value was determined at the point on curve at which the Youden's index was maximal. Confidence intervals were calculated by bootstrapping. Total risk score for each individual ranged from 0 to 26. Area under the receiver operating characteristic curve (95.8; P < 0.001) suggests strong predictive ability. A risk score criterion value of ≤10 produced optimal sensitivity (93.5%), specificity (71.1%), false-positive rate (28.8%), false-negative rate (6.4%), positive predictive value (74.8%), and negative predictive value (96.6%). This risk factor-based model has the potential of satisfactorily screening and detection of UADT cancers at its early stage in a high-risk population like India. The identified at-risk individuals can then be targeted for clinical examination and for focused preventive/treatment measures at the hospital. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  12. Uncertainties in Biologically-Based Modeling of Formaldehyde-Induced Respiratory Cancer Risk: Identification of Key Issues

    Science.gov (United States)

    Subramaniam, Ravi P.; Chen, Chao; Crump, Kenny S.; DeVoney, Danielle; Fox, John F.; Portier, Christopher J.; Schlosser, Paul M.; Thompson, Chad M.; White, Paul

    2009-01-01

    In a series of articles and a health-risk assessment report, scientists at the CIIT Hamner Institutes developed a model (CIIT model) for estimating respiratory cancer risk due to inhaled formaldehyde within a conceptual framework incorporating extensive mechanistic information and advanced computational methods at the toxicokinetic and toxicodynamic levels. Several regulatory bodies have utilized predictions from this model; on the other hand, upon detailed evaluation the California EPA has decided against doing so. In this article, we study the CIIT model to identify key biological and statistical uncertainties that need careful evaluation if such two-stage clonal expansion models are to be used for extrapolation of cancer risk from animal bioassays to human exposure. Broadly, these issues pertain to the use and interpretation of experimental labeling index and tumor data, the evaluation and biological interpretation of estimated parameters, and uncertainties in model specification, in particular that of initiated cells. We also identify key uncertainties in the scale-up of the CIIT model to humans, focusing on assumptions underlying model parameters for cell replication rates and formaldehyde-induced mutation. We discuss uncertainties in identifying parameter values in the model used to estimate and extrapolate DNA protein cross-link levels. The authors of the CIIT modeling endeavor characterized their human risk estimates as “conservative in the face of modeling uncertainties.” The uncertainties discussed in this article indicate that such a claim is premature. PMID:18564991

  13. Competition, Innovation, Risk-Taking, and Profitability in the Chinese Banking Sector: An Empirical Analysis Based on Structural Equation Modeling

    Directory of Open Access Journals (Sweden)

    Ti Hu

    2016-01-01

    Full Text Available We introduce a new perspective to systematically investigate the cause-and-effect relationships among competition, innovation, risk-taking, and profitability in the Chinese banking industry. Our hypotheses are tested by the structural equation modeling (SEM, and the empirical results show that (i risk-taking is positively related to profitability; (ii innovation positively affects both risk-taking and profitability, and the effect of innovation on profitability works both directly and indirectly; (iii competition negatively affects risk-taking but positively affects both innovation and profitability, and the effects of competition on risk-taking and profitability work both directly and indirectly; (iv there is a cascading relationship among market competition and bank innovation, risk-taking, and profitability.

  14. A model-based analysis of decision making under risk in obsessive-compulsive and hoarding disorders.

    Science.gov (United States)

    Aranovich, Gabriel J; Cavagnaro, Daniel R; Pitt, Mark A; Myung, Jay I; Mathews, Carol A

    2017-07-01

    Attitudes towards risk are highly consequential in clinical disorders thought to be prone to "risky behavior", such as substance dependence, as well as those commonly associated with excessive risk aversion, such as obsessive-compulsive disorder (OCD) and hoarding disorder (HD). Moreover, it has recently been suggested that attitudes towards risk may serve as a behavioral biomarker for OCD. We investigated the risk preferences of participants with OCD and HD using a novel adaptive task and a quantitative model from behavioral economics that decomposes risk preferences into outcome sensitivity and probability sensitivity. Contrary to expectation, compared to healthy controls, participants with OCD and HD exhibited less outcome sensitivity, implying less risk aversion in the standard economic framework. In addition, risk attitudes were strongly correlated with depression, hoarding, and compulsion scores, while compulsion (hoarding) scores were associated with more (less) "rational" risk preferences. These results demonstrate how fundamental attitudes towards risk relate to specific psychopathology and thereby contribute to our understanding of the cognitive manifestations of mental disorders. In addition, our findings indicate that the conclusion made in recent work that decision making under risk is unaltered in OCD is premature. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. A competing risk approach for the European Heart SCORE model based on cause-specific and all-cause mortality

    DEFF Research Database (Denmark)

    Stovring, H.; Harmsen, C. G.; Wisloff, T.

    2013-01-01

    Background: The European Heart SCORE model constitutes the basis for national guidelines for primary prevention and treatment of cardiovascular disease (CVD) in several European countries. The model estimates individuals' 10-year CVD mortality risks from age, sex, smoking status, systolic blood p...

  16. Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

    Science.gov (United States)

    Ganga, G M D; Esposto, K F; Braatz, D

    2012-01-01

    The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

  17. Finding Groups Using Model-Based Cluster Analysis: Heterogeneous Emotional Self-Regulatory Processes and Heavy Alcohol Use Risk

    Science.gov (United States)

    Mun, Eun Young; von Eye, Alexander; Bates, Marsha E.; Vaschillo, Evgeny G.

    2008-01-01

    Model-based cluster analysis is a new clustering procedure to investigate population heterogeneity utilizing finite mixture multivariate normal densities. It is an inferentially based, statistically principled procedure that allows comparison of nonnested models using the Bayesian information criterion to compare multiple models and identify the…

  18. Model Risk in Portfolio Optimization

    Directory of Open Access Journals (Sweden)

    David Stefanovits

    2014-08-01

    Full Text Available We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk.

  19. Risk assessment of agricultural water requirement based on a multi-model ensemble framework, southwest of Iran

    Science.gov (United States)

    Zamani, Reza; Akhond-Ali, Ali-Mohammad; Roozbahani, Abbas; Fattahi, Rouhollah

    2017-08-01

    Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025-2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

  20. Enterprise Risk Management Models

    CERN Document Server

    Olson, David L

    2010-01-01

    Enterprise risk management has always been important. However, the events of the 21st Century have made it even more critical. The top level of business management became suspect after scandals at ENRON, WorldCom, and other business entities. Financially, many firms experienced difficulties from bubbles. The problems of interacting cultures demonstrated risk from terrorism as well, with numerous terrorist attacks, to include 9/11 in the U.S. Risks can arise in many facets of business. Businesses in fact exist to cope with risk in their area of specialization. Financial risk management has focu

  1. The use of traits-based approaches and eco(toxico)logical models to advance the ecological risk assessment framework for chemicals

    NARCIS (Netherlands)

    Brink, van den P.J.; Baird, D.J.; Baveco, J.M.; Focks, A.

    2013-01-01

    This article presents a framework to diagnose and predict the effects of chemicals, integrating 2 promising tools to incorporate more ecology into ecological risk assessment, namely traits-based approaches and ecological modeling. Traits-based approaches are used increasingly to derive correlations

  2. Screening Models for Cardiac Risk Evaluation in Emergency Abdominal Surgery. I. Evaluation of the Intraoperative Period Risk based on Data from the Preoperative Period

    Directory of Open Access Journals (Sweden)

    Mikhail Matveev

    2008-04-01

    Full Text Available A classification of intraoperative cardio-vascular complications (CVC was performed, based on data from 466 patients subjected to emergency surgery, due to severe abdominal surgical diseases or traumas, in accordance with the severe criteria of ACC/AHA for CVC in noncardiac surgery. There were 370 intraoperative CVC registered, distributed as follows: groups with low risk (148, moderate risk (200, and high risk (22. Patient groups were formed, according to the CVC risk level, during the intraoperative period, for which the determinant factor for the group distribution of patients was the complication with the highest risk. Individual data was collected for each patient, based on 65 indices: age, physical status, diseases, surgical interventions, anaesthesiological information, intra and postoperative cardio-vascular complications, disease outcome, causes of death, cardiovascular disease anamnesis, anamnesis of all other nonsurgical diseases present, laboratory results, results from all imaging and instrumental examinations, etc. On the basis of these indices, a new distribution of the risk factors was implemented, into groups with different levels of risk of CVC during intraoperative period. This result is a solid argument, substantiating the proposal to introduce these adjustments for determining the severity of CVC in the specific conditions of emergency abdominal surgery.

  3. A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making.

    Science.gov (United States)

    Balasubramani, Pragathi P; Chakravarthy, V Srinivasa; Ravindran, Balaraman; Moustafa, Ahmed A

    2015-01-01

    There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making (Balasubramani et al., 2014). Despite this evidence, little is known about the computational principles and neural correlates of risk computation in this subcortical system. We have previously proposed a reinforcement learning (RL)-based model of the BG that simulates the interactions between dopamine (DA) and serotonin (5HT) in a diverse set of experimental studies including reward, punishment and risk based decision making (Balasubramani et al., 2014). Starting with the classical idea that the activity of mesencephalic DA represents reward prediction error, the model posits that serotoninergic activity in the striatum controls risk-prediction error. Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data. In this work, we expand the earlier model into a detailed network model of the BG and demonstrate the joint contributions of DA-5HT in risk and reward-punishment sensitivity. At the core of the proposed network model is the following insight regarding cellular correlates of value and risk computation. Just as DA D1 receptor (D1R) expressing medium spiny neurons (MSNs) of the striatum were thought to be the neural substrates for value computation, we propose that DA D1R and D2R co-expressing MSNs are capable of computing risk. Though the existence of MSNs that co-express D1R and D2R are reported by various experimental studies, prior existing computational models did not include them. Ours is the first model that accounts for the computational possibilities of these co-expressing D1R-D2R MSNs, and describes how DA and 5HT mediate activity in these classes of neurons (D1R-, D2R-, D1R-D2R- MSNs). Starting from the assumption that 5HT modulates all MSNs, our study predicts significant modulatory effects of 5HT on D2R and co-expressing D1R-D2R MSNs which in turn

  4. Nottingham knee osteoarthritis risk prediction models.

    Science.gov (United States)

    Zhang, Weiya; McWilliams, Daniel F; Ingham, Sarah L; Doherty, Sally A; Muthuri, Stella; Muir, Kenneth R; Doherty, Michael

    2011-09-01

    (1) To develop risk prediction models for knee osteoarthritis (OA) and (2) to estimate the risk reduction that results from modification of potential risk factors. This was a 12-year retrospective cohort study undertaken in the general population in Nottingham, UK. Baseline risk factors were collected by questionnaire. Incident radiographic knee OA was defined by Kellgren and Lawrence (KL) score ≥2. Incident symptomatic knee OA was defined by KL ≥2 plus knee pain. Progression of knee OA was defined by KL ≥1 grade increase from baseline. A logistic regression model was used for prediction. Calibration and discrimination of the models were tested in the Osteoarthritis Initiative (OAI) population and Genetics of Osteoarthritis and Lifestyle (GOAL) population. ORs of the models were compared with those obtained from meta-analysis of existing literature. From a community sample of 424 people aged over 40, 3 risk prediction models were developed. These included incidence of radiographic knee OA, incidence of symptomatic knee OA and progression of knee OA. All models had good calibration and moderate discrimination power in OAI and GOAL. The ORs lied within the 95% CIs of the published studies. The risk reduction due to modifying obesity at the individual and the population levels were demonstrated. Risk prediction of knee OA based on the well established, common modifiable risk factors has been established. The models may be used to predict the risk of knee OA, and risk reduction due to preventing a specific risk factor.

  5. Characterising Seismic Hazard Input for Analysis Risk to Multi-System Infrastructures: Application to Scenario Event-Based Models and extension to Probabilistic Risk

    Science.gov (United States)

    Weatherill, G. A.; Silva, V.

    2011-12-01

    The potential human and economic cost of earthquakes to complex urban infrastructures has been demonstrated in the most emphatic manner by recent large earthquakes such as that of Haiti (February 2010), Christchurch (September 2010 and February 2011) and Tohoku (March 2011). Consideration of seismic risk for a homogenous portfolio, such as a single building typology or infrastructure, or independent analyses of separate typologies or infrastructures, are insufficient to fully characterise the potential impacts that arise from inter-connected system failure. Individual elements of each infrastructure may be adversely affected by different facets of the ground motion (e.g. short-period acceleration, long-period displacement, cumulative energy input etc.). The accuracy and efficiency of the risk analysis is dependent on the ability to characterise these multiple features of the ground motion over a spatially distributed portfolio of elements. The modelling challenges raised by this extension to multi-system analysis of risk have been a key focus of the European Project "Systemic Seismic Vulnerability and Risk Analysis for Buildings, Lifeline Networks and Infrastructures Safety Gain (SYNER-G)", and are expected to be developed further within the Global Earthquake Model (GEM). Seismic performance of a spatially distributed infrastructure during an earthquake may be assessed by means of Monte Carlo simulation, in order to incorporate the aleatory variability of the ground motion into the network analysis. Methodologies for co-simulating large numbers of spatially cross-correlated ground motion fields are appraised, and their potential impacts on a spatially distributed portfolio of mixed building typologies assessed using idealised case study scenarios from California and Europe. Potential developments to incorporate correlation and uncertainty in site amplification and geotechnical hazard are also explored. Whilst the initial application of the seismic risk analysis is

  6. An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study.

    Science.gov (United States)

    Li, Huixia; Luo, Miyang; Zheng, Jianfei; Luo, Jiayou; Zeng, Rong; Feng, Na; Du, Qiyun; Fang, Junqun

    2017-02-01

    An artificial neural network (ANN) model was developed to predict the risks of congenital heart disease (CHD) in pregnant women.This hospital-based case-control study involved 119 CHD cases and 239 controls all recruited from birth defect surveillance hospitals in Hunan Province between July 2013 and June 2014. All subjects were interviewed face-to-face to fill in a questionnaire that covered 36 CHD-related variables. The 358 subjects were randomly divided into a training set and a testing set at the ratio of 85:15. The training set was used to identify the significant predictors of CHD by univariate logistic regression analyses and develop a standard feed-forward back-propagation neural network (BPNN) model for the prediction of CHD. The testing set was used to test and evaluate the performance of the ANN model. Univariate logistic regression analyses were performed on SPSS 18.0. The ANN models were developed on Matlab 7.1.The univariate logistic regression identified 15 predictors that were significantly associated with CHD, including education level (odds ratio  = 0.55), gravidity (1.95), parity (2.01), history of abnormal reproduction (2.49), family history of CHD (5.23), maternal chronic disease (4.19), maternal upper respiratory tract infection (2.08), environmental pollution around maternal dwelling place (3.63), maternal exposure to occupational hazards (3.53), maternal mental stress (2.48), paternal chronic disease (4.87), paternal exposure to occupational hazards (2.51), intake of vegetable/fruit (0.45), intake of fish/shrimp/meat/egg (0.59), and intake of milk/soymilk (0.55). After many trials, we selected a 3-layer BPNN model with 15, 12, and 1 neuron in the input, hidden, and output layers, respectively, as the best prediction model. The prediction model has accuracies of 0.91 and 0.86 on the training and testing sets, respectively. The sensitivity, specificity, and Yuden Index on the testing set (training set) are 0.78 (0.83), 0.90 (0.95), and 0

  7. Physically based dynamic run-out modelling for quantitative debris flow risk assessment: a case study in Tresenda, northern Italy

    Czech Academy of Sciences Publication Activity Database

    Quan Luna, B.; Blahůt, Jan; Camera, C.; Van Westen, C.; Apuani, T.; Jetten, V.; Sterlacchini, S.

    2014-01-01

    Roč. 72, č. 3 (2014), s. 645-661 ISSN 1866-6280 Institutional support: RVO:67985891 Keywords : debris flow * FLO-2D * run-out * quantitative hazard and risk assessment * vulnerability * numerical modelling Subject RIV: DB - Geology ; Mineralogy Impact factor: 1.765, year: 2014

  8. Wildfire Risk Main Model

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — The model combines three modeled fire behavior parameters (rate of spread, flame length, crown fire potential) and one modeled ecological health measure (fire regime...

  9. Software Development Risk Management Model

    OpenAIRE

    Islam, Shareeful

    2011-01-01

    Risk management is an effective tool to control risks in software projects and increases the likelihood of project success. Risk management needs to be integrated as early as possible in the project. This dissertation proposes a Goal-driven Software Development Risk Management Model (GSRM) and explicitly integrates it into requirements engineering phase. This integration provides an early warning of potential problems so that both preventive and corrective actions can be undertaken to avoid t...

  10. Operational Risk Modeling

    OpenAIRE

    Gabriela ANGHELACHE; Ana Cornelia OLTEANU

    2011-01-01

    Losses resulting from operational risk events from a complex interaction between organizational factors, personal and market participants that do not fit a simple classification scheme. Taking into account past losses (ex. Barings, Daiwa, etc.) we can say that operational risk is a major financial losses in the banking sector, although until recently have been underestimated, considering that they are generally minor, note setting survival of a bank.

  11. Operational Risk Modeling

    Directory of Open Access Journals (Sweden)

    Gabriela ANGHELACHE

    2011-06-01

    Full Text Available Losses resulting from operational risk events from a complex interaction between organizational factors, personal and market participants that do not fit a simple classification scheme. Taking into account past losses (ex. Barings, Daiwa, etc. we can say that operational risk is a major financial losses in the banking sector, although until recently have been underestimated, considering that they are generally minor, note setting survival of a bank.

  12. A rear-end collision risk assessment model based on drivers' collision avoidance process under influences of cell phone use and gender-A driving simulator based study.

    Science.gov (United States)

    Li, Xiaomeng; Yan, Xuedong; Wu, Jiawei; Radwan, Essam; Zhang, Yuting

    2016-12-01

    Driver's collision avoidance performance has a direct link to the collision risk and crash severity. Previous studies demonstrated that the distracted driving, such as using a cell phone while driving, disrupted the driver's performance on road. This study aimed to investigate the manner and extent to which cell phone use and driver's gender affected driving performance and collision risk in a rear-end collision avoidance process. Forty-two licensed drivers completed the driving simulation experiment in three phone use conditions: no phone use, hands-free, and hand-held, in which the drivers drove in a car-following situation with potential rear-end collision risks caused by the leading vehicle's sudden deceleration. Based on the experiment data, a rear-end collision risk assessment model was developed to assess the influence of cell phone use and driver's gender. The cell phone use and driver's gender were found to be significant factors that affected the braking performances in the rear-end collision avoidance process, including the brake reaction time, the deceleration adjusting time and the maximum deceleration rate. The minimum headway distance between the leading vehicle and the simulator during the rear-end collision avoidance process was the final output variable, which could be used to measure the rear-end collision risk and judge whether a collision occurred. The results showed that although cell phone use drivers took some compensatory behaviors in the collision avoidance process to reduce the mental workload, the collision risk in cell phone use conditions was still higher than that without the phone use. More importantly, the results proved that the hands-free condition did not eliminate the safety problem associated with distracted driving because it impaired the driving performance in the same way as much as the use of hand-held phones. In addition, the gender effect indicated that although female drivers had longer reaction time than male drivers in

  13. Fuel prices scenario generation based on a multivariate GARCH model for risk analysis in a wholesale electricity market

    Energy Technology Data Exchange (ETDEWEB)

    Batlle, C.; Barquin, J. [Universidad Pontifica Comillas, Madrid (Spain). Instituto de Investigacion Tecnologica

    2004-05-01

    This paper presents a fuel prices scenario generator in the frame of a simulation tool developed to support risk analysis in a competitive electricity environment. The tool feeds different erogenous risk factors to a wholesale electricity market model to perform a statistical analysis of the results. As the different fuel series that are studied, such as the oil or gas ones, present stochastic volatility and strong correlation among them, a multivariate Generalized Autoregressive Conditional Heteroskedastic (GARCH) model has been designed in order to allow the generation of future fuel prices paths. The model makes use of a decomposition method to simplify the consideration of the multidimensional conditional covariance. An example of its application with real data is also presented. (author)

  14. Globally-Applicable Predictive Wildfire Model   a Temporal-Spatial GIS Based Risk Analysis Using Data Driven Fuzzy Logic Functions

    Science.gov (United States)

    van den Dool, G.

    2017-11-01

    This study (van den Dool, 2017) is a proof of concept for a global predictive wildfire model, in which the temporal-spatial characteristics of wildfires are placed in a Geographical Information System (GIS), and the risk analysis is based on data-driven fuzzy logic functions. The data sources used in this model are available as global datasets, but subdivided into three pilot areas: North America (California/Nevada), Europe (Spain), and Asia (Mongolia), and are downscaled to the highest resolution (3-arc second). The GIS is constructed around three themes: topography, fuel availability and climate. From the topographical data, six derived sub-themes are created and converted to a fuzzy membership based on the catchment area statistics. The fuel availability score is a composite of four data layers: land cover, wood loads, biomass, biovolumes. As input for the climatological sub-model reanalysed daily averaged, weather-related data is used, which is accumulated to a global weekly time-window (to account for the uncertainty within the climatological model) and forms the temporal component of the model. The final product is a wildfire risk score (from 0 to 1) by week, representing the average wildfire risk in an area. To compute the potential wildfire risk the sub-models are combined usinga Multi-Criteria Approach, and the model results are validated against the area under the Receiver Operating Characteristic curve.

  15. GLOBALLY-APPLICABLE PREDICTIVE WILDFIRE MODEL   A TEMPORAL–SPATIAL GIS BASED RISK ANALYSIS USING DATA DRIVEN FUZZY LOGIC FUNCTIONS

    Directory of Open Access Journals (Sweden)

    G. van den Dool

    2017-11-01

    Full Text Available This study (van den Dool, 2017 is a proof of concept for a global predictive wildfire model, in which the temporal–spatial characteristics of wildfires are placed in a Geographical Information System (GIS, and the risk analysis is based on data-driven fuzzy logic functions. The data sources used in this model are available as global datasets, but subdivided into three pilot areas: North America (California/Nevada, Europe (Spain, and Asia (Mongolia, and are downscaled to the highest resolution (3-arc second. The GIS is constructed around three themes: topography, fuel availability and climate. From the topographical data, six derived sub-themes are created and converted to a fuzzy membership based on the catchment area statistics. The fuel availability score is a composite of four data layers: land cover, wood loads, biomass, biovolumes. As input for the climatological sub-model reanalysed daily averaged, weather-related data is used, which is accumulated to a global weekly time-window (to account for the uncertainty within the climatological model and forms the temporal component of the model. The final product is a wildfire risk score (from 0 to 1 by week, representing the average wildfire risk in an area. To compute the potential wildfire risk the sub-models are combined usinga Multi-Criteria Approach, and the model results are validated against the area under the Receiver Operating Characteristic curve.

  16. [Model-based estimates of the risk of HCV transmission from infected patients to gynaecologic and obstetric staff].

    Science.gov (United States)

    Gańczak, Maria; Szczeniowski, Adam; Jurewicz, Alina; Karakiewicz, Beata; Szych, Zbigniew

    2012-01-01

    The risk of acquiring the hepatitis C virus (HCV) through percutaneous occupational exposure is dependent on three key variables: number of injuries, probability of a percutaneous injury transmitting HCV and prevalence of HCV infection in the patient population. To estimate the prevalence of HCV infection in the gynaecological/obstetric patient population and thereafter estimate the risk of HCV transmission to personnel through occupational exposure. The prevalence of anti-HCV was estimated through an anonymous serosurvey of gynaecological/ obstetric patients in 15 randomly selected hospitals in West Pomerania, Poland, from February 2008 to January 2009. Using own published data on the percutaneous injuries during gynaecological/obstetric surgeries and results obtained from serologic survey, the risk of annual occupational transmission of HCV to personnel was then derived with the use of a mathematical model. The prevalence of anti-HCV infection for 528 gynaecological/obstetric patients, aged 18-83 (median 45), was 0.76% (4/528; 95%CI: 0.29-1.93%). The estimated risk of HCV transmission from an HCV infected patient to an uninfected staff member may vary over a wide range (0.00007-0.1%), being dependent on the type of exposure; the average risk for a midwife was 0.0038% per annum (0.15% risk over a 40 year professional career). The estimated risk for a gynaecologist/obstetrician was 0.0076% and 0.30% respectively. The risk of an individual member of a gynaecological/obstetric staff acquiring HCV through occupational exposure is low, however a credible hazard still exists. One in 130 patients hospitalized at gynaecological/obstetric wards showed markers of HCV infection. Therefore, staff members should be encouraged to observe standard precautions regarding sharps injury prevention and present themselves for post-exposure management in case of need.

  17. Risk Probability Estimating Based on Clustering

    DEFF Research Database (Denmark)

    Chen, Yong; Jensen, Christian D.; Gray, Elizabeth

    2003-01-01

    of prior experiences, recommendations from a trusted entity or the reputation of the other entity. In this paper we propose a dynamic mechanism for estimating the risk probability of a certain interaction in a given environment using hybrid neural networks. We argue that traditional risk assessment models...... from the insurance industry do not directly apply to ubiquitous computing environments. Instead, we propose a dynamic mechanism for risk assessment, which is based on pattern matching, classification and prediction procedures. This mechanism uses an estimator of risk probability, which is based...

  18. Risk assessment and management of brucellosis in the southern greater Yellowstone area (I): A citizen-science based risk model for bovine brucellosis transmission from elk to cattle.

    Science.gov (United States)

    Kauffman, Mandy; Peck, Dannele; Scurlock, Brandon; Logan, Jim; Robinson, Timothy; Cook, Walt; Boroff, Kari; Schumaker, Brant

    2016-09-15

    Livestock producers and state wildlife agencies have used multiple management strategies to control bovine brucellosis in the Greater Yellowstone Area (GYA). However, spillover from elk to domestic bison and cattle herds continues to occur. Although knowledge is increasing about the location and behavior of elk in the SGYA, predicting spatiotemporal overlap between elk and cattle requires locations of livestock operations and observations of elk contact by producers. We queried all producers in a three-county area using a questionnaire designed to determine location of cattle and whether producers saw elk comingle with their animals. This information was used to parameterize a spatially-explicit risk model to estimate the number of elk expected to overlap with cattle during the brucellosis transmission risk period. Elk-cattle overlap was predicted in areas further from roads and forest boundaries in areas with wolf activity, with higher slopes, lower hunter densities, and where the cost-distance to feedgrounds was very low or very high. The model was used to estimate the expected number of years until a cattle reactor will be detected, under alternative management strategies. The model predicted cattle cases every 4.28 years in the highest risk herd unit, a higher prediction than the one case in 26 years we have observed. This difference likely indicates that ongoing management strategies are at least somewhat effective in preventing potential elk-cattle brucellosis transmission in these areas. Using this model, we can infer the expected effectiveness of various management strategies for reducing the risk of brucellosis spillover from elk to cattle. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Big data based fraud risk management at Alibaba

    National Research Council Canada - National Science Library

    Chen, Jidong; Tao, Ye; Wang, Haoran; Chen, Tao

    2015-01-01

    .... This paper is to introduce the Fraud Risk Management at Alibaba under big data. Alibaba has built a fraud risk monitoring and management system based on real-time big data processing and intelligent risk models...

  20. Risk analysis based on hazards interactions

    Science.gov (United States)

    Rossi, Lauro; Rudari, Roberto; Trasforini, Eva; De Angeli, Silvia; Becker, Joost

    2017-04-01

    Despite an increasing need for open, transparent, and credible multi-hazard risk assessment methods, models, and tools, the availability of comprehensive risk information needed to inform disaster risk reduction is limited, and the level of interaction across hazards is not systematically analysed. Risk assessment methodologies for different hazards often produce risk metrics that are not comparable. Hazard interactions (consecutive occurrence two or more different events) are generally neglected, resulting in strongly underestimated risk assessment in the most exposed areas. This study presents cases of interaction between different hazards, showing how subsidence can affect coastal and river flood risk (Jakarta and Bandung, Indonesia) or how flood risk is modified after a seismic event (Italy). The analysis of well documented real study cases, based on a combination between Earth Observation and in-situ data, would serve as basis the formalisation of a multi-hazard methodology, identifying gaps and research frontiers. Multi-hazard risk analysis is performed through the RASOR platform (Rapid Analysis and Spatialisation Of Risk). A scenario-driven query system allow users to simulate future scenarios based on existing and assumed conditions, to compare with historical scenarios, and to model multi-hazard risk both before and during an event (www.rasor.eu).

  1. The high-density lipoprotein-adjusted SCORE model worsens SCORE-based risk classification in a contemporary population of 30,824 Europeans

    DEFF Research Database (Denmark)

    Mortensen, Martin B; Afzal, Shoaib; Nordestgaard, Børge G

    2015-01-01

    AIMS: Recent European guidelines recommend to include high-density lipoprotein (HDL) cholesterol in risk assessment for primary prevention of cardiovascular disease (CVD), using a SCORE-based risk model (SCORE-HDL). We compared the predictive performance of SCORE-HDL with SCORE in an independent......, contemporary, 'low-risk' European population, focusing on ability to identify those in need of intensified CVD prevention. METHODS AND RESULTS: Between 2003 and 2008, 46,092 individuals without CVD, diabetes, or statin use were enrolled in the Copenhagen General Population Study (CGPS). During a mean of 6.......8 years of follow-up, 339 individuals died of CVD. In the SCORE target population (age 40-65; n = 30,824), fewer individuals were at baseline categorized as high risk (≥5% 10-year risk of fatal CVD) using SCORE-HDL compared with SCORE (10 vs. 17% in men, 1 vs. 3% in women). SCORE-HDL did not improve...

  2. Risk-based decisionmaking (Panel)

    Energy Technology Data Exchange (ETDEWEB)

    Smith, T.H.

    1995-12-31

    By means of a panel discussion and extensive audience interaction, explore the current challenges and progress to date in applying risk considerations to decisionmaking related to low-level waste. This topic is especially timely because of the proposed legislation pertaining to risk-based decisionmaking and because of the increased emphasis placed on radiological performance assessments of low-level waste disposal.

  3. Risk-based decision making : environmental risk management

    Energy Technology Data Exchange (ETDEWEB)

    Graydon, C.F. [Lawson Lundell Lawson and McIntosh, Calgary, AB (Canada)

    1998-12-31

    Risk based environmental decision making was described as the process which involves the identification of any potential or existing environmental impacts, and which attempts to quantify the magnitude of such impacts. Each stage of the decision making process is influenced by ecological, political, economic, cultural and social concerns. The process of defining risk is outlined, and four Canadian examples of decision making processes dealing with environmental risk assessment are described. These are : (1) legislation provisions and definitions under the Alberta Environmental Enhancement and Protection Act which invite a risk based decision making approach, (2) examples of comments made and approaches taken by Courts and Tribunals in addressing risk based risk assessment of environmental matters, (3) environmental enforcement agencies and the approach adopted by the Alberta Department of Environmental Protection in dealing with underground storage tank contamination, and (4) the approach taken by the Courts under the Canadian Environmental Assessment Act. The issue of whether environmental management systems and risk based assessment should be built into the corporate model is also discussed.

