Uncertainty analysis

Laboratory practice plays a crucial role in engineering especially in thermal fluid education. The advancement of computational and computer technologies have ushered in a new horizon in learning and teaching of laboratory practices world-wide. Apart from traditional hands-on laboratory practice, the virtual/simulated laboratory practices are playing an increasingly dominant role. The virtual laboratory practices offer unique opportunities for students to visualise complex concepts and remove the time and location barrier. This paper presents a 3-step hybrid laboratory practice developed at RMIT University for thermal fluid course. It is evident that a combination of video clip, hands on laboratory practice and virtual/simulated laboratory practice enhances the student learning experience and learning outcomes.

En este trabajo se presenta el diseño y desarrollo de un sistema de información climático para el Estado de Querétaro, el cual tiene capacidad para administrar, manejar y reportar información proveniente de estaciones climatológicas instaladas y desarrolladas para su funcionamiento en el estado. Actualmente se han desarrollado dos estaciones climatológicas [1], y se tiene una de estas instalada en la comunidad de Adjuntas (comunidad en Querétaro), en la cual se están recolectando los primeros datos de la base de información y con ello se están realizando las pruebas finales al sistema. El sistema permite una administración eficiente de la información climatológica de acuerdo a las necesidades expresadas por expertos en el manejo de datos climáticos en el estado. Palabras clave: sistemas de información, climatología, administración, manejo de recursos bióticos.

In this paper, we created a classification for major sources of uncertainty in projects and categorized the studies in project scheduling literature with respect to the uncertainty source(s) they address. In addition, we investigated the approaches and methods to manage uncertainty, and studied the literature regarding these methods. Project management predominantly models the randomness in duration of activities; however, studies modeling the uncertainty due to other sources are scarce. We focused on these sources of uncertainty and highlighted the promising areas of research. The results presented in this paper will help researchers to identify the research gaps in modeling project uncertainty.

An approximation technique named the Probabilistic Collocation Method (PCM) is expected for Stochastic Load Flow (SLF) calculations has been proposed in this work. The suggested technique consists in wisely choosing a limited nominated points to estimate the Probability Distribution Function (PDF) of a arbitrary variable in the structure of the system, thus reducing the counting period considerably. Subsequently the grid integrated wind turbines are accepted, uncertain variation relay wind speed is characterized using normal Weibull distribution function. The efficiency of suggested technique is established by a comparison of results between proposed technique and Monte Carlo simulation (MCS) method, both of probabilistic methods compared with Deterministic approach Newton Raphson (NR), using IEEE 5-bus test system.

The EU FP5 VALCO project is partially based on results earlier obtained within the EU Phare project (SRR1/95) (Ref. [1,2]). Two selected transients, one for each a VVER-440 and a VVER-1000, were analysed in this former project by different coupled-code systems. The ...

A viable means of CO2 reduction in the atmosphere is to capture and concentrate CO2 from large point sources such as power plants and petroleum refineries and store it by underground injection. The main concern for commercial scale CO2 sequestration in geologic formations is the uncertainties associated with this process. The risks involved in different stages of a CO2 sequestration project are related to geological and operational uncertainties. This paper presents the application of a grid-based Surrogate Reservoir Model (SRM) to a real case CO2 sequestration project in which CO2 were injected into a depleted gas reservoir. This project is a part of the National Risk Assessment Partnership (NRAP)’s research on CO2 sequestration process. An SRM is a customized model that accurately mimics reservoir simulation behavior by using Artificial Intelligence & Data Mining techniques. Initial steps for developing the SRM included constructing a reservoir simulation model with a commercial s...

Risk assessment is an important and significant aid in the decision making process. Risk assessment is performed using 'model' and a model is a function of parameters which are usually affected by uncertainty. Some model parameters are affected by aleatory uncertainty and some others are affected by epistemic uncertainty. In this paper we propose a hybrid method to deal with propagation of both kinds of uncertainty within the same computation of risk.

