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Peristalsis is the most dynamic phenomenon that is significant in the biomedical domain and offers considerable promise in microscale fluids. For the past few years, this biomimetic (peristaltic) phenomenon attracted the attention of the research community because of its massive applications in numerous medical and industrial domains. In the present study, the steady, laminar rheology of biological fluid from a biomimetic (peristaltic) channel is considered. The rheological equations are expressed in the Cartesian system, and the porosity effects are modelled (added) on a body force term of the momentum equation. The current analysis depends on the creeping phenomena and long wavelength. The closed-form solutions are acquired by using integration on the rheological equations subject to suitable boundary conditions. The rheology is controlled by two embedded parameters, the porosity and non-Newtonian parameters. It is shown using graphs that the magnitude of axial velocity is strongly affected by the larger strength of porosity (Darcy's number) and non-Newtonian (couple stress) parameters. We noticed the impacts of involved rheological constraints on pumping and trapping phenomena in the light of porous effects. Additionally, the wavy pattern of sinusoidal waves is utilised in the present analysis to increase the efficiency of the peristaltic pump. By increasing the porosity impacts, the magnitudes of velocity profile and pressure gradient of a biofluid are increased. The porosity has a dynamic role in the augmentation of peristaltic pumping. The impacts of couple stress parameter reduces the viscous effects. These outcomes may be useful in bioengineering (drug delivery schemes) and chemical processes. [ABSTRACT FROM AUTHOR]

Comparing outputs of multiple climate and crop models is an option to assess the uncertainty in simulations in a changing climate. The use of multiple wheat models under five plausible future simulated climatic conditions is rarely found in literature. CERES-Wheat, DSSAT-Nwheat, CROPSIM-Wheat, and APSIM-Wheat models were calibrated with observed data form eleven sowing dates (15 October to 15 March) of irrigated wheat trails at Faisalabad, Pakistan, to explore close to real climate changing impacts and adaptations. Twenty-nine GCM of CMIP5 were used to generate future climate scenarios during 2040–2069 under RCP 8.5. These scenarios were categorized among five climatic conditions (Cool/Wet, Cool/Dry, Hot/Wet, Hot/Dry, Middle) on the basis of monthly changes in temperature and rainfall of wheat season using a stretched distribution approach (STA). The five GCM at Faisalabad and Layyah were selected and used in the wheat multimodels set to CO2 571 ppm. In the future, the temperature of both locations will elevate 2–3 °C under the five climatic conditions, although Faisalabad will be drier and Layyah will be wetter as compared with baseline conditions. Climate change impacts were quantified on wheat sown on different dates, including 1 November, 15 November, and 30 November which showed average reduction at semiarid and arid environment by 23.5%, 19.8%, and 31%, respectively. Agronomic and breeding options offset the climate change impacts and also increased simulated yield about 20% in all climatic conditions. The number of GCMs was considerably different in each quadrate of STA, showing the uncertainty in possible future climatic conditions of both locations. Uncertainty among wheat models was higher at Layyah as compared with Faisalabad. Under Hot/Dry and Hot/Wet climatic conditions, wheat models were the most uncertain to simulate impacts and adaptations. DSSAT-Nwheat and APSIM-Wheat were the most and least sensitive to changing temperature among years and climatic conditions, respectively. [ABSTRACT FROM AUTHOR]

We investigate the dynamical behavior of a modified Leslie–Gower prey–predator model with harvesting in prey population. In order to explore rich dynamics of the model, Euler approximation is implemented to obtain a discrete-time modified Leslie–Gower model. Existence of equilibria and their local asymptotic stabilities are carried out. Furthermore, with the help of bifurcation theory and center manifold theorem, existence and directions of period-doubling and Neimark–Sacker bifurcations are investigated at positive steady-state. In order to control chaos and bifurcations, the Ott–Grebogi–Yorke (OGY) method and the hybrid control strategy are introduced. Numerical simulations are also provided to illustrate the theoretical discussions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the complex dynamics of three-dimensional Ricker-type discrete-time competition model. We discuss the existence and uniqueness, and find parametric conditions for local asymptotic stability of positive fixed point of this model. It is also proved that the system undergoes Neimark-Sacker (NS) and period-doubling bifurcation (PDB) at certain parametric values for positive fixed point with the help of an explicit criterion for NS and PDB. The system shows chaotic dynamics at increasing values of bifurcation parameter. To control the chaos, we apply the hybrid control methodology. Finally, numerical simulations are provided to illustrate the theoretical discussions. These results of numerical simulations show chaotic long-term behavior over a broad range of parameters. The computation of the maximum Lyapunov exponents confirms the presence of chaotic behavior in the model. [ABSTRACT FROM AUTHOR]

