UHD Journal of Science and Technology

Investigating the Influence of Environmental Factors on Corrosion in Pipelines Using Geospatial Modeling

Thu, 04 Jan 2024 00:00:00 +0000

This study integrated geographic information system (GIS) and remote sensing technology to identify areas around pipelines that are more susceptible to corrosion having the Kurdistan pipeline as a case study. Geospatial data are used to target factors such as rainfall, temperature, rivers, and minerals which increase the corrosion rate. Spatial data such as, the direction of slope, rainfall, proximity to rivers, and minerals, were collected and analyzed; maps were created for every individual factor to visualize their distribution. By overlaying these maps, regions that are at higher risk of corrosion were identified, which can be prioritized for further investigation or preventive measures. This paper’s findings are significant for oil and gas industries, including pipeline operators and designers as corrosion can lead to devastating consequences. The novelty of this study is to identify areas along the pipeline at higher risk of corrosion through the application of geospatial information systems and remote sensing. This methodology holds immense potential for industries looking to proactively prevent corrosion through the implementation of preventative maintenance, monitoring programs, and the application of protective coatings and inhibitors. The results of this research demonstrate that environmental data, GIS, and remote sensing can predict corrosion in oil pipelines, offering valuable insights for better managing corrosion risk.

Kurdish Sorani Dialect Morphology Generation Using a Concatenative Strategy

Kardo O. Aziz, Ramyar A. Teimoor, Tofiq A. Tofiq, Dilman S. Abdulla — Wed, 10 Jan 2024 00:00:00 +0000

In natural language processing, morphological generation refers to the creation of the appropriate inflected forms of words based on a predetermined set of morphological rules. However, it might be difficult to generate morphology in languages with intricate morphological systems, like the Kurdish Sorani dialect. The concatenative morphology-based unique technique to morphological generation in Kurdish Sorani is proposed in this research. The suggested strategy tries to get over the drawbacks of current approaches and enhance the precision and effectiveness of morphological generation in Kurdish Sorani. The suggested technique generates all conceivable subjective and objective pronouns in both positive and negative forms, together with the various verb tenses for Kurdish morphology. The study presents a detailed examination of Kurdish Sorani’s morphology and points out the difficulties in coming up with the right verbforms. The authors suggest a concatenative morphology-based morphological generating system that comprises of a morphological analyzer and a morphological generator.

Performance Evaluation using Spanning Tree Protocol, Rapid Spanning Tree Protocol, Per-VLAN Spanning Tree, and Multiple Spanning Tree

Dana Faiq Abd, Rawyer Asaad Rashid, Danyar Awat Othman, Hero Muhammed Abdulqader — Sat, 20 Jan 2024 00:00:00 +0000

This paper examines the concepts and practical applications of the spanning tree protocol (STP). It also covers per-VLAN spanning tree (PVST), multiple spanning tree (MST), and rapid STP (RSTP). Moreover, practical scenarios are presented to help the reader understand the concepts and implementations of these protocols. This study analyzes protocols using seven metrics. All protocols have been evaluated using these metrics in both small and big topology scenarios to obtain the best results. In addition, all metrics are mentioned in the introduction chapter, and the way used to apply tests on the metrics is described in the methodology chapter. Based on the experiments, different STPs performance are compared, including STP, RSTP, PVST, and MST. In summary, findings show that STP is easy to use and performs well overall, but it consistently has high latency issues. RSTP is suitable for small networks and has quick convergence, but it cannot handle as much load as STP. PVST performed the best in the experiments, as it demonstrated high scalability and the ability to handle a lot of pressure, although it requires strong hardware. However, MST did not perform as well as expected, as it struggled with delay problems and high jitter. In conclusion, it is recommended to use RSTP for simple networks that require fast convergence with dependable delay and capacity, or STP for networks that require good scaling and bandwidth. PVST is an excellent option for those who can afford high-performance hardware, while MST is suitable for simple networks or those with outdated hardware.

