Category Archives: PhD Thesis

This dissertation investigated the effect arrangement of layers in glass and carbon composites, focusing on the effects of longitudinal fiber orientation, thickness, and the addition of SiO₂ nanoparticles. Three batches of composite plates were produced. The first category comprised unidirectional glass fiber sheets arranged in a stacking sequence of 0° [G/G/G/G]s  with and without 2% silica dioxide nanoparticles and carbon fiber sheets arranged in a stacking sequence of 0° [C/C/C/C]s with and without 2% silica dioxide nanoparticles. These sheets were specifically designed to assess the mechanical properties, namely the modulus of elasticity in the longitudinal and transverse directions E₁ and E₂, shear modulus (G₁₂), and Poisson's ratio (ν₁₂) for glass/epoxy and carbon/epoxy composites.

The second group, characterized by quasi-isotropic-balanced distinct stacking sequences, comprised three primary unidirectional fiber orientations: 0°, 45°, and 90°. These sequences varied in thickness, consisting of eight layers (2 mm), ten layers (2.5 mm), and twelve layers (3 mm). Additionally, SiO₂ nanoparticles were utilized as a reinforcing agent within the epoxy matrix. The third group consisted of cross-layer configurations, with various stacking sequences examined. These sequences involved two primary unidirectional fiber orientations: 0° and 90°, and they exhibited different thicknesses. Specifically, the group included eight layers (2 mm), twelve layers (3 mm), sixteen layers (4 mm), and twenty layers (5 mm).

Furthermore, SiO₂ nanoparticles were employed as a reinforcing agent within the epoxy matrix. The vacuum-assisted resin infusion process was utilized to fabricate fourteen combinations of fiber-reinforced epoxy composites, including those with and without the addition of 2% silicon dioxide nanoparticle composites, designated as QS1, QS1N, QS2, QS2N, QS3, QS3N, CS1, CS1N, CS2, CS2N, CS3, CS3N, CS4, and CS4N. The quasi-static mechanical properties (tensile and three-point bending test) and dynamic (axial and flexural fatigue test) behaviors of the material were examined through experimental analysis and validated using numerical simulations via the finite element method (ANSYS 2019/R3 Workbench).

The modulus of elasticity (E₁) and maximum stress for QS3 and QS3N increased by 20.97% and 18.65%, respectively. In comparison to CS1, which lacks SiO₂ nanoparticles, CS1N exhibited increases in E₁ and maximum stress of 2.5% and 12.7%, respectively. The incorporation of SiO₂ nanoparticles significantly enhanced the performance of glass/carbon hybrid composite materials. Axial fatigue tests demonstrated that the number of cycles of the hybrid composites (CS1 and CS1N), (CS2 and CS2N), and (CS3 and CS3N) increased by approximately 55%, 27%, and 58%, respectively, at a load level of 70%. In flexural fatigue testing, there was a stress increase of 17.4% between CS1 and CS₁N, and a similar increase of 13.11% between CS2 and CS2N. The sample pairs CS3 and CS3N showed a comparable percentage increase of 17.1%, while CS4 and CS4N exhibited an increase of 13.61%.

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).