ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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Durability Assessment of Green Concrete Incorporating Volcanic Tuff Pozzolan, Basalt, and Recycled Aggregate
Full text linkExtending the lifespan of building structures is a critical strategy for mitigating environmental impacts, particularly greenhouse gas emissions from cement production, like emissions from cement. Green concrete, made from pozzolana, basalt, and recycled materials, was tested for durability. Samples were immersed in 2% sulfuric acid for a week, then checked for resistance loss. This study investigates the performance of various concrete mixtures through experiments and simulations where Portland Cement was replacedby natural pozzolana ground into the bonding paste in proportions ranging from 10% to 50% with the use of four types of gravel structures I (natural gravel), II (recycled gravel), III (pozzolanic gravel with basalt sand), and IV (pozzolanic gravel and sand), where 128 cubes were poured with dimensions (10 × 10 × 10) cm to perform simple pressure tests on samples before and after immersion in a solution of sulfuric acid. The results showed that higher cement replacement percentages in mixtures with recycled aggregates resulted in greater durability reduction, with resistance losses exceeding 34% at 50% replacement, primarily due to the rounded aggregate morphology and lower acid resistance. In contrast, mixtures incorporating pozzolanic gravel and basalt sand showed superior performance, achieving only 18.7% resistance loss at 50% replacement (compared to 30.1% at lower replacement rates), highlighting basalt’s effective pore-filling capability. The optimal performance was observed in pozzolanic gravel-sand blends, which exhibited just 13.7% resistance loss, demonstrating enhanced synergistic pozzolanic activity. These findings validate that optimized pozzolana-basalt combinations significantly improve chemical resistance, offering promising solutions for sustainable concrete development
Investigating the Relationship Between Insulin-Like Growth Factor-I and Thyroid Hormones in Thyroid Disorder Patients
Full text linkThe insulin-like growth factor I (IGF-I) system plays a pivotal role in endocrine regulation, metabolism, and cellular growth; however, its interaction with thyroid hormones in the context of thyroid dysfunction remains insufficiently understood. This study aimed to investigate the correlations between IGF-I and thyroid hormones, TSH, T3, and T4, in females diagnosed with thyroid disorders. After removing 15 women due to pregnancy or medication use, 160 women from the Koya district were assessed. Serum levels of IGF-I and thyroid hormones were measured using the COBAS e411 analyzer, and body mass index (BMI) was also recorded. Statistical analysis was conducted using SPSS 25, employing Kruskal-Wallis ANOVA and Spearman correlation for thyroid groups: hyperthyroid, hypothyroid, and euthyroid. The results demonstrated significant variation in IGF-I levels across thyroid conditions. IGF-I was markedly elevated in hyperthyroid individuals and showed a moderate inverse correlation with TSH, along with a positive correlation with T3 and T4. Hypothyroid individuals exhibited significantly lower IGF-I levels, suggesting a regulatory role of IGF-I in thyroid hormone dynamics. Additionally, BMI varied significantly across groups (p < 0.001), with the highest values are observed in hypothyroid participants, supporting the metabolic implications of thyroid dysfunction and its association with IGF-I. The study concludes that IGF-I could serve as a valuable adjunct biomarker in the assessment of thyroid disorders, particularly in ambiguous or subclinical cases. Incorporating IGF-I into routine thyroid evaluation may enhance early detection, risk stratification, and individualized management strategies, contributing to more precise and effective endocrine care
Formalizing Public Transit in Mid-Sized Developing Cities: A Review of Strategies for Sustainable and Resilient Mobility
Full text linkMid-sized cities in developing cities face increasing demand to modernize their public transit (PT) systems to advance sustainability, equity, and resilience. Many of these cities remain dependent on informal transit modes such as minibuses, privately owned taxis, and shared vans which, despite their flexibility, often lead to operational inefficiencies, safety risks, and limited accessibility. This review examines strategies for transitioning to formal public bus transit (BT) systems through analysis of peer-reviewed literature. The analysis is organized around five core domains that directly reflect the structure of this study: assessment of the current state of PT systems, strategies for transitioning from informal to formal networks, selection of appropriate PT modes for mid-sized cities, planning processes for BT systems, and sustainable and resilient approaches for BT development. Based on these findings, this study proposes a structured decision-support framework in the form of a decision tree to guide context-sensitive formalization efforts. Future studies should prioritize long-term impact evaluation, inclusive transition mechanisms for informal operators, and the integration of smart and sustainable technologies
Radon in Commercial Cigarettes and Associated Health Risks: An Evaluation of Lung Cancer Risk and Radiation Dose in the Kurdistan Region of Iraq
