Set Of Model Parameters Research Articles

To what extent does river routing matter in hydrological modeling?

Abstract. Spatially distributed hydrology and land surface models are typically applied in combination with river routing schemes that convert instantaneous runoff into streamflow. Nevertheless, the development of such schemes has been somehow disconnected from hydrologic model calibration research, although both seek to achieve more realistic streamflow simulations. In this paper, we seek to bridge this gap to understand the extent to which the configuration of routing schemes affects hydrologic model parameter searches in water resources applications. To this end, we configure the Variable Infiltration Capacity (VIC) model coupled with the mizuRoute routing model in the Cautín River basin (2770 km2), Chile. We use the Latin hypercube sampling (LHS) method to generate 3500 different model parameters sets, for which basin-averaged runoff estimates are obtained directly (no routing or instantaneous runoff case) and are subsequently compared against outputs from four routing schemes (unit hydrograph, Lagrangian kinematic wave, Muskingum–Cunge, and diffusive wave) applied with five different routing time steps (1, 2, 3, 4, and 6 h). The results show that incorporating routing schemes may alter streamflow simulations at sub-daily, daily, and even monthly timescales. The maximum Kling–Gupta efficiency (KGE) obtained for daily streamflow increases from 0.64 (instantaneous runoff) to 0.81 (for the best routing scheme), and such improvements do not depend on the routing time step. Moreover, the optimal parameter sets may differ depending on the routing scheme configuration, affecting the baseflow contribution to total runoff. Including routing models decreases streamflow values in flood frequency curves and may alter the probabilistic distribution of the medium- and low-flow segments of the flow duration curve considerably (compared to the case without routing). More generally, the results presented here highlight the potential impacts of river routing implementations on water resources applications that involve hydrologic models and, in particular, parameter calibration.

Modeling of the magnetobarocaloric effect in the framework of the mean-field theory

The magnetobarocaloric effect (MBCE) is studied, which consists in the change of entropy and temperature in a layer of magnetostrictive material upon simultaneous application of a magnetic field and pressure. The MBCE is described in the framework of the generalized mean-field model accounting both the nonlinear dependence of the effective magnetic intensity on magnetization and its dependence on pressure. To test the developed MBCE model, a comparison with experimental data for La0.85Ag0.15MnO3 manganite ceramics is performed. Using a single set of model parameters, the results can be reproduced with good accuracy. Based on the obtained values for the material constants, the thermodynamic efficiency of the Brayton cycle on the magnetobarocaloric effect is calculated. The combined use of magnetic field and pressure allows to significantly increase the efficiency of this cycle (by 10%–20%), while the entropy can increase by 45%.

Dynamic mode decomposition with memory.

This study proposed a numerical method of dynamic mode decomposition with memory (DMDm) to analyze multidimensional time-series data with memory effects. The memory effect is a widely observed phenomenon in physics and engineering and is considered to be the result of interactions between the system and environment. Dynamic mode decomposition (DMD) is a linear operation-based, data-driven method for multidimensional time-series data proposed in 2008. Although DMD is a successful method for time-series data analysis, it is based on ordinary differential equationsand thus cannot incorporate memory effects. In this study, we formulated the abstract algorithmic structure of DMDm and demonstrate its utility in overcoming the memoryless restriction imposed by existing DMD methods on the time-evolution model. In the numerical demonstration, we utilized the Caputo fractional differential to implement an example of DMDm such that the time-series data could be analyzed with power-law memory effects. Thus, we developed a fractional DMD, which is a DMD-based method with arbitrary (real value) order differential operations. The proposed method was applied to synthetic data from a set of fractional oscillators and model parameters were estimated successfully. The proposed method is expected to be useful for scientific applications and aid in model estimation, control, and failure detection of mechanical, thermal, and fluid systems in factory machines, such as modern semiconductor manufacturing equipment.

