DIYALA JOURNAL OF ENGINEERING SCIENCES

The growing adoption of Internet of Things (IoT) and edge computing has increased the need for effective real-time object detectors that can operate in resource-constrained environments. YOLO (You Only Look Once), a leading state-of-the-art object detection algorithm, is well known for its remarkable real-time performance in a variety of applications. However, traditional YOLO models remain computationally heavy for resource-constrained environments since they are designed for high-performance systems, making them less practical for low-power, embedded platforms such as Raspberry Pi, ARM-based processors, and NVIDIA Jetson edge devices. This paper aims to investigate and analyse optimization strategies that enhance YOLO’s efficiency for edge deployments and provides a comprehensive review of various optimization techniques to overcome the deployment challenges. Two main approaches are explored, structural modification using lightweight modules like ShuffleNet, MobileNet, and GhostNet, and model compression via knowledge distillation, quantization, and pruning. The reviewed works demonstrate significant reductions in model size and complexity, with generally enhanced inference speed and improving accuracy, however, in certain cases, a slight drop in accuracy and frame rate occurs as the cost of achieving higher efficiency. Structural modifications generally support model stability, efficiency, and generalization, while compression-based techniques further improve models’ compactness and inference throughput. A combined or hybrid optimization strategy offers the most balanced solution, achieving strong detection accuracy with reductions in model size, GFLOPs, and overall inference cost. This narrative synthesis review provides guidance for developing scalable, energy-aware YOLO models suitable for edge-based detection applications in fields like autonomous vehicles, smart cities, and IoT-driven systems.

Expansive soils are responsible for extensive infrastructure damage worldwide, prompting the urgent search for innovative, sustainable stabilization methods. Nanomaterials have emerged as a promising next-generation solution capable of addressing both performance and environmental challenges. This review investigated the use of nanomaterials such as nano-silica, nano-alumina, nano-copper, nano-MgO, and nano-calcined clay and terrasil, and a combination of nanomaterials with lime or cement (hybrid) as sustainable stabilizers. It was found that plasticity index (PI) decreases by 20-60%, optimum moisture content decreases of 10-12%, and maximum dry density increases of 5-10% for ≤1% nonadditive. Swell potential and swell pressure typically reduced by 20-30% or more at dosages ≤1-1.5%, while hybrid mixes achieve even higher reductions. Strength gains were particularly significant: unconfined compressive strength (UCS) commonly doubles in the short term and exceeds 5 times untreated values with long-term curing, reaching up to 6 times with hybrid stabilization. Similarly, soaked CBR values improve by about 2 times with nanomaterials alone and up to 5-6 times with hybrid systems. These mechanical enhancements were supported by microstructural evidence showing pore filling, formation of cementitious gels (CSH, CAH, MSH, CASH), and surface coatings that densify soil, strengthen particle bonding, and reduce water ingress. Performance was strongly dosage- and material-dependent, with excessive amounts sometimes leading to agglomeration and reduced efficiency. Overall, findings confirmed that nanomaterials, especially when combined with lime or cement, provided a reliable, sustainable, and long-term stabilization approach, offering substantial improvements in expansive soil behavior and geotechnical infrastructure performance.

Latent Heat Storage Units (LHSUs) are widely used for low-temperature thermal applications, but their energy retrieval rate during solidification is restricted by the low thermal conductivity of most phase change materials (PCMs). Enhancing heat transfer during discharging is therefore essential for improving system efficiency and enabling practical solar thermal integration. This study experimentally evaluates the effectiveness of spiral fins in augmenting the discharging rate of a shell-and-tube LHSU filled with stearic acid as PCM and water as the heat transfer fluid (HTF). A custom-developed test rig with adjustable orientation (vertical, horizontal, and inclined) was fabricated, and temperature measurements were captured using 45 K-type thermocouples distributed over five axial planes within the annular PCM region. Discharging experiments were performed at an HTF inlet temperature of 28 °C and 5 LPM flow rate. The results confirm that spiral fins substantially accelerate solidification, reducing the timerequired to reach an average PCM temperature of 35 °C by 26.5%, 25.96% and 24.37% at the middle section for vertical, horizontal, and inclined orientations, respectively. For the entire annulus, the reduction ranged from 17.2% to 19.58%, demonstrating orientation-independent enhancement. The dominance of conduction during solidification minimizes buoyancy-driven convective effects, making spiral fins a reliable design feature for practical installations irrespective of mounting angle. The findings validatespiral fins as a robust heat transfer enhancement method for LHSUs and provide experimental insights for system optimization in real-world thermal energy storage applications

