Landslide susceptibility assessment along the southern coast of the Caspian Sea,Iran

China is a disaster-prone country, and these disasters have diverse characteristics, a wide scope of distribution, high frequency, and large losses. China has advanced community-based disaster management (CBDM) capacity. Community is the bottom unit of the society, and CBDM is the foundation of the entire society’s disaster management system. A series of domestic major emergency incidents and disasters and international disaster reduction activities have promoted the formation of the CBDM concept, the implementation of capacity building activities, and the improvement of policy and laws. Thus far, the CBDM system has been preliminarily formed in China, and relevant rules and regulations have been promulgated and implemented. Furthermore, disaster reduction activities, such as the construction of the national comprehensive disaster reduction community and national safe community, have been promoted nationwide. As a result, China’s disaster-resistance capacity has largely improved. However, it is only in the initial phase of CBDM implementation, which remains plagued by several challenges and problems, such as the deficiency of community resident participation, management organizations, disaster risk assessment methods, NGO development, and safety culture cultivation.     

Stochastic simulation of patterns using Bayesian pattern modeling

In this paper, a Bayesian framework is introduced for pattern modeling and multiple point statistics simulation. The method presented here is a generalized clustering-based method where the patterns can live on a hyper-plane of very low dimensionality in each cluster. The provided generalizationallows a remarkable increase in variability of the model and a significant reduction in the number of necessary clusters for pattern modeling which leads to more computational efficiency compared with clustering-based methods. The Bayesian model employed here is a nonlinear model which is composed of a mixture of linear models. Therefore, the model is stronger than linear models for data modeling and computationally more effective than nonlinear models. Furthermore, the model allows us to extract features from incomplete patterns and to compare patterns in feature space instead of spatial domain. Due to the lower dimensionality of feature space, comparison in feature space results in more computational efficiency as well. Despite most of the previously employed methods, the feature extraction filters employed here are customized for each training image (TI). This causes the features to be more informative and useful. Using a fully Bayesian model, the method does not require extensive parameter setting and tunes its parameters itself in a principled manner. Extensive experiments on different TIs (either continuous or categorical) show that the proposed method is capable of better reproduction of complex geostatistical patterns compared with other clustering-based methods using a very limited number of clusters.     

Provisional Elemental Values for IAEA‐Sewage Sludge by Instrumental Neutron Activation Analysis

The International Atomic Energy Agency (IAEA) organised a proficiency test (PT), IAEA‐CU‐2010‐02, for the determination of elements in sewage sludge. The PT sample was analysed by semi‐absolute standardless k₀‐instrumental neutron activation analysis (k₀‐INAA). Results for seven elements (As, Co, Cr, Fe, Hg, Se, Zn) were submitted to the IAEA by our laboratory. All of our results were scored ‘acceptable’ by the ‘result evaluation criteria’ adopted by the IAEA. The same analytical methodology produced quantitative results for twenty‐six additional elements. In total, thirty‐six elements were determined with uncertainty varying from 4 to 11%. This paper presents the provisional mass fractions of twenty‐six additional elements (Ag, Al, Br, Ca, Ce, Cl, Dy, Eu, Ga, Hf, I, K, La, Mg, Mn, Na, Rb, Sb, Sc, Sm, Ta, Tb, Th, V, U, W) not reported by the IAEA. The analytical methodology was discussed with important sources of spectral, nuclear and fission‐product interferences. It was shown that the important components of uncertainties were the k₀ factor, Q₀ factor, detector efficiency, mass and counting statistics. The methodology was validated by analysing the IAEA‐S7 reference material.     

Shallow and deep aquifer hydrochemistry and chemical variability of water quality at Shariatpur district, Bangladesh

Water quality and hydrochemistry of Shariatpur district were evaluated in terms of hydrochemical composition and some important physico-chemical parameters. The groundwater of the study area is good for drinking, domestic as well as for irrigation purposes. Among the major ions, shallow tube well waters give higher concentration of Ca²⁺ which ranges from 24 to 260 mg/L. The deep tubewell waters show higher concentration of Na⁺ which varies from 74 to 582 mg/L during dry season. Among the trace elements most of the shallow aquifer samples show higher concentration of Fe²⁺, Mn²⁺ and As. Concentration of Fe²⁺ varies from 0.655 to 18.8 mg/L, and Mn²⁺ from trace to 0.868 mg/L during dry period. Hydrochemical analyses reveal significant seasonal variation in water quality of shallow aquifer. Both the shallow aquifer and the surface water of the study area are predominantly of Ca–Mg–HCO₃ type, while the deep aquifer water is mainly of Na–K–Cl–SO₄ type with slight inclination to Ca–Mg–HCO₃ type. The study area is suitable for groundwater development if comprehensive and holistic approaches towards water resource management are taken into consideration.     