Climate change adversely affects food security all over the world, especially in developing countries where the increasing population is confronting food insecurity and malnutrition. Crop models can assist stakeholders for assessment of climate change in current and future agricultural production systems. The aim of this study was to use of system analysis approach through CSM-CERES-Millet model to quantify climate change and its impact on pearl millet under arid and semi-arid climatic conditions of Punjab, Pakistan. Calibration and evaluation of CERES-Millet were performed with the field observations for pearl millet hybrid 86M86. Mid-century (2040-2069) climate change scenarios for representative concentration pathway (RCP) 4.5 and RCP 8.5 were generated based on an ensemble of selected five general circulation models (GCMs). The model was calibrated with optimum treatment (15-cm plant spacing and 200 kg N ha−1) using field observations on phenology, growth and grain yield. Thereafter, pearl millet cultivar was evaluated with remaining treatments of plant spacing and nitrogen during 2015 and 2016 in Faisalabad and Layyah. The CERES-Millet model was calibrated very well and predicted the grain yield with 1.14% error. Model valuation results showed that there was a close agreement between the observed and simulated values of grain yield with RMSE ranging from 172 to 193 kg ha−1. The results of future climate scenarios revealed that there would be an increase in Tmin (2.8 °C and 2.9 °C, respectively, for the semi-arid and arid environment) and Tmax (2.5 °C and 2.7 °C, respectively, for the semi-arid and arid environment) under RCP4.5. For RCP8.5, there would be an increase of 4 °C in Tmin for the semi-arid and arid environment and an increase of 3.7 °C and 3.9 °C in Tmax, respectively, for the semi-arid and arid environment. The impacts of climate changes showed that pearl millet yield would be reduced by 7 to 10% under RCPs 4.5 and 8.5 in Faisalabad and 10 to 13% in Layyah under RCP 4.5 and 8.5 for mid-century. So, CSM-CERES-Millet is a useful tool in assessing the climate change impacts. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the qualitative behavior of a four-dimensional discrete-time predator-prey model with parasites. We investigate existence and uniqueness of positive steady state and find parametric conditions for local asymptotic stability of positive equilibrium point of given system. It is also proved that the system undergoes Neimark-Sacker bifurcation (NSB) at positive equilibrium point with the help of an explicit criterion for NSB. The system shows chaotic dynamics at increasing values of bifurcation parameter. Chaos control is also discussed through implementation of hybrid control strategy, which is based on feedback control methodology and parameter perturbation. Finally, numerical simulations are conducted to illustrate theoretical results. [ABSTRACT FROM AUTHOR]

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35-6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha−1, while remote sensing showed the RMSE of 397 kg ha−1. Overall strength of relationship between estimated and actual grain yields were good with R2 of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool. [ABSTRACT FROM AUTHOR]

