In recent days, the escalating need to seamlessly transfer data traffic without discontinuities across the Internet network has exerted immense pressure on the capacity of these networks. Consequently, this surge in demand has resulted in the disruption of traffic flow continuity. Despite the emergence of intelligent networking technologies such as software-defined networking, network cloudification, and network function virtualization, they still need to improve their performance. Our proposal provides a novel solution to tackle traffic flow continuity by controlling the selected packet header bits (Differentiated Services Code Point (DSCP)) that govern the traffic flow priority. By setting the DSCP bits, we can determine the appropriate precedence level for the data traffic flow. The control and modification of the DSCP bits directly impact the priority assigned to data packets, thereby shaping the traffic flow continuity accordingly. The evaluation and performance analysis of the proposed network were conducted using the Mininet simulator and the MATLAB platform. The outcomes of the comprehensive testing demonstrate that the implementation of our novel priority management technique has successfully reduced queuing delay and minimized packet loss. As a result, the overall data traffic continuity has been significantly enhanced. The obtained results demonstrate that the traffic flow continuity, handled by the Software-Defined Networking (SDN) controller with the support of DSCP modification, has increased by approximately 65% when implementing the proposed priority management based on DSCP bits modification in the SDN network.
Background: Tooth wear is one of the most concerning problems of the current dental practice especially among older subjects. The aim of this study is to determine the severity of tooth wear and its relation with selected salivary variables (salivary pH and vitamin C level) among a group of older adults in Mosul city/Iraq. Materials and methods: All subjects (30 subjects) of both gender tookpart in the current study; sixteen of them were older adults (55-65 years) and compared with fourteen middle-aged adults (30-40 years) at Textile factory in Mosul city/Iraq. Unstimulated salivary samples were collected and salivary pH was immediately measured. Salivary vitamin C level was determined colormetrically. Severity of tooth wear was determined
... Show MoreSeveral specimens of the avocet, Recurvirostra avocetta L. are found infected with
Himantocestus gigantivcus sp. nov. ( Cestoda , Diploposthidae) . This cestode is related to H.
blanksoni Ukoli 1965 but easily differentiated from it in having longer and wider strobila,
larger size of testes but lesser in number, cirrus situated in the middle of mature segment
histead of anterior third and slightly posterior to the middle in gravid segment instead of the
middle , ovary and vitelline gland are larger , and the uterus has more branches.
The aim of t his p aper is t o const ruct t he (k,r)-caps in t he p rojective 3-sp ace PG(3,p ) over Galois field GF(4). We found t hat t he maximum comp let e (k,2)-cap which is called an ovaloid, exist s in PG(3,4) when k = 13. Moreover t he maximum (k,3)-cap s, (k,4)-cap s and (k,5)-caps.
POSSIBILITY OF APPLICATION THE BALANCED SCORECARD IN THE IRAQI INDUSTRIAL COMPANIES: A PROPOSED MODEL
This study was conducted to investigate phytoplasma causing a virescence disease on Arabic jasmine Jasminum sambac based on microscopy and molecular approaches. Samples were collected from symptomatic Arabic jasmine plants grown in nurseries in Baghdad-Iraq. Specimens from infected plants were prepared and Dienes stained for light microscopy examination. Phytoplasma were detected in infected plants by polymerase chain reaction (PCR) using P1/P7 and SecAfor1/SecArev3 Candidatus Phytoplasma specific primer sets. Light microscopy test showed symptomatic Arabic jasmine plants were phytoplasms infected when phloem tissues were stained with a dark blue color. PCR test confirmed the symptomatic plants were phytoplasms infected when SecAfor1/Sec
... Show MoreThis research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.