ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY

Thermal Dynamics in Optical Networks

2024-10-08T08:12:15+00:00

The signal distribution of any fiber-optic network system is an important factor in optical communication, which determines the quality of the optical signal transmission. One of the important effects is the temperature degrees; that effect is on the main parameters of optical communication (of which the fiber optic is the main part). The main material in fiber optics is glass. And as is well known, temperature has a strong effect on the glass, especially the core of fiber optics, because the structure of fiber optics contains several glass layers with different refractive indexes. Hence, in the present article, the effect of temperature on the optical signal and other components of the optical network system has been analyzed and studied. The analysis includes aberration, dispersion, and distortion of the optical network communication signal. The result has been discussed and analyzed for variables in the BW of the spectral when the temperature changed.

New Thiazole Derivatives

2024-10-13T20:55:24+00:00

While bacterial superbugs have garnered much attention, the rise of antifungal resistance poses a growing threat. This study explores the potential of newly synthesized 2,5-Bis(3,4 Dialkoxy Phenyl) Thiazolo[5,4-d] Thiazoles (DATTn compounds) as antifungal agents. Notably, DATTn compounds demonstrated significant fungicidal activity against Candida albicans, a major fungal pathogen, whereas remaining largely ineffective against common bacterial strains, such as Staphylococcus aureus and Escherichia coli. In silico docking simulations using Schrödinger suites unveiled the molecular basis for this selectivity, revealing strong interactions between DATTn molecules and a crucial fungal protein (Portion Data Bank ID: 8JZN) in C. albicans. These findings highlight the potential of DATTn compounds as promising leads for the development of novel antifungal therapies, particularly in light of escalating drug resistance concerns.

Assessment of Radioactivity in Building Materials

2024-08-03T13:37:42+00:00

This research investigates the radioactivity levels of various rock types used in construction within the Kurdistan region and assesses their potential impact on human health, the measurements were performed using an HPGe gamma-ray spectrometer. The measured activity concentrations of ²²⁶Ra, ²³²Th, and ⁴⁰K radionuclides varied from ND (Chromitite) to 78.68 ± 4.54 Bq/kg (Marly Limestone), ND (Chromitite) to 109.52 ± 10.23 Bq/kg (Mudstone), and ND (Chromitite) to 2973.6 ± 152.1 Bq/kg (Claystone), respectively. The obtained Raeq values for all rock samples are well below the UNSCEAR, 2008 recommended value of 370 Bq/kg. 71.43% of DR, 66.66% of E_out, 71.43% of E_in, 100% of Hinand H_out, 71.43% of ELCR_out, 71.43% of ELCR_in, and 100% of activity utilization index of the rock samples are well below the recommended values declared by UNSCEAR, 2008. The radioactivity level of rock types that are prepared as building materials should be assessed by the producers and considered by the users to reduce the overall cancer risk. The outcomes of the RESRAD-BUILD computer code indicate that the maximum external and inhalation doses were calculated to be 19.7 and 0.105 μSv for R7 and R1 samples, respectively, over a period of 70 years.

Toward Optimizing Coarse Aggregate Types and Sizes in High-strength Concrete

2024-08-03T13:37:27+00:00

The 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.

The Recovery of Historical Buildings in Post-war Aleppo

2024-08-03T22:56:33+00:00

Throughout history, wars and armed conflicts have severely impacted cultural heritage, erased collective memories, and left entire populations seemingly non-existent. However, recognizing the significance of cultural heritage is the primary impetus for its subsequent reconstruction. Since the Syrian war outbreak in 2011, Aleppo, one of the world’s oldest continuously inhabited urban centers, has suffered significant damage to its historic sites such as shrines, khans, and other architectural treasures due to bombings and clashes. In 2017, a study by the Directorate of Antiquities and Museums in Aleppo revealed that over 70% of the city’s historic center was destroyed. As a result, the cultural heritage of Aleppo and its social and symbolic values have been threatened. This represents a huge loss not only to Syria but also to the international community. This paper explores the significant role that collective memories play in shaping a city’s identity in the aftermath of war. The research aims to analyze the best approach for intervention during the reconstruction phase, whether it be preservation, restoration, enhancement, or eventual reconstruction by reviewing some international experiences. Furthermore, it explains the current situation of the old city of Aleppo and presents some cultural buildings that have been severely damaged or destroyed during the conflict. The study will compare the essential values of each case study and finally conclude with suggestions about the suitable intervention of different case studies that represent the values mentioned before.

