Author Archives: Jihan Jihan

This research study includes an experimental and analytical study of steel fiber-reinforced concrete columns using natural and recycled aggregate (RA). To improve the structural application of recycled aggregate and to protect the environment and preserve natural resources, it is crucial to use recycled aggregate in construction. The recycled coarse aggregate reinforced concrete columns with the addition of steel fiber subjected to concentric and eccentric loadings for short and slender columns are examined experimentally and analytically in this research. Forty two column specimens were cast to examine the impact of steel fiber, recycled aggregate, slenderness, and eccentricity on the behavior of reinforced concrete columns. In addition to concentrically loaded columns, columns were loaded at 50% and 100% eccentricity, corresponding to e/h ratios of 0.5 and 1.0. Three different slenderness ratios were selected to examine the effects of height: 17.24 for short columns, 26 for moderately slender columns, and 34.5 for highly slender columns. The research examined the failure mode, maximum load-carrying capacity, strain in the concrete, and strain in the reinforcement, mid-height lateral displacement, vertical displacement and ductility. Based on the results of the current study, it can be concluded that employing recycled concrete aggregate is a potential approach that can meet design codes. Columns produced with recycled concrete aggregate behaved similarly to columns made with natural aggregate (NA). The addition of 1% steel fiber effectively prevented concrete from crushing and spalling. Steel fiber, however, improved the columns' ductility and strength. According to experimental results, the steel fiber addition narrowed the crack width which visually observed and had a comparable effect on columns constructed with recycled aggregate and columns constructed with natural aggregate. The experimental test maximum load carrying capacity agreed well with the results using ACI-318-19 equations. Furthermore, a model has been proposed for columns with both natural and recycled aggregate and accounts for eccentricity and slenderness to forecast the load-carrying capability. Additionally, the second-order effect due to the intentional such as given eccentricity and unintentional eccentricity such as alignment errors was investigated. The second-order effect is considered an excellent theoretical method to examine the behavior of columns. Using this method theoretical load path was drawn for each column tested as well as an experimental load path for comparison, later, using the same method, an axial load bending moment interaction diagram was plotted for all the tested columns. The outcomes demonstrated that the design principles were met well. Plots of load-moment interaction diagrams for short, slender columns prepared using the ACI-318-19 equations, 2nd order effect method, and proposed method. The experimental findings were added to the interaction diagrams for comparison.

Finally, it needs to be mentioned that recycling concrete waste cubes and other recycled materials decreases the amount of waste sent to landfills. This practice helps avoid the consumption of natural resources, thereby preventing their quick depletion and cutting down on the expenses and distractions linked to their extraction. Utilizing sustainable materials and creating a new pathway for their reuse, such as incorporating recycled aggregates, can reduce waste and conserve natural resources. 

The world's population has exponentially grown, which has an effect on usage of vehicles by individuals and leads to an increase in the number of cars in urbans. With the direct relationship between population and car usage, traffic management has become an important issue to be solved. For this purpose, an intelligent traffic signaling with a rapid urbanization is required to overcome the traffic congestions, and reduce cost and time of traveling. To overcome these problems, emerging computer vision and deep learning are vital candidates to handle this issue because they take an important role for managing and controlling traffic signals with great success. Nevertheless, detecting and distinguishing between objects are helpful for counting vehicles and other objects which avoid crowds and controlling signals in the traffic areas. Besides, detecting emergency vehicles and giving the priority to them is required for intelligent traffic signaling system.

The main objective of this study is to design and implement an efficient system for traffic signal systems based on custom vehicle detection.  Furthermore, the proposed system involves four phases; the first one is capturing images from both simulated and real time cameras from the roads. In the second phase, different image preprocessing algorithms are performed to the captured images as a pre-processing step. In addition, the deep learning techniques are applied to detect objects such as (regular car, police car, ambulance, and firefighter, etc..). In the last phase, the proposed system is tested to evaluate the performance accuracy of the detected vehicles.