  4. Modeling Risk Convergence for European Financial Markets

    Directory of Open Access Journals (Sweden)

    Radu LUPU

    2014-09-01

    Full Text Available This article studies the convergence of risk on a sample of 13 European indexes. We use a set of 31 model specifications of a significant number of models belonging to the GARCH class and on their estimates we build an aggregate index in a Value-at-Risk approach. We use this index as a base for our convergence analysis. The results indicate a positive and significant tendency of convergence growth for the European financial market

  5. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. A methodology for modeling regional terrorism risk.

    Science.gov (United States)

    Chatterjee, Samrat; Abkowitz, Mark D

    2011-07-01

    Over the past decade, terrorism risk has become a prominent consideration in protecting the well-being of individuals and organizations. More recently, there has been interest in not only quantifying terrorism risk, but also placing it in the context of an all-hazards environment in which consideration is given to accidents and natural hazards, as well as intentional acts. This article discusses the development of a regional terrorism risk assessment model designed for this purpose. The approach taken is to model terrorism risk as a dependent variable, expressed in expected annual monetary terms, as a function of attributes of population concentration and critical infrastructure. This allows for an assessment of regional terrorism risk in and of itself, as well as in relation to man-made accident and natural hazard risks, so that mitigation resources can be allocated in an effective manner. The adopted methodology incorporates elements of two terrorism risk modeling approaches (event-based models and risk indicators), producing results that can be utilized at various jurisdictional levels. The validity, strengths, and limitations of the model are discussed in the context of a case study application within the United States. © 2011 Society for Risk Analysis.

  11. Models for Pesticide Risk Assessment

    Science.gov (United States)

    EPA considers the toxicity of the pesticide as well as the amount of pesticide to which a person or the environments may be exposed in risk assessment. Scientists use mathematical models to predict pesticide concentrations in exposure assessment.

  12. A spatially-based modeling framework for assessing the risks of soil-associated metals to bats.

    Science.gov (United States)

    Hernout, Béatrice V; Somerwill, Kate E; Arnold, Kathryn E; McClean, Colin J; Boxall, Alistair B A

    2013-02-01

    Populations of some species of bats are declining in some regions of Europe. These declines are probably due to a range of pressures, including climate change, urbanization and exposure to toxins such as metals. This paper describes the development, paramaterisation and application of a spatially explicit modeling framework to predict the risks of soil-associated metals (lead, copper, zinc and cadmium) to bat health. Around 5.9% of areas where bats reside were predicted to have lead levels that pose a risk to bat health. For copper, this value was 2.8%, for cadmium it was 0.6% and for zinc 0.5%. Further work is therefore warranted to explore the impacts of soil-associated metals on bat populations in the UK. Copyright © 2012 Elsevier Ltd. All rights reserved.

  13. Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models

    Directory of Open Access Journals (Sweden)

    Testa Antonia C

    2010-10-01

    Full Text Available Abstract Background Hitherto, risk prediction models for preoperative ultrasound-based diagnosis of ovarian tumors were dichotomous (benign versus malignant. We develop and validate polytomous models (models that predict more than two events to diagnose ovarian tumors as benign, borderline, primary invasive or metastatic invasive. The main focus is on how different types of models perform and compare. Methods A multi-center dataset containing 1066 women was used for model development and internal validation, whilst another multi-center dataset of 1938 women was used for temporal and external validation. Models were based on standard logistic regression and on penalized kernel-based algorithms (least squares support vector machines and kernel logistic regression. We used true polytomous models as well as combinations of dichotomous models based on the 'pairwise coupling' technique to produce polytomous risk estimates. Careful variable selection was performed, based largely on cross-validated c-index estimates. Model performance was assessed with the dichotomous c-index (i.e. the area under the ROC curve and a polytomous extension, and with calibration graphs. Results For all models, between 9 and 11 predictors were selected. Internal validation was successful with polytomous c-indexes between 0.64 and 0.69. For the best model dichotomous c-indexes were between 0.73 (primary invasive vs metastatic and 0.96 (borderline vs metastatic. On temporal and external validation, overall discrimination performance was good with polytomous c-indexes between 0.57 and 0.64. However, discrimination between primary and metastatic invasive tumors decreased to near random levels. Standard logistic regression performed well in comparison with advanced algorithms, and combining dichotomous models performed well in comparison with true polytomous models. The best model was a combination of dichotomous logistic regression models. This model is available online

  14. Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

    Science.gov (United States)

    Wu, Qiang; Zhao, Dekang; Wang, Yang; Shen, Jianjun; Mu, Wenping; Liu, Honglei

    2017-11-01

    Water inrush from coal-seam floors greatly threatens mining safety in North China and is a complex process controlled by multiple factors. This study presents a mathematical assessment system for coal-floor water-inrush risk based on the variable-weight model (VWM) and unascertained measure theory (UMT). In contrast to the traditional constant-weight model (CWM), which assigns a fixed weight to each factor, the VWM varies with the factor-state value. The UMT employs the confidence principle, which is more effective in ordered partition problems than the maximum membership principle adopted in the former mathematical theory. The method is applied to the Datang Tashan Coal Mine in North China. First, eight main controlling factors are selected to construct the comprehensive evaluation index system. Subsequently, an incentive-penalty variable-weight model is built to calculate the variable weights of each factor. Then, the VWM-UMT model is established using the quantitative risk-grade divide of each factor according to the UMT. On this basis, the risk of coal-floor water inrush in Tashan Mine No. 8 is divided into five grades. For comparison, the CWM is also adopted for the risk assessment, and a differences distribution map is obtained between the two methods. Finally, the verification of water-inrush points indicates that the VWM-UMT model is powerful and more feasible and reasonable. The model has great potential and practical significance in future engineering applications.

  15. Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

    Science.gov (United States)

    Wu, Qiang; Zhao, Dekang; Wang, Yang; Shen, Jianjun; Mu, Wenping; Liu, Honglei

    2017-06-01

    Water inrush from coal-seam floors greatly threatens mining safety in North China and is a complex process controlled by multiple factors. This study presents a mathematical assessment system for coal-floor water-inrush risk based on the variable-weight model (VWM) and unascertained measure theory (UMT). In contrast to the traditional constant-weight model (CWM), which assigns a fixed weight to each factor, the VWM varies with the factor-state value. The UMT employs the confidence principle, which is more effective in ordered partition problems than the maximum membership principle adopted in the former mathematical theory. The method is applied to the Datang Tashan Coal Mine in North China. First, eight main controlling factors are selected to construct the comprehensive evaluation index system. Subsequently, an incentive-penalty variable-weight model is built to calculate the variable weights of each factor. Then, the VWM-UMT model is established using the quantitative risk-grade divide of each factor according to the UMT. On this basis, the risk of coal-floor water inrush in Tashan Mine No. 8 is divided into five grades. For comparison, the CWM is also adopted for the risk assessment, and a differences distribution map is obtained between the two methods. Finally, the verification of water-inrush points indicates that the VWM-UMT model is powerful and more feasible and reasonable. The model has great potential and practical significance in future engineering applications.

  16. An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning.

    Science.gov (United States)

    Balasubramani, Pragathi P; Chakravarthy, V Srinivasa; Ravindran, Balaraman; Moustafa, Ahmed A

    2014-01-01

    Although empirical and neural studies show that serotonin (5HT) plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL)-framework. The model depicts the roles of dopamine (DA) and serotonin (5HT) in Basal Ganglia (BG). In this model, the DA signal is represented by the temporal difference error (δ), while the 5HT signal is represented by a parameter (α) that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: (1) Risk-sensitive decision making, where 5HT controls risk assessment, (2) Temporal reward prediction, where 5HT controls time-scale of reward prediction, and (3) Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.

  17. AN EXTENDED REINFORCEMENT LEARNING MODEL OF BASAL GANGLIA TO UNDERSTAND THE CONTRIBUTIONS OF SEROTONIN AND DOPAMINE IN RISK-BASED DECISION MAKING, REWARD PREDICTION, AND PUNISHMENT LEARNING

    Directory of Open Access Journals (Sweden)

    Pragathi Priyadharsini Balasubramani

    2014-04-01

    Full Text Available Although empirical and neural studies show that serotonin (5HT plays many functional roles in the brain, prior computational models mostly focus on its role in behavioral inhibition. In this study, we present a model of risk based decision making in a modified Reinforcement Learning (RL-framework. The model depicts the roles of dopamine (DA and serotonin (5HT in Basal Ganglia (BG. In this model, the DA signal is represented by the temporal difference error (δ, while the 5HT signal is represented by a parameter (α that controls risk prediction error. This formulation that accommodates both 5HT and DA reconciles some of the diverse roles of 5HT particularly in connection with the BG system. We apply the model to different experimental paradigms used to study the role of 5HT: 1 Risk-sensitive decision making, where 5HT controls risk assessment, 2 Temporal reward prediction, where 5HT controls time-scale of reward prediction, and 3 Reward/Punishment sensitivity, in which the punishment prediction error depends on 5HT levels. Thus the proposed integrated RL model reconciles several existing theories of 5HT and DA in the BG.

  18. XPC Ala499Val and XPG Asp1104His polymorphisms and digestive system cancer risk: a meta-analysis based on model-free approach.

    Science.gov (United States)

    Yu, Guangsheng; Wang, Jianlu; Dong, Jiahong; Liu, Jun

    2015-01-01

    Many studies have reported the association between XPC Ala499Val and XPG Asp1104His polymorphisms and digestive system cancer susceptibility, but the results were inconclusive. We performed a meta-analysis, using a comprehensive strategy based on the allele model and a model-free approach, to derive a more precise estimation of the relationship between XPC Ala499Val and XPG Asp1104His polymorphisms with digestive system cancer risk. For XPC Ala499Val, no significant cancer risk was found in the allele model (OR = 0.98, 95% CI: 0.86-1.11) and with model-free approach (ORG = 0.97, 95% CI: 0.83-1.13). For XPG Asp1104His, there was also no association between this polymorphism and cancer risk in the allele model (OR = 1.03, 95% CI: 0.96-1.11) and with the model-free approach (ORG = 1.04, 95% CI: 0.95-1.14). Therefore, this meta-analysis suggests that the XPC Ala499Val and XPG Asp1104His polymorphisms were not associated with digestive system cancer risk. Further large and well-designed studies are needed to confirm these findings.

  19. Risk Measurement and Risk Modelling using Applications of Vine Copulas

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); A.K. Singh (Abhay)

    2014-01-01

    markdownabstract__abstract__ This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite nancial risk. Copula-based dependence modelling is a popular tool in nancial

  20. Risk measurement and risk modelling using applications of Vine copulas

    NARCIS (Netherlands)

    D.E. Allen (David); M.J. McAleer (Michael); A.K. Singh (Abhay)

    2017-01-01

    textabstractThis paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite financial risk. Copula-based dependence modelling is a popular tool in financial applications, but is

  1. Expert judgement models in quantitative risk assessment

    Energy Technology Data Exchange (ETDEWEB)

    Rosqvist, T. [VTT Automation, Helsinki (Finland); Tuominen, R. [VTT Automation, Tampere (Finland)

    1999-12-01

    Expert judgement is a valuable source of information in risk management. Especially, risk-based decision making relies significantly on quantitative risk assessment, which requires numerical data describing the initiator event frequencies and conditional probabilities in the risk model. This data is seldom found in databases and has to be elicited from qualified experts. In this report, we discuss some modelling approaches to expert judgement in risk modelling. A classical and a Bayesian expert model is presented and applied to real case expert judgement data. The cornerstone in the models is the log-normal distribution, which is argued to be a satisfactory choice for modelling degree-of-belief type probability distributions with respect to the unknown parameters in a risk model. Expert judgements are qualified according to bias, dispersion, and dependency, which are treated differently in the classical and Bayesian approaches. The differences are pointed out and related to the application task. Differences in the results obtained from the different approaches, as applied to real case expert judgement data, are discussed. Also, the role of a degree-of-belief type probability in risk decision making is discussed.

  2. Operational risk modeled analytically II: the consequences of classification invariance

    OpenAIRE

    Vivien Brunel

    2015-01-01

    Most of the banks' operational risk internal models are based on loss pooling in risk and business line categories. The parameters and outputs of operational risk models are sensitive to the pooling of the data and the choice of the risk classification. In a simple model, we establish the link between the number of risk cells and the model parameters by requiring invariance of the bank's loss distribution upon a change in classification. We provide details on the impact of this requirement on...

  3. Construction of an Early Risk Warning Model of Organizational Resilience: An Empirical Study Based on Samples of R&D Teams

    Directory of Open Access Journals (Sweden)

    Si-hua Chen

    2016-01-01

    Full Text Available Facing fierce competition, it is critical for organizations to keep advantages either actively or passively. Organizational resilience is the ability of an organization to anticipate, prepare for, respond to, and adapt to incremental change and sudden disruptions in order to survive and prosper. It is of particular importance for enterprises to apprehend the intensity of organizational resilience and thereby judge their abilities to withstand pressure. By conducting an exploratory factor analysis and a confirmatory factor analysis, this paper clarifies a five-factor model for organizational resilience of R&D teams. Moreover, based on it, this paper applies fuzzy integrated evaluation method to build an early risk warning model for organizational resilience of R&D teams. The application of the model to a company shows that the model can adequately evaluate the intensity of organizational resilience of R&D teams. The results are also supposed to contribute to applied early risk warning theory.

  4. Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies.

    Science.gov (United States)

    Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad

    2018-02-01

    The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Energy risk management and value at risk modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sadeghi, Mehdi [Economics department, Imam Sadiq University, P.B. 14655-159, Tehran (Iran, Islamic Republic of)]. E-mail: sadeghi@isu.ac.ir; Shavvalpour, Saeed [Economics department, Imam Sadiq University, P.B. 14655-159, Tehran (Iran, Islamic Republic of)]. E-mail: shavalpoor@isu.ac.ir

    2006-12-15

    The value of energy trades can change over time with market conditions and underlying price variables. The rise of competition and deregulation in energy markets has led to relatively free energy markets that are characterized by high price shifts. Within oil markets the volatile oil price environment after OPEC agreements in the 1970s requires a risk quantification.' Value-at-risk' has become an essential tool for this end when quantifying market risk. There are various methods for calculating value-at-risk. The methods we introduced in this paper are Historical Simulation ARMA Forecasting and Variance-Covariance based on GARCH modeling approaches. The results show that among various approaches the HSAF methodology presents more efficient results, so that if the level of confidence is 99%, the value-at-risk calculated through HSAF methodology is greater than actual price changes in almost 97.6 percent of the forecasting period.

  6. Energy risk management and value at risk modeling

    Energy Technology Data Exchange (ETDEWEB)

    Mehdi Sadeghi; Saeed Shavvalpour [Imam Sadiq University, Tehran (Iran). Economics Dept.

    2006-12-15

    The value of energy trades can change over time with market conditions and underlying price variables. The rise of competition and deregulation in energy markets has led to relatively free energy markets that are characterized by high price shifts. Within oil markets the volatile oil price environment after OPEC agreements in the 1970s requires a risk quantification. ''Value-at-risk'' has become an essential tool for this end when quantifying market risk. There are various methods for calculating value-at-risk. The methods we introduced in this paper are Historical Simulation ARMA Forecasting and Variance-Covariance based on GARCH modeling approaches. The results show that among various approaches the HSAF methodology presents more efficient results, so that if the level of confidence is 99%, the value-at-risk calculated through HSAF methodology is greater than actual price changes in almost 97.6 percent of the forecasting period. (author)

  7. Study on the Market Risk Measurement of the Style Portfolios in Stock Markets Based on EVT-t-Copula Model

    Directory of Open Access Journals (Sweden)

    Yuhong Zhou

    2013-03-01

    Full Text Available For the presence of non-normal distribution characteristics in the financial assets returns, the model of AR(1-GJR(1,1 is used to characterize the marginal distribution of the style assets in China stock market. The Copula function is introduced to analyze the dependency structure between the six style assets, combined with the marginal distributed residual sequences. And the joint return distribution of the style portfolios is simulated, combined with extreme value theory and Monte Carlo simulation method. Then the market risks (VaR and CVaR of the style portfolios in China stock markets are obtained. The results of the study show that the generalized Pareto distribution Model can well fit the non-normal distribution characteristics such as peak and fat tail in the style assets returns.

  8. Biases in Individualized Cost-effectiveness Analysis: Influence of Choices in Modeling Short-Term, Trial-Based, Mortality Risk Reduction and Post-Trial Life Expectancy.

    Science.gov (United States)

    van Klaveren, David; Wong, John B; Kent, David M; Steyerberg, Ewout W

    2017-10-01

    The benefits and costs of a treatment are typically heterogeneous across individual patients. Randomized clinical trials permit the examination of individualized treatment benefits over the trial horizon but extrapolation to lifetime horizon usually involves combining trial-based individualized estimates of short-term risk reduction with less detailed (less granular) population life tables. However, the underlying assumption of equal post-trial life expectancy for low- and high-risk patients of the same sex and age is unrealistic. We aimed to study the influence of unequal granularity between models of short-term risk reduction and life expectancy on individualized estimates of cost-effectiveness of aggressive thrombolysis for patients with an acute myocardial infarction. To estimate life years gained, we multiplied individualized estimates of short-term risk reduction either with less granular and with equally granular post-trial life expectancy estimates. Estimates of short-term risk reduction were obtained from GUSTO trial data (30,510 patients) using logistic regression analysis with treatment, sex, and age as predictor variables. Life expectancy estimates were derived from sex- and age-specific US life tables. Based on sex- and age-specific, short-term risk reductions but average population life expectancy (less granularity), we found that aggressive thrombolysis was cost-effective (incremental cost-effectiveness ratio below $50,000) for women above age 49 y and men above age 53 y (92% and 69% of the population, respectively). Considering sex- and age-specific short-term mortality risk reduction and correspondingly sex- and age-specific life expectancy (equal granularity), aggressive thrombolysis was cost-effective for men above age 45 y and women above age 50 y (95% and 76% of the population, respectively). Failure to model short-term risk reduction and life expectancy at an equal level of granularity may bias our estimates of individualized cost

  9. Future soil erosion risk - Results of GIS-based model simulations for a catchment in Saxony/Germany

    Science.gov (United States)

    Routschek, A.; Schmidt, J.; Enke, W.; Deutschlaender, Th.

    2014-02-01

    The aim of this study is to quantify the impact of climate change on soil loss at catchment scale at high temporal and spatial resolution. Simulations were performed for one example catchment in Saxony/Germany. The study is based on the B2 IPCC-scenario and model outputs of three models: ECHAM4-OPYC3 (general circulation model), WETTREG (statistical downscaling climate model) and EROSION 3D as a process-based soil erosion model. Soil loss was simulated for the future time period from 2031 to 2050. Results were compared to soil loss based on 20 years of measured precipitation from 1981 to 2000. The results of the simulations with EROSION 3D allow the quantification of the impacts of climate change on erosion rates. The impact of the expected increase of precipitation intensities leads to a significant increase of soil loss by 64% by 2050. Expected changes in land use due to changed crop rotation, and the influence of a shifted harvest are taken into account in the scenario studies. The impacts of land use, soil management and soil properties on soil loss are higher than the effects of the changed precipitation patterns.

  10. Quantifying population-level risks using an individual-based model: sea otters, Harlequin Ducks, and the Exxon Valdez oil spill.

    Science.gov (United States)

    Harwell, Mark A; Gentile, John H; Parker, Keith R

    2012-07-01

    Ecological risk assessments need to advance beyond evaluating risks to individuals that are largely based on toxicity studies conducted on a few species under laboratory conditions, to assessing population-level risks to the environment, including considerations of variability and uncertainty. Two individual-based models (IBMs), recently developed to assess current risks to sea otters and seaducks in Prince William Sound more than 2 decades after the Exxon Valdez oil spill (EVOS), are used to explore population-level risks. In each case, the models had previously shown that there were essentially no remaining risks to individuals from polycyclic aromatic hydrocarbons (PAHs) derived from the EVOS. New sensitivity analyses are reported here in which hypothetical environmental exposures to PAHs were heuristically increased until assimilated doses reached toxicity reference values (TRVs) derived at the no-observed-adverse-effects and lowest-observed-adverse-effects levels (NOAEL and LOAEL, respectively). For the sea otters, this was accomplished by artificially increasing the number of sea otter pits that would intersect remaining patches of subsurface oil residues by orders of magnitude over actual estimated rates. Similarly, in the seaduck assessment, the PAH concentrations in the constituents of diet, sediments, and seawater were increased in proportion to their relative contributions to the assimilated doses by orders of magnitude over measured environmental concentrations, to reach the NOAEL and LOAEL thresholds. The stochastic IBMs simulated millions of individuals. From these outputs, frequency distributions were derived of assimilated doses for populations of 500,000 sea otters or seaducks in each of 7 or 8 classes, respectively. Doses to several selected quantiles were analyzed, ranging from the 1-in-1000th most-exposed individuals (99.9% quantile) to the median-exposed individuals (50% quantile). The resulting families of quantile curves provide the basis for

  11. Quantifying Population-Level Risks Using an Individual-Based Model: Sea Otters, Harlequin Ducks, and the Exxon Valdez Oil Spill

    Science.gov (United States)

    Harwell, Mark A; Gentile, John H; Parker, Keith R

    2012-01-01

    Ecological risk assessments need to advance beyond evaluating risks to individuals that are largely based on toxicity studies conducted on a few species under laboratory conditions, to assessing population-level risks to the environment, including considerations of variability and uncertainty. Two individual-based models (IBMs), recently developed to assess current risks to sea otters and seaducks in Prince William Sound more than 2 decades after the Exxon Valdez oil spill (EVOS), are used to explore population-level risks. In each case, the models had previously shown that there were essentially no remaining risks to individuals from polycyclic aromatic hydrocarbons (PAHs) derived from the EVOS. New sensitivity analyses are reported here in which hypothetical environmental exposures to PAHs were heuristically increased until assimilated doses reached toxicity reference values (TRVs) derived at the no-observed-adverse-effects and lowest-observed-adverse-effects levels (NOAEL and LOAEL, respectively). For the sea otters, this was accomplished by artificially increasing the number of sea otter pits that would intersect remaining patches of subsurface oil residues by orders of magnitude over actual estimated rates. Similarly, in the seaduck assessment, the PAH concentrations in the constituents of diet, sediments, and seawater were increased in proportion to their relative contributions to the assimilated doses by orders of magnitude over measured environmental concentrations, to reach the NOAEL and LOAEL thresholds. The stochastic IBMs simulated millions of individuals. From these outputs, frequency distributions were derived of assimilated doses for populations of 500 000 sea otters or seaducks in each of 7 or 8 classes, respectively. Doses to several selected quantiles were analyzed, ranging from the 1-in-1000th most-exposed individuals (99.9% quantile) to the median-exposed individuals (50% quantile). The resulting families of quantile curves provide the basis for

  12. Investigating the effect of an education plan based on the health belief model on the physical activity of women who are at risk for hypertension.

    Science.gov (United States)

    Hoseini, Habibollah; Maleki, Fatemeh; Moeini, Mahin; Sharifirad, Gholam Reza

    2014-11-01

    Hypertension is the main risk factor of many diseases and the main reason of death all over the world. Because the signs of hypertension are not clear, people do not feel its dangers and do not believe they are at risk. This problem makes preventing hypertension a great challenge for the health system. One factor that is related to lifestyle and is effective in preventing hypertension is increasing exercise. The aim of this study is investigate the effect of an education plan based on the health belief model on the physical activity of women who are at risk for hypertension. This is a field experimental study. Field of study was two health care centers in Isfahan, which were selected through simple random sampling. Ninety-two females who were at risk for hypertension were the subjects of study. Subjects were selected through systematic sampling. Beck questionnaire was used to evaluate the physical activity of both experimental and control group subjects before and 2 months after the intervention. The intervention plan was three education sections that were conducted in 4 weeks. The data were analyzed by descriptive statistical tests and inferential tests of repetitive variance analysis and t-test through SPSS. The results showed that the average of physical activity increased significantly in the intervention group 2 months after education (P = 0.03). The findings of the study confirm the efficiency of education plan based on the health belief model on the physical activity of women who are at risk for hypertension.

  13. Development and external validation of preoperative risk models for operative morbidities after total gastrectomy using a Japanese web-based nationwide registry.

    Science.gov (United States)

    Kikuchi, Hirotoshi; Miyata, Hiroaki; Konno, Hiroyuki; Kamiya, Kinji; Tomotaki, Ai; Gotoh, Mitsukazu; Wakabayashi, Go; Mori, Masaki

    2017-11-01

    Total gastrectomy is a relatively difficult and invasive procedure among gastrointestinal surgeries, and major morbidities following total gastrectomy can be serious and fatal. This study aimed to develop and validate preoperative risk models of morbidities associated with total gastrectomy using a Japanese web-based nationwide registry. The national clinical database was used to retrieve the records of 39,253 patients who underwent total gastrectomy in 1,841 hospitals between January 1, 2011 and December 31, 2012. Mean patient age was 69.1 years, and 73.8% of the patients were male. The overall morbidity rate was 21.5%, which included 8.1% with surgical site infection (SSI), 4.5% with anastomotic leak, 5.0% with pancreatic fistula, 3.7% with pneumonia, 1.9% with prolonged ventilation, and 1.2% with renal failure. Sex, splenectomy, and Brinkman index were selected as common risk factors for SSI, anastomotic leak, and pancreatic fistula. Pancreatectomy was the most significant preoperative risk factor in the risk model of SSI and pancreatic fistula. Need of assistance with activities of daily living, chronic obstructive pulmonary disease, previous cerebrovascular disease, American Society of Anesthesiologists score class 3 and over, presence of esophageal cancer, and body mass index more than 25 were selected as common risk factors for pneumonia, prolonged ventilation over 48 h, and renal failure. We have created the first reported risk models of morbidities associated with total gastrectomy, using a Japanese nationwide database. The risk models developed in this study may be useful to preoperatively predict operative morbidities in patients undergoing total gastrectomy.

  14. Command Process Modeling & Risk Analysis

    Science.gov (United States)

    Meshkat, Leila

    2011-01-01

    Commanding Errors may be caused by a variety of root causes. It's important to understand the relative significance of each of these causes for making institutional investment decisions. One of these causes is the lack of standardized processes and procedures for command and control. We mitigate this problem by building periodic tables and models corresponding to key functions within it. These models include simulation analysis and probabilistic risk assessment models.

  15. Update on a Pharmacokinetic-Centric Alternative Tier II Program for MMT—Part II: Physiologically Based Pharmacokinetic Modeling and Manganese Risk Assessment

    Directory of Open Access Journals (Sweden)

    Michael D. Taylor

    2012-01-01

    Full Text Available Recently, a variety of physiologically based pharmacokinetic (PBPK models have been developed for the essential element manganese. This paper reviews the development of PBPK models (e.g., adult, pregnant, lactating, and neonatal rats, nonhuman primates, and adult, pregnant, lactating, and neonatal humans and relevant risk assessment applications. Each PBPK model incorporates critical features including dose-dependent saturable tissue capacities and asymmetrical diffusional flux of manganese into brain and other tissues. Varied influx and efflux diffusion rate and binding constants for different brain regions account for the differential increases in regional brain manganese concentrations observed experimentally. We also present novel PBPK simulations to predict manganese tissue concentrations in fetal, neonatal, pregnant, or aged individuals, as well as individuals with liver disease or chronic manganese inhalation. The results of these simulations could help guide risk assessors in the application of uncertainty factors as they establish exposure guidelines for the general public or workers.

  16. Cabin Environment Physics Risk Model

    Science.gov (United States)

    Mattenberger, Christopher J.; Mathias, Donovan Leigh

    2014-01-01

    This paper presents a Cabin Environment Physics Risk (CEPR) model that predicts the time for an initial failure of Environmental Control and Life Support System (ECLSS) functionality to propagate into a hazardous environment and trigger a loss-of-crew (LOC) event. This physics-of failure model allows a probabilistic risk assessment of a crewed spacecraft to account for the cabin environment, which can serve as a buffer to protect the crew during an abort from orbit and ultimately enable a safe return. The results of the CEPR model replace the assumption that failure of the crew critical ECLSS functionality causes LOC instantly, and provide a more accurate representation of the spacecraft's risk posture. The instant-LOC assumption is shown to be excessively conservative and, moreover, can impact the relative risk drivers identified for the spacecraft. This, in turn, could lead the design team to allocate mass for equipment to reduce overly conservative risk estimates in a suboptimal configuration, which inherently increases the overall risk to the crew. For example, available mass could be poorly used to add redundant ECLSS components that have a negligible benefit but appear to make the vehicle safer due to poor assumptions about the propagation time of ECLSS failures.

  17. Cancer risk assessment of human exposure to polycyclic aromatic hydrocarbons (PAHs) via indoor and outdoor dust based on probit model.

    Science.gov (United States)

    Kang, Yuan; Shao, Dingding; Li, Ning; Yang, Gelin; Zhang, Qiuyun; Zeng, Lixuan; Luo, Jiwen; Zhong, Wenfeng

    2015-03-01

    In the present study, the polycyclic aromatic hydrocarbons (PAHs) in indoor dust and outdoor dust including road and window dust around the traffic road in Hunan Province, China, were sampled and detected. The ∑PAHs in indoor dust ranged from 5007-24,236 ng g(-1), with a median of 14,049 ng g(-1). The ∑PAHs in road dust ranged from 3644-12,875 ng g(-1), with a median of 10,559 ng g(-1). The ∑PAHs in window dust ranged from 803-12,590 ng g(-1), with a median of 5459 ng g(-1). Similar pattern of PAHs was observed in road and window dust except in H3W and H4W samples, which was dominated by naphthalene (Nap), benzo(b+k)fluoranthene (B(b+k)F), phenanthrene (Phe), and fluorine (Fle). Indoor dust showed slightly different PAHs profiles, which was dominated by Nap, fluoranthene (Fla) and Phe. Risk assessment indicated that dermal contact and dust ingestion exposure pathways were more important than the inhalation pathway. Cancer risk of PAHs via dust varied from 2.73 × 10(-8)-8.04 × 10(-6), with a median of 2.06 × 10(-6) for children, and from 2 × 10(-8)-5.89 × 10(-6), with a median of 1.52 × 10(-6) for adult. Probit model showed that 76 and 71 % of samples in the sampling area would result in the risk of children and adult exposure to PAHs via dust higher than the acceptable level (1 × 10(-6)), respectively.

  18. The use of traits-based approaches and eco(toxico)logical models to advance the ecological risk assessment framework for chemicals.