Assessment of Health-care Professional’s Knowledge Regarding the Comorbidities of Vitamin D Deficiency and its Relationship with Uterine Fibroids

Wed, 24 Jan 2024 00:00:00 +0000

Vitamin D deficiency is a widespread global health issue, notably prevalent in the Middle East and more severe in women. Vitamin D deficiency increases the incidence of uterine fibroids in black and white women, the most frequent benign gynecologic malignancies. This study examined health-care providers’ understanding of the relationship between vitamin D insufficiency and uterine fibroid at Sulaimani Hospitals in Kurdistan, Iraq. A quantitative design, cross-sectional-descriptive study (non-probability purposive sample) of 113 female nurses and gynecologists. Data were collected using a checklist through Google Forms. The results showed that the majority of the participants were nurses (88.5%), and the remaining (11.5%) were gynecologist. Two-thirds of them work in maternity teaching hospital while others work in smart hospitals and Faroq medical cities. The results revealed that the level of knowledge was significantly associated with the position of participants and level of education (P < 0.05). As well, the study demonstrated that the majority of the participants were not trained regarding uterine fibroid and vitamin D deficiency. The study concluded that nearly a quarter of the health-care professionals had a medium level of knowledge, while nearly a quarter (24.8%) of them had a high level of knowledge. The study recommended to the Ministry of Health giving opportunities to health-care professionals, especially nurses, to participate in training courses, workshops, and conferences regarding the relationship between vitamin D deficiency and uterine fibroid.

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Hoshmen Murad Mohamedyusf, Hawar Othman Sharif, Mazen Ismaeel Ghareb — Tue, 13 Feb 2024 00:00:00 +0000

Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.

Comparative Analysis of Word Embeddings for Multiclass Cyberbullying Detection

Azhi Faraj, Semih Utku — Tue, 20 Feb 2024 00:00:00 +0000

Cyberbullying has emerged as a pervasive concern in modern society, particularly within social media platforms. This phenomenon encompasses employing digital communication to instill fear, threaten, harass, or harm individuals. Given the prevalence of social media in our lives, there is an escalating need for effective methods to detect and combat cyberbullying. This paper aims to explore the utilization of word embeddings and to discern the comparative effectiveness of trainable word embeddings, pre-trained word embeddings, and fine-tuned language models in multiclass cyberbullying detection. Distinguishing from previous binary classification methods, our research delves into nuanced multiclass detection. The exploration of word embeddings holds significant promise due to its ability to transform words into dense numerical vectors within a high-dimensional space. This transformation captures intricate semantic and syntactic relationships inherent in language, enabling machine learning (ML) algorithms to discern patterns that might signify cyberbullying. In contrast to previous research, this work delves beyond primary binary classification and centers on the nuanced realm of multiclass cyberbullying detection. The research employs diverse techniques, including convolutional neural networks and bidirectional long short-term memory, alongside well-known pre-trained models such as word2vec and bidirectional encoder representations from transformers (BERT). Moreover, traditional ML algorithms such as K-nearest neighbors, Random Forest, and Naïve Bayes are integrated to evaluate their performance vis-à-vis deep learning models. The findings underscore the promise of a fine-tuned BERT model on our dataset, yielding the most promising results in multiclass cyberbullying detection, and achieving the best-recorded accuracy of 85% on the dataset.

Iraqi Kurd or Arab Male Authenticity Detection Based on Facial Feature

Bnar Abdulsalam Abdulrahman, Nama Ezzaalddin Mustafa — Sun, 03 Mar 2024 00:00:00 +0000