Uncertainty analysis is useful to define the level of reliability of a modelling application, but operational methods are needed to identify the best modelling structure for a specific problem based on uncertainty reduction criteria. One interesting example is given by flood damage estimation problem where different possible modelling solution and flood damage estimation can be used depending on the case study. Past literature showed that several modelling structures may be equally reliable in terms of calibration ability but they may produce different uncertainty levels. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Model structure uncertainty is associated with the assumptions reflected in model conceptualization and mathematical structure. An unfortunate truth in model development is that no matter how many resources are invested in developing a particular model, there remain conditions and situations in which the model is unsuitable to give an accurate forecast. Reliance on a single model typically overestimates the confidence and increases the statistical bias of the forecast. Bayesian model-averaging (BMA) techniques look to overcome the limitations of a single model by linearly combining a number of competing models into a single new model forecast. This method showed that a pooled forecast of competing models outperformed any single model forecast. In the present paper, BMA was applied to several flood damage estimation models in order to identify the best combination of models to analyse urban flooding distribution in Palermo city centre (Italy).

In this work the heat flux generated in a cutting process is simulated using the inverse heat conduction technique based on conjugate gradient method. A three-dimensional formulation is used both to describe the physical phenomenon and to solve the inverse problem. The machining process (turning) is simulated and instrumented with nine thermocouples at the bottom face of the tool, opposite to its main rake face. The signals are automatically received and processed using a data acquisition system and a PC-Pentium. The direct solution, adjoint equation and sensitivity problem are numerically solved using finite volumes method. With this technique the cutting temperatures are estimated for various simulated cutting conditions. In order to validate the method the IHCP is applied in a well-controlled experiment where the heat flux input is known. An uncertainty analysis is also presented.

Evaluating measurement uncertainty of a physical quantity is a mandatory requirement for laboratories within the recognition ISO/IEC 17025 certification to access reliability of measured results. In this work, the uncertainty of ionizing radiation measurements such as air-kerma, personal dose equivalent Hp(d) was evaluated based on GUM method and Monte Carlo method. An uncertainty propagation software has been developed for evaluation of the measurement uncertainty more convenient.

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.

Sustainability and durability are the key requirements of pavement structure. Sustainability of asphalt pavement structure involves utilization of Warm Mix Asphalt (WMA) technologies with the addition of Reclaimed Asphalt Pavement (RAP), where durability of asphalt involves performance parameters like fatigue and fracture resistance properties etc. Utilizing the RAP content in asphalt mix increases the mixing and compaction temperature which may degrade the performance of asphalt. Hence, numerous studies have recommended different WMA technologies to decrease mixing and compaction temperature of asphalt mix containing RAP. The present research work evaluates the fatigue and fracture performance of WMA and Hot Mix Asphalt (HMA) with varying percentages of RAP and Sasobit. Different mixes of WMA and HMA were designed with varying percentages of RAP (0, 20, 40 and 60%) through Marshall Mix design. Sasobit (organic/wax-based additive) was used as WMA technology to prepare WMA at varying percentages (0, 2, 4 and 6%). The fatigue behavior of asphalt was evaluated using four-point bending test, where fracture resistance of asphalt was determined using Semi Circular Bending (SCB) test in the laboratory. Fatigue and fracture resistance of WMA were improved with the increase in percentages of Sasobit and RAP content, while the addition of RAP in HMA showed a decreasing trend of fatigue and fracture resistance due to the stiffer nature of RAP. Furthermore, WMA was identified as economical for construction besides other benefits like improved properties and environment friendly asphalt mix.

In a world that is becoming increasingly complex and unpredictable, people search for anchors of certainty and control. Conspiracy theories, though often dismissed as irrational, offer a seductive, albeit illusory, logic for grappling with confusing events. This essay argues that the psychological appeal of conspiracy thinking can be understood as an attempt to create a perspective for action. By reducing complex problems to a manageable narrative with a clear enemy, conspiracy thinking provides a cognitive and emotional framework that, although based on misconceptions, offers a sense of understanding, control, and empowerment to the individual. It is a maladaptive strategy to fulfill fundamental psychological needs in times of uncertainty.