Investigation on mantle peridotites from Neyriz ophiolite, south of Iran: geodynamic signals

Neyriz ophiolite in Abadeh Tashk area appears as four major separated massifs in an area with 125 km², south of Iran. Peridotites including harzburgite, dunite, and lesser low-Cpx lherzolite are the major constituents of the ophiolite with very minor mafic rocks. Usual gabbros of ophiolite complexes are virtually absent from the study area. Mineral modality associated with bulk rock and mineral chemistry of the peridotites show a progression from fertile to ultra-refractory character, reflected by a progressive decrease in modal pyroxenes and in Al₂O₃, CaO, SiO₂, Sc, Ta, V, and Ga values of the studied rocks by approaching chromite deposits. The Neyriz peridotites vary from low-Cpx lherzolite (MgO, 41.97–43.1 wt.%; Al₂O₃, 0.8–1.3 wt.%) with low content of Cr# spinel (36.7–37.6) and Fo olivine (90.79–91.5) to harzburgite (MgO, 44.31–45.25 wt.%;Al₂O₃, 0.29–0.45 wt.%; Cr# spinel, 58.2–73.45; Fo olivine, 91.23–91.56), and then to dunite (MgO, 45.9–49.2 wt.%; Al₂O₃, 0.18–0.48 wt.%) with higher content of Cr# spinel (74.34–79.36) and Fo olivine (91.75–94.68). Compared to modern oceanic settings, mineral and rock composition of low-Cpx lherzolite plot within the field of mid-ocean-ridge environment, whereas those of harzburgite and dunite fall in the field of fore-arc peridotites. As a result of the studies on minerals and whole rock chemistry along with rock interrelationships, we contend that the peridotites were subsequently affected by percolating hydrous boninitic melt from which the high-Cr–Mg, low-Ti chromitites were formed within mantle wedge above the supra-subduction zone in a fore-arc setting.     

Quantitative and qualitative simulation of groundwater by mathematical models in Rafsanjan aquifer using MODFLOW and MT3DMS

Agriculture sector by using 80% of freshwater is the greatest water consumer in Iran. Excessive use of agricultural fertilizers in last decade, caused accumulation of enormous amounts of salts and subsequence declined the physical properties of soil. In desert and dry regions such as Rafsanjan plain, use of the groundwater resources is more than the surface water resources. Therefore, information about the quality of these resources remains a necessary task for optimum management, protection of water resources, and stopping the future damages. In this study, the groundwater quantity and quality of Rafsanjan plain was investigated by MODFLOW and MT3DMS. The presented quantitative model for this aquifer was compared by observed data and calibrated. This model was used to predict a 10-year period. Results show that water elevation decreases approximately 15 m for 10 years to come in this plain. Qualitative model results show that most quality parameters will increase. Electrical conductivity will increase more than other parameters. As values of this parameter will reach 16,000 µs/l for next 5 years. Therefore, we suggest that exploitation of water from these resources should be reduced and discharge from some of agricultural wells stop; also we suggested that recharge to groundwater resources should be increased and agricultural activities should be limited or improved using of modern irrigation systems in this plain.     

A family of well behaved charge analogues of Durgapal’s perfect fluid exact solution in general relativity

This paper presents a new family of interior solutions of Einstein–Maxwell field equations in general relativity for a static spherically symmetric distribution of a charged perfect fluid with a particular form of charge distribution. This solution gives us wide range of parameter, K, for which the solution is well behaved hence, suitable for modeling of superdense star. For this solution the gravitational mass of a star is maximized with all degree of suitability by assuming the surface density equal to normal nuclear density, ρ _nm=2.5×10¹⁷ kg?m^?3. By this model we obtain the mass of the Crab pulsar, M _Crab, 1.36M _⊙ and radius 13.21 km, constraining the moment of inertia >?1.61×10³⁸ kg?m² for the conservative estimate of Crab nebula mass 2M _⊙. And M _Crab=1.96M _⊙ with radius R _Crab=14.38 km constraining the moment of inertia >?3.04×10³⁸ kg?m² for the newest estimate of Crab nebula mass, 4.6M _⊙. These results are quite well in agreement with the possible values of mass and radius of Crab pulsar. Besides this, our model yields moments of inertia for PSR J0737-3039A and PSR J0737-3039B, I _A =1.4285×10³⁸ kg?m² and I _B =1.3647×10³⁸ kg?m² respectively. It has been observed that under well behaved conditions this class of solutions gives us the overall maximum gravitational mass of super dense object, M _G(max)=4.7487M _⊙ with radius $R_{M_{\max}}=15.24~\mathrm{km}$ , surface redshift 0.9878, charge 7.47×10²⁰ C, and central density 4.31ρ _nm.     