As an inherent human characteristic, ethnicity plays a fundamental and critical role in biometric identification. On the other hand, the human face is the core of man’s identity, and facts such as age and race are often extrapolated automatically from the face. The objective is to utilize computer technologies to identify and categorize ethnic groups based on facial features. Convolutional neural networks (CNN), which can automatically identify underlying patterns from data, excel at learning image features and have shown state-of-the-art performance in several visual recognition challenges, such as ethnicity detection. Although the automated classification of traits such as age, gender, and ethnicity is a well-researched topic, Iraqi ethnic groupings have not yet been addressed. This study seeks to tackle the challenge of predicting the ethnicity of Iraqi male individuals based on their facial traits for the two largest ethnic groups, the Arabs, and the Kurds. Male Iraqi Kurds and Arabs were each represented by 260 image samples. The dataset underwent a diverse array of preprocessing and data enhancement techniques, including image resizing, isolation, gamma correction, and contrast stretching. Moreover, to augment the dataset and expand its diversity, various techniques such as brightness adjustment, rotation, horizontal flip, and grayscale augmentations were systematically applied, effectively increasing the overall number of images, and enriching the dataset for improved model performance. Face images of Kurds and Arabs were classified using the Faster region-based CNN (RCNN) approach of deep learning. Due to insufficient data in the dataset, we propose employing transfer learning to extract features using several pre-trained models. Specifically, we examined EfficientNetB4, ResNet-50, SqueezeNet, VGG16, and MobileNetV2, resulting in accuracies of 96.73%, 94.91%, 93.39%, 92.48%, and 90.32%, accompanied by corresponding precision values of 0.86, 0.81, 0.80, 0.70, and 0.69, respectively. It is essential to emphasize that the following inference speeds – VGG16 (4.5 ms), ResNet-50 (4.6 ms), SqueezeNet (3.8 ms), MobileNetV2 (3.7 ms), and EfficientNet-B4 (16 ms) – represent the computing times needed for each backbone. Moreover, to achieve a harmonious trade-off between precision and the time required for inference, we chose ResNet-50 as the foundational framework for our model aimed at classifying ethnicity. The study also acknowledges limitations such as the availability and diversity of the dataset. Nevertheless, despite these limitations, it provides valuable perspectives on the automated prediction of Iraqi male ethnicity through facial features, presenting potential applications in various domains. The findings contribute to the broader conversation surrounding biometric identification and ethnic categorization, underscoring the importance of ongoing research and heightened awareness of the inherent limitations associated with such studies.

Blood storage impacts on the hematological indices of healthy subjects and patients with iron-deficiency anemia and beta-thalassemia – A comparative study

Mudhafar Mohamed M. Saeed — Sun, 10 Mar 2024 00:00:00 +0000

Background: There are scientific evidence confirmed specific changes in blood cell counts, reducing the efficacy and feasibly the safeness of blood transmission when storing blood at 4°C for 5 weeks or more.
Objectives: The study aimed to investigate the effects of stored blood obtained from healthy subjects and patients with anemia due to iron deficiency and beta-thalassemia, on hematological indices.
Materials and Methods: A total of 37 participants, consisting of 14 healthy subjects, 13 patients with iron-deficiency anemia, and 10 patients with beta-thalassemia minor, were recruited from Hiwa Hospital between November 2021 and July 2022. Blood samples were obtained from the participants and stored at 4°C for 5 weeks. Hematological indices, including red cell distribution, platelet distribution width, and mean platelet volume, were determined using a hematology analyzer at weekly intervals.
Results: Blood storage caused significantly increased mean values of hematological indices among healthy subjects as well as among patients with iron-deficiency anemia and beta-thalassemia, although the pattern of changes was differed.
Conclusions: The storage of whole blood significantly increased hematological indices, showing variations in both healthy subjects and patients with iron-deficiency anemia and beta-thalassemia. The pattern of raise in these hematological indices is specific to iron-deficiency anemia and thalassemia when compared with healthy subjects.

Enhancing COVID-19 Detection Accuracy: Optimal Gene Combinations, Kit Performance, and Reliable Detection Intervals

Dara A. Tahir, Sehand Arif — Mon, 11 Mar 2024 00:00:00 +0000

A significant challenge and threat to public health have been created by the COVID-19 pandemic for the entire global population. The study aimed to compare the SARS-CoV-2 RNA detection capabilities of available primers and probes to identify the most reliable, efficient, and affordable method. From 200 previously detected samples of SARS CoV-2, 94 samples were selected randomly and used for the optimization of our primers and probes. We compared our results with two kits that have been approved by the health authority. In addition, we evaluated the detectability of each gene. The study compared the diagnostic performance of different gene combinations for COVID-19 detection using kits A and B and a novel approach combining RdRp, N, and E genes. Results showed that the combined approach exhibited superior discriminatory power, particularly with the inclusion of the E gene, boasting area under the curve (AUC) values of 83.3%, 79.1%, and 93.7% for the respective genes. Kit B, with Orf1ab and N genes, outperformed Kit A (RdRp and S genes), with AUC values of 81.2% and 90.6% versus 80.2% and 75%, respectively. The chart representation highlighted gene detection frequencies across various cycle threshold (Ct) ranges, demonstrating robust identification within the 20.1–30 Ct range across all kits and genes, emphasizing the reliability of detection within specific intervals. Combining RdRp, N, and E genes showed the highest accuracy for COVID-19 diagnosis, particularly with the E gene. Detection was most reliable within the 20.1–30 Ct range across all gene combinations and kits.