Full text linkThis study evaluates radon concentrations and associated health risks in 40 commercially available cigarette brands from the Kurdistan Region. Two measurement techniques are employed: RAD-7 (active method) and CR-39 (passive method). Using the CR-39 detector, radon concentrations ranged from 101.99 ± 10.1 Bq·m⁻3 in the S6 (Cigaronne) sample to 339.98 ± 18.44 Bq·m⁻3 in the S32 (Gauloises Gold) sample, with an average concentration of 190.84 ± 13.67 Bq·m⁻3. The RAD-7 measurements show values between 96.01 ± 9.8 Bq·m⁻3 and 282.72 ± 16.81 Bq·m⁻3 in the same samples, averaging 180.30 ± 13.00 Bq·m⁻3. A strong correlation (R² = 0.9271) is observed between the two methods, confirming the reliability of the results. The effective radium content in all cigarette samples remains below internationally recommended safety limits, with Gauloises Gold showing the highest levels. Estimated annual effective doses ranged from 2.57 ± 1.61 mSv·y−1 to 8.58 mSv·y−1, remaining within acceptable limits established by the International Commission on Radiological Protection. Lung cancer risk due to radon exposure varies among brands, with an average of 208 ± 14.27 cases per million individuals. A significant correlation is found between ²²²Rn concentration and estimated annual lung cancer incidence. These findings highlight radon exposure from cigarette smoke as a contributing risk factor for lung cancer, underscoring the need for public health awareness regarding the radiological hazards of smoking
Optimizing a Compact Ring Coupler with Neural Network Modeling for Enhanced Performance in Radio Frequency Applications
Full text linkThis paper presents the design and optimization of a compact 900 MHz hybrid ring coupler using lumped reactive components, aimed at achieving harmonic suppression and size reduction for Radio Frequency (RF) applications. Traditional hybrid ring couplers rely on quarter-wavelength transmission lines, resulting in large size device and limited harmonic rejection. To address these challenges, a novel coupler structure was developed that replaces long transmission lines with composite branches, significantly reducing device dimensions while enhancing performance. In the proposed coupler, instead of the six conventional 90-degree lines, six compact networks composed of microstrip lines, three inductors, and one capacitor are used. The inductors have values of L1, L1, L2, and the capacitor has a value of C. These four parameters significantly influence the coupler’s performance; thus, they were selected as inputs for the applied neural network, with the scattering parameters S11, S12, S13, S14, and frequency considered as the five output parameters. The dielectric constant (Ɛᵣ) of the substrate is 2.2, and the substrate material is RT/duroid 5880 with a thickness of 20 mils. By feeding the neural network model with these parameters as inputs, the coupler’s output response was predicted and analyzed, enabling the selection of optimal component values. Optimal responses were obtained with L1 = 10.1nH, L2 = 2.3nH and C = 2.1pF, which allows the coupler to operate effectively at 900 MHz. At this operating frequency, the values are S11 = −32.6dB, S12 = −3.05dB, S13 = −3.03dB, and S14 = −45.9dB, indicating excellent coupler performance
A Systematic Survey on Large Language Models for Code Generation
Full text linkThe rapid development of large language models (LLMs) has transformed code generation, offering powerful tools for automating software development tasks. However, evaluating generated code’s quality, security, and effectiveness remains a significant challenge. The present systematic survey comprehensively analyses studies published between 2021 and 2024, focusing on utilizing LLMs in the code generation process. The survey explored ten research questions, such as the most commonly used programming languages, the metrics employed to evaluate the quality of code, and scenarios in which LLMs are applied by developers during the software development process, outlining the scope in which prompt engineering influences code generation and security concerns with the types of benchmarks, models evaluated, and code analysis tools used in studies. The findings indicate that the most frequently used evaluation metrics in code generation are Pass@k and Bilingual Evaluation Understudy. It also shows that Python, Java, and C++ are the most widely used languages. Furthermore, identifying security vulnerabilities and establishing robust evaluation metrics remain challenges. This survey underlines present practices, detects gaps, and suggests future research to enhance the reliability and security of code generated by LLMs in real-world applications
A Hybrid Deep Learning Model with Self-Attention for the Classification of Lung Cancer Using Histopathology Image