An Optimal Parameter Extraction Procedure for SiC Power MOSFET Model

A simple and efficient parameter extraction method for Silicon Carbide (SiC) power MOSFET model is described. This method uses nonlinear optimization algorithm to find the optimal set of parameters to model. The optimizer algorithm starts with initial guess parameters, extracted from measurement, to provide a set of parameters minimizing errors between model and measurements data in entire operating regions of the device. The starting initial guess parameter values give to the algorithm a closed solution to obtain the optimal set of model parameters with reduced iteratives. The Levenberg-Marquardt (LM) algorithm will be used in this work. The efficiency of the proposed extraction method is proved with the good agreements obtained between the model and the measurements.

Multi-forward-step battery voltage prediction for real-world electric vehicles using gated recurrent units

Accurate battery voltage prediction performs a critical role in electric vehicles, especially safety risks prognosis in future periods. This paper proposes a multi-forward-step battery voltage prediction method using gated recurrent units. Actual vehicle operation data and weather data are used together for model training. A two-step procedure including data cleaning and correlation analysis is conducted to improve training efficiency and prediction accuracy. Furthermore, the effects of different model parameters on the prediction results are also investigated to obtain an optimal set of model parameters. Additionally, the model is tested with the data of the second year. The results proved that the model not only has a high prediction accuracy, but also has a strong stability and robustness. On this basis, a research plan for real-time multi-forward-step battery voltage prediction is further developed, whereby identifying occurrence of battery failure and mitigate the risk of thermal runaway by voltage predicting.

Parameterization of a National Groundwater Model for New Zealand

Groundwater is a vital source of water for humanity, with up to 50% of global drinking water and 43% of irrigation water being derived from such sources. Quantitative assessment and accounting of groundwater is essential to ensure its sustainable management and use. TopNet-GW is a parsimonious groundwater model that was developed to provide groundwater simulation at national, regional, and local scales across New Zealand. At a national scale, the model can help local government authorities estimate groundwater resource reliability within and between regions. However, as many catchments are ungauged, the model cannot be calibrated to local conditions against observed data. This paper, therefore, describes a method to derive an a priori, reach-scale groundwater model parameter set from national-scale hydrogeological datasets. The parameter set includes coefficients of lateral (kl) and vertical (kr) conductivity and effective aquifer storage (S). When the parameter set was used with the TopNet-GW model in the Wairau catchment in the Marlborough region (South Island, New Zealand), it produced a poor representation of peak river flows but a more accurate representation of low flows (overall NSE 0.64). The model performance decreased in the smaller Opawa catchment (NSE 0.39). It is concluded that the developed a priori parameter set can be used to provide national groundwater modeling capability in ungauged catchments but should be used with caution, and model performance would benefit greatly from local scale calibration. The parameter derivation method is repeatable globally if analogous hydrological and geological information is available and thus provides a basis for the parameterization of groundwater models in ungauged catchments. Future research will assess the spatial variability of parameter performance at a national scale in New Zealand.

Fusing Numerical Weather Prediction Ensembles with Refractivity Inversions during Surface Ducting Conditions

Abstract This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island field experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared with this ground truth refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the ground truth parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.

Ductile fracture prediction of HPDC aluminum alloy based on a shear-modified GTN damage model

In this paper, we investigate how the shear-modified Gurson-Tvergaard-Needleman (GTN) model can be used to reveal the effect of manufacturing-process-induced porosity on the scatter of ductile fracture properties of a high-pressure die-casting aluminum alloy. The GTN model exhibits great advantages in predicting the variation of failure strain/displacement by altering the initial void volume fraction (f0), compared with uncoupled ductile damage models. For a specific metallic material, one unique set of model parameters is usually determined from experiments, which leads to the fact that only a deterministic failure strain/displacement can be obtained under a fixed porosity. To overcome this shortcoming of the shear-modified GTN model, a novel parameter calibration scheme is proposed and validated in the present study. Following the physical background of the GTN model, the twelve material parameters of the shear-modified GTN model have been determined based on a combination of macroscopic mechanical tests covering a wide range of stress states and micromechanics-based unit cell simulations. Different from the existing calibration strategies, the simulation results of unit cells embedded with a spherical void have revealed a mutual dependence between the f0 and critical void volume fraction (fc). By conducting interrupted shear tests, the evolution of secondary nucleated void volume fraction was identified according to the statistical results of the fractured brittle silicon particles. The remaining parameters have been further iteratively determined. In the end, the effectiveness of the proposed parameter determination approach is confirmed based on the accurate prediction of stochastic ductile failure properties (both the global failure displacements and the local failure patterns) in different fracture tests. Given the proposed parameters identification strategy, the GTN material models have been enriched to predict the ductile failure behavior of metallic materials, which possess relatively high porosities and more pronounced scattering in failure properties.

Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data

Objective:Prediction of physiological mechanics are important in medical practice because interventions are guided by predicted impacts of interventions. But prediction is difficult in medicine because medicine is complex and difficult to understand from data alone, and the data are sparse relative to the complexity of the generating processes. Computational methods can increase prediction accuracy, but prediction with clinical data is difficult because the data are sparse, noisy and nonstationary. This paper focuses on predicting physiological processes given sparse, non-stationary, electronic health record data in the intensive care unit using data assimilation (DA), a broad collection of methods that pair mechanistic models with inference methods. Methods:A methodological pipeline embedding a glucose–insulin model into a new DA framework, the constrained ensemble Kalman filter (CEnKF) to forecast blood glucose was developed. The data include tube-fed patients whose nutrition, blood glucose, administered insulins and medications were extracted by hand due to their complexity and to ensure accuracy. The model was estimated using an individual’s data as if they arrived in real-time, and the estimated model was run forward producing a forecast. Both constrained and unconstrained ensemble Kalman filters were estimated to compare the impact of constraints. Constraint boundaries, model parameter sets estimated, and data used to estimate the models were varied to investigate their influence on forecasting accuracy. Forecasting accuracy was evaluated according to mean squared error between the model-forecasted glucose and the measurements and by comparing distributions of measured glucose and forecast ensemble means. Results:The novel CEnKF produced substantial gains in robustness and accuracy while minimizing the data requirements compared to the unconstrained ensemble Kalman filters. Administered insulin and tube-nutrition were important for accurate forecasting, but including glucose in IV medication delivery did not increase forecast accuracy. Model flexibility, controlled by constraint boundaries and estimated parameters, did influence forecasting accuracy. Conclusion:Accurate and robust physiological forecasting with sparse clinical data is possible with DA. Introducing constrained inference, particularly on unmeasured states and parameters, reduced forecast error and data requirements. The results are not particularly sensitive to model flexibility such as constraint boundaries, but over or under constraining increased forecasting errors.

New aspects of a single-crystal gradient plasticity model: Rate-dependency of size-dependent response

Gradient crystal plasticity and rate-dependent plasticity models have been widely developed. However, describing the rate-dependency of the size-dependent behavior remains a long-term challenge and is of great interest in the field. This study investigates the interaction between the rate and size dependency of single crystals. The main contribution is to determine whether the size dependency of the stress–strain responses increases or decreases with increasing deformation rates. A thermodynamically consistent model based on the finite-deformation single-crystal gradient plasticity framework proposed by Gurtin is presented. In the current formulation, the particular features are a rate-dependent function and a rate-sensitivity parameter incorporated into the dissipative gradient strengthening term. These features allowed us to observe the disparate rate-dependencies induced by the dissipative conventional and dissipative gradient terms. From an experimental perspective, few studies have investigated the interaction between the size- and rate-dependence of single crystals. Interestingly, these studies reported different modes of interaction. Here, we categorize existing experimental stress–strain data into two distinct groups: first, the stress data obtained at fixed overall strains, and second, those captured at offset strains, that is, at the yield or post-yield strains. The numerical results obtained through a two-dimensional framework and based on a common set of material and modeling parameters were qualitatively compared with existing experimental data reported for different materials. Trends in the interactions were numerically captured and discussed. The results indicate the promising potential of the model for describing the rate-dependency of the size-dependent yield stress and subsequent behaviors.

