Dijlah University College
Recent publications
Since the outbreak of the COVID-19 epidemic, several control strategies have been proposed. The rapid spread of COVID-19 globally, allied with the fact that COVID-19 is a serious threat to people’s health and life, motivated many researchers around the world to investigate new methods and techniques to control its spread and offer treatment. Currently, the most effective approach to containing SARS-CoV-2 (COVID-19) and minimizing its impact on education and the economy remains a vaccination control strategy, however. In this paper, a modified version of the susceptible, exposed, infectious, and recovered (SEIR) model using vaccination control with a novel construct of active disturbance rejection control (ADRC) is thus used to generate a proper vaccination control scheme by rejecting those disturbances that might possibly affect the system. For the COVID-19 system, which has a unit relative degree, a new structure for the ADRC has been introduced by embedding the tracking differentiator (TD) in the control unit to obtain an error signal and its derivative. Two further novel nonlinear controllers, the nonlinear PID and a super twisting sliding mode (STC-SM) were also used with the TD to develop a new version of the nonlinear state error feedback (NLSEF), while a new nonlinear extended state observer (NLESO) was introduced to estimate the system state and total disturbance. The final simulation results show that the proposed methods achieve excellent performance compared to conventional active disturbance rejection controls.
Laser ablation synthesis in liquid solution (PLAL) is a green technique that allows for the physical formation of nanomaterials. This study indicates the preparation of stable gold nanoparticles (AuNPs) in Gum Arabic (GA) solution via laser ablation as a CT contrast agent. The optical properties were achieved using the absorption spectroscopic technique whereas the morphology and size distribution were investigated by TEM and ImageJ software. TEM image shows greater stability and spherical shape of GA-AuNPs with smaller size at 1.85 ± 0.99 nm compared to AuNPs without GA. The absorption spectrum of pure AuNPs has a lower absorption peak height in the visible range at λ = 521 nm, while the spectrum of GA-AuNPs has a higher plasmon peak height at λ = 514 nm with a blue shift towards lower wavelengths. The concentration of GA that dissolved in 10 mL of DI water via laser ablation is set at 20 mg. Increasing the number of pulses has only a minor effect on particle size distribution, which remains tiny in the nanometer range (less than 3 nm). For energies greater than 200 mJ, there is a blue shift toward shorter wavelengths. As the concentration of GA-AuNPs increases, the CT number is also increased indicating good image contrast. It can be concluded that there is a positive and significant influence of GA as a reducing agent for AuNPs, and a contrast agent for CT imaging which highlights its superiority in future medical applications.
Abstract: Due to globalization in this modern age of technology and other uncontrollable influences, transportation parameters can differ within a certain range of a given period. In this situation, a managerial position’s objective is to make appropriate decisions for the decision-makers. However, in general, the determination of an exact solution to the interval data-based transportation problem (IDTP) becomes an NP-hard problem as the number of choices within their respective ranges increases enormously when the number of suppliers and buyers increases. So, in practice, it is difficult for an exact method to find the exact solution to the IDTP in a reasonable time, specifically the largesized problems with large interval sizes. This paper introduces solutions to the IDTP where supply, demand, and cost are all in interval numbers. One of the best interval approximations, namely the closed interval approximation of pentagonal fuzzy number, is proposed for solving the IDTP. First, in the proposed closed interval approximation method (Method-1), the pentagonal fuzzification method converts the IDTP to a fuzzy transportation problem (FTP). Subsequently, two new ranking methods based on centroid and in-center triangle concepts are presented to transfer the pentagonal fuzzy number into the corresponding crisp (non-fuzzy) value. Thereafter, the optimal solution was obtained using Vogel’s approximation method coupled with the modified distribution method. The proposed Method-1 is reported against a recent method and shows superior performance over the aforementioned and a proposed Method-2 via benchmark instances and new instances.
Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.
Virtual meeting platforms have been identified as the golden bullet to deliver the learning materials to students during the COVID-19 pandemic. While this is evident across thousands of universities across the globe, the literature is scarce on what impacts the continued use of these platforms during and beyond the COVID-19 pandemic. Therefore, this research develops a theoretical model to examine the impact of psychological, social, and quality factors on the continuous intention to use these platforms. Unlike the previous adoption studies, which mainly relied on structural equation modeling (SEM) analysis, the developed model was validated through a hybrid approach using SEM and artificial neural network (ANN) based on data collected from 470 students. The hypotheses testing results indicated that psychological, social, and quality factors have significant positive impacts on the continuous intention to use virtual meeting platforms. The sensitivity analysis results revealed that psychological factors have the most considerable effect on the continuous intention to use virtual meeting platforms with 100% normalized importance, followed by quality factors (72%), and social factors (31%). The contribution of this study lies behind the development of an integrated model that considers the psychological, social, and quality factors in understanding the continuous intention to use virtual meeting platforms during and beyond the COVID-19 pandemic.
