Sensitivity Of Model Parameters Research Articles

Reaction Kinetics-Based Modeling and Parameter Sensitivity Analysis of Direct Ethanol Fuel Cells

Ethanol is considered an alternative fuel to power fuel cells, especially due to its ease of transport and storage and renewable production on a large scale. However, its use in direct ethanol fuel cells (DEFC) is still limited by incomplete electro-oxidation and slow reaction kinetics. Modeling approaches have focused on investigating different reaction mechanisms, but so far, no formal analysis of model parameter sensitivity has been conducted. This work modeled and identified sensitive parameters for different types of Pt–Sn catalysts previously prepared by our research group that displayed good performance in the 5–15 mW/cm2 range (relative to a performance of 12 mW/cm2 achieved by a commercial ETEK catalyst). Analyses to study the effect of these parameters on coverage fraction distribution, reaction rates and possible correlations were also performed. The model was developed based on Butler–Volmer kinetics and on a reaction mechanism previously reported in the literature. Statistical developments were considered to compute parameter uncertainties for a non-linear system with non-linear restrictions. The model achieved very good fits to experimental data, with low RMSE values between 0.22 × 10−4 and 4.2 × 10−4 A/cm2, while also showing surface coverage fraction distributions in agreement with other experiment-based works from the literature. All catalysts taken into account, the most sensitive parameters were the reaction rate constants associated with the formation of adsorbed CH3CO, and the direct and reverse water dissociative adsorption reactions, respectively. Additional analyses suggested that there is not much correlation between the parameters. The results from this work could contribute to the state-of-the-art DEFC models by providing insights into which variables may be assumed constant or which ones have the greatest impact on the model output, thus helping to reduce the model size and computational time for future broader DEFC models.

Sensitivity Analysis of Conductivity Models in Simulation of High-Altitude Electromagnetic Pulse

The reasonable conductivity model and parameters are important for the accurate simulation of high-altitude electromagnetic pulse. While in most public literature, the fitting formulas of the conductivity parameters are given directly without discussion of their physical meaning. In addition, few investigations were carried out in systematic analysis on the parameter sensitivity of the air conductivity model. In this article, the sensitivity of the parameters in the equilibrium ohmic model is analyzed, indicating that electron mobility is the most sensitive conductivity parameter. The law of the peak electric field with the electron mobility under different conditions of observation direction, burst height, yield, gamma energy, and gamma pulse waveform is systematically explored. It is concluded that a reasonable mobility fitting formula should consider the effects of air density, electric field intensity, and nonequilibrium effect, and the fitting result should be as close as possible to the credible measurement value.

Using experimental design to assess rate-dependent numerical models

To enhance the accuracy of advanced constitutive models for soft natural clays, several parameters are necessary, resulting in complexity of numerical modelling. However, the detailed effects of these parameters are not rigorously quantified towards their constitutive relationships. Thus, the aim of the paper is to assess such advanced models in order to determine the most significant parameters over the time series data. Two methods for Global Sensitivity Analysis (GSA), i.e. Experimental Design and the Sobol method, were used and benchmarked to assess the model predictions on a discretised domain in time and space. A rate-dependent Finite Element model using Creep-SCLAY1S and a hydro-mechanically coupled formulation for consolidation was used to study a Constant Rate of Strain (CRS) test. The value of GSA approaches adopted herein was to investigate the model predictions both in the temporal and spatial domain. The temporal analyses indicate three sets of significant model parameters in different portions of the CRS compression curve. Furthermore, the non-stationary nature of sensitivity results is exposed, identifying the parameters that lead to unique solutions for the CRS loading path. The FE implementation enabled the quantification of the most sensitive model parameters in the spatial domain. The spatial results that are governed by the rate-dependent processes in the soil (i.e. consolidation and creep) illustrated that Experimental Design was capable of providing sensitivity maps with satisfactory accuracy similar to the Sobol method. Experimental Design was found to be the most efficient method, concerning execution time and storage costs, to assess rate-dependent problems in Geotechnics.

