ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY

Structural Characterization of Salts Using X-ray Fluorescence Technique

Sat, 06 Jan 2024 04:57:08 +0000

This study investigates the structure of 21 table salts that were collected from different local markets in the Kurdistan region of Iraq. The major trace elements and iodine concentrations in tablesalt are analyzed through the X-ray fluorescence (XRF) technique and the titration method, respectively. The study shows that using XRF spectral analysis, the collected table salt samples are rich in chlorine, sodium, and contain a lower percentage of bromine, strontium, tin, tellurium, and iodine. Moreover, these samples have a high percentage of sulfur and sirconium, where the molybdenum is >0.2%. Other elements such as zinc and copper are essential and found in low concentrations <0.0086% and 0.001%. Iodine is a trace element that is necessary nutrients for human life, and it is naturally present in some foods. Iodine deficiency is brought on by a lack of iodine consumption. Iodized salt is highly recommended as a source of iodine to prevent iodine deficiency disease. Iodine is added to table salt in two different ways, either through iodate or through iodine. The results show that only 25% of the salt samples have an adequate level of iodine, while the other samples have low or no iodine content. According to the World Health Organization, quality of salt depends on iodine concentration and other trace elements, which are necessary for human health.

Synthesis of Some novel Azomethine Oxide Derived from Aromatic Oximes and their Anti-microbial Studies

Jihad H. Mohammed, Akram N. M. Qaddo, Nabaz A. Muhammad Salih, Faeza B. Omar — Thu, 15 Feb 2024 00:00:00 +0000

The organic compound category known as azomethine oxides has garnered significant attention due to its potential for bioactive and pharmacological effects, as well as its role in organic syntheses. Existing literature offers various methods for producing derivatives of these compounds. This study, in particular, concentrates on the creation of several aromatic oximes through the reduction of corresponding aldehydes. Furthermore, these oximes are employed in the generation of new azomethine oxides through a condensation reaction with selected aldehydes. The molecular structure of the synthesized azomethine oxides is determined using techniques such as Fourier-transform infrared spectroscopy, ¹H-NMR, and ¹³C-NMR. Finally, the antimicrobial effectiveness of these compounds was assessed against Escherichia coli (gram-negative bacteria), Staphylococcus aureus (gram-positive bacteria), and Candida albicans (fungus). The synthesized compounds were obtained with high purity and yielded excellent results. Furthermore, the data demonstrates that these azomethine oxides exhibit significant antimicrobial activity when compared to standard drugs.

An Innovative Embedded Processor-Based Signal Phase Shifter Algorithm

Aven R. Hamza, Mohammed A. Hussein — Thu, 15 Feb 2024 00:00:00 +0000

Digital filtration is widely used today in many application fields, and with the increased use of low-cost embedded processors, it can be applied to vast areas. A drawback of digital filtration algorithms is the introduction of phase angle shifts in the filtered signals, thereby creating undesirable characteristics in many application fields. In this work, low-pass filters of finite impulse response and infinite impulse response types are designed with an innovative buffering scheme to delay a digitally low-passed signal by an angle ranging from 0° to 180° for real-time signals. The application of the filtration and buffering scheme on a cost-effective embedded processor with limited signal processing capabilities opens the horizons for its applicability in many signal processing fields. In assessing its practicality, the generated filtered output signal is correlated with the original signal (a low-passed version), revealing correlation values reaching 0.99 in certain instances. The novelty of the proposed approach enables its application to a broad-spectrum area of digital signal filtration.

Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques

Esraa Q. Naamha, Matheel E. Abdulmunim — Fri, 16 Feb 2024 06:18:11 +0000

Thanks to the rapid expansion of the Internet, anyone can now access a vast array of information online. However, as the volume of web content continues to grow exponentially, search engines face challenges in delivering relevant results. Early search engines primarily relied on the words or phrases found within web pages to index and rank them. While this approach had its merits, it often resulted in irrelevant or inaccurate results. To address this issue, more advanced search engines began incorporating the hyperlink structures of web pages to help determine their relevance. While this method improved retrieval accuracy to some extent, it still had limitations, as it did not consider the actual content of web pages. The objective of the work is to enhance Web Information Retrieval methods by leveraging three key components: text content analysis, link analysis, and log file analysis. By integrating insights from these multiple data sources, the goal is to achieve a more accurate and effective ranking of relevant web pages in the retrieved document set, ultimately enhancing the user experience and delivering more precise search results the proposed system was tested with both multi-word and single-word queries, and the results were evaluated using metrics such as relative recall, precision, and F-measure. When compared to Google’s PageRank algorithm, the proposed system demonstrated superior performance, achieving an 81% mean average precision, 56% average relative recall, and a 66% F-measure.

An Ensemble Model for Detection of Adverse Drug Reactions

Tue, 20 Feb 2024 12:51:34 +0000

The 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.

Bromination of Chalcone

Kosrat N. Kaka, Rebaz A. Omer, Dyari M. Mamand, Aryan F. Qader — Wed, 28 Feb 2024 18:27:18 +0000

In 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 devices

Permeability Prediction for Carbonate Rocks using a Modified Flow Zone Indicator Method

Ahmed J. Mahmood, Mohammed A. Jubair — Wed, 28 Feb 2024 19:04:37 +0000

Carbonate reservoir rocks are usually heterogeneous, so it is not an easy task to establish a relation between porosity and permeability in these types of reservoir rocks. First, Kozney and Kozney-carmen formulas were used to establish these relations. Later, the flow zone indicator (FZI) method was introduced, which was widely used to find such a relation since it shows better results than the two former methods. In this work, the classical FZI method and a modified form of the FZI method are utilized to identify the hydraulic flow units and rock quality index to predict permeability. In this FZI method, the cementation factor (m) was introduced in calculating the value of FZI. The data collected from core analysis of the cored intervals in the Tanuma and Khasib formations were used as a database for this work. The classical and the modified FZI methods were applied using the database to predict core permeability. The value of the cementation factor was tuned to get a better match between the predicted permeability resulting from applying the modified method and the measured permeability values. Results show that the correlation coefficients resulting from applying the modified FZI method are closer to unity compared with that resulting from the classical FZI method. Cementation factor (m) of m = 3 for Tanuma formation and m = 3 for Khasib formation are the best values used with the modified FZI method. The modified FZI method shows a regression factor of 0.9986 for Tanuma and 0.9942 for Khasib formation.

Electrocardiogram Heartbeat Classification using Convolutional Neural Network-k Nearest Neighbor

Zrar Kh. Abdul, Abdulbasit K. Al‑Talabani, Chnoor M. Rahman, Safar M. Asaad — Thu, 29 Feb 2024 08:02:15 +0000

Electrocardiogram (ECG) analysis is widely used by cardiologists and medical practitioners for monitoring cardiac health. A high-performance automatic ECG classification system is challenging because there is difficulty in detecting and categorizing different waveforms in the signal, especially in manual analysis of ECG signals, which means, a better classification system is needed in terms of performance and accuracy. Hence, in this paper, the authors propose an accurate ECG classification and monitoring system called convolutional neural network-k nearest neighbor (CNN-kNN). The proposed method utilizes 1D-CNN and kNN. Unlike the existing techniques, the examined technique does not need training during classifying the ECG signals. The CNN-kNN is evaluated against the PhysioNet’s MIT-BIH and PTB diagnostics datasets. The CNN is fed using the ECG beat raw signal directly. In addition, the learned features are extracted from the 1D-CNN model and its dimensions are reduced using two fully connected layers and then fed to the k-NN classifier. The CNN-kNN model achieved average accuracies of 98% and 97.4% on arrhythmia and myocardial infarction classifications, respectively. These results are evidence of the great ability of the proposed model compared to the mentioned models in this article.