In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment.
Due to the existence of system noise and unknown state variables, it is difficult to realize unbiased estimation with minimum variance for the parameter estimation of canonical state space model. This paper presents a new least squares estimator based on bias compensation principle to solve this problem, transforms canonical state space into the form suitable for the least square algorithm, introduces an augmented parameter vector and an auxiliary variable, derives parameter estimation formula based on noise compensation, realizes the unbiased estimation, and gives the specific algorithm. A simulation example is provided to verify the effectiveness of the estimator.
Among a high number of compositions explored, the typical structure of methylammonium trilead iodide (CH3NH3PbI3) has been considered as a common light harvester for high-efficiency and stable CH3NH3PbI3-based photovoltaics (PVs), and promoting the efficiency of this absorber material in such photovoltaics is of importance for the PV research society. In this work, we propose a solvent additive modification concept that employs ammonium acetate (NH4Ac) to passivate trap defects that are present between grain boundaries of the perovskites and to augment the crystallinity of the fabricated perovskites. The obtained PSCs yield an efficiency (PCE) of 16.6% and have long-term ambient stability (1200 h). Our research provides an important method for eliminating trap defects in perovskite films, allowing for easy access to deposition films with improved optoelectronic performances.
The adsorption process of 5-Fluorouracil (5FU) drugs on Aluminum nitride nanotubes surface (AlNNTs) have been evaluated through density functional theory (DFT). The DFT results show that the interaction of AlNNTs with the F atoms of 5FU drugs is strong due to the fact that the amount of adsorption energy was about −29.65 kcal.mol⁻¹. Conversely, the interaction of the 5FU through O atoms with the AlNNTs was weaker due to the lower value of adsorption energy. Also, based on the values of Gibbs free energy, the 5FU adsorption on the surfaces of AlNNTs was spontaneous. In addition, based on natural bond orbital (NBO) analysis, the direction of charge transfer was from fluorine’s σ orbitals of the drug to nitrogen’s and aluminum’s n* orbitals of AlNNTs with a considerable amount of transferred energy. Based on the obtained results, 5FU drug’s tendency toward interaction with AlNNTs is favorable. During the adsorption of 5FU drug onto the AlNNTs, a significant changed in the electrical band gap (Eg) were seen, resulting in increased electrical conductance. The current research is devoted to investigating the potentials of AlNNTs for 5FU anticancer drugs delivery in a bio-based environment.
Perovskite photovoltaics have appeared as a promising technology for new-generation photovoltaics. Developing a hole blocking layer (HBL) has also proven to be a successful method of enhancing the efficiency of perovskite solar cells (PSCs). Hereof, planar 3D/2D structured devices are developed using a reduced graphene oxide (rGO) modified tin oxide (SnO2). The results show that the modified HBL can improve perovskite crystallization, optical absorption, and passivate interface trap-defect at the perovskite/SnO2 interface. The modification impact accounts for the improvement of charge transfer and better work function alignment of SnO2. When an optimized 3% rGO additive was incorporated into the SnO2 layer, the PSC exhibited the champion performance in short-current density (Jsc), open-circuit voltage (Voc), and fill factor (FF) of the PSCs, which were 24.11 mA/cm², 1.093 V, and 79.64%, respectively, and the efficiency was boosted from 16.48% to 20.98%. This HBL modification route allows for defect passivation, which improves the performance of perovskite photovoltaics even more.