Soil Erosion, Sediment Yield, and Runoff Modeling of the Megech Watershed Using the GeoWEPP Model

Modeling soil erosion, sediment yield, and runoff are crucial for managing reservoir capacity, water quality, and watershed soil productivity. However, the monitoring and modeling of soil erosion and sedimentation rates in developing countries such as Ethiopia is not well practiced; thus, the reservoir capacity is diminishing at faster rates. In this study, the soil erosion, sediment yield, and runoff in the Megech watershed, Upper Blue Nile Basin, Ethiopia were modeled using the physically-based geospatial interface, the Water Erosion Prediction Project (GeoWEPP). The GoWEPP model was calibrated and validated at the Angereb sub-watershed and simulated to representative sites to capture the spatiotemporal variability of soil erosion and sediment yield of the Megech watershed. The model parameter sensitivity analysis showed that the hydraulic conductivity (Ke) for all soil types was found to be the dominant parameter for runoff simulation, while rill erodibility (Kr), hydraulic conductivity (Ke), critical shear stress (τc), and inter rill erodibility (Ki) were found to be sensitive for sediment yield and soil loss simulation. The model calibration (2000–2002) and validation (2003–2004) results showed the capability of the GeoWEPP model; with R2 and NSE values, respectively, of 0.94 and 0.94 for calibration; and 0.75 and 0.65 for validation. In general, the results show that the sediment yield in the study watershed varied between 10.3 t/ha/year to 54.8 t/ha/year, with a weighted mean value of 28.57 t/ha/year. The GeoWEPP model resulted in higher sediment value over that of the design sediment yield in the study basin, suggesting the implementation of the best watershed management practices to reduce the rates of watershed sediment yield. Moreover, the mean soil loss rate for the Angerb sub-watershed was found to be 32.69 t/ha/year.

Multimodal learning for fetal distress diagnosis using a multimodal medical information fusion framework

Cardiotocography (CTG) monitoring is an important medical diagnostic tool for fetal well-being evaluation in late pregnancy. In this regard, intelligent CTG classification based on Fetal Heart Rate (FHR) signals is a challenging research area that can assist obstetricians in making clinical decisions, thereby improving the efficiency and accuracy of pregnancy management. Most existing methods focus on one specific modality, that is, they only detect one type of modality and inevitably have limitations such as incomplete or redundant source domain feature extraction, and poor repeatability. This study focuses on modeling multimodal learning for Fetal Distress Diagnosis (FDD); however, exists three major challenges: unaligned multimodalities; failure to learn and fuse the causality and inclusion between multimodal biomedical data; modality sensitivity, that is, difficulty in implementing a task in the absence of modalities. To address these three issues, we propose a Multimodal Medical Information Fusion framework named MMIF, where the Category Constrained-Parallel ViT model (CCPViT) was first proposed to explore multimodal learning tasks and address the misalignment between multimodalities. Based on CCPViT, a cross-attention-based image-text joint component is introduced to establish a Multimodal Representation Alignment Network model (MRAN), explore the deep-level interactive representation between cross-modal data, and assist multimodal learning. Furthermore, we designed a simple-structured FDD test model based on the highly modal alignment MMIF, realizing task delegation from multimodal model training (image and text) to unimodal pathological diagnosis (image). Extensive experiments, including model parameter sensitivity analysis, cross-modal alignment assessment, and pathological diagnostic accuracy evaluation, were conducted to show our models' superior performance and effectiveness.

Assessment of the parameter sensitivity for the ORYZA model at the regional scale - A case study in the Yangtze River Basin

