ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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373 research outputs found
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Cryptocurrency Time Series Forecasting Based on Ensemble and Deep Learning Algorithms: A Comprehensive Review
Full text linkBlockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers
Dolomitization and Hypogenic Dissolution of the Eocene Avanah Formation, Iraqi Kurdistan
Full text linkThis study constrains the mechanism of extensive dolomitization and its impact on reservoir quality of the shallowwater marine ramp carbonates of the Avanah Formation (Eocene), Iraqi Kurdistan. The presence of shoal deposits, which semiisolate a lagoon water body from the open marine, suggests that dolomitization was by seepage reflux of brines. Nevertheless, the absence of eogenetic gypsum/anhydrite in the dolostones succession indicates that the dolomitizing fluids were mesohaline/penesaline brines formed during cycles of relative sea level (RSL) fall. Dolomitization resulted in the formation of abundant intercrystalline and moldic/vuggy pores. Restriction of dolomitization and related reservoir quality improvement to the lower part of the formation is attributed to an overall 3rd order fall in the RSL. Conversely, the lack of dolomitization in the upper part of the formation is attributed to deposition during 3rd order marine transgression, which prevented severe restriction and evaporation of the inner ramp and, consequently, inhibited the development of dolomitizing brines. It is suggested that hypogenic dissolution (karstification) by upward flow of aggressive fluids along faults and fractures during the Zagros Orogeny caused dissolution and considerable porosity and permeability improvement of the dolostones. A greater extent of dolostones dissolution in the flanks, which was accompanied by calcite cementation, compared to the crest, reflects the role of oil emplacement in the retardation of diagenetic reactions
Wild Cherry (Prunus microcarpa) Ameliorates Azoxymethane-Induced Aberrant Crypt Foci in vivo: Depicted Molecular Mechanisms
Full text linkColorectal cancer is the third diagnosed cancer across the globe despite modern therapeutic interventions. Prunus microcarpa has been consumed as a therapeutic tea for several human disorders; therefore, the study investigates the chemoprotective effects of Prunus microcarpa against azoxymethane-induced aberrant crypt foci (ACF) in rats. Fifty Sprague-Dawley rats were divided into five groups: a normal control group and untreated rats given saline; a reference group treated with 5-fluorouracil; and two groups treated with 500 mg/kg methanolic extracts of fruits and stems (MEPMF and MEPMS), separately, for two months. Additionally, all rats, except the normal controls, were injected with azoxymethane twice a week for two consecutive weeks and consumed sodium dextran sulfate-mixed water for seven days. The plant extracts exhibited significant resistance against AOM-induced colon carcinogenesis, as indicated by lower ACF formation, reduced glandular dysplasia, decreased hyperchromasia, and a higher organization of simple columnar epithelial cells compared to vehicle rats. Immunohistochemical results demonstrated increased modulatory effects of MEPMF on apoptosis mediators, evidenced by higher Bax and lower PCNA levels in colonic tissues compared to MEPMS. Prunus supplementation led to decreased oxidative stress and cellular infiltrations in colon tissues, as evidenced by increased endogenous antioxidants (SOD, CAT, and GPx) and reduced inflammatory mediators (TNF-α and IL-6) and lower levels of peroxidation byproducts (MDA), while preserving organ functions such as those of the liver and kidneys. This study presents safety margin and chemoprotective effects of P. microcarpa against AOM-induced colon cytotoxicity, evidencing a viable source for nutraceutical and biopharmaceutical formulation.
