Kurdistan Journal of Applied Research (KJAR)
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Identification of High-Risk Intersections in an Urban Street Network Using Local and Highway Safety Manual Crash Prediction Models
Get PDFNowadays, highway safety is a vital issue because vehicle crashes cause tremendous human, economic, social, and environment losses. This study asses intersections’ safety performance in Sulaimani urban street network where the number of vehicles has been rapidly growing, as the case study. Crash prediction models were developed and applied to assess the safety performance of the intersections. The crash data were reported from Sulaimani traffic police station, happened from January 2020 to September 2024. Besides the crash prediction models mentioned in the Highway Safety Manual (HSM), local crash prediction models for each selected intersections were developed, then the models were used as tools for assessing intersections safety performance. To know the intersections risk levels, five safety performance approaches were used namely Level of Safety Service, Excess Porengicted Average Crash Frequency using Safety Performance Function, Expected Average Crash Frequency with Empirical Bayes (EB) Adjustment, Equivalent Property Damage Only with EB Adjustment, and Excess Expected Average Crash Frequency with EB Adjustments. The results indicate that the local prediction model has a higher R² than the HSM model, indicating a better fit to the local traffic and road conditions specifically at four-leg signalized intersections, the local model achieved an R² value of 0.618, which is substantially higher than the 0.208 obtained from the HSM models. Moreoveresults show that four-leg signalized intersections have significantly higher crash rates, with 15 intersections identified as high-risk across both models. The findings offer practical insights for prioritizing safety improvements and resource allocation to enhance traffic safety in urban areas
Isolation and Characterization of Listeria monocytogenes in Selected Food Products
Get PDFListeria monocytogenes is a significant foodborne pathogen capable of causing severe illness with a high mortality rate, especially in vulnerable populations. Its ability to survive under adverse environmental conditions and contaminate a wide range of foods, including ready-to-eat products, makes it a major public health concern. In Sulaymaniyah and Halabja provinces, there is a lack of systematic data on the prevalence, virulence characteristics, and antimicrobial resistance of L. monocytogenes in locally consumed foods, which limits effective risk assessment and control strategies. This study aimed to determine the prevalence, virulence gene profiles, and antimicrobial resistance patterns of L. monocytogenes in selected dairy, vegetable, and meat products using cultural isolation, biochemical identification, and Polymers Chain Reaction based molecular confirmation. A total of 124 food samples were collected and tested, with molecular detection targeting prs, lmo1030, and 16S rRNA genes, and virulence profiling for hlyA, prfA, and inlA. Antimicrobial susceptibility was assessed against ten antibiotics using the disk diffusion method. Twelve samples (9.6%) were positive for L. monocytogenes, with the highest prevalence in traditional semi-hard cheese (40%), lettuce (25%), and celery (25%). The prfA and inlA genes were each detected in 41.6% of isolates, and hlyA in 33.3%. All isolates were resistant to ampicillin but remained susceptible to most other antibiotics. Thus, these findings provide essential baseline data that can guide targeted food safety interventions and strengthen public health protection measures in this region. Future studies should expand sampling to a wider range of food categories, include seasonal monitoring, and apply whole-genome sequencing to better understand the epidemiology and resistance mechanisms of L. monocytogenes.
Novel Fluorinated Pyrazoline Based Ethers: Synthesis, Characterization and Antimicrobial Evaluation
Get PDFIn response to the growing global threat of antimicrobial resistance, this work seeks to synthesize and analyze chalcone-derived pyrazoline derivatives and assess their antibacterial efficacy against the Staphylococcus aureus and Escherichia coli). A series of pyrazoline compounds were synthesized using both classical step-wise and one-pot synthetic strategies, involving Claisen–Schmidt condensation of 4-(4-fluorobenzyl) oxy acetophenone with various substituted benzaldehydes that subsequently undergo cyclization with phenyl hydrazine. The chemical structures of the produced chalcones and their corresponding pyrazolines were characterized using FTIR, ¹H-NMR, and ¹³C-NMR spectroscopy. Physicochemical characterization revealed the products were obtained in high yields and were sufficiently stable for isolation, with improved yields observed via the one-pot method. Antibacterial activity was assessed using the disk diffusion technique at multiple concentrations (200–800 ppm). The results demonstrated that pyrazoline derivatives exhibited significantly higher inhibition zones, particularly against S. aureus, compared to their chalcone precursors. Compounds 5a, 5c, and 5f had the most significant antibacterial efficacy, whereas chalcones showed minimal to no action against E. coli. The findings confirm the superior bioactivity of the pyrazoline ring system and suggest the crucial impact of electron- giving and electron-removing substituents on antibacterial potential. This research underscores one-pot synthesis as operationally simple and reducing waste generation by eliminating the need for intermediate isolation, thereby offering a more efficient and practical route, time-saving method for producing structurally varied, physiologically active pyrazolines, presenting attractive possibilities for the development of novel antibacterial medicines
Deep Learning Techniques for Early Fault Detection in Bearings: An Intelligent Approach
