DIYALA JOURNAL OF ENGINEERING SCIENCES

Metaheuristic algorithms have gained significant acceptance in large areas of optimization, giving unique and novel solutions to complicated problems across various areas. This research dives into the wide classification of state-of-the-art real-world applications that depend on metaheuristic algorithms, acknowledging their prevalence and the diversity of real-world applications where their performances are evaluated. The major goal is to evaluate forty-eight metaheuristic algorithms from 2020 to 2024 based on the results presented in their original research articles, emphasizing their effectiveness in tackling six prevalent real-world applications. In addition, the study classifies the algorithms and compares them to determine which ones are most effective for the particular applications. The results point out the necessity to solve the actual problems using opting for a metaheuristic algorithm. Nevertheless, it becomes very obvious that no algorithm works well in all the cases pointed out, as a demand for an informed selection based on the task complexity. This research contributes to the ongoing development and application of metaheuristic algorithms in diverse practical settings by providing valuable insights into the dynamic landscape of metaheuristics.

The upcoming sixth-generation (6G) mobile is designed with several purposes, such as incredible improvements, ultra-fast speed, ultra-low latency, and massive connections within a small area. Bandwidth Efficiency and extensive coverage, besides sub-millisecond synchronization using a new sub-terahertz spectrum, are also considered. In order to maintain frame consistency, adapt to diverse applications, enable scalability, and minimize interferences, in 6G networks, a multi-waveform architecture is required instead of the individual waveform, which cannot meet the requirements. In this paper, a new approach to the waveform framework is presented. The proposed method integrates multi waveforms, referred to as (Waveforms Interfering). This approach combines two multicarrier schemes within a single frame, and then their performances are independently and collectively checked. The suggested approach has a Power Spectral Density (PSD) with minimal out-of-band radiation (OOBR) and a reduced guard band to 6 subcarriers between the two waveforms without causing interference. The Bit Error Rates (BER) performance compared among different orders of Quadrature Amplitude Modulation (QAM). The minimum BER achieved at 20dB SNR is 9e-7. This comparison reveals diverse performance outcomes based on the modulation orders used. The Peak Average Power Ratio (PAPR) behaves similarly to an individual scheme. This approach can effectively satisfy the strict performance requirements of 6G networks.

Alzheimer's disease (AD) is one of the common neurodegenerative disorders presenting with progressive cognitive decline and synaptic impairment. The need for accurate and accessible diagnostic methods is especially pressing in resource-limited regions. This study presents the first open electroencephalography (EEG) dataset for Alzheimer's Disease (AD) in Iraq, recording electrophysiological activity in four stages of cognition, namely, Healthy, Mild, Moderate, and Severe. The dataset comprised recordings from 53 participants and was recorded using a 40-channel EEG device based on the 10-20 electrode placement standard. Considering this EEG dataset, advanced preprocessing, such as the removal of artifacts, Independent Component Analysis (ICA), and Automatic Subspace Reconstruction (ASR), was performed to ensure high signal quality suitable for all state-of-the-art Machine Learning (ML) and Deep Learning (DL) analyses. Benchmark classification was conducted with a wide variety of ML and DL models, including Random Forest (RF), Gradient Boosting methods, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and Long Short-Term Memory networks (LSTMs), showing promising results-a maximum of 96.85% by Random Forest within ML techniques and a maximum of 96.05% by DNNs in DL techniques. This dataset fills a critical gap in regional AD research and moves toward developing low-cost, noninvasive diagnostic tools. Future work may be performed on more extended and more diverse datasets with more sophisticated multimodal approaches for improving AD diagnosis and early intervention. The dataset can be downloaded from https://shorturl.at/Z0b8D.

The Low Noise Amplifier (LNA) is typically the initial step in any microwave receiver circuit and is crucial to the receiver\'s quality. The design, analysis, and modeling of single-stage and multi-stage low-noise amplifiers combined with an optimal matching network at wideband frequencies between 11 - 13 GHz are presented in this study. The amplifier circuit\'s heart, the embedded GaAs FET transistor MGF2407A in Advance Design System tool, operates in class AB mode with a drain source voltage of 4 V and a gate source voltage of -0.2 V. The matching circuit was constructed and optimized at the transistor\'s input and output after the source and load impedances were extracted using the source and load-pull technique. The stability factor of simulated amplifier was greater than 4 in the 11–13 GHz frequency range. The noise figure (NF) and power gain at 12 GHz were 34 dB and 0.391, respectively. The input and output sides have exceptionally low reflection coefficients, with values below -15 dB. According to simulation results, the LNA has a broad bandwidth of 2 GHz and an acceptable NF between 0.5 and 0.3 within the bandwidth range of the Ku-Band applications. This amplifier circuit model can be used to create and build various LNA circuits for a variety of uses.

