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Awareness of Menstrual Abnormalities among Female Nursing Students at the University of Sulaimani
Full text linkBackground: The menstrual cycle, which occurs on a monthly basis from menarche to menopause and facilitates fertilization and conception, is a normal function in the female reproductive system. A 28-day cycle is the typical length. Any variations from the typical menstrual cycle in terms of frequency, irregularity of onset, duration of flow, or volume of blood are referred to as menstrual abnormalities. Aim: The current study set out to evaluate nursing students’ awareness regarding menstrual abnormalities. Materials and Methods: In a descriptive study of the quantitative method, the sample of 100 female students was conducted at the University of Sulaimani/Nursing College from January 15 to May 30, 2024. A questionnaire format was created according to the aim of the study and delivered by a team of six experts, consisting of three parts. Part one: sociodemographic characteristics of students. Part two: Menstrual patterns of students. Part three. Awareness of students regarding menstrual abnormalities. Data were collected by direct interviews with the students. Statistical Package for the Social Science version 22 was used for analyzing the data. The frequency, percentage, and Chi-square test were used. Results: Results of the present study indicated that the highest percentage of participants were in the age group (20–24); they mostly dwelled in dormitory. Financial state for the majority was sufficient and the vast majority were unmarried. The majority of participants experienced painful menstruation which affected their academic performance. Moreover, only one-fifth of participants had a high awareness regarding menstrual abnormalities. Finally, the study showed that there was a significant association between the group age of students and their awareness regarding menstrual abnormalities. Conclusion and Recommendations: The research concludes that the majority of participants demonstrated low awareness of menstrual abnormalities. Information, education, and awareness programs need to be strengthened to spread awareness regarding menstrual abnormalities
Hybrid E-Recommendation System for Multi-Shop Environment
Full text linkIn the Kurdistan Regional Government, most computer shops and markets conduct their marketing offline and do not have electronic systems. Nevertheless, customers live in a digital age; they often face challenges in finding products among these markets and shops. The most common question that customers ask is which shop they should purchase from. Therefore, data from five laptop stores and ratings for markets were collected to build an integrated recommender system to help customers find products and select the best store. Our proposed system is a hybrid e-recommendation system that combines machine learning techniques to provide personalized shop and product recommendations. Methods include data collection from multiple laptop shops and dataset preparation. The system uses techniques such as hybrid/blended methods using singular value decomposition and K-nearest neighbors for collaborative filtering (CF) to recommend shops and products based on customer ratings, alongside term frequency-inverse document frequency vectorization and cosine similarity for content-based filtering. The CF’s performance was evaluated using metrics like RMSE = 0.14 and MAE = 0.11, which demonstrated positive results for product and market recommendation. Overall, this study offers solutions through HE-RS to address key challenges such as market fragmentation, cold-start problems, and data scarcity
An Efficient Hybrid Framework U-Net-based deep learning Technique of Early Detection of Pulmonary Nodules using LIDC-IDRI Dataset
Full text linkRadiologists still face difficulties and mistakes while screening lung computed tomography (CT) images for pulmonary nodules, particularly small and inconspicuous malignant lesions. Frequent radiation exposure, the complexity of radiomic characteristics in low-dose CT scans, and the high cost of imaging therapy are some of the challenges. Our technique proposed a novel automated computer-aided diagnostic technique to address these issues by increasing the accuracy of early lung nodule diagnosis. We suggested a four-step technique that involves (1) a preprocessing step consisting of contrast-limited adaptive histogram equalization to refine the contrast of contribution inputs, followed by extracting and combining texture and shape features in parallel using a gray level co-occurrence matrix for the first features and region of interest (ROI) properties for the second. In addition, we suggested a hybrid U-Net-based deep learning architecture for categorization that successfully blends automatically learned features with manually created features. This integration improves the precision and resilience of pulmonary nodule classification by utilizing the convolutional neural networks (CNN)’s capacity to capture spatial hierarchies. We implemented our proposed technique on the overtly accessible Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, employing the Python programming language for the implementation. Experimental results confirmed that our technique attained segmentation and classification results of 95.35% accuracy, 95.33% sensitivity, 94.23% specificity, and 95.44% AUC rates, outperforming several state-of-the-art methods. This high-performance approach offers a reliable solution for early detection, potentially reducing lung cancer mortality rates through timely diagnosis
