Time dependent chemical study of contracting interstellar clouds. I. Abundances of nitrogen- and carbon-bearing molecules

We have constructed a reaction system containing the chemical families of H, C, O, N, S, Si, Cl, metals (Me) and grains. A total of 104 species have been included and a network of 557 reactions has been studied. The chemical kinetic equations were integrated as a function of time by using gear program. The chemical reaction system was followed at low, intermediate and high cloud densities i.e. from 10–10⁷ particles cm^-3. The calculated fractional abundances of N₂, CN, HCN, and CH which are in good agreement with the results of observations and with those of previous theoretical studies.     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Mass Estimation in the Globular Cluster M4

Using the kinematical data we try to solve the Jeans equation to provide mass estimation of the globular cluster M4. The proper motion data provides two independent velocity dispersion profiles which we need in this estimation. Moreover, we have calculated the density distribution function and checked the anisotropy of the velocity dispersion. The comparison of the dynamical mass with the estimation stellar mass for M4 gives the best estimation of the mass inside a spherical shell centered on the cluster, with a radius corresponding to 840 arcsec on the sky, as 4-5× 10⁴ M . This revised version was published online in July 2006 with corrections to the Cover Date.     

Physicochemical behavior of tropical laterite soil stabilized with non-traditional additive

Non-traditional soil stabilizers are widely used for treating weak materials. These additives are cost- and time-effective alternatives to more traditional materials such as lime and cement. It has been well established that the treatment of natural soil with chemical additives will gradually affect the size, shape, and arrangement of soil particles. Furthermore, the degree of improvement is dependent on the quantity and the pattern of new products formed on and around the soil particles. In this paper, unconfined compressive strength (UCS) test was performed as an index of soil improvement on mix designs treated with calcium-based powder stabilizer (SH-85). The time-dependent changes in shear strength parameter and compressibility behavior of treated soil were also studied using standard direct shear and one-dimensional consolidation tests. In order to better understand the shape and surface area of treated particles, FESEM, N₂-BET, and particle size distribution analysis were performed on soil-stabilizer matrix. From engineering standpoint, the UCS results showed that the degree of improvement for SH-85-stabilized laterite soil was roughly five times stronger than the untreated soil at the early stages of curing (7-day period). Also, a significant increase in the compressibility resistance of treated samples with curing time was observed. Based on the results, less porous and denser soil fabric was seen on the surface of clay particles. FESEM images of the treated mix designs showed the formation of white lumps in the soil fabric with the cementitious gel filling the pores in the soil structure.     

A comparative study of kriging and simulation-based methods in classifying ore and waste blocks

Ore grade is the most important source of uncertainty in a mining operation which plays an important role to classify run-of-mine (ROM) material into ore and waste parcels. As a widely used method, kriging estimator is used to estimate the grade of ore blocks. In conventional mining practices, if the estimated grade of a parcel is above the cut-off grade, this parcel is classified as ore, otherwise, is labelled as a waste parcel. An alternative approach is to simultaneously consider the grade of parcels and the economic consequences of sending parcels to destinations by applying simulation-based methods. In this study, kriging and simulation-based methods including loss and profit functions are applied on a real-world case study to classify ore/waste material based on the initial exploration data. Then, the actual known data, collected from blast holes samples, are compared with the estimated results in order to validate the performance of the presented methods. Outcomes show that simulation-based methods can perform better and show more adjustability with real data.     