Molybdenum disulfide (MoS2) is a promising nanomaterial with a wide range of applications. Its outstanding properties, good biocompatibility, and band gap characteristics allowed it to be used in biosensing, electronics, optoelectronics, and biological and medical applications. Furthermore, Biopolymers, including chitosan and PEG, are regarded as the most promising materials in the medical domain. Therefore, in this work, MoS2, MoS2-CS, and MoS2-PEG nanocomposite materials are generated using a laser ablation process. A comprehensive examination of the manufactured composites was performed using X-ray diffraction analysis (XRD), transmission electron microscopy (TEM), ultraviolet-visible spectroscopy (UV–Vis), energy Dispersive X-ray (EDX), Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR) to validate the quality and content of the newly prepared composites. Based on the results, MoS2 NPs show a minimum average size of 20 nm generated using 127.3, 250mm/s, 3 minutes irradiation. Furthermore, the recorded average size of MoS2-CS and MoS2-PEG nanocomposite were 10.5 nm and 7.8 nm, respectively. The MoS2-CS hybrid nanocomposite showed the highest antibacterial activity against E. coli bacteria, while MoS2-CS and MoS2-PEG showed the highest antibacterial activity against S.aureus bacteria compared to pure MoS2 NPs. The anticancer analysis demonstrated that the MoS2-CS nanocomposite presented the highest cytotoxic effect against the A549 cell compared to MoS2 and MoS2-PEG.

This study presents a comprehensive numerical investigation into the thermal and hydraulic performance of a double-pipe heat exchanger equipped with various twisted tape configurations, including metal foam twisted tape, conventional (smooth) twisted tape, and porous media inserts. The analysis is conducted using Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent under steady-state conditions, with air and water as the working fluids. Key parameters such as outlet temperature, pressure drop, Nusselt number, thermal performance factor (TPF), and the pitch-to-height ratio (Pt/W = 3.6, 5.2, and 6.8) are evaluated over a wide range of Reynolds numbers (500 < Re < 13,000). The study explores three distinct cases with different external fin configurations to assess the impact of geometric design on heat transfer enhancement. The results indicate a significant improvement in thermal performance when using twisted tape inserts. Conventional twisted tape enhances heat transfer by up to 33% compared to a plain tube; while metal foam twisted tape achieves a 37% enhancement. Furthermore, metal foam twisted tape demonstrates an 11.2% superior thermal performance over conventional twisted tape, particularly in the laminar flow regime. However, at higher velocities (i.e., higher Reynolds numbers), the difference in performance decreases due to flow choking within the metal foam matrix. Maximum heat transfer augmentation is observed before the transition to turbulent flow, after which the performance gradually declines.

Construction project delays are a recurring problem that have a big influence on stakeholder satisfaction, project budgets, and schedules. The fuzzy Analytic Hierarchy Process (AHP) is used in this study to discover, classify, and rank delay causes as it relates to contractor-induced delays. Seventy components were first discovered and categorized into six groups: finance, project management, materials, equipment, external factors, and manpower. This was done using a thorough process that included literature reviews, case studies, expert interviews, and surveys. Financial considerations have the highest weight (0.36743), followed by project management (0.23959) and material-related factors (0.1601), according to the fuzzy AHP model. The results highlight how important it is to deal with payment delays and to mitigate them through efficient project planning, material procurement, and equipment maintenance. This study offers a thorough framework for comprehending and controlling delays caused by contractors, along with useful suggestions to enhance efficiency and reduce risks.

This study investigates the effect of secondary fluid inlet geometrical parameters on the performance of a two-phase water-steam ejector operating in the subsonic flow regime. A 3D numerical model, employing the Volume-of-Fluid (VOF) method, was developed to analyze the impact of varying secondary fluid inlet angles (30°, 45°, 60°, 75°, and 90°) and the number of secondary fluid inlets (1, 2, 3, and 4). The study examined these parameters across a range of primary fluid flow rates (6-24 L/min) to understand their interactions. The numerical model was validated through comparison with existing experimental data, demonstrating strong agreement between predicted and measured ejector entrainment ratios (Er). An optimal secondary fluid inlet angle of 45° was identified, providing the best balance between momentum transfer and perpendicular velocity components. It was found also that increasing the number of secondary fluid inlets from 1 to 3 significantly enhanced the ejector Er, while further increases yielded minimal additional improvements. The effects of secondary fluid inlet parameters were more pronounced at higher primary fluid flow rates. These results contribute to a deeper understanding of two-phase ejector performance and provide valuable insights for optimizing their design in various applications.

