Duhok Polytechnic University

Introduction: This study aimed to determine the prevalence and associated factors of coronavirus disease 2019 (COVID-19) and long COVID-19 in children in Duhok province and Zakho city in the Kurdistan region. Methodology: The study was conducted as a cross-sectional study and included children aged 5–12 years in Duhok and Zakho, two major neighboring cities in the Bahdenan region of northern Iraq. A total of 330 participants were included and the study was conducted between October 2022 and April 2023. The children were tested for the presence of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) antibodies. A questionnaire was used to collect demographic and personal data, and symptoms of each participant to determine the prevalence of long COVID-19. Results: Out of 330 participants, 302 (91.5%) were positive for IgG, and 156 (51.7%) of them were male. Only 4 participants (1.3%) had pneumonia, and 282 (93.4%) were asymptomatic. Fourteen out of 302 (4.6%) participants had long COVID-19. There were significant associations between long COVID-19 and history of previous COVID-19 episodes (p = 0.001), presence of pneumonia (p = 0.001), and family history of COVID-19 (p = 0.005). Conclusions: There was a high prevalence of COVID-19 among children in Duhok province and Zakho city, and 4.6% of them experienced long COVID-19. Factors such as prior COVID-19, pneumonia, and family history of COVID-19 were associated with long COVID-19. Continued monitoring, education, vaccination, preventive measures, and supportive care are recommended to effectively address the impact of COVID-19 on the pediatric population.

Background The last line of defense against risks is frequently regarded as personal protective equipment (PPE). Therefore, there is a shortage of PPE in places with high demand because of COVID-19’s widespread nature. Objective The aim of the study was to address the shortage of personal protective equipment (PPE), as well as to identify factors that increased the risk of mental health problems among healthcare workers during coronavirus disease 19 (COVID-19). Materials and Methods A cross-sectional study was conducted from June 22nd to August 22nd, 2020, in Iraqi Kurdistan region. A total of 337 healthcare professionals participated in an online survey that included questions about socio-demographic information, personal protective measures, and risk factors for mental health issues. SPSS software version 24.0 was used to analyze data. Results The majority of healthcare professionals 196 (58.2%) were men. The majority of the population was aged 25–34 years 211 (62.6%), with nursing, representing the highest percentage among all professions 151 (44.8%). At least 46.6% of the participants reported a lack of PPE. The most common shortages reported were hats, boots, N95 masks, goggles, and face shields. A significant positive correlation ( r = 0.181, P = 0.001) was observed between direct contact with COVID-19 patients and the spreading of fear and panic among healthcare professionals due to their concerns about transmitting the virus to their relatives. Also, results revealed that healthcare professionals’ non-receive training on the ways of facing the COVID-19 crisis was another risk factor affecting healthcare professionals’ mental health in all hospitals ( r = 0.119, P = 0.001). Conclusion According to the current study, healthcare professionals lacked the resources needed to treat COVID-19 patients. To avoid healthcare professionals’ mental health problems during medical emergencies, the government should take action, especially the Ministry of Health, which should address the challenges in the case of a future health crisis.

With the growing global emphasis on accessibility and inclusion for deaf and hard-of-hearing individuals, research in Sign Language Recognition (SLR) has gained significant momentum. Sign languages have unique grammar and syntax and rely on manual communication to convey meaning, which presents specific challenges for automated recognition systems. Unlike isolated sign language recognition, continuous sign language recognition (CSLR) must accurately interpret sequences of gestures without clear boundaries between signs. This requires advanced techniques for both segmentation and recognition. This paper presents a comprehensive review of Continuous Sign Language Recognition (CSLR), focusing specifically on deep learning (DL) techniques. It addresses key challenges, such as movement epenthesis (ME)—the transitional movements between signs. Implicit models, including Hidden Markov Models (HMMs) and Connectionist Temporal Classification (CTC), have demonstrated superior performance compared to traditional methods. The researchers reviewed 32 studies published by major publishers (IEEE, Elsevier, and Springer) and examined 12 benchmark datasets related to CSLR. This examination included an overview of their linguistic scope, recording setups, vocabulary size, and participant diversity. Additionally, the performance metrics and methods used in these studies were thoroughly analyzed, followed by a discussion of the results and their broader implications. The review highlights several limitations in existing research, underlining the need for ongoing innovation within the CSLR domain. The insights gained from this review not only enhance the understanding of Sign Language Recognition but also provide a foundation for future research aimed at tackling persistent challenges in this evolving field.