LectBench-95: Preparing a University-Lecture Corpus for A/B Evaluation of Lecture-Processing Techniques
Full text linkTools for processing educational lectures are rapidly advancing, but there is a need for a diverse, balanced, and high-quality university lecture transcript corpus. The existing datasets are either limited to K-12, tutorial styled, and lack interactivity, focus on narrow disciplines, paywalled/non-accessible, or are impractically large. We introduce LectBench-95, a publicly available corpus of 95 video lecture transcripts spanning 3 disciplines and 17 specific subjects within them. With strict filtering for high audio quality (SNR ≥25 dB), transcription confidence (Mean 0.84, Min 0.7), transcript quality controls, and a power analysis-guided sample size, the 94-h dataset aims to detect ≥20% performance differences between competing systems with 95% confidence for head-to-head A/B experiments. LectBench-95 contains 816 k words (unique ≈ 123 k), a mean Measure of Textual Lexical Diversity of 54.5, and a mean speech rate of 144 words/min, mirroring real-world university lectures. A toy A/B test on zero-shot summarization (Gemini-1.5-Flash vs. 1.5-Flash-8B) shows the corpus’s utility, resulting in a statistically significant 43% win-rate gap with P = 3 × 10−5. Released under CC BY-NC-SA 4.0, LectBench-95 provides a modest yet statistically robust dataset for future educational natural language processing research and prototyping
Adapting F5-TTS Model to Kurdish Sorani: Diffusion-based Speech Synthesis with a Specialized Dataset
Full text linkThe Kurdish language is one of the low-resource languages in the field of speech synthesis. Most of the currently available Kurdish text-to-speech (TTS) systems lack both accuracy and naturalness. To address this issue, this study fine-tunes the F5-TTS model for the Kurdish (Sorani) language, an advanced model that had not previously been fine-tuned for this language. The process began with the creation of a high-quality, well-constructed, single-speaker dataset containing more than 10 h of recorded speech that was collected from news, interviews, and short videos. The dataset was very carefully curated to ensure balanced sample durations, accurate transcriptions, emotional diversity, and a wide range of topics and speaking styles. After ensuring that the training data was sufficient, clean, and prepared, the F5-TTS model was fine-tuned on this data. Both objective and subjective evaluations were conducted to verify the model’s performance. For the objective evaluation, the model achieved a character error rate of 4.3% and a word error rate of 20.37%, indicating a high transcription accuracy in the generated audio. In the subjective evaluation, the mean opinion score reached 4.72, showing that the synthesized speech is very close to the original speaker’s voice. These results demonstrate that diffusion-based models like F5-TTS can be effectively adapted to low-resource languages when supported by a well-designed dataset
A Multi-Account Statistical Evaluation of ChatGPT Proficiency in the Kurdish Sorani Language
Full text linkThis research analyzes the strengths and weaknesses of ChatGPT in responding to questions posed in the Kurdish language, specifically its Sorani dialect, by evaluating its responses to a structured dataset of 50 multiple-choice questions across multiple topics such as language, history, culture, and general knowledge. Using four independent user accounts, each subjected to ten repeated testing cycles, the research assesses accuracy, consistency, and variation influenced by account identity, session timing, and model behavior. This study evaluates the multilingual capabilities of ChatGPT by comparing its performance in Kurdish (Sorani) and Arabic languages. The research establishes a framework to examine how artificial intelligence chatbots, such as ChatGPT, function as applied tools for language understanding and educational use. The analysis demonstrates that ChatGPT achieved an overall average accuracy rate of approximately 70%, indicating satisfactory performance in multilingual contexts. However, significant variations were observed across different user accounts, suggesting that factors such as user profile and temporal dynamics can considerably influence output consistency. The comparative findings highlight the developmental challenges in Arabic and Kurdish language processing, emphasizing the need for further refinement of ChatGPT’s linguistic performance and its effective integration into academic and technological applications. While ChatGPT exhibited proficiency in answering general knowledge questions, it demonstrated a limited understanding of specialized topics in Kurdish, particularly classical literature and historical content. The research presents the strengths and limitations of ChatGPT for under-resourced languages and provides feedback to developers, educators, and researchers. Observing patterns in accuracy, question difficulty, and error behavior, this research also contributes to ongoing efforts toward improving the linguistic and cultural adequacy of AI models for under-resourced languages
An Image Analysis for Designing an Optimal Stirrer in Metal Matrix Composites Manufacturing
