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The Clinical Neurological Manifestations of Patients Diagnosed with Carpal Tunnel Syndrome
Full text linkBackground: Carpal tunnel syndrome (CTS) is a condition, in which the median nerve becomes pressed or squeezed at the wrist. This causes pain and numbness in the fingers. Therefore, a neurological study is crucial to assess the condition.Objectives: The objective of this study was to assess the neurological manifestations of CTS and their association with demographic and clinical features from October 2022 to March 2023. Materials and Methods: A quantitative study was carried out over the period of 5 months by prospectively selecting and enrolling 100 CTS patients with a confirmed diagnosis. The CTS assessment questionnaire was modified and patients consented to the study before the data collection. Results: Adults aged 35–44 were the dominant group and the disease was found in females 10 times more than males. The least assigned symptoms were tingling and numbness in the little finger (4%) and neck pain 22%. All the patients with CTS presented with severe levels of CTS. Statistically significant associations were found between occupations, duration of the disease, affected side, other chronic diseases, and the prevalence of the symptoms at P ≤ 0.05. Self-management to sub-side pain and numbness had crucial impact on reducing the symptoms (P ≤ 0.05). Conclusion: The prevalence of the neurological symptoms varied depending on the sociodemographic and clinical features. Self-management had a significant positive impact on reducing some of the neurological symptoms, such as pain in the wrist at night and tingling and numbness in the morning
Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman
Full text linkTraffic congestion is a significant issue in urban road networks, particularly in Amman, where peak hours cause major delays for commuters. Developing an advanced traffic management system is essential to helping residents save time, reduce congestion, and alleviate traffic jams. To address this challenge, we have implemented a simulation model powered by machine learning techniques to effectively and accurately manage traffic flow on Amman's streets. This innovative system leverages real-world data from the Jordanian capital to dynamically optimize traffic control. By automating traffic management processes, the model aims to reduce congestion while easing the workload of traffic personnel. This approach promises to enhance urban mobility and contribute to building a smarter and more efficient traffic management infrastructure in Amman, ensuring a better quality of life for its residents. After implementing the metering strategy, the traffic flow became more balanced, with less congestion and smoother transitions between intersections. The metering points effectively regulated the entry of vehicles into the circles, preventing congestion buildup and improving overall traffic efficiency
Enhancing Pelican Optimization Algorithm with Differential Evolution: A Novel Hybrid Metaheuristic Approach
Full text linkIn the field of solutions for composite objective functions, the problem of identifying a proper trade-off between exploitation and exploration is still urgent. Classical methods can hardly avoid early iteration convergence or be insufficient in terms of searching throughout the space of potential solutions, especially when dealing with multi-variate multi-dimensional problems. To overcome this problem, this work proposes a combination of the pelican optimization algorithm (POA) and differential evolution (DE), known as the POA-DE metaheuristic method, which comprises the explorative characteristic of POA and the exploitative feature of DE. The main issue dealt with in this work relates to the conflict of global search and local exploitation in the context of solving complex optimization tasks. In global exploration, the POA technique is applied to improve the performances of the search in the large area, and the DE method is used in the local search space for improving the solution. To this end, the proposed solution hybrid model tries to avoid the shortcomings associated with using either of the two key aspects when used independently. To support the results obtained through POA-DE, it is necessary to perform the intensive empirical examination of several benchmark functions. The results also show that the proposed method has achieved better stability, efficiency, and convergence speed than the basic POA. Therefore, extending the hybrid optimization techniques is significant in enhancing the meta-heuristic algorithms that form a powerful tool to solve the optimization problems
An Ensemble-based Machine Learning Framework for Advanced Distributed Denial of Service Attack Detection in Software Defined Networks
Full text linkDistributed Denial of Service (DDoS) attacks pose a significant threat to modern network architectures, especially Software Defined Networking (SDN) due to its centralized controller. This study proposes an advanced framework for DDoS attack identification and prediction using state-of-the-art machine learning (ML) techniques in an SDN architecture. A comprehensive dataset was generated through a two-stage traffic generation procedure, simulating attack and normal scenarios over a 6-day period, from which fifteen were extracted to characterize network behavior. Multiple classifiers including Gradient Boosting Ensemble methods such as LightGBM, XGBoost, CatBoost, and Gradient Boosting Decision Trees, as well as additional ensemble methods such as AdaBoost and Bagging were evaluated alongside with One-Class SVM and Bayesian Networks. They were trained and evaluated using rigorous cross-validation. The results demonstrate near-perfect performance of ensemble models, achieving up to 99.98% accuracy with outstanding precision, recall, and area under curve metrics. To achieve efficient mitigation, the detection mechanism is deployed on local web servers, and a certificate authority-based secure communication channel transmits malicious IPs to the SDN controller, enabling low-latency, scalable, and real-time DDoS attack mitigation. This paper discusses the promise of applying cutting-edge ML models to enhance the robustness of SDN infrastructures against sophisticated cyber-attacks and offers a template for further research in dynamic network defense strategies