Natural clay is considered one of the most attractive substances due to its broad applications and environmental benignity. In this work, Kurdistan montmorillonite clay (KMC) has been easily separated from the soil without any chemical treatment. It is employed as an efficient adsorbent for removing the cationic toxic dyes from the influent. Different methods, such as BET, FESEM, TEM, UV–VIS, XRD, XRF, XPS, and Zeta potential, have been applied to study how well clay works. In an effort to match the isothermal data, Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich equations were used. The experimental findings have shown that a Langmuir isotherm equation provides a good fit for the equilibrium data (R2 = 0.999). The rate parameters were assessed using pseudo-first-order, pseudo-second-order, intra-particle diffusion, and liquid film diffusion equations and were consistent with the pseudo-second-order kinetic model (R2 = 0.999). Additionally, the results revealed that the clay exhibited a high adsorption capacity (2.45 mg/g) and removal for methylene blue (MB) dye (98%) in one minute. The outcomes show that KMC effectively adsorbs MB dye and may be used as a low-cost substitute in wastewater treatment to get rid of cationic dyes.

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards.

Objectives Crimean-Congo hemorrhagic fever (CCHF) is the most widespread tick-borne viral disease worldwide. CCHF was not recognized in Iraq before 1979, after which many outbreaks occurred, and the disease became endemic with the re-emergence of outbreaks. This study aimed to analyze the epidemiology of the largest outbreak in Iraqi history in 2023. Methods This retrospective study included human CCHF cases from 2023. Results 2186 suspected cases were investigated. There were 587 confirmed cases, and 83 deaths, and an overall case fatality rate (14%). Among the confirmed cases, only 539 cases had complete data, and the analysis was performed on these cases. The majority 70.9% of patients lived in the southern provinces. Approximately 58% of the patients were male and up to half of the patients resided in rural areas. Approximately 45% of cases were in the 25-44 years age group. The occupations of the patients were as follows: 30% were housewives, 22% were butchers, 18% were animal owners, 30% had other occupations, and up to 60% had a history of exposure to fresh raw meat. Conclusions The 2023 outbreak was the largest in Iraq in decades. The absence of preventive and control activities during the COVID-19 pandemic played an important role in the rise of cases and the presence of unlicensed and freelance slaughterers, especially during religious events played an important role in this epidemic.

The increasing demand for ultra-fast data, high capacity, and low latency in 5G and beyond networks is driving the adoption of millimeter-Wave (mm-Wave) frequencies (3–300 GHz), which utilize spatial multiplexing and beamforming for improved performance. However, environmental factors like humidity, temperature, dust, and sandstorms, particularly in the Middle East, pose significant challenges. The parameters of the channel model and how it behaves statistically when exposed to dust and sandstorms have been analyzed using NYUSIM simulator and MATLAB. Wireless communication channels face challenges like time variability, frequency-selective fading, and interference from adjacent subcarriers, making traditional estimation methods less effective. This paper introduces a DNN model based on Bidirectional Long Short-Term Memory (BI-LSTM) networks, which excel at processing time-series data with long-range dependencies, outperforming standard RNNs. An end-to-end channel estimation and signal detection process using the Bi-LSTM-based detector is simulated and compared with traditional techniques such as LS, ZF, and MMSE. Results show that DNN provides higher estimation accuracy, especially with fewer pilots, achieving SER 30 to 35 times lower than ZF and MMSE. Additionally, the DNN model offers an SNR gain of approximately 10–15 dB or more compared to conventional methods. The proposed approach shows cases promising advancements for not only 5G but also for the evolving requirements of beyond-5G networks, offering a reliable solution for maintaining efficient Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing MIMO-OFDM communication under diverse weather conditions.