    Science.gov (United States)

    Van den Brink, Paul J; Baird, Donald J; Baveco, Hans J M; Focks, Andreas

    2013-07-01

    This article presents a framework to diagnose and predict the effects of chemicals, integrating 2 promising tools to incorporate more ecology into ecological risk assessment, namely traits-based approaches and ecological modeling. Traits-based approaches are used increasingly to derive correlations between the occurrence of species traits and chemical exposure from biological and chemical monitoring data. This assessment can also be used in a diagnostic way, i.e., to identify the chemicals probably posing the highest risks to the aquatic ecosystems. The article also describes how ecological models can be used to explore how traits govern the species-substance interactions and to predict effects at the individual, population, and community and ecosystem level, i.e., from the receptor to the landscape level. This can be done by developing models describing the toxicokinetics and toxicodynamics of the chemical in the individual, the life-history of species and the connectivity of populations, determining their recovery, and the food web relations at the community and ecosystem level that determine the indirect effects. Special attention is given on how spatial aspects can be included in the ecological risk assessments using ecological models. The components of the framework are introduced and critically discussed. We describe how the different tools and data generated through experimentation (laboratory and semifield) and biomonitoring can be integrated. The article uses examples from the aquatic compartment, but the concepts that are used, and their integration within the framework, can be generalized to other environmental compartments. Copyright © 2013 SETAC.

  19. Formation of translational risk score based on correlation coefficients as an alternative to Cox regression models for predicting outcome in patients with NSCLC

    Directory of Open Access Journals (Sweden)

    ElAidi Tina

    2011-07-01

    Full Text Available Abstract Background Personalised cancer therapy, such as that used for bronchial carcinoma (BC, requires treatment to be adjusted to the patient's status. Individual risk for progression is estimated from clinical and molecular-biological data using translational score systems. Additional molecular information can improve outcome prediction depending on the marker used and the applied algorithm. Two models, one based on regressions and the other on correlations, were used to investigate the effect of combining various items of prognostic information to produce a comprehensive score. This was carried out using correlation coefficients, with options concerning a more plausible selection of variables for modelling, and this is considered better than classical regression analysis. Methods Clinical data concerning 63 BC patients were used to investigate the expression pattern of five tumour-associated proteins. Significant impact on survival was determined using log-rank tests. Significant variables were integrated into a Cox regression model and a new variable called integrative score of individual risk (ISIR, based on Spearman's correlations, was obtained. Results High tumour stage (TNM was predictive for poor survival, while CD68 and Gas6 protein expression correlated with a favourable outcome. Cox regression model analysis predicted outcome more accurately than using each variable in isolation, and correctly classified 84% of patients as having a clear risk status. Calculation of the integrated score for an individual risk (ISIR, considering tumour size (T, lymph node status (N, metastasis (M, Gas6 and CD68 identified 82% of patients as having a clear risk status. Conclusion Combining protein expression analysis of CD68 and GAS6 with T, N and M, using Cox regression or ISIR, improves prediction. Considering the increasing number of molecular markers, subsequent studies will be required to validate translational algorithms for the prognostic

  20. Assessment of Industry-Induced Urban Human Health Risks Related to Benzo[a]pyrene based on a Multimedia Fugacity Model: Case Study of Nanjing, China

    Science.gov (United States)

    Xu, Linyu; Song, Huimin; Wang, Yan; Yin, Hao

    2015-01-01

    Large amounts of organic pollutants emitted from industries have accumulated and caused serious human health risks, especially in urban areas with rapid industrialization. This paper focused on the carcinogen benzo[a]pyrene (BaP) from industrial effluent and gaseous emissions, and established a multi-pathway exposure model based on a Level IV multimedia fugacity model to analyze the human health risks in a city that has undergone rapid industrialization. In this study, GIS tools combined with land-use data was introduced to analyze smaller spatial scales so as to enhance the spatial resolution of the results. An uncertainty analysis using a Monte Carlo simulation was also conducted to illustrate the rationale of the probabilistic assessment mode rather than deterministic assessment. Finally, the results of the case study in Nanjing, China indicated the annual average human cancer risk induced by local industrial emissions during 2002–2008 (lowest at 1.99×10–6 in 2008 and highest at 3.34×10–6 in 2004), which was lower than the USEPA prescriptive level (1×10–6–1×10–4) but cannot be neglected in the long term.The study results could not only instruct the BaP health risk management but also help future health risk prediction and control. PMID:26035663

  1. Assessment of Industry-Induced Urban Human Health Risks Related to Benzo[a]pyrene based on a Multimedia Fugacity Model: Case Study of Nanjing, China

    Directory of Open Access Journals (Sweden)

    Linyu Xu

    2015-05-01

    Full Text Available Large amounts of organic pollutants emitted from industries have accumulated and caused serious human health risks, especially in urban areas with rapid industrialization. This paper focused on the carcinogen benzo[a]pyrene (BaP from industrial effluent and gaseous emissions, and established a multi-pathway exposure model based on a Level IV multimedia fugacity model to analyze the human health risks in a city that has undergone rapid industrialization. In this study, GIS tools combined with land-use data was introduced to analyze smaller spatial scales so as to enhance the spatial resolution of the results. An uncertainty analysis using a Monte Carlo simulation was also conducted to illustrate the rationale of the probabilistic assessment mode rather than deterministic assessment. Finally, the results of the case study in Nanjing, China indicated the annual average human cancer risk induced by local industrial emissions during 2002–2008 (lowest at 1.99´10–6 in 2008 and highest at 3.34´10–6 in 2004, which was lower than the USEPA prescriptive level (1´10–6–1´10–4 but cannot be neglected in the long term. The study results could not only instruct the BaP health risk management but also help future health risk prediction and control.

  2. Risk Factors Analysis and Death Prediction in Some Life-Threatening Ailments Using Chi-Square Case-Based Reasoning (χ2 CBR) Model.

    Science.gov (United States)

    Adeniyi, D A; Wei, Z; Yang, Y

    2018-01-30

    A wealth of data are available within the health care system, however, effective analysis tools for exploring the hidden patterns in these datasets are lacking. To alleviate this limitation, this paper proposes a simple but promising hybrid predictive model by suitably combining the Chi-square distance measurement with case-based reasoning technique. The study presents the realization of an automated risk calculator and death prediction in some life-threatening ailments using Chi-square case-based reasoning (χ2 CBR) model. The proposed predictive engine is capable of reducing runtime and speeds up execution process through the use of critical χ2 distribution value. This work also showcases the development of a novel feature selection method referred to as frequent item based rule (FIBR) method. This FIBR method is used for selecting the best feature for the proposed χ2 CBR model at the preprocessing stage of the predictive procedures. The implementation of the proposed risk calculator is achieved through the use of an in-house developed PHP program experimented with XAMP/Apache HTTP server as hosting server. The process of data acquisition and case-based development is implemented using the MySQL application. Performance comparison between our system, the NBY, the ED-KNN, the ANN, the SVM, the Random Forest and the traditional CBR techniques shows that the quality of predictions produced by our system outperformed the baseline methods studied. The result of our experiment shows that the precision rate and predictive quality of our system in most cases are equal to or greater than 70%. Our result also shows that the proposed system executes faster than the baseline methods studied. Therefore, the proposed risk calculator is capable of providing useful, consistent, faster, accurate and efficient risk level prediction to both the patients and the physicians at any time, online and on a real-time basis.

  3. Building caries risk assessment models for children.

    Science.gov (United States)

    Gao, X-L; Hsu, C-Y S; Xu, Y; Hwarng, H B; Loh, T; Koh, D

    2010-06-01

    Despite the well-recognized importance of caries risk assessment, practical models remain to be established. This study was designed to develop biopsychosocial models for caries risk assessment in various settings. With a questionnaire, an oral examination, and biological (salivary, microbiological, and plaque pH) tests, a prospective study was conducted among 1782 children aged 3-6 years, with 1576 (88.4%) participants followed in 12 months. Multiple risk factors, indicators, and protective factors were identified. Various risk assessment models were constructed by the random selection of 50% of the cases and further validated in the remaining cases. For the prediction of a "one-year caries increment", screening models without biological tests achieved a sensitivity/specificity of 82%/73%; with biological tests, full-blown models achieved the sensitivity/specificity of 90%/90%. For identification of a quarter of the children with high caries burden (baseline dmft > 2), a community-screening model requiring only a questionnaire reached a sensitivity/specificity of 82%/81%. These models are promising tools for cost-effective caries control and evidence-based treatment planning. decayed, missing, filled teeth in primary dentition (dmft); receiver operation characteristics (ROC); relative risk (RR); confidence interval (CI); National Institutes of Health (NIH); World Health Organization (WHO); US Department of Health and Human Services (US/DHHS); American Academy of Pediatric Dentistry (AAPD).

  4. Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply.

    Science.gov (United States)

    Narani, Akash; Coffman, Phil; Gardner, James; Li, Chenlin; Ray, Allison E; Hartley, Damon S; Stettler, Allison; Konda, N V S N Murthy; Simmons, Blake; Pray, Todd R; Tanjore, Deepti

    2017-11-01

    Commercial-scale bio-refineries are designed to process 2000tons/day of single lignocellulosic biomass. Several geographical areas in the United States generate diverse feedstocks that, when combined, can be substantial for bio-based manufacturing. Blending multiple feedstocks is a strategy being investigated to expand bio-based manufacturing outside Corn Belt. In this study, we developed a model to predict continuous envelopes of biomass blends that are optimal for a given pretreatment condition to achieve a predetermined sugar yield or vice versa. For example, our model predicted more than 60% glucose yield can be achieved by treating an equal part blend of energy cane, corn stover, and switchgrass with alkali pretreatment at 120°C for 14.8h. By using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160°C for 2.2h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Network analysis of swine shipments in Ontario, Canada, to support disease spread modelling and risk-based disease management.

    Science.gov (United States)

    Dorjee, S; Revie, C W; Poljak, Z; McNab, W B; Sanchez, J

    2013-10-01

    Understanding contact networks are important for modelling and managing the spread and control of communicable diseases in populations. This study characterizes the swine shipment network of a multi-site production system in southwestern Ontario, Canada. Data were extracted from a company's database listing swine shipments among 251 swine farms, including 20 sow, 69 nursery and 162 finishing farms, for the 2-year period of 2006 to 2007. Several network metrics were generated. The number of shipments per week between pairs of farms ranged from 1 to 6. The medians (and ranges) of out-degree were: sow 6 (1-21), nursery 8 (0-25), and finishing 0 (0-4), over the entire 2-year study period. Corresponding estimates for in-degree of nursery and finishing farms were 3 (0-9) and 3 (0-12) respectively. Outgoing and incoming infection chains (OIC and IIC), were also measured. The medians (ranges) of the monthly OIC and IIC were 0 (0-8) and 0 (0-6), respectively, with very similar measures observed for 2-week intervals. Nursery farms exhibited high measures of centrality. This indicates that they pose greater risks of disease spread in the network. Therefore, they should be given a high priority for disease prevention and control measures affecting all age groups alike. The network demonstrated scale-free and small-world topologies as observed in other livestock shipment studies. This heterogeneity in contacts among farm types and network topologies should be incorporated in simulation models to improve their validity. In conclusion, this study provided useful epidemiological information and parameters for the control and modelling of disease spread among swine farms, for the first time from Ontario, Canada. Copyright © 2013 Elsevier B.V. All rights reserved.

  6. The effectiveness of flood risk communication strategies and the influence of social networks: insights from an agent-based model

    NARCIS (Netherlands)

    Haer, T.; Botzen, W.J.W.; Aerts, J.C.J.H.

    2016-01-01

    Flood risk management is becoming increasingly important, because more people are settling in flood-prone areas, and flood risk is increasing in many regions due to extreme weather events associated with climate change. It has been proposed that appropriately designed flood risk communication

  7. Integration of Human Reliability Analysis Models into the Simulation-Based Framework for the Risk-Informed Safety Margin Characterization Toolkit

    Energy Technology Data Exchange (ETDEWEB)

    Boring, Ronald [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rasmussen, Martin [Norwegian Univ. of Science and Technology, Trondheim (Norway). Social Research; Herberger, Sarah [Idaho National Lab. (INL), Idaho Falls, ID (United States); Ulrich, Thomas [Idaho National Lab. (INL), Idaho Falls, ID (United States); Groth, Katrina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Smith, Curtis [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2016-06-01

    This report presents an application of a computation-based human reliability analysis (HRA) framework called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER). HUNTER has been developed not as a standalone HRA method but rather as framework that ties together different HRA methods to model dynamic risk of human activities as part of an overall probabilistic risk assessment (PRA). While we have adopted particular methods to build an initial model, the HUNTER framework is meant to be intrinsically flexible to new pieces that achieve particular modeling goals. In the present report, the HUNTER implementation has the following goals: • Integration with a high fidelity thermal-hydraulic model capable of modeling nuclear power plant behaviors and transients • Consideration of a PRA context • Incorporation of a solid psychological basis for operator performance • Demonstration of a functional dynamic model of a plant upset condition and appropriate operator response This report outlines these efforts and presents the case study of a station blackout scenario to demonstrate the various modules developed to date under the HUNTER research umbrella.

  8. APPROACHING TO THE EUROPEAN MODEL OF ALCOHOL CONSUMPTION ON BASE OF LOWERING RISKS OF UKRAINIAN CONSUMERS AT WINE MARKET

    Directory of Open Access Journals (Sweden)

    A. Starostina

    2016-09-01

    Full Text Available Development of theoretical principles of conception of the perceived risk is considered. Methodology of marketing research of the perceived risks at the market of dry wine of Kyiv is shown. Searching questions, hypotheses, methods of statistical verification of hypotheses of marketing research are shown. Estimation of dry wine customers of separate components of the perceived risk and strategies of their decline are described. Concrete directions of the development of market strategy and marketing-mix, which take into account the estimation of the perceived risks and strategies of their diminishing, are offered.

  9. Centre characteristics associated with the risk of peritonitis in peritoneal dialysis: a hierarchical modelling approach based on the data of the French Language Peritoneal Dialysis Registry.

    Science.gov (United States)

    Béchade, Clémence; Guillouët, Sonia; Verger, Christian; Ficheux, Maxence; Lanot, Antoine; Lobbedez, Thierry

    2017-06-01

    This study investigated the centre effect on the risk of peritonitis in peritoneal dialysis (PD) patients. This was a retrospective cohort study based on data from the French Language Peritoneal Dialysis Registry. We analysed 5017 incident patients starting PD between January 2008 and December 2012 in 127 PD centres. The end of the observation period was 1 January 2014. The event of interest was the first peritonitis episode. The analysis was performed with a multilevel Cox model and a Fine and Gray model. Among the 5017 patients, 3190 peritonitis episodes occurred in 1796 patients. There was significant heterogeneity between centres (variance of the random effect: 0.11). The variance of the centre effect was reduced by 9% after adjusting for patient characteristics and by 35% after adjusting on centre covariate. In the multivariate analysis with a multilevel Cox model, centre with a nurse specialized in PD or centre providing home visits before dialysis initiation decreased the centre effect on peritonitis. Patients treated in centres with a nurse specialized in PD or in centres providing home visits before dialysis initiation had a lower risk of peritonitis [cause-specific hazard ratio (cs-HR): 0.75 (95% confidence interval, CI, 0.67-0.83) and cs-HR: 0.87 (95% CI 0.76-0.97), respectively]. The data show that neither centre type nor centre volume influenced peritonitis risk. In the competing risk analysis, centre with a nurse specialized in PD and centre with home visits had a protective effect on peritonitis [sub-distribution HR (sd-HR): 0.77 (95% CI 0.70-0.85) and sd-HR: 0.85 (95% CI 0.77-0.94), respectively]. There is a significant centre effect on the risk of peritonitis that can be decreased by home visits before dialysis initiation and by the presence of a nurse specialized in PD.

  10. Customer classification in banking system of Iran based on the credit risk model using multi-criteria decision-making models

    Directory of Open Access Journals (Sweden)

    Khalil Khalili

    2015-11-01

    Full Text Available One of the most important factors of survival of financial institutes and banks in the current competitive markets is to create balance and equality among resources and consumptions as well as to keep the health of money circulation in these institutes. According to the experiences obtained from recent financial crises in the world. The lack of appropriate management of the demands of banks and financial institutions can be considered as one of the main factors of occurrence of this crisis. The objective of the present study is to identify and classify customers according to credit risk and decisions of predictive models. The present research is a survey research employing field study in terms of the data collection method. The method of collecting theoretical framework was library research and the data were collected by two ways of data of a questionnaire and real customers’ financial data. To analyze the data of the questionnaire, analytical hierarchy process and to analyze real customers’ financial data, the TOPSIS method were employed. The population of the study included files of real customers in one of the branches of RefahKargaran Bank in city of Tabriz, Iran. From among 800 files, 140 files were completed and using Morgan’s table, 103 files were investigated. The final model was presented and with 95% of probability, if the next customer’s data is entered the model, it will capable of identifying accurately the degree of customer risk.

  11. THE MODEL FOR RISK ASSESSMENT ERP-SYSTEMS INFORMATION SECURITY

    Directory of Open Access Journals (Sweden)

    V. S. Oladko

    2016-12-01

    Full Text Available The article deals with the problem assessment of information security risks in the ERP-system. ERP-system functions and architecture are studied. The model malicious impacts on levels of ERP-system architecture are composed. Model-based risk assessment, which is the quantitative and qualitative approach to risk assessment, built on the partial unification 3 methods for studying the risks of information security - security models with full overlapping technique CRAMM and FRAP techniques developed.

  12. Evaluation of the Effectiveness of Nutritional Education based on Health Belief Model on Self-Esteem and BMI of Overweight and at Risk of Overweight Adolescent Girls

    Directory of Open Access Journals (Sweden)

    Leili Rabiei

    2017-08-01

    Full Text Available Background Due to significant increases in the prevalence of overweight and obesity in adolescents in developed countries, much attention has been focused on this issue. This study aimed to determine the effectiveness of nutritional education based on Health Belief Model (HBM on self-esteem and body mass index (BMI of overweight and at risk of overweight adolescent girls. Materials and Methods: The study subjects consist of 140 female students recruited from two high schools, who were randomly allocated to the intervention (n=70 and control (n=70 groups. The data collection instrument included sections on socio-demographic status, transportation method, physical status, and knowledge and attitudes of the students towards nutrition, which was designed according to HBM. As the intervention, model-based educational program was implemented through six 60-minute sessions, focusing on the overweight and at-risk students. Results were compared in the beginning, and three months after the intervention to find the possible impacts. Results: Average score of model structures and self-esteem of students in both groups had no significant difference at baseline, but immediately after the intervention and 3 months after treatment, the mean component scores were significantly higher in intervention group than controls (P

  13. Risk-driven security testing using risk analysis with threat modeling approach.

    Science.gov (United States)

    Palanivel, Maragathavalli; Selvadurai, Kanmani

    2014-01-01

    Security testing is a process of determining risks present in the system states and protects them from vulnerabilities. But security testing does not provide due importance to threat modeling and risk analysis simultaneously that affects confidentiality and integrity of the system. Risk analysis includes identification, evaluation and assessment of risks. Threat modeling approach is identifying threats associated with the system. Risk-driven security testing uses risk analysis results in test case identification, selection and assessment to prioritize and optimize the testing process. Threat modeling approach, STRIDE is generally used to identify both technical and non-technical threats present in the system. Thus, a security testing mechanism based on risk analysis results using STRIDE approach has been proposed for identifying highly risk states. Risk metrics considered for testing includes risk impact, risk possibility and risk threshold. Risk threshold value is directly proportional to risk impact and risk possibility. Risk-driven security testing results in reduced test suite which in turn reduces test case selection time. Risk analysis optimizes the test case selection and execution process. For experimentation, the system models namely LMS, ATM, OBS, OSS and MTRS are considered. The performance of proposed system is analyzed using Test Suite Reduction Rate (TSRR) and FSM coverage. TSRR varies from 13.16 to 21.43% whereas FSM coverage is achieved up to 91.49%. The results show that the proposed method combining risk analysis with threat modeling identifies states with high risks to improve the testing efficiency.

  14. The DGAV risk calculator: development and validation of statistical models for a web-based instrument predicting complications of colorectal cancer surgery.

    Science.gov (United States)

    Crispin, Alexander; Klinger, Carsten; Rieger, Anna; Strahwald, Brigitte; Lehmann, Kai; Buhr, Heinz-Johannes; Mansmann, Ulrich

    2017-10-01

    The purpose of this study is to provide a web-based calculator predicting complication probabilities of patients undergoing colorectal cancer (CRC) surgery in Germany. Analyses were based on records of first-time CRC surgery between 2010 and February 2017, documented in the database of the Study, Documentation, and Quality Center (StuDoQ) of the Deutsche Gesellschaft für Allgemein- und Viszeralchirurgie (DGAV), a registry of CRC surgery in hospitals throughout Germany, covering demography, medical history, tumor features, comorbidity, behavioral risk factors, surgical procedures, and outcomes. Using logistic ridge regression, separate models were developed in learning samples of 6729 colon and 4381 rectum cancer patients and evaluated in validation samples of sizes 2407 and 1287. Discrimination was assessed using c statistics. Calibration was examined graphically by plotting observed versus predicted complication probabilities and numerically using Brier scores. We report validation results regarding 15 outcomes such as any major complication, surgical site infection, anastomotic leakage, bladder voiding disturbance after rectal surgery, abdominal wall dehiscence, various internistic complications, 30-day readmission, 30-day reoperation rate, and 30-day mortality. When applied to the validation samples, c statistics ranged between 0.60 for anastomosis leakage and 0.85 for mortality after rectum cancer surgery. Brier scores ranged from 0.003 to 0.127. While most models showed satisfactory discrimination and calibration, this does not preclude overly optimistic or pessimistic individual predictions. To avoid misinterpretation, one has to understand the basic principles of risk calculation and risk communication. An e-learning tool outlining the appropriate use of the risk calculator is provided.

  15. Model risk analysis for risk management and option pricing

    NARCIS (Netherlands)

    Kerkhof, F.L.J.

    2003-01-01

    Due to the growing complexity of products in financial markets, market participants rely more and more on quantitative models for trading and risk management decisions. This introduces a fairly new type of risk, namely, model risk. In the first part of this thesis we investigate the quantitative

  16. Risk management model of winter navigation operations.

    Science.gov (United States)

    Valdez Banda, Osiris A; Goerlandt, Floris; Kuzmin, Vladimir; Kujala, Pentti; Montewka, Jakub

    2016-07-15

    The wintertime maritime traffic operations in the Gulf of Finland are managed through the Finnish-Swedish Winter Navigation System. This establishes the requirements and limitations for the vessels navigating when ice covers this area. During winter navigation in the Gulf of Finland, the largest risk stems from accidental ship collisions which may also trigger oil spills. In this article, a model for managing the risk of winter navigation operations is presented. The model analyses the probability of oil spills derived from collisions involving oil tanker vessels and other vessel types. The model structure is based on the steps provided in the Formal Safety Assessment (FSA) by the International Maritime Organization (IMO) and adapted into a Bayesian Network model. The results indicate that ship independent navigation and convoys are the operations with higher probability of oil spills. Minor spills are most probable, while major oil spills found very unlikely but possible. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Determination of risk factors for child fall based on the Calgary Family Assessment Model - doi:10.5020/18061230.2010.p101

    Directory of Open Access Journals (Sweden)

    Aline de Souza Pereira

    2012-01-01

    Full Text Available Objective: To determine risk factors for falls in children based on the Calgary Family Assessment Model (CFAM. Method: A qualitative approach, in which we interviewed six relatives of children who were admitted to an emergency hospital in Fortaleza, Ceara due to fall in the period from August to September, 2005. According to the CFAM we did the genogram and eco-map of two families (1 and (2. Results: By the genogram and eco-map of the families, we observed that (1 is a single parent family with six children, Roman Catholic, earns one minimum wage and attends both school and Family Health Basic Unit (UBSF. (2 is a nuclear family, with two children, Roman Catholic, earns three or more minimum wages and attends school, work and UBSF. Conclusion: The Calgary Family Assessment Model enabled to know the family structures of the children who had suffered falls and helped in defining the risk factors that exist within families and social environments in which these children attend. Family income, number of children, the presence or absence of fathers, schooling and lack of spaces for education support represent risk factors for these accidents.

  18. A regression-based method for estimating risks and relative risks in case-base studies.

    Science.gov (United States)

    Chui, Tina Tsz-Ting; Lee, Wen-Chung

    2013-01-01

    Both the absolute risk and the relative risk (RR) have a crucial role to play in epidemiology. RR is often approximated by odds ratio (OR) under the rare-disease assumption in conventional case-control study; however, such a study design does not provide an estimate for absolute risk. The case-base study is an alternative approach which readily produces RR estimation without resorting to the rare-disease assumption. However, previous researchers only considered one single dichotomous exposure and did not elaborate how absolute risks can be estimated in a case-base study. In this paper, the authors propose a logistic model for the case-base study. The model is flexible enough to admit multiple exposures in any measurement scale-binary, categorical or continuous. It can be easily fitted using common statistical packages. With one additional step of simple calculations of the model parameters, one readily obtains relative and absolute risk estimates as well as their confidence intervals. Monte-Carlo simulations show that the proposed method can produce unbiased estimates and adequate-coverage confidence intervals, for ORs, RRs and absolute risks. The case-base study with all its desirable properties and its methods of analysis fully developed in this paper may become a mainstay in epidemiology.

  19. A regression-based method for estimating risks and relative risks in case-base studies.

    Directory of Open Access Journals (Sweden)

    Tina Tsz-Ting Chui

    Full Text Available Both the absolute risk and the relative risk (RR have a crucial role to play in epidemiology. RR is often approximated by odds ratio (OR under the rare-disease assumption in conventional case-control study; however, such a study design does not provide an estimate for absolute risk. The case-base study is an alternative approach which readily produces RR estimation without resorting to the rare-disease assumption. However, previous researchers only considered one single dichotomous exposure and did not elaborate how absolute risks can be estimated in a case-base study. In this paper, the authors propose a logistic model for the case-base study. The model is flexible enough to admit multiple exposures in any measurement scale-binary, categorical or continuous. It can be easily fitted using common statistical packages. With one additional step of simple calculations of the model parameters, one readily obtains relative and absolute risk estimates as well as their confidence intervals. Monte-Carlo simulations show that the proposed method can produce unbiased estimates and adequate-coverage confidence intervals, for ORs, RRs and absolute risks. The case-base study with all its desirable properties and its methods of analysis fully developed in this paper may become a mainstay in epidemiology.

  20. Public sector risk management: a specific model.

    Science.gov (United States)

    Lawlor, Ted

    2002-07-01

    Risk management programs for state mental health authorities are generally limited in scope and reactive in nature. Recent changes in how mental health care is provided render it necessary to redirect the risk management focus from its present institutional basis to a statewide, network-based paradigm that is integrated across public and private inpatient and community programs alike. These changes include treating an increasing number of individuals in less-secure settings and contracting for an increasing number of public mental health services with private providers. The model proposed here is closely linked to the Quality Management Process.

  1. Estimating the risk of gestational diabetes mellitus : a clinical prediction model based on patient characteristics and medical history

    NARCIS (Netherlands)

    van Leeuwen, M.; Opmeer, B. C.; Zweers, E. J. K.; van Ballegooie, E.; ter Brugge, H. G.; de Valk, H. W.; Visser, G. H. A.; Mol, B. W. J.

    Objective To develop a clinical prediction rule that can help the clinician to identify women at high and low risk for gestational diabetes mellitus (GDM) early in pregnancy in order to improve the efficiency of GDM screening. Design We used data from a prospective cohort study to develop the

  2. Quantitative microbial risk assessment for spray irrigation of dairy manure based on an empirical fate and transport model

    Science.gov (United States)

    Background: Spray irrigation for land-applying livestock manure is increasing in the United States as farms become larger and economies of scale make manure irrigation affordable. However, human health risks from exposure to zoonotic pathogens aerosolized during manure irrigation are not well-unders...

  3. Groundwater Risk Assessment Model (GRAM: Groundwater Risk Assessment Model for Wellfield Protection

    Directory of Open Access Journals (Sweden)

    Nara Somaratne

    2013-09-01

    Full Text Available A groundwater risk assessment was carried out for 30 potable water supply systems under a framework of protecting drinking water quality across South Australia. A semi-quantitative Groundwater Risk Assessment Model (GRAM was developed based on a “multi-barrier” approach using likelihood of release, contaminant pathway and consequence equation. Groundwater vulnerability and well integrity have been incorporated to the pathway component of the risk equation. The land use of the study basins varies from protected water reserves to heavily stocked grazing lands. Based on the risk assessment, 15 systems were considered as low risk, four as medium and 11 systems as at high risk. The GRAM risk levels were comparable with indicator bacteria—total coliform—detection. Most high risk systems were the result of poor well construction and casing corrosion rather than the land use. We carried out risk management actions, including changes to well designs and well operational practices, design to increase time of residence and setting the production zone below identified low permeable zones to provide additional barriers to contaminants. The highlight of the risk management element is the well integrity testing using down hole geophysical methods and camera views of the casing condition.

  4. A Probabilistic Asteroid Impact Risk Model

    Science.gov (United States)

    Mathias, Donovan L.; Wheeler, Lorien F.; Dotson, Jessie L.

    2016-01-01

    Asteroid threat assessment requires the quantification of both the impact likelihood and resulting consequence across the range of possible events. This paper presents a probabilistic asteroid impact risk (PAIR) assessment model developed for this purpose. The model incorporates published impact frequency rates with state-of-the-art consequence assessment tools, applied within a Monte Carlo framework that generates sets of impact scenarios from uncertain parameter distributions. Explicit treatment of atmospheric entry is included to produce energy deposition rates that account for the effects of thermal ablation and object fragmentation. These energy deposition rates are used to model the resulting ground damage, and affected populations are computed for the sampled impact locations. The results for each scenario are aggregated into a distribution of potential outcomes that reflect the range of uncertain impact parameters, population densities, and strike probabilities. As an illustration of the utility of the PAIR model, the results are used to address the question of what minimum size asteroid constitutes a threat to the population. To answer this question, complete distributions of results are combined with a hypothetical risk tolerance posture to provide the minimum size, given sets of initial assumptions. Model outputs demonstrate how such questions can be answered and provide a means for interpreting the effect that input assumptions and uncertainty can have on final risk-based decisions. Model results can be used to prioritize investments to gain knowledge in critical areas or, conversely, to identify areas where additional data has little effect on the metrics of interest.

