From A to B,randomly: a point-to-point random trajectory generator for animal movement

For applications in animal movement, we propose a random trajectory generator (RTG) algorithm that combines the concepts of random walks, space-time prisms, and the Brownian bridge movement model and is capable of efficiently generating random trajectories between a given origin and a destination point, with the least directional bias possible. Since we provide both a planar and a spherical version of the algorithm, it is suitable for simulating trajectories ranging from the local scale up to the (inter-)continental scale, as exemplified by the movement of migrating birds. The algorithm accounts for physical limitations, including maximum speed and maximum movement time, and provides the user with either single or multiple trajectories as a result. Single trajectories generated by the RTG algorithm can be used as a null model to test hypotheses about movement stimuli, while the multiple trajectories can be used to create a probability density surface akin to Brownian bridges.     

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.     

Analysis and farmers’ perception of climate change in the Kashmir Valley,India

Theoretical and Applied Climatology - Analysis of climatic variables is important for the detection and attribution of climate change trends and has received considerable attention from researchers...     

Petroleum geochemistry of the Potwar Basin,Pakistan: II – Oil classification based on heterocyclic and polycyclic aromatic hydrocarbons

In a previous study, oils in the Potwar Basin (Upper Indus) of Pakistan were correlated based on the dissimilarity of source and depositional environment of organic matter (OM) using biomarkers and bulk stable isotopes. This study is aimed at supporting the classification of Potwar Basin oils into three groups (A, B and C) using the distribution of alkylnaphthalenes, alkylphenanthrenes, alkyldibenzothiophenes, alkyldibenzofurans, alkylfluorenes, alkylbiphenyls, triaromatic steroids, methyl triaromatic steroids, retene, methyl retenes and cadalene. The higher relative abundance of specific methyl isomers of naphthalene and phenanthrene and the presence of diagnostic aromatic biomarkers clearly indicate the terrigenous and oxic depositional environment of OM for group A oil. Group B and C oils are of marine origin and the aforementioned heterocyclic and polycyclic aromatic hydrocarbons (HCs) differentiate them clearly into two different groups. The relative percentages of heterocyclic aromatic HCs reveal that the distribution of these compounds is controlled by the depositional environment of the OM. Sulfur-containing heterocyclic aromatic HCs are higher in crude oils generated from source rocks deposited in suboxic depositional environments, while oxygen-containing heterocyclic aromatic HCs in combination with alkylfluorenes are higher in marine oxic and deltaic oils. Biomarker and aromatic HC parameters do not indicate significant differences in the thermal maturity of Potwar Basin oils. Triaromatic and methyl triaromatic steroids support the division of Potwar Basin oils into the three groups and their relative abundances are related to source OM rather than thermal maturity. Significantly higher amounts of C₂₀ and C₂₁ triaromtic steroids and the presence or absence of long chain triaromatic steroids (C₂₅, C₂₆, C₂₇, and C₂₈) indicates that these compounds are probably formed from different biological precursors in each group. Different isomers of methyl substituted triaromatic steroids are present only for short chain compounds (C₂₀–C₂₂) and the origin of these compounds may be short chain methyl steranes from unknown biological precursors.     

Anomalous subsidence on the rifted volcanic margin of Pakistan: No influence from Deccan plume

The role of hotter than ambient plume mantle in the formation of a rifted volcanic margin in the northern Arabian Sea is investigated using subsidence analysis of a drill site located on the seismically defined Somnath volcanic ridge. The ridge has experienced > 4 km of subsidence since 65 Ma and lies within oceanic lithosphere. We estimate crustal thickness to be 9.5–11.5 km. Curiously < 400 m of the thermal subsidence occurred prior to 37 Ma, when subsidence rates would normally be at a maximum. We reject the hypothesis that this was caused by increasing plume dynamic support after continental break-up because the size of the thermal anomalies required are unrealistic (> 600 °C), especially considering the rapid northward drift of India relative to the Deccan-Réunion hotspot. We suggest that this reflects very slow lithospheric growth, possibly caused by vigorous asthenospheric convection lasting > 28 m.y., and induced by the steep continent–ocean boundary. Post-rift slow subsidence is also recognized on volcanic margins in the NE Atlantic and SE Newfoundland and cannot be used as a unique indicator of plume mantle involvement in continental break-up.     