Assessing E-Government Effectiveness

2024-10-08T08:47:55+00:00

In this study, we address the pressing need to improve e-government services by conducting a comprehensive evaluation of document management practices within the Ministry of Higher Education in the Kurdistan Region of Iraq. Employing structural equation modeling and regression modeling techniques, we assess the effectiveness of key variables using the six factors (digitalization, utility, reliability, efficiency, user satisfaction, and accessibility) under the e-government assessment model. Our findings reveal that user satisfaction and ease of access are pivotal for the success of e-government services, with digitization and efficiency significantly impacting operational effectiveness. Furthermore, it also concerns that without improving the overall user satisfaction, the level of acceptance and use of e-government remains low. High user satisfaction can make a lot of difference in overall engagement besides improving service delivery among organizations. Through insights specific to the Kurdistan Region, we underscore the importance of balancing technical infrastructure with user experience. Our proposed six-factor model offers valuable guidance for policymakers and practitioners globally, aiming to enhance e-government services.

Transmission Power Reduction Based on an Enhanced Particle Swarm Optimization Algorithm in Wireless Sensor Network for Internet of Things

2024-08-23T11:49:54+00:00

A wireless sensor network (WSN) consists of several sensor nodes; all these nodes can sense physical events, including light, heat, and pressure. These networks are essential in smart homes, smart agriculture, and smart water management, swelling with the concept of the Internet of Things. However, WSN needs to address the challenges of energy issues; thus, energy-conserving techniques have been pursued for communication. Optimization of energy is normally solved using the Particle Swarm Optimization (PSO) algorithm since it offers high accuracy but is prone to local optima, thus resulting in early convergence. To tackle this challenge, this paper proposes the development of an enhanced particle swarm optimization for the node power estimation (EPSO-NPE) model. EPSO-NPE calculates distinct transmission powers for each node, preventing the formation of isolated areas within a sensor cluster. Unlike the original PSO, the EPSO algorithm enhances exploration capabilities by avoiding stagnation on search space boundaries. A comparative analysis with the original PSO-based model (PSO-NPE), where nodes adopt maximum power for connectivity, reveals superior performance by EPSO-NPE. The enhanced model exhibits heightened energy-saving capabilities, ultimately extending the network’s lifetime.

Synthesis Development and Molecular Docking Study of New Azo Chalcone Derivatives

2024-08-23T11:50:20+00:00

This work is divided into two main parts. The first part involves the synthesis of new azo chalcone compounds through a two-step process. Initially, azo compounds are synthesized by diazotizing 3-nitroaniline, followed by a coupling reaction with 4-hydroxyacetophenone, which has a terminal ketone group. Subsequently, the resulting product undergoes a Claisen–Schmidt condensation reaction with various aromatic aldehyde substrates to produce new α, β-unsaturated ketones, known as azo chalcone compounds. The successful synthesis of these compounds is confirmed using Fourier-transform infrared spectroscopy, ¹HNMR, and ¹³C NMR spectral analyses. The second part of this study explores the theoretical biological activity of the synthesized compounds against severe acute respiratory syndrome coronavirus 2 through molecular docking studies. The results indicate potential antiviral properties for each compound, with compounds B5 and B8 exhibiting the most promising results. These compounds achieved higher docking scores (ΔG −6.235 kcal/mol and −5.832 kcal/mol, respectively) and each formed four hydrogen bonds with the target protein.

