Duhok Polytechnic University
Recent publications
One of the most effective ways to understand nonlinear quantum systems is with lump solutions. The objective of this study is to acquire more about the (3+1)-dimensional soliton equation. We successfully present this equation with various solitons and M-lump solutions. We adopt specific parameter values to accentuate the physical features of the provided exact solutions through 3D and contour plots as doing so is of extreme significance. The submitted results indicate the physical qualities of lump-and-lump interaction events in various nonlinear physical processes.
The wide application of reclaimed asphalt pavement (RAP) is hindered due to the highly brittle nature of the material, which contributes a major factor towards cracking-related distresses. While the utilisation of rejuvenating agents has been shown to enhance the flexibility of RAP, they also trigger certain negative effects on the performance of asphalt mixtures. In view of this, potential rejuvenators should be able to alter the rheological properties of asphalts to limit fatigue issues and enhance the potential of low-temperature cracking. Therefore, this study aimed to investigate the possibility of extraction and characterisation of maltene from virgin asphalt (VA) as a potential rejuvenating agent in RAP. Several physicochemical characteristics were examined, including density, viscosity, gas chromatography–mass spectrometry (GC–MS), Fourier-transform infrared (FTIR) spectroscopy, CHNS elemental analysis, and energy dispersive X-ray (EDX) analysis. Finally, the stiffness modulus characteristics of the different types of asphalt binders were evaluated at low and high temperatures. The results demonstrated that maltene was successfully extracted from VA using petroleum ether. In addition, the GC–MS showed that the extracted maltene contained polar and non-polar compounds with low molecular weights compared to VA. Furthermore, the spectra curve of maltene was very similar to that of asphalt, indicating its compatibility with asphalt binder and prospective use. Finally, adding maltene to aged asphalt decreased stiffness values to 0.0063, 0.0499, and 0.0108 MPa, which are equivalent to VA values (0.0061, 0.0481, and 0.0104 MPa) at loading times of 1.0, 0.1, and 0.55 s, respectively. Meanwhile, the stiffness modulus characteristics at low temperature were restored with the addition of maltene.
Asian seabass, Lates calcarifer frys were exposed to polystyrene (MP: 0.5 mg/l), oil (0.83 ml/l) and agglomerates (MP + oil + Corexit) as eight treatments in three replicates, and fresh synthetic marine water (control) for 15 days. The synergistic effect was confirmed (P ˂ 0.05) by bio-indicators including RBC count, total plasma protein, aspartate aminotransferase (AST), catalase (CAT), glutathione S-transferase (GST), basophils, thrombocyte and eosinophils percentages. Most of the significant and synergistic effects were observed in the highest doses (5 mg/l MP and 5 mg/l MP-oil-dispersant). Exposure to MP and a combination of MP+ oil caused tissue lesions in gill, liver and intestine. Our results suggest there are no critical health issues for Asian seabass in natural environments. However, the bioaccumulation of MPs, oil, and their agglomerates in consumers' bodies may remain a concern.
Propomacrus bimucronatus (Pallas, 1781), the Mediterranean long-armed scarab, is a large saproxylic beetle, occurring in the east Mediterranean and south-east Europe, sparse throughout its entire distributional range, often considered as rare, threatened or extinct species. Propomacrus bimucronatus is recorded for the first time from Kurdistan, Iraq. The new data on its distribution and phenology in Iraq and in Israel is published for the first time, compared with the previously-published data and analysed.
In the present era of architecture, different cross-sectional shapes of structural concrete elements have been in use. However, this change in shape has a great effect on load-carrying capacity. To restore this capacity utilization of confinements of the column with elliptical sections has gained attention. This paper aimed to investigate the effect of elliptical sections of confined concrete with Carbon Fiber Reinforced Polymer (CFRP) and steel tube on axial load carrying capacity using Finite Element (FE) in Abaqus, analytical and Artificial Neural Networks (ANN) modeling. The study involved a column, 500 mm in height with three sets of aspect ratios, 1.0, 1.5, and 2.0. In each aspect ratio, three different layers of CFRP were used i.e., 0.167, 0.334, and 0.501 mm. Analytical results show that with the increase of aspect ratio from 1 to 2, there is a decrease in ultimate axial load of about 23.2% on average. In addition, the combined confining pressure of steel tube and CFRP increases with a decrease in dilation angle as several CFRP layers increases. The failure mode for the column with a large aspect ratio is a local buckling at its mid-height along the minor axis. The result showed a good correlation between FE and experimental results of ultimate stress and strains with the mean squared error of 2.27 and 0.001, respectively. Moreover, ANN and analytical models showed a delightful correlation of R2 of 0.97 for stress models and 0.88 for strain models, respectively. The elliptical concrete section of the column confined with steel tubes can be adopted for a new architectural type of construction, however, with more than three aspect ratios, the wrapping of the section with CFRP jackets is highly recommended.