The aim of this study is to develop a thermodynamic model for designing the primary nozzle (P-N) of a phase change ejector utilized in the Air - Water Heat Pump (AWHP) cycle. This expansion device is proposed to replace the conventional expansion devices with high irreversibilities losses in order to enhance the overall cycle efficiency. This simulation study is modelled using MATLAB software with REFPROP database used to obtain R134a thermodynamic properties. A comparison analysis of AWHP using valve, turbine or ejector as expansion devices is conducted. The ejector results is then used in the P-N design simulation. In addition, a sensitivity analysis to investigate the effects of varying R134a velocity at the P-N inlet on the design parameters is performed. The results show that ejector cycle has achieved higher COPh and second law efficiency by 3.25 and 6.6% respectively. In addition, the exergy evaluation demonstrates that using the ejector reduces the total exergy destruction by 51.7 and 48.5% compared to valve and turbine cycles respectively. The results also show that 20.7 J of exergy is destroyed in the ejector, of which 15.9 J of this destruction occurs in the P-N. The sensitivity analysis show that the P-N has a converging-diverging configuration with an inlet diameter ranging between 4.9-3.2 mm, while the throat diameter is between 3.11-2.7 mm and the outlet diameter is almost constant at 7mm. In addition, at the throat section, R134a reaches supersonic speed and highest mass flux and maintain the supersonic speed at the nozzle exit.

The use of solar panels is becoming increasingly important now as a source of renewable energy. With the different systems designed to invest solar energy and the different electronic attacks on renewable energy systems, the importance of designing a photovoltaic system with different features appears in terms of detecting the attack on the input of the control unit to the system, and thus knowing the electronic attacks targeting the control unit. In this paper, a solar farm system based on cyberattack detection is designed and analyzed using MATLAB Simulink. First, the proposed system detects and identifies cyberattacks, such as Time Delay Attacks (TDA), on the PV controller. Second, it diagnoses physical attacks, including Short Circuit Faults (SCF), and evaluates their impact on the photovoltaic controller. The simulation comprises TDA and SCF attacks and their impact on current and voltage waveforms in the PV system. These types of attacks mainly depend on delaying or changing the triggering signal, which leads to an effect on the output signals. In addition, the modulation-index feature is adopted in verifying the presence of attacks and diagnosing their type on the photovoltaic farm configuration, it reaches its highest value of 0.91 when the solar farm is operating properly. On the other hand, it is disturbed and fluctuates to reach 0.66 and 0.16 in the case of SCA and TDA, respectively. The Simulink results demonstrate that the electronic attack affected the current and voltage for each solar cell at the attack simulation time of 0.4 seconds.

Gingival and periodontal diseases, such as gingivitis and periodontitis, are critical public health concerns that can lead to severe complications if left untreated. Early and precise diagnosis is crucial to mitigate the progression of these conditions and improve oral health outcomes. This study investigates the application of convolutional neural networks (CNNs) in diagnosing gingival diseases using medical images, including X-rays and intraoral photographs. Several CNN architectures, including VGG16, Sequential CNN, MobileNet, InceptionV3, and suggestions for a voting method to enhance the prediction, were evaluated for their performance in classifying gingival conditions. MobileNet emerged as the most effective model, achieving a test accuracy of 92.73%; the suggested method relies mainly on its positive result. When the MobileNet's result is false, the process takes the voting result using the other methods. This boosts the accuracy to 96%. Surpassing other models in precision and recall metrics. Pre-processing techniques such as normalization using the CIELAB color space and data augmentation significantly enhanced model accuracy. The study employed robust evaluation methods, including 10-fold cross-validation and hyperparameter tuning, to ensure model reliability and generalizability. The findings highlight the transformative potential of AI-powered diagnostic tools in dental healthcare. By leveraging lightweight and efficient architectures like MobileNet, these tools can be deployed in resource-limited settings, offering real-time diagnostic support to healthcare professionals. Future work will focus on expanding datasets, exploring ensemble models, and improving interpretability to further enhance diagnostic accuracy and clinical applicability.

Hybrid aluminum composites are frequently used in industrial applications because of their attractive mechanical and tribological properties to weight ratio, such as hardness, strength, and wear resistance. This work investigated the mechanical properties, microstructure, density, and wear behavior of Al-6061 matrix reinforced with different percentages (2 and 4 Wt.% for each Al2O3, SiO2, SiC, and TiO2) and (2 and 4 Wt.% for each Al2O3, TiO2, and SiC, SiO2) hybrid composites at 50 μm average size using the powder metallurgy technique. The process of creating a component in net or nearly net shape is known as powder metallurgy. Due to its benefits, including great dimensional control and the avoidance of intermediate machining operations, P/M is one of the fastest-expanding pathways in many industrial applications. According to the results, the optimum hardness and density were 95 HV and 3.1 g/cm3, respectively, for the hybrid composite (Al- 2SiC 2SiO2) after the sintering operation. The highest hardness was (95HV) at the addition hybrid composite (Al- 2SiC 2SiO2). Microstructural characterization using optical microscopy shows that the hybrid composite (Al-2%SiC 2%SiO2) was the best. The SEM photographs of the worn surface of the Al-2%SiC/2%SiO2 hybrid composite exhibited the least wear losses and grooves. The lowest wear rate was (13*10ˉ6) at the addition of hybrid composite (Al- 2SiC 2SiO2) at load (5N).