  5. Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

    Directory of Open Access Journals (Sweden)

    Jianxin Shi

    2016-12-01

    Full Text Available Recent heritability analyses have indicated that genome-wide association studies (GWAS have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS, a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2 for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017 and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5. Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

  6. Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

    Science.gov (United States)

    Shi, Jianxin; Park, Ju-Hyun; Duan, Jubao; Berndt, Sonja T; Moy, Winton; Yu, Kai; Song, Lei; Wheeler, William; Hua, Xing; Silverman, Debra; Garcia-Closas, Montserrat; Hsiung, Chao Agnes; Figueroa, Jonine D; Cortessis, Victoria K; Malats, Núria; Karagas, Margaret R; Vineis, Paolo; Chang, I-Shou; Lin, Dongxin; Zhou, Baosen; Seow, Adeline; Matsuo, Keitaro; Hong, Yun-Chul; Caporaso, Neil E; Wolpin, Brian; Jacobs, Eric; Petersen, Gloria M; Klein, Alison P; Li, Donghui; Risch, Harvey; Sanders, Alan R; Hsu, Li; Schoen, Robert E; Brenner, Hermann; Stolzenberg-Solomon, Rachael; Gejman, Pablo; Lan, Qing; Rothman, Nathaniel; Amundadottir, Laufey T; Landi, Maria Teresa; Levinson, Douglas F; Chanock, Stephen J; Chatterjee, Nilanjan

    2016-12-01

    Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

  7. Managing multiple international risks simultaneously with an optimal hedging model

    OpenAIRE

    Gboroton F. Sarassoro; Raymond M. Leuthold

    1991-01-01

    A risk management model based on portfolio theory which accounts jointly for price, quantity, interest rate and exchange rate risks is developed and applied to cocoa and coffee production and exports in the Ivory Coast. Utilizing commodity and financial futures markets jointly, the results show that a government export agency can reduce risks from 27% to 89% by following a multicommodity hedging program which manages several risks simultaneously. The model and technique developed are applicab...

  8. MANAGING BRAND EQUITY RISK: ADDING EXOGENOUS RISKS TO AN EVALUATION MODEL

    OpenAIRE

    Catalin Mihail Barbu; Sorin Tudor; Dorian Laurentiu Florea

    2014-01-01

    Risk can no longer be ignored when talking about brand management, as risk management can no longer disregard brands for manifold reasons. Building on the risk-based brand equity model, this paper contributes to the development of an evaluation model, by suggesting formulas for 3 exogenous risk sources related to the market and competitive structure: the new brand marketing effort, consumer behavior change, and the extant brands adaptation.

  9. Modelling of Systemic Risk of Banking Sector

    Directory of Open Access Journals (Sweden)

    Laura Gudelytė

    2014-03-01

    Full Text Available Purpose – to evaluate the general networking and simulation approaches of modelling of systemic risk and the financial contagion and their ability to assess the banking sector resilience in the case of external economic shocks and collapse of idiosyncratic financial institutions.Design/methodology/approach – a general overview of research papers presenting concepts and methodologies of assessment of systemic risk of the banking sector.Findings – limitations of the networking approach and possible ways to improve modelling of systemic risk. The network approach cannot explain the causes of initial default of bank. On the other hand, assumptions made on LGD and interbank exposures are very strong. These features are important limitations of network and simulation approaches.Research limitations/implications – the application of reviewed methods in the case of Lithuanian banking sector falls, however, due to the lack of exhaustive data. On the other hand, until now, applied methods for systemic risk due to the lack of data have been limited. Also, because of this reason, there are difficulties to create adequate dynamic assessment for systemic risk models. Therefore, in assessing systemic risk of the banking sector, the same problem remains: is it possible to parameterize the financial crisis, its spread and speed and other characteristics according to quantitative methods. Knowing the liquidity, credit risk and other standards set in Basel Accords, it is also not enough to properly manage the systemic risk of the whole banking sector because for the proper activity of the banking sector not only characteristics related to capital requirements have influence on it, but also external (mostly the macroeconomic, political factors.Practical implications – determination of the explicit connection based on quantitative methods determining the systemic risk of the banking sector would be exact and objective assessment and useful not only for the

  10. Risk Measurement and Risk Modelling Using Applications of Vine Copulas

    Directory of Open Access Journals (Sweden)

    David E. Allen

    2017-09-01

    Full Text Available This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite financial risk. Copula-based dependence modelling is a popular tool in financial applications, but is usually applied to pairs of securities. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using the rich variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. The paper features the use of Regular Vine copulas in an analysis of the co-dependencies of 10 major European Stock Markets, as represented by individual market indices and the composite STOXX 50 index. The sample runs from 2005 to the end of 2013 to permit an exploration of how correlations change indifferent economic circumstances using three different sample periods: pre-GFC (January 2005–July 2007, GFC (July 2007– September 2009, and post-GFC periods (September 2009–December 2013. The empirical results suggest that the dependencies change in a complex manner, and are subject to change in different economic circumstances. One of the attractions of this approach to risk modelling is the flexibility in the choice of distributions used to model co-dependencies. The practical application of Regular Vine metrics is demonstrated via an example of the calculation of the VaR of a portfolio made up of the indices.

  11. Construction Safety Risk Modeling and Simulation.

    Science.gov (United States)

    Tixier, Antoine J-P; Hallowell, Matthew R; Rajagopalan, Balaji

    2017-10-01

    By building on a genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data-driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute-based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency-magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety-related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers. © 2017 Society for Risk Analysis.

  12. Incentivising flood risk adaptation through risk based insurance premiums : Trade-offs between affordability and risk reduction

    NARCIS (Netherlands)

    Hudson, Paul F.; Botzen, W.J.W.; Feyen, L.; Aerts, Jeroen C.J.H.

    2016-01-01

    The financial incentives offered by the risk-based pricing of insurance can stimulate policyholder adaptation to flood risk while potentially conflicting with affordability. We examine the trade-off between risk reduction and affordability in a model of public-private flood insurance in France and

  13. Risk matrix model for rotating equipment

    Directory of Open Access Journals (Sweden)

    Wassan Rano Khan

    2014-07-01

    Full Text Available Different industries have various residual risk levels for their rotating equipment. Accordingly the occurrence rate of the failures and associated failure consequences categories are different. Thus, a generalized risk matrix model is developed in this study which can fit various available risk matrix standards. This generalized risk matrix will be helpful to develop new risk matrix, to fit the required risk assessment scenario for rotating equipment. Power generation system was taken as case study. It was observed that eight subsystems were under risk. Only vibration monitor system was under high risk category, while remaining seven subsystems were under serious and medium risk categories.

  14. Landslide risk models for decision making.

    Science.gov (United States)

    Bonachea, Jaime; Remondo, Juan; de Terán, José Ramón Díaz; González-Díez, Alberto; Cendrero, Antonio

    2009-11-01

    This contribution presents a quantitative procedure for landslide risk analysis and zoning considering hazard, exposure (or value of elements at risk), and vulnerability. The method provides the means to obtain landslide risk models (expressing expected damage due to landslides on material elements and economic activities in monetary terms, according to different scenarios and periods) useful to identify areas where mitigation efforts will be most cost effective. It allows identifying priority areas for the implementation of actions to reduce vulnerability (elements) or hazard (processes). The procedure proposed can also be used as a preventive tool, through its application to strategic environmental impact analysis (SEIA) of land-use plans. The underlying hypothesis is that reliable predictions about hazard and risk can be made using models based on a detailed analysis of past landslide occurrences in connection with conditioning factors and data on past damage. The results show that the approach proposed and the hypothesis formulated are essentially correct, providing estimates of the order of magnitude of expected losses for a given time period. Uncertainties, strengths, and shortcomings of the procedure and results obtained are discussed and potential lines of research to improve the models are indicated. Finally, comments and suggestions are provided to generalize this type of analysis.

  15. Risk and risk perception of knee osteoarthritis in the US: a population-based study.

    Science.gov (United States)

    Michl, G L; Katz, J N; Losina, E

    2016-04-01

    We sought to investigate risk perception among an online cohort of younger US adults compared with calculated risk estimates. We recruited a population-based cohort 25-44 years of age with no history of knee osteoarthritis (OA) using Amazon's Mechanical Turk, an online marketplace used extensively for behavioral research. After collecting demographic and risk factor information, we asked participants to estimate their 10-year and lifetime risk of knee OA. We compared perceived risk with risk derived from the OA risk calculator (OA Risk C), an online tool built on the basis of the validated OA Policy Model. 375 people completed the study. 21% reported having 3+ risk factors for OA, 25% reported two risk factors, and 32% reported one risk factor. Using the OA Risk C, we calculated a mean lifetime OA risk of 25% and 10-year risk of 4% for this sample. Participants overestimated their lifetime and 10-year OA risk at 48% and 26%, respectively. We found that obesity, female sex, family history of OA, history of knee injury, and occupational exposure were all significantly associated with greater perceived lifetime risk of OA. Risk factors are prevalent in this relatively young cohort. Participants consistently overestimated their lifetime risk and showed even greater overestimation of their 10-year risk, suggesting a lack of knowledge about the timing of OA onset. These data offer insights for awareness and risk interventions among younger persons at risk for knee OA. Copyright © 2015 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

  16. Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level.

    Science.gov (United States)

    Onega, Tracy; Beaber, Elisabeth F; Sprague, Brian L; Barlow, William E; Haas, Jennifer S; Tosteson, Anna N A; D Schnall, Mitchell; Armstrong, Katrina; Schapira, Marilyn M; Geller, Berta; Weaver, Donald L; Conant, Emily F

    2014-10-01

    Breast cancer screening holds a prominent place in public health, health care delivery, policy, and women's health care decisions. Several factors are driving shifts in how population-based breast cancer screening is approached, including advanced imaging technologies, health system performance measures, health care reform, concern for "overdiagnosis," and improved understanding of risk. Maximizing benefits while minimizing the harms of screening requires moving from a "1-size-fits-all" guideline paradigm to more personalized strategies. A refined conceptual model for breast cancer screening is needed to align women's risks and preferences with screening regimens. A conceptual model of personalized breast cancer screening is presented herein that emphasizes key domains and transitions throughout the screening process, as well as multilevel perspectives. The key domains of screening awareness, detection, diagnosis, and treatment and survivorship are conceptualized to function at the level of the patient, provider, facility, health care system, and population/policy arena. Personalized breast cancer screening can be assessed across these domains with both process and outcome measures. Identifying, evaluating, and monitoring process measures in screening is a focus of a National Cancer Institute initiative entitled PROSPR (Population-based Research Optimizing Screening through Personalized Regimens), which will provide generalizable evidence for a risk-based model of breast cancer screening, The model presented builds on prior breast cancer screening models and may serve to identify new measures to optimize benefits-to-harms tradeoffs in population-based screening, which is a timely goal in the era of health care reform. © 2014 American Cancer Society.

  17. Model Averaging Software for Dichotomous Dose Response Risk Estimation

    Directory of Open Access Journals (Sweden)

    Matthew W. Wheeler

    2008-02-01

    Full Text Available Model averaging has been shown to be a useful method for incorporating model uncertainty in quantitative risk estimation. In certain circumstances this technique is computationally complex, requiring sophisticated software to carry out the computation. We introduce software that implements model averaging for risk assessment based upon dichotomous dose-response data. This software, which we call Model Averaging for Dichotomous Response Benchmark Dose (MADr-BMD, fits the quantal response models, which are also used in the US Environmental Protection Agency benchmark dose software suite, and generates a model-averaged dose response model to generate benchmark dose and benchmark dose lower bound estimates. The software fulfills a need for risk assessors, allowing them to go beyond one single model in their risk assessments based on quantal data by focusing on a set of models that describes the experimental data.

  18. A model for environmental risk assessment and standard setting based on biomagnification. Top predators in terrestrial ecosystems

    NARCIS (Netherlands)

    Jongbloed RH; Pijnenburg J; Mensink BJWG; Traas TP; Luttik R; ACT; LWD; RIKZ

    1994-01-01

    Soil contaminants accumulating through food chains may exert toxic effects on birds and mammals (secondary poisoning). In the current procedure for setting soil quality objectives, the maximum permissable concentration for a chemical in the soil (MPC) for secondary poisoning is based solely on the

  19. Prediction of Drug Attitude in Adolescents Based on Family Training Risk Factors for Mental Health in Society: Designing a Model for Prevention of Addiction

    National Research Council Canada - National Science Library

    M Parsian; K Hashemian; Kh Abolmaali; M Mirhashemi

    2015-01-01

    ... . However, this study was done in order to predict drug addiction attitude in adolescents according to the family training risk factors to prevent substance abuse and to design a model for the prevention of addiction .   Methods...

  20. The Influence of Base Rate and Case Information on Health-Risk Perceptions: A Unified Model of Self-Positivity and Self-Negativity

    OpenAIRE

    Dengfeng Yan; Jaideep Sengupta

    2013-01-01

    This research examines how consumers use base rate (e.g., disease prevalence in a population) and case information (e.g., an individual's disease symptoms) to estimate health risks. Drawing on construal level theory, we propose that consumers' reliance on base rate (case information) will be enhanced (weakened) by psychological distance. A corollary of this premise is that self-positivity (i.e., underestimating self-risk vs. other-risk) is likely when the disease base rate is high but the cas...

  1. Risk-based high-throughput chemical screening and prioritization using exposure models and in vitro bioactivity assays

    DEFF Research Database (Denmark)

    Shin, Hyeong-Moo; Ernstoff, Alexi; Arnot, Jon

    2015-01-01

    relevant use scenarios (e.g., dermal application, indoor emissions). For each chemical and use scenario, exposure models are then used to calculate a chemical intake fraction, or a product intake fraction, accounting for chemical properties and the exposed population. We then combine these intake fractions...... with use scenario-specific estimates of chemical quantity to calculate daily intake rates (iR; mg/kg/day). These intake rates are compared to oral equivalent doses (OED; mg/kg/day), calculated from a suite of ToxCast in vitro bioactivity assays using in vitro-to-in vivo extrapolation and reverse dosimetry...

  2. Predicting points of departure for risk assessment based on in vitro cytotoxicity data and physiologically based kinetic (PBK) modeling: The case of kidney toxicity induced by aristolochic acid I.

    Science.gov (United States)

    Abdullah, Rozaini; Alhusainy, Wasma; Woutersen, Jasper; Rietjens, Ivonne M C M; Punt, Ans

    2016-06-01

    Aristolochic acids are naturally occurring nephrotoxins. This study aims to investigate whether physiologically based kinetic (PBK) model-based reverse dosimetry could convert in vitro concentration-response curves of aristolochic acid I (AAI) to in vivo dose response-curves for nephrotoxicity in rat, mouse and human. To achieve this extrapolation, PBK models were developed for AAI in these different species. Subsequently, concentration-response curves obtained from in vitro cytotoxicity models were translated to in vivo dose-response curves using PBK model-based reverse dosimetry. From the predicted in vivo dose-response curves, points of departure (PODs) for risk assessment could be derived. The PBK models elucidated species differences in the kinetics of AAI with the overall catalytic efficiency for metabolic conversion of AAI to aristolochic acid Ia (AAIa) being 2-fold higher for rat and 64-fold higher for mouse than human. Results show that the predicted PODs generally fall within the range of PODs derived from the available in vivo studies. This study provides proof of principle for a new method to predict a POD for in vivo nephrotoxicity by integrating in vitro toxicity testing with in silico PBK model-based reverse dosimetry. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

    Science.gov (United States)

    Yahyaei, Mohsen; Bashiri, Mahdi

    2017-03-01

    The hub location problem arises in a variety of domains such as transportation and telecommunication systems. In many real-world situations, hub facilities are subject to disruption. This paper deals with the multiple allocation hub location problem in the presence of facilities failure. To model the problem, a two-stage stochastic formulation is developed. In the proposed model, the number of scenarios grows exponentially with the number of facilities. To alleviate this issue, two approaches are applied simultaneously. The first approach is to apply sample average approximation to approximate the two stochastic problem via sampling. Then, by applying the multiple cuts Benders decomposition approach, computational performance is enhanced. Numerical studies show the effective performance of the SAA in terms of optimality gap for small problem instances with numerous scenarios. Moreover, performance of multi-cut Benders decomposition is assessed through comparison with the classic version and the computational results reveal the superiority of the multi-cut approach regarding the computational time and number of iterations.

  4. A benefit–risk assessment model for statins using multicriteria decision analysis based on a discrete choice experiment in Korean patients

    Science.gov (United States)

    Byun, Ji-Hye; Kwon, Sun-Hong; Ha, Ji-Hye; Lee, Eui-Kyung

    2016-01-01

    Purpose The benefit–risk balance for drugs can alter post approval owing to additional data on efficacy or adverse events. This study developed a quantitative benefit–risk assessment (BRA) model for statins using multicriteria decision analysis with discrete choice experiments and compared a recent BRA with that at the time of approval. Patients and methods Following a systematic review of the literature, the benefit criteria within the statin BRA model were defined as a reduction in the plasma low-density lipoprotein cholesterol level and a reduction in myocardial infarction incidence; the risk criteria were hepatotoxicity (Liv) and fatal rhabdomyolysis (Rha). The scores for these criteria were estimated using mixed treatment comparison methods. Weighting was calculated from a discrete choice experiment involving 203 Korean patients. The scores and weights were integrated to produce an overall value representing the benefit–risk balance, and sensitivity analyses were conducted. Results In this BRA model, low-density lipoprotein (relative importance [RI]: 37.50%) was found to be a more important benefit criterion than myocardial infarction (RI: 35.43%), and Liv (RI: 16.28%) was a more important risk criterion than Rha (RI: 10.79%). Patients preferred atorvastatin, and the preference ranking of cerivastatin and simvastatin was switched post approval because of the emergence of additional risk information related to cerivastatin. Conclusion A quantitative statin BRA model confirmed that the preference ranking of statins changed post approval because of the identification of additional benefits or risks. PMID:27358567

  5. A benefit-risk assessment model for statins using multicriteria decision analysis based on a discrete choice experiment in Korean patients.

    Science.gov (United States)

    Byun, Ji-Hye; Kwon, Sun-Hong; Ha, Ji-Hye; Lee, Eui-Kyung

    2016-01-01

    The benefit-risk balance for drugs can alter post approval owing to additional data on efficacy or adverse events. This study developed a quantitative benefit-risk assessment (BRA) model for statins using multicriteria decision analysis with discrete choice experiments and compared a recent BRA with that at the time of approval. Following a systematic review of the literature, the benefit criteria within the statin BRA model were defined as a reduction in the plasma low-density lipoprotein cholesterol level and a reduction in myocardial infarction incidence; the risk criteria were hepatotoxicity (Liv) and fatal rhabdomyolysis (Rha). The scores for these criteria were estimated using mixed treatment comparison methods. Weighting was calculated from a discrete choice experiment involving 203 Korean patients. The scores and weights were integrated to produce an overall value representing the benefit-risk balance, and sensitivity analyses were conducted. In this BRA model, low-density lipoprotein (relative importance [RI]: 37.50%) was found to be a more important benefit criterion than myocardial infarction (RI: 35.43%), and Liv (RI: 16.28%) was a more important risk criterion than Rha (RI: 10.79%). Patients preferred atorvastatin, and the preference ranking of cerivastatin and simvastatin was switched post approval because of the emergence of additional risk information related to cerivastatin. A quantitative statin BRA model confirmed that the preference ranking of statins changed post approval because of the identification of additional benefits or risks.

  6. Big data based fraud risk management at Alibaba

    OpenAIRE

    Chen, Jidong; Tao, Ye; Wang, Haoran; Chen, Tao

    2015-01-01

    With development of mobile internet and finance, fraud risk comes in all shapes and sizes. This paper is to introduce the Fraud Risk Management at Alibaba under big data. Alibaba has built a fraud risk monitoring and management system based on real-time big data processing and intelligent risk models. It captures fraud signals directly from huge amount data of user behaviors and network, analyzes them in real-time using machine learning, and accurately predicts the bad users and transactions....

  7. Mitigating risk during strategic supply network modeling

    OpenAIRE

    Müssigmann, Nikolaus

    2006-01-01

    Mitigating risk during strategic supply network modeling. - In: Managing risks in supply chains / ed. by Wolfgang Kersten ... - Berlin : Schmidt, 2006. - S. 213-226. - (Operations and technology management ; 1)

  8. Paternal programming of breast cancer risk in daughters in a rat model: opposing effects of animal- and plant-based high-fat diets.

    Science.gov (United States)

    Fontelles, Camile Castilho; Guido, Luiza Nicolosi; Rosim, Mariana Papaléo; Andrade, Fábia de Oliveira; Jin, Lu; Inchauspe, Jessica; Pires, Vanessa Cardoso; de Castro, Inar Alves; Hilakivi-Clarke, Leena; de Assis, Sonia; Ong, Thomas Prates

    2016-07-26

    Although males contribute half of the embryo's genome, only recently has interest begun to be directed toward the potential impact of paternal experiences on the health of offspring. While there is evidence that paternal malnutrition may increase offspring susceptibility to metabolic diseases, the influence of paternal factors on a daughter's breast cancer risk has been examined in few studies. Male Sprague-Dawley rats were fed, before and during puberty, either a lard-based (high in saturated fats) or a corn oil-based (high in n-6 polyunsaturated fats) high-fat diet (60 % of fat-derived energy). Control animals were fed an AIN-93G control diet (16 % of fat-derived energy). Their 50-day-old female offspring fed only a commercial diet were subjected to the classical model of mammary carcinogenesis based on 7,12-dimethylbenz[a]anthracene initiation, and mammary tumor development was evaluated. Sperm cells and mammary gland tissue were subjected to cellular and molecular analysis. Compared with female offspring of control diet-fed male rats, offspring of lard-fed male rats did not differ in tumor latency, growth, or multiplicity. However, female offspring of lard-fed male rats had increased elongation of the mammary epithelial tree, number of terminal end buds, and tumor incidence compared with both female offspring of control diet-fed and corn oil-fed male rats. Compared with female offspring of control diet-fed male rats, female offspring of corn oil-fed male rats showed decreased tumor growth but no difference regarding tumor incidence, latency, or multiplicity. Additionally, female offspring of corn oil-fed male rats had longer tumor latency as well as decreased tumor growth and multiplicity compared with female offspring of lard-fed male rats. Paternal consumption of animal- or plant-based high-fat diets elicited opposing effects, with lard rich in saturated fatty acids increasing breast cancer risk in offspring and corn oil rich in n-6 polyunsaturated fatty

  9. Social norm influences on evaluations of the risks associated with alcohol consumption: applying the rank-based decision by sampling model to health judgments.

    Science.gov (United States)

    Wood, Alex M; Brown, Gordon D A; Maltby, John

    2012-01-01

    The research first tested whether perceptions of other people's alcohol consumption influenced drinkers' perceptions of the riskiness of their own consumption. Second, the research tested how such comparisons are made-whether, for example, people compare their drinking to the 'average' drinker's or 'rank' their consumption amongst other people's. The latter untested possibility, suggested by the recent Decision by Sampling Model of judgment, would imply different cognitive mechanisms and suggest that information should be presented differently to people in social norm interventions. Study 1 surveyed students who provided information on (a) their own drinking, (b) their perceptions of the distribution of drinking in the UK and (c) their perceived risk of various alcohol-related disorders. Study 2 experimentally manipulated the rank of 'target' units of alcohol within the context of units viewed simultaneously. In both studies, the rank of an individual's drinking in a context of other drinkers predicted perceptions of developing alcohol-related disorders. There was no evidence for the alternative hypothesis that people compared with the average of other drinkers' consumptions. The position that subjects believed they occupied in the ranking of other drinkers predicted their perceived risk, and did so as strongly as how much they actually drank. Drinking comparisons are rank-based, which is consistent with other judgments in social, emotional and psychophysical domains. Interventions should be designed to work with people's natural ways of information processing, through providing clients with information on their drinking rank rather than how their drinking differs from the average.

  10. The Effectiveness of an Educational Intervention Based on the Health Belief Model in the Empowerment of Stockbreeders Against High-Risk Behaviors Associated with Brucellosis

    Directory of Open Access Journals (Sweden)

    Vahid Babaei

    2014-12-01

    Full Text Available Background and Objectives: Brucellosis is among the most common zoonotic diseases. Educational programs can be effective in the prevention of this disease in humans. The present study was conducted to assess the effectiveness of an educational intervention based on the Health Belief Model (HBM in the empowerment of stockbreeders against high risk behaviors associated with brucellosis in Charuymaq county, East Azerbaijan. Materials and Methods: The present quasi-experimental study was conducted in 2014 in Charuymaq county. A total of 200 people selected through stratified random sampling participated in the study. Data were collected using a researcher-designed questionnaire including items on participants' demographic information, knowledge and the HBM constructs. Training sessions were then designed and held for the intervention group. Three months after the intervention was held, data were collected from both groups and then analyzed using descriptive statistics including the Mann-Whitney U test and the Wilcoxon test. Results: The mean scores obtained for knowledge, HBM constructs (perceived susceptibility, severity, barriers and benefits and self-efficacy and brucellosis preventive behaviors showed no significant differences between the two groups before the intervention however, after the educational intervention, significant differences were observed between the mean scores obtained by the intervention group and the control group (P<0.05. Conclusion: The cooperation of charismatic individuals with intervention programs and the use of education theories can be more effective in modifying high-risk behaviors these programs should therefore be widely implemented across the country.

  11. Risk assessment based on a combination of historical analysis, a detailed field study and numerical modeling on the alluvial fan Gadeinerbach as a basis for a risk management concept

    Science.gov (United States)

    Moser, M.

    2009-04-01

    The catchment Gadeinerbach in the District of Lungau/Salzburg/Austria is prone to debris flows. Large debris flow events dates back from the years 1934 and 1953. In the upper catchment large mass movements represent debris sources. A field study shows the debris potential and the catchment looks like a "sleeping torrential giant". To carry out mitigation measures a detailed risk management concept, based on a risk assessment in combination of historical analysis, field study and numerical modeling on the alluvial fan was conducted. Human activities have partly altered the surface of the alluvial fan Gadeinerbach but nevertheless some important hazard indicators could be found. With the hazard indicators and photo analysis from the large debris flow event 1934 the catchment character could be pointed out. With the help of these historical data sets (hazard indicators, sediment and debris amount...) it is possible to calibrate the provided numerical models and to win useful knowledge over the pro and cons and their application. The results were used to simulate the design event and furthermore to derive mitigation measures. Therefore the most effective protection against debris with a reduction of the high energy level to a lower level under particular energy change in combination with a debris/bedload deposition place has been carried out. Expert opinion, the study of historical data and a field work is in addition to numerical simulation techniques very necessary for the work in the field of natural hazard management.

  12. Competing Risks and Multistate Models with R

    CERN Document Server

    Beyersmann, Jan; Schumacher, Martin

    2012-01-01

    This book covers competing risks and multistate models, sometimes summarized as event history analysis. These models generalize the analysis of time to a single event (survival analysis) to analysing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models). Both R and multistate methods are promoted with a focus on nonparametric methods.

  13. Effects of education based on the health belief model on screening behavior in high risk women for breast cancer, Tehran, Iran.

    Science.gov (United States)

    Hajian, Sepideh; Vakilian, Katayon; Najabadi, Khadijeh Mirzaii; Hosseini, Jalil; Mirzaei, Hamid Reza

    2011-01-01

    Breast cancer is the most common malignancy in women. Early diagnosis allows efficient treatment and increases survival, but the efficacy of breast self examination (BSE) is not sufficiently well established. The American Cancer Society aims to give women the opportunity to recognize the utility, limitations and adverse effects of breast cancer screening through education models based on psychological theories. With the Health Belief Model, people's health perceptions and attitudes influence their practices, for example with screening. The purpose of this randomized controlled clinical trial was to determine the effect of education based on this model on breast cancer screening in high risk Iranian women. Participants were women with a family history of breast cancer (mother, sister, and daughter). After explanation of the study objectives to participants, they were recruited on obtaining oral consent and each filled out the study questionnaire based on the Health Belief Model. Allocation was into two groups by computerized randomization, control and intervention, receiving education on breast cancer screening. Perceived susceptibility to and seriousness of breast cancer, perceived usefulness of and barriers to BSE, clinical breast examination, and mammography, and self-efficacy in the ability to perform these, were assessed, with comparison of scores for BSE practice before and after education and doing mammography and clinical examination by a physician in intervention and control group. The mean age was 37.8 ± 11.7 (range 19-60). The mean rank in the intervention group significantly differed before and after the education, but except for " perceived threat" and "perceived usefulness of breast self examination", we did not find any significant differences from the control group. After educational sessions, breast self examination and clinical examination practice rates were elevated. Health education based on well known psychological theories for breast cancer

  14. Adoption of Building Information Modelling in project planning risk management

    Science.gov (United States)

    Mering, M. M.; Aminudin, E.; Chai, C. S.; Zakaria, R.; Tan, C. S.; Lee, Y. Y.; Redzuan, A. A.

    2017-11-01

    An efficient and effective risk management required a systematic and proper methodology besides knowledge and experience. However, if the risk management is not discussed from the starting of the project, this duty is notably complicated and no longer efficient. This paper presents the adoption of Building Information Modelling (BIM) in project planning risk management. The objectives is to identify the traditional risk management practices and its function, besides, determine the best function of BIM in risk management and investigating the efficiency of adopting BIM-based risk management during the project planning phase. In order to obtain data, a quantitative approach is adopted in this research. Based on data analysis, the lack of compliance with project requirements and failure to recognise risk and develop responses to opportunity are the risks occurred when traditional risk management is implemented. When using BIM in project planning, it works as the tracking of cost control and cash flow give impact on the project cycle to be completed on time. 5D cost estimation or cash flow modeling benefit risk management in planning, controlling and managing budget and cost reasonably. There were two factors that mostly benefit a BIM-based technology which were formwork plan with integrated fall plan and design for safety model check. By adopting risk management, potential risks linked with a project and acknowledging to those risks can be identified to reduce them to an acceptable extent. This means recognizing potential risks and avoiding threat by reducing their negative effects. The BIM-based risk management can enhance the planning process of construction projects. It benefits the construction players in various aspects. It is important to know the application of BIM-based risk management as it can be a lesson learnt to others to implement BIM and increase the quality of the project.

  15. Estimating the Value-at-Risk for some stocks at the capital market in Indonesia based on ARMA-FIGARCH models

    Science.gov (United States)

    Sukono; Lesmana, E.; Susanti, D.; Napitupulu, H.; Hidayat, Y.

    2017-11-01

    Value-at-Risk has already become a standard measurement that must be carried out by the financial institution for both internal interest and regulatory. In this paper, the estimation of Value-at-Risk of some stocks with econometric models approach is analyzed. In this research, we assume that the stock return follows the time series model. To do the estimation of mean value we are using ARMA models, while to estimate the variance value we are using FIGARCH models. Furthermore, the mean value estimator and the variance are used to estimate the Value-at-Risk. The result of the analysis shows that from five stock PRUF, BBRI, MPPA, BMRI, and INDF, the Value-at-Risk obtained are 0.01791, 0.06037, 0.02550, 0.06030, and 0.02585 respectively. Since Value-at-Risk represents the maximum risk size of each stock at a 95% level of significance, then it can be taken into consideration in determining the investment policy on stocks.

  16. Risk Modelling for Passages in Approach Channel

    Directory of Open Access Journals (Sweden)

    Leszek Smolarek

    2013-01-01

    Full Text Available Methods of multivariate statistics, stochastic processes, and simulation methods are used to identify and assess the risk measures. This paper presents the use of generalized linear models and Markov models to study risks to ships along the approach channel. These models combined with simulation testing are used to determine the time required for continuous monitoring of endangered objects or period at which the level of risk should be verified.