While global sensitivity analysis (GSA) has been widely implemented for crop models, few studies have assessed the sensitivity of model parameters at the regional scale. To fully understand the overall sensitivity of parameters in a specific area, this study carried out a regional-scale global sensitivity analysis (RGSA) by selecting multiple stations through grids within the study area and integrated GSA results obtained by extended Fourier amplitude sensitivity test (EFAST) from these stations by statistical indicators to quantify parameter sensitivity at the regional scale. Taking the Yangtze River Basin (YRB) as the study area, RGSA was implemented for the ORYZA model for paddy rice under two water and nitrogen situations (WNCs) and four levels of number of stations for sensitivity analysis (NSSA). The results suggested that the applicable area for the results of sensitive and insensitive parameters partitioning, sensitivity ranks and sensitivity indices at a specific station were not larger than grid sizes of 8° × 8°, 4° × 4° and 2° × 2° around it, respectively. Thus, the NSSA needed for sensitive parameter identification, sensitivity ranking and sensitivity indices variability analysis at regional scale increased sequentially. It was recommended that a suitable number of stations should be chosen for analysis according to the sensitivity analysis purpose at the regional scale. Since WNC will affect the results of RGSA, such analysis was recommended to be performed separately for different WNCs. Highly sensitive parameters of the ORYZA model for paddy rice in YZB were screened out through RGSA, which were DVRJ, DVRP and RGRLMX. Model users can adopt the RGSA workflow used in this study for parameter sensitivity assessment of other crop models.

Adjoint-based sensitivity analysis of viscoelastic fluids at a low deborah number

This work performs an adjoint-based sensitivity analysis of a viscoelastic fluid flow around a cylinder in a confined channel. The viscoelastic behavior of the fluid is modeled by the Oldroyd-B constitutive equation and the quantity of interest of the flow is set to the resistance of the cylinder. A set of continuous adjoint governing equations is formulated to quantify the local sensitivities of rheological model parameters on the drag of the cylinder. The adjoint equations are solved together with the original resistance prediction problem, at the Deborah number 0.5. Final outcomes are the field resistance-sensitivity maps for the Oldroyd-B model parameters that imply the local relative importance of different underlying physical mechanisms reflected in the model parameters. In particular, the sensitivity map of relaxation parameter distinguishes two opposing effects of fluid elasticity to resistance of the cylinder depending on the location. While the fluid elasticity near the top and bottom surfaces reduces the resistance, it works adversely near the front and back surfaces. The example illustrates how adjoint-based sensitivity analysis can be utilized for developing effective flow control strategies in many applications working with viscoelastic fluids.

Modelling wavelength position of absorption feature using covariance weighted least square inversion

Reflectance spectroscopy is a rapid and non-destructive technique for probing the chemical composition of the earth surface. The minerals are characterised by absorption features whose wavelength is function of its crystal chemistry. In the present study, we propose a covariance weighted least square inversion approach to model the wavelength position of the absorption features. The approach takes into consideration the noise in the reflectance spectra and estimates the wavelength position with root mean square value of less than 5 nm for library spectra. The algorithm is then implemented on AVIRIS-NG datasets of Hutti-Maski schist belt to map the spatial variations in wavelength position of absorption features in wavelength range 2290–2360 nm. Sensitivity of model parameters to number of points, effect of number of data points on striping noise and limitations of the proposed algorithm are also discussed. It is observed that the algorithm based wavelength map is smooth and enables identification of gradients in absorption wavelength position. Mapping of such gradients are very important for understanding subtle changes in crystal chemistry due to hydrothermal alteration and possible mineralization. The generated wavelength map can be used in conjunction with alteration mineral assemblage to identify prospective zones for mineral exploration.

How the climate shapes stalagmites—A comparative study of model and speleothem at the Sofular Cave, Northern Turkey

Understanding how stalagmites grow under changing climate conditions is of great significance for their application as a paleoclimate archive. In this study, we present a shape modeling approach to stalagmite growth by combining three existing models accounting for climate variables, karst water chemistry, and speleothem deposition. The combined model requires only four input parameters: calcium concentration of the water drop, drip interval, cave temperature, and cave carbon dioxide (CO2) concentration. Using the output of the coupled atmosphere–ocean–land surface model MPI-ESM1.2 and the CaveCalc model for speleothem chemistry, we simulated stalagmite growth at Sofular Cave, Northern Turkey, (in the last 25 kyr) and compared the results to those of the existing So-1 stalagmite from the same cave. This approach allows simulating, completely independent of measured boundary conditions, a stalagmite geometry that follows the trend of the experimental data for the growth rate, with input parameters within the respective error ranges. When testing the sensitivity of the individual model parameters, the model suggests that the stalagmite radius mainly depends on the drip interval, whereas the growth rate is driven by the calcium concentration of the water drop. The model is also capable of showing some basic phenomena, like a decrease in growth rate (as observed in the real stalagmite), as CO2 concentration in the cave increases. The coupling of input parameters for the model to climate models represents the first attempt to understand an important climate archive in its shape and isotope content and opens the possibility for a new inverse approach to paleoclimate variables and model constraints.

Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest

Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 %). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.

A new fractional-order model for time-dependent damage of rock under true triaxial stresses

To reasonably describe the time-dependent characteristics of rock under true triaxial stresses and analyze the long-term stability of deep underground engineering, based on the understanding of time-dependent mechanical properties of rock under true triaxial stresses, a new fractional-order damage element and the method in which the crack initiation stress is taken as the starting point of creep and the crack damage stress as the starting point of creep accelerating stage were established. Then, combined with the Mogi-Coulomb strength criterion, a fractional-order creep damage model of rock under true triaxial stresses was proposed. The proposed model was further verified based on uniaxial, conventional triaxial and true triaxial creep test results. The theoretical curves of the model are highly consistent with the test curves, indicating that the proposed model has wide applicability and rationality. Meanwhile the sensitivity of true triaxial stress states and model parameters to the creep characteristics was further investigated. Finally, based on the proposed creep model, the time-dependent characteristics of rock under untested true triaxial stress levels were predicted. The research provides a theoretical basis for the prediction of time-dependent disasters and the long-term stability analysis of surrounding rock in deep engineering.

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

A finite control set model predictive direct speed controller for PMSM application with improved parameter robustness

Model predictive control (MPC) is an advanced control method that relies on a mathematical model containing the motor parameters in the field of Permanent Magnet Synchronous Motor (PMSM) application. However, the internal parameters of the motor and external load characteristic parameters are uncertain, and their actual values ​​may be inconsistent with the nominal values, resulting in prediction errors, which in turn affect the performance of the control algorithm. Based on the analysis of the parameter sensitivity of the motor prediction model, traditional finite control set model predictive direct speed controller (FCS-MPDSC) predicts state parameters based on a mathematical model, and there is a problem that the parameters of the motor model are uncertain, which will lead to prediction errors. In order to improve the robustness of FCS-MPDSC parameters, this paper proposes an improved FCS-MPDSC for PMSM application, which abandons the traditional PMSM control cascade structure and has the advantages of fast speed response performance, small speed error and strong parameter robustness. In order to solve the problem of load characteristic parameter uncertainty, Luenberger observer and the Model Reference Adaptive System (MRAS) algorithm are used to estimate the variation of load characteristic parameters in this paper. Moreover, a proportional-integral (PI) control term of speed error is introduced into the cost function to achieve the zero steady state error of speed and torque, which make full use of the robustness of integral action and also a faster dynamic response of direct speed control is maintained. In this paper, the HIL experimental platform is built according to the simulation. The proposed method reduces the steady-state error of the traditional method under the parameter mismatch. When the inductance parameter and the permanent magnet flux parameters deviate two times larger than nominal values, the steady-state error of the motor speed are 0.1125% and 0.8725%, respectively, demonstrating the effectiveness of the proposed method.

Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization

Abstract The prediction skill of a numerical model can be enhanced by calibrating the sensitive parameters that significantly influence the model forecast. The objective of the present study is to improve the prediction of surface wind speed and precipitation by calibrating the Weather Research and Forecasting (WRF) Model parameters for the simulations of tropical cyclones over the Bay of Bengal region. Ten tropical cyclones across different intensity categories between 2011 and 2017 are selected for the calibration experiments. Eight sensitive model parameters are calibrated by minimizing the prediction error corresponding to 10-m wind speed and precipitation, using a multiobjective adaptive surrogate model-based optimization (MO-ASMO) framework. The 10-m wind speed and precipitation simulated by the default and calibrated parameter values across different aspects are compared. The results show that the calibrated parameters improved the prediction of 10-m wind speed by 17.62% and precipitation by 8.20% compared to the default parameters. The effect of calibrated parameters on other model output variables, such as cyclone track and intensities, and 500-hPa wind fields, is investigated. Eight tropical cyclones across different categories between 2011 and 2018 are selected to corroborate the performance of the calibrated parameter values for other cyclone events. The robustness of the calibrated parameters across different boundary conditions and grid resolutions is also examined. These results will have significant implications for improving the predictability of tropical cyclone characteristics, which allows us to better plan adaptation and mitigation strategies and thus help in reducing the adverse effects of tropical cyclones on society.