Analyzing Colorectal Cancer at the Molecular Level through Next-generation Sequencing in Erbil City

Vyan A. Qadir, Kamaran K. Abdoulrahman — Mon, 04 Mar 2024 20:49:42 +0000

Colorectal cancer (CRC) ranks as the third leading cause of cancer-related deaths globally. It is characterized as a genomic disorder marked by diverse genomic anomalies, including point mutations, genomic rearrangements, gene fusions, and alterations in chromosomal copy numbers. This research aims to identify previously undisclosed genetic variants associated with an increased risk of CRC by employing next-generation sequencing technology. Genomic DNA was extracted from blood specimens of five CRC patients. The sequencing data of the samples are utilized for variant identification. In addition, the Integrative Genomic Viewer software (IGV) is used to visualize the identified variants. Furthermore, various in silico tools, including Mutation Taster and Align GVGD, are used to predict the potential impact of mutations on structural features and protein function. Based on the findings of this research, 12 different genetic variations are detected among individuals with CRC. Inherited variations are located within the following genes: MSH6, MSH2, PTPRJ, PMS2, TP53, BRAF, APC, and PIK3CA.

Deep Learning-Based Optical Music Recognition for Semantic Representation of Non-overlap and Overlap Music Notes

Rana L. Abdulazeez, Fattah Alizadeh — Mon, 11 Mar 2024 00:00:00 +0000

In the technology era, the process of teaching a computer to interpret musical notation is termed optical music recognition (OMR). It aims to convert musical note sheets presented in an image into a computer-readable format. Recently, the sequence-to-sequence model along with the attention mechanism (which is used in text and handwritten recognition) has been used in music notes recognition. However, due to the gradual disappearance of excessively long sequences of musical sheets, the mentioned OMR models which consist of long short-term memory are facing difficulties in learning the relationships among the musical notations. Consequently, a new framework has been proposed, leveraging the image segmentation technique to break up the procedure into several steps. In addition, an overlap problem in OMR has been addressed in this study. Overlapping can result in misinterpretation of music notations, producing inaccurate findings. Thus, a novel algorithm is being suggested to detect and segment the notations that are extremely close to each other. Our experiments are based on the usage of the Convolutional Neural Network block as a feature extractor from the image of the musical sheet and the sequence-to-sequence model to retrieve the corresponding semantic representation. The proposed approach is evaluated on The Printed Images of Music Staves dataset. The achieved results confirm that our suggested framework successfully solves the problem of long sequence music sheets, obtaining SER 0% for the non-overlap symbols in the best scenario. Furthermore, our approach has shown promising results in addressing the overlapping problem: 23.12 % SER for overlapping symbols.

Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model

Mohammed Al-Mukhtar, Ameer H. Morad, Hussein L. Hussein, Mina H. Al-hashimi — Sat, 30 Mar 2024 17:00:30 +0000

The brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (segmentation), followed by the application of deep learning techniques for the identification and classification of brain tumors. The detection and measurement of a brain tumor involve a series of steps, namely, preprocessing, skull stripping, and tumor segmentation. The overfitting of BTNet-convolutional neural network (CNN) models occurs after a lot of training time because training the model with a large number of images. Moreover, the tuned CNN model shows a better performance for classification step by achieving an accuracy rate of 98%. The performance metrics imply that the BTNet model can reach the optimal classification accuracy for the brain tumor (BraTS 2020) dataset identification. The model analysis segment has a WT specificity of 0.97, a TC specificity of 0.925914, an ET specificity of 0.967717, and Dice scores of 79.73% for ET, 91.64% for WT, and 87.73% for TC.

Bridging the Gap

Soran S. Badawi — Wed, 03 Apr 2024 07:50:01 +0000

Effective 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.

Synthesis, Characterization, and Bioactivity Studies of the Schiff Base Ligand and its Zinc(II) Complex

Nabaz A. Muhammad Salih — Mon, 08 Apr 2024 00:00:00 +0000

One 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.