The filtering algorithm, Kalman Filter was used in real-time estimation and correction of classical hydrological models parameters. The Unit Hydrograph and Autoregressive Model were applied to Jangada river basin, with a drainage area of 1050 km2. Two different algorithms of Kalman Filter were described in a crescent level of generality. The
results showed it is necessary to impose restrictions to the values of the models parameters to guarantee the convergence to acceptable values according to theory of the hydrological models used here. It concludes that Kalman Filter is useful to formulate hydrological models with real-time correction of the models parameters.

The aim of this paper is to present the stochastic flow simulation in saturated porous media, based on a real case of soil and groundwater contamination. The flow simulation is an initial step for the stochastic simulation of contaminant transport and also for the probabilistic human health risk assessment due to ingestion of contaminated groundwater. The stochastic approach consisted in generating hydraulic conductivity random fields for the study area, in order to characterize the uncertainty related to the spatial variability of hydraulic conductivity. Two hundred
and fifty random field realizations were performed for a 2 km x 5 km rectangular study area, discretized into 2500 cells. The hydraulic conductivity random fields were generated using the Sequential Gaussian Simulation (SGS) method. The spatial structure of the hydraulic conductivity was determined using Geostatistics. From the generated
random fields, 200 groundwater flow simulations were performed, resulting both in phreatic level and Darcy’s velocity fields for later use both in simulations of contaminant transport and in definition of risk frequency distributions.

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Uncertainty Analysis of Primary Resource Savings at Cogeneration The article provides the uncertainty analysis for electricity and heat energy production measurements at a cogeneration plant. It is analysed how the uncertainty in the input data affects the uncertainty in the data on primary resource savings. In the work, the standard uncertainties have been estimated for both data sets. The uncertainty budget is defined for determination of the primary energy resource savings.

Background. Uncertainty about climate change impacts on forests can hinder mitigation and adaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet different uncertainty components emerge in different studies. Consequently, inconsistent understanding of uncertainty among different climate impact studies (from the impact analysis to implementing solutions) can be an additional reason for delaying action. In this review we (a) expanded existing uncertainty assessment frameworks into one harmonised framework for characterizing uncertainty, (b) used this framework to identify and classify uncertainties in climate change impacts studies on forests, and (c) summarised the uncertainty assessment methods applied in those studies. Methods. We systematically reviewed climate change impact studies published between 1994 and 2016. We separated these studies into those generating information about climate change impacts on forests using models –‘modelling studie...

TORT has been in service more than 10 years now. Although the original version was developed for the single purpose of calculating the penetration of radiation into large concrete buildings, more general versions have achieved widespread use and acceptance in many other applications. This paper discusses current features and capabilities of TORT and related peripheral codes.

This study aims to reconstruct NDI45 missing values due to cloud cover while outlining the importance of vegetation health for the climate-carbon cycle and the benefits of the NDI45 index for high canopy area indices. The methods include a novel hybrid framework that combines a deterministic Kalman filter (KF) and a clustering-based LSTM network to generate gap-free NDI45 series with 20 m spatial and 5-day temporal resolution. The innovation of the applied method relies on achieving a single-sensor workflow, provides a pixel-level uncertainty map, and minimizes LSTM overfitting through clustering based on a correlation threshold. In the northern Pampas (South America), this hybrid approach reduces the MAE by 22-35% on average and narrows the 95% confidence interval by 25-40% compared to the Kalman filter or LSTM alone. The three-dimensional spatiotemporal analysis demonstrates that the KF-LSTM hybrid provides better spatial homogeneity and reliability across the entire study area. The proposed framework can generate gap-free, high-resolution NDI45 time series with quantified uncertainties, enabling more reliable detection of vegetation stress, yield fluctuations, and long-term resilience trends. These capabilities make the method directly applicable to operational drought monitoring, crop insurance modeling, and climate risk assessment in agricultural systems, particularly in regions prone to frequent cloud cover. The framework can be further extended by including radar backscatter and multi-model ensembles, thus providing a promising basis for the reconstruction of global, high-resolution vegetation time series.

