Cihan University-Erbil Scientific Journal

Single and Double Stub Tuner Impedance Matching of Microwave Circuits Using Mathcad Software

2024-07-08T06:54:05+00:00

In this paper impedance matching, using tuning stubs of different types and forms are designed and analyzed using Mathcad Software. The aim of this paper is impedance matching which is to connect an arbitrary complex-valued load impedance to a source with a given resistive internal impedance (Intrinsic Impedance Z_o) without causing input reflection and to ensure transfer of maximum power from source to load (Z_L). Smith chart is designed in this paper with a Mathcad software. This tool is considered as a very powerful tool to show the results on Smith chart. Single and double stub tuners are designed and analyzed with examples. The designed software is used in this paper for different cases, such as single stub and double stub tuners (open-ended and short-ended) for matching capacitive and inductive loads. The software designed in this paper is useful for designers working in this field as well as in education process.

A Recently Formulated Individual Control Chart Designed for Quality Control Applications within the Health-care Domain

2024-07-21T07:37:51+00:00

Quality control charts are based on data that is also used for various statistical analyses, such as regression, time series analysis, and other types of analyses. This implies that the data used to create these charts may contain outliers, potentially compromising their accuracy. To eliminate any potential noise or pollution from the data, a researcher proposed using wavelet shrinkage with a threshold and comparing the resulting data with Shewart's individual chart. Based on this, a new quality control chart was proposed and built using a universal approach for assessing the level of thresholding, representing the individual chart for wavelet haar with soft and hard thresholding. Following that, a comparison was performed between them as well as with Shewart's individual chart. This letter's most significant conclusion is that, in addition to using two real data points—triglyceride, which was recorded by a laboratory blood analyzer in Al-Jumhuri and at the specialized Center for Cardiology Hospital in Erbil Governorate—it may be possible to use wavelet shrinkage with a threshold to address issues of noise or pollution when Shewart's individual chart was made and used. Moreover, the wavelet method effectively reduces deviation, as demonstrated in both sets of data.

Knowledge and Awareness Towards Food Drug Interaction Among Pharmacists in Erbil City

2024-07-21T07:40:18+00:00

In the evolving landscape of healthcare, understanding the potential interactions between pharmaceutical drugs and food is imperative yet often overlooked. This study aimed to assess pharmacists' knowledge of food-drug interactions (FDI) in Erbil city, focusing on comprehension, risk factors, counseling practices, and familiarity with drug databases. Conducted through a survey-based approach, the research involved 100 pharmacists from varying educational backgrounds, sampled purposively from Doctors Street in Erbil. Data collection occurred over two months via a self-administered questionnaire comprising 34 questions covering basic FDI awareness and advanced topics. Results indicate a correlation between higher education levels and a deeper understanding of FDI, particularly in recognizing specific drug-nutrient interactions. Nonetheless, knowledge gaps persist, underscoring the complexity of FDI comprehension. Intriguingly, pharmacists with institute certificates exhibited nuanced understanding in certain scenarios, challenging assumptions about the linear relationship between education and awareness. The study emphasizes the necessity for targeted interventions in pharmaceutical education to address these nuances effectively. These findings contribute to the growing body of knowledge on FDI awareness among pharmacists, highlighting the importance of continuous education to ensure optimal patient care, treatment efficacy, and safety.

Epidemiological Insights into TORCH Infections in the Population of Erbil City

2024-07-28T08:59:33+00:00

A seroepidemiological study of the TORCH panel was conducted on inhabitants of Erbil City to provide updated baseline data on TORCH prevalence. 508 individuals were included in this study. 218 (42.91%) were females and 290 (57.09%) were males. Their ages ranged from 13 to 63 year, the majority being within the age group (21-30 years), 162 (31.89%), (P < 0.05). 20 individuals (3.94%) tested positive for anti-TOX IgG antibodies. One of the 20 (5%) tested positive for anti-TOX IgM antibodies, who was a 32-year-old female. 128 individuals (25.2%) (all females) tested positive for anti-Rubella IgG antibodies. 40 individuals (7.87%) were positive for anti-CMV IgG antibodies. One of the 40 (2.5%) also tested positive for anti-CMV IgM antibodies, who was a 26-year-old male. 18 individuals (3.54%) tested positive for anti-HSV-1 IgG antibodies. One of the 18 (5.56%) also tested positive for anti-HSV-1 IgM antibodies. One 18-year-old male (0.2%) tested positive for anti-HSV-2 IgG antibodies, and none were positive for anti-HSV-2 IgM antibodies. The results suggest the presence of Toxoplasma, CMV, HSV-1 and HSV-2 infections among the community, the majority (P < 0.05) being CMV followed by TOX and HSV-1 and finally HSV-2. The positive IgG results of Rubella are most probably due to the obligatory vaccination program for females. Despite the fact that the majority of positive cases were for IgG, enhancing vaccination efforts and providing comprehensive health education is crucial for enhancing the well-being of the Erbil population.

