ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
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373 research outputs found
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Molecular Analysis and Genotyping of Drug-Resistant Acinetobacter baumannii Isolates from Clinical Specimens
Full text linkAcinetobacter baumannii is a globally concerning hospital infection because it has developed resistance to many antibiotics, including last-resort carbapenems. In this study, 46 non-duplicate A. baumannii isolates from hospitalized patients are identified by the Phoenix BD Diagnostic System, which are used for bacterial identification and antimicrobial susceptibility profiles. Various clinical specimens, including endotracheal aspiration, urine, wound, blood, burns, and cerebrospinal fluid, were collected between 2023 and 2024 at different hospital wards. To provide further understanding of the epidemiology of multidrug-resistant (MDR) A. baumannii isolates, this study attempts to; (1) utilize enterobacterial repetitive intergenic consensus polymerase chain reaction (ERIC-PCR) DNA fingerprinting to estimate genetic diversity, which reveals a significant level of genetic relationship is established amongst A. baumannii isolates from hospitalized patients, suggesting cross-transmission, and (2) clarifies the genetic characteristics of the antimicrobial resistance profiles contributing to the antibiotics widely used for MDR A. baumannii isolates. All isolates are classified as MDR (54.3%), extensively drug-resistant (39.1%), and pandrug-resistant (6.5%). According to Clinical and Laboratory Standards Institute-2024 standards, 93.4% and 91.3% of isolates are resistant to meropenem and imipenem, respectively, while colistin and tigecycline are the most effective antibiotics. Furthermore, the most common of the genes present among clinical isolates are blaOXA-51 (100%) and pmrC (97.80%), while the less common detected genes are blaIMP (0.0%) and blaOXA-58 (46%). ERIC-PCR could provide a rapid and dependable scheme to recognize clonal relationships among isolates from a multiplicity of clinical samples. Controlling endemic A. baumannii strains, particularly in intensive care unit settings, is essential
A Systematic Survey on Large Language Models for Static Code Analysis
Full text linkStatic code analysis aids in improving software quality, security, and maintainability by detecting vulnerabilities, errors, and programming issues in source code without executing it. The latest advancements in Artificial Intelligence (AI), especially the development of Large Language Models (LLMs) such as ChatGPT, have enabled transformational opportunities in this domain. Thus, it is essential to explore this hot field of research alongside many directions. This systematic survey focuses on the use of LLMs for static code analysis, detailing their applications, advantages, contexts, limitations, etc. In this study, the research papers that have been published on the topic from well-known literature databases were examined to answer several research questions regarding state-of-the-art use of LLMs for static code analysis. Also, different research gaps and challenges were identified and discussed alongside many directions. The results of this study demonstrate how LLMs can be useful for static code analysis and overcome different constraints. Thus, it opens the doors for developers and researchers to employ LLMs for affordable and effective static code analysis to improve software development process
Hybrid Cryptosystem with Computational Ghost Imaging Based on Integer Wavelet Transform and Chaotic Maps
Full text linkComputational ghost imaging encryption (CGIE) has gained increasing attention from researchers in the field of optical cryptography due to its unique phenomenon. However, traditional CGIE suffers from long imaging time, inherent system linearity, and an enormous number of random phase masks that must be transmitted as secret keys, which limits its application in practical communication. In this paper, a hybrid optical image encryption approach is proposed using CGIE based on integer wavelet transform and chaotic maps. In addition, Hadamard basis patterns are employed to reduce sampling times and improve reconstructed image quality. Simulation results demonstrate that the proposed system is robust against different types of attacks with high key sensitivity and low execution times of 0.03 s for encryption and 0.14 s for decryption. This approach will ensure broader adoption of this technology by facilitating its integration into cryptosystems
Performance Evaluation of Membrane Bioreactor Operational Design in Sewage Treatment Plant using GPS-X Software