  17. Prediction of Drug Attitude in Adolescents Based on Family Training Risk Factors for Mental Health in Society: Designing a Model for Prevention of Addiction

    Directory of Open Access Journals (Sweden)

    M Parsian

    2015-06-01

    Full Text Available Background & objectives: Substance abuse is one of the worst humanitarian issues in recent years which undermines the base and foundations of human society. Its prevention requires the application of multiple theories in various disciplines together with diverse methods and techniques. Several studies have been emphasized on the role of personal and familial variables as risk factors for substance use . However, this study was done in order to predict drug addiction attitude in adolescents according to the family training risk factors to prevent substance abuse and to design a model for the prevention of addiction .   Methods: This study is a descriptive and survey research performed on a sample of 373 male and female students selected randomly among the five high school students in Ghaemshahr city. Then a questionnaire including parenting styles, attitude to addiction and social problem solving skill as well as a socioeconomic questionnaire distributed among the students. For data analysis, the statistical method of descriptive statistics and path analysis has been used.   Results: Results of this study have shown that the component of parenting styles has a direct and positive impact on attitudes to drug addiction. In addition, there was a direct and positive non-significant relationship between the adaptive social problem solving skills and attitude to drug addiction and also direct and negative significant relationship between the non-adaptive social problem solving skills on this attitudes. A direct and negative significant relationship was also seen between parenting styles and attitude to drug addiction.   Conclusions: Based on the results of present study, the components of parenting styles have a direct and negative impact on attitudes to drug addiction. Also there is a direct and significant relationship between the components of non-adaptive social problem solving skills and the variable of social attitude in adolescents . But the

  18. Metodology for Risk-based Indicators Impementation

    Directory of Open Access Journals (Sweden)

    Vladimír Plos

    2016-01-01

    Full Text Available The article describes the principle of creating a riskbased indicators in companies operating in an air transport. The first part deals with the description of safety indicators and introduce the concept of risk-based indicators. The next section describes the procedure for creating the base of risk-based indicators and describes specific examples of developed indicators.

  19. Modeling Financial Risk in Telecommunication Field

    Directory of Open Access Journals (Sweden)

    Natalia V. Kuznietsova

    2017-10-01

    Full Text Available Background. The telecommunication field in Ukraine is dynamically developing continuously renewing its proposals for the market and consumer requirements. That is why a timely estimation of financial risks and optimization of financial expenses regarding development of new components and possible losses of clients is especially urgent problem today. Objective. The aim of the paper is to suggest an approach for estimation of financial risks and forecasting of the client loss and optimal service time utilization based on intellectual data analysis and behavior models. Methods. To determine the probability of customer loss the neural networks theory, gradient busting, random forest and logistic regression are used. The survival analysis models for possible client transition time to another company are developed. Results. The best model for forecasting the clients intending for transition to another telecommunication company turned out to be the one based on gradient busting. Conclusions. It was shown that timely estimation of financial losses, provoked by possible loss of clients, is an urgent task for intellectual data analysis. A perspective approach for optimization of the company financial resources is determining the time period related to possible loss of clients.

  20. A comparative review of radiation-induced cancer risk models

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seung Hee; Kim, Ju Youl [FNC Technology Co., Ltd., Yongin (Korea, Republic of); Han, Seok Jung [Risk and Environmental Safety Research Division, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2017-06-15

    With the need for a domestic level 3 probabilistic safety assessment (PSA), it is essential to develop a Korea-specific code. Health effect assessments study radiation-induced impacts; in particular, long-term health effects are evaluated in terms of cancer risk. The objective of this study was to analyze the latest cancer risk models developed by foreign organizations and to compare the methodology of how they were developed. This paper also provides suggestions regarding the development of Korean cancer risk models. A review of cancer risk models was carried out targeting the latest models: the NUREG model (1993), the BEIR VII model (2006), the UNSCEAR model (2006), the ICRP 103 model (2007), and the U.S. EPA model (2011). The methodology of how each model was developed is explained, and the cancer sites, dose and dose rate effectiveness factor (DDREF) and mathematical models are also described in the sections presenting differences among the models. The NUREG model was developed by assuming that the risk was proportional to the risk coefficient and dose, while the BEIR VII, UNSCEAR, ICRP, and U.S. EPA models were derived from epidemiological data, principally from Japanese atomic bomb survivors. The risk coefficient does not consider individual characteristics, as the values were calculated in terms of population-averaged cancer risk per unit dose. However, the models derived by epidemiological data are a function of sex, exposure age, and attained age of the exposed individual. Moreover, the methodologies can be used to apply the latest epidemiological data. Therefore, methodologies using epidemiological data should be considered first for developing a Korean cancer risk model, and the cancer sites and DDREF should also be determined based on Korea-specific studies. This review can be used as a basis for developing a Korean cancer risk model in the future.

  1. 76 FR 1889 - Risk-Based Capital Guidelines: Market Risk

    Science.gov (United States)

    2011-01-11

    ... Risk Determination of the Multiplication Factor 7. VaR-Based Capital Requirement Quantitative....org/press/p100618/annex.pdf . These revisions to the market risk framework and other proposed... preceding 250 business days, it is generally required to apply a multiplication factor in excess of 3 when...

  2. Annual effective dose due to residential radon progeny in Sweden: Evaluations based on current risk projections models and on risk estimates from a nation-wide Swedish epidemiological study

    Energy Technology Data Exchange (ETDEWEB)

    Doi, M. [National Inst. of Radiological Sciences, Chiba (Japan); Lagarde, F. [Karolinska Inst., Stockholm (Sweden). Inst. of Environmental Medicine; Falk, R.; Swedjemark, G.A. [Swedish Radiation Protection Inst., Stockholm (Sweden)

    1996-12-01

    Effective dose per unit radon progeny exposure to Swedish population in 1992 is estimated by the risk projection model based on the Swedish epidemiological study of radon and lung cancer. The resulting values range from 1.29 - 3.00 mSv/WLM and 2.58 - 5.99 mSv/WLM, respectively. Assuming a radon concentration of 100 Bq/m{sup 3}, an equilibrium factor of 0.4 and an occupancy factor of 0.6 in Swedish houses, the annual effective dose for the Swedish population is estimated to be 0.43 - 1.98 mSv/year, which should be compared to the value of 1.9 mSv/year, according to the UNSCEAR 1993 report. 27 refs, tabs, figs.

  3. A benefit–risk assessment model for statins using multicriteria decision analysis based on a discrete choice experiment in Korean patients

    Directory of Open Access Journals (Sweden)

    Byun JH

    2016-06-01

    Full Text Available Ji-Hye Byun,1 Sun-Hong Kwon,1 Ji-Hye Ha,2 Eui-Kyung Lee1 1School of Pharmacy, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 2Ministry of Food and Drug Safety, Cheongju-si, Chungcheongbuk-do, South Korea Purpose: The benefit–risk balance for drugs can alter post approval owing to additional data on efficacy or adverse events. This study developed a quantitative benefit–risk assessment (BRA model for statins using multicriteria decision analysis with discrete choice experiments and compared a recent BRA with that at the time of approval. Patients and methods: Following a systematic review of the literature, the benefit criteria within the statin BRA model were defined as a reduction in the plasma low-density lipoprotein cholesterol level and a reduction in myocardial infarction incidence; the risk criteria were hepatotoxicity (Liv and fatal rhabdomyolysis (Rha. The scores for these criteria were estimated using mixed treatment comparison methods. Weighting was calculated from a discrete choice experiment involving 203 Korean patients. The scores and weights were integrated to produce an overall value representing the benefit–risk balance, and sensitivity analyses were conducted. Results: In this BRA model, low-density lipoprotein (relative importance [RI]: 37.50% was found to be a more important benefit criterion than myocardial infarction (RI: 35.43%, and Liv (RI: 16.28% was a more important risk criterion than Rha (RI: 10.79%. Patients preferred atorvastatin, and the preference ranking of cerivastatin and simvastatin was switched post approval because of the emergence of additional risk information related to cerivastatin. Conclusion: A quantitative statin BRA model confirmed that the preference ranking of statins changed post approval because of the identification of additional benefits or risks. Keywords: multicriteria decision analysis, statin, quantitative benefit–risk assessment, discrete choice experiment

  4. 77 FR 53059 - Risk-Based Capital Guidelines: Market Risk

    Science.gov (United States)

    2012-08-30

    ... Documentation 6. Capital Requirement for Market Risk Determination of the Multiplication Factor 7. VaR-based... at http://bis.org/press/p100618/annex.pdf . Both the 2005 and 2009 revisions include provisions that...

  5. Development of Science-Based Permitting Guidance for Geological Sequestration of CO2 in Deep Saline Aquifers Based on Modeling and Risk Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Jean-Philippe Nicot; Renaud Bouroullec; Hugo Castellanos; Susan Hovorka; Srivatsan Lakshminarasimhan; Jeffrey Paine

    2006-06-30

    Underground carbon storage may become one of the solutions to address global warming. However, to have an impact, carbon storage must be done at a much larger scale than current CO{sub 2} injection operations for enhanced oil recovery. It must also include injection into saline aquifers. An important characteristic of CO{sub 2} is its strong buoyancy--storage must be guaranteed to be sufficiently permanent to satisfy the very reason that CO{sub 2} is injected. This long-term aspect (hundreds to thousands of years) is not currently captured in legislation, even if the U.S. has a relatively well-developed regulatory framework to handle carbon storage, especially in the operational short term. This report proposes a hierarchical approach to permitting in which the State/Federal Government is responsible for developing regional assessments, ranking potential sites (''General Permit'') and lessening the applicant's burden if the general area of the chosen site has been ranked more favorably. The general permit would involve determining in the regional sense structural (closed structures), stratigraphic (heterogeneity), and petrophysical (flow parameters such as residual saturation) controls on the long-term fate of geologically sequestered CO{sub 2}. The state-sponsored regional studies and the subsequent local study performed by the applicant will address the long-term risk of the particular site. It is felt that a performance-based approach rather than a prescriptive approach is the most appropriate framework in which to address public concerns. However, operational issues for each well (equivalent to the current underground injection control-UIC-program) could follow regulations currently in place. Area ranking will include an understanding of trapping modes. Capillary (due to residual saturation) and structural (due to local geological configuration) trappings are two of the four mechanisms (the other two are solubility and mineral trappings

  6. Backtesting for Risk-Based Regulatory Capital

    NARCIS (Netherlands)

    Kerkhof, F.L.J.; Melenberg, B.

    2002-01-01

    In this paper we present a framework for backtesting all currently popular risk measurement methods (including value-at-risk and expected shortfall) using the functional delta method.Estimation risk can be taken explicitly into account.Based on a simulation study we provide evidence that tests for

  7. ISM Approach to Model Offshore Outsourcing Risks

    Directory of Open Access Journals (Sweden)

    Sunand Kumar

    2014-07-01

    Full Text Available In an effort to achieve a competitive advantage via cost reductions and improved market responsiveness, organizations are increasingly employing offshore outsourcing as a major component of their supply chain strategies. But as evident from literature number of risks such as Political risk, Risk due to cultural differences, Compliance and regulatory risk, Opportunistic risk and Organization structural risk, which adversely affect the performance of offshore outsourcing in a supply chain network. This also leads to dissatisfaction among different stake holders. The main objective of this paper is to identify and understand the mutual interaction among various risks which affect the performance of offshore outsourcing.  To this effect, authors have identified various risks through extant review of literature.  From this information, an integrated model using interpretive structural modelling (ISM for risks affecting offshore outsourcing is developed and the structural relationships between these risks are modeled.  Further, MICMAC analysis is done to analyze the driving power and dependency of risks which shall be helpful to managers to identify and classify important criterions and to reveal the direct and indirect effects of each criterion on offshore outsourcing. Results show that political risk and risk due to cultural differences are act as strong drivers.

  8. Concordance for prognostic models with competing risks

    DEFF Research Database (Denmark)

    Wolbers, Marcel; Blanche, Paul; Koller, Michael T

    2014-01-01

    The concordance probability is a widely used measure to assess discrimination of prognostic models with binary and survival endpoints. We formally define the concordance probability for a prognostic model of the absolute risk of an event of interest in the presence of competing risks and relate i...... of the working model. We further illustrate the methods by computing the concordance probability for a prognostic model of coronary heart disease (CHD) events in the presence of the competing risk of non-CHD death.......The concordance probability is a widely used measure to assess discrimination of prognostic models with binary and survival endpoints. We formally define the concordance probability for a prognostic model of the absolute risk of an event of interest in the presence of competing risks and relate...

  9. Cost-effectiveness of a new urinary biomarker-based risk score compared to standard of care in prostate cancer diagnostics - a decision analytical model.

    Science.gov (United States)

    Dijkstra, Siebren; Govers, Tim M; Hendriks, Rianne J; Schalken, Jack A; Van Criekinge, Wim; Van Neste, Leander; Grutters, Janneke P C; Sedelaar, John P Michiel; van Oort, Inge M

    2017-11-01

    To assess the cost-effectiveness of a new urinary biomarker-based risk score (SelectMDx; MDxHealth, Inc., Irvine, CA, USA) to identify patients for transrectal ultrasonography (TRUS)-guided biopsy and to compare this with the current standard of care (SOC), using only prostate-specific antigen (PSA) to select for TRUS-guided biopsy. A decision tree and Markov model were developed to evaluate the cost-effectiveness of SelectMDx as a reflex test vs SOC in men with a PSA level of >3 ng/mL. Transition probabilities, utilities and costs were derived from the literature and expert opinion. Cost-effectiveness was expressed in quality-adjusted life years (QALYs) and healthcare costs of both diagnostic strategies, simulating the course of patients over a time horizon representing 18 years. Deterministic sensitivity analyses were performed to address uncertainty in assumptions. A diagnostic strategy including SelectMDx with a cut-off chosen at a sensitivity of 95.7% for high-grade prostate cancer resulted in savings of €128 and a gain of 0.025 QALY per patient compared to the SOC strategy. The sensitivity analyses showed that the disutility assigned to active surveillance had a high impact on the QALYs gained and the disutility attributed to TRUS-guided biopsy only slightly influenced the outcome of the model. Based on the currently available evidence, the reduction of over diagnosis and overtreatment due to the use of the SelectMDx test in men with PSA levels of >3 ng/mL may lead to a reduction in total costs per patient and a gain in QALYs. © 2017 The Authors BJU International © 2017 BJU International Published by John Wiley & Sons Ltd.

  10. Adult Human Primary Cardiomyocyte-Based Model for the Simultaneous Prediction of Drug-Induced Inotropic and Pro-arrhythmia Risk

    Directory of Open Access Journals (Sweden)

    Nathalie Nguyen

    2017-12-01

    Full Text Available Cardiac safety remains the leading cause of drug development discontinuation. We developed a human cardiomyocyte-based model that has the potential to provide a predictive preclinical approach for simultaneously predicting drug-induced inotropic and pro-arrhythmia risk.Methods: Adult human primary cardiomyocytes from ethically consented organ donors were used to measure contractility transients. We used measures of changes in contractility parameters as markers to infer both drug-induced inotropic effect (sarcomere shortening and pro-arrhythmia (aftercontraction, AC; contractility escape (CE; time to 90% relaxation (TR90. We addressed the clinical relevance of this approach by evaluating the effects of 23 torsadogenic and 10 non-torsadogenic drugs. Each drug was tested separately at four multiples of the free effective therapeutic plasma concentration (fETPC.Results: Human cardiomyocyte-based model differentiated between torsadogenic and non-torsadogenic drugs. For example, dofetilide, a torsadogenic drug, caused ACs and increased TR90 starting at 10-fold the fETPC, while CE events were observed at the highest multiple of fETPC (100-fold. Verapamil, a non-torsadogenic drug, did not change TR90 and induced no AC or CE up to the highest multiple of fETPCs tested in this study (222-fold. When drug pro-arrhythmic activity was evaluated at 10-fold of the fETPC, AC parameter had excellent assay sensitivity and specificity values of 96 and 100%, respectively. This high predictivity supports the translational safety potential of this preparation and of the selected marker. The data demonstrate that human cardiomyocytes could also identify drugs associated with inotropic effects. hERG channel blockers, like dofetilide, had no effects on sarcomere shortening, while multi-ion channel blockers, like verapamil, inhibited sarcomere shortening.Conclusions: Isolated adult human primary cardiomyocytes can simultaneously predict risks associated with inotropic

  11. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  12. Cytogenetic bases for risk inference

    Energy Technology Data Exchange (ETDEWEB)

    Bender, M A

    1980-01-01

    Various enviromental pollutants are suspected of being capable of causing cancers or genetic defects even at low levels of exposure. In order to estimate risk from exposure to these pollutants, it would be useful to have some indicator of exposure. It is suggested that chromosomes are ideally suited for this purpose. Through the phenonema of chromosome aberrations and sister chromatid exchanges (SCE), chromosomes respond to virtually all carcinogens and mutagens. Aberrations and SCE are discussed in the context of their use as indicators of increased risk to health by chemical pollutants. (ACR)

  13. Flexible competing risks regression modeling and goodness-of-fit

    DEFF Research Database (Denmark)

    Scheike, Thomas; Zhang, Mei-Jie

    2008-01-01

    In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....

  14. A software quality model and metrics for risk assessment

    Science.gov (United States)

    Hyatt, L.; Rosenberg, L.

    1996-01-01

    A software quality model and its associated attributes are defined and used as the model for the basis for a discussion on risk. Specific quality goals and attributes are selected based on their importance to a software development project and their ability to be quantified. Risks that can be determined by the model's metrics are identified. A core set of metrics relating to the software development process and its products is defined. Measurements for each metric and their usability and applicability are discussed.

  15. Why operational risk modelling creates inverse incentives

    NARCIS (Netherlands)

    Doff, R.

    2015-01-01

    Operational risk modelling has become commonplace in large international banks and is gaining popularity in the insurance industry as well. This is partly due to financial regulation (Basel II, Solvency II). This article argues that operational risk modelling is fundamentally flawed, despite efforts

  16. Risk-based SMA for Cubesats

    Science.gov (United States)

    Leitner, Jesse

    2016-01-01

    This presentation conveys an approach for risk-based safety and mission assurance applied to cubesats. This presentation accompanies a NASA Goddard standard in development that provides guidance for building a mission success plan for cubesats based on the risk tolerance and resources available.

  17. Risk-Based Operation and Maintenance Using Bayesian Networks

    DEFF Research Database (Denmark)

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2011-01-01

    This paper describes how risk-based decision making can be used for maintenance planning of components exposed to degradation such as fatigue in offshore wind turbines. In fatigue models, large epistemic uncertainties are usually present. These can be reduced if monitoring results are used...... to update the models, and hereby a better basis for decision making is obtained. An application example shows how a Bayesian network model can be used as a tool for updating the model and assist in risk-based decision making....

  18. Risk-Based Operation and Maintenance Using Bayesian Networks

    DEFF Research Database (Denmark)

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2011-01-01

    This paper describes how risk-based decision making can be used for maintenance planning of components exposed to degradation such as fatigue in offshore wind turbines. In fatigue models, large epistemic uncertainties are usually present. These can be reduced if monitoring results are used to upd...... to update the models, and hereby a better basis for decision making is obtained. An application example shows how a Bayesian network model can be used as a tool for updating the model and assist in risk-based decision making....

  19. Tests of risk premia in linear factor models

    NARCIS (Netherlands)

    Kleibergen, F.R.

    2005-01-01

    We show that inference on risk premia in linear factor models that is based on the Fama-MacBeth and GLS risk premia estimators is misleading when the ß’s are small and/or the number of assets is large. We propose some novel statistics that remain trustworthy in these cases. The inadequacy of

  20. Does nutritional education improve the risk factors for cardiovascular diseases among elderly patients with type 2 diabetes? A randomized controlled trial based on an educational model.

    Science.gov (United States)

    Sharifirad, Gholamreza; Najimi, Arash; Hassanzadeh, Akbar; Azadbakht, Leila

    2013-06-01

    To evaluate the effects of a nutritional education program on cardiovascular risk among elderly patients with type 2 diabetes (T2D). Ninety-seven elderly patients with T2D were enrolled in the present randomized controlled educational trial study. Patients were divided into intervention and control groups. The belief, attitude, subjective norm, enabling factors (BASNEF) educational model was used to design the educational intervention. Patients in the control group received their usual care during the study. Anthropometric data, lipid profiles and blood pressure measurements were collected at baseline and Week 12 in each group. Significant declines were observed in the intervention compared with control group in terms of body weight (-1.3 ± 1.1 vs. 0.11 ± 0.58 kg, respectively), body mass index (-0.48 ± 0.37 vs. 0.05 ± 0.22 kg/m2, respectively), serum triglyceride levels (-18.25 ± 32.15 vs. -3.67 ± 22.61 mg/dL, respectively; P intervention and control groups in terms of high-density lipoprotein-cholesterol (-1.02 ± 4.35 vs. -1.10 ± 6.93 mg/dL, respectively; P = 0.9) or low-density lipoprotein-cholesterol (-4.04 ± 11.64 vs. -1.08 ± 4.35 mg/dL, respectively; P = 0.2). Short-term nutritional education based on the BASNEF educational model improves serum triglyceride levels and anthropometric indices in elderly patients with T2D. © 2012 Wiley Publishing Asia Pty Ltd and Ruijin Hospital, Shanghai Jiaotong University School of Medicine.

  1. Automating risk analysis of software design models.

    Science.gov (United States)

    Frydman, Maxime; Ruiz, Guifré; Heymann, Elisa; César, Eduardo; Miller, Barton P

    2014-01-01

    The growth of the internet and networked systems has exposed software to an increased amount of security threats. One of the responses from software developers to these threats is the introduction of security activities in the software development lifecycle. This paper describes an approach to reduce the need for costly human expertise to perform risk analysis in software, which is common in secure development methodologies, by automating threat modeling. Reducing the dependency on security experts aims at reducing the cost of secure development by allowing non-security-aware developers to apply secure development with little to no additional cost, making secure development more accessible. To automate threat modeling two data structures are introduced, identification trees and mitigation trees, to identify threats in software designs and advise mitigation techniques, while taking into account specification requirements and cost concerns. These are the components of our model for automated threat modeling, AutSEC. We validated AutSEC by implementing it in a tool based on data flow diagrams, from the Microsoft security development methodology, and applying it to VOMS, a grid middleware component, to evaluate our model's performance.

  2. Automating Risk Analysis of Software Design Models

    Directory of Open Access Journals (Sweden)

    Maxime Frydman

    2014-01-01

    Full Text Available The growth of the internet and networked systems has exposed software to an increased amount of security threats. One of the responses from software developers to these threats is the introduction of security activities in the software development lifecycle. This paper describes an approach to reduce the need for costly human expertise to perform risk analysis in software, which is common in secure development methodologies, by automating threat modeling. Reducing the dependency on security experts aims at reducing the cost of secure development by allowing non-security-aware developers to apply secure development with little to no additional cost, making secure development more accessible. To automate threat modeling two data structures are introduced, identification trees and mitigation trees, to identify threats in software designs and advise mitigation techniques, while taking into account specification requirements and cost concerns. These are the components of our model for automated threat modeling, AutSEC. We validated AutSEC by implementing it in a tool based on data flow diagrams, from the Microsoft security development methodology, and applying it to VOMS, a grid middleware component, to evaluate our model's performance.

  3. Latent Model Analysis of Substance Use and HIV Risk Behaviors among High-Risk Minority Adults

    Science.gov (United States)

    Wang, Min Qi; Matthew, Resa F.; Chiu, Yu-Wen; Yan, Fang; Bellamy, Nikki D.

    2007-01-01

    Objectives: This study evaluated substance use and HIV risk profile using a latent model analysis based on ecological theory, inclusive of a risk and protective factor framework, in sexually active minority adults (N=1,056) who participated in a federally funded substance abuse and HIV prevention health initiative from 2002 to 2006. Methods: Data…

  4. Aeromedical Risk Assessment of Pharmaceuticals Using Evidence-Based Medicine.

    Science.gov (United States)

    Prudhomme, Michael B; Ropp, Lincoln G; Sauer, Samual W; LaVan, Joseph T

    2015-09-01

    Using concepts from evidence-based medicine, systems theory, and risk assessment, a standardized model was developed to accept or reject medications for use in flight. The model calculates the risk scores of medications, which can then be compared to an organization's acceptable risk tolerance. Risk scores for each medication were established by summing the products of incidence rates and severity scores for all published side effects. The incidence of each side effect was obtained in an evidence-based manner and each assigned a severity multiplier. Using statistical analysis of the calculated risk scores of approved medications, an acceptance control chart was generated. Range of calculated risk scores of historically approved medications was 10-9140. Six Sigma Acceptance Control Line was calculated at 1.5 SDs above the mean and was 9822. Risk score range of medications generally felt unsafe was 27,010-41,294. Risk score range of medications under consideration for approval was 986-6863. This novel approach to medication approval is the first in aerospace medicine to attempt to combine evidence-based medicine, risk analysis, and control charts to standardize and streamline the medication approval process within an organization. The model was validated by testing against medications generally accepted to be unsafe for use in flight. These medications fell several deviations above the control line. Other medications not yet authorized fall well below the acceptance line and could be considered for approval.

  5. Risk matrix model applied to the outsourcing of logistics' activities

    Directory of Open Access Journals (Sweden)

    Fouad Jawab

    2015-09-01

    Full Text Available Purpose: This paper proposes the application of the risk matrix model in the field of logistics outsourcing. Such an application can serve as the basis for decision making regarding the conduct of a risk management in the logistics outsourcing process and allow its prevention. Design/methodology/approach: This study is based on the risk management of logistics outsourcing in the field of the retail sector in Morocco. The authors identify all possible risks and then classify and prioritize them using the Risk Matrix Model. Finally, we have come to four possible decisions for the identified risks. The analysis was made possible through interviews and discussions with the heads of departments and agents who are directly involved in each outsourced activity. Findings and Originality/value: It is possible to improve the risk matrix model by proposing more personalized prevention measures according to each company that operates in the mass-market retailing. Originality/value: This study is the only one made in the process of logistics outsourcing in the retail sector in Morocco through Label’vie as a case study. First, we had identified as thorough as we could all possible risks, then we applied the Risk Matrix Model to sort them out in an ascending order of importance and criticality. As a result, we could hand out to the decision-makers the mapping for an effective control of risks and a better guiding of the process of risk management.

  6. Cost-effectiveness of a new urinary biomarker-based risk score compared to standard of care in prostate cancer diagnostics - a decision analytical model

    NARCIS (Netherlands)

    Dijkstra, S.; Govers, T.M.; Hendriks, R.J.; Schalken, J.A.; Criekinge, W. van; Neste, L. Van; Grutters, J.P.C.; Sedelaar, J.P.M.; Oort, I.M. van

    2017-01-01

    OBJECTIVE: To assess the cost-effectiveness of a new urinary biomarker-based risk score (SelectMDx; MDxHealth, Inc., Irvine, CA, USA) to identify patients for transrectal ultrasonography (TRUS)-guided biopsy and to compare this with the current standard of care (SOC), using only prostate-specific

  7. Individualized Angiotensin-Converting Enzyme (ACE)-Inhibitor Therapy in Stable Coronary Artery Disease Based on Clinical and Pharmacogenetic Determinants: The PERindopril GENEtic (PERGENE) Risk Model.

    Science.gov (United States)

    Oemrawsingh, Rohit M; Akkerhuis, K Martijn; Van Vark, Laura C; Redekop, W Ken; Rudez, Goran; Remme, Willem J; Bertrand, Michel E; Fox, Kim M; Ferrari, Roberto; Danser, A H Jan; de Maat, Moniek; Simoons, Maarten L; Brugts, Jasper J; Boersma, Eric

    2016-03-28

    Patients with stable coronary artery disease (CAD) constitute a heterogeneous group in which the treatment benefits by angiotensin-converting enzyme (ACE)-inhibitor therapy vary between individuals. Our objective was to integrate clinical and pharmacogenetic determinants in an ultimate combined risk prediction model. Clinical, genetic, and outcomes data were used from 8726 stable CAD patients participating in the EUROPA/PERGENE trial of perindopril versus placebo. Multivariable analysis of phenotype data resulted in a clinical risk score (range, 0-21 points). Three single-nucleotide polymorphisms (rs275651 and rs5182 in the angiotensin-II type I-receptor gene and rs12050217 in the bradykinin type I-receptor gene) were used to construct a pharmacogenetic risk score (PGXscore; range, 0-6 points). Seven hundred eighty-five patients (9.0%) experienced the primary endpoint of cardiovascular mortality, nonfatal myocardial infarction or resuscitated cardiac arrest, during 4.2 years of follow-up. Absolute risk reductions ranged from 1.2% to 7.5% in the 73.5% of patients with PGXscore of 0 to 2. As a consequence, estimated annual numbers needed to treat ranged from as low as 29 (clinical risk score ≥10 and PGXscore of 0) to 521 (clinical risk score ≤6 and PGXscore of 2). Furthermore, our data suggest that long-term perindopril prescription in patients with a PGXscore of 0 to 2 is cost-effective. Both baseline clinical phenotype, as well as genotype determine the efficacy of widely prescribed ACE inhibition in stable CAD. Integration of clinical and pharmacogenetic determinants in a combined risk prediction model demonstrated a very wide range of gradients of absolute treatment benefit. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  8. A Network Model of Credit Risk Contagion

    Directory of Open Access Journals (Sweden)

    Ting-Qiang Chen

    2012-01-01

    Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.

  9. A Hierarchal Risk Assessment Model Using the Evidential Reasoning Rule

    Directory of Open Access Journals (Sweden)

    Xiaoxiao Ji

    2017-02-01

    Full Text Available This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1 formulation of risk assessment hierarchy; (2 representation of both qualitative and quantitative information; (3 elicitation of attribute weights and information reliabilities; (4 aggregation of assessment information using the ER rule and (5 quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

  10. The Application of Asymmetric Liquidity Risk Measure in Modelling the Risk of Investment

    Directory of Open Access Journals (Sweden)

    Garsztka Przemysław

    2015-06-01

    Full Text Available The article analyses the relationship between investment risk (as measured by the variance of returns or standard deviation of returns and liquidity risk. The paper presents a method for calculating a new measure of liquidity risk, based on the characteristic line. In addition, it is checked what is the impact of liquidity risk to the volatility of daily returns. To describe this relationship dynamic econometric models were used. It was found that there was an econometric relationship between the proposed measure liquidity risk and the variance of returns.

  11. Prediction of survival in resected non-small cell lung cancer using a protein expression-based risk model: implications for personalized chemoprevention and therapy.