Full text linkLung cancer remains a prevalent health burden and is one of the leading causes of cancer mortality worldwide. Its high mortality rate is partly attributable to histological heterogeneity and the difficulty of detecting it at early stages. An accurate distinction of lung cancer subtypes in histopathological images is crucial for improving the accuracy of diagnosis and planning an appropriate treatment to improve the quality of life of patients. This study proposes a hybrid deep-learning model for classifying cancer types using histopathology images. The ConvNeXt-Tiny is an extension of the ResNet-50 base architecture. This architecture is inspired by both models and introduces self-attention layers to improve both feature extraction and classification performance, leading to a unique model design. The proposed model and two other deep learning models were trained and tested using the public Lung and Colon Cancer Histopathological Image (LC25000) dataset and a private clinical dataset, and their effectiveness was evaluated. The proposed model outperformed the best classification accuracy among the other architectures (98.73% public and 93.17% private), outperforming baseline models, such as ConvNeXt-Tiny (96.27% public and 89.33% private) and ResNet-50 (94.00% public and 87.67% private). The results confirm the robustness and generalization ability of the proposed architecture
Broadband Metamaterial Absorbers for Organic Solar Cells: A Review
Full text linkOrganic solar cells (OSCs) often suffer from weak absorption in the visible and infrared spectrum, which directly restricts their efficiency. Since light harvesting is central to solar conversion, improving absorption across a broad range is critical. Broadband metamaterial absorbers (BMMAs) present a promising solution by enhancing light-matter interaction and extending absorption over a broader spectrum. This improvement directly makes the process of converting energy more efficient. This review aims to systematically examine the recent progress of metamaterial absorbers (MMAs), highlighting broadband, polarization independent, and wide-angle designs areas that remain unexplored in recent reviews. Different categories of strategies, such as planar, vertical, lumped-element, and nanostructured plasmonic designs, are discussed to highlight how material choice and design geometry affect absorption. In addition, it describes the physical concepts of perfect absorption and assesses how applicable they are to OSC integration. Our analysis shows that the most of the progress has been theoretical approaches with a limited experiment. These studies demonstrate that BMMAs have an excellent opportunity to significantly improve energy conversion efficiency. At the same time, most challenges remain, like scalability, material losses, and easy integration into OGCs. This research also points out that there are future investigations into affordable, low-loss materials that can be easily integrated. Overall, this study emphasizes how important MMBAs are to advancing the efficiency and sustainability of next generation OSCs
Modification of the Optical Properties of Polyvinyl Alcohol through Incorporating Cu2O Nanoparticles Prepared by Laser Ablation
Full text linkA focused, high‐intensity pulsed laser ablated 99.9% - pure copper targets submerged in deionized water and in Polyvinyl alcohol (PVA) solution, respectively, was utilized to produce Cu2O nanoparticles (NPs). Nano-plasmonic cuprous oxide was incorporated using the nanosecond Nd: YAG pulsed laser ablation in liquids technique, which advances the physiochemical characteristics of Cu2O/PVA nanocomposite. Optical characterization was carried out for the induced Cu2O NPs and Cu2O/PVA nanocomposites with six different mass concentrations of Cu2O. The concentrations of the Cu2O NPs were 0.007, 0.017, 0.027, 0.04, 0.047, and 0.057 mg/mL in the PVA matrix. X-ray diffraction confirms that copper ions were reduced to form crystalline Cu2O NPs. Furthermore, DLS showed the presence of NP agglomeration, which revealed polydispersity of Cu2O NPs. The band gap of pristine PVA, determined from Tauc plots, was 5.00 eV. The optical band gap decreased progressively with increasing mass concentration of Cu2O NPs. This band-gap reduction is attributed to changes in the PVA electronic structure caused by incorporated Cu2O NPs. A distinctive feature of this work is the use of pulsed-laser ablation in liquid to generate plasmonic Cu2O NPs and incorporate them in situ into PVA at room temperature in a single step; NP concentration was precisely controlled by the number of laser pulses
A Comprehensive Review of Facial Beauty Prediction Using Multi-task Learning and Facial Attributes
Full text linkBeauty multi-task prediction from facial attributes is a multidisciplinary challenge at the intersection of computer vision, machine learning, and psychology. Despite the centrality of beauty in human perception, its subjective nature—shaped by individual, social, and cultural influences—complicates its computational modeling. This review addresses the pressing need to develop robust and fair predictive models for facial beauty assessments by leveraging deep learning techniques. Using facial attributes such as symmetry, skin complexion, and hairstyle, we explore how these features influence perceptions of attractiveness. The study adopts advanced computational methodologies, including convolutional neural networks and multi-task learning frameworks, to capture nuanced facial cues. A comprehensive analysis of publicly available datasets reveals critical gaps in diversity, biases, and ground truth annotation for training effective models. We further examine the methodological challenges in defining and measuring beauty, such as data imbalances and algorithmic fairness. By synthesizing insights from psychology and machine learning, this work highlights the potential of interdisciplinary approaches to enhance the reliability and inclusivity of automated beauty prediction systems