River Yamuna has been extensively studied with regard to physical and chemical characteristics. It originates from the Yamunotri Glacier at a height 6,387 metres. Earlier the waters of the Yamuna were distinguishable as "clear blue", as compared to the silt-laden yellow of the Ganges. However, due to high density population growth and rapid industrialization today Yamuna is one of the most polluted rivers in India, especially around New Delhi, the capital of India, which dumps about 58% of its treated or partially treated waste into the river. The paper presents pollutional aspects of river Yamuna at Delhi (Capital of India). 9 locations were selected and samples were collected on monthly basis from June 2011 to June 2012 during the entire stretch of Yamuna River in Delhi. Parameters studied were Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and pH. Results shows that despite of all efforts pollution load is still increasing making water unfit for consumption.

Data assimilation (DA) was revealed as a highly efficient approach to enhance the prediction's accuracy, demonstrating the reliability of the simulation through uncertainty quantification analysis. However, in the numerical simulations, most of the tasks are highly nonlinear relationships between the parameters that directly affect the efficiency of the DA such as the large search space dimension, resulting in significant computational costs. This limitation can be overcome by implementing machine learning (ML) predictions that can efficiently expand the DA search space. Therefore, this study is aimed at enhancing the performance of DA by integrating ML and DA (IMD), proposing a new method to overcome the stand-alone DA limitations. The approach involved implementing a stand-alone DA using the multidimensional analysis of reactor safety (MARS) code to enhance the prediction of reflood tests. The output datasets obtained from the stand-alone DA were then used to train the deep neural networks (DNNs), and the accuracy of the DNN's prediction was thoroughly evaluated with varying dataset sizes. This DNN subsequently can predict the enormous unobserved samples, enabling the identification and investigation of the prospective candidates for the subsequent DA process. The results demonstrated that the stand-alone DA achieved an accuracy enhancement of up to 41.3% in reflood test predictions, while the IMD yielded even more significant improvements, with a performance enhancement of 47.0%. These findings reveal that the IMD approach outperformed the stand-alone DA approach, particularly in the case of high flooding rate tests. In this context, the proposed integrating system can effectively overcome the high computational cost and enhance the performance of stand-alone DA.

Data assimilation is the process that can enhance the predictions by adjusting the input parameters inside their uncertainties range. In this study, the predicted results of the reflood tests, which were simulated by the SPACE code, were examined using the data assimilation technique. Crucially, a newly proposed data assimilation framework, STARU (Sampling meThod for highly non-lineAR system Uncertainty analysis), was developed. To propose the new data assimilation framework, we combined a new accuracy evaluation method with the Monte Carlo sampling algorithm to enhance the predictions for such a complex system as reflooding phenomena (many input parameters and non-linear responses). Subsequently, the outcomes of STARU were compared with the performances of data assimilation in PAPIRUS, a toolkit with many functions and modules for uncertainty analysis, which was developed by Heo and Kim (2015). Conversely, STARU only focused on solving problems with many parameters and highly non-linear systems in which implementation of the Monte Carlo sampling method is required because of the complexity of the systems. The main objectives of developing STARU are to reduce the computation time and increase the acceptance rate of the data assimilation process for complex systems. Consequently, we found that STARU effectively enhanced the data assimilation results for the reflood tests with higher acceptance rates and faster convergence. Furthermore, better improvements and more stable convergence of the system states were also observed, which effectively facilitated the search process for the most sensitive physical models in the predictions. In the future, STARU may be suitable for implementation in the other complex problems to calibrate the models, examine the uncertainty propagation, and study the sensitivity analysis.

In this paper we propose a novel four parameter continuous univariate distribution that can be motivated from at least two approached. The first one views the distribution as a generalization of the uniform one that allows for uncertainty specification at the vicinity of its bounds (gradually) represented via two Pareto tails. The second one is that of an asymmetric heavy-tailed, peaked distribution with an unbounded domain with the property that the location of the mode is not uniquely determined but rather is described by an uniform range. Properties of the distribution are described and a maximum likelihood estimation (MLE) procedure for the mode location and the Pareto tails parameters is presented. The procedure is illustrated by means of an i.i.d. sample of standardized log-differences of bi-monthly 30-year US certificate deposit interest rates for the period from 1964-2004. The sample is constructed utilizing the Auto-Regressive Conditional Heteroscedastic (ARCH) time series model devised by the Nobel Laureate R.F. .