    Science.gov (United States)

    Gold, Kathryn A; Kim, Edward S; Liu, Diane D; Yuan, Ping; Behrens, Carmen; Solis, Luisa M; Kadara, Humam; Rice, David C; Wistuba, Ignacio I; Swisher, Stephen G; Hofstetter, Wayne L; Lee, J Jack; Hong, Waun K

    2014-04-01

    Patients with resected non-small cell lung cancer (NSCLC) are at risk for recurrence of disease, but we do not have tools to predict which patients are at highest risk. We set out to create a risk model incorporating both clinical data and biomarkers. We assembled a comprehensive database with archival tissues and clinical follow-up from patients with NSCLC resected between 2002 and 2005. Twenty-one proteins identified from our preclinical studies as related to lung carcinogenesis were investigated, including pathways related to metabolism, DNA repair, inflammation, and growth factors. Expression of proteins was quantified using immunohistochemistry. Immunohistochemistry was chosen because it is widely available and can be performed on formalin-fixed paraffin-embedded specimens. Cox models were fitted to estimate effects of clinical factors and biomarkers on recurrence-free survival (RFS) and overall survival (OS). A total of 370 patients are included in our analysis. With median follow-up of 5.3 years, median OS is 6.4 years. A total of 209 cases with recurrence or death were observed. Multicovariate risk models for RFS and OS were developed including relevant biomarkers, age, and stage. Increased expression of phospho-adenosine monophosphate-activated protein kinase (pAMPK), phospho-mTOR (pmTOR), epithelial cell adhesion molecule (EpCAM), and calcium/calmodulin-dependent serine protein kinase were significant (P < 0.05) predictors for favorable RFS; insulin receptor, chemokine (C-X-C motif) receptor 2 (CXCR2), and insulin-like growth factor-1 receptor predicted for unfavorable RFS. Significant (P < 0.05) predictors for favorable OS include pAMPK, pmTOR, and EpCAM; CXCR2 and flap structure-specific endonuclease-1 predicted unfavorable OS. We have developed a comprehensive risk model predictive for recurrence in our large retrospective database, which is one of the largest reported series of resected NSCLC. ©2013 AACR.

  12. Quantifying the risks of unexploded ordnance at closed military bases.

    Science.gov (United States)

    MacDonald, Jacqueline A; Small, Mitchell J; Morgan, M Granger

    2009-01-15

    Some 1,976 sites at closed military bases in the United States are contaminated with unexploded ordnance (UXO) left over from live-fire weapons training. These sites present risks to civilians who might come into contact with the UXO and cause it to explode. This paper presents the first systems analysis model for assessing the explosion risks of UXO at former military training ranges. We develop a stochastic model for estimating the probability of exposure to and explosion of UXO, before and after site cleanup. An application of the model to a 310-acre parcel at Fort Ord, California, shows that substantial risk can remain even after a site is declared clean. We estimate that risk to individual construction workers of encountering UXO that explodes would range from 4 x 10(-4) to 5 x 10(-2), depending on model assumptions, well above typical Occupational Safety and Health Administration (OSHA) and U.S. Environmental Protection Agency (EPA) target risk levels of 10(-4) to 10(-6). In contrast, a qualitative UXO risk assessment method, the Munitions and Explosives of Concern Hazard Assessment (MEC HA), developed by an interagency work group led by the EPA, indicates that the explosion risk at the case study site is low and "compatible with current and determined or reasonably anticipated future risk." We argue that a quantitative approach, like that illustrated in this paper, is necessary to provide a more complete picture of risks and the opportunities for risk reduction.

  13. Model-Based Testing

    NARCIS (Netherlands)

    Timmer, Mark; Brinksma, Hendrik; Stoelinga, Mariëlle Ida Antoinette; Broy, M.; Leuxner, C.; Hoare, C.A.R.

    This paper provides a comprehensive introduction to a framework for formal testing using labelled transition systems, based on an extension and reformulation of the ioco theory introduced by Tretmans. We introduce the underlying models needed to specify the requirements, and formalise the notion of

  14. Competing Risks Copula Models for Unemployment Duration

    DEFF Research Database (Denmark)

    Lo, Simon M. S.; Stephan, Gesine; Wilke, Ralf

    2017-01-01

    The copula graphic estimator (CGE) for competing risks models has received little attention in empirical research, despite having been developed into a comprehensive research method. In this paper, we bridge the gap between theoretical developments and applied research by considering a general...... class of competing risks copula models, which nests popular models such as the Cox proportional hazards model, the semiparametric multivariate mixed proportional hazards model (MMPHM), and the CGE as special cases. Analyzing the effects of a German Hartz reform on unemployment duration, we illustrate...

  15. Population-based absolute risk estimation with survey data.

    Science.gov (United States)

    Kovalchik, Stephanie A; Pfeiffer, Ruth M

    2014-04-01

    Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level.

  16. Investigating the effect of an education plan based on the health belief model on the physical activity of women who are at risk for hypertension

    OpenAIRE

    Hoseini, Habibollah; Maleki, Fatemeh; Moeini, Mahin; Sharifirad, Gholam Reza

    2014-01-01

    Background: Hypertension is the main risk factor of many diseases and the main reason of death all over the world. Because the signs of hypertension are not clear, people do not feel its dangers and do not believe they are at risk. This problem makes preventing hypertension a great challenge for the health system. One factor that is related to lifestyle and is effective in preventing hypertension is increasing exercise. The aim of this study is investigate the effect of an education plan base...

  17. Risk based limits for Operational Safety Requirements

    Energy Technology Data Exchange (ETDEWEB)

    Cappucci, A.J. Jr.

    1993-01-18

    OSR limits are designed to protect the assumptions made in the facility safety analysis in order to preserve the safety envelope during facility operation. Normally, limits are set based on ``worst case conditions`` without regard to the likelihood (frequency) of a credible event occurring. In special cases where the accident analyses are based on ``time at risk`` arguments, it may be desirable to control the time at which the facility is at risk. A methodology has been developed to use OSR limits to control the source terms and the times these source terms would be available, thus controlling the acceptable risk to a nuclear process facility. The methodology defines a new term ``gram-days``. This term represents the area under a source term (inventory) vs time curve which represents the risk to the facility. Using the concept of gram-days (normalized to one year) allows the use of an accounting scheme to control the risk under the inventory vs time curve. The methodology results in at least three OSR limits: (1) control of the maximum inventory or source term, (2) control of the maximum gram-days for the period based on a source term weighted average, and (3) control of the maximum gram-days at the individual source term levels. Basing OSR limits on risk based safety analysis is feasible, and a basis for development of risk based limits is defensible. However, monitoring inventories and the frequencies required to maintain facility operation within the safety envelope may be complex and time consuming.

  18. Prototype Biology-Based Radiation Risk Module Project

    Science.gov (United States)

    Terrier, Douglas; Clayton, Ronald G.; Patel, Zarana; Hu, Shaowen; Huff, Janice

    2015-01-01

    Biological effects of space radiation and risk mitigation are strategic knowledge gaps for the Evolvable Mars Campaign. The current epidemiology-based NASA Space Cancer Risk (NSCR) model contains large uncertainties (HAT #6.5a) due to lack of information on the radiobiology of galactic cosmic rays (GCR) and lack of human data. The use of experimental models that most accurately replicate the response of human tissues is critical for precision in risk projections. Our proposed study will compare DNA damage, histological, and cell kinetic parameters after irradiation in normal 2D human cells versus 3D tissue models, and it will use a multi-scale computational model (CHASTE) to investigate various biological processes that may contribute to carcinogenesis, including radiation-induced cellular signaling pathways. This cross-disciplinary work, with biological validation of an evolvable mathematical computational model, will help reduce uncertainties within NSCR and aid risk mitigation for radiation-induced carcinogenesis.

  19. Bounded Error Approximation Algorithms for Risk-Based Intrusion Response

    Science.gov (United States)

    2015-09-17

    AFRL-AFOSR-VA-TR-2015-0324 Bounded Error Approximation Algorithms for Risk-Based Intrusion Response K Subramani West Virginia University Research...2015. 4. TITLE AND SUBTITLE Bounded Error Approximation Algorithms for Risk-Based Intrusion Response 5a. CONTRACT NUMBER FA9550-12-1-0199. 5b. GRANT...SUPPLEMENTARY NOTES 14. ABSTRACT Our research consisted of modeling the intrusion response problem as one of finding a partial vertex cover in

  20. A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.

    Science.gov (United States)

    Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia

    2016-10-01

    To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. © 2016 John Wiley & Sons Ltd.

  1. Risk Based Optimal Fatigue Testing

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Faber, M.H.; Kroon, I.B.

    1992-01-01

    Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value of the maxi......Optimal fatigue life testing of materials is considered. Based on minimization of the total expected costs of a mechanical component a strategy is suggested to determine the optimal stress range levels for which additional experiments are to be performed together with an optimal value...

  2. Effects of education based on the health belief model on screening behavior in high risk women for breast cancer, Tehran, Iran

    National Research Council Canada - National Science Library

    Hajian, Sepideh; Vakilian, Katayon; Najabadi, Khadijeh Mirzaii; Hosseini, Jalil; Mirzaei, Hamid Reza

    2011-01-01

    ...) is not sufficiently well established. The American Cancer Society aims to give women the opportunity to recognize the utility, limitations and adverse effects of breast cancer screening through education models based on psychological theories...

  3. Modeling foreign exchange risk premium in Armenia

    NARCIS (Netherlands)

    Poghosyan, Tigran; Kocenda, Evnen; Zemcik, Petr

    2008-01-01

    This paper applies stochastic discount factor methodology to modeling the foreign exchange risk premium in Armenia. We use weekly data on foreign and domestic currency deposits, which coexist in the Armenian banking system. This coexistence implies elimination of the cross-country risks and

  4. Appendix 2: Risk-based framework and risk case studies. Risk Assessment for two bird species in northern Wisconsin.

    Science.gov (United States)

    Megan M. Friggens; Stephen N. Matthews

    2012-01-01

    Species distribution models for 147 bird species have been derived using climate, elevation, and distribution of current tree species as potential predictors (Matthews et al. 2011). In this case study, a risk matrix was developed for two bird species (fig. A2-5), with projected change in bird habitat (the x axis) based on models of changing suitable habitat resulting...

  5. Evaluating Water Supply Risk in the Middle and Lower Reaches of Hanjiang River Basin Based on an Integrated Optimal Water Resources Allocation Model

    OpenAIRE

    Xingjun Hong; Shenglian Guo; Le Wang; Guang Yang; Dedi Liu; Haijin Guo; Jun Wang

    2016-01-01

    The rapid socio-economic development and expanding human-induced hydrological alteration have strengthened the interactions between the social and hydrologic systems. To assess regional water supply security under changing water supply and demand condition in strongly human-impacted area, an integrated water resources management model that fully incorporates water demand prediction, optimal water resources allocation and water supply risk analysis is proposed and applied in the mid-lower reac...

  6. Biological Based Risk Assessment for Space Exploration

    Science.gov (United States)

    Cucinotta, Francis A.

    2011-01-01

    Exposures from galactic cosmic rays (GCR) - made up of high-energy protons and high-energy and charge (HZE) nuclei, and solar particle events (SPEs) - comprised largely of low- to medium-energy protons are the primary health concern for astronauts for long-term space missions. Experimental studies have shown that HZE nuclei produce both qualitative and quantitative differences in biological effects compared to terrestrial radiation, making risk assessments for cancer and degenerative risks, such as central nervous system effects and heart disease, highly uncertain. The goal for space radiation protection at NASA is to be able to reduce the uncertainties in risk assessments for Mars exploration to be small enough to ensure acceptable levels of risks are not exceeded and to adequately assess the efficacy of mitigation measures such as shielding or biological countermeasures. We review the recent BEIR VII and UNSCEAR-2006 models of cancer risks and their uncertainties. These models are shown to have an inherent 2-fold uncertainty as defined by ratio of the 95% percent confidence level to the mean projection, even before radiation quality is considered. In order to overcome the uncertainties in these models, new approaches to risk assessment are warranted. We consider new computational biology approaches to modeling cancer risks. A basic program of research that includes stochastic descriptions of the physics and chemistry of radiation tracks and biochemistry of metabolic pathways, to emerging biological understanding of cellular and tissue modifications leading to cancer is described.

  7. WTS - Risk Based Resource Targeting (RBRT) -

    Data.gov (United States)

    Department of Transportation — The Risk Based Resource Targeting (RBRT) application supports a new SMS-structured process designed to focus on safety oversight of systems and processes rather than...

  8. Risk-Based Bi-Level Model for Simultaneous Profit Maximization of a Smart Distribution Company and Electric Vehicle Parking Lot Owner

    Directory of Open Access Journals (Sweden)

    S. Muhammad Bagher Sadati

    2017-10-01

    Full Text Available In this paper, the effect of renewable energy resources (RERs, demand response (DR programs and electric vehicles (EVs is evaluated on the optimal operation of a smart distribution company (SDISCO in the form of a new bi-level model. According to the existence of private electric vehicle parking lots (PLs in the network, the aim of both levels is to maximize the profits of SDISCO and the PL owners. Furthermore, due to the uncertainty of RERs and EVs, the conditional value-at-risk (CVaR method is applied in order to limit the risk of expected profit. The model is transformed into a linear single-level model by the Karush–Kuhn–Tucker (KKT conditions and tested on the IEEE 33-bus distribution system over a 24-h period. The results show that by using a proper charging/discharging schedule, as well as a time of use program, SDISCO gains more profit. Furthermore, by increasing the risk aversion parameter, this profit is reduced.

  9. Developing points-based risk-scoring systems in the presence of competing risks.

    Science.gov (United States)

    Austin, Peter C; Lee, Douglas S; D'Agostino, Ralph B; Fine, Jason P

    2016-09-30

    Predicting the occurrence of an adverse event over time is an important issue in clinical medicine. Clinical prediction models and associated points-based risk-scoring systems are popular statistical methods for summarizing the relationship between a multivariable set of patient risk factors and the risk of the occurrence of an adverse event. Points-based risk-scoring systems are popular amongst physicians as they permit a rapid assessment of patient risk without the use of computers or other electronic devices. The use of such points-based risk-scoring systems facilitates evidence-based clinical decision making. There is a growing interest in cause-specific mortality and in non-fatal outcomes. However, when considering these types of outcomes, one must account for competing risks whose occurrence precludes the occurrence of the event of interest. We describe how points-based risk-scoring systems can be developed in the presence of competing events. We illustrate the application of these methods by developing risk-scoring systems for predicting cardiovascular mortality in patients hospitalized with acute myocardial infarction. Code in the R statistical programming language is provided for the implementation of the described methods. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

  10. Skull base tumor model.

    Science.gov (United States)

    Gragnaniello, Cristian; Nader, Remi; van Doormaal, Tristan; Kamel, Mahmoud; Voormolen, Eduard H J; Lasio, Giovanni; Aboud, Emad; Regli, Luca; Tulleken, Cornelius A F; Al-Mefty, Ossama

    2010-11-01

    Resident duty-hours restrictions have now been instituted in many countries worldwide. Shortened training times and increased public scrutiny of surgical competency have led to a move away from the traditional apprenticeship model of training. The development of educational models for brain anatomy is a fascinating innovation allowing neurosurgeons to train without the need to practice on real patients and it may be a solution to achieve competency within a shortened training period. The authors describe the use of Stratathane resin ST-504 polymer (SRSP), which is inserted at different intracranial locations to closely mimic meningiomas and other pathological entities of the skull base, in a cadaveric model, for use in neurosurgical training. Silicone-injected and pressurized cadaveric heads were used for studying the SRSP model. The SRSP presents unique intrinsic metamorphic characteristics: liquid at first, it expands and foams when injected into the desired area of the brain, forming a solid tumorlike structure. The authors injected SRSP via different passages that did not influence routes used for the surgical approach for resection of the simulated lesion. For example, SRSP injection routes included endonasal transsphenoidal or transoral approaches if lesions were to be removed through standard skull base approach, or, alternatively, SRSP was injected via a cranial approach if the removal was planned to be via the transsphenoidal or transoral route. The model was set in place in 3 countries (US, Italy, and The Netherlands), and a pool of 13 physicians from 4 different institutions (all surgeons and surgeons in training) participated in evaluating it and provided feedback. All 13 evaluating physicians had overall positive impressions of the model. The overall score on 9 components evaluated--including comparison between the tumor model and real tumor cases, perioperative requirements, general impression, and applicability--was 88% (100% being the best possible

  11. Biophysics of risk aversion based on neurotransmitter receptor theory

    CERN Document Server

    Takahashi, Taiki

    2011-01-01

    Decision under risk and uncertainty has been attracting attention in neuroeconomics and neuroendocrinology of decision-making. This paper demonstrated that the neurotransmitter receptor theory-based value (utility) function can account for human and animal risk-taking behavior. The theory predicts that (i) when dopaminergic neuronal response is efficiently coupled to the formation of ligand-receptor complex, subjects are risk-aversive (irrespective of their satisfaction level) and (ii) when the coupling is inefficient, subjects are risk-seeking at low satisfaction levels, consistent with risk-sensitive foraging theory in ecology. It is further suggested that some anomalies in decision under risk are due to inefficiency of the coupling between dopamine receptor activation and neuronal response. Future directions in the application of the model to studies in neuroeconomics of addiction and neuroendocrine modulation of risk-taking behavior are discussed.

  12. Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: a decade follow-up in a Middle East prospective cohort study.

    Science.gov (United States)

    Ramezankhani, Azra; Hadavandi, Esmaeil; Pournik, Omid; Shahrabi, Jamal; Azizi, Fereidoun; Hadaegh, Farzad

    2016-12-01

    The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. Prospective cohort study. Tehran Lipid and Glucose Study (TLGS). A total of 6647 participants (43.4% men) aged >20 years, without T2D at baselines ((1999-2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. T2D was primary outcome which defined if fasting plasma glucose (FPG) was ≥7 mmol/L or if the 2h-PCPG was ≥11.1 mmol/L or if the participant was taking antidiabetic medication. During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78% and 0.75%) and (78% and 0.78%) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-to-height ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG≤4.9 mmol/L and 2h-PCPG≤7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG≤5.2 mmol/L and WHtR≤0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. Our study emphasises the utility of DT for exploring interactions between predictor variables. Published by the BMJ Publishing Group Limited. For permission

  13. Dementia risk prediction in the population: are screening models accurate?

    Science.gov (United States)

    Stephan, Blossom C M; Kurth, Tobias; Matthews, Fiona E; Brayne, Carol; Dufouil, Carole

    2010-06-01

    Early identification of individuals at risk of dementia will become crucial when effective preventative strategies for this condition are developed. Various dementia prediction models have been proposed, including clinic-based criteria for mild cognitive impairment, and more-broadly constructed algorithms, which synthesize information from known dementia risk factors, such as poor cognition and health. Knowledge of the predictive accuracy of such models will be important if they are to be used in daily clinical practice or to screen the entire older population (individuals aged >or=65 years). This article presents an overview of recent progress in the development of dementia prediction models for use in population screening. In total, 25 articles relating to dementia risk screening met our inclusion criteria for review. Our evaluation of the predictive accuracy of each model shows that most are poor at discriminating at-risk individuals from not-at-risk cases. The best models incorporate diverse sources of information across multiple risk factors. Typically, poor accuracy is associated with single-factor models, long follow-up intervals and the outcome measure of all-cause dementia. A parsimonious and cost-effective consensus model needs to be developed that accurately identifies individuals with a high risk of future dementia.

  14. Risk-based maintenance of ethylene oxide production facilities.

    Science.gov (United States)

    Khan, Faisal I; Haddara, Mahmoud R

    2004-05-20

    This paper discusses a methodology for the design of an optimum inspection and maintenance program. The methodology, called risk-based maintenance (RBM) is based on integrating a reliability approach and a risk assessment strategy to obtain an optimum maintenance schedule. First, the likely equipment failure scenarios are formulated. Out of many likely failure scenarios, the ones, which are most probable, are subjected to a detailed study. Detailed consequence analysis is done for the selected scenarios. Subsequently, these failure scenarios are subjected to a fault tree analysis to determine their probabilities. Finally, risk is computed by combining the results of the consequence and the probability analyses. The calculated risk is compared against known acceptable criteria. The frequencies of the maintenance tasks are obtained by minimizing the estimated risk. A case study involving an ethylene oxide production facility is presented. Out of the five most hazardous units considered, the pipeline used for the transportation of the ethylene is found to have the highest risk. Using available failure data and a lognormal reliability distribution function human health risk factors are calculated. Both societal risk factors and individual risk factors exceeded the acceptable risk criteria. To determine an optimal maintenance interval, a reverse fault tree analysis was used. The maintenance interval was determined such that the original high risk is brought down to an acceptable level. A sensitivity analysis is also undertaken to study the impact of changing the distribution of the reliability model as well as the error in the distribution parameters on the maintenance interval.

  15. A model for the optimal risk management of (farm) firms

    DEFF Research Database (Denmark)

    Rasmussen, Svend

    Current methods of risk management focus on efficiency and do not provide operational answers to the basic question of how to optimise and balance the two objectives, maximisation of expected income and minimisation of risk. This paper uses the Capital Asset Pricing Model (CAPM) to derive...... an operational criterion for the optimal risk management of firms. The criterion assumes that the objective of the firm manager is to maximise the market value of the firm and is based on the condition that the application of risk management tools has a symmetric effect on the variability of income around...... the mean. The criterion is based on the expected consequences of risk management on relative changes in the variance of return on equity and expected income. The paper demonstrates how the criterion may be used to evaluate and compare the effect of different risk management tools, and it illustrates how...

  16. Glacial lakes in the Indian Himalayas--from an area-wide glacial lake inventory to on-site and modeling based risk assessment of critical glacial lakes.

    Science.gov (United States)

    Worni, Raphael; Huggel, Christian; Stoffel, Markus

    2013-12-01

    Glacial lake hazards and glacial lake distributions are investigated in many glaciated regions of the world, but comparably little attention has been given to these topics in the Indian Himalayas. In this study we present a first area-wide glacial lake inventory, including a qualitative classification at 251 glacial lakes >0.01 km(2). Lakes were detected in the five states spanning the Indian Himalayas, and lake distribution pattern and lake characteristics were found to differ significantly between regions. Three glacial lakes, from different geographic and climatic regions within the Indian Himalayas were then selected for a detailed risk assessment. Lake outburst probability, potential outburst magnitudes and associated damage were evaluated on the basis of high-resolution satellite imagery, field assessments and through the use of a dynamic model. The glacial lakes analyzed in the states of Jammu and Kashmir and Himachal Pradesh were found to present moderate risks to downstream villages, whereas the lake in Sikkim severely threatens downstream locations. At the study site in Sikkim, a dam breach could trigger drainage of ca. 16×10(6)m(3) water and generate maximum lake discharge of nearly 7000 m(3) s(-). The identification of critical glacial lakes in the Indian Himalayas and the detailed risk assessments at three specific sites allow prioritizing further investigations and help in the definition of risk reduction actions. Copyright © 2012 Elsevier B.V. All rights reserved.

  17. Managing risks in business model innovation processes

    DEFF Research Database (Denmark)

    Taran, Yariv; Boer, Harry; Lindgren, Peter

    2010-01-01

    ) innovation is a risky enterprise, many companies are still choosing not to apply any risk management in the BM innovation process. The objective of this paper is to develop a better understanding of how risks are handled in the practice of BM innovation. An analysis of the BM innovation experiences of two...... industrial companies shows that both companies are experiencing high levels of uncertainty and complexity during their innovation processes and are, consequently, struggling to find new processes for handling the risks involved. Based on the two companies’ experiences, various testable propositions are put...... forward, which link success and failure to the way companies appreciate and handle the risks involved in BM innovation....

  18. Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models.

    Science.gov (United States)

    Guikema, Seth

    2012-07-01

    Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis. © 2011 Society for Risk Analysis.

  19. Proliferation Risk Characterization Model Prototype Model - User and Programmer Guidelines

    Energy Technology Data Exchange (ETDEWEB)

    Dukelow, J.S.; Whitford, D.

    1998-12-01

    A model for the estimation of the risk of diversion of weapons-capable materials was developed. It represents both the threat of diversion and site vulnerability as a product of a small number of variables (two to eight), each of which can take on a small number (two to four) of qualitatively defined (but quantitatively implemented) values. The values of the overall threat and vulnerability variables are then converted to threat and vulnerability categories. The threat and vulnerability categories are used to define the likelihood of diversion, also defined categorically. The evaluator supplies an estimate of the consequences of a diversion, defined categorically, but with the categories based on the IAEA Attractiveness levels. Likelihood and Consequences categories are used to define the Risk, also defined categorically. The threat, vulnerability, and consequences input provided by the evaluator contains a representation of his/her uncertainty in each variable assignment which is propagated all the way through to the calculation of the Risk categories. [Appendix G available on diskette only.

  20. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

    Science.gov (United States)

    Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

    2014-05-21

    Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to

  1. A Corrosion Risk Assessment Model for Underground Piping

    Science.gov (United States)

    Datta, Koushik; Fraser, Douglas R.

    2009-01-01

    The Pressure Systems Manager at NASA Ames Research Center (ARC) has embarked on a project to collect data and develop risk assessment models to support risk-informed decision making regarding future inspections of underground pipes at ARC. This paper shows progress in one area of this project - a corrosion risk assessment model for the underground high-pressure air distribution piping system at ARC. It consists of a Corrosion Model of pipe-segments, a Pipe Wrap Protection Model; and a Pipe Stress Model for a pipe segment. A Monte Carlo simulation of the combined models provides a distribution of the failure probabilities. Sensitivity study results show that the model uncertainty, or lack of knowledge, is the dominant contributor to the calculated unreliability of the underground piping system. As a result, the Pressure Systems Manager may consider investing resources specifically focused on reducing these uncertainties. Future work includes completing the data collection effort for the existing ground based pressure systems and applying the risk models to risk-based inspection strategies of the underground pipes at ARC.

  2. Quantitative risk assessment modeling for nonhomogeneous urban road tunnels.

    Science.gov (United States)

    Meng, Qiang; Qu, Xiaobo; Wang, Xinchang; Yuanita, Vivi; Wong, Siew Chee

    2011-03-01

    Urban road tunnels provide an increasingly cost-effective engineering solution, especially in compact cities like Singapore. For some urban road tunnels, tunnel characteristics such as tunnel configurations, geometries, provisions of tunnel electrical and mechanical systems, traffic volumes, etc. may vary from one section to another. These urban road tunnels that have characterized nonuniform parameters are referred to as nonhomogeneous urban road tunnels. In this study, a novel quantitative risk assessment (QRA) model is proposed for nonhomogeneous urban road tunnels because the existing QRA models for road tunnels are inapplicable to assess the risks in these road tunnels. This model uses a tunnel segmentation principle whereby a nonhomogeneous urban road tunnel is divided into various homogenous sections. Individual risk for road tunnel sections as well as the integrated risk indices for the entire road tunnel is defined. The article then proceeds to develop a new QRA model for each of the homogeneous sections. Compared to the existing QRA models for road tunnels, this section-based model incorporates one additional top event-toxic gases due to traffic congestion-and employs the Poisson regression method to estimate the vehicle accident frequencies of tunnel sections. This article further illustrates an aggregated QRA model for nonhomogeneous urban tunnels by integrating the section-based QRA models. Finally, a case study in Singapore is carried out. © 2010 Society for Risk Analysis.

  3. Dynamic Project Risk Analysis and Management Based on Influence Diagrams

    Science.gov (United States)

    Liu, Xiaohua; Yue, Chaoyuan

    This paper presents a real-time process-oriented project risk analysis and management model which can be combined with the project general management, based on the hierarchical risk influence diagram which is constructed on the basis of network planning. Through network planning, it can solve the problems of the dynamic and overall identification of risk elements, and the showing and analysis of risk transfer along with the working procedure, and the timely and dynamic risk prevention and control as well. The influence diagram can effectively represent the risk combination and transfer in time and logic order. And it is good at the analysis of the sensitivity and control value of risk elements, as well as being convenient for communicating between experts, managers and owner. So the hierarchical risk influence diagram can make the decision of risk management timelier and more accurate. The problems of risk description, diagrams construction and risk evaluation are solved very well in applying the general influence diagram to dynamic project risk analysis. In the end, good result is attained in the example.

  4. Techniques and Simulation Models in Risk Management

    Directory of Open Access Journals (Sweden)

    Mirela GHEORGHE

    2012-12-01

    Full Text Available In the present paper, the scientific approach of the research starts from the theoretical framework of the simulation concept and then continues in the setting of the practical reality, thus providing simulation models for a broad range of inherent risks specific to any organization and simulation of those models, using the informatics instrument @Risk (Palisade. The reason behind this research lies in the need for simulation models that will allow the person in charge with decision taking inside the field of risk management to adopt new corporate strategies which will answer their current needs. The results of the research are represented by two simulation models specific to risk management. The first model follows the net profit simulation as well as simulating the impact that could be generated by a series of inherent risk factors such as losing some important colleagues, a drop in selling prices, a drop in sales volume, retrofitting, and so on. The second simulation model is associated to the IT field, through the analysis of 10 informatics threats, in order to evaluate the potential financial loss.

  5. A review of unmanned aircraft system ground risk models

    Science.gov (United States)

    Washington, Achim; Clothier, Reece A.; Silva, Jose

    2017-11-01

    There is much effort being directed towards the development of safety regulations for unmanned aircraft systems (UAS). National airworthiness authorities have advocated the adoption of a risk-based approach, whereby regulations are driven by the outcomes of a systematic process to assess and manage identified safety risks. Subsequently, models characterising the primary hazards associated with UAS operations have now become critical to the development of regulations and in turn, to the future of the industry. Key to the development of airworthiness regulations for UAS is a comprehensive understanding of the risks UAS operations pose to people and property on the ground. A comprehensive review of the literature identified 33 different models (and component sub models) used to estimate ground risk posed by UAS. These models comprise failure, impact location, recovery, stress, exposure, incident stress and harm sub-models. The underlying assumptions and treatment of uncertainties in each of these sub-models differ significantly between models, which can have a significant impact on the development of regulations. This paper reviews the state-of-the-art in research into UAS ground risk modelling, discusses how the various sub-models relate to the different components of the regulation, and explores how model-uncertainties potentially impact the development of regulations for UAS.

  6. Model Based Definition

    Science.gov (United States)

    Rowe, Sidney E.

    2010-01-01

    In September 2007, the Engineering Directorate at the Marshall Space Flight Center (MSFC) created the Design System Focus Team (DSFT). MSFC was responsible for the in-house design and development of the Ares 1 Upper Stage and the Engineering Directorate was preparing to deploy a new electronic Configuration Management and Data Management System with the Design Data Management System (DDMS) based upon a Commercial Off The Shelf (COTS) Product Data Management (PDM) System. The DSFT was to establish standardized CAD practices and a new data life cycle for design data. Of special interest here, the design teams were to implement Model Based Definition (MBD) in support of the Upper Stage manufacturing contract. It is noted that this MBD does use partially dimensioned drawings for auxiliary information to the model. The design data lifecycle implemented several new release states to be used prior to formal release that allowed the models to move through a flow of progressive maturity. The DSFT identified some 17 Lessons Learned as outcomes of the standards development, pathfinder deployments and initial application to the Upper Stage design completion. Some of the high value examples are reviewed.