Oxidative Stress Assessment in Colorectal Cancer Patients

Vyan A. Qadir, Kamaran K. Abdoulrahman — Fri, 10 May 2024 08:23:13 +0000

Colorectal cancer (CRC), a global health challenge, exhibits rising incidence in low-income nations due to lifestyle changes. Oxidative stress, indicated by reactive oxygen species imbalance and Malondialdehyde (MDA), is linked to CRC. This study investigates oxidative stress markers, antioxidant enzymes, genetic markers, cellular regulation markers, and Vitamin E in CRC patients in Erbil. Ninety CRC patients and 30 healthy controls provided blood samples, processed and stored at –20°C. Enzyme-linked immunosorbent assay kits quantified oxidative stress, antioxidant markers, and Vitamin E. Oxidative stress markers showed significant differences, with elevated MDA and 8-hydroxy-2’-deoxyguanosine levels in patients. Nitrotyrosine exhibited lower expression in patients. Antioxidant enzymes glutathione peroxidase and superoxide dismutase were enhanced in patients, while glutathione (GSH), glutathione reductase and catalase levels were significantly lower in patients. The genetic marker KRAS showed a substantial decrease in patients (<0.0001) but both adenomatous polyposis coli (APC) and CRC antigen (CCA) were higher. Serum vitamin E levels were significantly lower in patients (71.78 ± 6.368) compared to controls (142.3 ± 4.828, p < 0.0001). Elevated oxidative stress, altered enzymatic activity, significantly lower expression of KRAS, and higher expression of APC and CCA in the patient group. Furthermore, reduced Vitamin E levels were observed in the patient group, highlighting potential challenges in antioxidant defense.

Analysis and Design of a Box Culvert Using Bentley Culvert Master Software

Anwer H. Dawood; Dana K. Mawlood — Fri, 10 May 2024 08:23:52 +0000

Box culverts are utilized in situations where natural stream flow intersects with roads and railway lines. This research utilizes a digital elevation model and the water Modeling System software to assess the catchment area of the primary valley and identify the factors contributing to flooding in Qoshtapa City. The study involves an analysis of the existing culvert and generated the necessary data for the design of a new culvert. Despite the presence of the existing culvert, floodwater levels rose to over 1 m above the roadway elevation of Erbil-Kirkuk during the last flood event in 2021–2022. The research collected hydrological and climatic data for the study area, conducted soil type analysis using the Harmonized World Soil Database software, and performed hydraulic calculations to estimate the maximum flood discharge of the valley using the Hydrological Engineering Center-Hydrological Modeling System software for flood return periods of 50, 100, and 200 years, for design, to select the best economic alternative. The new culvert design was executed using Bentley Culvert Master software to ensure that floodwaters can flow through the culvert without rising to street level. The results indicated that the new culvert design surpasses the capacity of the existing one. The results show that the best economic alternative hydraulic design is the first alternative capacity of 201 m³/s of a 100-year return period; the new design cross-section area of the culvert is 52.5 m².

Efficient and Simplified Modeling for Kerosene Processing Quality Detection Using Partial Least Squares-Discriminant Analysis Regression

Hayder M. Issa, Rezan H. Hama Salih — Fri, 10 May 2024 09:54:54 +0000

Kerosene from various refineries and crudes is used for heating and other purposes in many countries like Iraq;

therefore, it is important to identify its source to recognize and tax any adulteration. In this study, a fast classification technique for kerosene marketed in Iraq was developed with the goal of identifying its quality. The samples were categorized using a supervised partial least squares discriminant analysis (PLS-DA) approach. Multivariate analyses using agglomerative hierarchal clustering and principal component analysis were utilized to identify outliers and sample dissimilarities. The dataset was divided into calibration and prediction sets. The prediction set was used to evaluate the model’s separation performance. The Q² cross-validation was applied. The PLS-DA models achieved significant accuracy, sensitivity, and specificity, showing strong segregation ability, notably for the calibration set (100% accuracy and 1.00 sensitivity). It was found that kerosene processing can be classified rapidly and non-destructively without the need for complicated analyses, demonstrating the best results for classification even when compared with the classification outcomes of other fuels. This PLS-DA approach has never been looked at before for process quality detection, and the results are comparable to direct kerosene classification with soft independent modeling of class analogy and support vector machines.

