Journal of Education and Science

Determination of Factors Affecting the Isolation of Candida Species Among Diabetic Patients with Oral Candidiasis in Duhok Governorate, Iraq

2025-09-01T08:22:51+00:00

Oral candidiasis, a fungal infection primarily caused by Candida albicans, poses a significant health risk for diabetics due to their weakened immune systems and altered oral environments. The prevalence and risk factors for oral candidiasis were evaluated in this cross-sectional study of 367 diabetic patients at Azadi Teaching Hospital in Duhok City from August 2024 to April 2025. Sterile swabs were used to collect clinical samples, which were then cultured on various selective media to identify the species of Candida. According to the results, 34.1% of patients tested positive for Candida species, with the most common species being Candida albicans (13.9%), followed by Candida tropicalis (9.8%) and Candida glabrata (3.0%). A noteworthy correlation was observed between comorbid conditions such as dental problems, high blood pressure, and high cholesterol with oral candidiasis. Additionally, type 2 diabetes and advanced age were prevalent among those affected. The study emphasises the importance of preventive measures and regular oral health examinations to reduce the risk of fungal infections in diabetic patients.

Antimicrobial Activity of Cladophora Glomerata and Chara Vulgaris Against Pathogenic Bacteria and Fungi

2025-09-01T11:40:46+00:00

There are several significant secondary metabolites found in freshwater macroalgae, which remain an unexplored source of therapeutic substances. The present study investigated the antimicrobial activity of two different species of macroalgae, Cladophora glomerata and Chara vulgaris. These were extracted using acetonitrile and tested against one Gram-positive bacterium, one Gram-negative bacterium, and one type of fungus using the well diffusion technique. Eleven different concentrations of the algal extract (2, 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 mg/mL) were prepared for the study. Additionally, gas chromatography-mass spectrometry (GC-MS) was used to analyses the components of the extracts. The results showed that the extract of Cladophora glomerata and Chara vulgaris prepared with acetonitrile was most effective against the pathogenic fungus Candida albicans (ATCC: 10231) at a concentration of 15 mg/mL, followed by Staphylococcus aureus (ATCC: 25923) at 25 mg/mL, and Escherichia coli (ATCC: 35218) at 30 mg/mL. Furthermore, the extract of Cladophora glomerata was more effective against the Gram-positive bacterium Staphylococcus aureus. Numerous chemical components with antibacterial and antifungal properties were identified in the extracts. These included alkaloids, alcohols, carboxylic acids, ketones, benzenes, amines, fatty acids, alkenes, furans, heterocycles, and other natural compounds. The antimicrobial investigation of Cladophora glomerata and Chara vulgaris demonstrated a significant effect in inhibiting or killing bacteria and fungi.

Exploring the Distribution of Microplastics and Heavy Metals in Agricultural Soils and the Evaluation of Risk Indices in Erbil City

2025-09-01T12:40:38+00:00

Microplastics (MPs) and heavy metals (HMs) are emerging contaminants that pose potential risks to both environmental and human health. This investigation aims to evaluate the concentration, dispersion, and potential health hazards of microplastics and heavy metals in agricultural soils of Erbil City, Iraq. The quality of microplastics will be assessed by analysing soil samples collected from six agricultural sites using FT-IR spectroscopy. The study sites and plants contained various forms of microplastics, including PET, PA, PE, PS, and PP. Samples from S3–S5 may contain traces of aromatic structures, suggesting either PS contamination or degradation products of PET. The highest concentration of plastic particles per gram of soil was observed in S4 for PET, ranging from 0 to 2.88 ± 0.55. Heavy metals such as Ba, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were evaluated in soil samples and two plant species (Barbarea verna and Anethum graveolens) using ICP. The concentrations of chromium, manganese, nickel, iron, and zinc in the samples exceeded the FAO's permitted levels. Based on microplastic and heavy metal contamination, sites S2 and S6 were classified as medium risk, while site S4 demonstrated a high ecological risk. According to the data obtained from MPs and HMs, the strongest positive correlation was observed between aluminium and PET (Polyethylene Terephthalate) MPs (r = 0.82), while the correlation between Pb and PS was r = 0.78. Additionally, the study will assess whether MPs and HMs could enter the food chain, posing a risk to human health.