  7. Risk Classification and Risk-based Safety and Mission Assurance

    Science.gov (United States)

    Leitner, Jesse A.

    2014-01-01

    Recent activities to revamp and emphasize the need to streamline processes and activities for Class D missions across the agency have led to various interpretations of Class D, including the lumping of a variety of low-cost projects into Class D. Sometimes terms such as Class D minus are used. In this presentation, mission risk classifications will be traced to official requirements and definitions as a measure to ensure that projects and programs align with the guidance and requirements that are commensurate for their defined risk posture. As part of this, the full suite of risk classifications, formal and informal will be defined, followed by an introduction to the new GPR 8705.4 that is currently under review.GPR 8705.4 lays out guidance for the mission success activities performed at the Classes A-D for NPR 7120.5 projects as well as for projects not under NPR 7120.5. Furthermore, the trends in stepping from Class A into higher risk posture classifications will be discussed. The talk will conclude with a discussion about risk-based safety and mission assuranceat GSFC.

  8. Model-Based Security Testing

    Directory of Open Access Journals (Sweden)

    Ina Schieferdecker

    2012-02-01

    Full Text Available Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.

  9. Mode-of-Action Uncertainty for Dual-Mode Carcinogens: A Bounding Approach for Naphthalene-Induced Nasal Tumors in Rats Based on PBPK and 2-Stage Stochastic Cancer Risk Models

    Energy Technology Data Exchange (ETDEWEB)

    Bogen, K T

    2007-05-11

    A relatively simple, quantitative approach is proposed to address a specific, important gap in the appr approach recommended by the USEPA Guidelines for Cancer Risk Assessment to oach address uncertainty in carcinogenic mode of action of certain chemicals when risk is extrapolated from bioassay data. These Guidelines recognize that some chemical carcinogens may have a site-specific mode of action (MOA) that is dual, involving mutation in addition to cell-killing induced hyperplasia. Although genotoxicity may contribute to increased risk at all doses, the Guidelines imply that for dual MOA (DMOA) carcinogens, judgment be used to compare and assess results obtained using separate 'linear' (genotoxic) vs. 'nonlinear' (nongenotoxic) approaches to low low-level risk extrapolation. However, the Guidelines allow the latter approach to be used only when evidence is sufficient t to parameterize a biologically based model that reliably o extrapolates risk to low levels of concern. The Guidelines thus effectively prevent MOA uncertainty from being characterized and addressed when data are insufficient to parameterize such a model, but otherwise clearly support a DMOA. A bounding factor approach - similar to that used in reference dose procedures for classic toxicity endpoints - can address MOA uncertainty in a way that avoids explicit modeling of low low-dose risk as a function of administere administered or internal dose. Even when a 'nonlinear' toxicokinetic model cannot be fully validated, implications of DMOA uncertainty on low low-dose risk may be bounded with reasonable confidence when target tumor types happen to be extremely rare. This concept was i illustrated llustrated for a likely DMOA rodent carcinogen naphthalene, specifically to the issue of risk extrapolation from bioassay data on naphthalene naphthalene-induced nasal tumors in rats. Bioassay data, supplemental toxicokinetic data, and related physiologically based p

  10. Risk Management Technologies With Logic and Probabilistic Models

    CERN Document Server

    Solozhentsev, E D

    2012-01-01

    This book presents intellectual, innovative, information technologies (I3-technologies) based on logical and probabilistic (LP) risk models. The technologies presented here consider such models for structurally complex systems and processes with logical links and with random events in economics and technology.  The volume describes the following components of risk management technologies: LP-calculus; classes of LP-models of risk and efficiency; procedures for different classes; special software for different classes; examples of applications; methods for the estimation of probabilities of events based on expert information. Also described are a variety of training courses in these topics. The classes of risk models treated here are: LP-modeling, LP-classification, LP-efficiency, and LP-forecasting. Particular attention is paid to LP-models of risk of failure to resolve difficult economic and technical problems. Amongst the  discussed  procedures of I3-technologies  are the construction of  LP-models,...

  11. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  12. Models to Assess the Bankruptcy Risk

    Directory of Open Access Journals (Sweden)

    Simona Valeria TOMA

    2013-08-01

    Full Text Available Closely related to financial risk assessment, one of the main concerns of the organizations should be the evaluation of bankruptcy risk, in this period of slow economic growth. Organization bankruptcies have increased in recent years worldwide. The aim of this paper is to demonstrate that the methods and models for forecasting bankruptcy of organizations, for the bankruptcy risk assessment are seeing for the health financing of an entity in financial accounting diagnosis and that the organizations requires assessment of risks accompanying the work, in which some signals fragility (vulnerable health this and other projected bankruptcy (insolvability threatens its survival (continuity. The bankruptcy risk assessment is important for profit-seeking investors because they must know how to value a company in or near bankruptcy is an important skill, but to detect any signs of looming bankruptcy is necessary to calculate and to analyse all kinds of financial rations: working capital, profitability, debt levels and liquidity.

  13. Measuring Risk Structure Using the Capital Asset Pricing Model

    Directory of Open Access Journals (Sweden)

    Zdeněk Konečný

    2015-01-01

    Full Text Available This article is aimed at proposing of an inovative method for calculating the shares of operational and financial risks. This methodological tool will support managers while monitoring the risk structure. The method is based on the capital asset pricing model (CAPM for calculation of equity cost, namely on determination of the beta coefficient, which is the only variable, that is dependent on entrepreneurial risk. There are combined both alternative approaches for calculation betas, which means, that there are accounting data used and there is distinguished unlevered beta and levered beta. The novelty of the proposed method is based on including of quantities for measuring operational and financial risks in beta calculation. The volatility of cash flow, as a quantity for measuring of operational risk, is included in the unlevered beta. Return on equity based on the cash flow and the indebtedness are variables used in calculation of the levered beta. This modification makes it possible to calculate the share of operational risk as the proportion of the unlevered/levered beta and the share of financial risk, which is the remainder of levered beta. The modified method is applied on companies from two sectors of the Czech economy. In the data set there are companies from one cyclical sector and from one neutral sector to find out potential differences in the risk structure. The findings show, that in both sectors the share of operational risk is over 50%, however, in the neutral sector is this more dominant.

  14. Analysis of intervention strategies for inhalation exposure to polycyclic aromatic hydrocarbons and associated lung cancer risk based on a Monte Carlo population exposure assessment model.

    Directory of Open Access Journals (Sweden)

    Bin Zhou

    Full Text Available It is difficult to evaluate and compare interventions for reducing exposure to air pollutants, including polycyclic aromatic hydrocarbons (PAHs, a widely found air pollutant in both indoor and outdoor air. This study presents the first application of the Monte Carlo population exposure assessment model to quantify the effects of different intervention strategies on inhalation exposure to PAHs and the associated lung cancer risk. The method was applied to the population in Beijing, China, in the year 2006. Several intervention strategies were designed and studied, including atmospheric cleaning, smoking prohibition indoors, use of clean fuel for cooking, enhancing ventilation while cooking and use of indoor cleaners. Their performances were quantified by population attributable fraction (PAF and potential impact fraction (PIF of lung cancer risk, and the changes in indoor PAH concentrations and annual inhalation doses were also calculated and compared. The results showed that atmospheric cleaning and use of indoor cleaners were the two most effective interventions. The sensitivity analysis showed that several input parameters had major influence on the modeled PAH inhalation exposure and the rankings of different interventions. The ranking was reasonably robust for the remaining majority of parameters. The method itself can be extended to other pollutants and in different places. It enables the quantitative comparison of different intervention strategies and would benefit intervention design and relevant policy making.

  15. A study of the effects of company size on systematic risk based on the capital asset pricing model among accepted companies in Tehran Stock Market ,

    Directory of Open Access Journals (Sweden)

    Reza Rostami

    2012-08-01

    Full Text Available Systematic risk (beta is one of the most effective factors in predicting the appropriate required rate of return of portfolios. Understanding systematic risk of usual portfolio of various companies helps investors consider financial investment, more confidentially. The aim of this study is to determine if there is any significant relationship between Company Size (Market value of stocks, Book value of stocks, level of company sale, trade volume of stocks, Price dividend ratio as independent variables and Systematic risk (Beta as dependent variables. The study chooses 112 companies accepted in Tehran Stock Market based on screening (systematic deletion in a six-year- period from 2005 to 2010. The required data were gathered from basic financial statement, committee reports, and other available documents in Tehran Stock Market. Regression and Pearson correlation were used to analyze the data. The results of the study revealed that there is a significant relationship between the variables. Some suggestions regarding the topic of the research are given too.

  16. Evaluation of Foreign Exchange Risk Capital Requirement Models

    Directory of Open Access Journals (Sweden)

    Ricardo S. Maia Clemente

    2005-12-01

    Full Text Available This paper examines capital requirement for financial institutions in order to cover market risk stemming from exposure to foreign currencies. The models examined belong to two groups according to the approach involved: standardized and internal models. In the first group, we study the Basel model and the model adopted by the Brazilian legislation. In the second group, we consider the models based on the concept of value at risk (VaR. We analyze the single and the double-window historical model, the exponential smoothing model (EWMA and a hybrid approach that combines features of both models. The results suggest that the Basel model is inadequate to the Brazilian market, exhibiting a large number of exceptions. The model of the Brazilian legislation has no exceptions, though generating higher capital requirements than other internal models based on VaR. In general, VaR-based models perform better and result in less capital allocation than the standardized approach model applied in Brazil.

  17. Sustainability and Risk: Towards a Risk-Based Sustainability Rating for Real Estate Investments

    OpenAIRE

    Erika Meins; Daniel Sager

    2013-01-01

    Purpose - To identify the relative contribution of selected sustainability features to property value risk with the aim of generating a risk-based weighting system for a property sustainability rating.Approach - For a given set of sustainability features, a discounted cash flow (DCF) model is used to derive the weights. The anticipated demand for each sustainability feature is described by three future states of nature. Subjective probability distributions describe the occurrence of the futur...

  18. Development of a new risk model for predicting cardiovascular events among hemodialysis patients: Population-based hemodialysis patients from the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS).

    Science.gov (United States)

    Matsubara, Yukiko; Kimachi, Miho; Fukuma, Shingo; Onishi, Yoshihiro; Fukuhara, Shunichi

    2017-01-01

    Cardiovascular (CV) events are the primary cause of death and becoming bedridden among hemodialysis (HD) patients. The Framingham risk score (FRS) is useful for predicting incidence of CV events in the general population, but is considerd to be unsuitable for the prediction of the incidence of CV events in HD patients, given their characteristics due to atypical relationships between conventional risk factors and outcomes. We therefore aimed to develop a new prognostic prediction model for prevention and early detection of CV events among hemodialysis patients. We enrolled 3,601 maintenance HD patients based on their data from the Japan Dialysis Outcomes and Practice Patterns Study (J-DOPPS), phases 3 and 4. We longitudinaly assessed the association between several potential candidate predictors and composite CV events in the year after study initiation. Potential candidate predictors included the component factors of FRS and other HD-specific risk factors. We used multivariable logistic regression with backward stepwise selection to develop our new prediction model and generated a calibration plot. Additinially, we performed bootstrapping to assess the internal validity. We observed 328 composite CV events during 1-year follow-up. The final prediction model contained six variables: age, diabetes status, history of CV events, dialysis time per session, and serum phosphorus and albumin levels. The new model showed significantly better discrimination than the FRS, in both men (c-statistics: 0.76 for new model, 0.64 for FRS) and women (c-statistics: 0.77 for new model, 0.60 for FRS). Additionally, we confirmed the consistency between the observed results and predicted results using the calibration plot. Further, we found similar discrimination and calibration to the derivation model in the bootstrapping cohort. We developed a new risk model consisting of only six predictors. Our new model predicted CV events more accurately than the FRS.

  19. Predictors of reducing sexual and reproductive risk behaviors based on the information-motivation-behavioral skills (IMB) model among unmarried rural-to-urban female migrants in Shanghai, China.

    Science.gov (United States)

    Cai, Yong; Wang, Ying; Zheng, Zhijie; Wang, Jin; Yao, Wen; Ma, Jin

    2013-01-01

    Due to the increase of premarital sex and the lack of reproductive health services, unmarried rural-to-urban female migrants experience more risks of sex and reproductive health (SRH). This study was designed to describe SRH related knowledge, attitude and risk behaviors among unmarried rural-to-urban female migrants and examine the predictors of reducing sexual and reproductive risk behaviors based on information-motivation-behavioral skills (IMB) model and to describe the relationships between the constructs. We conducted a cross-sectional study to assess SRH related information, motivation, behavioral skills and preventive behaviors among unmarried rural-to-urban female migrants in Shanghai, one of the largest importers of migrant laborers in China. Structural equation modeling (SEM) was used to assess the IMB model. A total of 944 subjects completed their questionnaires. The mean age was 21.2 years old (SD = 2.3; range 16 to 28). Over one-fourth of participants reported having had premarital sex (N = 261, 27.6%) and among whom 15.3% reported having had the experience of unintended pregnancy, 14.6% with the experience of abortion. The final IMB model provided acceptable fit to the data (CFI = 0.99, RMSEA = 0.034). Reducing sexual and reproductive risk behaviors was significantly predicted by SRH related information (β = 0.681, Psexual and reproductive risk behaviors mediated through behavioral skills. The results highlight the importance and necessity of conducting reproductive health promotion among unmarried rural-to-urban female migrants in China. The IMB model could be used to predict reducing sexual and reproductive risk behaviors and it suggests future interventions should focus on improving SRH related information and behavioral skills.

  20. A semi-quantitative model for risk appreciation and risk weighing

    DEFF Research Database (Denmark)

    Bos, Peter M.J.; Boon, Polly E.; van der Voet, Hilko

    2009-01-01

    Risk managers need detailed information on (1) the type of effect, (2) the size (severity) of the expected effect(s) and (3) the fraction of the population at risk to decide on well-balanced risk reduction measures. A previously developed integrated probabilistic risk assessment (IPRA) model...... provides quantitative information on these three parameters. A semi-quantitative tool is presented that combines information on these parameters into easy-readable charts that will facilitate risk evaluations of exposure situations and decisions on risk reduction measures. This tool is based on a concept...... of health impact categorization that has been successfully in force for several years within several emergency planning programs. Four health impact categories are distinguished: No-Health Impact, Low-Health Impact, Moderate-Health Impact and Severe-Health Impact. Two different charts are presented...

  1. Assessment of credit risk based on fuzzy relations

    Science.gov (United States)

    Tsabadze, Teimuraz

    2017-06-01

    The purpose of this paper is to develop a new approach for an assessment of the credit risk to corporate borrowers. There are different models for borrowers' risk assessment. These models are divided into two groups: statistical and theoretical. When assessing the credit risk for corporate borrowers, statistical model is unacceptable due to the lack of sufficiently large history of defaults. At the same time, we cannot use some theoretical models due to the lack of stock exchange. In those cases, when studying a particular borrower given that statistical base does not exist, the decision-making process is always of expert nature. The paper describes a new approach that may be used in group decision-making. An example of the application of the proposed approach is given.

  2. Incentivising flood risk adaptation through ris based insurance premiums: trade-offs between affordability and risk reduction

    NARCIS (Netherlands)

    Hudson, P.G.M.B.; Botzen, W.J.W.; Feyen, L.; Aerts, J.C.J.H.

    2016-01-01

    The financial incentives offered by the risk-based pricing of insurance can stimulate policyholder adaptation to flood risk while potentially conflicting with affordability. We examine the trade-off between risk reduction and affordability in a model of public-private flood insurance in France and

  3. Modeling the operational risk in Iranian commercial banks: case study of a private bank

    Science.gov (United States)

    Momen, Omid; Kimiagari, Alimohammad; Noorbakhsh, Eaman

    2012-08-01

    The Basel Committee on Banking Supervision from the Bank for International Settlement classifies banking risks into three main categories including credit risk, market risk, and operational risk. The focus of this study is on the operational risk measurement in Iranian banks. Therefore, issues arising when trying to implement operational risk models in Iran are discussed, and then, some solutions are recommended. Moreover, all steps of operational risk measurement based on Loss Distribution Approach with Iran's specific modifications are presented. We employed the approach of this study to model the operational risk of an Iranian private bank. The results are quite reasonable, comparing the scale of bank and other risk categories.

  4. Behavioural analytics: Beyond risk-based MFA

    CSIR Research Space (South Africa)

    Dlamini, Thandokuhle M

    2017-09-01

    Full Text Available risk-based multi-factor authentication system. It adds a behavioural analytics component that uses keystroke dynamics to grant or deny users access. Given the increasing number of compromised user credential stores, we make the assumption that criminals...

  5. World Equity Premium based Risk Aversion Estimates

    NARCIS (Netherlands)

    L.C.G. Pozzi (Lorenzo)

    2010-01-01

    textabstractThe equity premium puzzle holds that the coefficient of relative risk aversion estimated from the consumption based CAPM under power utility is excessively high. Moreover, estimates in the literature vary considerably across countries. We gauge the uncertainty pertaining to the country

  6. Calibration plots for risk prediction models in the presence of competing risks.

    Science.gov (United States)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-08-15

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.

  7. Risk modelling and management: An overview

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); D.E. Allen (David); M.J. McAleer (Michael); T. Pérez-Amaral (Teodosio)

    2013-01-01

    textabstractThe papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on "Risk Modelling and Management" (RMM2011). The papers cover the following topics: currency

  8. Risk Modelling and Management: An Overview

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); D.E. Allen (David); M.J. McAleer (Michael); T. Pérez-Amaral (Teodosio)

    2013-01-01

    textabstractThe papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on “Risk Modelling and Management” (RMM2011). The papers cover the following topics: currency

  9. Malignancy risk models for oral lesions.

    Science.gov (United States)

    Zarate, Ana-María; Brezzo, María-Magdalena; Secchi, Dante-Gustavo; Barra, José-Luis; Brunotto, Mabel

    2013-09-01

    The aim of this work was to assess risk habits, clinical and cellular phenotypes and TP53 DNA changes in oral mucosa samples from patients with Oral Potentially Malignant Disorders (OPMD), in order to create models that enable genotypic and phenotypic patterns to be obtained that determine the risk of lesions becoming malignant. Clinical phenotypes, family history of cancer and risk habits were collected in clinical histories. TP53 gene mutation and morphometric-morphological features were studied, and multivariate models were applied. Three groups were estabished: a) oral cancer (OC) group (n=10), b) oral potentially malignant disorders group (n=10), and c) control group (n=8). An average of 50% of patients with malignancy were found to have smoking and drinking habits. A high percentage of TP53 mutations were observed in OC (30%) and OPMD (average 20%) lesions (p=0.000). The majority of these mutations were GC TA transversion mutations (60%). However, patients with OC presented mutations in all the exons and introns studied. Highest diagnostic accuracy (p=0.0001) was observed when incorporating alcohol and tobacco habits variables with TP3 mutations. Our results prove to be statistically reliable, with parameter estimates that are nearly unbiased even for small sample sizes. Models 2 and 3 were the most accurate for assessing the risk of an OPMD becoming cancerous. However, in a public health context, model 3 is the most recommended because the characteristics considered are easier and less costly to evaluate.

  10. Distributionally Robust Return-Risk Optimization Models and Their Applications

    Directory of Open Access Journals (Sweden)

    Li Yang

    2014-01-01

    Full Text Available Based on the risk control of conditional value-at-risk, distributionally robust return-risk optimization models with box constraints of random vector are proposed. They describe uncertainty in both the distribution form and moments (mean and covariance matrix of random vector. It is difficult to solve them directly. Using the conic duality theory and the minimax theorem, the models are reformulated as semidefinite programming problems, which can be solved by interior point algorithms in polynomial time. An important theoretical basis is therefore provided for applications of the models. Moreover, an application of the models to a practical example of portfolio selection is considered, and the example is evaluated using a historical data set of four stocks. Numerical results show that proposed methods are robust and the investment strategy is safe.

  11. Issues in Value-at-Risk Modeling and Evaluation

    NARCIS (Netherlands)

    J. Daníelsson (Jón); C.G. de Vries (Casper); B.N. Jorgensen (Bjørn); P.F. Christoffersen (Peter); F.X. Diebold (Francis); T. Schuermann (Til); J.A. Lopez (Jose); B. Hirtle (Beverly)

    1998-01-01

    textabstractDiscusses the issues in value-at-risk modeling and evaluation. Value of value at risk; Horizon problems and extreme events in financial risk management; Methods of evaluating value-at-risk estimates.

  12. Study on Risk of Enterprise' Technology Innovation Based on ISM

    Science.gov (United States)

    Li, Hongyan

    The risk in the process of enterprise' technology innovation is concluted five subsystems: environmental risk, market risk, enterprise capacity risk, project risk and project management risk, 16 risk factors under each subsystem are identified. A Interpretative Structural Modeling(ISM) of of risk factors is established, the relationship and influence levels of them is confirmed, the purpose is to help enterprise assessing risks and taking countermeasure to minimize the potential loss and increase the innovation income.

  13. Launch Vehicle Debris Models and Crew Vehicle Ascent Abort Risk

    Science.gov (United States)

    Gee, Ken; Lawrence, Scott

    2013-01-01

    For manned space launch systems, a reliable abort system is required to reduce the risks associated with a launch vehicle failure during ascent. Understanding the risks associated with failure environments can be achieved through the use of physics-based models of these environments. Debris fields due to destruction of the launch vehicle is one such environment. To better analyze the risk posed by debris, a physics-based model for generating launch vehicle debris catalogs has been developed. The model predicts the mass distribution of the debris field based on formulae developed from analysis of explosions. Imparted velocity distributions are computed using a shock-physics code to model the explosions within the launch vehicle. A comparison of the debris catalog with an existing catalog for the Shuttle external tank show good comparison in the debris characteristics and the predicted debris strike probability. The model is used to analyze the effects of number of debris pieces and velocity distributions on the strike probability and risk.

  14. Validation of Three Scoring Risk Stratification Models for Thyroid Nodules.

    Science.gov (United States)

    Ha, Su Min; Ahn, Hye Shin; Baek, Jung Hwan; Ahn, Hwa Young; Chung, Yun Jae; Cho, Bo Youn; Park, Sung Bin

    2017-11-06

    To minimize potential harm from overuse of fine-needle aspiration, Thyroid Imaging Reporting and Data Systems (TIRADSs) were developed for thyroid nodule risk stratification. The purpose of this study was to perform validation of three scoring risk stratification models for thyroid nodules using ultrasonography features, a web-based malignancy risk stratification system at website (http://www.gap.pe.kr/thyroidnodule.php) and those developed by the Korean Society of Thyroid Radiology (KSThR) and the American College of Radiology (ACR). Using ultrasonography images, radiologists assessed thyroid nodules according to the following criteria: internal content, echogenicity of the solid portion, shape, margin, and calcifications. 954 patients (mean age, 50.8 years; range, 13-86 years) with 1112 nodules were evaluated in our institute from January 2013 to December 2014. The discrimination ability of the three models was assessed by estimating the area under the receiver operating characteristic (ROC) curve. Additionally, Hosmer-Lemeshow goodness-of-fit statistics (calibration ability) were used to evaluate the agreement between the observed and expected number of nodules that were benign or malignant. Thyroid malignancy was present in 37.2% of nodules (414/1112). According to the 14-point web-based scoring risk stratification system, malignancy risk ranged from 4.5% to 100.0% and was positively associated with an increase in risk scores. The areas under the ROC curve of the validation set were 0.884 in the web-based, 0.891 in the KSThR, and 0.875 in the ACR scoring risk stratification models. The Hosmer-Lemeshow goodness-of-fit test indicated that the web-based scoring system showed the best-calibrated result with a p value of 0.078. The three scoring risk stratification models using the ultrasonography features of thyroid nodules to stratify malignancy risk showed acceptable predictive accuracy and similar areas under the curve. The web-based scoring system demonstrated

  15. Mathematical modelling of risk reduction in reinsurance

    Science.gov (United States)

    Balashov, R. B.; Kryanev, A. V.; Sliva, D. E.

    2017-01-01

    The paper presents a mathematical model of efficient portfolio formation in the reinsurance markets. The presented approach provides the optimal ratio between the expected value of return and the risk of yield values below a certain level. The uncertainty in the return values is conditioned by use of expert evaluations and preliminary calculations, which result in expected return values and the corresponding risk levels. The proposed method allows for implementation of computationally simple schemes and algorithms for numerical calculation of the numerical structure of the efficient portfolios of reinsurance contracts of a given insurance company.

  16. Risk Assessment in Fractured Clayey Tills - Which Modeling Tools?

    DEFF Research Database (Denmark)

    Chambon, Julie Claire Claudia; Bjerg, Poul Løgstrup; Binning, Philip John

    2012-01-01

    The article presents different tools available for risk assessment in fractured clayey tills and their advantages and limitations are discussed. Because of the complex processes occurring during contaminant transport through fractured media, the development of simple practical tools for risk...... assessment is challenging and the inclusion of the relevant processes is difficult. Furthermore the lack of long-term monitoring data prevents from verifying the accuracy of the different conceptual models. Further investigations based on long-term data and numerical modeling are needed to accurately...

  17. A quality risk management model approach for cell therapy manufacturing.

    Science.gov (United States)

    Lopez, Fabio; Di Bartolo, Chiara; Piazza, Tommaso; Passannanti, Antonino; Gerlach, Jörg C; Gridelli, Bruno; Triolo, Fabio

    2010-12-01

    International regulatory authorities view risk management as an essential production need for the development of innovative, somatic cell-based therapies in regenerative medicine. The available risk management guidelines, however, provide little guidance on specific risk analysis approaches and procedures applicable in clinical cell therapy manufacturing. This raises a number of problems. Cell manufacturing is a poorly automated process, prone to operator-introduced variations, and affected by heterogeneity of the processed organs/tissues and lot-dependent variability of reagent (e.g., collagenase) efficiency. In this study, the principal challenges faced in a cell-based product manufacturing context (i.e., high dependence on human intervention and absence of reference standards for acceptable risk levels) are identified and addressed, and a risk management model approach applicable to manufacturing of cells for clinical use is described for the first time. The use of the heuristic and pseudo-quantitative failure mode and effect analysis/failure mode and critical effect analysis risk analysis technique associated with direct estimation of severity, occurrence, and detection is, in this specific context, as effective as, but more efficient than, the analytic hierarchy process. Moreover, a severity/occurrence matrix and Pareto analysis can be successfully adopted to identify priority failure modes on which to act to mitigate risks. The application of this approach to clinical cell therapy manufacturing in regenerative medicine is also discussed. © 2010 Society for Risk Analysis.

  18. The effectiveness of flood risk communication strategies and the influence of social networks-Insights from an agent-based model

    NARCIS (Netherlands)

    Haer, Toon; Botzen, W.J.W.; Aerts, Jeroen C.J.H.

    2016-01-01

    Flood risk management is becoming increasingly important, because more people are settling in flood-prone areas, and flood risk is increasing in many regions due to extreme weather events associated with climate change. It has been proposed that appropriately designed flood risk communication

  19. Value at Risk models for Energy Risk Management

    OpenAIRE

    Novák, Martin

    2010-01-01

    The main focus of this thesis lies on description of Risk Management in context of Energy Trading. The paper will predominantly discuss Value at Risk and its modifications as a main overall indicator of Energy Risk.

  20. Crop insurance: Risks and models of insurance

    Directory of Open Access Journals (Sweden)

    Čolović Vladimir

    2014-01-01

    Full Text Available The issue of crop protection is very important because of a variety of risks that could cause difficult consequences. One type of risk protection is insurance. The author in the paper states various models of insurance in some EU countries and the systems of subsidizing of insurance premiums by state. The author also gives a picture of crop insurance in the U.S., noting that in this country pays great attention to this matter. As for crop insurance in Serbia, it is not at a high level. The main problem with crop insurance is not only the risks but also the way of protection through insurance. The basic question that arises not only in the EU is the question is who will insure and protect crops. There are three possibilities: insurance companies under state control, insurance companies that are public-private partnerships or private insurance companies on a purely commercial basis.

  1. Modeling perceptions of climatic risk in crop production.

    Science.gov (United States)

    Reinmuth, Evelyn; Parker, Phillip; Aurbacher, Joachim; Högy, Petra; Dabbert, Stephan

    2017-01-01

    In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of "still-good yield" (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in

  2. A critical appraisal of logistic regression-based nomograms, artificial neural networks, classification and regression-tree models, look-up tables and risk-group stratification models for prostate cancer.

    Science.gov (United States)

    Chun, Felix K-H; Karakiewicz, Pierre I; Briganti, Alberto; Walz, Jochen; Kattan, Michael W; Huland, Hartwig; Graefen, Markus

    2007-04-01

    To evaluate several methods of predicting prostate cancer-related outcomes, i.e. nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group stratification (RGS) models, all of which represent valid alternatives. We present four direct comparisons, where a nomogram was compared to either an ANN, a look-up table, a CART model or a RGS model. In all comparisons we assessed the predictive accuracy and performance characteristics of both models. Nomograms have several advantages over ANN, look-up tables, CART and RGS models, the most fundamental being a higher predictive accuracy and better performance characteristics. These results suggest that nomograms are more accurate and have better performance characteristics than their alternatives. However, ANN, look-up tables, CART analyses and RGS models all rely on methodologically sound and valid alternatives, which should not be abandoned.

  3. Risk Classification with an Adaptive Naive Bayes Kernel Machine Model.

    Science.gov (United States)

    Minnier, Jessica; Yuan, Ming; Liu, Jun S; Cai, Tianxi

    2015-04-22

    Genetic studies of complex traits have uncovered only a small number of risk markers explaining a small fraction of heritability and adding little improvement to disease risk prediction. Standard single marker methods may lack power in selecting informative markers or estimating effects. Most existing methods also typically do not account for non-linearity. Identifying markers with weak signals and estimating their joint effects among many non-informative markers remains challenging. One potential approach is to group markers based on biological knowledge such as gene structure. If markers in a group tend to have similar effects, proper usage of the group structure could improve power and efficiency in estimation. We propose a two-stage method relating markers to disease risk by taking advantage of known gene-set structures. Imposing a naive bayes kernel machine (KM) model, we estimate gene-set specific risk models that relate each gene-set to the outcome in stage I. The KM framework efficiently models potentially non-linear effects of predictors without requiring explicit specification of functional forms. In stage II, we aggregate information across gene-sets via a regularization procedure. Estimation and computational efficiency is further improved with kernel principle component analysis. Asymptotic results for model estimation and gene set selection are derived and numerical studies suggest that the proposed procedure could outperform existing procedures for constructing genetic risk models.