Optimizing Emotional Insight through Unimodal and Multimodal Long Short-term Memory Models

Hemin F. Ibrahim, Chu K. Loo, Shreeyash Y. Geda, Abdulbasit K. Al-Talabani — Sun, 09 Jun 2024 22:43:04 +0000

The field of multimodal emotion recognition is increasingly gaining popularity as a research area. It involves analyzing human emotions across multiple modalities, such as acoustic, visual, and language. Emotion recognition is more effective as a multimodal learning task than relying on a single modality. In this paper, we present an unimodal and multimodal long short-term memory model with a class weight parameter technique for emotion recognition on the CMU-Multimodal Opinion Sentiment and Emotion Intensity dataset. In addition, a critical challenge lies in selecting the most effective fusion method for integrating multiple modalities. To address this, we applied four different fusion techniques: Early fusion, late fusion, deep fusion, and tensor fusion. These fusion methods improved the performance of multimodal emotion recognition compared to unimodal approaches. With the highly imbalanced number of samples per emotion class in the MOSEI dataset, adding a class weight parameter technique leads our model to outperform the state of the art on all three modalities — acoustic, visual, and language — as well as on all the fusion models. The challenges of class imbalance, which can lead to biased model performance, and using an effective fusion method for integrating multiple modalities often result in decreased accuracy in recognizing less frequent emotion classes. Our proposed model shows 2–3% performance improvement in the unimodal and 2% in the multimodal over the state-of-the-art achieved results.

Encryption of Color Images with a New Framework

Mardan A. Pirdawood, Shadman Kareem, Omar Al-Rassam — Fri, 14 Jun 2024 11:02:52 +0000

The significance of image encryption has risen due to the widespread use of images as a key means of sharing data across different applications. Encryption methods are crucial in defending the confidentiality and integrity of valuable image data. This work proposes a novel method of image encryption technique based on the Elzaki transformation and substitution process, which is made possible by the extension of the Maclaurin series coefficients. The image is encrypted using an infinite series of hyperbolic functions and the Elzaki transform; the inverse Elzaki transform is then used to decrypt the image. Using modular arithmetic, the coefficients that result from the transformation are keyed.

Geotechnical Assessment of the Slopes of Hamamok Dam, NE of Koya, Kurdistan Region of Iraq

Bahra Dh. Ghafour, Mohammed J. Hamawandy, Varoujan K. Sissakian — Mon, 17 Jun 2024 00:00:00 +0000

The Hamamok Dam is an earthfill dam with a height of 25 m and length of 125 m; constructed in 2011; located northwest of Koya town on a deep canyon-like valley that flows along the southeastern plunge of the Bana Bawi anticline; which forms Bawagi Mountain. The exposed rocks in the site belong to the Pila Spi and Gercus formations; however, rocks of the Khurmala and Kolosh formations are exposed upstream from the dam’s reservoir. The difference in the hardness of the carbonate rocks of the Pila Spi Formation which forms the uppermost parts of the cliffs surrounding the dam site and those of soft reddish brown clastics of the Gercus Formation caused steep slopes that suffer from slope instability problems. To assess a geotechnical study of slopes in the dam site, we have collected different field data to perform a Kinematic assessment method using Dip Analyst 2.0 software and draw the stereographic projection for the studied 10 stations using Stereonet v.11 software. Besides Bejerman’s method, which is based on field data only and indicates the Landslide Possibility Index (LPI). The results showed that the L.P.I. values range between 23 and 27, whereas the results of the Kinematic analysis showed that the right bank (Stations 1 – 5) suffers from plane sliding, whereas the left bank (Stations 6 – 10) suffers from toppling. In both cases, Joint 2 has the main role in the developed failures.

