The research discussed the possibility of adsorption of Brilliant Blue Dye (BBD) from wastewater using 13X zeolite adsorbent, which is considered a byproduct of the production process of potassium carbonate from Iraqi potash raw materials. The 13X zeolite adsorbent was prepared and characterized by X-ray diffraction that showed a clear match with the standard 13X zeolite. The crystallinity rate was 82.15% and the crystal zeolite size was 5.29 nm. The surface area and pore volume of the obtained 13X zeolite were estimated. The prepared 13X zeolite showed the ability to remove BBD contaminant from wastewater at concentrations 5 to 50 ppm and the removal reached 96.60% at the lower pollutant concentration. Adsorption measurements versus tim

In current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL^-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi

Одной из активно развивающихся отраслей лексикологии является неология, объект её изучения - новое слово или неологизм. В задачу неологии входит выявление новых слов и новых значений у уже существующих в языке слов, анализ причин и способов их появления, описание факторов, влияющих на появление нового в лексической системе языка, разработка языковой политики в отношении новых номинаций.  Лексикограф

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit

Intrusion detection systems detect attacks inside computers and networks, where the detection of the attacks must be in fast time and high rate. Various methods proposed achieved high detection rate, this was done either by improving the algorithm or hybridizing with another algorithm. However, they are suffering from the time, especially after the improvement of the algorithm and dealing with large traffic data. On the other hand, past researches have been successfully applied to the DNA sequences detection approaches for intrusion detection system; the achieved detection rate results were very low, on other hand, the processing time was fast. Also, feature selection used to reduce the computation and complexity lead to speed up the system

This paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that

Malaria is a curative disease, with therapeutics available for patients, such as drugs that can prevent future malaria infections in countries vulnerable to malaria. Though, there is no effective malaria vaccine until now, although it is an interesting research area in medicine. Local descriptors of blood smear image are exploited in this paper to solve parasitized malaria infection detection problem. Swarm intelligence is used to separate the red blood cells from the background of the blood slide image in adaptive manner. After that, the effective corner points are detected and localized using Harris corner detection method. Two types of local descriptors are generated from the local regions of the effective corners which are Gabor based f