  4. Model-based distance sampling

    OpenAIRE

    Buckland, Stephen Terrence; Oedekoven, Cornelia Sabrina; Borchers, David Louis

    2015-01-01

    CSO was part-funded by EPSRC/NERC Grant EP/1000917/1. Conventional distance sampling adopts a mixed approach, using model-based methods for the detection process, and design-based methods to estimate animal abundance in the study region, given estimated probabilities of detection. In recent years, there has been increasing interest in fully model-based methods. Model-based methods are less robust for estimating animal abundance than conventional methods, but offer several advantages: they ...

  5. Modelling Web-Based Instructional Systems

    NARCIS (Netherlands)

    Retalis, Symeon; Avgeriou, Paris

    2002-01-01

    The size and complexity of modern instructional systems, which are based on the World Wide Web, bring about great intricacy in their crafting, as there is not enough knowledge or experience in this field. This imposes the use of new instructional design models in order to achieve risk-mitigation,

  6. The Global Earthquake Model and Disaster Risk Reduction

    Science.gov (United States)

    Smolka, A. J.

    2015-12-01

    Advanced, reliable and transparent tools and data to assess earthquake risk are inaccessible to most, especially in less developed regions of the world while few, if any, globally accepted standards currently allow a meaningful comparison of risk between places. The Global Earthquake Model (GEM) is a collaborative effort that aims to provide models, datasets and state-of-the-art tools for transparent assessment of earthquake hazard and risk. As part of this goal, GEM and its global network of collaborators have developed the OpenQuake engine (an open-source software for hazard and risk calculations), the OpenQuake platform (a web-based portal making GEM's resources and datasets freely available to all potential users), and a suite of tools to support modelers and other experts in the development of hazard, exposure and vulnerability models. These resources are being used extensively across the world in hazard and risk assessment, from individual practitioners to local and national institutions, and in regional projects to inform disaster risk reduction. Practical examples for how GEM is bridging the gap between science and disaster risk reduction are: - Several countries including Switzerland, Turkey, Italy, Ecuador, Papua-New Guinea and Taiwan (with more to follow) are computing national seismic hazard using the OpenQuake-engine. In some cases these results are used for the definition of actions in building codes. - Technical support, tools and data for the development of hazard, exposure, vulnerability and risk models for regional projects in South America and Sub-Saharan Africa. - Going beyond physical risk, GEM's scorecard approach evaluates local resilience by bringing together neighborhood/community leaders and the risk reduction community as a basis for designing risk reduction programs at various levels of geography. Actual case studies are Lalitpur in the Kathmandu Valley in Nepal and Quito/Ecuador. In agreement with GEM's collaborative approach, all

  7. Risk-Based Explosive Safety Analysis

    Science.gov (United States)

    2016-11-30

    REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection of information is estimated to average 1...currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 30 November 2016 2. REPORT TYPE...Technical Paper 3. DATES COVERED (From - To) 01 November 2016 – 30 November 2016 4. TITLE AND SUBTITLE Risk-Based Explosive Safety Analysis 5a

  8. Method for gesture based modeling

    DEFF Research Database (Denmark)

    2006-01-01

    A computer program based method is described for creating models using gestures. On an input device, such as an electronic whiteboard, a user draws a gesture which is recognized by a computer program and interpreted relative to a predetermined meta-model. Based on the interpretation, an algorithm...... is assigned to the gesture drawn by the user. The executed algorithm may, for example, consist in creating a new model element, modifying an existing model element, or deleting an existing model element....

  9. Big data based fraud risk management at Alibaba

    Directory of Open Access Journals (Sweden)

    Jidong Chen

    2015-12-01

    Full Text Available With development of mobile internet and finance, fraud risk comes in all shapes and sizes. This paper is to introduce the Fraud Risk Management at Alibaba under big data. Alibaba has built a fraud risk monitoring and management system based on real-time big data processing and intelligent risk models. It captures fraud signals directly from huge amount data of user behaviors and network, analyzes them in real-time using machine learning, and accurately predicts the bad users and transactions. To extend the fraud risk prevention ability to external customers, Alibaba also built up a big data based fraud prevention product called AntBuckler. AntBuckler aims to identify and prevent all flavors of malicious behaviors with flexibility and intelligence for online merchants and banks. By combining large amount data of Alibaba and customers', AntBuckler uses the RAIN score engine to quantify risk levels of users or transactions for fraud prevention. It also has a user-friendly visualization UI with risk scores, top reasons and fraud connections.

  10. A predictive model to identify hospitalized cancer patients at risk for 30-day mortality based on admission criteria via the electronic medical record.

    Science.gov (United States)

    Ramchandran, Kavitha J; Shega, Joseph W; Von Roenn, Jamie; Schumacher, Mark; Szmuilowicz, Eytan; Rademaker, Alfred; Weitner, Bing Bing; Loftus, Pooja D; Chu, Isabella M; Weitzman, Sigmund

    2013-06-01

    This study sought to develop a predictive model for 30-day mortality in hospitalized cancer patients, by using admission information available through the electronic medical record. Observational cohort study of 3062 patients admitted to the oncology service from August 1, 2008, to July 31, 2009. Matched numbers of patients were in the derivation and validation cohorts (1531 patients). Data were obtained on day 1 of admission and included demographic information, vital signs, and laboratory data. Survival data were obtained from the Social Security Death Index. The 30-day mortality rate of the derivation and validation samples were 9.5% and 9.7% respectively. Significant predictive variables in the multivariate analysis included age (P < .0001), assistance with activities of daily living (ADLs; P = .022), admission type (elective/emergency) (P = .059), oxygen use (P < .0001), and vital signs abnormalities including pulse oximetry (P = .0004), temperature (P = .017), and heart rate (P = .0002). A logistic regression model was developed to predict death within 30 days: Score = 18.2897 + 0.6013*(admit type) + 0.4518*(ADL) + 0.0325*(admit age) - 0.1458*(temperature) + 0.019*(heart rate) - 0.0983*(pulse oximetry) - 0.0123 (systolic blood pressure) + 0.8615*(O2 use). The largest sum of sensitivity (63%) and specificity (78%) was at -2.09 (area under the curve = -0.789). A total of 25.32% (100 of 395) of patients with a score above -2.09 died, whereas 4.31% (49 of 1136) of patients below -2.09 died. Sensitivity and positive predictive value in the derivation and validation samples compared favorably. Clinical factors available via the electronic medical record within 24 hours of hospital admission can be used to identify cancer patients at risk for 30-day mortality. These patients would benefit from discussion of preferences for care at the end of life. Copyright © 2013 American Cancer Society.

  11. Human Plague Risk: Spatial-Temporal Models

    Science.gov (United States)

    Pinzon, Jorge E.

    2010-01-01

    This chpater reviews the use of spatial-temporal models in identifying potential risks of plague outbreaks into the human population. Using earth observations by satellites remote sensing there has been a systematic analysis and mapping of the close coupling between the vectors of the disease and climate variability. The overall result is that incidence of plague is correlated to positive El Nino/Southem Oscillation (ENSO).

  12. 12 CFR 932.3 - Risk-based capital requirement.

    Science.gov (United States)

    2010-01-01

    ... 12 Banks and Banking 7 2010-01-01 2010-01-01 false Risk-based capital requirement. 932.3 Section... CAPITAL STANDARDS FEDERAL HOME LOAN BANK CAPITAL REQUIREMENTS § 932.3 Risk-based capital requirement. (a... credit risk capital requirement, its market risk capital requirement, and its operations risk capital...

  13. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population.

    Science.gov (United States)

    Selvarajah, Sharmini; Kaur, Gurpreet; Haniff, Jamaiyah; Cheong, Kee Chee; Hiong, Tee Guat; van der Graaf, Yolanda; Bots, Michiel L

    2014-09-01

    Cardiovascular risk-prediction models are used in clinical practice to identify and treat high-risk populations, and to communicate risk effectively. We assessed the validity and utility of four cardiovascular risk-prediction models in an Asian population of a middle-income country. Data from a national population-based survey of 14,863 participants aged 40 to 65 years, with a follow-up duration of 73,277 person-years was used. The Framingham Risk Score (FRS), SCORE (Systematic COronary Risk Evaluation)-high and -low cardiovascular-risk regions and the World Health Organization/International Society of Hypertension (WHO/ISH) models were assessed. The outcome of interest was 5-year cardiovascular mortality. Discrimination was assessed for all models and calibration for the SCORE models. Cardiovascular risk factors were highly prevalent; smoking 20%, obesity 32%, hypertension 55%, diabetes mellitus 18% and hypercholesterolemia 34%. The FRS and SCORE models showed good agreement in risk stratification. The FRS, SCORE-high and -low models showed good discrimination for cardiovascular mortality, areas under the ROC curve (AUC) were 0.768, 0.774 and 0.775 respectively. The WHO/ISH model showed poor discrimination, AUC=0.613. Calibration of the SCORE-high model was graphically and statistically acceptable for men (χ(2) goodness-of-fit, p=0.097). The SCORE-low model was statistically acceptable for men (χ(2) goodness-of-fit, p=0.067). Both SCORE-models underestimated risk in women (p<0.001). The FRS and SCORE-high models, but not the WHO/ISH model can be used to identify high cardiovascular risk in the Malaysian population. The SCORE-high model predicts risk accurately in men but underestimated it in women. Copyright © 2014. Published by Elsevier Ireland Ltd.

  14. Flood risk assessment model using the fuzzy analytic hierarchy process

    Directory of Open Access Journals (Sweden)

    Marija Kerkez

    2017-07-01

    Full Text Available Sustainable development and natural disasters are closely interlinked. The impact of catastrophic events on the environment is still very difficult to determine, and such losses are generally underestimated. Development is never neutral in relation to catastrophes: it creates, enhances or reduces the risk of their occurrence. Selection of appropriate methods and mathematical models for risk assessment in relation to the specific features and characteristics of the considered system and available information and resources, is a key parameter of reliability assessment. Numerous authors applied AHP methods with flood risk assessment, but very limited literature is avaliable on the use of fuzzy multiobjective analysis in flood studies. In the recent years, the fuzzy approach for flood risk assessments has gained greater importance. In this paper, we present the fuzzy analytic hierarchy process (FAHP model for flood risk assessments. Two flood hazard indexes were defined, one based on natural factors and one based on anthropogenic factors. FAHP is applied to data sets to illustrate a model.

  15. Model-Based Reasoning

    Science.gov (United States)

    Ifenthaler, Dirk; Seel, Norbert M.

    2013-01-01

    In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related reasoning is a slowly developing capability of cognitive systems that emerges effectively with proper…

  16. Modelling Web-Based Instructional Systems

    OpenAIRE

    Symeon Retalis; Paris Avgeriou

    2002-01-01

    The size and complexity of modern instructional systems, which are based on the World Wide Web, bring about great intricacy in their crafting, as there is not enough knowledge or experience in this field. This imposes the use of new instructional design models in order to achieve risk-mitigation, cost and time efficiency, high pedagogical quality of the end product, which will capitalise on the potential of the networked technologies. This paper presents a model for constructing such systems,...

  17. Network Security Risk Assessment Based on Item Response Theory

    Directory of Open Access Journals (Sweden)

    Fangwei Li

    2015-08-01

    Full Text Available Owing to the traditional risk assessment method has one-sidedness and is difficult to reflect the real network situation, a risk assessment method based on Item Response Theory (IRT is put forward in network security. First of all, the novel algorithms of calculating the threat of attack and the successful probability of attack are proposed by the combination of IRT model and Service Security Level. Secondly, the service weight of importance is calculated by the three-demarcation analytic hierarchy process. Finally, the risk situation graph of service, host and network logic layer could be generated by the improved method. The simulation results show that this method can be more comprehensive consideration of factors which are affecting network security, and a more realistic network risk situation graph in real-time will be obtained.

  18. Risk-based management of invading plant disease.

    Science.gov (United States)

    Hyatt-Twynam, Samuel R; Parnell, Stephen; Stutt, Richard O J H; Gottwald, Tim R; Gilligan, Christopher A; Cunniffe, Nik J

    2017-05-01

    Effective control of plant disease remains a key challenge. Eradication attempts often involve removal of host plants within a certain radius of detection, targeting asymptomatic infection. Here we develop and test potentially more effective, epidemiologically motivated, control strategies, using a mathematical model previously fitted to the spread of citrus canker in Florida. We test risk-based control, which preferentially removes hosts expected to cause a high number of infections in the remaining host population. Removals then depend on past patterns of pathogen spread and host removal, which might be nontransparent to affected stakeholders. This motivates a variable radius strategy, which approximates risk-based control via removal radii that vary by location, but which are fixed in advance of any epidemic. Risk-based control outperforms variable radius control, which in turn outperforms constant radius removal. This result is robust to changes in disease spread parameters and initial patterns of susceptible host plants. However, efficiency degrades if epidemiological parameters are incorrectly characterised. Risk-based control including additional epidemiology can be used to improve disease management, but it requires good prior knowledge for optimal performance. This focuses attention on gaining maximal information from past epidemics, on understanding model transferability between locations and on adaptive management strategies that change over time. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

  19. Model-based Software Engineering

    DEFF Research Database (Denmark)

    Kindler, Ekkart

    2010-01-01

    The vision of model-based software engineering is to make models the main focus of software development and to automatically generate software from these models. Part of that idea works already today. But, there are still difficulties when it comes to behaviour. Actually, there is no lack in models...

  20. Risk Prediction Models for Colorectal Cancer: A Systematic Review.

    Science.gov (United States)

    Usher-Smith, Juliet A; Walter, Fiona M; Emery, Jon D; Win, Aung K; Griffin, Simon J

    2016-01-01

    Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes. ©2015 American Association for Cancer Research.

  1. Validation of a multifactorial risk factor model used for predicting future caries risk with nevada adolescents

    Directory of Open Access Journals (Sweden)

    Mobley Connie

    2011-05-01

    Full Text Available Abstract Background The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting. Methods This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008. The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural, tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP, negative predictive value (PVN, and prevalence across all six years of screening to assess the validity of the Risk Factor Model. Results Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%. Conclusions Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.

  2. Principles of models based engineering

    Energy Technology Data Exchange (ETDEWEB)

    Dolin, R.M.; Hefele, J.

    1996-11-01

    This report describes a Models Based Engineering (MBE) philosophy and implementation strategy that has been developed at Los Alamos National Laboratory`s Center for Advanced Engineering Technology. A major theme in this discussion is that models based engineering is an information management technology enabling the development of information driven engineering. Unlike other information management technologies, models based engineering encompasses the breadth of engineering information, from design intent through product definition to consumer application.

  3. 12 CFR 652.70 - Risk-based capital level.

    Science.gov (United States)

    2010-01-01

    ... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Risk-based capital level. 652.70 Section 652.70... CORPORATION FUNDING AND FISCAL AFFAIRS Risk-Based Capital Requirements § 652.70 Risk-based capital level. The risk-based capital level is the sum of the following amounts: (a) Credit and interest rate risk. The...

  4. Predictors of reducing sexual and reproductive risk behaviors based on the information-motivation-behavioral skills (IMB model among unmarried rural-to-urban female migrants in Shanghai, China.

    Directory of Open Access Journals (Sweden)

    Yong Cai

    Full Text Available BACKGROUND: Due to the increase of premarital sex and the lack of reproductive health services, unmarried rural-to-urban female migrants experience more risks of sex and reproductive health (SRH. This study was designed to describe SRH related knowledge, attitude and risk behaviors among unmarried rural-to-urban female migrants and examine the predictors of reducing sexual and reproductive risk behaviors based on information-motivation-behavioral skills (IMB model and to describe the relationships between the constructs. METHODS: We conducted a cross-sectional study to assess SRH related information, motivation, behavioral skills and preventive behaviors among unmarried rural-to-urban female migrants in Shanghai, one of the largest importers of migrant laborers in China. Structural equation modeling (SEM was used to assess the IMB model. RESULTS: A total of 944 subjects completed their questionnaires. The mean age was 21.2 years old (SD = 2.3; range 16 to 28. Over one-fourth of participants reported having had premarital sex (N = 261, 27.6% and among whom 15.3% reported having had the experience of unintended pregnancy, 14.6% with the experience of abortion. The final IMB model provided acceptable fit to the data (CFI = 0.99, RMSEA = 0.034. Reducing sexual and reproductive risk behaviors was significantly predicted by SRH related information (β = 0.681, P<0.001 and behavioral skills(β = 0.239, P<0.001. Motivation (β = 0.479, P<0.001 was the significant indirect predictor of reducing sexual and reproductive risk behaviors mediated through behavioral skills. CONCLUSIONS: The results highlight the importance and necessity of conducting reproductive health promotion among unmarried rural-to-urban female migrants in China. The IMB model could be used to predict reducing sexual and reproductive risk behaviors and it suggests future interventions should focus on improving SRH related information and behavioral skills.

  5. A Risk Management Model for Merger and Acquisition

    OpenAIRE

    B. S. Chui

    2011-01-01

    In this paper, a merger and acquisition risk management model is proposed for considering risk factors in the merger and acquisition activities. The proposed model aims to maximize the probability of success in merger and acquisition activities by managing and reducing the associated risks. The modeling of the proposed merger and acquisition risk management model is described and illustrated in this paper. The illustration result shows that the proposed model can help to screen the best targe...

  6. Combining engineering and data-driven approaches: Development of a generic fire risk model facilitating calibration

    DEFF Research Database (Denmark)

    De Sanctis, G.; Fischer, K.; Kohler, J.

    2014-01-01

    are not detailed enough. Engineering risk models, on the other hand, may be detailed but typically involve assumptions that may result in a biased risk assessment and make a cost-benefit study problematic. In two related papers it is shown how engineering and data-driven modeling can be combined by developing......Fire risk models support decision making for engineering problems under the consistent consideration of the associated uncertainties. Empirical approaches can be used for cost-benefit studies when enough data about the decision problem are available. But often the empirical approaches...... a generic risk model that is calibrated to observed fire loss data. Generic risk models assess the risk of buildings based on specific risk indicators and support risk assessment at a portfolio level. After an introduction to the principles of generic risk assessment, the focus of the present paper...

  7. Regional scale ecological risk assessment: using the relative risk model

    National Research Council Canada - National Science Library

    Landis, Wayne G

    2005-01-01

    ...) in the performance of regional-scale ecological risk assessments. The initial chapters present the methodology and the critical nature of the interaction between risk assessors and decision makers...

  8. gis-based hydrological model based hydrological model upstream

    African Journals Online (AJOL)

    eobe

    Hydrological. Hydrological modeling tools have been increasingl modeling tools have been increasingl watershed watershed level. The application of these tools hav. The application of these tools hav sensing and G sensing and Geographical Information System (GIS) eographical Information System (GIS) based models ...

  9. Study of operational risk-based configuration control

    Energy Technology Data Exchange (ETDEWEB)

    Vesely, W E [Science Applications International Corp., Dublin, OH (United States); Samanta, P K; Kim, I S [Brookhaven National Lab., Upton, NY (United States)

    1991-08-01

    This report studies aspects of a risk-based configuration control system to detect and control plant configurations from a risk perspective. Configuration control, as the term is used here, is the management of component configurations to achieve specific objectives. One important objective is to control risk and safety. Another is to operate efficiently and make effective use of available resources. PSA-based evaluations are performed to study configuration to core-melt frequency and core-melt probability for two plants. Some equipment configurations can cause large core-melt frequency and there are a number of such configurations that are not currently controlled by technical specifications. However, the expected frequency of occurrence of the impacting configurations is small and the core-melt probability contributions are also generally small. The insights from this evaluation are used to develop the framework for an effective risk-based configuration control system. The focal points of such a system and the requirements for tools development for implementing the system are defined. The requirements of risk models needed for the system, and the uses of plant-specific data are also discussed. 18 refs., 25 figs., 10 tabs.

  10. Applying the welfare model to at-own-risk discharges.

    Science.gov (United States)

    Krishna, Lalit Kumar Radha; Menon, Sumytra; Kanesvaran, Ravindran

    2017-08-01

    "At-own-risk discharges" or "self-discharges" evidences an irretrievable breakdown in the patient-clinician relationship when patients leave care facilities before completion of medical treatment and against medical advice. Dissolution of the therapeutic relationship terminates the physician's duty of care and professional liability with respect to care of the patient. Acquiescence of an at-own-risk discharge by the clinician is seen as respecting patient autonomy. The validity of such requests pivot on the assumptions that the patient is fully informed and competent to invoke an at-own-risk discharge and that care up to the point of the at-own-risk discharge meets prevailing clinical standards. Palliative care's use of a multidisciplinary team approach challenges both these assumptions. First by establishing multiple independent therapeutic relations between professionals in the multidisciplinary team and the patient who persists despite an at-own-risk discharge. These enduring therapeutic relationships negate the suggestion that no duty of care is owed the patient. Second, the continued employ of collusion, familial determinations, and the circumnavigation of direct patient involvement in family-centric societies compromises the patient's decision-making capacity and raises questions as to the patient's decision-making capacity and their ability to assume responsibility for the repercussions of invoking an at-own-risk discharge. With the validity of at-own-risk discharge request in question and the welfare and patient interest at stake, an alternative approach to assessing at-own-risk discharge requests are called for. The welfare model circumnavigates these concerns and preserves the patient's welfare through the employ of a multidisciplinary team guided holistic appraisal of the patient's specific situation that is informed by clinical and institutional standards and evidenced-based practice. The welfare model provides a robust decision-making framework for

  11. Precursor Analysis for Flight- and Ground-Based Anomaly Risk Significance Determination

    Science.gov (United States)

    Groen, Frank

    2010-01-01

    This slide presentation reviews the precursor analysis for flight and ground based anomaly risk significance. It includes information on accident precursor analysis, real models vs. models, and probabilistic analysis.

  12. Risk-Based Assessment of Structural Robustness

    Directory of Open Access Journals (Sweden)

    Oana-Mihaela Ioniţă

    2010-01-01

    Full Text Available Providing safety of structures is one of the main aims of design. In traditional design it is achieved by designing structural components against specified limit states. However, as showed the Ronan Point collapse in UK in 1968, when a gas explosion in one of flats on the 18-th floor of the residential building caused the failure of an entire section of the building, this approach is not sufficient. The approach does not exclude the risk of local damage to a structure due to accidental events that can occur during service life of the structure. While probability of occurrence of such events for ordinary structures is low, and, therefore, they are not considered explicitly in design, their effect on structural safety becomes significant if the structure is not robust, that is when some local damage can trigger a chain reaction of failures causing collapse of the whole structure or of a major part of it, the so called progressive collapse. The purpose of this paper is to outline the basic premises for the utilization of risk assessment in evaluating the robustness of structures. In the following the robustness assessment is understood as a process of decision making based on risks.

  13. [Application of three risk assessment models in occupational health risk assessment of dimethylformamide].

    Science.gov (United States)

    Wu, Z J; Xu, B; Jiang, H; Zheng, M; Zhang, M; Zhao, W J; Cheng, J

    2016-08-20

    Objective: To investigate the application of United States Environmental Protection Agency (EPA) inhalation risk assessment model, Singapore semi-quantitative risk assessment model, and occupational hazards risk assessment index method in occupational health risk in enterprises using dimethylformamide (DMF) in a certain area in Jiangsu, China, and to put forward related risk control measures. Methods: The industries involving DMF exposure in Jiangsu province were chosen as the evaluation objects in 2013 and three risk assessment models were used in the evaluation. EPA inhalation risk assessment model: HQ=EC/RfC; Singapore semi-quantitative risk assessment model: Risk= (HR×ER) 1/2; Occupational hazards risk assessment index=2Health effect level×2exposure ratio×Operation condition level. Results: The results of hazard quotient (HQ>1) from EPA inhalation risk assessment model suggested that all the workshops (dry method, wet method and printing) and work positions (pasting, burdening, unreeling, rolling, assisting) were high risk. The results of Singapore semi-quantitative risk assessment model indicated that the workshop risk level of dry method, wet method and printing were 3.5 (high) , 3.5 (high) and 2.8 (general) , and position risk level of pasting, burdening, unreeling, rolling, assisting were 4 (high) , 4 (high) , 2.8 (general) , 2.8 (general) and 2.8 (general) . The results of occupational hazards risk assessment index method demonstrated that the position risk index of pasting, burdening, unreeling, rolling, assisting were 42 (high) , 33 (high) , 23 (middle) , 21 (middle) and 22 (middle) . The results of Singapore semi-quantitative risk assessment model and occupational hazards risk assessment index method were similar, while EPA inhalation risk assessment model indicated all the workshops and positions were high risk. Conclusion: The occupational hazards risk assessment index method fully considers health effects, exposure, and operating conditions and

  14. NGNP Risk Management Database: A Model for Managing Risk

    Energy Technology Data Exchange (ETDEWEB)

    John Collins

    2009-09-01

    To facilitate the implementation of the Risk Management Plan, the Next Generation Nuclear Plant (NGNP) Project has developed and employed an analytical software tool called the NGNP Risk Management System (RMS). A relational database developed in Microsoft® Access, the RMS provides conventional database utility including data maintenance, archiving, configuration control, and query ability. Additionally, the tool’s design provides a number of unique capabilities specifically designed to facilitate the development and execution of activities outlined in the Risk Management Plan. Specifically, the RMS provides the capability to establish the risk baseline, document and analyze the risk reduction plan, track the current risk reduction status, organize risks by reference configuration system, subsystem, and component (SSC) and Area, and increase the level of NGNP decision making.

  15. A Hydrological Modeling Framework for Flood Risk Assessment for Japan

    Science.gov (United States)

    Ashouri, H.; Chinnayakanahalli, K.; Chowdhary, H.; Sen Gupta, A.

    2016-12-01

    Flooding has been the most frequent natural disaster that claims lives and imposes significant economic losses to human societies worldwide. Japan, with an annual rainfall of up to approximately 4000 mm is extremely vulnerable to flooding. The focus of this research is to develop a macroscale hydrologic model for simulating flooding toward an improved understanding and assessment of flood risk across Japan. The framework employs a conceptual hydrological model, known as the Probability Distributed Model (PDM), as well as the Muskingum-Cunge flood routing procedure for simulating streamflow. In addition, a Temperature-Index model is incorporated to account for snowmelt and its contribution to streamflow. For an efficient calibration of the model, in terms of computational timing and convergence of the parameters, a set of A Priori parameters is obtained based on the relationships between the model parameters and the physical properties of watersheds. In this regard, we have implemented a particle tracking algorithm and a statistical model which use high resolution Digital Terrain Models to estimate different time related parameters of the model such as time to peak of the unit hydrograph. In addition, global soil moisture and depth data are used to generate A Priori estimation of maximum soil moisture capacity, an important parameter of the PDM model. Once the model is calibrated, its performance is examined during the Typhoon Nabi which struck Japan in September 2005 and caused severe flooding throughout the country. The model is also validated for the extreme precipitation event in 2012 which affected Kyushu. In both cases, quantitative measures show that simulated streamflow depicts good agreement with gauge-based observations. The model is employed to simulate thousands of possible flood events for the entire Japan which makes a basis for a comprehensive flood risk assessment and loss estimation for the flood insurance industry.

  16. Web Applications Vulnerability Management using a Quantitative Stochastic Risk Modeling Method

    Directory of Open Access Journals (Sweden)

    Sergiu SECHEL

    2017-01-01

    Full Text Available The aim of this research is to propose a quantitative risk modeling method that reduces the guess work and uncertainty from the vulnerability and risk assessment activities of web based applications while providing users the flexibility to assess risk according to their risk appetite and tolerance with a high degree of assurance. The research method is based on the research done by the OWASP Foundation on this subject but their risk rating methodology needed de-bugging and updates in different in key areas that are presented in this paper. The modified risk modeling method uses Monte Carlo simulations to model risk characteristics that can’t be determined without guess work and it was tested in vulnerability assessment activities on real production systems and in theory by assigning discrete uniform assumptions to all risk charac-teristics (risk attributes and evaluate the results after 1.5 million rounds of Monte Carlo simu-lations.

  17. Uncertain Risk Assessment of Knowledge Management: Based on Set Pair Analysis

    OpenAIRE

    Guibin Yang; Hongyu Gao

    2016-01-01

    Since the knowledge resource becomes an important part of enterprise resources, the knowledge management risks could significantly affect the enterprise operation efficiency. Controlling knowledge management risks is one of the enterprise management tasks; the managers and researchers focus on how to effectively evaluate the risks. This paper aims to solve this problem and puts forward an evaluation model of the uncertain risks of knowledge management. Based on set pair theory, a model is est...

  18. Gambler Risk Perception: A Mental Model and Grounded Theory Analysis.

    Science.gov (United States)

    Spurrier, Michael; Blaszczynski, Alexander; Rhodes, Paul

    2015-09-01

    Few studies have investigated how gamblers perceive risk or the role of risk perception in disordered gambling. The purpose of the current study therefore was to obtain data on lay gamblers' beliefs on these variables and their effects on decision-making, behaviour, and disordered gambling aetiology. Fifteen regular lay gamblers (non-problem/low risk, moderate risk and problem gamblers) completed a semi-structured interview following mental models and grounded theory methodologies. Gambler interview data was compared to an expert 'map' of risk-perception, to identify comparative gaps or differences associated with harmful or safe gambling. Systematic overlapping processes of data gathering and analysis were used to iteratively extend, saturate, test for exception, and verify concepts and themes emerging from the data. The preliminary findings suggested that gambler accounts supported the presence of expert conceptual constructs, and to some degree the role of risk perception in protecting against or increasing vulnerability to harm and disordered gambling. Gambler accounts of causality, meaning, motivation, and strategy were highly idiosyncratic, and often contained content inconsistent with measures of disordered gambling. Disordered gambling appears heavily influenced by relative underestimation of risk and overvaluation of gambling, based on explicit and implicit analysis, and deliberate, innate, contextual, and learned processing evaluations and biases.

  19. Activity-based DEVS modeling

    DEFF Research Database (Denmark)

    Alshareef, Abdurrahman; Sarjoughian, Hessam S.; Zarrin, Bahram

    2018-01-01

    Use of model-driven approaches has been increasing to significantly benefit the process of building complex systems. Recently, an approach for specifying model behavior using UML activities has been devised to support the creation of DEVS models in a disciplined manner based on the model driven...... architecture and the UML concepts. In this paper, we further this work by grounding Activity-based DEVS modeling and developing a fully-fledged modeling engine to demonstrate applicability. We also detail the relevant aspects of the created metamodel in terms of modeling and simulation. A significant number...... of the artifacts of the UML 2.5 activities and actions, from the vantage point of DEVS behavioral modeling, is covered in details. Their semantics are discussed to the extent of time-accurate requirements for simulation. We characterize them in correspondence with the specification of the atomic model behavior. We...

  20. Ecological models for regulatory risk assessments of pesticides: Developing a strategy for the future.

    NARCIS (Netherlands)

    Thorbek, P.; Forbes, V.; Heimbach, F.; Hommen, U.; Thulke, H.H.; Brink, van den P.J.

    2010-01-01

    Ecological Models for Regulatory Risk Assessments of Pesticides: Developing a Strategy for the Future provides a coherent, science-based view on ecological modeling for regulatory risk assessments. It discusses the benefits of modeling in the context of registrations, identifies the obstacles that