Flexible Job Shop Scheduling Problem-Solving Using Apiary Organizational-Based Optimization Algorithm

2024-10-08T07:38:42+00:00

Flexible job shop scheduling problem (FJSSP) is a complex and challenging problem that plays a crucial role in industrial and manufacturing production. FJSSP is an expansion of the standard job shop scheduling problem (JSSP). One of FJSSP’s objectives that the manufacturing system competing for is minimizing the makespan. This paper uses a new nature-inspired metaheuristic optimization algorithm called the Apiary Organizational-Based Optimization algorithm (AOOA) to solve the FJSSP. This Algorithm simulates the organizational behavior of honeybees inside the apiary and translates their activities and vital processes during their lifecycle into phases that can solve such NP-hard problems. Two benchmark datasets, Brandimarte and Hurink, with 10 MK instances and 24 (edata, rdata, and vdata) instances respectively, were used to demonstrate the ability of AOOA to solve FJSSP. Moreover, the results of AOOA were compared with a set of state-of-the-art algorithms and statistically measured using the paired samples t-test and p-value, RPD, and group-based superiority statistical analysis to test its performance. AOOA outperformed Elitism GA, Enhanced GA, Improved GA, and MOGWO in solving all 10 MK instances and HICSA in solving 9 MK instances out of 10. Moreover, AOOA overcame CS, CS-BNG, CS-ILF, CHA, and MCA in solving 24, 12, 12, 23, and 24 instances of edata, rdata, and vdata, respectively. AOOA proved its robustness, showing promising outcomes.

Strategies for Sustainable Water Management

2024-09-08T16:38:58+00:00

Groundwater in the Rania basin, Iraqi Kurdistan region, has been under intensive exploitation in the last two decades, where quantity and quality are both affected. Hence, any attempt to protect the aquifers has become an urgent need. Saruchawa, Qulai Rania, and Qulai Kanimaran are the three large springs, among dozens of others in the area, that are heavily relied on as the sole or main source of water supply. Hydrochemical analysis, the first and most practical step to evaluating the water quality, was carried out through 60 water samples collected from 13 springs and 17 wells in both dry and wet seasons (October 2018 and May 2019). Laboratory results show a high calcium bicarbonate concentration with weak acids’ dominance. Protection zones are delineated for these springs using aquifer susceptibility to contamination and analysis of the recession part of the spring curves. The equivalent relationship between the protection factor (Fp) produced by the Epikarst, protective cover, infiltration condition, and Karst network development mapping method and the groundwater protection zone (S) is considered. Qulai Rania and Kanimaran Springs are mapped to be in S2 (a highly vulnerable area), whereas Saruchawa Spring is located in S1 (very highly vulnerable). Based on the second method results (recession curve analyses), all three selected springs fall under the (D-type) vulnerability category. As a result, the immediate protection zone was going to be surrounded by the inner protection zone, and both are enclosed within the outer protection zone, which covers the remainder of the catchment area.

Time Series-Based Spoof Speech Detection Using Long Short-Term Memory and Bidirectional Long Short-Term Memory

2024-09-14T23:28:11+00:00

Detecting fake speech in voice-based authentication systems is crucial for reliability. Traditional methods often struggle because they can't handle the complex patterns over time. Our study introduces an advanced approach using deep learning, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models, tailored for identifying fake speech based on its temporal characteristics. We use speech signals with cepstral features like Mel-frequency cepstral coefficients (MFCC), Constant Q cepstral coefficients (CQCC), and open-source Speech and Music Interpretation by Large-space Extraction (OpenSMILE) to directly learn these patterns. Testing on the ASVspoof 2019 Logical Access dataset, we focus on metrics such as min-tDCF, Equal Error Rate (EER), Recall, Precision, and F1-score. Our results show that LSTM and BiLSTM models significantly enhance the reliability of spoof speech detection systems.