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics.

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either (1) top-down and interpretable, but not flexible enough to account for experimental data, or (2) bottom-up and biologically realistic, but too intricate to interpret and hard to fit to data. To avoid the shortcomings of these approaches, we present a new plasticity rule based on a geometrical readout mechanism that flexibly maps synaptic enzyme dynamics to predict plasticity outcomes. We apply this readout to a multi-timescale model of hippocampal synaptic plasticity induction that includes electrical dynamics, calcium, CaMKII and calcineurin, and accurate representation of intrinsic noise sources. Using a single set of model parameters, we demonstrate the robustness of this plasticity rule by reproducing nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Our model also predicts that in vivo-like spike timing irregularity strongly shapes plasticity outcome. This geometrical readout modelling approach can be readily applied to other excitatory or inhibitory synapses to discover their synaptic plasticity rules.

Investigating the electrode-electrolyte interface modelling in cochlear implants

Objective. Proposing a good electrode-electrolyte interface (EEI) model and properly identifying relevant parameters may help designing safer and more optimized auditory nerve fiber stimulation and recording in cochlear implants (CI). However, in literature, EEI model parameter values exhibit large variability. We aim to explain some root causes of this variability using the Cole model and its simpler form, the Basic RC model. Approach. We use temporal and spectral methods and fit the models to stimulation pulse voltage response (SPVR) and electrochemical impedance spectroscopy (EIS) data. Main Results. Temporal fittings show that there are multiple sets of model parameters that provide a good fit to the SPVR data. Therefore, small methodological differences in literature may result in different model fits. While these models share similar characteristics at high frequencies >500 Hz, the SPVR fitting is blind to low frequencies, thus it cannot correctly estimate the Faradaic resistor. Similarly, the polarization capacitor and its fractional order are not estimated robustly (capacitor variations in the nano- to micro-farad range) due to limited observation of mid-range frequencies. EIS provides a good model fit down to ∼3Hz, and thus robust estimation for the polarization capacitor. At lower frequencies charge mechanisms may modify the EEI, requiring multi-compartment Cole model fitting to EIS to improve the estimation of Faradaic characteristics. Our EIS data measurements down to 0.05Hz show that a two-compartment Cole model is sufficient to explain the data. Significance. Our study describes the scope and limitation of SPVR and EIS fitting methods, by which literature variability is explained among CI EEI models. The estimation of mid-to-low-frequency characteristics of the CI EEI is not in the scope of the SPVR method. EIS provides a better fit; however, its results should not be extrapolated to unobserved frequencies where new charge transfer mechanisms may emerge at the EEI.

Similarity-Aware Multimodal Prompt Learning for fake news detection

The standard paradigm for fake news detection relies on utilizing text information to model the truthfulness of news. However, the subtle nature of online fake news makes it challenging to solely rely on textual information for debunking. Recent studies that focus on multimodal fake news detection have demonstrated superior performance compared with text-only methods, thereby establishing a new paradigm for detecting fake news. However, this paradigm may require a large number of training instances or updating the entire set of pre-trained model parameters. Furthermore, existing multimodal approaches typically integrate cross-modal features without considering the potential introduction of noise from unrelated semantic representations. To address these issues, this paper proposes the Similarity-Aware Multimodal Prompt Learning (SAMPLE) framework. Incorporating prompt learning into multimodal fake news detection, we used three prompt templates with a soft verbalizer to detect fake news. Moreover, we introduced a similarity-aware fusing method, which adaptively fuses the intensity of multimodal representation so as to mitigate noise injection from uncorrelated cross-modal features. Evaluation results show that SAMPLE outperformed previous work, achieving higher F1 and accuracy scores on two multimodal benchmark datasets, demonstrating its feasibility in real-world scenarios, regardless of data-rich or few-shot settings.