For various heating applications like space heating, timber seasoning, food drying, HVAC processes, water desalination processes, etc., where a low to medium heating source is required, the solar air heater is the most promising device to supply the appropriate heated working fluid with least possible cost of heating processes. Over the years, the improvement in the performance of the SHAs has become a fascinating area of research. The thermal efficiency of SAH is low because the boundary layer generation on the absorber plate causes resistance to heat transmission. Artificial roughness is the most efficient technique to break the laminar boundary layer, hence improving heat transmission. In this paper, various artificial roughness geometries used by various researchers have been reported. Based on the correlations and optimum parameters for the respective roughness provided by the respective authors, Nu, f, and thermohydraulic performance parameter have been calculated for the eleven roughness geometries using Microsoft excel 2021, and a comparison has been made to opt for the best roughness among them. At Re = 24000, internal conical ring obstacles with impinging jets give the highest Nu compared to other roughness geometries; which has Nu 4.89 times higher than the smooth SAH. Similarly, at Re = 24000, f for discrete reverse NACA 0040 profile roughness has been found to have the lowest value of f compared to the other roughness geometries, and it has f as 1.32 times higher compared to the smooth SAH. Thermohydraulic performance parameter (η) was found highest for perforated conical disc inserts with helical corrugations roughness at Re<16000 and lowest for perforated delta roughness at Re>16000. The thermohydraulic performance parameter (η) has a maximum value of 2.440 for a hybrid broken arc with staggered ribs roughness at Re = 24000. It has been noted that tremendous numerical and experimental work has been reported in the literature. In this study, key findings are discussed, the influence of the various roughness parameters on heat transmission and pressure drop are summarized, and recommendations are also provided for future research.
Density functional theory (DFT) calculations were used to evaluate the capability of Glutamine (Gln) and its derivative chemicals as inhibitors for the anti-corrosive behavior of iron. The current work is devoted to scrutinizing reactivity descriptors (both local and global) of Gln, two states of neutral and protonated. Also, the change of Gln upon the incorporation into dipeptides was investigated. Since the number of reaction centers has increased, an enhancement in dipeptides’ inhibitory effect was observed. Thus, the adsorption of small-scale peptides and glutamine amino acids on Fe surfaces (111) was performed, and characteristics such as adsorption energies and the configuration with the highest stability and lowest energy were calculated. Based on previous researches, it is understood that the adsorption of dipeptides on the aforementioned moieties has a chemical nature. The protonation of configuration leads to an increase in the amount of energy of adsorption on the surface of metal among the inhibitors. Theoretically speaking, it is more likely for peptides to adsorb on the surface of iron, and this fact reveals that these moieties are highly effective in terms of inhibitive applications. According to the obtained findings, small peptides can be used as favorable “green” corrosion inhibitors.
Density functional theory calculations were performed for investigating the effect of doping the Ni atom on the sensing capability of a B24N24 nanocluster ((BN)24) in detecting the carbonyl fluoride (CF) gas. We predicted that the interaction of pristine (BN)24 with CF was a physisorption, and the sensing response (SR) of (BN)24 was 5.1. The adsorption energy of CF changed from -4.9 to -21.4 kcal/mol after doping the Ni atom. Also, the corresponding SR increased significantly to 77.8, indicating that the Ni transition metal significantly increased the sensitivity of the nanocluster. It was shown that the [email protected](BN)24 may selectively detect the CF gas among O2, CF4, SiF4, C2F6, and HF gases. Our theoretical results further supported the fact that the [email protected] nano-structures have practical applications.
Density functional theory was used to investigate the possible use of the B36 borophene in the detection of H2S, CS2, COS, and SO2 gases. In general, the order of interaction stability for the studied gases is as follows: SO2 > CS2 >H2S > COS. There seems to be a relation between the energy of absorption and the electric dipole moment of the molecules. The B36 borophene is indeed a Ф-type sensor that only detects SO2 and also an electronic sensor that detects SO2 and CS2. As a Ф-type sensor, it can be extrapolated that the B36 borophene is able to identify SO2 in the presence of H2S, COS, and CS2. Furthermore, it may work selectively between SO2 and CS2 as an electronic sensor by changing different electronic conductivity values in the presence of the as-mentioned gases. It is an electronic or function-type sensor for the detection of COS and SO2. The B36 borophene has a short recovery time of around 0.7 s and 0.1 s for the desorption of CS2 and SO2 from the surface at ambient temperature. It has been determined that this borophene is able to function in a moist environment.