Energy-autonomous systems have evolved in response to the quick deployment of 5G networks and growing presence of Internet of Things (IoT) devices. One reasonable approach is hybrid RF energy collecting using rectenna designs. Optimizing antenna performance for multi-band operation is a significant design difficulty. This article presents a complex rectenna design targeted toward hybrid energy harvesting in 5G networks. The design maximizes the geometry of a microstrip patch antenna running at 2.4GHz and 5.8GHz using a Binary Genetic Algorithm (BGA) based on Artificial Intelligence (AI). By use of binary representation of the antenna's patch form, the problem becomes combinatorial optimization. Using Schottky diodes from the Skyworks SMS7630 and Avago HSMS 285B families, the optimized antenna combines a commercial RF rectifier with nine-stage voltage doubler branches. After 250 generations with a 50,000-population count, the BGA produced antenna designs with widths of 810 MHz (5.14–5.95 GHz) and 200 MHz (2.38–2.58 GHz). Obtained were return losses of –41 dB at 2.4 GHz and –38 dB at 5.8 GHz along with matching gains of 6.2 dBi and 7.12 dBi. With input power levels kept at 6 dBm, the rectifier showed maximum conversion efficiencies of 70% at 2.4 GHz and 42% at 5.8 GHz. Using a 1 kΩ load resistor to provide impedance matching and preserve a power conversion efficiency of 40%, outside testing generated DC output voltages of 92.6 mV and 64 mV. For ambient RF energy collecting in 5G and IoT devices, the suggested AI-optimized rectenna design shows great efficiency and dependability.

educing carbon dioxide (CO2) emissions into the atmosphere is the most crucial objective since this results in higher temperatures, pollution, health problems and acid rain. This research focuses on the modeling and optimization of CO2 capture from flue gas by deep eutectic solvent (DES) synthesized from choline chloride/monoethanolamine (ChCl/MEA) using statistical design of experiments (DoE). The synthesized DES and the raw materials were characterized for the presence of functional groups using Fourier transform infrared (FTIR) spectrometry. The impact of three process parameters, operating temperature (25–45°C), molar ratio of ChCl to MEA (0.1–0.5) and inlet CO2 concentration (5–20%) on the CO2 absorption loading performance were investigated. A model to correlate the impact of process parameters on CO2 absorption loading was constructed using the response surface methodology (RSM) in conjunction with central composite design (CCD). The analysis of variance (ANOVA) validated the quadratic model's high significance at a 95% confidence interval to identify the optimal process parameters for the absorption performance. Furthermore, CO2 absorption loading was computed according to the experimental data; the optimal process parameters to achieve the maximum CO2 absorption loading at 8.647 mole CO2/kg solvent at a molar ratio of ChCl to MEA 0.1, an inlet CO2 concentration of 20% and an operating temperature of 32°C

Medical diagnostics Interpretation of images is a important activity: the number of images is growing continuously, and the number of specialist radiologists is limited globally, which often results in late diagnosis and possible clinical misinformation. The paper under analysis analyzes the BLIP model, which is an automatic medical image clinical captioning model. To refine the BLIP model, a methodology was designed based on more than 81,000 radiology images with Unified Medical Language System (UMLS) identifiers, which were obtained by the ROCO (Radiology Objects in Context) dataset. A representative subset of 1,000 images was chosen to fit within computational limitations- 800 images were used in training, 100 in validation and 100 in testing, but with the preservation of representation across major imaging modalities. They trained the model on transformer-based encoder-decoder with cross-attention mechanisms. The four key contributions of this work are (1) domain-specific fine-tuning of the model to the radiological setting, (2) the use of standardized medical terminology by using UMLS concept unique identifiers, (3) integration of explainable AI with attention heatmaps and post-hoc explanations (SHAP and LIME), and (4) evaluation of performance using accepted NLP metrics. The model attained a high semantic and clinical agreement with quantitative scores of 0.7300 (BLEU-4), 0.6101 (METEOR), and 0.8405 (ROUGE). These results prompt the idea that AI-based image captioning has a considerable potential in facilitating clinical documentation and increasing the reliability of radiological assessments.