Application of the WEPP model to determine sources of run‐off and sediment in a forested watershed

This study investigates critical run‐off and sediment production sources in a forested Kasilian watershed located in northern Iran. The Water Erosion Prediction Project (WEPP) watershed model was set up to simulate the run‐off and sediment yields. WEPP was calibrated and validated against measured rainfall–run‐off–sediment data. Results showed that simulated run‐off and sediment yields of the watershed were in agreement with the measured data for the calibration and validation periods. While low and medium values of run‐off and sediment yields were adequately simulated by the WEPP model, high run‐off and sediment yield values were underestimated. Performance of the model was evaluated as very good and satisfactory during the calibration and validation stages, respectively. Total soil erosion and sediment load of the study watershed during the study period were determined to be 10 108 t yr^?1 and 8735 t yr^?1, respectively. The northern areas of the watershed with dry farming were identified as the critical erosion prone zones. To prioritize the subwatersheds based on their contribution to the run‐off and sediment production at the watershed's main outlet, unit response approach (URA) was applied. In this regard, subwatersheds close to the main outlet were found to have the highest contribution to sediment yield of the whole watershed. Results indicated that depending on the objective of land and water conservation practices, particularly, for controlling sediment yield at the main outlet, critical areas for implementing the best management practices may be identified through conjunctive application of WEPP and URA. Copyright © 2014 John Wiley & Sons, Ltd.     

Generalized poroelastic wellbore problem

This paper presents a novel analytical solution to the transient, z‐dependent, and asymmetric problem of an infinite wellbore drilled into a fluid‐saturated porous medium. The formulations are based on Biot's linear theory of poroelasticity, in which the dependency of poroelastic field variables to spatial coordinates as well as time domain is considered in the most general form. This gives flexibility to the solution in cases that cannot be analyzed using the conventional plane strain or symmetric models. One such case is when calculating the stress variations around an inclined wellbore where the far‐field stresses are acting over a finite vertical section. The results of our solution to this case with a three‐dimensional state of far‐field stress are used to analyze the stability of inclined wellbores passing through abnormally stressed formations. The presented solution is capable of finding expressions for fundamental solutions with stress or flow boundary conditions at the wellbore. These solutions are here adopted to analyze the pressure disturbances generated by multiprobe formation tester, a standard wireline device that is designed for downhole fluid sampling as well as estimating the directional permeabilities of subsurface earth formations. A comparison with the conventional solution for the relevant pressure diffusion equation indicates that the poroelastic effect is relatively significant in relation to the transient response of the pore pressure. Further, it is shown that the finite dimensions of sink probe would, to a great extent, contribute to the formation's pore pressure variations at its immediate proximity. Copyright © 2013 John Wiley & Sons, Ltd.     

Prediction of hourly actual evapotranspiration using neural networks,genetic programming,and statistical models

The complexity of the evapotranspiration process and its variability in time and space have imposed some limitations on previously developed evapotranspiration models. In this study, two data‐driven models: genetic programming (GP) and artificial neural networks (ANNs), and statistical regression models were developed and compared for estimating the hourly eddy covariance (EC)‐measured actual evapotranspiration (AET) using meteorological variables. The utility of the investigated data‐driven models was also compared with that of HYDRUS‐1D model, which makes use of conventional Penman–Monteith (PM) model for the prediction of AET. The latent heat (LE), which is measured using the EC method, is modelled as a function of five climatic variables: net radiation, ground temperature, air temperature, relative humidity, and wind speed in a reconstructed landscape located in Northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg–Marquardt and Bayesian regularization. The GP technique was used to generate mathematical equations correlating AET to the five climatic variables. Furthermore, the climatic variables, as well as their two‐factor interactions, were statistically analysed to obtain a regression equation and to indicate the climatic factors having significant effect on the evapotranspiration process. HYDRUS‐1D model as an available physically based model was examined for estimating AET using climatic variables, leaf area index (LAI), and soil moisture information. The results indicated that all three proposed data‐driven models were able to approximate the AET reasonably well; however, GP and regression models had better generalization ability than the ANN model. The results of HYDRUS‐1D model exhibited that a physically based model, such as HYDRUS‐1D, might be comparable or even inferior to the data‐driven models in terms of the overall prediction accuracy. Based on the developed GP and regression models, net radiation and ground temperature had larger contribution to the AET process than other variables. Copyright © 2010 John Wiley & Sons, Ltd.