Thirty-two soil samples were collected from the study area in October 2020 for geochemical and pollutants investigation of Shwan Sub-basin soil. All soil samples were analysed for different geochemical analyses. The analysis results revealed that the pH values in soil samples ranged from 7.12 to 7.56 with a mean of 7.327. According to the pH values detected in the soil samples, the soil is classified as neutral soil. The electrical conductivity ranged from 0.92 mmhos/cm to 7.8 mmhos/cm with a mean of 1.53 mmhos/cm. Thus, according to the detected electrical conductivity values, the soil was classified as non-saline to slightly saline. The organic matter ranged from 1.14% to 1.45% with a mean value of 1.326 %, while total organic carbon ranged from 0.66 % to 0.84 % with a mean value of 0.769 % which indicated the soil was characterized by low organic content. The results of the geochemical analysis revealed that the major and minor element mean concentrations were in the order Si> Ca> Al> Fe> Mg > K> Ti> Na> P> Mn> S> Cl> N. The average concentrations of trace elements in soil samples followed the decreasing order Sr > Cr> Ba> Zr> Ni> V> Zn> Ta> Rb> Cu> Nb> Y> Pb> Co> Ga> Mo> As> Th> Br> Sn> I. Furthermore, the comparison between heavy metal concentrations in the soil of the study area and metal concentrations in the world soil limit and Indirect Geochemical Background revealed an increase in metal concentrations of Cr, Ni, Zn, Co, As, Mo and Ta. Multivariate statistical analyses, such as Principal Component Analysis and Agglomerative Hierarchal Cluster Analysis, identified the potential sources of pollutants in the soil. Most metals are from natural sources and some of them are from anthropogenic sources mostly from agricultural activities mainly fertilizers use and the waste of animals breeding on farms. Besides industrial activities such as deposits of pollutants from emissions of petroleum refineries located inside or close to the study area. In addition, building blocks and paint factories.
Modeling the microclimate of a greenhouse located in Baghdad under its weather conditions to calculate the heating and cooling loads by computer simulation. Solar collectors with a V-corrugated absorber plate and an auxiliary heat source were used as a heating system. A rotary silica gel desiccant dehumidifier, a sensible heat exchanger, and an evaporative cooler were added to the collectors to form an open-cycle solar assisted desiccant cooling system. A dynamic model was adopted to predict the inside air and the soil surface temperatures of the greenhouse. These temperatures are used to predict the greenhouse heating and cooling loads through an energy balance method which takes into account the soil heat gain. This is not included in
... Show MoreAn optoelectronic flow-through detector for active ingredients determination in pharmaceutical formulations is explained. Two consecutive compact photodetector’s devices operating according to light-emitting diodes-solar cells concept where the LEDs acting as a light source and solar cells for measuring the attenuated light of the incident light at 180˚ have been developed. The turbidimetric detector, fabricated of ten light-emitting diodes and five solar cells only, integrated with a glass flow cell has been easily adapted in flow injection analysis manifold system. For active ingredients determination, the developed detector was successfully utilized for the development and validation of an analytical method for warfarin determination
... Show MoreThe diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Sca
... Show MorePurpose: The purpose of this study was to clarify the basic dimensions, which seeks to indestructible scenarios practices within the organization, as a final result from the use of this philosophy.
Methodology: The methodology that focuses adoption researchers to study survey of major literature that dealt with this subject in order to provide a conceptual theoretical conception of scenarios theory .
The most prominent findings: The only successful formulation of scenarios, when you reach the decision-maker's mind wa takes aim to form a correct mental models, which appear in the expansion of Perception managers, and adopted as the basis of the decisions taken. The strength l
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