Full text linkThis study evaluates the operational performance of a full-scale membrane bioreactor wastewater treatment plant located in Kuala Lumpur using GPS-X 8.0 simulation software. Key performance indicators–including mixed liquor suspended solids (MLSS), transmembrane pressure (TMP), dissolved oxygen, and effluent total suspended solids (TSS)were monitored over 30 days. The simulations were conducted using the advanced mode and calibrated with actual plant data. Results show that although the plant complies with Malaysian effluent discharge standards, it operates well below its design capacity, with MLSS levels significantly lower than recommended. This operational underload contributes to increased energy consumption and reduced treatment efficiency, particularly in terms of TSS and chemical oxygen demand removal
Influence of Patient Size and Photon Energies on IMRT Plans for Localized Prostate Cancer
Full text linkThe localized prostate cancer treatment with intensitymodulated radiation therapy (IMRT) produces precise radiation doses that protect healthy tissues but continues to generate toxicity in the bladder and rectum. Our study aims to evaluate how patient size and photon energy influence IMRT plan quality in localized prostate cancer. Patients with localized prostate cancer (n = 20) received IMRT treatment in a retrospective analysis through two groups (small n = 12 and large n = 8) based on planning target volume isocenter diameter measurements. The same planning objectives guided the build of 6 MV and 10 MV photon plans for each patient. The assessment included monitor units (MUs) as well as examination of homogeneity index and conformity index, and dose–volume histogram metrics when comparing different energies and patient size groups. The target coverage and dose uniformity between 6 MV and 10 MV were equivalent, but 10 MV plans cut down MUs by 6.6% in a small group. The size of patients played a stronger influence on organs at risk (OAR) than energy utilization during treatment planning. Large patients received 22% lower mean bladder doses, 30% lower bladder V10–V50 values, and 3396% (6 MV) less V20–V50 doses in the left femoral head compared to smaller patients. The size of the patient influenced OAR sparing more than the selection of photon energy, which had limited and size‐dependent effects. Therefore, patient size has to be considered in energy choice, treatment planning, and clinical trial design to individualize treatment and minimize toxicity
Influence of Structural Properties on Fusion Cross-Sections Using Proximity Potentials
Full text linkIn this study, an optimal nuclear proximity potential is used to get more accurate fusion cross-section predictions. For 111 colliding systems, we evaluate the predictive accuracy of several proximity potential models interfaced with Wong’s formula in reproducing the fusion cross-section experimental data. For the purpose of Chi-square minimization technique, Christensen and Winther 1976 potential is selected. The analysis examines fusion dynamics across a wide range of nuclear configurations (6 ≤ Zp (projectile atomic number)≤28, 6 ≤ Zt (target atomic number) ≤94, and 36 ≤ Zp Zt ≤ 1880). To increase accuracy and match experimental data, a Python code that calculates cross-sections for all proximity potentials is established using the Nelder–Mead algorithm. The extensive range of calculations facilitates a systematic study of the effects of structural factors, including magic numbers, shell structure, neutron excess, and pairing effect. The results reveal that shell effects can sometimes overcome neutron excess and produce unexpected fusion trends, as seen in the 28Si + 90Zr and 28Si + 94Zr. In other reactions, the shell effect eliminated the effect of the neutron excess, such as in the 16O + 62Ni versus 16O + 58Ni and 12C + 208Pb versus 12C + 204Pb reactions. Our findings also highlighted the important role of the projectile in the process of fusion. The titanium isotopes (46Ti, 50Ti) in particular fused more effectively with 12C than with 16O. Nickel isotopes show similar projectiledependent behavior
A Novel Skin Cancer Detection Approach Using Deep Learning Algorithm with Image Segmentation Filters
Full text linkSkin cancer is considered one of the most common and dangerous diseases in the world because so many people do not pay attention to it. In addition, skin cancer is a medical condition that a doctor cannot accurately diagnose from imaging data during a manual examination. Therefore, there is a great need to apply deep learning methods for early detection of skin cancer, as these methods are excellent in the field of medical image processing. This paper presents a deep learning model based on the convolutional neural network algorithm to provide automatic detection of skin cancer. The model basically consists of two scenarios: binary classification (benign and malignant) of the data set without an image segmentation process and binary classification of the same data set after applying four image segmentation methods (threshold-based segmentation, edge-based segmentation, binary fill holes technique, and removing small objects). The input images in the first scenario are three channels and one channel in the second scenario. These image segmentation techniques have significantly improved the accuracy of the proposed model, as the proposed model achieved 92.18% before applying segmentation and 96.83% after applying image segmentation