A Comprehensive Framework for Integrating Robotics and Digital Twins in Façade Perforation

Ahmed K. Ali — Wed, 19 Jun 2024 10:48:27 +0000

In contemporary design practices, the conflict between initial design approaches and subsequent manufacturing and construction stages presents a notable challenge. To address this disparity, our study aims to establish a comprehensive digital design workflow, bridging these gaps. The authors introduce a conceptual framework that seamlessly integrates the imperatives of LEED with the realm of robotic manufacturing, specifically tailored for construction sites. The proposed methodology encompasses four distinct iFOBOT modules: iFOBOT-environment, iFOBOT-design, iFOBOT-construct, and iFOBOT-monitor. The integration of these modules allows for a holistic approach to design and construction, fostering efficient collaboration between multidisciplinary teams. To validate the efficacy of the author’s approach, we conducted an empirical study involving the creation of a double-skin facade panel perforation using this integrated process. Initial findings emphasize the enhanced constructability achieved through simulated robotic interventions utilizing a heuristic function. Moreover, this research presents a functional prototype as a tangible embodiment of the method’s practical application and potential impact on the field of architectural design and construction.

Upright Pyramid Surface Textures for Light Trapping and MoOx Layer in Ultrathin Crystalline Silicon Solar Cells

Halo D. Omar — Tue, 25 Jun 2024 11:03:23 +0000

In this work, ray tracing is used to investigate the optical characteristics of various surface structures in ultrathin crystalline silicon (c-Si) for solar cells. Ultrathin c-Si with a thickness of 20 μm is used as the substrate. The light trapping includes front upright pyramids with a molybdenum oxides (MoOx) anti-reflection (AR) layer. Planar ultrathin c-Si (without a MoOx AR layer and upright pyramids) is used as a reference. The wafer ray tracer was developed by a photovoltaic (PV) lighthouse to model the MoOx AR layer to reduce the front surface reflectance and impacts of the AR layer on ultrathin Si solar cells. The optical properties are calculated on the AM1.5 global solar energy spectrum across the 200–1200 nm wavelength region. From the absorbance profile, the photogenerated current density (J_ph) in the substrate is also calculated with various surface structures. The front upright pyramids with the MoOx layer result in the largest absorbance enhancement due to the enhanced light scattering by the pyramids and MoOx AR layer. The J_ph of 37.41 mA/cm² is improved when compared to the planar ultrathin c-Si reference. This study is significant as it illustrates the potential of ultrathin c-Si as a promising PV module technology in the future.

High-resolution biostratigraphic zonation across the Cretaceous/Paleogene (K/Pg) boundary from the Sulaymaniyah area, Kurdistan Region, Northeastern Iraq

Imad M. Ghafor, Asaad I. Mustafa, Ibrahim M. Mohialdeen, Howri Mansurbeg — Wed, 26 Jun 2024 20:01:17 +0000