Airside velocity measurements over the wind-sheared water surface using particle image velocimetry

An experimental investigation of the airflow structure in the near surface region over the wind-sheared air–water interface is reported. The two-dimensional velocity fields in a plane perpendicular to the water surface were measured using particle image velocimetry (PIV) technique over a wind speed range from 1.5 to 4.4 m s⁻¹. The results show a reduction in the mean velocity magnitudes and the tangential stresses when gravity waves appear on the surface. An enhanced vorticity layer was observed immediately above the water surface that extended to a height of approximately 2 cm. The vorticity was enhanced by an order of magnitude, and the energy dissipation rate was enhanced by a factor of 7 in this layer at all wind speeds. The vertical profiles of Reynolds stress, energy production, and dissipation indicate the contribution of surface waves in the enhanced transfer of momentum and energy between the two fluids. The results in this study show that the flow dynamics in a layer immediately adjacent to the water surface whose thickness is of the order of the significant wave height is significantly different from that at greater heights. Thus, it is concluded that the quantitative investigation of the flow in the immediate vicinity of the interface is vital for an improved understanding of the heat, mass, and momentum exchange between air and water. The present study demonstrates that PIV is an effective technique to accurately measure the velocity fields in this region.     

Geochemical modeling,fate distribution,and risk exposure of potentially toxic metals in the surface sediment of the Shyok suture zone,northern Pakistan

The objective of the current study was to assess the contamination of potentially toxic metals (PTMs) in weathered surface sediment, along stream tributaries, and surrounding area of the river Chitral, Shyok suture zone district Chitral, Pakistan. To understand the geochemical features of 113 sediment, samples were collected from the Mirkhani and Drosh area. Then, different statistical tools including the geo-accumulation index (Igeo), cluster analysis (CA), principal component analysis (PCA), and ecological risk assessment (ERA) were used to unravel the origin, intensity, and exposure level of PTMs to control risk and restore the ecosystem within the study area. The results for the PTMs namely nickle (Ni), chromium (Cr), copper (Cu), cadmium (Cd), lead (Pb), zinc (Zn), and cobalt (Co) in Mirkhani and Drosh were in the following ranges: 10–150, 15–210, 15–250, 0.08–1.00, 10–70, 76–240 and 14–51; and 13–240, 17–210, 15–150, 0.08–0.60, 7–140, 47–150 and 13–36 mg/kg, respectively. In consequence, the potential ecological risk caused by Pb, Ni, Cu, Co, Cr, and Zn is reflected by the percentages of samples with an ecological risk index (ERI) greater than one which were 100%, 91%, 100%, 100%, 92%, and 100%, respectively. However, the overall mean decreasing order of ecological risk of PTMs in the district Chitral was Pb > Ni > Cu > Co > Cr > Zn > Cd. Moreover, the PCA yielded 78% variability which indicated that mineral prospects play an important role in the contamination of sediment. Furthermore, the mineral phases of Pb and Zn suggested supersaturation, while that for Cd revealed unsaturation. The results of Igeo, ERI, and CA indicated contamination of PTMs in the study area. The ERI value of Pb, Ni, Cu, Co, Cr, and Zn was higher than 1 suggesting an ecological risk in the study area. Moreover, the current study showed the dominance of geogenic contamination with major contributions from ultramafic rock and known mineral prospects. Therefore, contaminated sediment of the Shyok suture zone is extremely detrimental to the aquatic ecosystem of the study area.     

Nickel sulphide fire assay for the collection of the platinum group elements and gold from chromitites using reduced bead size

Summary Fire assay is a classical method for the separation of the platinum group elements and gold (PGE + Au). In nickel sulphide fire assay nickel and sulphur are used for the preconcentration of PGE + Au. In the past the authors have successfully reduced the weight of the collectors used without affecting the recovery of the elements from silicate material and eliminated the reagent blank problem. In this work the authors have applied the small bead size to chromitites. Chromitites can be difficult to fuse, and the authors have applied their modified technique to improve the dissolution of chromitites. This paper describes the application of the reduced bead size to the preconcentration of the PGE + Au from chromitites, by using only 0.5 g of nickel compared to the 10 g used by other fire assay workers.

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Nickelsulfid Fire assay zur Anreicherung von Platingruppenelementen und Gold aus Chromititen

Zusammenfassung Fire assay ist die klassische Methode um Platingruppenelemente und Gold (PGE + Au) vor der Analyse anzureichern und von der Matrix abzutrennen. Zum Sammeln der PGE und Au wird Nickelsulfid verwendet. Durch Verwendung möglichst kleiner Mengen konnte das Problem eventueller Verunreinigung der verwendeten Reagenzien ausgeschaltet werden. In der vorliegenden Arbeit wird diese Methode bei Chromititen angewendet, wobei hier ein modifiziertes Verfahren zum Lösen dieser Gesteine verwen det wird. Es werden 0.5 g Nickel zum Abtrennen der PGE und Au von Chromititen verwendet, was einer 20-fachen Mengenreduktion des Kollektormaterials gegenüber herkömmlichen Methoden entspricht.

     

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.