Model Selection for the Mean Function of Kriging Models

2025-09-01T18:43:26+00:00

Kriging models are used in many scientific disciplines to investigate the behavior of physical systems. In the Kriging model (KM), the response of the computer simulation code (CSC) is considered to have a Gaussian process (GP). To discover variables influencing responses, choosing a selection of variables or creating a strongly reduced regression model is a crucial process. Selecting some variables can prevent over-fitting or under-fitting in the predictions of data in KM. There have been just a few studies on the variable selection in KM. In this work, we suggest performing variable selection to construct a good model among the KM. The results of the proposed model selection are compared in terms of prediction accuracy with other models based on different forms of the mean function. The comparison is achieved by several measures that investigate the behavior of the KMs. Based on the results, the performance of the Forward selection and Backward selection based on AIC is the best. We apply KMs to several examples of computer simulation codes.

Enhancing IoT Security: A Machine Learning-Based Intrusion Detection System for Real-Time Threat Detection and Mitigation

2025-09-01T19:24:10+00:00

Rapid growth in usage of Internet of Things (IoT) devices has created a situation where security is highly vulnerable, and people require more sophisticated and evolving solutions. Conventional security solutions cannot overcome the issue of heterogeneity, resource scarcity, and dynamism of IoT environments. This paper suggests the use of a machine learning-based Intrusion Detection System (IDS) to identify and attempt to reduce the presence of real-time threats within IoT networks. The results of different machine learning models which include the Logistic Regression, the Decision Tree, the Random Forest, the XGBoost, the AdaBoost, the Gradient Boosting, Bagging, K-Nearest Neighbors (KNN), and the Naive Bayes are compared based on some of the key performance indicators that are accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Our findings indicate that ensemble algorithms, especially Random Forest, Decision Tree, and Bagging, can be more effective than other models in identifying a large number of detections with low false positives, and Random Forest offers an accuracy of 99.99%, precision of 99.96%, a recall rate of 99.96% and ROC-AUC of 99.99%. By contrast, the results of Naive Bayes were much worse, showing an accuracy rate of 74.28 %, a precision rate of 23.32% and an F1-score of 37.71. These findings underline that ensemble algorithms, in particular Random Forest, are also very successful in real-time intrusion detection on IoT systems. The given approach proves that ensemble learning, which possesses the capability to merge several classifiers, is an effective solution to enhancing the IoT safety of systems.

Predicting Arrest Release Outcomes: A Comparative Analysis of Machine Learning Models

2025-09-21T10:07:07+00:00

This comparative study evaluates machine learning models for predicting arrest release outcomes using 5,226 marijuana possession cases from the Toronto Police Service (1997-2002). The dataset exhibited significant class imbalance, with only 17.1% detention outcomes versus 82.9% releases. After preprocessing to handle missing values and convert categorical variables, we implemented two modeling approaches: a 500-tree Random Forest classifier with feature importance measurement and a binomial Logistic Regression model. Both algorithms demonstrated strong predictive capability for release cases, achieving comparable overall accuracy (83.2-83.4%) and excellent sensitivity (>98%), though they struggled with the critical minority class as evidenced by poor specificity (<7%). The models showed similar discriminative power, with Logistic Regression achieving a marginally higher AUC-ROC (0.733 vs 0.726). Feature importance analysis identified employment status and prior police background checks as the strongest predictors, while demographic factors, including race, also contributed significantly to predictions. These results highlight both the technical challenges of imbalanced classification in justice system data and the ethical considerations surrounding potential algorithmic bias, particularly given the high false positive rate for detention predictions that could exacerbate existing disparities. The study underscores the need for careful model evaluation and responsible implementation when applying predictive analytics to sensitive criminal justice decisions, balancing statistical performance with considerations of fairness and social impact.

Deep Learning-Based Structural Health Monitoring: A Multi-Scale Neural Network Approach for Real-Time Damage Detection in Composite Materials

2025-09-01T19:20:48+00:00

For the structural health monitoring of composite materials, data analysis technology must be very sophisticated, capable of detecting fault patterns that are multi-level and complicated. A comprehensive deep learning paradigm was designed for real-time damage detection in this paper. It used advanced neural network architectures with hierarchies and then trained the model on an extensive dataset until it was ready to be published. In other words, the whole process began from scratch. We adopt Cartesian neural network architectures at different levels of scale: from micro- to macro. This system processes damage in composite materials logistically speaking. Through this hierarchical deep learning approach, even if the neural network system is unable to recognize a certain type of spatial damage pattern, it can still be recognized at an earlier stage. The method proposed herein integrates convolutional neural networks with recurrent neural networks and attention mechanisms to effectively capture spatial temporal patterns of damage. Our deep learning method calculates 94.2% damage localization accuracy under carbon fiber reinforced polymer test specimens and decreases false positive rates by 67% compared with traditional signal processing methodologies. This framework has established a new benchmark in industry practice and offers a suite of user-friendly tools with excellent performance repetitive in diverse situations but highly efficient from the computational perspective.