Get PDFBearings are essential for spinning machines. An unexpected bearing failure could disrupt production. This study describes a sophisticated method for diagnosing deep groove ball bearing issues. We designed and built an experimental setup to collect precise data in many scenarios, including inner race fault, outer race fault, cage fault, and normal state. Machine learning (ML) and deep learning (DL) algorithms have improved image processing, speech recognition, defect detection, item identification, and medical sciences. Experts anticipate a surge in equipment problems as intelligent machinery becomes more prevalent. Deep learning methods for equipment failure detection and diagnosis have increased steadily. Research papers have used deep learning to study and share open-source and closed source data. The Case Western Reserve University (CWRU) bearing data set identifies abnormalities in machinery bearings. Popularity makes this dataset simple to access. This dataset is 'ideal' for model verification and is widely accepted. This article describes current deep learning research using the CWRU bearing dataset to diagnose machinery faults precisely. Using the CWRU dataset, this article has the potential to be of significant service to future academics who desire to begin their work on the detection and diagnosis of machinery failures. This is our view.This paper focuses on utilizing the CWRU bearing dataset combined with Elastic Weight Consolidation (EWC (algorithm to achieve a notable accuracy of 97.06%. The streamlined approach emphasizes the use of raw data and advanced methodologies, showcasing the significance of achieving high diagnostic accuracy while providing a reliable alternative to conventional fault classification techniques
Protecting Digital Footprints: A Selective Web History Sanitization Tool for Domestic Violence Survivors
Get PDFThis study introduces a novel solution that is designed to protect the privacy of domestic violence survivors when seeking online support. It enables survivors to access essential online resources without leaving digital footprints that may lead to further abuse by their violent partners. The study employs the principles of the design science framework to develop and evaluate a system for selective web history sanitization. The implemented system effectively deletes traces related to support websites while preserving unrelated browsing history to avoid the abuser’s suspicion, ensuring both usability and privacy. This work contributes a novel artifact that addresses a critical societal challenge, providing domestic violence survivors with a secure, adaptive, and privacy-preserving tool to seek online supports with confidence. It offers a significant step forward in protecting and empowering vulnerable populations, particularly those with limited technical expertise in the digital age. Additionally, this work offers practical insights for organizations that support survivors by advocating for the development and use of privacy-focused tools and practices to make their digital services more effective and safer
A Comprehensive Review and Analysis of Blockchain-Based Security Solutions for Cloud Computing Ecosystems
No full textCloud computing is a rapidly evolving technology that defines a modern digital computing framework and business approach for utilizing software and hardware resources. While it provides numerous advantages, it also brings with it considerable security risks and challenges. This paper discusses cloud computing and blockchain technology with a focus on the security implications of integrating these two technologies for solving three security issues in the cloud environment. Three major security domains—data security, identity authentication and management, and access control—have been analyzed through an in-depth review of recent studies to evaluate the strengths of integrating both technologies, identify existing limitations, and propose suggestions for future research directions. Relevant literature was retrieved from five major scientific databases: Google Scholar, ScienceDirect, IEEE Xplore, Scopus, and Web of Science. The selection of studies was guided by predefined research questions and specific inclusion and exclusion criteria. The findings reveal that the combination of blockchain and cloud computing establishes a new era of enhanced security. This fusion of blockchain and cloud computing represents the most promising path forward, offering robust security with decentralization and delivering significant advancements in authentication, authorization, data integrity, and privacy protection. This approach will open up vast opportunities in various sectors like healthcare, business, supply chain, and industry due to its guaranteed data security, improved efficiency, and reduced costs. Future research areas need further investigation, such as designing innovative consensus mechanisms to boost scalability and leveraging artificial intelligence and machine learning to improve security and privacy by incorporating blockchain technology into cloud environments
AI-Based Load Balancing Using Decision Tree Regressor for Parallel Matrix Computation in Cloud Environments
Get PDFCloud computing is an evolving technology of current information systems that supports dynamic sharing and elastic provision of resources and services. With increasing demands for computational resources, efficient workload assignment has become an important challenge. Current load balancing methods based on traditional approaches fail to suit dynamic server performance and contribute to the inefficient utilization of available resources, latency, and delays. In response to this challenge, this paper suggests an AI-driven load balancer based on a decision tree regressor to dynamically control task allocation within a parallel cloud system. The system operates to handle computationally heavy tasks, i.e., matrix multiplication, across different servers based on real-time performance measures such as Central Processing Unit (CPU) usage, memory utilization, time of execution, and networking latency. Model training was done with historical data obtained from past executions, incorporated into the web server to facilitate adaptive decision-making. It was tested experimentally with different levels of server scalability as well as matrix complexity. It was contrasted with a static, manual load balancer. All critical performance measures were found to be significantly improved by the AI-based methodology, with the total execution time reduced from 7,060 milliseconds to 1,000 milliseconds; network latency was also reduced to 5.12 ms, down from 214 ms; and the method reduced the overall use of CPU by 33% and overall use of memory by more than 85%. These findings confirm that intelligent, data-driven load balancing offers superior scalability, responsiveness, and efficiency for cloud-based parallel processing systems