The present study interprets the high resolution of the biostratigraphy across the Cretaceous/Paleogene (K/Pg) boundary from Sulaymaniyah, Kurdistan Region, and north-east Iraq, based on planktic foraminifera. The Dartw section was selected for this study, within the High Folded Zone. The biozone contact consists of lithostratigraphic resemblance and is represented by the upper part of the Tanjero Formation (late Maastrichtian) with the overlying Kolosh Formation (Danian). Four late Maastrichtian planktic foraminiferal biozones have been recorded from the Tanjero Formation: Racemiguembelina fructicosa Interval Zone (CF4), Pseudoguembelina hariaensis Concurrent Range Zone (CF3),Pseudoguembelina palpebra Partial Range Zone (CF2), and Plummerita hantkeninoides Total Range Zone (CF1), while three Danian planktic foraminiferal biozones and two subzones have been recorded from the Kolosh Formation: (Guembelitria cretacea (P0) Interval Zone, Parvularugoglobigerina eugubina (Pα) Total Range Zone, and Parasubbotina pseudobulloides (P1) Partial-Range Zone (Globoanomalina archaeocompressa (P1a) Partial Range Subzone, and Subbotina triloculinoides (P1b) Interval Subzone). High biostratigraphic resolution indicates a complete K/Pg transition with no hiatus at the studied section in the Sulaymaniyah area. The ranges of the species recognized in this study are given. Correlations with other sections in Iraq and other parts of the world, including the typeMaastrichtian and Danian areas, are discussed and represented in correlation charts, together with the ranges of the important Upper Maastrichtian and Paleocene species.

Glucuronidase Gene

Hikmat M. Masyab, Qutaiba S. Al-Nema, Mozahim Q. Al-Mallah — Mon, 08 Jan 2024 11:49:14 +0000

Gluconacetobacter diazotrophicus lives inside plant tissue cells in the form of colonies and excretes about half of the fixed nitrogen, which offers potential power that improves plant growth. The aim of this study is to find the interaction of glucuronidase (GUS)-labeled G. diazotrophicus with spinach seedlings and the detection of GUS genes using X-gluc dye (5-bromo-4-chloro-3-indolyl-β-D- glucuronic acid). The GUS protocol is used to detect GUS-labeled G. diazotrophicus in spinach seedling tissues by chemical detection using X-gluc dye. The results show that the spinach seedlings are successfully infected with GUS-labeled G. diazotrophicus , with the survival of the seedlings throughout their growth period and an improvement in the growth of pollinated seedlings. The outcomes of the microscopic inspection of the root slices reveal the presence of bacterial cells at the root tips and their concentration in the area of the cell walls of the peripheral cells. Furthermore, the findings of microscopic examinations of longitudinal sections for cotyledons show the presence of a number of bacteria within epidermal cell walls. This indicates that the determinants of the interaction between these bacteria and spinach seedlings are suitable for the expression of the gene responsible for the formation of the nitrogenase enzyme.

A Review on Adverse Drug Reaction Detection Techniques

Ahmed A. Nafea, Manar AL-Mahdawi, Mohammed M. AL-Ani , Nazlia Omar — Fri, 07 Jun 2024 06:20:19 +0000

The detection of adverse drug reactions (ADRs) is an important piece of information for determining a patient’s view of a single drug. This study attempts to consider and discuss this feature of drug reviews in medical opinion-mining systems. This paper discusses the literature that summarizes the background of this work. To achieve this aim, the first discusses a survey on detecting ADRs and side effects, followed by an examination of biomedical text mining that focuses on identifying the specific relationships involving ADRs. Finally, we will provide a general overview of sentiment analysis, particularly from a medical perspective. This study presents a survey on ADRs extracted from drug review sentences on social media, utilizing and comparing different techniques.

Microstrip Passive Components for Energy Harvesting and 5G Applications

Leila Nouri, Salah I. Yahya, Abbas Rezaei, Sohrab Majidifar — Mon, 10 Jun 2024 07:51:44 +0000

This review paper provides a comprehensive overview of microstrip passive components for energy harvesting and 5G applications. The paper covers the structure, fabrication and performance of various microstrip passive components such as filters, couplers, diplexers and triplexers. The size and performance of several 5G and energy harvester microstrip passive devices are compared and discussed. The review highlights the importance of these components in enabling efficient energy harvesting and high-speed communication in 5G networks. Additionally, the paper discusses the latest advancements in microstrip technology and identifies key research challenges and future directions in this field. Overall, this review serves as a valuable resource for researchers and engineers working on microstrip passive components for energy harvesting and 5G applications.