Hydrobiological variables as a regulatory factor on the abundance of heterotrophic flagellates in an urban pond

The seasonal abundance of flagellates has been monitored over a period of 1 year from December2013 to November 2014(divided into 4 conjugative seasons namely winter, spring, summer, and autumn) in an experimental pond located in Rajshahi City Corporation area, Bangladesh. To our knowledge, this study is the first to shed light on the occurrence and possible interrelationships among heterotrophic flagellates(HF),bacteria and zooplankton in Bangladesh and the result obtained by this study will be beneficial for similar water ecosystem all over the world. Standard methods were used to determine the prescribed hydrological parameters and zooplankton cell density. Maximum HF abundance(14 346.00 cells/mL) was found in the spring and the minimum(5 215.00 cells/mL) occurred in the summer. Inverse to HF, significantly(P0.05)higher zooplankton abundance was found during the winter(782.00±47.62 cells/mL) and the lowest value was found in the autumn(448.00±39.15 cells/mL). Whereas similar to the HF, total bacterial abundance was significantly higher during the spring((2.25±1.05)×10~5 cells/mL) and lower in the summer((0.79±0.06)×105 cells/mL). Multivariate analyses(ANOSIM and MDS) have shown significant seasonal differences for cell numbers where MDS ordination plot and cluster analysis based on similarity in the genera abundance of HF revealed overlapping condition between winter and spring. Canonical correspondence analysis(CCA) also showed a distinct separation among the genera based on the prevailing hydrological situation and indicated that temperature, pH, BOD_5, and NO_3~- were the most important environmental variables in determining the observed variation in HF community structure. Among the biological factors, zooplankton showed negative but total bacteria were positively correlated with HF abundance.     

Microseismicity and tectonics of Sinai

Five microearthquake seismographs were used at 11 sites in northern Sinai in the period February 1987 to February 1988 to study the microseismicity of the area around the Maghara coal mine for mine-development studies. A total of 270 events were recorded on 850 records. The magnitude, epicenter and depth of each event were calculated using suitable software for an Egyptian Geological Survey and Mining Authority's (EGSMA) XT computer. The interpretation of the seismic activity in the area is given in view of plate tectonics, the Sinai subplate boundary follows the Gulf of Aqaba and the Dead Sea. The plate and subplate boundaries are presently active, and there seems to be a diffuse zone of deformation between “NW Africa” and “Nubia” affecting the Cairo-Suez district.     

Observed Drawdown Pattern Around a Well Partially Penetrating a Vertically Extensive Water-Table Aquifer

In order to understand the flow pattern around a pumping well partially penetrating a vertically extensive aquifer, a specially designed pumping test was carried out in Pakistan. In this paper salient features of the test have been described. The spatial distributions of drawdown have been shown graphically. Some of the preliminary conclusions made from the drawdown pattern include:

• • The distance beyond which the flow is likely to be horizontal increases with decrease in the degree of aquifer penetration.
• • In equidistant observation wells open at different depths, (1) the drawdowns tend to merge at larger times, provided the observation point is located within the screened section of the aquifer; (2) the less the depth of penetration is, the earlier the drawdowns start merging; and (3) the initial rate of drawdown near the aquifer top is slow but catches up with time to exceed those at deeper points.

     

Spectral wheat yield prediction modeling using SPOT satellite imagery and leaf area index