A novel variational robust filter with Gaussian mixture model for unknown non-Gaussian noises

In numerous practical applications, the presence of unpredictable abnormal behaviors often leads to random measurement outliers, causing the random measurement noises are no longer Gaussian but unknown non-Gaussian distributions, and it is intractable to obtain accurate a priori information about the noises. To address this challenge and enable nonlinear state estimation under such measurement noises, we propose a novel robust filter based on Gaussian mixture model (GMM). Firstly, a GMM is introduced to model the unknown non-Gaussian measurement likelihood probability density function (ML-PDF). Without relying on any prior noise statistics, the GMM can accurately model the ML-PDF utilizing a set of model parameters adaptively learned by the variational Bayesian (VB) method. Then, considering the high computational burden associated with the existing variational iterative process, we develop an improved VB method, which independently calculates the posterior distributions of state and unknown model parameters, thereby improving the computational efficiency. Finally, a new robust filter is derived by integrating the GMM with the improved VB approach, The effectiveness and superiority of the filter are demonstrated through both numerical simulations and real-world data analysis.

Application of methods of economic cybernetics to the design of a sports financing model

One of the priority directions of the state policy is the development of the sphere of physical culture and sports as a significant branch of national economy and socio-economic practice of effective management. In the current economic conditions, on the one hand, there are tendencies to increase commercialization in the sports industry. On the other hand, the need for financial support, diversification of sources and modernization of the sports financing model, increasing the effectiveness of public-private partnership mechanisms, and developing state order tools in order to achieve sustainable industry dynamics are announced. The severity and unresolved financial and economic problems of sports development determined the choice of the direction of this study and its relevance. In the process of research based on the synthesis of system and process approaches, we used methods of economic cybernetics, organization theory, construction of structural-information decision-making schemes using analytical assessments, tools of structural-logical and criteria analysis, methodological approaches of unit economics. As a result, the poly-subject environment of the industry ecosystem was determined, a conceptual approach to the formation of the financial model of sports was developed, the structural and functional space of the ecosystem business model was designed taking into account its digital component, sources, business processes and alternative financing objects were identified, a set of model parameters and infrastructure components were developed.

The Unscented Kalman Filter for Nonlinear Parameter Identification of Adaptive Cruise Control Systems

This paper develops and investigates a dual unscented Kalman filter (DUKF) for the joint nonlinear state and parameter identification of commercial adaptive cruise control (ACC) systems. Although the core functionality of stock ACC systems, including their proprietary control logic and parameters, is not publicly available, this work considers a car-following scenario with a human-driven vehicle (leader) and an ACC engaged ego vehicle (follower) that employs a constant time-headway policy (CTHP). The objective of the DUKF is to determine the CTHP parameters of the ACC by using real-time observations of space-gap and relative velocity from the vehicle's onboard sensors. Real-time parameter identification of stock ACC systems is essential for assessing their string stability, large-scale deployment on motorways, and impact on traffic flow and throughput. In this regard, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_{2}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_\infty$</tex-math></inline-formula> string stability conditions are considered. The observability rank condition for nonlinear systems is adopted to evaluate the ability of the proposed estimation scheme to estimate stock ACC system parameters using empirical data. The proposed filter is evaluated using empirical data collected from the onboard sensors of two 2019 SUV vehicles, namely Hyundai Nexo and SsangYong Rexton, equipped with stock ACC systems; and is compared with batch and recursive least-squares optimization. The set of ACC model parameters obtained from the proposed filter revealed that the commercially implemented ACC system of the considered vehicle (Hyundai Nexo) is neither <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_{2}$</tex-math></inline-formula> nor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathfrak {L}}_\infty$</tex-math></inline-formula> string stable.