Influence of Microstructure and Droplet Volume on Atmospheric Pitting Corrosion of 304L Austenitic Stainless Steel
Full text linkThis research investigates the atmospheric pitting corrosion behavior of 304L austenitic stainless steel subjected to MgCl2 droplets, emphasizing the effects of microstructure and droplet volume. X-ray diffraction and scanning electron microscopy (SEM) show that both austenite and ferrite are present, and it is observed that the ferrite bands dissolved more in the direction the steel is rolled. SEM-energy-dispersive X-ray spectroscopy analysis identified mixed oxides and MnS inclusions. The shape of the pits changed depending on the direction of the plate: Layered pits mostly occurred on the longitudinal–transverse side, while striped pits are seen on the longitudinal–short transverse and short transverse sides, indicating variations in the material’s structure. An increase in droplet volume from 0.5 µL to 2.5 µL led to a linear rise in total pit area and a measurable increase in pit depth. These findings show that the direction of the microstructure and the size of the droplets significantly affect how likely pitting is to occur, which is important for designing and using stainless steels in environments with a lot of chloride
Gene Polymorphism of Antigen B Subunit 2 and Pathogenesis of Cystic Echinococcosis in Murine Model
Full text linkA complex genetic diversity among the causative agent, Echinococcus granulosus, is documented. Antigen B (AgB) is a major antigenic fraction of hydatid fluid and hydatid cyst tissues. This study aims to investigate the role of antigen B subunit 2 (AgB2) gene polymorphism in the pathogenesis of cystic echinococcosis (CE) in murine model. Ovine liver hydatid cysts are obtained from Erbil Slaughterhouse. Protoscoleces from each isolate are separated into two batches. First preserved at −20°C for molecular analysis whereas the second is used for experimental infection in mice. Parasite DNA was extracted, and AgB2 genome was amplified and sequenced. The sequencing profile of six of the isolates (1, 2, 3, 5, 8, and 11) revealed a 100% analogy with AgB2 gene of E. granulosus genotype G2. Minor sequence polymorphisms, 1.67%, are observed in one of the isolates, whereas remarkable DNA sequence polymorphisms are noticed in three of the isolates. The polymerase chain reaction (PCR) products sequencing profiles revealed 100% polymorphisms in four of the isolates in comparison with the source gene (AY569356.1), instead, those isolates reveal various degrees of analogy, 80.33%, 80.87–89.05%, and 89.36% to G1, G3, and G6, respectively. Polymorphic sequencing profile of the PCR-amplified product (250 bp) of E. granulosus clone EgB2G2v13 AgB2 gene (Accession no.: AY569356.1) has no significant impact on the pathogenicity of the CE in murine model. To upgrade the diagnostic sensitivity rates of the immunological techniques, a mixture of native hydatid antigens containing AgB is recommended to be used in the ser-diagnosis of this infection
The Improved Kurdish Dialect Classification Using Data Augmentation and ANOVA-Based Feature Selection
No full textAnalyzing dialects in the Kurdish language proves to be tough because of the tiny phonetic distinctions among the dialects. We applied advanced methods to enhance the precision of Kurdish dialect classification in this research. We examined the dataset’s stability and variation through the use of time-stretching and noise-augmenting methods. Analysis of variance (ANOVA) filter approach is applied to improve feature selection (FS) more efficiently and highlight the most relevant features for dialect classification. The ANOVA filter method ranks features based on the means from different dialect groups, which made FS better. To make dialect classification work better, a 1D convolutional neural network model was given a dataset that had ANOVA FS added to it. The model showed a very strong performance, reaching a remarkable accuracy of 99.42%. This noteworthy increase in accuracy beat former research with an accuracy of 95.5%. The findings demonstrate how combining time stretch and FS methods can improve the accuracy of Kurdish dialect classification. This project improves our understanding and implementation of machine learning in the field of linguistic diversity and dialectology
Dual-Band Power Divider with Wide Suppression Band: Artificial Intelligence Modeling for Performance Confirmation
Full text linkIn this paper, a planar dual-band Wilkinson power divider (DWPD) with a triangular-shaped resonator is designed. This work stands out from existing designs by addressing key limitations in conventional power dividers, i.e., physical size, harmonic suppression, and insertion loss. The proposed triangular shaped resonator has a compact size of 9.9 mm × 3.4 mm (0.26 λg × 0.09 λg ), where λg is electrical wavelength at 5.9 GHz, and provides a wide suppression band from 7.1 GHz to 20.6 GHz with a 20 dB attenuation level. In the proposed DWPD structure, two triangular shaped resonators are used in two branches. It works at 3.6 GHz and 5.5 GHz with <0.1 dB insertion loss at both operating bands. The input and output return losses and ports isolation parameters at both bands are better than 20 dB, which show good performance of the divider at operating bands. Besides the acceptable performance, the proposed DWPD provides a wide suppression band from 6.8 GHz to 20.5 GHz with more than 20dB attenuation level. In the divider design, the neural network is employed to model a triangular-shaped resonator. The proposed neural network has two outputs (S11 and S21), and two hidden layers with eight neurons at each layer. The weights of each neuron are obtained using particle swarm optimization algorithms. The proposed neural network model has accurate results, and the mean relative error of the train and test data for both outputs is <0.1 , which validates the accurate results of the proposed model