Landslides are significant geological hazards in mountainous regions, arising from both natural forces and human actions, presenting serious environmental challenges through their extensive damage to properties and infrastructure, often leading to casualties and alterations to the landscape. This study employed GIS-based techniques to evaluate and map the landslide susceptibility in the Bekhair structure located within the Zagros mountains of Kurdistan, northern Iraq. An inventory map containing 282 landslide occurrences was compiled through intensive field investigations, as well as the interpretation of remote sensing data and Google Earth images. Ten potential influencing factors, including elevation, rainfall, lithology, slope, curvature, aspect, LULC, NDVI, distance to roads and rivers, were selected to construct susceptibility maps by integrating the frequency ratio (FR) and analytical hierarchy process (AHP) approaches, with the goal of understanding how these factors relate to landslides occurrence. The Bekhair core area was divided into 5 hazard zones on the landslide susceptibility maps. The regions classified as very low and low hazard zones are mainly occur in flat or gently sloping plains that characterized by resistant rocks, dense vegetation, minimal rainfall, shallow valleys, and are distant from riverbanks and roads. The areas designated as high and very high hazard zones are found in steep slopes and rough terrain with bare soil, intense weathering, high rainfall, sparse vegetation, highly fractured rocks, deep valleys, and close proximity to construction projects. The moderate hazard zones are mainly located between the other 4 zones across the Bekhair anticline. Results of the susceptibility analysis indicate that the occurrence of landslides in Kurdistan mountains are primarily controlled by factors related to the tectonic structure, surface characteristics and environmental conditions, such as rock lithology (competency), terrain slope, rainfall intensity, and human impacts. The delineation of landslide hazard zones offers important guides for government decision-makers engaged in regional planning, infrastructure development, and the formulation of strategies to mitigate landslides and protect lives and properties in Kurdistan. The accuracy of susceptibility maps was evaluated using the R-index and the AUC-ROC curve. The landslide susceptibility index (LSI) values allocated to different susceptibility classes derived from both FR and AHP models are consistent with the values obtained from the R-index. Moreover, the FR model demonstrated superior performance compared to the AHP model, with a success rate of 85.3% and a predictive rate of 81.2%, in contrast to the AHP model’s success rate of 75.2% and predictive rate of 72.4%.

The aim of this study was to compare the physical, sensorial, and microstructural properties of selected meat products with their plant-based alternatives to assess how closely the alternatives mimic the original products. Six meat analogue products, including Frankfurter sausage (SuA), steak (StA), Hungarian sausage (KA), minced meat (MA), salami (SaA), and burger (BA), were compared with their corresponding meat products (SuM, StM, KM, MM, SaM, and BM, respectively). The study measured colour indicators, texture parameters, sensory attributes, and microstructural properties. The redness values (a*) of the external surfaces of SuM and KM, as well as the hardness of MM, were similar to those of their alternative products, with no statistically significant differences (p > 0.05). Sensory evaluation revealed similar ratings for two attributes: product similarity and overall appearance. However, significant differences were found in the descriptors for animal character and meat taste.

The voltage generated by power plants is increased using step-up transformers and then transferred using high-voltage transmission lines. In a distribution system, the voltage is stepped down to certain levels and is utilized by consumers. The losses in distribution networks are very high compared with the transmission line losses because of the high value of the line resistance (R) compared with the reactance (X), high current, and low voltage. Distribution companies have an economic incentive to minimize network losses. Generally, the incentive is the difference between the actual losses and standard losses. Therefore, when the actual losses are greater than the standard losses, distribution companies are fined. If the actual losses are less than the standard losses, distribution companies earn profits. Consequently, the issue of power losses in distribution networks has attracted the attention of researchers. Numerous methods and techniques have been examined and implemented to reduce distribution system losses. These methods differ based on the selection of the loss reduction mechanism, formulation of the problem, technique utilized, and solution obtained. Many techniques are used to minimize losses, such as power factor correction, reconfiguration, distributed generation allocation, load balancing, voltage upgrades, and conductor upgrades. In this study, a literature review, general background on distribution loss minimization, and a comprehensive comparison of the main techniques are presented to examine the best methods for minimizing power losses.