Vitamin D levels in Coronavirus-infected Individuals and its Correlation with Certain Hematological Markers

2024-07-28T09:03:32+00:00

The world and nearly all our health care system were confronted by a significant threat which was declared by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Determining the SARS-CoV-2 or COVID-19’s prognostic and modifiable measures surely would largely contribute to outcome improving despite the deficiency of causative therapy that affected nearly 200 million people, 3.5 million fatal incidences, and it is still airborne. The research in hand examined probable relations between the levels of vitamin D (VitD) and immunological markers. Hence, a sample of 200 lecturers and students from Cihan University-Erbil were assigned, (150 males, 50 females) whom ages were between 22 to 25, with some respondents were treated from a previous COVID-19 infection. The results showed that 98 (or 49%) of the 200 samples had Covid-19 infection. Males 60/98 had a greater prevalence rate (61%), whilst females 38/98 had a lower rate (39%). According to immunological ELISA results, every patient had positive IgG antibodies and negative IgM antibodies. Compared to the healthy group, the serum levels of IgE rose considerably in Covid-19 patients. However, when compared to the healthy group, the serum level of vitamin D3 in the Covid-19 recovered patients showed a considerable reduction. The mean results for hematological parameters indicated a non-significant increase in the number of red blood cells relative to the healthy group, but a significant increase in the WBC count, Hb concentration, and platelets. Thus, the result indicates the necessity for interventional studies on vitamin D supplementation in SARS-CoV-2 infected persons by showing a correlation between vitamin D deficit and WBC count during COVID-19 recovery.

Multi-Level Feedback Queue Scheduling Technique

2024-08-03T19:03:27+00:00

Abstract - In recent decades, computer technology has been highly developed and applied in numerous aspects of daily life, and the healthcare sector is becoming more aware of the significance of information development. Healthcare systems, including their divisions, can indeed use software applications to minimize risk and enhance performance. This study used the MLFQ technique, assigning a time slot to each queue for MLFQ scheduling so that it changes dynamically with each round of performance. Implementing MLFQS techniques enhances patient satisfaction, minimizes risk, and ultimately delivers high-quality care to patients. It also optimizes patient flow and resource allocation, reducing wait times and delays. The technique divides the ready queue into several queues with different priorities, and each queue has its own scheduling technique. Based on the prioritization, MLFQST reduced the waiting time in the queue over the maximum safety requirements for optimal management by 78 arrivals per 4 hours per day in a week, while the percentage of station time utilization was 22, 19.81, 17.62, and 17.62, respectively. While the average waiting time was 24.56, 29.68, 23.5, and 22.62 minutes, the estimated average support units used by the four stations in the system are Ws = Ls/λ = 1/λ – μ = 0.215 hours = 0.215 * 60 = 12.9 minutes per patient in the waiting queue. The findings indicate a substantial reduction in risk, waiting times in the queue, and enhanced performance.