This study investigates the simultaneous effect of nano-silica and nano-alumina with and without polypropylene fiber on the chemical-resistant of alkali-activator mortar (AAM) exposed to (5% Sulfuric Acid, 5% Magnesium Sulphate, and 3.5% Sodium chloride) attack. Design-expert software provided the central composite design (CCD) for mixed proportions. Nano-silica (NS) and nano-alumina (NA) at 0, 1%, and 2%, and with polypropylene fiber (0, 0.5%, and 1%) were used in the production of AAM. The alkali activator mortar mixes were created using an alkaline activator to binder ratio of 0.5. The binder materials include 50% fly ash Class F (FA) and 50% ground granulated blast furnace slag (GGBS). A sodium silicate solution (Na2SiO3) and sodium hydroxide solution (NaOH) were combined in the alkaline activator at a ratio of 2.5 (Na2SiO3/NaOH). The mechanical properties of AAM were tested via compressive strength and flexural strength tests. The results show that the acid attack, more than the sulphate and chloride attacks, significantly influenced the AAM. The addition of both nanomaterials improved the mechanical properties and chemical resistance. The use of nanomaterials with PPF showed a superior effect, and the best results were indicated through the use of 2%NA–1%PPF.
In this study, the mechanical and long-term performance of alkali-activated mortar (AAM) were investigated. In the fresh phase, the flow table and fresh density were evaluated. The compressive and flexural strengths were investigated at 28 and 56 days. In addition, the water absorption, sorptivity, and chemical resistance (sulfuric acid, magnesium sulfate, and sodium chloride) of AAM were investigated after 56 days of exposure Nano silica with and without polypropylene fiber was added to the AAM mixtures and cured at ambient temperature (23 ± 2 • C). Sodium silicates (Na2SiO3) and sodium hydroxide (NaOH) were used as alkaline-activated solution with a ratio of 1.5 to 2.5. The results show that the workability, water absorption, and sorptivity were better, with a ratio of 2.5 (the ratio of sodium silicates to sodium hydroxide). Nevertheless, the simultaneous use of fiber and nano-silica was adversely influenced. On the other hand, better hardened performanc was achieved with the ratio of 1.5. Moreover, the together use of fiber and nano-silica were reduced the flexural and compressive strengths when exposed to sulfuric acid, magnesium sulfate, and sodium chloride. In contrast, an increment of compressive strength was recorded for sodium chloride exposure.
Fabrication of metal nanostructures using natural products has attracted scientists and researchers due to its renewable and environmentally benign availability. This work has prepared an eco-friendly, low-cost, and rapid colorimetric sensor of silver nanoparticles using tree gum as a reducing and stabilizing agent. Several characterization techniques have been exploited to describe the synthesized nanosensor morphology and optical properties. Ultraviolet-Visible (UV-Vis)spectroscopy has been used for monitoring the localized plasmon surface area. High-resolution transmission electron microscopy (HR-TEM) illustrated the size and shape of silver nanoparticles. X-ray diffraction spectra showed the crystallography and purity of the product. Silver nanoparticles decorated with almond gum molecules (AgNPs@AG) demonstrated high sensitivity and colorimetric detection of mercury ions in water samples. The method is based on the aggregation of AgNPs and the disappearing yellow color of AgNPs via a spectrophotometer. The detection limit of this method was reported to be 0.5 mg/L. This work aimed to synthesize a rapid, easy-preparation, eco-friendly, and efficient naked-eye colorimetric sensor to detect toxic pollutants in aqueous samples.
Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization.
There are many petrochemical industries that need adequate knowledge of multiphase flow phenomena inside pipes. In such industries, measuring the void fraction is considered to be a very challenging task. Thus, various techniques have been used for void fraction measurements. For determining more accurate multiphase flow measurements, this study employed dual non-intrusive techniques, gamma-ray and electrical capacitance sensors. The techniques using such sensors are considered non-intrusive as they do not cause any perturbation of the local structure of the phases' flow. The first aim of this paper is to analyze both techniques separately for the void fraction data obtained from practical experiments. The second aim is to use both techniques' data in a neural network model to analyze measurements more efficiently. Accordingly, a new system is configured to combine the two techniques' data to obtain more precise results than they can individually. The simulations and analyzing procedures were performed using MATLAB. The model shows that using gamma-ray and capacitance-based sensors gives Mean Absolute Errors (MAE) of 3.8% and 2.6%, respectively, while using both techniques gives a lower MAE that is nearly 1%. Consequently, measurements using two techniques have the ability to enhance the multiphase flows' observation with more accurate features. Such a hybrid measurement system is proposed to be a forward step toward an adaptive observation system within related applications of multiphase flows.