Impact of Phytogenic Feed Additives on Growth Performance, Antioxidant Defense, and Serum Biochemical Indices in Cirrhinus mrigala

2025-09-21T10:32:11+00:00

This study investigated the effects of dietary supplementation with Moringa oleifera, Allium sativum, Zingiber officinale, and Curcuma longa powders (each at 2% inclusion) on Cirrhinus mrigala over a 60-day feeding trial on growth, antioxidant status, and serum biochemistry. A total of 270 juvenile fish (15.8 ± 0.12 g) were distributed into five groups (control and four treatments) in triplicate. Growth performance, antioxidant enzyme activities (Superoxide dismutase; SOD, Catalase; CAT, and Malondialdehyde; MDA), serum biochemical parameters (Total protein, albumin, globulin), and hepatic enzyme activities (Alanine aminotransferase; ALT, and Aspartate aminotransferase; AST) were measured. Turmeric (T4) and garlic (T2) significantly improved growth performance, with T4 exhibiting the highest weight gain and best feed conversion ratio. Both T4 and T2 markedly increased SOD and CAT activities while reducing MDA levels. Serum analysis revealed improved hepatic health. These results demonstrate the potential of turmeric and garlic as sustainable phytogenic additives for improving productivity, oxidative resilience, and health in C. mrigala aquaculture, providing viable alternatives to synthetic growth promoters.

Coxsackie Virus and its Effect on Human Health: A review

2025-09-01T12:50:40+00:00

In low- and middle-income countries, coxsackievirus infections constitute a serious public health concern, especially for young children and newborns. Many human ailments, such as hand, foot, and mouth disease, as well as diseases of the heart, lungs, and muscles, are brought on by these infections, which are common around the world. Coxsackie virus infections typically cause mild flu-like symptoms and resolve on their own. However, in certain situations, they might progress to more serious diseases. According to previous studies, around 50% of affected children do not exhibit any symptoms. Others experience a sudden high temperature, muscle aches, and headache; some also experience nausea, stomach pain, or sore throats. Coxsackievirus infections have a brief incubation period, lasting one to five days. A youngster with a Coxsackievirus infection may only feel hot and have no other symptoms. Most children's fevers endure around three days and then disappear.

In this review, we will provide a brief virus from its initial discovery to the laboratory techniques used to diagnose it, in addition to reviewing the epidemiological data, which offer detailed information on the diversity and distribution of the many Coxsackievirus types. We will also highlight the most important disease it causes, which is hand, foot, and mouth disease.

The Influence of Ambient Temperature and Rainfall on Foodborne Infections Caused by Vibrio, Campylobacter, and Pathogenic Escherichia coli: A Review

2025-09-01T10:12:00+00:00

Foodborne bacterial infections caused by Vibrio species, Campylobacter species, and pathogenic Escherichia coli (STEC/EHEC) pose significant public health risks globally. Environmental factors, particularly ambient temperature and rainfall, play crucial roles in modulating the ecology, transmission, and epidemiology of these pathogens. This review examines recent scientific literature (post-2015) on the associations between these key weather variables and infections caused by Vibrio, Campylobacter, and STEC. Evidence confirms that rising temperatures strongly promote the proliferation and geographic expansion of Vibrio species in aquatic environments. For Campylobacter and STEC, temperature influences seasonality and environmental survival, often through indirect mechanisms affecting hosts, vectors, or human behaviour. Rainfall, particularly heavy rainfall events, is a major driver of pathogen transport via runoff, leading to contamination of water sources and agricultural produce, thereby increasing exposure risk for all three pathogen groups. Understanding these complex, pathogen-specific relationships is critical for developing effective public health strategies, including enhanced surveillance, predictive modelling, and climate change adaptation measures in food safety and water management. Integrated approaches combining environmental monitoring, epidemiology, and climate science are essential to mitigating the growing threat of weather-influenced foodborne diseases.