Growth, development, and economic yield of agricultural crops rely on moisture, temperature, light, and carbon dioxide concentration. However, the amount of these parameters is varying with time due to climate change. Climate change is factual and ongoing so, first principle of agronomy should be to identify climate change potential impacts and adaptation measures to manage the susceptibilities of agricultural sector. Crop models have ability to predict the crop’s yield under changing climatic conditions. We used OILCROP-SUN model to simulate the influence of elevated temperature and CO2 on crop growth duration, maximum leaf area index (LAI), total dry matter (TDM), and achene yield of sunflower under semi-arid conditions of Pakistan (Faisalabad, Punjab). The model was calibrated and validated with the experimental data of 2012 and 2013, respectively. The simulation results showed that phenological events of sunflower were not changed at higher concentration of CO2 (430 and 550 ppm). However LAI, achene yield, and TDM increased by 0.24, 2.41, and 4.67% at 430 ppm and by 0.48, 3.09, and 9.87% at 550 ppm, respectively. Increased temperature (1 and 2 °C) reduced the sunflower duration to remain green that finally led to less LAI, achene yield, and TDM as compared to present conditions. However, the drastic effects of increased temperature on sunflower were reduced to some extent at 550 ppm CO2 concentration. Evaluation of different adaptation options revealed that 21 days earlier (as compared to current sowing date) planting of sunflower crop with increased plant population (83,333 plants ha−1) could reduce the yield losses due to climate change. Flowering is the most critical stage of sunflower to water scarcity. We recommended skipping second irrigation or 10% (337.5 mm) less irrigation water application to conserve moisture under possible water scarce conditions of 2025 and 2050. [ABSTRACT FROM AUTHOR]

In this article, we propose a method for the solution of the generalized Burger-Fisher equation. The method is developed using CAS wavelets in conjunction with quasi-linearization technique. The operational matrices for the CAS wavelets are derived and constructed. Error analysis and procedure of implementation of the method are provided. We compare the results produce by present method with some well known results and show that the present method is more accurate, efficient, and applicable. [ABSTRACT FROM AUTHOR]

The combination of nitrogen and plant population expresses the spatial distribution of crop plants. The spatial distribution influences canopy structure and development, radiation capture, accumulated intercepted radiation (Sa), radiation use efficiency (RUE), and subsequently dry matter production. We hypothesized that the sunflower crop at higher plant populations and nitrogen (N) rates would achieve early canopy cover, capture more radiant energy, utilize radiation energy more efficiently, and ultimately increase economic yield. To investigate the above hypothesis, we examined the influences of leaf area index (LAI) at different plant populations (83,333, 66,666, and 55,555 plants ha) and N rates (90, 120, and 150 kg ha) on radiation interception (Fi), photosynthetically active radiation (PAR) accumulation (Sa), total dry matter (TDM), achene yield (AY), and RUE of sunflower. The experimental work was conducted during 2012 and 2013 on sandy loam soil in Punjab, Pakistan. The sunflower crop captured more than 96% of incident radiant energy (mean of all treatments), 98% with a higher plant population (83,333 plants ha), and 97% with higher N application (150 kg ha) at the fifth harvest (60 days after sowing) during both study years. The plant population of 83,333 plants ha with 150 kg N ha ominously promoted crop, RUE, and finally productivity of sunflower (AY and TDM). Sunflower canopy (LAI) showed a very close and strong association with Fi ( R = 0.99 in both years), PAR ( R = 0.74 and 0.79 in 2012 and 2013, respectively), TDM ( R = 0.97 in 2012 and 0.91 in 2013), AY ( R = 0.95 in both years), RUE for TDM (RUE) ( R = 0.63 and 0.71 in 2012 and 2013, respectively), and RUE for AY (RUE) ( R = 0.88 and 0.87 in 2012 and 2013, respectively). Similarly, AY ( R = 0.73 in 2012 and 0.79 in 2013) and TDM ( R = 0.75 in 2012 and 0.84 in 2013) indicated significant dependence on PAR accumulation of sunflower. High temperature during the flowering stage in 2013 shortened the crop maturity duration, which reduced the LAI, leaf area duration (LAD), crop growth rate (CGR), TDM, AY, Fi, Sa, and RUE of sunflower. Our results clearly revealed that RUE was enhanced as plant population and N application rates were increased and biomass assimilation in semi-arid environments varied with radiation capture capacity of sunflower. [ABSTRACT FROM AUTHOR]

