UHD Journal of Science and Technology

Relation between Thyroid Function Tests and Body Mass Index among Thyroid Dysfunction Patients

2024-04-22T00:27:53+00:00

Background: Obesity has the potential to impact thyroid function through various pathways, even in individuals considered euthyroid. The relationship between thyroid function and body mass index (BMI) remains a subject of ongoing debate. Therefore, the primary objective of this study was to ascertain the correlation existing among thyroid stimulating hormone (TSH), thyroid hormones, and BMI in patients experiencing thyroid dysfunction within the context of the Smart Health Tower. Methods: This cross-sectional study was conducted in Smart Health Tower in Sulaimani city from November 9, 2021, to March 1, 2022. One hundred and sixty-six patients with thyroid dysfunction (128 individuals had hypothyroidism, eight individuals had hyperthyroidism, and 30 individuals were the other type of thyroid dysfunction) were enrolled in this study. Their mean age was 43.62 ± 11.17 and 50.6% of the participation were male. Patients were divided into four groups based on BMI value: Underweight (BMI <18.5 kg/m2), normal (BMI: 18.5–24.9 kg/m2), overweight (BMI: 25–29.9 kg/m2), and obese (BMI ≥30 kg/m2). Results: The highest rate of age group was between (30 and 40) years old (84%) of them were male. The participants with higher BMI had higher TSH and this trend continued from underweight to Obese. The mean TSH of the underweight group was 0.47 ± 0.61 mIU/L, the normal weight group 1.5 ± 1.91 mIU/L, the overweight group 2.8± 3.87 mIU/L and the obese group 2.7 ± 2.37 mIU/L. Conclusion: A significant relationship between serum TSH and BMI and mean TSH increased as BMI increased. Further large-scale data from the population are required to confirm these findings.

Evaluating Aggregate Functions and Machine Learning Integration: A Comparative Analysis of Performance, Security, and NoSQL Connectivity in Oracle, SQL Server, and MySQL

2024-08-22T15:41:55+00:00

This 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.

Assessment of Acute Respiratory Infection and Common Medication Use in Children below Five Years in Sulaimani, Kurdistan, Iraq

2024-07-27T19:28:42+00:00

In children under 5 years of age, acute respiratory infections (ARIs) represent a significant cause of morbidity and mortality, potentially leading to severe outcomes such as hearing loss and developmental delays. This study aims to assess the prevalence of frequent medication use and identify epidemiological risk factors associated with ARIs in this age group. The research was conducted at Dr. Jamal Pediatric Hospital in Sulaimani city, employing a cross-sectional descriptive approach that included a sample of 42 patients, from June 11th to July 1st, 2024. Data collection involved interviewing mothers and reviewing their children’s medical records. The findings indicated that 45.2% of the children were under 1-year-old, 52.3% resided in metropolitan areas, and 66.7% were male. Notably, 47.6% of the children had received only partial vaccinations, 42.8% consumed bottle milk, and 66.7% experienced recurring illnesses. The majority of illnesses lasted between 1 and 5 days (83.3%). The medications administered included amoxicillin (26.2%), acetaminophen (54.8%), ventolin nebulizer (52.4%), dexamethasone (88%), and antihistamines (4.8%). Regarding parental education, 40.4% of mothers were illiterate, and 76.1% were unemployed. In contrast, 42.9% of fathers had completed elementary school. In addition, 73.9% of families reported insufficient financial resources. Smoking prevalence was high among fathers (71.4%) and lower among mothers (26.1%). Moreover, 12% of fathers had previously smoked, and 28.5% of mothers had been exposed to secondhand smoke. The majority of families (52.3%) were nuclear, with 66.7% consisting of three or more members. Data analysis was performed using Statistical Package for the Social Sciences version 24. The study concluded that significant risk factors for ARIs include male gender, incomplete immunization, exposure to smoking, low parental education and economic status, and inadequate diet. Public health initiatives should focus on improving nutrition, educating parents, reducing smoking exposure, and ensuring complete immunization to effectively decrease the prevalence of ARIs.

Effects of Nano Silver and Indole Butyric Acid Application on Growth and Some Physiological Characteristics on Hardwood Cutting of Dalbergia sissoo Roxb