A Performance-Driven Enhancement of the Mud Ring Algorithm for Global Optimization Challenges
Get PDFNowadays, real-world optimization problems are becoming increasingly complex tasks, prompting the development of nature-inspired algorithms that mimic biological phenomena to improve search performance and solution quality. The Mud Ring Algorithm (MRA), inspired by the cooperative hunting behavior of bottlenose dolphins, has shown promise but remains sensitive to parameter settings, especially when balancing exploration and exploitation. To address these limitations, this paper proposes the Enhanced Mud Ring Algorithm (EMRA), which introduces a novel mechanism to more effectively manage the exploration-exploitation tradeoff. This modification allows the algorithm to escape local minima and explore the solution space more effectively. Numerical experiments on some standard benchmark functions as well as the difficult Congress on Evolutionary Computation 2019 benchmark suite show that EMRA outperforms the original MRA in terms of accuracy performance and computational cost, especially when dealing with high-dimension and multi-peak functions. In addition, EMRA was used in three complex engineering optimization problem designs (welded beam, pressure vessel, and tension spring), and the results were found to be more accurate and reliable than MRA. These results validate the strength and applicability of EMRA as a general optimization tool to tackle complex problems from many different disciplines, including real-world problems requiring the exhaustive exploration of multiple options. In summary, this study shows that EMRA is an effective extension of metaheuristic optimization that is applicable in real-world problems
An IoT-Enabled Machine Learning Framework for Automated Teacher Performance Feedback to Enhance Teaching Quality
Get PDFGiven the crucial role of teachers in the education system, robust mechanisms are necessary to enhance their teaching effectiveness. Through leveraging advanced technological methods, both teacher and student evaluation processes can be conducted with high accuracy. This study proposes an IoT-based automated teacher performance evaluation system that utilizes machine learning algorithms and computer vision techniques to provide immediate feedback on teaching performance to supervisors. The system analyzes key elements such as hand movements and the teacher's position in the classroom. By enhancing teaching performance, the model aims to improve student learning outcomes. In addition, to develop and test the system, a hypothetical dataset - called the teacher dataset - was created for this proposed model by collecting 35 publicly available videos from YouTube. This approach employs a ResNet50 pre-trained neural network for transfer learning and feature extraction to classify teacher behavior into 8 classes. Fuzzy logic converts the predictions into three teaching quality ratings (poor/medium/good). Using this custom dataset, the model achieved an accuracy of 84.8%, indicating strong performance. This approach enables automated feedback on teaching style, reducing the need for in-person evaluations by educational supervisors. The proposed system has the potential to significantly enhance the overall quality of teaching and learning
A Multilingual Hybrid News Recommendation Framework for Educational Web Portals
Get PDFUniversity web portals increasingly serve as vital platforms for academic information sharing, yet effective news recommendation in resource-constrained, multilingual environments remains challenging due to limited labeled data, sparse user profiles, and linguistic diversity. This study presents a modular hybrid news recommendation framework tailored for educational web portals in low-resource settings. The approach integrates lexical methods, specifically Term Frequency–Inverse Document Frequency (TF–IDF) and Best Match 25 (BM25), with semantic retrieval based on Sentence-BERT (SBERT), combined with unsupervised clustering for topical diversification and a fuzzy-logic fusion layer to integrate heterogeneous similarity signals. A publicly available multilingual dataset of 1,389 university news articles was collected via a custom crawler, and a Flask-based API was implemented for real-time recommendation. Evaluation relies on an automatic hybrid ground truth generated by fusing SBERT, TF–IDF, and BM25 signals. On the ground truth subset, the hybrid model attains Precision@5 = 0.96 and NDCG@5 = 0.945, outperforming SBERT (Precision@5 = 0.93; NDCG@5 = 0.859), with improvements shown to be statistically significant (paired t-test on NDCG@5, p < 1e-5). Clustering enhances thematic diversity (entropy 1.697 vs. 0.032), reducing concentration on repeated announcements. Multilingual experiments demonstrate consistently high precision across Arabic, Kurdish, and English but reveal substantially lower recall for underrepresented languages, highlighting dataset imbalance and representation challenges. Fusion weights were tuned on a validation split to balance precision and recall while mitigating the dominance of any single signal across languages and content types. The proposed framework provides an interpretable and extensible solution for multilingual academic news recommendation in scenarios where interaction data are scarce, offering a practical foundation for future work on language-aware preprocessing, human validation of labels, and supervised re-ranking