A modified transfer learning approach has been applied to the DenseNet201 model for multiple classifications, including non-emergency cars, ambulances, police, and firefighters. The approach involves freezing the architecture of the model's layers. A high accuracy rate is obtained with this model and reaches 98.6%. Also, various optimization methods, including (Adam, Adamax, Nadam, and RMSprob) are used to improve the detection performance based on the best optimizer selection and yielded an accuracy of 98.84%. In addition, a modified version of YOLOv5 was proposed for vehicle detection, which aims to enhance the mean average precision (mAP) detection by 3%. Finally, the proposed system was simulated to reduce the waiting time at traffic signal. The experimental results demonstrate a significant reduction in waiting time, ranging from 30 to 100 seconds depending on the status.

Breast cancer is the most common type of cancer among women; every year, millions of new cases are detected worldwide, and the cases increase dramatically. Despite the fact that most of the cases are caused by non-genetic factors, hereditary and familial breast cancer also contribute and are considered risk factors that are responsible for about 20% of the cases. The present study aimed to be the first study to investigate the frequency of hereditary breast cancer caused by the high penetrance genes BReast CAncer gene 1 (BRCA1) and BReast CAncer gene 2 (BRCA2) using net generation sequencing (NGS) among Iraqi Kurdish women in Erbil province. Also, investigate several important parameters that some of them have studied for the first time among Kurdish breast cancer patients in Erbil, Iraq.

The present study included 150 participants who were already diagnosed with breast cancer and registered at Nanakali Hospital for Blood Diseases and Cancer, Erbil, Iraq. For mutation analysis and variant detection, 70 participants were selected for NGS. Samples underwent DNA extraction, estimation of the extracted DNA, polymerase chain reaction (PCR) for amplification of all exomes of the BRCA1 and BRCA2 genes, and NGS for sequencing of all coding regions (exomes) through (Illumina Inc., San Diego, CA). Results of NGS obtained in different formats (BAM, BAI, VCF, and FASTA) files. Variant viewing and detection were carried out through the Integrative Genomic Viewer (IGV) and MutationTaster websites. Finally, for interpretation of the clinical significance of the variants, different databases were used, including mainly: NCBI/ClinVar, BRCAExchange, ENIGMA, gnomAD, and COSMIC.

Many variants were detected on these two genes, variants in intronic regions were neglected (except one on BRCA2 that was not benign). At the end, 42 variants were included in the present study, 20 (47.6%) on BRCA1 and 22 (52.4%) on BRCA2. Regarding the clinical significance of the variants, 9 (21.4%) of them were clinically significant. On BRCA1, 4 (9.5%) pathogenic variants were detected (c.3607C>T, c.3544C>T, c.224_227delAAAG, c.68_69del), while on BRCA2, 2 (4.76%) pathogenic variants (c.100G>T, c.1813delA), 2 (4.76%) conflict interpretations of pathogenicity (c.3318C>G, c.1909+12delT), and 1 (2.38%) variant of uncertain significance (c.6966G>T) were detected. Also, 29 (69%) other benign variants were detected on these two genes.

An important finding of the present study was the detection of four new variants, three on the BRCA1 gene (c.463dupC, c.3190A>C, c.981del) and one on the BRCA2 gene (c.3787A>G). Those exact variants were not reported in any databases or articles before. Those new variants were submitted to NCBI/ClinVar, and unique accession numbers were obtained for each of them (SCV005196609, SCV005199865, SCV005199845, SCV005196610), respectively. Detecting new variants on these two genes is popular, especially among low- and middle-income countries, where little or no studies have been done among those populations.

Besides the molecular part, several other important parameters were investigated in the present study, including 150 participants. The mean age at the time of diagnosis with breast cancer was 49.5 years of age, with highly significant differences between the age groups (P<0.0001). The level of awareness by assessing previous knowledge about breast cancer was very low; 120 (80%), had no previous information about breast cancer, and the rest had simple knowledge about different aspects of the disease (P<0.0001). Most of the participants, 131 (87.3%) didn’t undergo any pre-tests before being diagnosed, and the rest underwent a few attempts or just once during their lifetime (P<0.0001). About half of the cases 72 (48%) were detected at advanced stages (stages III and IV), followed by stage I, then stage II (P<0.0001).

