Effect of Silica Fume on Compressive Strength of Oil-Polluted Concrete in Different Marine Environments

In the present research, effect of silica fume as an additive and oil polluted sands as aggregates on compressive strength of concrete were investigated experimentally. The amount of oil in the designed mixtures was assumed to be constant and equal to 2% of the sand weight. Silica fume accounting for 10%, 15% and 20% of the weight is added to the designed mixture. After preparation and curing, concrete specimens were placed into the three different conditions: fresh, brackish and saltwater environments (submerged in fresh water, alternation of exposed in air & submerged in sea water and submerged in sea water). The result of compressive strength tests shows that the compressive strength of the specimens consisting of silica fume increases significantly in comparison with the control specimens in all three environments. The compressive strength of the concrete with 15% silica fume content was about 30% to 50% higher than that of control specimens in all tested environments under the condition of using polluted aggregates in the designed mixture.     

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.     

Mapping of groundwater prospective zones integrating remote sensing,geographic information systems and geophysical techniques in El-Qaà Plain area,Egypt

The geospatial mapping of groundwater prospective zones is essential to support the needs of local inhabitants and agricultural activities in arid regions such as El-Qaà area, Sinai Peninsula, Egypt. The study aims to locate new wells that can serve to cope with water scarcity. The integration of remote sensing, geographic information systems (GIS) and geophysical techniques is a breakthrough for groundwater prospecting. Based on these techniques, several factors contributing to groundwater potential in El-Qaà Plain were determined. Geophysical data were supported by information derived from a digital elevation model, and from geologic, geomorphologic and hydrologic data, to reveal the promising sites. All the spatial data that represent the contributing factors were integrated and analyzed in a GIS framework to develop a groundwater prospective model. An appropriate weightage was specified to each factor based on its relative contribution towards groundwater potential, and the resulting map delineates the study area into five classes, from very poor to very good potential. The very good potential zones are located in the Quaternary deposits, with flat to gentle topography, dense lineaments and structurally controlled drainage channels. The groundwater potential map was tested against the distribution of groundwater wells and cultivated land. The integrated methodology provides a powerful tool to design a suitable groundwater management plan in arid regions.     

Biostratigraphy and seismic data analysis to detect the sequence stratigraphic depositional environment of the Miocene succession: Gulf of Suez (Egypt)

This work comprises a study of the sequence stratigraphy, seismic-facies analysis, biostratigraphy and depositional environments of the northern part of the Gulf of Suez, Egypt, using a set of 24 3D seismic profiles, composite logs and sonic logs from ten wells. The syn-rift formations in the studied ten wells are described lithologically and interpreted based on investigating two seismic profiles. Biostratigraphically, the Miocene fossils are identified to correlate the five planktonic foraminiferal biozones in the examined boreholes (RB-A1, RB-B1, RB-B3, EE85-2 and RB-C1). The sequence stratigraphic analysis suggests that the Miocene succession can be subdivided into two major third order depositional sequences (S1 and S2) separated by the three major sequence boundaries (DSB1, DSB2 and DSB3).     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.     

Nature of serpentinization and carbonation of ophiolitic peridotites (Eastern Desert,Egypt): constrains from stable isotopes and whole-rock geochemistry

Serpentinites are widespread in the Arabian-Nubian Shield (ANS) of the Eastern Desert of Egypt and usually enclose a tremendous carbonate alteration. Combined investigation of the stable isotope compositions of both O-H in serpentines and O-C in the whole-rock and the chemistry of the fluid-mobile elements (FMEs) in whole-rock serpentinites from Wadi (W.) Alam, Gabal (G.) El-Maiyit, and W. Atalla (Eastern Desert of Egypt) allowed to better understand the subsequent fluid sources of serpentinization and carbonation, as well as impact of these processes on the geochemistry of protolith ultramafic rocks. δ ¹⁸O values of W. Alam and W. Atalla serpentine minerals are close to the unaltered mantle and propose a lower temperature serpentinization if compared with those of G. El-Maiyit rocks. Moreover, δD values of W. Alam and W. Atalla serpentines (? 94 to ? 65‰) correspond to an igneous source that might be hydrothermal solutions mixed with the seawater in the mid-ocean ridge-arc transition setting. On the other hand, G. El-Maiyit serpentine is more depleted in ¹⁸O (with lower δ ¹⁸O values = 4.08–4.85‰), and its δD values (? 73 to 56 ‰) are most probably caused by an interaction with metamorphic fluids, acquired during on-land emplacement of oceanic peridotites or during burial in fore-arc setting. In addition, the oceanic oxygen isotope composition of most studied ophiolitic serpentinites points to the preservation of the pre-obduction δ ¹⁸O signatures and thus local-scale fluid flow at low water/rock ratios. Serpentinization fluids were CO₂-poor and the carbonation of the serpentinites resulted from infiltration of externally derived fluids. δ ¹⁸O_VSMOW values of carbonates in the studied serpentinites vary between heavier oxygen isotope composition in G. El-Maiyit samples (av. = 25.32‰) to lighter composition in W. Alam samples (av. = 19.43‰). However, δ ¹³C values of all serpentinites point mantle source of carbon. This source might have been evolved in mid-ocean ridge (W. Atalla) and subduction zone (W. Alam and G. El-Maiyit) settings. The studied serpentinites are usually enriched in FMEs, particularly Pb, Sr, Cs, and U. These enrichments were most probably the result of serpentinization and/or carbonation.     