BCl3 is toxic gas and its detection is of great importance. Thus, here, B3LYP, M06-2X, and B97D density functionals are utilized for probing the effect of decorating Zn, Cd, and Au on the sensing performance of an AlP nano-sheet (AlPNS) in detecting the BCl3. We predict that the interaction of pure AlPNS with BCl3 is physisorption, and the sensing response (SR) of AlPNS is approximately 9.2. The adsorption energy of BCl3 changes from -4.1 to -18.8, -19.1, and -19.5 kcal/mol by decorating the Zn, Cd, and Au metals into the AlPNS surface, respectively. Also, the corresponding SR meaningfully rises to 40.4, 59.0, and 80.9, indicating that by increasing the atomic number of metals, the sensitivity of metal decorated AlPNS ([email protected]) is increased. Therefore, we found that Au-decorating much more increases the sensitivity of AlPNS toward BCl3. As energy decomposing analysis reveals the electrostatic, also known as cation-lone pair interaction, is mostly the nature of the interaction between the BCl3 and [email protected]
Infrared thermometers are increasingly used to measure human body temperature during the COVID-19 epidemic. Mishandling the instrument and varying ambient conditions result in inaccurate measurements in numerous situations. The ambient temperature heavily influences the recorded body temperature value. The thermodynamic word entropy can be used to determine the actual heat of a body. The temperatures of a body and the ambient are used to calculate the entropy of a body. Experiments on the human body were conducted day and night time using a newly developed direct entropy measuring instrument. This investigation yielded a precise entropy range for a commensurate temperature of the human body. It was found that the mean entropy of a healthy human body was found to be 0.042 and 0.146 kJ/K during the day and night, respectively. In comparison to simulated entropy values, the measured values varied by 4%. The findings will aid in identifying people who have a slight fever but no significant thermal symptoms. This entropy value can be used to lessen the errors caused by environmental factors while detecting a feverish person.
Organometal halide perovskite (OHP) has drawn extensive research interest because of its high efficiency, cheap cost, and facile production procedure. However, the commercialization of OHP is still limited owing to its unstable behaviour in open‐air conditions and toxicity due to the presence of lead in commonly used methyalammonium lead perovskite. The mismatch between the current density and voltage curves concerning the scan direction, also known as J‐V hysteresis, is one of the instabilities that creates a serious problem in the overall device performance in MAPbI3 bases perovskite solar cells. In this manuscript, all inorganic lead‐free double perovskite‐based solar cell is computationally simulated, and overall device performance is optimised using solar cell capacitance software. Proposed solar cell is composed of lead‐free Cs2AuBiCl6 double perovskite as the main absorber material and Zn(O, S), CuSCN as an electron transport material and hole transport material, respectively. Due to its high absorption coefficient (≈105 cm−1) and low reflectance, Cs2AuBiCl6 is investigated as a lead‐free double perovskite substitute for OHP. The layered architecture consists of FTO/Zn(O, S)/ Cs2AuBiCl6/CuSCN/Au is optimised by varying the thickness of the absorber layer, the defect density of main absorber layer, interfacial defect and operating temperature. Once the device is optimised, then J‐V hysteresis is performed using two different defect model‐based strategies. Simulation finding gives decent power conversion efficiency of 20.5% with almost negligible hysteresis. These simulation‐based studies on Cs2AuBiCl6 will give guidance for designing and developing a high‐efficiency eco‐friendly lead‐free perovskite solar cell as an alternative to traditional lead‐based perovskite solar cells. All inorganic lead‐free double perovskite‐based solar cell is computationally simulated, and overall device performance is optimised using solar cell capacitance software.
Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.
This paper presents the research results on the contribution of user-centered data mining based on the standard principles, focusing on the analysis of survival and mortality of lung cancer cases. Researchers used anonymized data from previously diagnosed instances in the health database to predict the condition of new patients who have not had their results yet. Medical professionals specializing in this field provided feedback on the usefulness of the new software, which was constructed using WEKA data mining tools and the Naive Bayes method. The results of this article provide elements of interest to discuss the value of identifying or discovering relationships in apparently “hidden” information to propose strategies to counteract health problems or prevent future complications and thus contribute to improving the quality of care. Life of the population, as would be the case of data mining in the health area, has shown applicability in the early detection and prevention of diseases for the analysis of genetic markers to determine the probability of a satisfactory response to medical treatment, and the most accurate model was Naive Bayes (91.1%). The Naive Bayes algorithm’s closest competitor, bagging, came in second with 90.8%. The analysis found that the ZeroR algorithm had the lowest success rate at 80%.
Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information to reflect on the practices currently used in assessing the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders. This study, based on genuine and current facts, used data from 292 children with an autism spectrum disorder. The input dataset has 20 characteristics, and the output dataset has one attribute. The output property indicates whether or not a certain person has autism. The research study first and foremost performed data pretreatment activities such as filling in missing data gaps in the data collection, digitizing categorical data, and normalizing. The features were then clustered using k -means and x -means clustering methods, then artificial neural networks and a linguistic strong neurofuzzy classifier were used to classify them. The outcomes of each strategy were examined, and their respective performances were compared.
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482 members
Abdulla Suhail
  • Optics Techniques Department
Sinan Salih
  • Computer Sciences Department
Mustafa K. A. Mohammed
  • Radiology Techniques Department
Baghdad, Iraq
Head of institution
Prof. Dr. Saeed. A. Almurhij