Nawroz University

Given the global spread of COVID-19, there is an urgent need for accurate point-of-care tests in settings where RT-PCR has a long turnaround time or is unavailable. Early screening with chest X-ray (CXR) imaging is attractive because it is widely available and low cost. Nevertheless, the manual interpretation of X-ray images is time consuming, subjective and necessitates expert radiological skills. We propose a lightweight, efficient automated COVID-19 detection model by combining handcrafted features with deep learning features to classify CXR images. Preprocessing normalizes image dimensions and enhances contrast. Local Binary Pattern (LBP) features are employed to enrapture local texture information of lung regions. For context-aware representation, we extract high-level features with a pretrained CNN (ResNet-50). The LBP and CNN features are fused into a single feature vector, which is fed into a support vector machine (SVM) classifier. We evaluated our proposed model on a public dataset available for COVID-19 radiography, containing normal, pneumonia, and other confirmed COVID-19 cases. The results show high classification performance with an accuracy of 97.91%, precision of 97.8%, recall of 97.83% and F1-score of 97.23%. The large burden of clinical screening demonstrates that the fusion of LBP and CNN features dramatically promotes the differentiability between infectious patients with COVID-19 and non-infected cases, which provides a valid approach to address scalable accuracy in clinical examination.

Youth unemployment remains a critical socioeconomic issue in Duhok City. This study used an online cross-sectional survey (Google Forms) of 2,076 urban residents aged 18–30, conducted from January to March 2025, to assess employment status and sociodemographic predictors of joblessness. The respondents were 37.7 percent female (n = 782) and 62.3 percent male (n = 1,294). Educational attainment included 33.9 percent secondary‐school graduates, 28.2 percent postgraduate degree holders, 21.9 percent undergraduates, 14.2 percent diploma holders, 1.4 percent with no formal education, and 0.3 percent with vocational training. Employment breakdown showed 30.5 percent in full‐time work, 12.9 percent in part‐time roles, 14.0 percent self‐employed, and 38.2 percent unemployed; 3.3 percent were unable to work and 1.1 percent were retired. Enhanced logistic regression analysis (Stata 17) with comprehensive model diagnostics revealed that female sex (OR = 1.43), younger age (OR = 0.97 per year), and lower educational attainment significantly increased the odds of unemployment, with diploma (OR = 0.75), undergraduate (OR = 0.68), and postgraduate (OR = 0.52) credentials offering graduated protection. Gender-stratified analysis and interaction effects (gender × education) provided deeper insights into differential employment barriers. Model fit statistics showed Pseudo R² = 0.142, with good model performance (Hosmer-Lemeshow p = .189). These findings highlight the need for gender-sensitive policies, age-appropriate training, and expanded vocational and higher-education pathways to reduce youth joblessness in the Kurdistan Region. However, the online sampling methodology limits generalizability to digitally connected youth populations only.

This experimental research investigates the effects of different nano-alumina (NA) percentages partially replaced with ground granulated blast slag (GGBS) and fly ash (FA) using a variety of tests, including compressive and flexural strengths, flowability, dry density, microstructural analysis (scanning electron microscope, energy dispersive spectrometer, thermogravimetry analysis), water absorption, and sorptivity at ages of 28 days. All mixture results were also contrasted with those obtained from a control mixture of 50% GGBS and 50% FA without NA. All of the mixtures had the same alkaline activator and solid base, and the proportion of sodium hydroxide solution (S.H)/sodium silicate solution (S.S) was 2.5; the molarity of S.H is 12 M. The aim of this research is to investigate the optimal percentages of NA that showed significant results. The mix containing 2% NA reached its maximum strength of 61.9 MPa at 28 days. The results show that the percentage of NA significantly influences alkali-activated mortar properties, including water absorption, which is affected differently at varying concentrations. The proposed alkali-activated mortar may offer stable substitutes for conventional building materials that are also environmentally friendly.

ABSTRACTAmidst the dual challenges of aggregate scarcity and the environmental impact of carbon dioxide (CO2) emissions from cement production, this study investigates the viability of palm oil clinker (POC) as a sustainable aggregate in geopolymer concrete (GPC). The lack of appropriate alternative coarse and fine aggregates essential in concrete production is one of the critical issues faced by the construction industry. This review evaluates its environmental benefits, chemical and physical attributes, and influence on GPC's microstructure. Previous studies have shown that incorporating POC in GPC significantly reduces density from 2345 to 1821 kg/m3 while maintaining competitive compressive strength, thus proving its applicability in various structural and nonstructural contexts. Moreover, GPC with POC demonstrates enhanced resistance to aggressive environmental conditions such as water absorption and resistance against acid and sulfate environments. Geopolymer mortar (GPM) exposed to sulfate attack recorded the lowest decrease in strength than GPM containing POC fine aggregates by about 20%. The use of 100% POC aggregates in GPC mix has a 3.2% water absorption, which is lower than the limit for high‐performance concrete. The results advocate for the development of POC‐aggregate GPC as an environmentally friendly construction material, contributing to the sustainable advancement of the building industry.

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.

This study investigates the quality of groundwater sources in the rural areas of Duhok City, Iraq, with a particular focus on heavy metal contamination, chemical composition, and properties of water. Water samples from 14 wells, serving as the main water source for the surrounding areas, were collected and analyzed. Water quality parameters including calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), potassium (K⁺), and sulfate (SO₄²⁻) values ranged from 24 to 105.6 mg/L, 6.832 to 50.752 mg/L, 18 to 34 mg/L, 1 to 7 mg/L, and 3.4 to 38 mg/L respectively. Common heavy metals like manganese (Mn), lead (Pb), copper (Cu), and cobalt (Co) exhibited varying concentrations. Most parameters meet the WHO standards, except for the elevated potassium in one sample, requiring attention. Additionally, 50% of the sampled wells showed elevated cadmium (Cd) levels. Possible sources of contamination include industrial activities, agricultural runoff, and geological factors, highlighting the importance of ongoing monitoring and targeted interventions to ensure access to clean and safe water.

Wireless localization technologies have undergone a paradigm shift with the advent of the emerging trend which is the Reconfigurable Intelligent Surfaces (RISs). This paper presents a comprehensive survey of the state-of-the-art RIS-aided localization techniques, exploring the transformative impact of intelligent surfaces on location-based services. The survey comprehensively reviews existing wireless localization methods, ranging from the localization techniques in the wireless communications generations. It addition the challenges posed by conventional localization methods are discussed, including accuracy and coverage issues. In response to these challenges, the incorporation of RIS is explored as a promising paradigm to enhance the precision and reliability of wireless localization. The core of the paper focuses on the integration of RIS into localization systems, highlighting how these RISs can be strategically deployed to enhance accuracy, mitigate multipath effects, integration in different propagation environment and solve non feasible localization problems. The survey encompasses a wide range of applications, including outdoor positioning, indoor positioning, and Internet of Things (IoT) devices, showcasing the versatility of RIS-aided localization across various scenarios. Also, critical design aspects are examined, including propagation regions, hardware requirements, deployment strategies, necessary measurements, multiplexing, and signalling systems.