Nitrogen fertilizer availability to plants is strongly linked with water availability. Excessive or insufficient use of nitrogen can cause reduction in grain yield of wheat and environmental issues. The per capita per annum water availability in Pakistan has reduced to less than 1000 m and is expected to reach 800 m during 2025. Irrigating crops with 3 or more than 3 in. of depth without measuring volume of water is not a feasible option anymore. Water productivity and economic return of grain yield can be improved by efficient management of water and nitrogen fertilizer. A study was conducted at post-graduate agricultural research station, University of Agriculture Faisalabad, during 2012-2013 and 2013-2014 to optimize volume of water per irrigation and nitrogen application. Split plot design with three replications was used to conduct experiment; four irrigation levels (I = 300 mm, I = 240 mm, I = 180 mm, I = 120 mm for whole growing season at critical growth stages) and four nitrogen levels (N = 60 kg ha, N = 120 kg ha, N = 180 kg ha, and N = 240 kg ha) were randomized as main and sub-plot factors, respectively. The recorded data on grain yield was used to develop empirical regression models. The results based on quadratic equations and economic analysis showed 164, 162, 158, and 107 kg ha nitrogen as economic optimum with I, I, I, and I mm water, respectively, during 2012-2013. During 2013-2014, quadratic equations and economic analysis showed 165, 162, 161, and 117 kg ha nitrogen as economic optimum with I, I, I, and I mm water, respectively. The optimum irrigation level was obtained by fitting economic optimum nitrogen as function of total water. Equations predicted 253 mm as optimum irrigation water for whole growing season during 2012-2013 and 256 mm water as optimum for 2013-2014. The results also revealed that reducing irrigation from I to I mm during 2012-2013 and 2013-2014 did not reduce crop yield significantly ( P < 0.01). The excessive nitrogen application ranged from 31.2 to 55.4% at N and N kg ha for different levels of irrigation. It is concluded from study that irrigation and nitrogen relationship can be used for efficient management of irrigation and nitrogen and to reduce nitrogen losses. The empirical equations developed in this study can help farmers of semi-arid environment to calculate optimum level of irrigation and nitrogen for maximum economic return from wheat. [ABSTRACT FROM AUTHOR]

In this article, a numerical technique is developed for solving delay differential equations. The proposed method combines the method of steps with the radial basis function networks. A delay differential equation is transformed to an ordinary differential equation and then the radial basis function collocationmethod is implemented to find the solution of the ordinary differential equation. The procedure for implementation of the radial basis function collocation method and the proposed method for delay differential equation is presented in detail. Numerical experiments are carried out on a number of examples to show the advantages of the proposed technique over the radial basis function collocation method. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present two numerical techniques for solving generalized fractional differential equations. We develop Haar wavelets operational matrices to approximate the solution of generalized Caputo–Katugampola fractional differential equations. Moreover, we introduce Green–Haar approach for a family of generalized fractional boundary value problems and compare the method with the classical Haar wavelets technique. In the context of error analysis, an upper bound for error is established to show the convergence of the method. Results of numerical experiments have been documented in a tabular and graphical format to elaborate the accuracy and efficiency of addressed methods. Further, we conclude that accuracy-wise Green–Haar approach is better than the conventional Haar wavelets approach as it takes less computational time compared to the Haar wavelet method. [ABSTRACT FROM AUTHOR]

The increasing frequency of urban flooding episodes is a cause for concern for urban management and deciders. In this research, two benchmark urban flood inundation modeling (FIM) studies under the European Union-funded IMPACT Project are modeled with the 2D Sedimentation and River Hydraulics model, SRH2D. The two studies are "the model city flooding experiment" and the "isolated building" test case. The model result comparison with the measured datasets are carried out in terms of time series of flow depths at gauge locations as well as the surface flow velocity maps over the whole domain. The model is also assessed for different building treatments in the computational mesh. The model performed admirably in reproducing the flow depths at the gauge locations with a mean Nash-Sutcliffe efficiency value of 0.89. The model discrepancy is mostly due to 3D effects, which are predominant, right after the dam-break flow release from the reservoir. The second case study investigates dam-break flow around a single building as opposed to the first case study, which is focused on a locality exposed to flood. The study points to the promise held by the 2D shallow water equation-based FIM models in tracking the flood progression and providing a reliable spatiotemporal numerical dataset for the flow depths and discharges, which is a key ingredient that goes into the generation of flood hazard maps. [ABSTRACT FROM AUTHOR]