Antibacterial and Wound Healing Assessment of Silver Nanoparticles against Multidrug-Resistant Klebsiella variicola

2024-08-19T09:38:52+00:00

The increase in antimicrobial resistance and the absence of novel antibiotic development cause problems in controlling infections for wound healing. Antibacterial properties of nanomaterials have emerged as a potentially effective approach in the pursuit of superior alternatives, nevertheless, the toxicity associated with higher concentrations has emerged as a significant obstacle. According to this study, green nanoparticle synthesis is demonstrated to be both economical and biocompatible on account of the bioactive compounds present. In the present work, silver nanoparticles prepared by using Pistacia khinjuk gum, then, nanoparticle-based cream was prepared and compared with Fusidin and Vaseline creams via assessment in vitro antibacterial and in vivo wound healing activity on Wistar albino rats infected with multi drug resistant Gram negative bacteria which was newly recorded in Iraq, namely Klebsiella variicola. The findings revealed that the application of nanoparticle cream resulted in more rapid and effective wound healing (11 days), demonstrating a significant synergistic effect in comparison to Fusidin (16 days), Vaseline (20 days), and the untreated control group rats (32 days). The results indicated that green nanoparticles proved to be a significant strategy in the fight against multidrug resistant bacteria, without any toxicity concerns.

Correlation between Klebsiella Species’ Biofilm Formation and Antibiotic Resistance from Erbil Hospital Patients

2024-08-26T10:17:25+00:00

The primary cause of nosocomial infections is Klebsiella pneumonia, an environmental gram-negative, encapsulated, non-motile bacterium. Antibiotic resistance and biofilm formation are two crucial characteristics that contribute to the pathogenicity of this bacterium. This research aimed to examine the relationship between antibiotic resistance and the capability of Klebsiella species isolated from Erbil hospital patients to produce biofilms. Therefore, in this study, 38 samples were collected from different laboratories and hospitals. At first, the samples were rejuvenated in Tryptic soy broth and then cultured on MacConkey agar. Gram staining and urease test were performed to identify the bacterial isolates while VITEK 2 system was performed to determine both the bacterial isolates and the antibiotic susceptibility. At last, Biofilm detection was assessed by the microliter plate technique as well as the Congo red agar method. The results revealed positive Klebsiella samples that were both resistant to some antibiotics and biofilm producers.

Determination of Single Sampling Attribute Plans Based Upon Dodge Roaming Model with Application

2024-08-26T10:31:40+00:00

Bayesian sampling plans for production inspection involve using a sampling method to assess the features of the plan, under the assumption that defect rates fluctuate randomly among different production batches. This results in a likelihood distribution that can be established through experience and the quality information at hand. In this study, the parameters of a single Bayesian sampling plan were determined using the Beta-Binomial distribution, and were subsequently contrasted with parameters from other single sampling plans. Based on research findings, (Ala corporation for soft drinks) oversees the control of product quality. Since the variable fluctuates randomly between manufacturing batches, 120 batches were selected to calculate the defect rate by analyzing batch size and number of defective items. Applying Bayesian and decision-making models can lead to the development of a single sampling inspection procedure that closely approximates the actual quality level. When the decision-making model was used, the researchers discovered that the sample size was smaller and led to lower inspection costs compared to other inspection plans.

Optimizing Health Pattern Recognition Particle Swarm Optimization Approach for Enhanced Neural Network Performance

2024-09-14T13:49:25+00:00

Health pattern recognition is vital for advancing personalized healthcare interventions. This research introduces a synergistic approach, combining Fuzzy C-Means clustering with Particle Swarm Optimization (PSO), to optimize the hyperparameters of an Artificial Neural Network (ANN) and enhance health pattern recognition. Leveraging key features such as 'Smoker,' 'BMI,' and 'GenHlth,' Fuzzy C-Means reveals distinctive health clusters, providing nuanced insights into diverse health profiles within the dataset. Subsequently, the PSO algorithm systematically optimizes critical ANN hyperparameters, significantly decreasing the training loss to 0.004. This reduction underscores the effectiveness of the optimization process, indicating improved learning and predictive capabilities of the ANN. The proposed methodology not only refines health pattern recognition but also holds promise for personalized healthcare analytics. The identified clusters offer actionable insights for tailored interventions, addressing specific health profiles within the population. This research contributes to the evolving landscape of healthcare analytics by integrating advanced clustering and optimization techniques, paving the way for more effective and individualized healthcare strategies.

