ABSTRACT

          Herbs and spices have long been used to support various food products, including dairy products because of their flavoring, taste, texture and general appearance as well as therapeutic properties such as antioxidant activities, infections, microbes, anti-diabetes and hypertension. Therefore, this study aimed to demonstrate the effect of adding ginger, cinnamon, lycopene extract and olive oil on the physiochemical properties, the quality of the soft cheese produced and the extent of its acceptance by the Iraqi consumer, who prefers this product in abundance to other types of cheese. So, this study was prepared

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),

The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

In this research,  Fuzzy Analytic Hierarchy Process technique is applied (Fuzzy AHP) which is one of  multi-criteria decision making techniques to evaluate the criteria for urban planning projects, the project of developing master plan of Al-Muqdadiyah city to 2035 has been chosen as a case study. The researcher prepared a list of criteria in addition to the authorized departments criteria and previous researches in order to choose optimized master plan according to these criteria. This research aims at employing the foundations of (Fuzzy AHP) technique in evaluating urban planning criteria precisely and flexible. The results of the data analysis to the individuals of the sample who are specialists, in this aspect.  The la

The optical absorption data of Hydrogenated Amorphous Silicon was analyzed using a Dunstan model of optical absorption in amorphous semiconductors. This model introduces disorder into the band-band absorption through a linear exponential distribution of local energy gaps, and it accounts for both the Urbach and Tauc regions of the optical absorption edge.Compared to other models of similar bases, such as the O’Leary and Guerra models, it is simpler to understand mathematically and has a physical meaning. The optical absorption data of Jackson et al and Maurer et al were successfully interpreted using Dunstan’s model. Useful physical parameters are extracted especially the band to the band energy gap , which is the energy gap in the a

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

In the light of the globalization Which surrounds the business environment and whose impact has been reflected on industrial economic units  the whole world has become a single market that affects its variables on all units and is affected by the economic contribution of each economic unit as much as its share. The problem of this research is that the use of Pareto analysis enables industrial economic units to diagnose the risks surrounding them , so the main objective of the research was to classify risks into both internal and external types and identify any risks that require more attention.

The research was based on the hypothesis that Pareto analysis used, risks can be identified and addressed before they occur.

This paper proposes a novel method for generating True Random Numbers (TRNs) using electromechanical switches. The proposed generator is implemented using an FPGA board. The system utilizes the phenomenon of electromechanical switch bounce to produce a randomly fluctuated signal that is used to trigger a counter to generate a binary random number. Compared to other true random number generation methods, the proposed approach features a high degree of randomness using a simple circuit that can be easily built using off-the-shelf components. The proposed system is implemented using a commercial relay circuit connected to an FPGA board that is used to process and record the generated random sequences. Applying statistical testing on th