Optimization of Spring Constant of a Pneumatic Artificial Muscle-Spring Driven Antagonistic Structure

Pneumatic artificial muscles (PAMs) have been widely applied to robotic systems, especially assistive devices, which could benefit from PAMs’ intrinsic viscoelasticity. However, nonlinearity and hysteresis make it challenging to achieve high-accuracy control. Using pre-tensioned springs to replace one side PAMs in an antagonistic structure is one efficient way to improve control accuracy. However, in the previous study, the selection method for spring, especially the range of spring constant has not been sufficiently investigated. This study designed a method to find the optimal spring constant based on gradient descent algorithm; and verified it with prototype and simulation experiments. Results showed that the group with optimal spring constant leads to a faster response time, smaller overshoot, and lower steady-state error, which indicated that the proposed method could find optimal spring constant. Moreover, the sensitivity of PAMs dynamic model parameters to the optimal spring stiffness was investigated. This study provides insights to practical use of soft actuators.

Identification of High-Impact Uncertainty Sources for Urban Flood Models in Hillside Peri-Urban Catchments

Climate change, as well as increasing urbanization, lead to an increase in urban flooding events around the world. Accurate urban flood models are an established tool to predict flooding areas in urban as well as peri-urban catchments, to derive suitable measures to increase resilience against urban flooding. The high computational cost and complex processes of urban flooding with numerous subprocesses are the reason why many studies ignore the discussion of model uncertainties as well as model calibration and validation. In addition, the influence of steep surface (hillside) conditions on calibration parameters such as surface roughness are frequently left out of consideration. This study applies a variance-based approach to analyze the impact of three uncertainty sources on the two variables—flow and water depth—in a steep peri-urban catchment: (i) impact of DEM validation; (ii) calibration of the model parameter; (iii) differences between 1D/2D and 2D models. The results demonstrate the importance of optimizing sensitive model parameters, especially surface roughness, in steep catchments. Additional findings of this work indicate that the sewer system cannot be disregarded in the context of urban flood modeling. Further research with real heavy storm events is to be pursued to confirm the main results of this study.

Mesoscale investigation on concrete creep behaviors based on discrete element method

Studying creep behaviors of concrete materials at a mesoscale level is crucial to understanding their mechanism. Discrete element method (DEM) could provide an effective numerical tool to explain the mesoscale mechanism of concrete materials. In this study, the creep characteristics of concrete are analyzed by combining DEM and Burgers model. Firstly, a multi-phase concrete numerical model consisting of matrix, aggregate, and interfacial transition zone (ITZ) was developed. Then, the sensitivity of the Burgers model parameters was conducted. Finally, to investigate concrete creep behaviors at a mesoscale level, a series of numerical simulations were carried out. Simulation results show that Burgers model can reflect the creep behavior of concrete well. The porosity gradually decreased as the loading duration increase, and deceleration became stable at the later stage. The creep contribution of ITZ decreases with the increase of the ratio of ITZ strength to matrix strength. Force-chain strength in ITZ was greater than that in matrix, and viscoelastic sliding or squeezing occurs at Burger contact. Meanwhile, the direction distribution of force-chain strength in the matrix is regular and the main direction of the contact distribution is about 90° or 270°. In addition, the probability function of contact force is exponential, and the strength probability distribution of matrix and ITZ contact reached the peak at 0.7 f/f¯ and then decreased, and the proportion of strong contact and weak contact in matrix and ITZ was about 70% and 30%, respectively. The current study can bridge the gap between simulation and experimental research.