The Reality of Extension Works in the Directorate of Agricultural Extension and Agricultural Research In Kurdistan Region of Iraq

Ahmed Sajid Hameed — Thu, 04 Apr 2024 00:00:00 +0000

The study aims to identify the reality of extension works in the Directorate of Agricultural Extension and Agricultural Researches in the Kurdistan region of Iraq, and the study area included all governorates of the Kurdistan Region of Iraq (Germian, Erbil, Sulaymaniyah, Dohuk). The research community included all workers in agricultural extension, agricultural research, and agricultural directorates in all governorates, there were 215 respondents. The sample of the respondents includes (71) representing 33% of the population, taken by a simple random sampling method. The data were collected through personal interviews; questionnaire was prepared for this purpose. To confirm the validity, the questionnaire was reviewed by some experts. In general, the reality of extension work was described by the respondents (extension workers and agricultural engineers working in the Agricultural Research and Extension Directorate) as weak. Conducting training courses for agricultural extension workers regarding methods and methods of communication to work with farmers, emphasizing the importance and necessity of linking and coordinating between agricultural extension and education (universities) to graduate a qualified agricultural guide. Cooperation with other research agencies that work on producing and generating agricultural scientific information and practices approved by the Ministry of Agriculture.

Using the Cuckoo Optimization to Initialize the Harmony Memory in Harmony Search Algorithm to Find New Hybrid (CSHS) Algorithm

Fariaa Abdalmajeed Hameed, Kanar R. Tariq, Harith Raad Hasan, Riyam Alaa Johni — Sun, 07 Apr 2024 00:00:00 +0000

The current paper seeks to present a hybrid harmony search algorithm, using CS and HS. The proposed algorithm takes advantage of using a method to initialize the harmony memory (HM) with the help of the cuckoo search optimization algorithm. The HS algorithm is inspired by the method used by musicians to create and enhance harmony in music. It follows 3 main principles of pitch adjustment (PA), HM consideration (HMC), and random selection (RS). The performance of HS can be affected by its poor accuracy in optimization and speed of convergence, 2 main issues with it. However, to improve the HS algorithm, Cuckoo search can be used as it can do a local search using one parameter only, other than the size of the population, making it work more efficiently. This current method has been tested for its validity and efficiency in performance by being implemented on many international standard optimization problems. The results confirm the fact that the performance of the currently proposed algorithm is better and more efficient in finding solutions compared to HS and other algorithms.

Training Needs of Farmers in the Field of Fig Fruit Breeding in Rania District: Sulaymaniyah Governorate and Relationship with Some Variables

Ahmed Sajid Hameed — Tue, 09 Apr 2024 00:00:00 +0000

The study aimed to identify the training needs of the farmers in the field of fig fruit breeding and to determine the relationship between training needs and some variables. The study area included Rania District in Sulaymaniyah Governorate. The research population involved 5205 farmers. The research sample was 104 respondents who were taken using a simple random sampling method, representing 2% of the study population. The research included 20 villages that were included in the research. Data were collected through the questionnaire and personal interviews. The results showed that more than 74% of fig farmers were between (medium and high levels) in need of training in their field of work, and the average training need reached 53.25°. The results showed that there is a statistically significant relationship between the training need and the economic level and the number of years working in the field of fig breeding. The study also showed that there is no significant relationship between the training need and (the area used for raising figs). The study recommended that the responsible authorities increase extension services and activities (such as seminars, training courses, and extension magazines) to increase farmers’ skills and thus increase production.