The aim of this research is to explore the time and space distribution of traffic volume demand and investigate its vehicle compositions. The four selected links presented the activity of transportation facilities and different congestion points according to directions. The study area belongs to Al-Rusafa sector in Baghdad city that exhibited higher rate of traffic congestions of working days at peak morning and evening periods due to the different mixed land uses. The obtained results showed that Link (1) from Medical city intersection to Sarafiya intersection, demonstrated the highest traffic volume in both peak time periods morning AM and afternoon PM where the demand exceeds the capacity along the link corridor. Also, higher values f

We introduced the nomenclature of orthogonal G -m-derivations and orthogonal generalized G -m-derivations in semi-prime G -near-rings and provide a few essentials and enough provision for generalized G -n-derivations in semi-prime G -near-rings by orthogonal.

At present, smooth movement on the roads is a matter which is needed for each user. Many roads, especially in urban areas geometrically improved because of the number of vehicles increase from time to time.

In this research, Highway capacity software, HCS, 2000, will be adopted to determine the effectiveness of roundabout in terms of capacity of roundabout, delay and level of service of roundabout.

The results of the analysis indicated that the Ahmed Urabi roundabout operates under level of service F with an average control delay of 300 seconds per vehicle during the peak hours.

The through movements of Alkarrada- Aljadiriya direction (Major Direction) represent the heaviest traff

In networking communication systems like vehicular ad hoc networks, the high vehicular mobility leads to rapid shifts in vehicle densities, incoherence in inter-vehicle communications, and challenges for routing algorithms. It is necessary that the routing algorithm avoids transmitting the pockets via segments where the network density is low and the scale of network disconnections is high as this could lead to packet loss, interruptions and increased communication overhead in route recovery. Hence, attention needs to be paid to both segment status and traffic. The aim of this paper is to present an intersection-based segment aware algorithm for geographic routing in vehicular ad hoc networks. This algorithm makes available the best route f

   Rotating cylinder electrode (RCE) is used . in weight loss technique , the salinity is 200000 p.p.m, temperatures are (30,5060,7080Co) . the velocity of (RCE) are (500,1500,3000 r.p.m). the water cut (30% , 50%). The corrosion rate of carbon steel increase with increasing rotating cylinder velocity. In single phase flow, an increase im rotational velocity from 500 to 1500 r.p.m, the corrosion rate increase from 6.88258 mm/y to 10.11563 mm/y respectively.

   In multiphase flow, an increase in (RCE) from 500 to 1500 r.p.m leads to increase in corrosion rate from 0.786153 to 0.910327 mm/y respectively. Increasing brine concentration leads to increase in corrosion rate at water cut 30%.

The plant Papaver rhoeas ,which belongs to family Papaveraceae and known as common poppy is wildly grown in Iraq .It was used in traditional medicine in wide range of diseases including inflammation, diarrhea, sleep disorders, treatment of cough, analgesia, and also to reduce the withdrawal signs of opioid addiction.

The project provide the first comprehensive research done in Iraq to study the phytochemical and the methods of extraction and separation of alkaloids from Papaver rhoeas wildly grown in Iraq .The plant was harvested in April 2019 from Zurbatiya is an Iraqi town located at the northeast of Waist province in Iraq.The collected plant was washed thoroughly, dries under shade, and grounding in a mechani

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an