Al-Qudis power plant was chosen, as one of the power stations of Baghdad, to investigate the effects of Cadmium that emitted from combustion of crude oil in that power plant on the workers' glutathione serum level. Air samples were taken seasonally during August 2011- July 2012 from four sites at Al- Qudis power plant which are oil treatment unit 1(site 1), oil treatment 2 (site2), pre –chimney 1(site 3) and pre-chimney 2 (site 4), to measure levels of heavy metal (cadmium) under study. Blood samples were collected from the workers to estimate the heavy metal Cadmium. Air cadmium levels during summer (August and September) were varied from 6.26 ± 0.6 μg/m3 at site 1 to 6.89 ± 0.67 μg/m3 at site 3, while in spring (end of March, and

Al-Qudis power plant was chosen, as one of the power stations of Baghdad, to investigate the effects of Cadmium that emitted from combustion of crude oil in that power plant on the workers' glutathione serum level. Air samples were taken seasonally during August 2011- July 2012 from four sites at Al- Qudis power plant which are oil treatment unit 1(site 1), oil treatment 2 (site2), pre –chimney 1(site 3) and pre-chimney 2 (site 4), to measure levels of heavy metal (cadmium) under study. Blood samples were collected from the workers to estimate the heavy metal Cadmium. Air cadmium levels during summer (August and September) were varied from 6.26 ± 0.6 μg/m3 at site 1 to 6.89 ± 0.67 μg/m3 at site 3, while in spring (end of March, and

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

     In Iraq; there is a great demand for handling  some epidemic diseases such as hyperglycemia and hyperlipidema through searching some medical plant extracts which the country is rich with and contains an important phytochemicals that may solve  the incidence of such cases. Aloe vera that cultivated in Iraq, is known to be rich with biological active constituents.    The aim of  the  present study; the plant gel extract was subjected  to treat an induced diabetic lab mice through injecting  streptozotocin dose with  high fatty food to elevate lipid profile before induction of the gel oral treatment of the plant. In the study results: the oral administration of A.ver

ABSTRACT

            The results showed that the organic fertilizer mixture (1:1) 30 tons/ha with chemical fertilization recorded the lowest level of bulk density of 1.2 g/cm³, the organic fertilizer mixture (1:1) 30 tons/ha with chemical fertilization recorded the highest percentage of aggregation stability amounting to 16.17%, the organic fertilizer palm fronds recorded the highest level of ready water with an average of 5.50 cm³/cm³ and the organic fertilizer mixture (1:1) 30 tons/ha without chemical fertilization recorded the highest level of ready water as it reached 6.93%, the or

In this work, we have developed a model that describes the relationships between top predators (such as tigers, hyenas, and others), crop raiders (such as baboons, warthogs, and deer), and prey (such as deer) in the coffee forests of southwest Ethiopia. Various potential equilibrium points are identified. Additionally, the model's stability in the vicinity of these equilibrium points is examined. An investigation of the model's Hopf bifurcation is conducted concerning several significant parameters. It is found that prey species may be extinct due to a lower growth rate and consumption by top predators in the absence of human interference in the carrying capacity of prey. It is observed that top predators may be extinct due to human interfe

     The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mea

The amount of protein in the serum depends on the balance between the rate of its synthesis, and that of its catabolism or loss. Abnormal metabolism may result from nutritional deficiency, enzyme deficiency, abnormal secretion of hormones, or the actions of drugs and toxins. Renal cancer is the third most common malignancy of the genitourinary system, and accounts for 3% of adult malignancies globally. Total serum proteins were measured in malignant kidney tumor, benign kidney tumors, and non tumoral kidney diseases patient groups, as well as in healthy individuals. A significant decrease (p< 0.001) of total serum protein levels in patients with malignant kidney tumors when compared with those of benign tumors, non tumoral diseases, and hea

Multiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of

Most reinforced concrete (RC) structures are constructed with square/rectangular columns. The cross-section size of these types of columns is much larger than the thickness of their partitions. Therefore, parts of these columns are protruded out of the partitions. The emergence of columns edges out of the walls has some disadvantages. This limitation is difficult to be overcome with square or rectangular columns. To solve this problem, new types of RC columns called specially shaped reinforced concrete (SSRC) columns have been used as hidden columns. Besides, the use of SSRC columns provides many structural and architectural advantages as compared with rectangular columns. Therefore, this study was conducted to explain the structura