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Antimicrobial Susceptibility in Acute Myeloid Leukemia Patients in Erbil: Antibacterial Effectiveness Against Bacteria in AML Patients
No full textPatients with acute myeloid leukemia (AML) demonstrate significant sensitivity to bacterial infections. Appropriate and efficient antibiotic treatment is essential to diminish the morbidity and mortality rate of the disease. This study aimed to determine the antibacterial susceptibility profile of bacterial isolates in AML patients in Erbil City. From August 2024 to January 2025, samples were obtained from the blood, gut, and urine of 40 AML patients at Nanakali Hospital in Erbil city. Among 40 cases of AML, 49 bacteria were isolated. Gram-negative bacteria (63.27%) were more prevalent than Gram-positive bacteria (36.73%). Most Gram-negative isolates were Escherichia coli (34.70%) and Klebsiella pneumoniae (16.43%), while Staphylococcus hominis (10.20%) was the most common Gram-positive. Against Gram-negative isolates, colistin (100%) showed the best antibacterial action, while vancomycin and imipenem both with percentage of 100% were most successful against Gram-positive isolates. Colistin, vancomycin, and imipenem had significant efficacy, confirming their application in therapy. Continuous surveillance of resistance is essential
Knowledge and Attitude toward Human Papillomavirus among Students at the University of Sulaimani/City Campus
Full text linkHuman papillomavirus (HPV) is a sexually transmitted infection, which poses a significant global health concern. HPV affects sexually active individuals worldwide and is the primary cause of cancers in the cervix, anus, vagina, penis, and/or oropharynx. The aim of the study was to assess knowledge and attitude toward HPV among students at the University of Sulaimani City Campus. A cross-sectional study was conducted between December 08, 2024, and February 20, 2025, involving 210 students from the colleges of nursing, pharmacy, education, and Islamic science. Participants were predominantly aged 19–23 years. Female participants (55.7%) outnumbered male participants (44.3%). The most significant representation came from the pharmacy college (34.3%), followed by nursing (28.1%), education (21%), and Islamic science (16.7%). Most participants (86.7%) were in their fourth year of study. Rural residency was reported by 64.8%. Only 29.5% of students demonstrated good knowledge of HPV, and 24.8% showed positive attitudes. While 31% strongly agreed on the importance of vaccination, just 35.7% recognized its role in cancer prevention. Significant associations were found between knowledge levels and age, gender, and father’s education (P < 0.005). Moreover, there was a significant association between age, gender, father’s education, and level of knowledge (P < 0.005). Findings reveal substantial gaps in HPV awareness and vaccine attitudes among university students. Targeted educational interventions, particularly within nursing programs, are essential to equip future healthcare professionals with the knowledge needed to promote HPV prevention and reduce cervical cancer risk
Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets
Full text linkThe global rise in cardiovascular disease (CVD) cases underscores the critical need for accurate and early diagnostic solutions. This study introduces a robust machine learning (ML) framework for predicting CVD risk by integrating two large, feature-identical datasets containing clinical and biological indicators along with patient history. Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. A stratified sampling strategy was used to ensure balanced class distribution, and model performance was further validated using k-fold cross-validation to enhance robustness and generalizability. The datasets, sourced from the UCI repository, were pre-processed and evaluated using metrics such as accuracy, precision, F1-score, log loss, and error rate, with performance further assessed using confusion matrices. Results revealed that ensemble models, particularly RF and DT, achieved optimal performance with 100% accuracy, while stratification significantly improved the outcomes of SVM, GNB, and GB. The integration of datasets, stratified sampling, and k-fold validation effectively enhanced model reliability while minimizing overfitting. These findings highlight the potential of ML to support early CVD diagnosis and lay the groundwork for future research on hybrid models and real-world clinical applications
Statistical Analysis of Realized Volatility of Bitcoin Price using Heterogeneous Autoregressive and Generalized Autoregressive Conditional Heteroskedasticity Models
Full text linkBitcoin has recently gained extra attention in the financial industry and the blockchain community in general; it’s considered the most popular form of technology. As a result, the purpose of the study is to predict the actual volatility of the bitcoin price using generalized autoregressive conditional heteroskedasticity (GARCH) and heterogeneous autoregressive (HAR). In this research, the researcher attempted to utilize the appropriate statistical methodology, such as GARCH and HAR models. GARCH models were created to address the issue of volatility aggregation, which is the tendency for prices to cluster together as large changes occur. With the GARCH model, we can represent the conditional heteroskedasticity and the fat tail of financial market data. The primary objective was to directly observe and predict the behavior of volatility in time series data. Overall, the model’s architecture appears simple and is capable of reproducing the primary characteristics of financial information. The primary concept of this model is that investors with different time frames perceive and respond to different levels of volatility. Sample information about the price of the bitcoin cryptocurrency was distributed worldwide. It includes daily updates of the variable for the time period 31-Jun-17 to 31-Jan-22. The investigation has demonstrated that the HAR model is more effective at predicting variance for this period in comparison to GARCH (1, 1). The result shows that 1 day of previous year’s variance estimates and jump estimates have a significant impact on the future variance (h = 1)
Prevalence of Hepatitis B Core Antibodies and Occult Hepatitis B Infection among Blood Donors in Erbil Governorate, Iraq