A systematic review of strong gravitational lens modeling software

Despite expanding research activity in gravitational lens modeling, there is no particular software which is considered a standard. Much of the gravitational lens modeling software is written by individual investigators for their own use. Some gravitational lens modeling software is freely available for download but is widely variable with regard to ease of use and quality of documentation. This review of 13 software packages was undertaken to provide a single source of information. Gravitational lens models are classified as parametric models or non-parametric models, and can be further divided into research and educational software. Software used in research includes the GRAVLENS package (with both gravlens and lensmodel), Lenstool, LensPerfect, glafic, PixeLens, SimpLens, Lensview, and GRALE. In this review, GravLensHD, G-Lens, Gravitational Lensing, lens and MOWGLI are categorized as educational programs that are useful for demonstrating various aspects of lensing. Each of the 13 software packages is reviewed with regard to software features (installation, documentation, files provided, etc.) and lensing features (type of model, input data, output data, etc.) as well as a brief review of studies where they have been used. Recent studies have demonstrated the utility of strong gravitational lensing data for mass mapping, and suggest increased use of these techniques in the future. Coupled with the advent of greatly improved imaging, new approaches to modeling of strong gravitational lens systems are needed. This is the first systematic review of strong gravitational lens modeling software, providing investigators with a starting point for future software development to further advance gravitational lens modeling research.     

Assessment of Trace Metal Contamination of Drinking Water in the Pearl Valley,Azad Jammu and Kashmir

The aim of this study was to assess trace metal contamination of drinking water in the Pearl Valley, Azad Jammu and Kashmir (Pakistan). The objectives were to determine physical properties and the dissolved concentration of five trace metals, i. e., lead, copper, nickel, zinc, and manganese, in drinking water samples collected from various sites of municipal water supply, natural water springs and wells in the valley. Concentrations of the metals in the water samples were determined by flame atomic absorption spectrometry. Results showed physical parameters, i. e., appearance, taste and odor within acceptable limits and pH was between 5.5 and 7.0. The observed concentrations of the metals varied between sources of water samples and between sampling sites. Maximum dissolved concentration observed was 4.7 mg/L for Pb and Mn, 4.6 mg/L for Zn, 2.9 mg/L for Ni and 2.8 mg/L for Cu. The observed concentrations of the metals were compared with the World Health Organization's guideline values for drinking water. Overall, the quality of water samples taken from the water springs at Mutyal Mara and Bonjosa was good; however, the water quality was unsuitable for drinking in Kiraki, Kharick, and Pothi Bala localities particularly. Finally, the authors discuss possible causes for increased concentrations of the trace metals in drinking water in the study area.     

Input data selection for solar radiation estimation

Model input data selection is a complicated process, especially for non‐linear dynamic systems. The questions on which inputs should be used and how long the training data should be for model development have been hard to solve in practice. Despite the importance of this subject, there have been insufficient reports in the published literature about inter‐comparison between different model input data selection techniques. In this study, several methods (i.e. the Gamma test, entropy theory, AIC (Akaike's information criterion)/BIC (Bayesian information criterion) have been explored with the aid of non‐linear models of LLR (local linear regression) and ANN (artificial neural networks). The methodology is tested in estimation of solar radiation in the Brue Catchment of England. It has been found that the conventional model selection tools such as AIC/BIC failed to demonstrate their functionality. Although the entropy theory is quite powerful and efficient to compute, it failed to pick up the best input combinations. On the other hand, it is very encouraging to find that the new Gamma test was able to choose the best input selection. However, it is surprising to note that the Gamma test significantly underestimated the required training data while the entropy theory did a better job in this aspect. This is the first study to compare the performance of those techniques for model input selections and still there are many unsolved puzzles. Copyright © 2009 John Wiley & Sons, Ltd.