134 samples of plants and animals wastes were taken from three different regions outside Baghdad and three different regions in Baghdad. 24 cellulolytic isolates fungi AO, C1, TH1, AN1, R1, TV, PG, AF, B1, L1, AP, TH, AP1, AN3, AO2, A, A1, C, F, AO1, C2, F1, CL and AP2 independent were chosen out of 48 selected fungi. The best optimal conditions for growth were 30ºC and pH 7. The isolates were identified and screened according to the colony diameter, biomass and density of spores in addition of capability to produce the hydrolytic enzymes for cellulose.

The aim of this research is to identify the strengths and weaknesses of organizational health in the Iraqi Central Handball Federation from the point of view of those who manage the implementation of the annual curriculum, and adopt a descriptive approach in the method of studying the case. This is based on a sample of administrators of the Iraqi Handball Federation curriculum [trainers, governors, members, president, the 138 members of the Central Federation's Administrative Authority, the President, members of the sub-federations of the sports market (2021/2022) selected deliberately by 100% and then divided into statistical analysis sample (30), reconnaissance sample (10), and application sample (98)]. The regulatory health questionnaire

The aim of this study is to investigate the existence of some heavy metals (lead, cadmium, chromium) in colored plastic table dishes and study the migration of these metals to the food meals and the affecting factors in migration , such as storage period and food temperature. Six kinds of colored plastic table dishes were collected from Baghdad markets. The heavy metals in table dishes and in the prepared food meals put in them were estimated using atomic absorption spectrophotometer (Shimadzu A5000). The results indicated the existence of lead in all samples (1.61_1.00 mg/ kg) and chromium in three samples (0.85_0.97 mg/ kg) while other samples are free of chromium, and cadmium. Investigating the migration of these metals to food at dif

Anxiety has become a highly paramount field of research attention in psychopharmacology today. Sundry studies have shown a nitric oxide role in the regulation of anxiety. The goal of the study was to investigate sodium nitroprusside ability to affect anxiety-like behavior in mice and to compare this effect with the standard anxiolytic drug, diazepam, using both plus maze test and light/dark box test. The results revealed that sodium nitroprusside at a dose of 1 mg/kg had a significant effect on the behavior in both of the elevated plus maze test and light/dark test. However, at higher dose (3 mg/kg), it has significantly increased the anxiogenic-like effect in the light/dark box test. Diazepam at a dose of 2 mg/kg increased the time spen

In recent years, the migration of the computational workload to computational clouds has attracted intruders to target and exploit cloud networks internally and externally. The investigation of such hazardous network attacks in the cloud network requires comprehensive network forensics methods (NFM) to identify the source of the attack. However, cloud computing lacks NFM to identify the network attacks that affect various cloud resources by disseminating through cloud networks. In this paper, the study is motivated by the need to find the applicability of current (C-NFMs) for cloud networks of the cloud computing. The applicability is evaluated based on strengths, weaknesses, opportunities, and threats (SWOT) to outlook the cloud network. T

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

     In this paper, we study some cases of a common fixed point theorem for classes of firmly nonexpansive and generalized nonexpansive maps. In addition, we establish that the Picard-Mann iteration is faster than Noor iteration and we used Noor iteration to find the solution of delay differential equation.

    Current study was carried out to determine the adsorption ability of the Multiwall carbon nanotubes (MWCNTs) by adsorption Malachite Green dye from an aqueous solution. Crystal structure of the materials was measured using powder X-rays diffraction (PXRD), UV–Vis diffuse reflectance and specific surface area (BET). Many parameters that affecting the adsorption process such as contact time, pH, adsorbent dosage, initial dye concentration and temperature were studied. The outcome showed that an increasing occurred in the adsorbent dosage and the rate of dye removal, and the best efficiency for Malachite Green dye removal was amounted 99. 11 %. The results were obtained at optimal reaction conditions were pH = 5.5, cata

In general, path-planning problem is one of most important task in the field of robotics. This paper describes the path-planning problem of mobile robot based on various metaheuristic algorithms. The suitable collision free path of a robot must satisfies certain optimization criteria such as feasibility, minimum path length, safety and smoothness and so on. In this research, various three approaches namely, PSO, Firefly and proposed hybrid FFCPSO are applied in static, known environment to solve the global path-planning problem in three cases. The first case used single mobile robot, the second case used three independent mobile robots and the third case applied three follow up mobile robot.  Simulation results, whi

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class