Evaluating the COVID-19 Lockdown’s Effects on Land Surface Temperature, NO2, and O3 in the Erbil City, Kurdistan Region of Iraq

2024-09-20T08:12:28+00:00

Humans’ lifestyles were radically changed after the 2020 COVID-19 epidemic. To mitigate the effects of the disease, most countries have implemented lockout systems. This research aims to provide some recommendations for long-term, sustainable environmental management by analyzing how the COVID-19 lockdown affected nitrogen dioxide (NO2), Ozone (O3), the land surface temperature (LST), and the normalized difference vegetation index (NDVI). Sentinel Hub data and LANDSAT 8 satellite imagery from 2017 to 2022 were used to create land use land cover (LULC), NDVI, and LST maps. Digital thematic maps of NO2 and O3 with figures that demonstrate the correlation between NDVI-LST and LULC-LST were derived using Arc geographic information system 10.8. The spatial analysis showed that LST and LULC are noticeably correlated, in addition to an inverse proportion between NDVI and LST, as well as between NO2 and O3. The lockdown in Erbil drastically decreased NO2 emissions by 54.5% and improved O3 levels by 7.6%. While the NDVI and O3 levels have increased significantly, the LST and NO2 levels have declined considerably due to the shutdown of all industrial and transportation activities during the lockdown. Although COVID-19 caused a lot of damage, the lockdown contributed to healthier conditions of the environment.

Influence of Maternal Body Mass Index on Fetal Ultrasound Biometry

2024-10-05T08:08:30+00:00

The worldwide prevalence of obesity has increased almost multiple times in recent decades. This study aims to estimate and evaluate the correlation between the body mass index (BMI) of pregnant women and the weight as well as the biometric parameters of the fetus. In the present study, from 245 pregnant women who participated, 210 participants of pregnant women were enrolled in a private clinical center in Erbil Governorate in the Kurdistan Region, Iraq. The data were collected from December 2022 to September 2023. 210 participants were enrolled, and their ages ranged from 17 to 45 years, with a means ± SD of 30.89 ± 6.317 years, 17.1% of participant’s ages were <25 years, 52% were between 25 and 34 years, and 30% were 35 years and more. The relation between maternal BMI and fetal weight in grams for <2000, between 2000 and 3000, and more than 3000 g was statistically significant with a P = 0.027, while the association between maternal BMI and fetal gender statistically was not significant. According to our result in the present study, BMI was not statistically significant with fetal parameters. These findings show the relationship between the fetal weight and the mother’s BMI is statistically significant. However, there is no significant relationship between maternal age and all maternal BMI categories. In addition, in the third trimester of pregnancy, not all categories of the mother’s BMI have an impact on fetal biometry.

Intelligent Handwritten Identification Using Novel Hybrid Convolutional Neural Networks – Long-short-term Memory Architecture

2024-10-05T08:11:10+00:00

Handwritten character identification finds broad applications in document analysis, digital forensics, and human-computer interaction. Conventional methods encounter challenges in accurately deciphering a range of handwriting styles and variations. Consequently, investigating intelligent system for handwriting identification becomes crucial to enhance accuracy and efficiency. This paper introduces an innovative hybrid deep learning architecture, seamlessly integrating the strengths of convolutional neural networks (CNN) and long-short term memory (LSTM) within a one single framework. The combination of these structures enables the model to effectively capture both spatial features and temporal dependencies inherent in handwritten strokes, resulting in improved recognition performance. The proposed 2DCNN-LSTM algorithm has been tested on MNIST dataset. The proposed hybrid CNN-LSTM structure has been compared to conventional intelligent machine learning methods, and the results demonstrate the superior performance of the hybrid CNN-LSTM, showcasing heightened accuracy, sensitivity, specificity, and other evaluation metrics.

Evaluation of ChatGPT’s Configuration Support for Network Connectivity and Security

2024-08-09T07:40:17+00:00

ChatGPT is the world most famous AI interface operates by analyzing the prompt input text and generating coherent responses that predicts effectively your query by utilizing the knowledge it has acquired from its training data. Although this process may appear straightforward and authentic, it may give misleading results for more deep analysis specially for the network engineers. In this paper, evaluation of ChatGPT’s configuration support for network connectivity and security will be analyzed, by applying the commands generated by the ChatGPT AI to configure and secure an enterprise network designed with simulated cisco hardware, and analyzing the full network connectivity and security to determine if the ChatGPT AI prediction was accurately sufficient to run a full network