A Novel Class of Hybrid Conjugate Gradient Methods for Unconstrained Optimization with Applications to Image Denoising
Full text linkThis paper presents a new hybrid conjugate gradient (CG) method for solving large-scale unconstrained optimization problems where classical CG algorithms may not perform well. This new method integrates four classical algorithms, namely, Liu and Storey, Fletcher and Reeves, Dai and Yuan, and Polak, Ribiere, and Polyak, using a convex combination of CGs and an inexact line search based on strong Wolfe conditions, and adheres to the Dai-Liao conjugate condition to enhance convergence properties. The theoretical study established the conditions for sufficient descent and global convergence of the hybrid CG method. Numerical studies demonstrated that the proposed method significantly reduced the number of iterations and computational time, achieving superior evaluation efficiency compared with other CG methods. In particular, the effectiveness of the proposed algorithm is validated for image impulse denoising, where it can recover high-quality images while preserving significant features, implying its practical applicability to real-world signal and image processing problems
Knowledge, Attitude, and Practice towards Menstrual Hygiene Management among Adolescent School Girls in Sulaymaniyah City/Iraq
Full text linkBackground: Menstrual hygiene management (MHM) remains a significant public health challenge for adolescent girls in many communities. Despite its importance for reproductive health and overall well-being, many girls lack adequate information, resources, and supportive environments to manage menstruation effectively. MHM being crucial for adolescent girls’ health, gaps in knowledge and practices persist among schoolgirls across different educational levels. Objectives: The objective of the study was to assess knowledge, attitude, and practices (KAP) regarding MHM among adolescent schoolgirls across different grades as well as to find out the association between sociodemographic characteristics with KAP scores. Methods: A quantitative descriptive analytic (Cross-sectional study) was conducted with 432 adolescent girls across different grade levels, The data collection started from November 20, 2024, to February 10, 2025, using an interviewed questionnaire. Results: Among participants, 45.8% had poor knowledge, 31.5% had negative attitudes, and 42.6% demonstrated poor practices toward MHM. KAP scores were significantly associated with age, class level, parental education, and maternal occupation (P < 0.05). Conclusion: The findings indicate a need for comprehensive menstrual health education programs targeting adolescent girls, particularly in lower grades, to improve MHM and address the gaps in KAP
Regression Analysis of Soil Properties for Small Dam Bodies samples in Chamchamal and Qaradagh Districts, Sulaymaniyah Governorate
Full text linkEffective geotechnical engineering relies on accurately predicting soil parameters such as cohesion and the angle of internal friction, which are critical for ensuring the stability of structures such as small dams. Traditional laboratory testing can be prohibitively expensive and time-consuming, highlighting the need for efficient predictive models. This article aims to develop regression equations that estimate these parameters using easily obtainable soil properties in Chamchamal and Qaradagh Districts, Sulaymaniyah Governorate. Using soil water content, soil density, and plasticity index as key predictors, the one-way analysis of variance analysis achieved an R-squared value of approximately 0.87, with a root mean square error of 0.15 and a bias of about −1.2%, demonstrating high accuracy and robustness across different datasets. The analysis further revealed that increases in plasticity index significantly impacted the angle of internal friction (P = 0.014), while dry density showed a strong positive influence on cohesion. These findings underscore the role of soil parameters in estimating the soil compression index and demonstrate that a simplified, empirically derived model can offer practical insights for geotechnical applications. However, given the moderate correlation levels observed (R2 = 0.38 for cohesion and R2 = 0.61 for internal friction angle), the predictive capability of the models is limited. Therefore, the developed regression models should be regarded as preliminary tools, useful for initial assessments, but must be complemented by thorough field investigations and comprehensive engineering analyses to ensure the reliability and safety of dam structures
Intelligent System for Screening Epileptic Seizures in the Erbil Electroencephalogram Epilepsy Dataset Images Utilizing Cascaded Histogram of Oriented Gradients-Gray Level Co-occurrence Matrix Features