The present study explains a steady-state method of measuring convective heat transfer coefficient on the casing of an axial flow turbine. The goal is to develop an accurate steadystate heat transfer method for the comparison of various casing surface and tip designs used for turbine performance improvements. The free-stream reference temperature, especially in the tip gap region of the casing varies monotonically from the rotor inlet to rotor exit due to work extraction in the stage. In a heat transfer problem of this nature, the definition of the free-stream temperature is not as straight forward as constant free-stream temperature type problems. The accurate determination of the convective heat transfer coefficient strongly depends on the magnitude of the local freestream reference temperature varying in axial direction, from the rotor inlet to exit. The current study explains a strategy for the simultaneous determination of the steady-state heat transfer coefficient and free-stream reference temperature on the smooth casing of a single stage rotating turbine facility. The heat transfer approach is also applicable to patterned casing surfaces. A detailed uncertainty analysis follows a detailed description of the casing heat transfer measurements. The overall uncertainty of the method developed is between 5 % and 8 % of convective heat transfer coefficient.

This paper presents a set of modular test parts specially designed for the evaluation of the extraction operation using CT measuring systems. The motivation in developing the test parts was to individually quantify different types of CT-induced measurement errors and their influence on measurement results. The characteristics of the test parts and the evaluation procedure used to access different types of measurement errors are described. An experimental study using the test parts is included. Discussions on the use of the test parts for evaluating the metrological performance of CT systems are carried out.

The application of computed tomography (CT) is growing rapidly in the field of dimensional metrology. In the current state of the art, however, CT-systems cannot provide the same accuracy as tactile coordinate measuring systems, and one of the main reasons is the high level of noise generated in CT measurements. This paper presents an experimental approach to investigate the influence of the extraction operation on geometrical evaluations performed with CT-systems. The methodology consists in performing systematic experiments using different setup parameters on calibrated workpieces; and comparing characteristics of the extracted integral features in both the space and the frequency domains. The approach was used to analyse a set of CT measurement data extracted with different setup parameters and evaluated with different verification operators. The results demonstrate that distortions generated during the extraction operation with CT systems may have great impact on the results of geometrical evaluations. In this context, the analysis of the integral extracted features obtained with systematic experiments showed to be a useful tool to evaluate the quality of the extraction operation, and thus, to define the setup parameters in order to improve the accuracy of geometrical evaluations.

The early detection of anomalies through the analysis of the neutron noise recorded by incore and ex-core instrumentation gives the possibility to take proper actions before such problems lead to safety concerns or impact plant availability. The study of the neutron fluctuations permits to detect and differentiate anomalies depending on their type and possibly to characterize and localize such anomalies. This method is non-intrusive and does not require any external perturbation of the system. To effectively use the neutron noise for reactor diagnostics it is essential to accurately model the effects of the anomalies on the neutron field. This paper deals with the development and validation of a neutron noise simulator for reactors with different geometries. The neutron noise is obtained by solving the frequency-domain two-group neutron diffusion equation in the first order approximation. In order to solve this partial differential equation a code based on a high order finite element method is developed. The novelty of this simulator resides on the possibility of dealing with rectangular meshes in any kind of geometry, thus allowing for complex domains and any location of the perturbation. The finite element method also permits automatic refinements in the cell size (h-adaptability) and in its polynomial degree (p-adaptability) that lead to a fast convergence. In order to show the possibilities of the neutron noise simulator developed a perturbation in a hexagonal two-dimensional reactor is investigated in this paper.