In recent years, the proliferation of Internet of Things (IoT) devices has introduced significant vulnerabilities in cybersecurity, particularly with the rise of sophisticated malware targeting these systems. Traditional detection methods, often based on static signatures, struggle to keep pace with evolving threats, such as zero-day attacks. This paper explores the application of Large Language Models (LLMs), specifically BERT and GPT-2, in detecting IoT malware by analyzing network traffic and identifying anomalies. Using the contextual understanding and adaptability of LLM, our approach significantly enhances detection accuracy compared to conventional methods. We evaluated the models using the ToN-IoT dataset, demonstrating their capability to detect complex malware patterns with higher precision. The results indicate that BERT outperforms GPT-2 across multiple metrics, highlighting its effectiveness in generalizing to various attack types. Despite promising advancements, challenges such as computational resource demands and model interpretability persist. Future research should focus on optimizing LLMs for real-time detection in resource-constrained environments and improving transparency to enhance trust among cybersecurity professionals. Our study underscores the potential of LLMs as powerful tools in the ongoing battle against IoT malware, offering a robust framework for enhancing cybersecurity defenses.

p class="ICST-abstracttext"> In this paper, we propose a hybrid image denoising method that combines wavelet transform and deep learning techniques to effectively remove noise from digital images. The wavelet transform is applied to each color channel of the noisy image, decomposing it into different frequency components. The approximation coefficients are then denoised using a convolutional neural network (CNN), specifically designed for this task. The denoised coefficients are subsequently reconstructed to form the final denoised image. Our experimental results demonstrate that this hybrid approach outperforms traditional denoising methods, achieving superior noise reduction while preserving image details. The proposed method is validated using synthetic noisy images, and the results are visually and quantitatively evaluated to confirm its effectiveness. </p

This systematic review explores the role of urban green infrastructure (UGI) in enhancing climate resilience, focusing mainly on heat mitigation modelling and its application at both urban and building scales. The study analyses 207 articles published in the last five years at the screening stage and 50 at the inclusion stage, highlighting the effectiveness of UGIs in reducing ambient temperatures and improving building energy efficiency through shading and evapotranspiration. Advanced simulation tools like Computational Fluid Dynamics (CFD) and Building Performance Simulation (BPS) are increasingly relied upon, though challenges remain in accurately modelling vegetation and urban-climate interactions. The review identifies critical research gaps, particularly in evaluating UGI’s performance under future climate change and seasonal variation scenarios, emphasising the need for refined simulation techniques. Moreover, the evapotranspiration modelling of UGIs needs to be developed on the BPS scale. Addressing these gaps is essential for optimising UGI design to ensure their effectiveness in future urban climates. The review calls for further studies on long-term UGI resilience, especially in rising global temperatures and evolving urban environments.

Predicting groundwater drawdown is crucial to the Duhok Governorate’s sustainable management of its water resources. To ensure long-term water availability as extraction from population growth and development intensifies, predicting drawdown helps to prevent overuse, provide a continuous supply of water, and enable effective planning for urbanization, agriculture, and industrial needs. In this work, a novel approach based on Multi-layer perceptron neural network (MLP), support vector regression (SVR), k-nearest neighbor algorithm (KNN), and extreme learning Machine (ELM) optimized by whale optimization algorithm (WOA) were proposed for estimating the total drawdown at Zakho region, Duhok Governorate, Northern Iraq for the first time. The input variables of the models include the rate of water extraction from the well (Q), well depth (D), and various meteorological parameters such as rainfall (R), evapotranspiration (E), Maximum Temperature (Tmax), and Minimum Temperature (Tmin). It is found that ELM showed the highest performance in modeling groundwater drawdown (R² = 0.911, RMSE = 5.674 m, and MAE = 4.937 m). Moreover, the novelty of the research work is to enhance the accuracy of the individual models using two ensemble techniques including simple averaging ensemble (SAE) and weighted average ensemble (WAE). Based on the findings, the WAE technique increased the performance of individual models by up to 20%, proving the reliability of the WAE technique for groundwater drawdown prediction.