2024-06-06T10:34:42+00:00

Nanosilver (NS) and indole-3-butyric acid (IBA) can improve cutting performance and subsequent growth. This study was performed in Taqtaq city in a randomized complete block design to study the effects of NS (30, 60, and 90 mg/L) and IBA (50, 100, and 200 mg/L) in addition to distilled water as a control on growth and some characteristics of hardwood cutting of Dalbergia sissoo Roxb. Application of IBA enhanced significantly buds sprouting, where the cutting treated with 50 mg/L IBA sprouted after 35.53 days. IBA at 200 mg/L increased plant leaf area significantly to 204.11 dm2 in comparison to the control cuttings (122.00 dm2). Furthermore, IBA at 50 and 200 mg/L increased the number of leaves to 194.66 and 193.00 leaves/plant, compared to control cuttings (158 leaves/plant). The lowest peroxidase activity (902.00 and 903.30 absorbing units/ g fresh leaves) was observed in the cuttings soaked in 30 and 60 mg/L NS, respectively. Both NS and IBA had a significant effect on macro and microelements in the shoot except Mg and Fe. The shoot content of elements was different in response to NS and IBA applications, whereas the high level of IBA decreased significantly K content (25.80 %) it increased significantly the shoot content of Zn (0.22%). However the lowest concentration of NS (30 mg/L) decreased significantly the Cu content (0.02 %) and increased significantly the shoot content of Mn (0.58%). Root response to NS and IBA also was different, where 90 mg/L NS increased significantly each of K (26.40%) and Zn content (0.68 %), whereas it decreased significantly the root content of Fe (8.78%). The enhancement effects of IBA were more than that of NS on most studied characteristics.

Innovative Machine Learning Strategies for DDoS Detection: A Review

2024-08-22T01:10:16+00:00

This 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.

Male Nursing Students’ Perceptions of Their Clinical Practice in Maternity Hospital at Sulaymaniyah Polytechnic University and University of Sulaymaniyah Colleges of Nursing

2024-08-22T15:40:54+00:00

Background: The field of nursing remains dominated by women in many countries. Males joining the nursing and midwifery fields are becoming more numerous, and with that comes more problems during training. It is crucial to comprehend the experiences and difficulties faced by male nursing students during their nursing education, as these obstacles differ depending on the culture. Aims: The objectives of the study are to explore the perceptions of male nursing students at Sulaimani Polytechnic University and University of Sulaimani Colleges of Nursing about their clinical maternity hospital. Materials and Methods: A quantitative-descriptive design has been carried out in Sulaimani city. A non-probability, convenience sample size of (100) students was included in this study over a period of 1 month. Through an extensive review of relevant literature, a questionnaire was designed for data collection. Results: The results confirmed that male nursing students generally feel more comfortable performing procedures that do not involve exposure to private parts compared to those that do involve such exposure. The association between age groups, marital status, Residency College, and comfort level is not significant (P > 0.05). While, the P = 0.045, indicating that the association between class and comfort level is significant (P < 0.05). Conclusion: The study indicated that while the participants regarded the maternity clinical practice to be difficult, they did not show any interest in doing the maternity course. It was believed that male nursing students’ experiences with maternity clinical practice would be enhanced by acceptance and more specialized training in the clinical setting.

The Social Support Perceived by Patients with Schizophrenia at Ali-Kamal Center in Sulaimani City, Kurdistan of Iraq

2024-08-17T20:10:44+00:00

Background: Schizophrenia is a major mental disorder marked by a diverse range of symptoms. Patients’ perception of social support affects the progress of schizophrenia. Aim: This study aimed mainly to assess social support perceived by patients with schizophrenia. Methods: The present study quantitative- descriptive study on a convenience sample of 100 patients experiencing schizophrenia recruited from the outpatient psychiatric clinic of Ali-kamal Center in Sulaiamani City/Iarq. A valid and reliable questionnaire was used which includes sociodemographic and clinical characteristics and a multidimensional of perceived social support scale. The interview method was employed to collect the data. Results: Low social support was perceived by patients with schizophrenia. Gender P = 0.05; economic status,P = 0.04; and educational level P = 0.01 were statistically significantly associated with low social support among the sample. Conclusion and Recommendation: The role of social support emphasized by schizophrenic patients and the essential need for treatment that includes psychological treatment is also addressed to improve prognosis; moreover, additional studies are needed about the effect of social support on schizophrenic patients.

A Hybrid Genetic Algorithm-Particle Swarm Optimization Approach for Enhanced Text Compression

2024-10-16T19:01:48+00:00

Text compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression.

Prediction of Lung Cancer Disease Using Machine Learning Techniques

2024-07-22T13:41:41+00:00

The pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML). One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of this work is to evaluate supervised learning algorithms, explain SML classification methodologies, and identify the most effective classification algorithm given the available data. Two distinct machine learning (ML) techniques were examined: Random Forest (RF) and Neural Networks (NN). The algorithms were implemented using Python for knowledge analysis. For the categorization, 310 cases from a lung cancer data set were employed, with 15 features serving as independent variables and one serving as the dependent variable. In comparison to NN classification methods, RF was found to be the algorithm with the highest precision and accuracy, according to the results. The study reveals that while the kappa statistic and mean square error (MSE) are factors on the one hand, the time required to create a model and precision (accuracy) are factors on the other. Consequently, to have supervised predictive ML algorithms need to be precise, accurate, and minimum error. Thus, as a consequence of the research, we are currently at this analysis. The categorizing of NNs accuracy is 0.75 the MSE is 0.25, The RF classification accuracy is 0.89 and the MSE is 0.21.