Utilization of waste materials in the treatment of challenging polluted soils is a cost-effective and environmentally responsible strategy, as it helps to reduce disposal issues created by diverse industrial wastes. The aim of this research is to utilize waste material Ground Granulated Blast Furnace Slag (GGBFS) to enhance several engineering characteristics of polluted soil with crude oil. The engineering characteristics of clean and polluted soils with crude oil were investigated and compared to controls. Three percentages of crude oil 3%, 6%, and 9% were artificially mixed with clayey soils by weight. The effects of Ground Granulated Blast Furnace Slag on the compaction properties (OMC and MDD) and shear strength characteristics (cohesion and angle of friction) of the soil were studied. Different percentages of GGBFS (12%, and 18%) by dry weight were utilized in mixtures of soil samples for different experiments. Ultimately, bases on the experimental results, it is summarized that the use of industrial wastes, i.e. GGBFS are affected in shear strength and compaction properties. Although, they have environment-friendly behavior for the construction project purpose.
Emotion identification is an essential task for human-computer-interaction (HCI) systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition. So far, there have been several EEG-based emotion recognition datasets that the researchers have used to validate their developed models. Hence, we have used a new ICBrainDB EEG dataset to classify angry, neutral, happy, and sad emotions in this work. Signal processing-based wavelet transform (WT), tunable Q-factor wavelet transform (TQWT), and image processing-based histogram of oriented gradients (HOG), local binary pattern (LBP), and convolutional neural network (CNN) features have been used extracted from the EEG signals. The WT is used to extract the rhythms from each channel of the EEG signal. The instantaneous frequency and spectral entropy are computed from each EEG rhythm signal. The average, and standard deviation of instantaneous frequency, and spectral entropy of each rhythm of the signal are the final feature vectors. The spectral entropy in each channel of the EEG signal after performing the TQWT is used to create the feature vectors in the second signal side method. Each EEG channel is transformed into time-frequency plots using the synchrosqueezed wavelet transform (SWT). Then, the feature vectors are constructed individually using windowed HOG and LBP features. Also, each channel of the EEG data is fed to a pretrained CNN to extract the features. In the feature selection process, the ReliefF feature selector is employed. Various feature classification algorithms namely, k-nearest neighbor (KNN), support vector machines (SVM), and neural networks (NN) are used for the automated classification of angry, neutral, happy, and sad emotions.
The field of smart homes has gained notable attention from both academia and industry. The majority of the work has been directed at regular users, and less attention has been placed on users with special needs, particularly those with mobility problems or quadriplegia. Brain computer interface has started the mission of helping people with special needs in smart homes by developing an environment that allows them to make more independent decisions. This study investigates the efforts made in the literature for smart homes that have been established to manage and control home components by disabled people and makes a comparison between the reviewed papers, in terms of the controlled devices, the central controller, the people with disabilities the system is meant for, whether or not machine learning was used in the system, and the system's command method. In the field of machine learning-based smart homes for disabled people, the limitations have been pointed out and talked about. Current challenges and possible future directions for further progress have also been given.
The Internet of Drone Things (IoDT) is a trending research area where drones are used to gather information from ground networks. In order to overcome the drawbacks of the Internet of Vehicles (IoV), such as congestion issues, security issues, and energy consumption, drones were introduced into the IoV, which is termed drone-assisted IoV. Due to the unique characteristics of the IoV, such as dynamic mobility and unsystematic traffic patterns, the performance of the network is reduced in terms of delay, energy consumption, and overhead. Additionally, there is the possibility of the existence of various attackers that disturb the traffic pattern. In order to overcome this drawback, the drone-assisted IoV was developed. In this paper, the bio-inspired dynamic trust and congestion-aware zone-based secured Internet of Drone Things (BDTC-SIoDT) is developed, and it is mainly divided into three sections. These sections are dynamic trust estimation, congestion-aware community construction, and hybrid optimization. Initially, through the dynamic trust estimation process, triple-layer trust establishment is performed, which helps to protect the network from all kinds of threats. Secondly, a congestion-aware community is created to predict congestion and to avoid it. Finally, hybrid optimization is performed with the combination of ant colony optimization (ACO) and gray wolf optimization (GWO). Through this hybrid optimization technique, overhead occurs during the initial stage of transmission, and the time taken by vehicles to leave and join the cluster is reduced. The experimentation is performed using various threats, such as flooding attack, insider attack, wormhole attack, and position falsification attack. To analyze the performance, the parameters that are considered are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput. The outcome of the proposed BDTC-SIoDT is compared with earlier research works, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIoDT achieves high performance when compared with earlier research works.
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