ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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373 research outputs found
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Bridging the Gap: Enhancing Kurdish News Classification with RFA-CNN Hybrid Model
Full text linkEffective organization and retrieval of news content are heavily reliant on accurate news classification. While the mountainous research has been conducted in resourceful languages like English and Chinese, the researches on under-resourced languages like the Kurdish language are severely lacking. To address this challenge, we introduce a hybrid approach called RFO-CNN in this paper. The proposed method combines an improved version of red fox optimization algorithm (RFO) and convolutional neural network (CNN) for finetuning CNN’s parameters. Our model’s efficacy was tested on two widely used Kurdish news datasets, KNDH and KDC-4007, both of which contain news articles classified into various categories. We compared the performance of RFO-CNN to other cutting-edge deep learning models such as bidirectional long short-term memory networks and bidirectional encoder representations from transformers (BERT) transformers, as well as classical machine learning approaches such as multinomial naive bayes, support vector machine, and K-nearest neighbors. We trained and tested our datasets using four different scenarios: 60:40, 70:30, 80:20, and 90:10. Our experimental results demonstrate the superiority of the RFO-CNN model across all scenarios, outperforming the benchmark BERT model and other machine learning models in terms of accuracy and F1-score
Micropollutant Control in Wastewater Treatment: A Review of Harnessing Nitrification and Denitrification Biotransformation of Micropollutant
Full text linkMicropollutants, an array of organic compounds such as pharmaceuticals, personal care products, and agrochemicals, are pervasive in contemporary ecosystems, posing significant threats to environmental health even in trace concentrations. Therefore, exploring an efficient and effective technique to remediate these pollutants is essential. Nitrification–denitrification (ND) have emerged as one of the most sustainable treatment methods that effectively mitigate micropollutants while facilitating their biotransformation. This review provides a comprehensive analysis of the intricate interactions fundamentally and mechanically between the ND process and the influencing factors, such as dissolved oxygen (DO) concentration and pH optimization, which are vital to the success of micropollutant biotransformation. Insights gained from this examination contribute to a deeper understanding of microbial strategies, which offer potential avenues for sustainable environmental management and the protection of ecosystem integrity
Artificial Intelligence Integration in Academic Writing: Insights from the University of Duhok
Full text linkThis study investigates the use of artificial intelligence (AI) technologies among academics at the University of Duhok (UoD), focusing on their perspectives, preferences, and intentions toward integrating AI within academic and research environments. A survey was conducted through Google Forms, targeting postgraduate students, recent alumni (since 2020), and faculty members of UoD in the Kurdistan region of Iraq. A total of 674 participants, aged 22–70 years, responded. The findings indicate that only 36.94% had employed AI technologies. Among AI users (n = 249), primary sources of information were friends or colleagues (46.59%) and social media (35.74%). Younger individuals and those holding master’s degrees exhibited a stronger tendency toward AI usage (p < 0.0001), whereas gender and academic discipline had minimal influence. ChatGPT was the most widely used tool (70.68%), followed by Quill Bot (42.17%), Grammarly (34.94%), and Google Bard (29.32%). The main AI applications were text paraphrasing (33.73%) and information retrieval (15.26%). Notably, 47.58% of respondents recommended AI for various academic tasks, including scientific research and idea generation. In conclusion, the study shows that only one-third of UoD faculty members utilize AI, predominantly for text paraphrasing. Nearly half of the participants suggested the adoption of AI by postgraduate students and academic staff
Bromination of Chalcone: A Study on Synthesis, Characterization, and Optoelectronic Properties