A highly accurate peak time formula of epidemic outbreak from the SIR model

Forecasting the epidemic peak time right from the origination of a disease is vital to take over dynamical behaviour of its spread over time. The decision of isolation, social distance and lock down strategic progresses does all rely on an accurate prediction of the peak time so that reduction of the time of peak or of the infected size of population will be made possible. Therefore, recent efforts concentrated on deriving elaborative and analytically accessible expressions representing the peak time of the infected compartment from the classical SIR epidemic mathematical model. In this research paper, two closed-form formulae are introduced to yield a straightforward computation of peak time of an infectious disease with no restrictions on the SIR quantities. In addition to this, the calculations can be implemented on a usual calculator, without requiring the use of advanced mathematical functions, having provided the initial fractions of infected and susceptible populations as well as the recovery to infectious ratio. A comparison including the COVID-19 data is fulfilled with the very recent formulas available in the open literature. With the proposed new scalings, evaluation of the peak time is reduced only to two parameter space and the accuracy of the present formulas in reduced form is ultimately confirmed yielding an error of order of magnitude 10−4 valid for the complete regime of the set of SIR model parameters. Even in the case of an endemic, the past peak time of the illness can also be captured accurately by the given formulae. Two simple approximations in terms of usual geometric series are also provided. These can be safely used with a pocket calculator without sophisticated laboratory equipments.

Simulation analysis of visual perception model based on pulse coupled neural network

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

A framework to assess future water-resource under climate change in northern Morocco using hydro-climatic modelling and water-withdrawal scenarios

Study regionThe Bas-Loukkos catchment, a Mediterranean catchment in northern Morocco exposed to growing water withdrawal caused mainly by agricultural development. Study focusFor adaptation to climate change, water managers have to consider the high and various uncertainties. To assess impacts of climate change on projected water resources, this study aimed to develop a smart analysis framework to provide scientific information by exploring the complexity of many projections combined with hydrological models. Uncertainties were quantified using 13 pair-wise combinations of 5 regional climate models forced by 4 global climate models under two emissions scenarios (RCP4.5 and RCP8.5), data with and without bias correction (using empirical quantile mapping), and two sets of GR2M hydrological model parameters corresponding to different precipitation conditions. The Budyko hypothesis was used to analyse combined effects of climate change on water resources according to water-withdrawal scenarios. Climate and hydrological projections have been analyzed over three periods: short-term [2020–2040], medium-term [2041–2060] and long-term [2081–2100]. New hydrological insights for the regionResults from all simulations indicate that, in the long term (2081–2100), precipitation and discharge will decrease by ca. 21–38% and ca. 50–71%, respectively, compared to the reference period (1981–2005). Consequently, this decline in water resources will require water management strategies to adapt to the future climatic conditions and water demand.

Experiment and thermodynamic modelling of phase equilibria in PbO−“CuO0.5” and PbO−“CuO0.5”−“FeO1.5” slag systems with metal

The phase equilibria of the PbO−“CuO0.5” and PbO−“CuO0.5”−“FeO1.5” slag systems in equilibrium with solid metallic copper and/or liquid metallic lead−copper alloy were studied as part of the investigation of the 19-element PbO−ZnO−Cu2O−FeO−Fe2O3−CaO−SiO2−Al2O3−MgO−S−(As, Sn, Sb, Bi, Ag, Au, Ni, Cr, Co as minor elements) multicomponent slag/matte/metal/speiss system, supporting the operation and development of the existing and emerging pyrometallurgical processes. In the experimental portion of this study, samples underwent high temperature equilibration followed by quenching, after which the compositions in the liquid slag, solid oxide and metallic phases were directly measured by electron probe microanalysis (EPMA). Phase equilibria data on the massicot (PbO), spinel ((Fe, Cu)Fe2O4), cuprite (Cu2O), lead ferrite (Pb2+xFe2O5+x), magnetoplumbite (Pb1+xFe12−xO19−x), copper plumbite (Cu2PbO2), and delafossite (CuFeO2) primary phase fields were obtained between 688 and 1000 °C. A key finding of the experimental study of the PbO−“CuO0.5” subsystem was that the copper plumbite (Cu2PbO2) phase melted incongruently, contradicting previous studies that suggested it apparently decomposed below the binary eutectic temperature to form massicot (PbO) and cuprite (Cu2O). Based on the results of past and present experimental studies, a self-consistent set of thermodynamic model parameters describing all phases in the system was derived using the FactSage software package.