Integrated Storage and Querying of Spatially Varying Data Quality Information in a Relational Spatial Database

This article investigates a new, integrated technique for storing and retrieving spatially varying data quality information in a relational spatial database. Rather than storing global data quality statements, the system enables data quality information to be referenced to a spatial framework, individual spatial objects, or even parts of spatial objects. The integrated model, called as RDBMS for Spatial Variation in Quality (RSVQ), allows flexible storage of spatially varying data quality information, and seamless querying irrespective of the underlying storage model. RSVQ is founded on a formal model of relational databases, defining a new derived, polymorphic query operator      to join quality data with spatial data. The      operator is implemented in an extension to SQL as a new WITHQUALITY keyword. A performance evaluation of RSVQ was conducted, using an Oracle Spatial database and a case study of cadastral data for parts of Victoria, Australia. The results of this evaluation demonstrated that the system is practical and efficient for a wide range of queries, as well as indicating the performance trade-offs associated with the different data quality storage models. Using the integrated RSVQ approach provides the potential for a single, consistent, database engine for a wide range of existing and proposed spatial data quality management systems.     

Stochastic Forecasting Models of the Monthly Streamflow for the Blue Nile at Eldiem Station

Egypt is almost totally dependent on the River Nile for satisfying about 95% of its water requirements. The River Nile has three main tributaries: White Nile, Blue Nile, and River Atbara. The Blue Nile contributes about 60% of total annual flow reached the River Nile at Aswan High Dam. The goal of this research is to develop a reliable stochastic model for the monthly streamflow of the Blue Nile at Eldiem station, where the Grand Ethiopian Renaissance Dam (GERD) is currently under construction with a storage capacity of about 74 billion m³. The developed model may help to carry out a reliable study on the filling scenarios of GERD reservoir and to minimize its expected negative side effects on Sudan and Egypt. The linear models: Deseasonalized AutoRegressive Moving Average (DARMA) model, Periodic AutoRegressive Moving Average (PARMA) model and Seasonal AutoRegressive Integrated Moving Average (SARIMA) model; and the nonlinear Artificial Neural Network (ANN) model are selected for modeling monthly streamflow at Eldiem station. The performance of various models during calibration and validation were evaluated using the statistical indices: Mean Absolute Error, Root Mean Square Error and coefficient of determination (R2) which indicate the strength of fitting between observed and forecasted values. The results show that the performance of the nonlinear model (ANN) was much better than all investigated linear models (DARMA, PARMA and SARIMA) in forecasting the monthly flow discharges at Eldiem station.     

Better constrained selection of the Paleozoic West Gondwana (South America) paleomagnetic poles for the APWP determination

The reliability of an Apparent Polar Wander Path (APWP) obviously depends on the paleomagnetic poles used to determine it. The APWP of Africa and South America are fairly well defined for the 330–260 Ma interval. However, this study pointed out a moderate shift between these two curves, and an incoherency of the South American data, contrary to the African ones, which are homogeneous. A number of South American pole positions were re-evaluated in an effort to better constrain the APWP for the entire continent. Most of discarded poles correspond to sites at the area of the junction of Cordillera with the stable craton. That could have structural implications for the evolution of the western margin of the Gondwana. A new criterion for the evaluation of paleomagnetic poles reliability for APWP is presented. Based on comparison of data from different continents and labeled “coherence” criterion, it is independent from Van der Voo’s ones.     

GIS based DRASTIC model for groundwater vulnerability assessment: Case study of the shallow mio-plio-quaternary aquifer (Southeastern Tunisia)

Groundwater is the main source of water in arid regions. Thus, groundwater pollution becomes a major issue due to the increasing contamination, which poses serious and harmful risk to the environment. Groundwater vulnerability maps can be used as a tool to help decision makers to protect groundwater resources from contamination. The vulnerability of the Mio-Plio-Quaternary shallow aquifer (Southeast Tunisia) has been assessed using a DRASTIC model based on Geographic Information System (GIS). The different parameters of the model were collected from several sources and converted into thematic maps using ArcGis©. Each DRASTIC parameter was assigned a weight and rating based on a range of information within the parameter. Groundwater vulnerability map shows a large area (48%) with high risk of pollution. It indicates that the Southern part of the aquifer and the wadi beds are the most susceptible to contamination. The measured nitrate concentration is coherent with the DRASTIC model results.     

Current State of Crustal Deformation and Seismic Activity from Seismic and Geodetic Data in Aswan Region,Egypt

Geotectonics - Aswan region is a very important region for Egypt where the High Dam is situated. The wellbeing of the dam and its encompassing region is of extreme worry to all the Egyptian...     

B.S. Daya Sagar,Qiuming Cheng and Frits Agterberg: Handbook of Mathematical Geosciences: Fifty Years of IAMG

Mathematical Geosciences -     

Finite strain analysis for deformed Neoproterozoic rock associations of the Al Amar fault zone,Eastern Arabian Shield,Saudi Arabia

Arabian Journal of Geosciences - In this work, quantitative analysis of coastal zone landform morphology is carried out through the integration of landscape index, remote sensing (RS) and...     

Component Analysis and Antifungal Activity of the Compounds Extracted from Four Brown Seaweeds with Different Solvents at Different Seasons

The polar and nonpolar extracts of Colpomenia sinuosa, Padina pavonia, Cystoseira barbata and Sargassum vulgare collected during spring, summer and autumn were evaluated for their antifungal activities. The phytochemicals of unexplored seaweeds were analyzed by gas chromatography and mass spectrometric (GC/MS). The algal extracts were tested for their antifungal activities against Aspergillus niger, A. flavus, Penicillium parasiticus, Candida utilis and Fusarium solani. Phytochemicals were extracted from the four seaweeds with various solvents including methanol, ethanol, acetone, chloroform and dimethyl ether. Among the various extracts, methanolic extract showed the highest inhibition activity on all fungal species. Seasonal variation in antifungal activity was studied, while methanol extracts showed the best antifungal activity in spring. Cystoseira barbata was the most effective seaweed, having antifungal activity throughout the whole year. The UV-Vis phytochemical procedure and GC-MS analysis of the methanol extracts from tested species indicated the existence of different constituents. In conclusions, the compounds with antifungal activity were identified as indoles, terpenes, acetogenins, phenols, and volatile halogenated hydrocarbons.