Full text linkThe Hepatitis B Virus (HBV) remains a considerable risk to blood transfusion safety, especially through occult hepatitis B infection (OBI), defined by undetectable Hepatitis B surface antigen (HBsAg) yet the presence of HBV DNA in the bloodstream. Identifying and investigating the prevalence of OBI is essential as these infections can get past normal screening tests, which can lead to accidental transmission through transfusion. This study aimed to evaluate the prevalence of total hepatitis B core antibody (HBcAb) and identify OBI among blood donors in Erbil Governorate, Iraq. A total of 31,631 blood donors were tested for total HBcAb between September 2024 and January 2025, using the Liaison XL chemiluminescence immunoassay machine. Out of these 31,631 blood donors, 388 (1.23%) showed positive results for the total HBcAb. Among the positive cases, 65 samples were randomly chosen to detect OBI by viral load detection using quantitative real-time polymerase chain reaction. All samples were negative for HBsAg during routine screenings. Occult OBI was detected within 17 (26.15%) of the HBcAb-positive, HBsAg-negative blood donors. Despite the application of HBcAb screening, the absence of molecular testing may continue to provide an opportunity for HBV transmission. Incorporating HBV DNA testing for positive cases may enhance the safety of blood transfusions
Hybrid U-Net Architectures with ResNet50 and VGG19 for Accurate CT-Based Kidney Disease and Stone Segmentation
Full text linkKidney illness is a major worldwide health issue requiring prompt and precise diagnosis for optimal management. This paper presents a comprehensive evaluation of hybrid deep learning (DL) architectures that integrate U-Net with ResNet50 and VGG19 for the automatic segmentation of kidney stones and renal disorders from computed tomography (CT) images. We assembled a dataset of 118 individuals from a private hospital, comprising 13,035 kidney-specific CT scans, while also using the publicly accessible Kaggle Kidney Stone Segmentation Dataset. Three experimental situations were established: (1) Concurrent segmentation of kidney disease and stones, (2) segmentation of kidney stones alone, and (3) segmentation of kidney disease exclusively. The hybrid U-Net+ResNet50 model attained superior performance in stone-only segmentation, with an F1-score of 0.8653, an IoU of 0.7626, and an accuracy of 0.9998 at a resolution of 256 × 256. The U-Net+VGG19 model exhibited strong performance in all situations, attaining an F1-score and DC of 0.8663 for stone segmentation. Both models demonstrated exceptional generalization ability when evaluated on external datasets. The findings indicate that hybrid architectures markedly improve segmentation accuracy compared to conventional methods, providing dependable automated tools for clinical kidney pathology evaluation while ensuring computational efficiency with average processing durations below 0.05 s per scan
Performance Evaluation using Spanning Tree Protocol, Rapid Spanning Tree Protocol, Per-VLAN Spanning Tree, and Multiple Spanning Tree
Full text linkThis 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
Evaluating Aggregate Functions and Machine Learning Integration: A Comparative Analysis of Performance, Security, and NoSQL Connectivity in Oracle, SQL Server, and MySQL
Full text linkThis paper is a comparison study on aggregate functions and windows function between the three major Relational Database Management Systems (RDBMSs): Oracle, SQL Server, and MySQL. These functions are essential to handle a huge data set and prepare it for effective analysis. The research is conducted to analyse the performance of these systems, their utilization of resources, while executing aggregate queries. Further, this paper examines the integration of machine-learning abilities and NoSQL database connectivity within these platforms. All these were measured under a constant benchmarking framework. It also discusses the analysis on how indexing affects query performance and the integration of machine-learning (ML) models with these databases. The results are indicative of considerable performance variation, resource efficiency, and ML integration among the three RDBMSs. Oracle is the best solution for implementing complex aggregations and ML integration, making it the best alternative to work on large datasets. Where MySQL is very efficient for most simple tasks, it lacks advanced features and does not have native ML support. It further provides optimization strategies for each RDBMS and gives insight into securing data and integrating with NoSQL databases. This research is set out to guide database administrators and developers in choosing the most appropriate RDBMS in relation to their specific needs in aggregation, ML, NoSQL integration. However, the factor of indexing is generally what brought most success to query optimization in these databases: Oracle, SQL Server, and MySQL. Among these, Oracle still was significantly outdoing both others, which further improved by indexing. In general, MySQL was less performant and lacked some functionality in window functions. Aggregation queries seem to profit more from indexing, but the less improvement was seen for window functions (STRING_AGG). All in all, indexing is a very effective technique in optimizing query efficiency
Innovative Machine Learning Strategies for DDoS Detection: A Review
Full text linkThis is a broad survey that investigates the use of machine learning (ML) methods for detecting distributed denial of service (DDoS) attacks. Traditional intrusion detection systems face difficulties in application-layer DDoS attacks because they target legal web traffic forms using standard transmission control protocol connections. This paper reviews different ML methods used in recent studies to tackle these issues. These studies use various data sets, such as UNSW-np-15, CICDDoS2019, and the novel dataset LATAM-DDoS-Internet of Things., which prove the efficacy of the proposed models in terms of accuracy and performance metrics. The second group of studies shows more advanced designs, such as protocol-based deep intrusion detection and autoencoder-multi-layer perceptron. These use deep learning to find features and group attacks. All of these approaches present favorable outcomes when it comes to distinguishing normal, DoS, and DDoS traffic with a high level of accuracy. Furthermore, the review discusses works that emphasize the early detection of noise-robust models and distributed frameworks. Different techniques, such as snake optimizer with ensemble learning, metastability theory, and spark-based anomaly detection, highlight the trend of predicting DDoS attacks, whereas hyperband-tuned deep neural networks and evolutionary support vector machine models show higher accuracy in cloud systems as well as software-defined networking environments. Hence, this review gives a general observation of how DDoS attacks develop on their way and proves that ML techniques help to strengthen network security
Training Needs of Farmers in the Field of Fig Fruit Breeding in Rania District: Sulaymaniyah Governorate and Relationship with Some Variables
Full text linkThe 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