Removing the big bang singularity: the role of the generalized uncertainty principle in quantum gravity

The possibility of avoiding the big bang singularity by means of a generalized uncertainty principle is investigated. In relation with this matter, the statistical mechanics of a free-particle system obeying the generalized uncertainty principle is studied and it is shown that the entropy of the system has a finite value in the infinite temperature limit. It is then argued that negative temperatures and negative pressures are possible in this system. Finally, it is shown that this model can remove the big bang singularity.     

Organochlorinated compounds in Caspian Sea sediments

Several organochlorinated contaminants, including numerous pesticides, were determined in coastal sediments from the Caspian Sea. The most important contaminants were p,p'-DDT (up to 7400 pg g(-1)) and its breakdown products, p,p'-DDD (up to 3400 pg g(-1)) and p,p'-DDE (up to 1300 pg g(-1)). Although the contamination was most severe in Azerbaijan, the sediment concentrations and percentage distribution of the three DDT-related compounds indicated that such contamination constitutes a contemporary and ubiquitous problem in the Caspian Sea. Lindane represented the second most significant contaminant, particularly in the Russian Federation, with concentrations up to 609 pg g(-1). The concentrations of HCB and other chlorinated pesticides (cis- and trans-chlordane, methoxychlor, heptachlor, heptachlor epoxide, aldrin, endrin, and endosulfans) were lower and not generally of concern, but the pesticides did demonstrate markedly different distributions reflecting differing agricultural usage in the region. The concentrations of Sigma PCBs were also quite low and ranged from 0.03 to 6.4 ng g(-1), with the highest amounts in the Russian Federation and Azerbaijan.     

Modeling and Prediction of Habitat Suitability for Ferula gummosa Medicinal Plant in a Mountainous Area

Natural Resources Research - Habitat suitability modeling and mapping are important aspects of long-term strategies for sustaining plant ecosystems. In this study, seven state-of-the-art machine...     

Independent Base Vector Representation to Address Endmember Variability in Hyperspectral Unmixing

The normal compositional model (NCM) is a well-known and powerful model in hyperspectral unmixing which represents endmembers as independent Gaussian vectors to capture endmember variability. However, the assumption of independent endmembers diminishes the model accuracy because the high degree of correlation between endmembers of a scene and identical sources of variability demonstrate that the endmembers are dependent. This paper proposes a new hyperspectral unmixing algorithm which represents endmembers using dependent Gaussian vectors to estimate abundance fractions. To overcome the higher complexity caused by dependence assumption, this algorithm introduces new independent Gaussian vectors named Base Vectors to represent different endmembers by a weighted linear combination. Also, the proposed unmixing algorithm uses maximum likelihood method to estimate weight coefficients of Base Vectors which are used to represent mixed pixel. Finally, abundance estimation can be done using the new representation for endmembers and mixed pixel. The proposed algorithm is evaluated and compared with other state-of-the-art unmixing algorithms using simulated and real hyperspectral images. Experimental results demonstrate that the proposed unmixing algorithm can unmix pixels composed of correlated endmembers in hyperspectral images in the presence of spectral variability more accurately than previous methods.     

Improved Minimum Miscibility Pressure Prediction for Gas Injection Process in Petroleum Reservoir

Determination of gas–oil minimum miscibility conditions is one of the important design parameters to improve the displacement efficiency of the hydrocarbon reservoir during enhanced oil recovery with gas injection. In this work, a support vector regression (SVR) model is developed using experimental data to estimate the minimum miscibility pressure (MMP) for various reservoir fluids and injection gases. Experimental MMP data taken from the reliable literature were used as input. Each data point input includes methane and intermediate components mole percent, plus fraction properties and reservoir temperature related to reservoir fluid and CO₂, H₂S, N₂ and intermediate mole fractions, and intermediate properties of the injected gas. Experimental MMP is regarded as the model output. The database contains 135 datasets, from which 125 datasets were used for model development, and the rest were used for model evaluation. Genetic algorithm was implemented to optimize the SVR model parameters. The proposed data-driven model was verified by statistical validation data. The model results illustrate a correlation coefficient (R²) of 0.999. In addition, the SVR results demonstrate the proposed model to be a fast tool and a robust approach to map input space to output features. The SVR model was compared to popular data-driven MMP estimation models as well. This comparison presents an acceptable accuracy relative to this estimation model. Finally, the presented model was evaluated against a comprehensive theoretical model of slim tube compositional simulation on a trusted literature dataset.     

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes,multilayer perceptron neural networks,and functional trees methods

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.     

Shearing response of geotextile-reinforced calcareous soils using monotonic triaxial tests

Abstract

Different types of geosynthetic reinforcements have been widely used for improving shear strength of soil materials in recent decades. In this study, two different calcareous soils reinforced by geotextile layers were evaluated by monotonic compression triaxial tests. The calcareous soils were obtained from Hormuz Island and Boushehr Port located in the north coast of the Persian Gulf. Different reconstituted samples were prepared at dense conditions with and without geotextile layers and consolidated under different effective confining pressures ranging from 100 to 600?kPa. Different layers of reinforcements including one, two and three geotextile layers were placed in the reinforced samples and sheared under drained condition. The results show that confining pressure has a significant effect on the efficiency of reinforcements. Evidently, increasing the reinforcement layers decreased the particle breakage of the calcareous soils used in this study.     

An ontological structure for semantic interoperability of GIS and environmental modeling

Service-oriented architecture in a distributed computing environment, with loosely coupled geo-services is a new approach for using GIS services in environmental modeling. The messages exchanged must follow a set of standard protocols which support syntactic interoperability, but do not address application semantics.     

Landslide susceptibility mapping using random forest,boosted regression tree,classification and regression tree,and general linear models and comparison of their performance at Wadi Tayyah Basin,Asir Region,Saudi Arabia

The purpose of the current study is to produce landslide susceptibility maps using different data mining models. Four modeling techniques, namely random forest (RF), boosted regression tree (BRT), classification and regression tree (CART), and general linear (GLM) are used, and their results are compared for landslides susceptibility mapping at the Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslide locations were identified and mapped from the interpretation of different data types, including high-resolution satellite images, topographic maps, historical records, and extensive field surveys. In total, 125 landslide locations were mapped using ArcGIS 10.2, and the locations were divided into two groups; training (70 %) and validating (25 %), respectively. Eleven layers of landslide-conditioning factors were prepared, including slope aspect, altitude, distance from faults, lithology, plan curvature, profile curvature, rainfall, distance from streams, distance from roads, slope angle, and land use. The relationships between the landslide-conditioning factors and the landslide inventory map were calculated using the mentioned 32 models (RF, BRT, CART, and generalized additive (GAM)). The models’ results were compared with landslide locations, which were not used during the models’ training. The receiver operating characteristics (ROC), including the area under the curve (AUC), was used to assess the accuracy of the models. The success (training data) and prediction (validation data) rate curves were calculated. The results showed that the AUC for success rates are 0.783 (78.3 %), 0.958 (95.8 %), 0.816 (81.6 %), and 0.821 (82.1 %) for RF, BRT, CART, and GLM models, respectively. The prediction rates are 0.812 (81.2 %), 0.856 (85.6 %), 0.862 (86.2 %), and 0.769 (76.9 %) for RF, BRT, CART, and GLM models, respectively. Subsequently, landslide susceptibility maps were divided into four classes, including low, moderate, high, and very high susceptibility. The results revealed that the RF, BRT, CART, and GLM models produced reasonable accuracy in landslide susceptibility mapping. The outcome maps would be useful for general planned development activities in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.