Uncertainty of groundwater model predictions has in the past mostly been related to uncertainty in the hydraulic parameters, whereas uncertainty in the geological structure has not been considered to the same extent. Recent developments in theoretical methods for quantifying geological uncertainty have made it possible to consider this factor in groundwater modeling. In this study we have applied the multiple-point geostatistical method (MPS) integrated in the Stanford Geostatistical Modeling Software (SGeMS) for exploring the impact of geological uncertainty on groundwater flow patterns for a site in Denmark. Realizations from the geostatistical model were used as input to a groundwater model developed from Modular three-dimensional finitedifference ground-water model (MODFLOW) within the Groundwater Modeling System (GMS) modeling environment. The uncertainty analysis was carried out in three scenarios involving simulation of groundwater head distribution and travel time. The first scenario implied 100 stochastic geological models all assigning the same hydraulic parameters for the same geological units. In the second scenario the same 100 geological models were subjected to model optimization, where the hydraulic parameters for each of them were estimated by calibration against observations of hydraulic head and stream discharge. In the third scenario each geological model was run with 216 randomized sets of parameters. The analysis documented that the uncertainty on the conceptual geological model was as significant as the uncertainty related to the embedded hydraulic parameters.

As already well known, Terna leads the Working Package 5 of The Italian demo of the H2020 Osmose project, which stands for Optimal System-Mix Of flexibility Solutions for European electricity. The main purpose of OSMOSE is developing a novel Energy Management System (EMS), which allows managing distributed Renewable Energy Sources (RES) and grid congestions, by properly coordinating innovative flexibility resources which include Dynamic Thermal Rating (DTR) and Demand Side Response (DSR). In this innovative approach, one of the two DTR investigated methods, which have been both developed by Ensiel, a consortium of Italian universities active in power systems research, is the Weather Based DTR. This technique is based on a thermo-mechanical model of the monitored line and on a detailed weather forecast of the involved area, having as final output the loadability curve of the line. The main features of the Weather Based DTR solution are described in this paper, and the first experimental results obtained on real case studies are presented and discussed in order to prove their effectiveness.

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the "with measures" scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance. Implications: A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.

Sensitivity analysis methods have recently received much attention for identifying important uncertainty sources (or uncertain inputs) and improving model calibrations and predictions for hydrological models. However, it is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models (PBHMs) because of its variant uncertainty sources and high computational cost. Therefore, a global sensitivity analysis method that is capable of simultaneously analyzing multiple uncertainty sources of PBHMs and providing quantitative sensitivity analysis results is still lacking. In an effort to develop a new tool for overcoming these weaknesses, we improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indices for subdivided parameters. A new binning method and Latin hypercube sampling (LHS) were implemented for estimating these new sensitivity indices. For test and demonstration purposes, this improved global sensitivity analysis method was implemented to quantify three different uncertainty sources (parameters, models, and climate scenarios) of a three-dimensional large-scale process-based hydrologic model (Process-based Adaptive Watershed Simulator, PAWS) with an application case in an ∼ 9000 km 2 Amazon catchment. The importance of different uncertainty sources was quantified by sensitivity indices for two hydrologic out-puts of interest: evapotranspiration (ET) and groundwater contribution to streamflow (Q G ). The results show that the parameters, especially the vadose zone parameters, are the most important uncertainty contributors for both outputs. In addition, the influence of climate scenarios on ET predictions is also important. Furthermore, the thickness of the aquifers is important for Q G predictions, especially in main stream areas. These sensitivity analysis results provide useful information for modelers, and our method is mathematically rigorous and can be applied to other large-scale hydrological models.