Full text linkIn this research work, a new compound, namely 2,6-dibromo-2,6-bis(bromo(phenyl)methyl)cyclohexanone (1), is synthesized and characterized for possible applications in organic electronic devices. The formation of the compound was confirmed by Fourier-transform infrared spectroscopy, 1H-, and 13C-NMR spectroscopy measurements. Furthermore, the spectroscopic and optoelectronic properties of the chemical compound were theoretically investigated using density-functional theory (DFT). Herein, the B3LYP/cc-pVDZ level was used to discover the compound electrostatic potentials and frontier molecular orbitals. The theoretical investigations predicted by DFT were compared with the experimentally obtained results from the ultraviolet visible spectra of the compound after being dissolved in various solvents. Results showed that the experimental band-gap energy of the compound is 3.17 eV, whereas its theoretical value was calculated to be 3.33 eV. The outcome of the achieved results suggests the viability of 2,6-dibromo-2,6-bis(bromo(phenyl)methyl)cyclohexanone for possible applications in organic electronic device
Distributed Software-Defined Networking Management: An Overview and Open Challenges
Full text linkDistributed software-defined networking (SDN) architecture satisfies the minimum requirements for WANs. The distributed controllers are connected in various topologies, including hierarchical and flat, which include logically centralized, physically distributed, and fully distributed controllers. The distributed SDN architectures are qualitatively explored as a more suitable solution for managing fluctuating networks in large-scale deployments, with the goal of optimizing overall network performance, particularly for applications that can tolerate some level of inconsistency, such as load balancing or routing. The logically centralized, physically distributed SDN controller architecture allows SDN controllers, in conjunction with the deployed SDN applications, to centrally coordinate the network due to the conciliated global network view. That is created through the synchronization process between controllers. However, inter-controller synchronization creates an overhead that affects the system’s performance. Additionally, the amount of inter-controller synchronization is vulnerable to the chosen consistency approach the application can tolerate. Although static eventual consistency is frequently employed in modern SDN systems to provide effective scalability, it is argued that it doesn’t place limits on the state inconsistencies that SDN applications will tolerate. Hence, the adaptive consistency models need to be investigated. The study showed that a flat, logically centralized, physically distributed architecture with an adaptive consistency approach would be more suitable for solving large-scale fluctuating network management considering scalability, reliability, and maximizing performance
Sonication Enhancement of Capsaicin Formation in Callus of Chili Pepper, Capsicum annuum L.
Full text linkThe current study investigates the induction of callus from leaf explants of chili pepper Capsicum annuum L. coupled with the isolation of capsaicin from alcoholic extracts. To determine which isolated alkaloid has a positive reaction, the DragenDroff test is used. Alkaloid is identified using conventional diagnostic techniques, such as measuring the absorbance values of the isolated alkaloid with an ultraviolet spectrophotometer, the alkaloid is identified. The results show a complete identity among them, and with control. Thin layer chromatography data showed a 0.8 cm distance between one location from each tested sample with the same rate, which is 0.8 cm from the control’s rate flow value. The chemical structure of studied samples is subsequently determined using nuclear magnetic resonance, which reveals similarities between the isolated alkaloid’s structure and standard capsaicin. A quantitative analysis of the isolated alkaloids revealed variations in the amounts for generated explants relative to other explants. This study shows that fruits are the most effective source of alkaloids. It’s interesting to note that the composition of the explant and the sonicated callus are identical. Since capsaicin discovery, it is used as a homeopathic remedy to treat burning pain using the concept of “treating like with like” or counterirritant, relieve minor pain associated with rheumatoid arthritis or muscle sprains and strains and due to large consumption of this fruit recently, the current study done to find out the structure and quantity
AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors
Full text linkMetering fluids is critical in various industries, and researchers have extensively explored factors affecting measurement accuracy. As a result, numerous sensors and methods are developed to precisely measure volume fractions in multi-phase fluids. A significant challenge in multi-phase fluid pipelines is the formation of scale within the pipes. This issue is particularly problematic in the petroleum industry, leading to narrowed internal diameters, corrosion, increased energy consumption, reduced equipment lifespan, and, most crucially, compromised flow measurement accuracy. This paper proposes a non-destructive metering system incorporating an artificial neural network with capacitive and photon attenuation sensors to address this challenge. The system simulates scale thicknesses from 0 mm to 10 mm using COMSOL multiphysics software and calculates counted rays through Beer Lambert equations. The simulation considers a 10% interval of volume variation in each phase, generating 726 data points. The proposed network, with two inputs—measured capacity and counted rays-and three outputs—volume fractions of gas, water, and oil—achieves mean absolute errors of 0.318, 1.531, and 1.614, respectively. These results demonstrate the system’s ability to accurately gauge volume proportions of a three-phase gas-water-oil fluid, regardless of pipeline scale thickness
Toward Optimizing Coarse Aggregate Types and Sizes in High-strength Concrete
Full text linkThe development of very effective coarse aggregate types and sizes can lead to a rapid increase in the production of high strengthconcrete (HSC). This research investigates the effects of five different coarse aggregate types and a range of maximum coarse aggregate sizes on the mechanical properties of concrete through experimental tests and numerical analysis. The workability of fresh concrete is examined using the slump cone test, whereas the mechanical performance of hardened concrete is assessed through compressive strength and splitting tensile strength tests. The experimental results are compared to the predicted results from the codes and design guidelines to assess their predictions. Both coarse aggregate types and sizes show a significant influence on the mechanical properties of HSC performance, especially the compressive strength of HSC, which could be increased on average by 25%. Moreover, the predictions of splitting tensile strength using the ACI 318 and ACI 363 equations are not very accurate, particularly at a high strength range. Therefore, this study develops a new equation for predicting splitting tensile strength based on both experimental test results conducted in this research and a significant amount of data collected from the literature. Evaluation metrics, including R2, RMSE, MAPE, and MAE, demonstrate the superior accuracy of the proposed equation compared to the design guidelines equations. The findings of this research can contribute toward the optimization of aggregate type and size in concrete mix design for enhanced performance and provide valuable insights into the relationship between compressive and splitting tensile strengths in HSC
An Ensemble Model for Detection of Adverse Drug Reactions
Full text linkThe detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant under reporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model that combines decision trees, support vector machines, random forests, and adaptive boosting (ADA-boost) to improve ADR detection. The experimental evaluation applied the benchmark data set and many preprocessing techniques such as tokenization, stop-word removal, stemming, and utilization of Point-wise Mutual Information. In addition, two term representations, namely, term frequency-inverse document frequency and term frequency, are utilized. The proposed ensemble model achieves an F-measure of 89% on the dataset. The proposed ensemble model shows its ability in detecting ADR to be a favored option in achieving both accuracy and clarity
Synthesis, Characterization, and Bioactivity Studies of the Schiff Base Ligand and its Zinc(II) Complex
Full text linkOne of the largest concerns to global health in recent decades has been identified as the growth of bacteria resistance to antibiotics. The Schiff base (SBs) and the zinc(II) SBs complex compounds category have attracted a lot of interest because of their function in chemical syntheses and their potential for bioactive and pharmacological effects. The present study includes the synthesis of various SBs with different substituents. Equimolar mixtures of benzaldehyde derivatives (1, 2) and aniline derivatives (3, 4) are used to carry out a series of condensation reactions to get compounds (5-7). By stoichiometrically combining Zn (II) acetate and ZnCl2 separately with the SBs ligand (7) in ethanol, it has been possible to prepare the SBs zinc(II) complex (8). The structure of the ligand and its metal complex are analyzed using (Fourier transform infrared spectroscopy, 1H-NMR, 13C-NMR) spectroscopy, scanning electron microscopy, and liquid chromatography–mass spectrometry. Moreover, the synthesized compounds are verified in vitro against Escherichia coli Gram negative, Staphylococcus aureus Gram positive, and fungi (Candida albicans). Compounds (5, 7, and 8) indicated significant growth inhibition against E. coli Gram negative and fungi (C. albicans) with different inhibition zones starting from 7 to 17.5 mm