Full text linkThis study proposes a novel approach for epileptic seizure detection from EEG signals using a statistical feature extraction method that being derived from a cascaded Histogram of Oriented Gradients (HOG) and Gray Level Co-occurrence Matrix (GLCM) techniques for (117 normal) non-elliptical seizures and (117 abnormal) elliptical seizures diagnosed EEG signal images collected from Erbil teaching hospital. Four classification algorithms namely—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), and Discriminator (DR)— with rigorous hyperparameter optimization using Bayesian techniques were utilized to improve classification: three feature extraction approaches: cascaded HOG-GLCM, GLCM, based statistical features extraction and HOG were calculated. The proposed comprehensive simulation results revealed that the cascaded HOG-GLCM approach significantly outperforms single-feature methods. The SVM and KNN classifiers achieved exceptional performance with the cascaded features, both approximately reaching 98.57% accuracy ensuring almost no epileptic events went undetected, which represents a substantial improvement over GLCM (best: 92.86% accuracy) and HOG approaches (best: 94.29% accuracy). The synergistic effect observed between gradient-based and texture-based features demonstrates how HOG captures directional patterns characteristic of seizure activity, while GLCM extracts spatial relationships within the signal. Neither feature type alone provides sufficient discriminative power, as evidenced by the 5-8% accuracy drop in single-feature approaches
Effects of Salicylic Acid on Barley Growth and Productivity under Cadmium Stress Conditions in Rania, Kurdistan Region, Iraq
Full text linkPlant health and agricultural productivity are seriously threatened by heavy metal contamination. Due to the expansion of cereal agricultural lands into marginal areas close to urban areas and unpaved roads that are polluted by many pollutants, especially that of cadmium (Cd), this study examined the effects of Cd and salicylic acid (SA) separately and in combination on the growth, physiological, biochemical, and reproductive responses of barley (Hordeum vulgare L.). Three SA treatments (0, 86, and 172 mg/kg soil) and four Cd concentrations (0, 10, 20, and 30 mg/kg soil) were used in a factorial pot experiment. The findings showed that high Cd (Cd30) reduced vegetative growth but increased spike number and harvest index, indicating reproductive compensation, moderate Cd levels (Cd20) improved some growth traits, including plant height by 4.00% and flag leaf area by 13.11% compared to the control treatment, suggesting a possible hormetic effect. Particularly under moderate Cd stress, SA at 86 mg/kg markedly enhanced plant height, yield components, and antioxidant balance. On the other hand, SA at 172 mg/kg increased grain number and spike length but decreased overall yield, as a result of metabolic effects and hormonal interferences. Under extreme stress, endogenous defense mechanisms might be sufficient, and external SA could upset homeostasis, according to the interaction effects, which showed that Cd20 × SA86 maximized growth and productivity while Cd30 × SA0 produced an increase in grain and biological yields by an amount of 10.95 and 9.73%, respectively, compared to the control treatment. These results show that the performance of growth and yield components of H. vulgare L. was significantly and variably affected by both Cd and SA, both separately and in combination, depending on the concentration and the interaction. Data suggest that SA is most effective at moderate Cd stress levels
An Effective Computer-aided diagnosis Technique for Alzheimer’s Disease Classification using U-net-based Deep Learning
Full text linkThe diagnosis of Alzheimer’s disease (AD), a common neurodegenerative disease that impairs thinking and memory abilities in older adults and ultimately results in cognitive impairment and dementia, is made possible in large part by computer-aided diagnosis (CAD). The idea has been to use either machine learning models or deep learning models to develop classification techniques for this disease. CAD techniques and mechanisms have emerged to help and facilitate early detection of this disease as a fundamental step in its treatment plan. As part of our approach, we proposed a model that included the following two pre-processing steps: Contrast Limited Adaptive Histogram Equalization (CLAHE) was utilized to enhance image contrast, especially in low-contrast areas. Normalization was then incorporated to ensure reliable training and faster convergence. A Gray-level co-occurrence matrix technique was used to extract seven texture features from the images following pre-processing: contrast, homogeneity, energy, correlation, variance, dissimilarity, and entropy. After that, these characteristics were added to the model output before the last classification layer. The best hybrid framework out of the five models we examined in this paper was utilized to build a convolutional neural network that can be used to identify AD characteristics from magnetic resonance images. As discussed in Section IV of this article, the U-Net model was selected because of its superior performance. The experimental results demonstrate that this technique showed great accuracy in segmentation and classification for each of the five AD Neuroimaging Initiative categories when a specific diagnosis was made. These results are as follows: Overall, the five classes’ final average scores for the four measures were as follows: 94.46% for Accuracy, 94.32% for Precision, 94.49% for Recall, and 94.41% for F1-score