Five dimensional universe model with variable cosmological term Λ and a big bounce

We assume the four dimensional induced matter of the 5D Ricci flat bouncing cosmological solution contains a perfect fluid. The big bounce singularity of simple 5D cosmological model is studied with the cosmological term Λ=α ρ and Λ=β H ² where α and β are constants and ρ and H are respectively energy density and Hubble parameter. This big bounce singularity is found to be an event horizon at which the scale factor and mass density of the universe are finite, while the pressure is infinite.      

PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping

This paper describes the application of a well-known multi-criteria decision-making technique, called preference ranking organization method for enrichment evaluation (PROMETHEE II), in combination with fuzzy analytical hierarchy process (FAHP), as a weighting technique to explore landslide susceptibility mapping (LSM). To this end, eight landslide-related geodata layers of the Minoo Dasht located in the Gorgan province of Iran, involving slope, aspect, distance to river, drainage density, distance to fault, mean annual rainfall, distance to road and lithology have been integrated using the PROMETHEE II enhanced by FAHP technique. Afterward, the receiver operating characteristics (ROC) curves for the proposed LSM were drawn using an inventory of landslides containing 83 recent and historic landslide points, and the area under curve = 0.752 value was calculated accordingly. Additionally, to further verify the practicality of such susceptibility map, it was also evaluated against the landslide inventory using simple overlay. The outcome was that about 11 % of the occurred landslide points fall into the very high susceptibility class of the LSM, but approximately 52 % of them indeed fall into the high and very high susceptibility zones together. Also, it resulted that no recorded landslide occurred in the zone of very low susceptibility. According to the results of the ROC curves analysis and simple overlay evaluation, the produced map has exhibited good performance.     

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.     

Assessing desertification by using soil indices

Desertification generally refers to land degradation in arid, semiarid, and dry semi-humid climatic zones. It involves five principal processes: vegetation degradation, water erosion, wind erosion, salinization and waterlogging, and soil crusting and compaction. The aim of this study is assessing desertification using soil criteria. For this purpose, nine indices including sodium absorption ratio (SAR), soil gypsum percentage, soil texture, the content of HCO₃ ^?, the percentage of the organic matter, electrical conductivity (EC), pH, the content of the soil sodium, and chloride were used. The soil samples were taken in the north of Zayandeh-Rood River in Isfahan province of Iran, using soil data randomly sampled in a depth of 0–20 cm. After assessing the normality of the samples using Kolmogorov-Smirnov test, indices were imported into GIS environment and interpolated with IDW and normal and discrete kriging methods for delineating soil characteristics maps based on MEDALUS model. In this model, the data were firstly changed from 100 to 200. Thus 100 and 200 are estimated as the best and worst quality, respectively. Then the final map of soil criteria has been created by geometric mean of its indicators. The results showed that the maximum area is related to the medium class of desertification and is equal to 44,746 ha. The areas of severe and very severe classes of desertification are equal to 30,949 and 351 ha, respectively. The results also revealed that the indices of the organic matter and soil gypsum percentage are the most influential indices which affect desertification phenomenon.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

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.     

Cu (II) removal intensification using Fe<Subscript>3</Subscript>O<Subscript>4</Subscript> nanoparticles under inert gas and magnetic field in a microchannel

In the present study, Cu (II) ions removal from aqueous solution was intensified by exciting magnetic nanoparticles under inert gas, magnetic field and combination of these two mixing methods in a T-type microchannel. The flow patterns and liquid–liquid two-phase mass transfer were studied in three different magnet distances from mixing channel (3, 6 and 10 mm) and also in the presence of different inert gas flow rates (1, 3 and 5 mL/min). Depending on the mixing method and the flow rate of both phases, several distinct flow patterns were observed including slugs, droplet, parallel and dispersed flows. The performances of mixing techniques for mass transfer enhancement based on relative removal efficiency ratio (λ) and mass transfer coefficient ratio (γ) were compared with simple layout (without nanoparticles, magnetic field and inert gas). The results showed that simultaneous using of inert gas and magnetic field can drive the nanoparticles as mixer. Liquid–liquid mass transfer with 27–62% enhancement in E and 235–285% in K _L a compared with plain one was observed.     

Cement Deep Soil Mixing (CDSM) for Solidification of Soft Estuarine Sediments

A preliminary study was conducted to determine the potential for cement deep soil mixing (CDSM) technology as a method for in-situ solidification of contaminated river and estuarine sediments. The study was conducted in Newark Bay, near the mouth of the Passaic River, New Jersey. The primary objective of the study was to evaluate the viability of CDSM for the in-situ S/S with a focus on: 1) determining the correct mix of the cement slurry, which provides rapid stabilization of the sediment matrix, 2) potential resuspension of solids during CSDM operations, 3) the effects of high organic content on the solidification process, and 4) the feasibility of using conventional dredging/extraction methods once the sediments have been stabilized and allowed to cure. The results of the study show CDSM slurry mixtures, as low as 7% in cement content, result in significant solidification and strength gain of in-situ sediments under ambient conditions. In sediments with very high organic contents (> 20%), the slurry mix would need to be adjusted to account for retardation effects of organics on cement hydration. Sediment resuspension during application was shown to be minimal at a distance of as little as 75 feet from the mixing head. Strength gains were considerable, effectively consolidating the sediment particles in a secure matrix, but not so high as to preclude extraction of solidified sediments with conventional dredging equipment. Dredged solidified sediment exhibited characteristics of a stiff glacial clay, and as such was easier to handle and transport than untreated dredged sediments. This technique has high potential to be used as an interim remedial measure prior to either extraction and decontamination/disposal or proper capping.