Analysis of pervasive precipitation in similar gradient areas of Iran

Precipitation is a major climatic element with high spatial variations. Temporal and spatial variations may differ in large and small scales. It is, therefore, of utmost importance to study areas with similar gradients in terms of precipitation patterns in order to shed light on the complexities of precipitation variations. In the present study, attempts were made to identify areas with similar gradients experiencing the same precipitation pattern over a 50-year period (1964–2013). To this end, data were collected from synoptic stations in Iran in two phases (i.e., 1434 stations in the first phase and 673 stations in the second one). Alexanderson’s technique was adopted to examine sudden changes in precipitation patterns. The results showed that five regions with similar gradients could be identified in terms of precipitation patterns: negative and high variations, negative and moderate variations, positive and high variations, positive and moderate variations, and little or no variations. The distribution of such regions indicated that the regions with positive trends experienced more annual variations and had further spatial distribution. Furthermore, the findings revealed that the regions with negative precipitation patterns experienced more sudden changes in comparison with those with positive precipitation patterns. Additionally, more variations were observed in the precipitation patterns in recent years.     

Shigar valley gemstones,their chemical composition and origin,Skardu, Gilgit-Baltistan,Pakistan

A variety of gemstones is being mined in the Shigar valley, Skardu, Pakistan. These include beryl (goshenite and aquamarine), tourmaline (schorl), garnet (almandine–spessartine), apatite, topaz, fluorite, zoisite, clinozoisite, and axinite, mostly occurring in complex or zoned pegmatites and metamorphic rocks. These have been analyzed using electron probe micro-analyzer and X-ray diffractometer. The mineral chemistry of each gemstone is similar to its respective typical gemstone variety with homogenous chemical composition. Field and chemical characteristics suggest that beryl, tourmaline, garnet, apatite, topaz, and fluorite are occurring in zoned pegmatites which are largely formed by magmatic hydrothermal fluids in the cavities and vugs within the intermediate zone. However, zoisite, clinozoisite, and axinite may have a metamorphic and/or metasomatic origin.     

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.     

Exploring Real‐Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real‐time geospatial applications (e.g. navigation and location‐based services) involve data‐ and/or compute‐intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real‐time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real‐time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data‐ and/or compute‐intensive geospatial applications, current cloud platforms require improvements and special tools for handling real‐time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real‐time geoprocessing in clouds.     

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and <Emphasis Type="Italic">t</Emphasis>-distributed deviations

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student’s) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.     

A probabilistic framework to make a decision on the post-earthquake functionality of bridges considering the damage,residual displacement,and aftershock

Bulletin of Earthquake Engineering - Making a decision on the post-earthquake functionality of structures has always been one of the most challenging issues in earthquake engineering. There is an...     

Reliability framework for characterizing heat wave and cold spell events

Natural Hazards - Extreme weather events such as heat waves and cold spells affect people’s lives. This study develops a probabilistic framework to evaluate heat waves and cold spells. As...