Sensitivity analysis methods have recently received much attention for identifying important uncertainty sources (or uncertain inputs) and improving model calibrations and predictions for hydrological models. However, it is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models (PBHMs) because of its variant uncertainty sources and high computational cost. Therefore, a global sensitivity analysis method that is capable of simultaneously analyzing multiple uncertainty sources of PBHMs and providing quantitative sensitivity analysis results is still lacking. In an effort to develop a new tool for overcoming these weaknesses, we improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indices for subdivided parameters. A new binning method and Latin hypercube sampling (LHS) were implemented for estimating these new sensitivity indices. For test and demonstration purposes, this improved global sensitivity analysis method was implemented to quantify three different uncertainty sources (parameters, models, and climate scenarios) of a three-dimensional large-scale process-based hydrologic model (Process-based Adaptive Watershed Simulator, PAWS) with an application case in an ∼ 9000 km 2 Amazon catchment. The importance of different uncertainty sources was quantified by sensitivity indices for two hydrologic out-puts of interest: evapotranspiration (ET) and groundwater contribution to streamflow (Q G ). The results show that the parameters, especially the vadose zone parameters, are the most important uncertainty contributors for both outputs. In addition, the influence of climate scenarios on ET predictions is also important. Furthermore, the thickness of the aquifers is important for Q G predictions, especially in main stream areas. These sensitivity analysis results provide useful information for modelers, and our method is mathematically rigorous and can be applied to other large-scale hydrological models.

Summary
Viewing psychotherapy conversations from an epistemic perspective involves analysing how psychotherapist and client manage their knowledge, insufficient knowledge (uncertainty) and lack of knowledge (unknowledge). This article focuses on the use of the epistemic disclaimer “I don’t know”, i.e., a linguistic expressions that speakers employ to indicate uncertainty or unknowledge, in a corpus of psychotherapy sessions. The main aims, both qualitative and quantitative, are to identify the syntactic and pragmatic manifestations of “I don’t know” in the corpus (linguistic analysis), which of them express unknowledge or uncertainty and why (epistemic analisys), how many of them are used by client and psychotherapist respectively. The practical purpose of this study is to give psychotherapists, counselors, clients etc. a few easy criteria for establishing when and why I don’t know communicates uncertainty or unknowledge.

This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times.

In this paper we are looking for answers: are domestic companies operating in small market economies such as the Baltics with little or no direct foreign involvement also at risk, taking into account that our companies mainly as a mean of exchange are using euro. The aim of this study is to examine the foreign exchange rate exposure of domestic corporations in the Baltic States. The study shows that companies in the Baltic States tend not to manage their foreign exchange risk properly and some of the companies are thus exposed to significant losses due to fluctuations in currency exchange rates. The VaR estimates are proposed and evaluated as a method to measure the position that is necessary to hedge.

The VIS/NIR bands polarization sensitivity of Joint Polar Satellite Sensor 1 (JPSS1) Visible/Infrared Imaging Radiometer Suite (VIIRS) instrument was measured using a broadband source. While polarization sensitivity for bands M5-M7, I1, and I2 was less than 2.5%, the maximum polarization sensitivity for bands M1, M2, M3, and M4 was measured to be 6.4%, 4.4%, 3.1%, and 4.3%, respectively with a polarization characterization uncertainty of less than 0.3%. A detailed polarization model indicated that the large polarization sensitivity observed in the M1 to M4 bands was mainly due to the large polarization sensitivity introduced at the leading and trailing edges of the newly manufactured VISNIR bandpass focal plane filters installed in front of the VISNIR detectors. This was confirmed by polarization measurements of bands M1 and M4 bands using monochromatic light. Discussed are the activities leading up to and including the instruments two polarization tests, some discussion of the polarization model and the model results, the role of the focal plane filters, the polarization testing of the Aft-Optics-Assembly, the testing of the polarizers at Goddard and NIST and the use of NIST's T-SIRCUS for polarization testing and associated analyses and results.

Carbon dioxide (CO2) has reached a higher level of emissions in the last decades, and as it is widely known, CO2 is responsible for numerous environmental problems, such as climate change. Thus, there is a great need for the application of CO2 capture and storage, as well as of CO2 utilization technologies (CCUS). This review article focuses on summarizing the current CCUS state-of-the-art methods used in Europe. Special emphasis has been given to mineralization methods/technologies, especially in basalts and sandstones, which are considered to be suitable for CO2 mineralization. Furthermore, a questionnaire survey was also carried out in order to investigate how informed about CO2 issues European citizens are, as well as whether their background is relative to their positive or negative opinion about the establishment of CCUS technologies in their countries. In addition, social acceptance by the community requires contact with citizens and stakeholders, as well as ensuring mutual t...