Because of their Physico‐chemical characteristics and its composition, the development of new specific analytical methodologies to determine some highly polar pesticides are required. The reported methods demand long analysis time, expensive instruments and prior extraction of pesticide for detection. The current work presents a new flow injection analysis method combined with indirect photometric detection for the determination of Fosetyl‐Aluminum (Fosetyl‐Al) in commercial formulations, with rapid and highly accurate determination involving only construction of manifold system combined with photometric detector without need some of the pre‐treatments to the sample before the analysis such as extraction or separation. The proposed method is based on the reaction between the Fosetyl‐Al with the colorimetric reagent (Aluminon dye) in an aqueous medium of sodium nitrite resulting in the formation of a red complex and its absorbance is measured using the photometric detector. The proposed method included optimization of several parameters such as sodium nitrite concentration, flow rate, sample volume, Aluminon dye concentration, mixing coil and light intensity. The linear range of the detector response was in the range of 0.005–1.8 mmol/L for the sample with r (coefficient of determination) 0.9909, R2 (correlation coefficient) 0.9954 at 95 % and 0.0041 mmol/L as a limit of detection. The relative standard deviation was less than 1.5 % for 0.005 mmol/L of the analyte (
This research a study model of linear regression problem of autocorrelation of random error is spread when a normal distribution as used in linear regression analysis for relationship between variables and through this relationship can predict the value of a variable with the values of other variables, and was comparing methods (method of least squares, method of the average un-weighted, Thiel method and Laplace method) using the mean square error (MSE) boxes and simulation and the study included fore sizes of samples (15, 30, 60, 100). The results showed that the least-squares method is best, applying the fore methods of buckwheat production data and the cultivated area of the provinces of Iraq for years (2010), (2011), (2012),
... Show MoreThis paper is submitted as anew approach to simulate manufacturing control & planning system to define the problem of designing control system on the needs for materials.
Production planning & control is a total and complex operation, resides in the essence of manufacturing companies operations. The successful process of production planning and control systems is critical for the staying of manufacturing organizations in markets leading to the increasing consumer competition and which dominate most of manufacturing sectors because of the market oriented economy , thus , what has happened previously , that the companies possessed a great inventory of crude material, components, and groupings and they use in flexible techni
... Show MoreIt highlights the importance of research through its focus on the assessment of tax for settling accounts Mmakhr medicines and annual statement controls and its role in determining the taxable income of the real tax Mmakhr drugs and achieve tax equity through tax settling accounts. The tax authority relies annual controls laid down by the tax settling accounts for Mmakhr medicines despite their inclusion bookkeeping business No. 2 system for the year 1985 average .ually this basis formulated hypothesis (that the adoption of the tax authority on annual controls in the tax settling accounts for Mmakhr medicine does not contribute in determining income taxable real tax for this Almmakr). the resulting search for a number of conclusions and
... Show MoreThe hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s
... Show MoreThe research seeks to identify the effectiveness of a selective program in modifying irrational thinking that causes anger among an important class of societies in general. namely teachers. Specifically female teachers because of the pressures they suffer because of the nature of this profession. It may develop into anger outburst, which may cause irrational thinking arose as a result of the different situations they are going through. The sample of the program consisted of (12) teachers in Egypt, from different educational sectors. The researcher intended to clarify the emotion of anger and the irrational thoughts associated with it and the contribution of the selective counseling program in modifying those irrational thinking. This was
... Show MoreThe internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of dat
... Show MoreThe purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals
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