Levofloxacin Determination in Pharmaceutical Tablets by Sensitive Spectrofluorometric Method with L-Tryptophan as a Fluorescent Probe
Full text linkProper dosage, therapeutic effectiveness, patient safety, and quality control throughout manufacture and storage can only be achieved by closely monitoring the concentration of pharmaceutical products. Aprecise and reliable spectrofluorometric approach for quantitative analysis and detection of levofloxacin (LEVO) in various pharmaceutical products was developed in this work using the fluorescent reagent L-tryptophan. When L-tryptophan, which has its inherent fluorescence signal quenched by LEVO, is mixed with Britton-Robinson buffer solution (pH 9.0), a stable ion-associated complex forms. The fluorescence intensity of L-tryptophan decreased at 365 nm after excitation at 281 nm. The method showed linearity for LEVO concentrations from 0.3 to 18.0 μg/mL, with a minimum detectable value of 0.10 μg/mL. An effective linear relationship (R2 = 0.9985) between the concentration and fluorescence intensity (ΔF) was obtained. This technique has been well-proven to be minimally affected by impurities commonly found in pharmaceutical formulations. The results were validated through comparative analyses with high-performance liquid chromatography. The study revealed that both equivalence levels and analytical quality (as measured by precision and accuracy) are very satisfactory. This study addresses the increasing demand for established and reliable methods in the quality control of pharmaceutical products
The Role of Immune Defense in Serratia marcescens Nosocomial Infections
Full text linkDeveloping resistance mechanisms leads to various nosocomial infections caused by opportunistic bacteria. Serratia marcescens are well known to be opportunistic and are equipped with an armory of virulence factors against host immune response. The study aims to detect the immune defense in patients infected with multidrug-resistant S. marcescens. The study includes 132 clinical samples, including burn, wound, otitis media, and urinary tract infection (UTI) at several hospitals in Baghdad, Iraq. All isolates are identified by cultivation on MacConkey agar, nutrient agar, and blood agar, followed by biochemical tests and assessment with the VITEK 2 compact system. The isolates are tested for antibiotic susceptibility tests, interleukin-12 (IL12) levels, neutrophil ability to phagocytosis, and complement C3 and C4 levels. Out of 120 positive cultures, six isolates are identified as S. marcescens. The urine samples are the most isolated source and a higher level of antibiotic resistance was noticed in ampicillin and cefotaxime (100%), whereas a lower level is in imipenem. Stimulation (p ꞊ 0.005) provided a significant increase in IL-12 production. The infection with the S. marcescens stimulated the neutrophil’s phagocytosis process compared with the control. The interplay role of virulence factors in S. marcescens influences its pathogenesis, antibiotic resistance, and immune response, particularly involving neutrophils and IL-12. Understanding these interactions is crucial for developing effective therapeutic strategies
Enhancing Cancer Diagnosis: A Hybrid Level-Set and Edge Detection Approach for Accurate Medical Image Segmentation
Full text linkEarly diagnosis of cancer is crucial for improved patient results. With the aim of improving the effectiveness of cancer diagnosis, this paper introduces a new proposed method, computer-aided diagnosis, utilizing the level-set algorithm based on the edge detection approach for medical image segmentation. To assess the performance of our method, it was proven on a highly varied dataset that comprised liver cancer, Magnetic Resonance Imaging (MRI) brain cancer, and dermoscopy color images. By effectively integrating edge information into the level-set evolution process, the proposed method achieved impressive results. For liver cancer images, we obtained an accuracy of 0.9913, a sensitivity of 0.9165, and a Dice coefficient of 0.8820. Similarly, for dermoscopy color images, the method achieved an accuracy of 0.9979, a sensitivity of 0.9301, and a Dice coefficient of 0.9301. In the case of MRI images, the method demonstrated an accuracy of 0.9933, a sensitivity of 0.8591, and a Dice coefficient of 0.8591. The proposed method outperforms traditional techniques such as Simulated Annealing combined with Artificial Neural Network and Fuzzy Entropy with Level Set thresholding. This method demonstrates superior segmentation accuracy and robustness. By enabling precise identification of cancerous regions, this approach supports early diagnosis, reduces misdiagnosis, and enhances treatment planning, offering significant potential for improving cancer care and patient results