As wheat represents the main staple food and strategic crop in Egypt and worldwide and since remote sensing satellite imagery is the tool to obtain synoptic, multi-temporal, dynamic, and time-efficient information about any target on the Earth, the main objective of the current study is to use remote sensing satellite imagery to generate remotely sensed empirical preharvest wheat yield prediction models. The main input parameters of these models are spectral data either in the form of spectral reflectance data released from Satellite Pour l’Observation de la Terre (SPOT) 4 satellite imagery or in the form of spectral vegetation indices. The other input factor is leaf area index (LAI) that was measured by LAI Plant Canopy Analyzer. The four spectral bands of SPOT4 imagery are green, red, near-infrared, and middle infrared; the five vegetation indices that are forms of ratios between red and near-infrared bands are normalized difference vegetation index, ratio vegetation index, soil-adjusted vegetation index, difference vegetation index, and infrared percentage vegetation index. Another vegetation index is green vegetation index that is calculated through a ratio between green band and near-infrared band. Each of the above-mentioned factors was used as an input factor against wheat yield to generate wheat yield prediction models. All generated models are site-specific limited to the area and the environment and could be applicable under similar conditions in Egypt. The study was carried out in Sakha experimental station by using the dataset from two wheat season 2007/2008 and 2009/2010. The total wheat area was 1.3 ha cultivated by Sakha 93 cultivar. Modeling and validation process were carried out for each season independently. Modeled yield was tested against reported yield through two common statistical tests; the standard error of estimate between modeled yield and reported yield, and the correlation coefficient for a direct regression analysis between modeled and reported yield with each generated model. Generally, as shown from the correlation coefficient of the generated models, green and middle infrared bands did not show good accuracy to predict wheat yield, while the other spectral bands (red and near-infrared) bands showed high accuracy and sufficiency to predict yield. This was proven through the correlation coefficient of the generated models and through the generated models with the wheat crops for the two seasons. Accordingly, the green vegetation index that is generally calculated from green and near-infrared bands showed relatively lower accuracy than the rest of the vegetation index models that are calculated from red and near-infrared bands. LAI showed high accuracy to predict yield as shown from the statistical analysis. The models are applicable after 90 days from sowing stage and applicable in similar regions with the same conditions.     

Horizontal soil stress changes around displacement piles

A simplified analysis of the problem of horizontal soil stress changes around circular displacement piles, caused by pile placement, is presented. Pile installation is assumed to cause soil displacements in the horizontal direction only, thus yielding an axisymmetric problem. The soil surrounding the pile is modelled as a weightless non-linear elastic material. Material non-linearity is handled in a simplified manner by adopting secant shear moduli defined in terms of a proportionality coefficient and a softening factor. The resulting equilibrium equation is solved analytically and an expression is obtained which is also conveniently presented in graphical form. The derived expression can be used to estimate horizontal soil stress changes and is incorporated into a simple procedure to estimate the ultimate load or the efficiency of pile groups. Comparisons are made between efficiencies calculated according to this procedure and efficiencies measured in full-scale group load tests in sand.     

Analysis of groundwater quality for irrigation purposes in shallow aquifers: A case study from West Aceh,Indonesia

The aim of this study is to evaluate the groundwater quality in shallow aquifers from West Aceh, Sumatra, for irrigation uses. Groundwater samples have been collected from 32 stations for pH, electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solids (TDS), sodium percentage (per cent Na) and residual sodium carbonate (RSC) analysis. Evaluation of water quality for irrigation using the United States Salinity Laboratory classifications suggests that the majority of the groundwater samples are good for irrigation. The crop tolerance of irrigation water salinity, as based on EC value, showed that paddy (rice), soybeans and sweet tomatoes are suitable for agricultural cultivation, but that corn and field beans are not suitable. Results indicate that, if used for agricultural irrigation, the groundwater quality ranges from excellent to good, except for a few locations (e.g. Meureubo sub‐district) which indicate signs of deterioration.     

Modeling of soil penetration resistance using multiple linear regression (MLR)

In agricultural areas, the use of machinery leads to improved yields. Nevertheless, its inadequate implementation and excessive utilization can seriously affect the soil efficiency. In fact, latter can be generated by increasing the penetration resistance and subsequently, it results in the compaction phenomenon. This problem becomes considerable with the increasing report wheel/soil. The aim of this work was to evaluate the efficiency through the prediction of soil penetration resistance (Rp) using a statistical model based on moisture content, density, tractor weight, number of passes, and the wheel inflation pressure. Experimental works (211 measurements) were analyzed and the penetration resistance was modeled using multiple linear regressions (MLR). Besides, the developed model elucidates the variables affecting the accentuation of soil Rp and allows the investigation of equations for novel sampled soils. Our results showed that the parameters related to soil and tractors were significant to explain Rp. The adopted model in the MLR analysis emphasizes that the mechanical parameters of ground measurements are statistically significant in estimating and evaluating Rp. The statistical calculation of the R ² expresses 83% of the variance in Rp generated by the various parameters related to soil and tractor. In view of the importance of estimating the penetration resistance (Rp), the regression equation shows that the weight of the tractor and the number of passages contributed the most to the proposed model for the soil.