Assessment of Leakage Radiation and Radiobiological Impacts in Gamma Knife Radiosurgery: Dosimetric and Biological Analysis
Full text linkGamma Knife radiosurgery is a non-invasive radiotherapy technique for brain lesions. However, radiation leakage from collimators and high-dose exposure may alter blood parameters, potentially increasing the risk of secondary cancers and other complications. The purpose of this study is to measure the leakage radiation produced during trigeminal neuralgia and meningiomas lesion treatments and impact on various radiosensitive organs. In addition, the radiobiological impact on patients’ blood parameters is investigated for both short-term and long-term treatment exposure. Scatter radiation was measured using dosimeters placed at various body regions. Blood samples were collected from 20 patients at three different times. Changes in parameters were statistically analyzed using one-way analysis of variance, to assess significant differences across the time points. The highest scatter radiation levels were recorded at the face and neck significantly exceeding other body regions about 110 μSv and 350 μSv, respectively. Statistical analysis revealed that long-term exposure (58.2 min at 80 Gy) in trigeminal neuralgia cases resulted in significantly greater blood parameter changes (p ≤ 0.05) compared to short-term exposure (19.4 min at 20 Gy) in meningiomas. These findings reveal dose-dependent blood changes and highlight the importance of radiation protection measures to enhance patient safety, particularly during high-dose treatments
An Ensemble-based Model for Sentiment Analysis of Kurdish Tweets
Full text linkThousands of comments are generated daily on social media in the Kurdistan Region. Sentiment analysis (SA) of these comments is valuable for organizations. The Kurdish language has three main dialects: Sorani (Central), Northern, and Southern. This study focuses on Sorani SA, where existing methods have limited accuracy. The proposed ensemble combines diverse models to improve sentiment classification. Preprocessing and word embedding using Roberta is the first phase of the method. The second phase consists of four proposed models, namely K-nearest neighbor, support vector machine, multilayer perceptron long short-term memory (LSTM), and bidirectional-LSTM (Bi-LSTM), which are used as classifiers. Finally, the ensemble weighted averaging technique is utilized to generate the final classification. To evaluate the performance of the proposed model, a dataset including 24211 unbalanced Soran tweets is first used, and after balancing, the dataset is used. The Bi-LSTM model attained an accuracy of 89.87% on the balanced dataset, and the proposed ensemble method increased the accuracy to 91.76%, which is better than the established state-of-the-art methods of Kurdish SA
Using Structural Data to Indicate the Folding Type of Anticlines Examples from the Kurdistan Region, Iraq
Full text linkUsing seismic data is the most relevant and sound method to indicate the folding type in anticlines. However, when seismic data are not available or not allowed for use, then structural data are recommended to indicate the folding type of any anticline. Such usage is known and considered worldwide with excellent results. We have adopted a sound and published opinion of using structural data to indicate the folding type, and applied the method on three anticlines, namely, Qara Boutaq, Korek, and Mateen. We have selected the three anticlines from three different tectonic zones in the Iraqi Kurdistan Region, which is located in the northern and northeastern parts of Iraq, and forms the northeastern part of the Arabian Plate. The plate has been colliding with the Eurasian Plate since the Late Cretaceous and is still in collision; accordingly, all the existing anticlines are developed. We used satellite images to measure the required structural parameters, including aspect ratio, fold symmetry index, and Mountain Front Sinuosity Index in these anticlines. We also measured the dip amounts along the three anticlines from satellite images, where there are no dip measurements in the geological maps, to indicate the geometrical type of the three anticlines. Part of the interpreted data was checked and confirmed in the field, wherever it was accessible. Accordingly, we indicated the folding type to be Detachment Fold for the Qara Boutaq and Korek anticlines and Fault-bent Fold for the Mateen anticline