Full text linkThe global market for aluminum-based composites, widely used in manufacturing and construction, is expected to grow significantly. However, enhancing the cost-to-performance ratio is essential to improving their commercial viability. Efficient mixing plays a critical role in many industrial and chemical applications. Stir casting is the leading method for producing aluminum alloy matrix composites, but achieving a uniform particle distribution remains a significant challenge. In this study, the optimal stirrer design was identified using image processing techniques to analyze the distribution of ceramic grains. The stirrer that achieved the most uniform grain distribution was selected, eliminating the need for destructive testing. The mechanical properties of the final products validated the accuracy of the image analysis results
A review: Multi-Objective Algorithm for Community Detection in Complex Social Networks
Full text linkRecently, research on multi-objective optimization algorithms for community detection in complex networks has grown considerably. Community detection based on multi-objective algorithms (MOAs) in complex social networks is a fundamental scheduler, and it supports knowing the dynamics of a society, finding influential groups, and improving information dissemination. The traditional methodologies often cannot cope with the features that real-world network usually present, related to optimizing various and sometimes conflicting objectives. This paper provides an overview of some recent works on MOAs for community detection in complex social networks. This paper will explore the balance of the reached objectives, such as modularity, community size, and edge density. Which are analyzed by 15 different approaches in order to choose from works published during the period 2019–2024. These strengths and limitations of various MOAs are reviewed with a comparative analysis to provide insights into both the effectiveness and computational efficiency of these methods. The present trends and future research are discussed that underline the need for the development of solutions to be more adaptive and scalable in coping with the gradually increasing complexity of social networks
Optimization of Lattice-Based Cryptographic Key Generation using Genetic Algorithms for Post-Quantum Security
Full text linkThe progress of quantum computing has posed serious threats to classical cryptographic systems, necessitating much research into developing post-quantum cryptography (PQC). Of the schemes available in PQC, the strongest candidates appear to be lattice-based cryptography (LBC), which encompasses an ample security basis and good computation efficiency. However, practically implementing LBC is faced with key-generation and optimization difficulties, mainly because of its enormous key sizes and computational overhead. The research proposes a novel concept whereby genetic algorithms (GAs) are blended with LBC to increase the merits of key generation while guaranteeing security. Through the evolutionary capacity of GAs, the proposed method optimizes lattice-based keys through selection, crossover, and mutation to ensure high entropy and computationally feasible with experimental results indicating that the GA-based method can cut down memory requirements and computational complexity, making it favorable for resource-constrained environments such as the Internet of Things and embedded systems. The method thus suggested accelerates encryption speed and simultaneously strengthens the security of the optimized key structures. This study emphasizes evolutionary algorithms’ potential to facilitate PQC advancement and provides a scalable and efficient framework for cryptographic systems
Climatic Impacts on Drug Therapy Usage: A Comparative Study of Kurdish Populations in Sulaimani, Iraq, and Stockholm, Sweden
Full text linkThe geographical area is influenced by climate impacts, which, in turn, affect the use of different drug therapies during seasonal weather fluctuations. Thus, this study investigates how geographical climate differences influence drug therapy usage by comparing two Kurdish populations residing in Sulaimani, Iraq, and Stockholm, Sweden. It also highlights significant variations in healthcare practices, demonstrating how environmental conditions shape medication patterns. Data collection was conducted through a structured online survey, covering sociodemographic factors, health behaviors, and medication practices, followed by statistical analysis using Python and SPSS. Geographic Information System (GIS) tools were applied to spatially analyze environmental variables across the two cities, enabling the validation of sampling locations and the statistical determination of optimal limitations for the sample collection dataset. In Stockholm, 73.33% of respondents reported that the cold and humid climate affected their health behavior, whereas in Sulaimani, 50.27% described the climate as moderate but highly variable. The study revealed that the key statistical values such as antibiotic usage were significantly higher in Sulaimani (38.03%) than Stockholm (14.00%, P < 0.001), indicating a more treatment-focused approach in Sulaimani versus a preventive focus in Stockholm. Similarly, painkiller usage was significantly higher in Sulaimani, correlating with climate-related seasonal illnesses. Meanwhile, multivitamin usage in Stockholm reached 44.67%, surpassing Sulaimani’s 37.77%, reflecting a stronger emphasis on preventive healthcare strategies in colder climates. These findings emphasize that climate, more than cultural differences, significantly influences drug therapy patterns. The study determines that healthcare strategies should integrate climate variability, prioritizing preventive care in colder climates and infection control in warmer regions. Finally, the study concludes with key findings and outlines directions for future research, emphasizing the need for further investigation into climate-adaptive healthcare approaches