Woody species diversity and forest structure from lowland to montane forest in Hyrcanian forest ecoregion

Alborz Mountains host Caspian Hyrcanian forest ecoregion along the northern slopes and forest steppe ecoregion in highlands. Hyrcanian forest covers the southeastern part of Caucasus biodiversity hotspot and is of great biogeographic importance. Altitudinal pattern and correlation between woody species biodiversity (DIV), forest structure ((stem density (DEN), mean basal area (MBA) and mean height class (MHC)) and disturbance (DIS) were explored along 2,400 m altitudinal gradient in Hyrcanian relict forest, Central Alborz Mountains. Vegetation changes from lowland forest (LoF) to mid- altitude forest (MiF) and montane forest (MoF) in this area. The altitudinal gradient was divided into twelve 200 m elevational belts. Point centered quarter method (PCQM) with 96 sampling points and 83 vegetation samples by plot method (PM) were used to record field data. Shannon-Wiener index and Pearson coefficient were used for diversity and correlation analysis. The results showed that DEN decreased linearly, MBA and MHC showed relatively hump shaped and DIS showed a reverse hump shaped pattern of change along altitudinal gradient. Woody species diversity decreased non-steadily from LoF to MoF. Transitional vegetations of Carpinus-Fagus and Fagus-Quercus represented higher diversity of woody taxa compared to adjacent homogenous communities. Significant correlation was observed between altitude and all parameters: DEN with MBA, DIS and DIV; MBA with DIS; MHC with DIS along with DIV; and DIS with DIV at the study area scale. Surprisingly,correlation between studied parameters differed within each vegetation type. Altitude probably acts as a proxy for human and environmental driving forces in this area. Stability of warm and wet condition, season length, soil depth along with forest accessibility probably influences the altitudinal pattern of the studied parameters. Disturbance affects forest structure and consequently diversity; especially in lowlands. The obtained results recommend using both forest biodiversity and mensuration data in management process of forest ecosystems.     

基于深度学习的区域生态安全时空模拟与预测

区域作为人类、自然、社会共同作用和互相影响的复杂系统,对区域进行生态量化建模与模拟仿真,是实现区域可持续发展战略的关键。传统机器学习方法对区域生态系统建模取得了一定的成果,但难以确定学习特征和实现时空模拟。深度学习不需事先确定训练特征,具有优异的特征学习能力,能够提高模型预测精度,因此利用深度学习进行建模具有显著优势。本文使用植被净初级生产力（NPP）、气溶胶光学厚度（AOD）和人口格网数据,充分利用深度学习的优点,采用最优深度神经网络时空模拟,得到了河南省2007-2014年3 km分辨率的生态赤字空间分布图和河南省2015-2020年的生态赤字时间预测结果并进行分析,为区域生态的科学管理和建设供科学依据和参考。     

Effects of a sill on selective withdrawal through a point sink in a linearly stratified fluid

Reservoir topography has significant effects on mechanism of selective withdrawal. We study selective withdrawal of a linearly stratified fluid through a point sink in a long rectangular reservoir with a sill at the bed experimentally and analytically. Experiments were conducted for various flow rates and sill heights to evaluate their effects on withdrawal layer in the inertial-buoyancy regime. Transition to the steady state is discussed in terms of dynamics of shear waves over the sill. The results show that in presence of a sill the withdrawal layer is thicker and higher in elevation. For flows controlled at the sill crest, the withdrawal layer thickness in the point-sink flow is almost equal to that in the similar line-sink flow. We propose an analytical relation for the withdrawal layer thickness in presence of a sill and confirm it by experiments.