Abstract
In the present study, composites were prepared by Hand lay-up molding. The composites constituents were epoxy resin as a matrix, 6% volume fractions of glass fibers (G.F) as reinforcement and 3%, 6% volume fractions of preparation natural material (Rice Husk Ash, Carrot Powder, and Sawdust) as filler. Studied the erosion wear behavior and coating by natural wastes (Rice Husk Ash) with epoxy resin after erosion. The results showed the non – reinforced epoxy have lower resistance erosion than natural based material composites and the specimen (Epoxy+6%glass fiber+6%RHA) has higher resistance erosion than composites reinforced with carrot powder and sawdust at 30cm , angle 60
... Show MoreIn this paper, we have investigated some of the most recent energy efficient routing protocols for wireless body area networks. This technology has seen advancements in recent times where wireless sensors are injected in the human body to sense and measure body parameters like temperature, heartbeat and glucose level. These tiny wireless sensors gather body data information and send it over a wireless network to the base station. The data measurements are examined by the doctor or physician and the suitable cure is suggested. The whole communication is done through routing protocols in a network environment. Routing protocol consumes energy while helping non-stop communic
... Show MorePot experiment was carried out at the College of Agriculture – Baghdad University during autumn season, 2007. Thirteen treatments were formulated to evaluate the effectiveness of four applications of Phosphorus (0, 60, 60×2 and 120 Kg P. h-1) and three applications of Zinc (0, 25×2 mg Zn. L-1 and 50 mg Zn. Kg soil-1) along with inoculating seeds of bean with strains mixture 889 and 1865 and non-inoculated treatment, on nodulation, yield and protein content in seeds (N%). The results showed that inoculated plants exceeded on non-inoculated one in all the studied characteristics. While, P and Zn, applications at the rate of 60×2 kg/ha and 25×2 mg/L respectively, significantly, increased, nodulation, yield, protein content in se
... Show MoreThe purpose of this paper is to apply different transportation models in their minimum and maximum values by finding starting basic feasible solution and finding the optimal solution. The requirements of transportation models were presented with one of their applications in the case of minimizing the objective function, which was conducted by the researcher as real data, which took place one month in 2015, in one of the poultry farms for the production of eggs
... Show MoreData scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan
... Show MoreThis study specifically contributes to the urgent need for novel methods in Training of Trainers (ToT) programs which can be more effective and efficient through incorporation of AI tools. By exploring scenarios in which AI could be used to dramatically advance trainer preparation, knowledge-sharing, and skill-building across sectors, the research aims to understand the possibility. This study uses a mixed-methods approach, it surveys 500 trainers and conducts in-depth interviews with a further 50 ToT program directors across diverse industries to evaluate the impact of AI-enhanced ToT programs. The results showcase that the use of AI has a substantial positive effect on trainer performance and program outcomes. AI-enhanced ToT programs, fo
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