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Go to Editorial ManagerRoad transport undeniably constitutes the predominant mechanism for facilitating the transportation of both goods and individuals on a global scale, serving as an essential backbone for economic and social interactions across diverse regions and cultures. The noticeable decrease in the flow of vehicles, which can be attributed to a plethora of internal and external factors, with a particular emphasis on the phenomenon of congestion, has profound implications that significantly influence fuel consumption rates, contribute to pollution associated with emissions, adversely affect the health and well-being of bystanders, and culminate in a considerable loss of time for individuals navigating these congested environments. In light of their elevated population densities coupled with their classification as emerging economies, South Asian countries find themselves necessitated to implement automated systems for the critical processes of predicting, identifying, and effectively addressing the challenges posed by road traffic congestion in order to enhance urban mobility and overall transport efficiency. This thorough research carefully explores the various techniques that have been utilized to recognize traffic congestion, presenting an extensive assessment of their individual strengths and weaknesses, thus offering insightful observations about the existing situation in this field of study. The examination of the diverse approaches and advanced technologies that have been utilized for the operation of lane-less roadways have been conducted, revealing substantial potential for further innovations that could greatly assist future researchers in their endeavors to enhance traffic management and improve roadway safety and efficiency.
Soil’s characteristics are essential for the successful design of projects such as airports runway and flexible pavement. CBR (California Bering Ratio) is one of the significant soil characteristics for highways and airports projects. Thus, the CBR property can be used to determine the subgrade reaction of soil through correlations. Many of the soil geotechnical parameters such as compaction characteristics (Maximum Dry Density, MDD; Optimum Moisture Content, OMC), and consistency parameters (Liquid Limit, LL; Plastic Limit, PL; Plasticity Index, PI) can be in charge of changes that happen in soil CBR value. Soaked and/or non-soaked conditions of soils also affect CBR value. Hence, testing soils in a laboratory for CBR calculation is time-consuming that needs notable effort. Therefore, this study aims to generate some useful correlations for soil’s CBR with compaction and consistency parameters for 85 samples of fine-grained soils. The study trials were applied on natural soil samples of various places in Sulaimani Governorate, Northern Iraq. Statistical analysis has been carried out by using SPSS software (Version 28). Soaked CBR is counted, which is important for conditions such as rural roads that remain prone to water for few days. Based on the statistical analysis, there is a significant correlation between LL, PL, PI, MDD, and OMC with CBR as the dependent variable as a single variable equation with R2 of 0.7673, 0.5423, 0.5192, 0.6489, and 0.51, respectively. In addition, the highest value of R2 correlation was obtained between CBR value with consistency and compaction properties as a multiple regression equation with R2 of 0.82. The obtained equations for correlation purposes are successfully achieved and can be used, notably, to estimate CBR value.
In the present work the effect of Corrosion & Scale Inhibitor was evaluated by using of the commercial product (Kurita S2050) that mainly containing of (Na2HPO4) sodium phosphate as corrosion inhibitor and (C6H11NaO7) sodium glocunate as scale inhibitor & dispersant. The dosing rate of this chemical was controlled according to the treatment system depend mainly on the monitoring of LI & RI indexes for (30) days treatment in the cooling tower unit of Al-Dora Oil refinery-Baghdad. The corrosion rate and the corrosion inhibitors efficiency were calculated by measurement of weight loss in standard test coupon (AISI 1010). After 30 day of the Field Test, the result show that the treatment program performance was effective in the corrosion & scale inhibition through an acceptable corrosion rate less than 0.018 in gmd. Also the result of corrosion rate was analyzed statistically by using of (ANN) to formulate a prediction equation to corrosion rate identification.
The aim of this work is to investigate the effect of soil corrosion on the critical buckling load of circular columns made of 2014-T4 aluminum alloy. In this work, 24 specimens were used and buried in the soil for 120 days. The samples divided into two groups (12 columns with corrosion before shot penning (SP) and ultrasonic impact treatment (UIT), and 12 columns with corrosion after combined surface treatments (SP+UIT)). The experimental1results revealed1that the corrosion negatively1affects the mechanical properties1of the material, and the1reduction percentage (R%) for1ultimate tensile strength (UTS) and1yield strength (YS) was (1.95% and 4.57%) respectively. After combined surface treatments (SP+UIT) for the corroded columns, the ultimate1tensile strength (UTS) and yield1strength (YS) were improved with (2.42%, and 2.87%) respectively. Perry-Robertson, Rankine, and ANSYS were used to estimate the critical buckling load (Pcr) and compare it with the experimental results. Rankine and Perry's formulas have been achieved a good agreement with the experimental without and with (1.5) factor of safety respectively. While ANSYS gave satisfactory prediction with a safety factor of (2.2, and 2.7) and (1.9, and 2.7) for long and intermediate columns before and after (SP+UIP) respectively.
The goal of the study is to predict the Man in the Middle attack in the packets of Wireshark program by using Support Vector Machines (SVM).In the time of using the internet, it has become a tool targeted by attackers and hackers; it is a serious threat to the devices. A uniqueness of an attack that appears in multiple identities for legitimate agencies. It is very necessary to know the behavior attack and predict the possible actions of an attacker. In this research a detection of Man in the Middle attack by monitoring the Wireshark program and recording any changes can be recognized in packet information. The classification of packets is divided into two categories (normal and abnormal). The proposed model is designed in many stages: loading data, processing data, training data, and testing data. The detection of SVM based on abnormal network packet through movement packets in the Wireshark program that needs to deal with current packets to recognize a new attack that one does not have prior knowledge of its detection, and there is a need for an intelligent way to separate network packets that represent normal. The proposed approach achieved an accuracy of 97.34% in detecting attacks. The results show that the proposed model effectively visualizes attacker behavior from data that represents abnormal network attackers. Research achieves successful accuracy in predicting abnormalities.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.
In term of sustainable practices, recycling plays a crucial role, particularly in the construction industry where the disposal of old structures generates significant waste. Recycling old concrete not only reduces the need for new natural resources but also eliminate waste accumulation. Numerous research study the behaviors of recycled aggregate concretes, practically focusing on the long term behaviours. A large number of studies have demonstrated that concrete made from recycled aggregate exhibits poorer long-term characteristics in comparison to aggregate from nature concrete. The long-term behaviour can be affected by three factor which is creep, shrinkage and tension stiffening. Greater management of these variables can enhance the RAC's long-term properties. The review will specifically focus on the influence of time dependent parameters i.e., creep, shrinkage, and loss of tension stiffening with time. Furthermore, it will explore the long-term deflection predicted from code used for deflection prediction, considering three codes: ACI, EC2, and the CSA code. The purpose of this paper is to enhance the understanding of long-term deflection of recycled aggregate concrete beam and evaluate the effectiveness of various factors that impact their structural performance.