The performance of a diesel engine was tested with diesel oil contaminated with glycol at the engineering workshop/Department of Agricultural Machines and Equipment / College of the Agricultural Engineering Sciences at the University of Baghdad. To investigate the impact of different concentrations of glycol on the performance of a diesel engine, an experimental water-cooled four-stroke motor was utilized, with oil containing 0, 100, and 200 parts per million (ppm). Specific fuel consumption, thermal efficiency, friction power, and exhaust gas temperature were examined as performance indicators. To compare the significance of the treatments, the study employed a full randomization design (CRD), with three replicates for each treatment at th

The present study is concerned with studying the effect of aquatic plant Hydrella vorticellata with the concentration of 10 and 20 gm/2 K gm soil on percentage and growth rate of germinating seeds of Hordeum vulgare and Vicia faba. More overs, the quantitative amount of NPK in both tested plants and Hydrella vorticellata, are estimated as an organic fertilizer. It has also been find the total number of root cells, the number of dividing cells, and stages of mitosis. The study reveales, that there are no significant differences between the concentration of hydrella used in germination percentage, growth rate, wet and dry weight. While there are differences in the plants containing NPK. The number of cells dividing stages and number of divid

Suffer most of the industrial sector companies from high Kperfi magnitude of the costs of industrial indirect, lack of equitable distribution of these costs on the objectives of cost, increased competition, and the lack of proper planning in line and changes faced by the industrial sector (general) and sample (private), as well as the difficulty in re- directing efforts to improve profitability and in-depth analysis of activities, and to identify untapped resource activities, then link these activities to the final products  The research aims to apply the technology review and evaluate programs with the method (ABC) through the application stages of planning, scheduling and control and a comparison to get to the products of dev

الغرض - تعتمد هذه الدراسة على المنهج الوصفي التحليلي من خلال جمع البيانات اللازمة وتحليلها، كون هذا المنهج يركز على استطلاع الآراء لعينة البحث وتوجهاتها ، وتهدف إلى تطوير نموذج يدرس العلاقة بين خلق المعرفة والبراعة التنظيمية في المصارف الخاصة العراقية والتحقق من صحته تجريبياً. التصميم / المنهجية / المدخل- تم إجراء مسح عبر استمارة استبيان لجمع البيانات من عينة من (113) مدير من مصارف تجارية خاصة بالإضافة إلى ذلك ا

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s

Intrusion detection systems (IDS) are useful tools that help security administrators in the developing task to secure the network and alert in any possible harmful event. IDS can be classified either as misuse or anomaly, depending on the detection methodology. Where Misuse IDS can recognize the known attack based on their signatures, the main disadvantage of these systems is that they cannot detect new attacks. At the same time, the anomaly IDS depends on normal behaviour, where the main advantage of this system is its ability to discover new attacks. On the other hand, the main drawback of anomaly IDS is high false alarm rate results. Therefore, a hybrid IDS is a combination of misuse and anomaly and acts as a solution to overcome the dis

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an