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

2024-09-22T17:15:45+00:00

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Dual Electrochemical Methods for Determination of Anesthetic Procaine

2024-10-13T21:09:48+00:00

Procaine belongs to a type of medicine in which excessive dosage form creates cardiac problems and many allergenic reactions. Thus, continuous monitoring of this drug and its metabolite is crucial for sustainable health management during treatment. In this study, electrochemical techniques such as square wave voltammetry (SWV) and differential pulse polarography (DPP) are utilized for assaying procaine amounts in standard and pharmaceutical formulations. In SWV, the reduction of diazotized procaine gives a reduction peak at −0.05 V which is directly proportional with procaine hydrochloride concentration, whereas in DPP, the interaction of the drug with lead cation at −0.4 V is followed by the decrease in peak current of the lead cation reduction peak, which is directly proportional with the concentration of the drug. Both methods indicate high accuracy, sensitivity and precision. Linear concentration ranges of both methods are 0.0999–5.996 × 10^-7M for SWV and 0.1999–5.996 × 10^-7 M for DPP. The limit of detection (LOD) and limit of quantification (LOQ) are calculated for both SWV and DPP techniques, and found that LOD equals 1.984 × 10^-9 M and LOQ equals 6.611 × 10^-9 M for SWV, while for (DPP) LOD and LOQ were found to be 3.519 × 10^-9 M and 1.173 × 10^-8 M, respectively.

AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors

2024-11-17T14:32:51+00:00

Metering 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.

Surveillance of Antimicrobial Resistance in Iraq

2024-11-20T17:01:39+00:00

Antimicrobial resistance (AMR) generates serious negative impacts on health-care systems worldwide, and Iraq is not an exception. To uncover the prevalence of AMR and to visualize the magnitude of the multidrug-resistant (MDR) dilemma in Iraqi hospitals, this study is carried out. A total of 11592 clinical records from ten different health-care facilities in seven Iraqi provinces are collected and analyzed. Our data show that 4984 (43.0%) of all clinical samples are negative for bacterial growth. In adults, Gram-negative bacteria (GNB) represented 48.9% and Gram-positive bacteria (GPB) represented 51.1% of clinical isolates; in children, GNB represented 60.8% and GPB represented 39.2%. Furthermore, in adults, Klebsiella pneumoniae (30.1%) and Staphylococcus aureus (40.8%) are among the most common GNB and GPB isolates, respectively. In children, K. pneumoniae (37.9%) and Staphylococcus haemolyticus (41.8%) are the most common GNB and GPB, respectively. Adults’ samples showed that Escherichia coli and Proteus mirabilis were the most resistant GNB; S. aureus and Staphylococcus epidermidis are among the most resistant GPB. In children, K. pneumoniae is found to be the most resistant GNB. This study confirms the persistence of antimicrobial resistance and multidrug-resistant gram-negative and gram-positive bacteria in adults and children alike. Ampicillin and oxacillin have been recognized as ineffective drugs in adults, and ampicillin, nafcillin, cefoxitin, and benzylpenicillin have been found to be highly resisted by pathogenic bacteria in children. The outcomes confirm the necessity of conducting AMR surveillance on a regular basis and establishing national antibiotic prescription guidelines to manage AMR development in Iraq.

Artificial Intelligence Integration in Academic Writing

2024-11-23T00:06:22+00:00

This 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.

Graphical User Authentication Algorithms Based on Recognition

2024-08-23T23:15:18+00:00

In cyber security, the most crucial subject in information security is user authentication. Robust text-based password methods may offer a certain level of protection. Strong passwords are hard to remember, though, so people who use them frequently write them on paper or store them in file for computer .Numerous of computer systems, networks, and Internet-based environments have experimented with using graphical authentication techniques for user authentication in recent years. The two main characteristics of all graphical passwords are their security and usability. Regretfully, none of these methods could adequately address both of these factors concurrently. The ISO usability standards and associated characteristics for graphical user authentication and possible attacks on nineteen recognition-based authentication systems were discussed. In this study, differentiation table of attack patterns for all recognition-based techniques is shown. Finally, the positive and negative aspects of nineteen methods were explained in the form of a detailed table.

Micropollutant Control in Wastewater Treatment

2024-09-27T07:53:44+00:00

Micropollutants, 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.

Distributed Software-Defined Networking Management

2024-10-04T10:07:38+00:00

Distributed 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.