Stability Evaluation of Proppant in Fractures of Gas Storage in Yulin Gas Field

In nearly a hundred years of construction, underground gas storage has become the main natural gas storage and peak regulation means in the world. For the gas storage, the large production and high flow rate of the gas well in the actual production process will cause the backflow of proppant filled in the supporting fracture, which will bring great harm to the gas field production. In this article, when the proppant fracture reaches a stable state in the process of gas injection and production, the stress of proppant particles is analyzed, the critical velocity of proppant reflux is calculated, and then the critical production model is established; calculate the permeability change during proppant migration, then calculate the fluid velocity and production, and determine the fluid velocity range of injection and production wells in gas storage. The parameter sensitivity of velocity and flow model is analyzed. The results show that with the increase of closure stress, the critical gas flow and critical gas velocity of proppant backflow gradually increase, and the proppant filling layer is more stable. The smaller the thickness and width of the filling layer, the greater the critical gas flow and critical gas velocity of proppant backflow, the more stable the proppant filling layer, and the lesser the chance of backflow. The higher the saturation, the lower the critical gas velocity, and the more prone the proppant to reflux. It has important guiding significance for realizing the optimization of gas well production and maintaining efficient production efficiency.

Complex dynamics and control analysis of an epidemic model with non-monotone incidence and saturated treatment.

In this manuscript, we consider an epidemic model having constant recruitment of susceptible individuals with non-monotone disease transmission rate and saturated-type treatment rate. Two types of disease control strategies are taken here, namely vaccination for susceptible individuals and treatment for infected individuals to minimize the impact of the disease. We study local as well as global stability analysis of the disease-free equilibrium point and also endemic equilibrium point based on the values of basic reproduction number R_0. Therefore, disease eradicates from the population if basic reproduction number less than unity and disease persists in the population if basic reproduction number greater than unity. We use center manifold theorem to study the dynamical behavior of the disease-free equilibrium point for R_0 = 1. We investigate different bifurcations such as transcritical bifurcation, backward bifurcation, saddle-node bifurcation, Hopf bifurcation and Bogdanov–Takens bifurcation of co-dimension 2. The biological significance of all types of bifurcations are described. Some numerical simulations are performed to check the reliability of our theoretical approach. Sensitivity analysis is performed to identify the influential model parameters which have most impact on the basic reproduction number of the proposed model. To control or eradicate the influence of the emerging disease, we need to control the most sensitive model parameters using necessary preventive measures. We study optimal control problem using Pontryagin’s maximum principle. Finally using efficiency analysis, we determine most effective control strategy among applied controls.

Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya

Study region19 flood prone catchments in Kenya, Eastern Africa Study focusFlooding is a major natural hazard especially in developing countries, and the need for timely, reliable, and actionable hydrological forecasts is paramount. Hydrological modelling is essential to produce forecasts but is a challenging task, especially in poorly gauged catchments, because of the inadequate temporal and spatial coverage of hydro-meteorological observations. Open access global meteorological reanalysis datasets can fill in this gap, however they have significant errors. This study assesses the performance of four reanalysis datasets (ERA5, ERA-Interim, CFSR and JRA55) over Kenya for the period 1981–2016 on daily, monthly, seasonal, and annual timescales. We firstly evaluate the reanalysis datasets by comparing them against observations from the Climate Hazards group Infrared Precipitation with Station. Secondly, we evaluate the ability of these reanalysis datasets to simulate streamflow using GR4J model considering both model performance and parameters sensitivity and identifiability. New hydrological insights for the regionWhile ERA5 is the best performing dataset overall, performance varies by season, and catchment and therefore there are marked differences in the suitability of reanalysis products for forcing hydrological models. Overall, wetland catchments in the western regions and highlands of Kenya obtained relatively better scores compared to those in the semi-arid regions, this can inform future applications of reanalysis products for setting up hydrological models that can be used for flood forecasting, early warning, and early action in data scarce regions, such as Kenya.