Many participants 103 (68.7%) indicated that the cases were observed by the patients themselves (P<0.0001), either by feeling a tumor or pain under the armpit. Despite the fact that cancer is known to be a silent disease, especially in its early stages, more than half 89 (59.3%) of the cases stated that they experienced some signs before the disease was detected; the most popular signs were swelling of the breast, while a few cases felt some pain, vomiting, stiffness of the breast, a shortage in breathing, and finally abnormal stuns in the breath and discharges of liquids, seen rarely (P<0.0001). For family history, 49 (32.7%) of the patients had relatives with breast cancer (P<0.0001). Regarding breast removing surgery, 62 (41.3%) already underwent mastectomy (P<0.04); among the rest of them, 73 (82.9%) stated they would take the choice of mastectomy if needed and recommended in the future.

Regarding the results of the psychological impact, 118 (78.7%) stated that the disease had a bad impact on their lives (P<0.0000.); most of them suffered from depression, and the quality of their sleep lowered dramatically after being diagnosed with cancer. For receiving sufficient information about their status, more than one-third, 53 (35.3%) of the participants stated that they were either little informed or not informed by the physician (P<0.0001). Regarding family support, 140 (93.3%) of them stated that they received good family, relatives, and friends’ support (P<0.0001). The majority 148 (98.7%) were taking one or two types of medications; chemotherapy was the most popular 129 (86%), followed by mastectomy (P<0.0001).

The emerging Fifth-Generation (5G) technology towards Internet of Vehicles (IoV) provides numerous advantages, such as lower levels of latency, stable link connections, and support for high mobility. However, avoiding vehicle collisions in IoV is a challenging task due to disseminating Emergency Safety Messages (ESMs) without strict delay and reliability requirements. To address this issue, this study proposes a novel intelligent Software-Defined Networking-based Collision Avoidance (SDNCA) framework assisted 5G. The proposed SDNCA framework employs two system models, each comprising three proposed algorithms. In the first system model, primarily, SDNCA performs the Vehicular Federated Learning (VFL) algorithm that accurately estimates the risk severity for each vehicle via training the proposed Risk Severity-Artificial Neural Network (RS-ANN) model through the implementation of federated learning among vehicles. The SDNCA framework applies the SDN algorithm to achieve three main objectives. First, it calculates the Quality of Service (QoS) of the ESM. Second, it dynamically allocates both 5G network and computing resources for three Virtual Networks (VNs). Third, it selects the optimal 5G base station (gNB) for routing the ESM to the destination vehicle. To ensure effective forwarding for each ESM, SDNCA deploys the gNB algorithm at the selected gNB to schedule the ESMs considering their priorities and configures the 5G network resources and computing resources based on the OpenFlow control message received from the SDN.

The implementation of the second system model integrates the VFL, SDN, and gNB algorithms, focusing on the risk distance between the source and destination vehicles. The objective of the second system model is to ensure the successful transmission of ESMs in scenarios when considering the risk distances between vehicles.

The two system models have been implemented using three simulation tools: Network Simulator (NS3), Python programming language, and a Mininet network emulator. The real-time simulation results demonstrate the evaluation of the SDNCA framework into two sections, compared with the existing related research. The first section assesses the performance of the SDNCA framework by varying the density and speed of the vehicles. These results include 17% and 20% Network Overhead (NO), 17% and 20% Computational Complexity (CC), 0% Collision Rate (CR), 18 ms End-to-End (E2E) Delay, 89%–90% Packet (ESM) Transmission Reliability (TR), 99.5% and 99.4% Successful Routing Ratio (SRR), 0.0050 ms Routing Efficiency (RE), 0% Packet Drop Ratio (PDR), 0.25    and 0.5    Channel Utilization (CU), and 4.5 ms and 4 ms E2E Delay with different values of the allocated bandwidth. The second section evaluates the performance of the SDNCA framework at distances ranging up to 30 meters between the source and destination vehicles, taking into account different vehicle densities and speeds. These results include 97%–99.5% and 98.4%–99.8% SRR, 4 ms and 3.5 ms RE, 0% CR, and 4.5 ms E2E Delay.