The dramatic rise in the number of Internet of Things (IoT) devices has greatly increased the size of the attack surface of network-based threats, especially high-volume, non-portable DDoS botnet attacks. Our hypothesis is to suggest an explainable intrusion detection system and analyse digital signals of raw IoT network traffic. We train a Histogram-based Gradient Boosting Classifier (HGBC) to identify benign and malicious traffic based on 11 classes (10 attack-related, 1 benign) on the N-BaIoT dataset. To reduce bias, the model has been trained on a strictly pre-processed and balanced subset of the data. We apply SHapley Additive exPlanations (SHAP), a game theory-based framework, to gain insight into complex model predictions that are security-relevant, despite the black-box nature of the model. This SHAP-enhanced method classifies and orders the most significant features, and it is found that mutual information and packet jitter characteristics descriptors (e.g., MI_dir_L0.1_mean) are decisive when identifying coordinated attack actions. The model reported the macro-averaged accuracy, recall and F1-score as 1.00 on a held-out test set. The three contributions of the work can be summarised as: (i) an end-to-end interpretable multi-class IoT DDoS detector; (ii) a transparent data curation framework that tackles imbalance and redundancy; and (iii) empirical support on how HGBC with SHAP can be highly performant yet offer actionable insight into the feature semantics that will inform future security design.

This paper presents a numerical evaluation of pipeline damage caused by an external circumferential semi-elliptical crack. The study utilizes the Abaqus software and the Extended Finite Element Method (XFEM) to model and analyze the crack behavior. Various parameters such as crack size, internal pressure, and loading conditions are investigated to assess their influence on pipeline integrity. The results reveal that as internal pressure increases from 300 to 600 bar, the pipe’s bearing capacity significantly decreases due to heightened stress concentrations around crack tips, leading to increased hoop stress and Stress Intensity Factors (SIFs), which accelerate crack propagation. Higher pressures also promote crack nucleation and growth, further reducing the effective cross-sectional area and weakening the pipe\'s load-carrying ability. Additionally, the analysis highlights the critical influence of defect size (a/t ratio) on stress distribution and residual strength: as the a/t ratio increases, the pipe becomes more vulnerable to failure at lower stress levels. The critical crack size is identified at the intersection of the resistance curve and the ultimate stress line, beyond which failure occurs before reaching the material’s full strength. Non-physical regions, where resistance exceeds ultimate stress, are disregarded to ensure realistic defect assessments. Under an internal pressure of 30 MPa, pipes with a/t ratios of 0.50 and 0.62 remain within the safety zone, while those with a/t ratios of 0.75 and 0.80 enter the failure zone, indicating a substantial loss of structural integrity and an increased risk of fracture.

Steelcorrosion in acidic environments is a critical industrial challenge, necessitating effective yet eco-friendly inhibitors. This study aims to address this problem by introducing a novel, green alternative: frankincense extract (FE). The distinctive contribution of this work lies in the comprehensive investigation of FE natural, sustainable, and economically viable resin as an effective corrosion inhibitor for carbon steel in 1 M HCl. The research employs an integrated methodology, including electrochemical techniques (potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS)), adsorption isotherm modeling, surface analysis (FT-IR and FESEM/EDX), and density functional theory (DFT) calculations. Key results demonstrated that FE exhibited excellent inhibition performance, achieving a remarkable efficiency of 87.2% at a concentration of 16 g/L and 303 K. PDP analysis confirmed FE acts as a mixed-type inhibitor. EIS results corroborated this performance, showing 75.89% inhibition efficiency. Adsorption behavior adhered to the Langmuir isotherm, and thermodynamic parameters revealed a spontaneous and exothermic process indicative of mixed physisorption and chemisorption mechanisms. Kinetic studies further supported this by showing an increased activation energy barrier for corrosion in the presence of the inhibitor. Surface analysis confirmed the formation of a protective adsorbed film on the steel. Quantum chemical computations provided molecular-level insights, correlating the electronic structure of key FE constituents with their adsorption strength. The study establishes FE as a cost-effective, sustainable, and highly efficient green corrosion inhibitor, offering a viable solution for protecting carbon steel infrastructure in aggressive acidic media.

Turbojet engines are now being given serious consideration owing to the high thrust power that they can deliver. The challenge that has arisen for the producers and designers of these engines relates to the problem of matching the compressor and the turbine. These components are crucial for the overall performanceof the engine but differ distinctly. The problem of stallsmay arise due to these disparities, thereby impacting onthe flow of air and the subsequent output. In order to counter such a problem, a simulation approach has been designed by us that has an error margin of less than 0.03 as far as fuel consumption per unit thrust force. The thrust force has been determined within an accuratedeviation of less than 1000 pounds. The accuracy of the simulation approach has been checked by using the data of the Pratt & Whitney J57 Turbojet engine.The simplified experimental outcomes estimated fuel consumption per unit thrust with an error margin of under 0.03. Furthermore, the thrust force for various engines was assessed with an accuracy deviation of less than 1000 pounds. The determination of the compatibility line was additionally corroborated by comparing the simulation results with established reference values for different turbojet engine components. The simulated compatibility line exhibited a 92% overlap accuracy for the Pratt-Whitney J57 engine, thereby affirming the reliability of the method.

The number of Internet of Things (IoT) devices is increasing rapidly, with estimates predicting more than 41 billion devices by 2025. This growth has also expanded the attack surface, making IoT networks highly vulnerable to cyberattacks. Traditional intrusion detection systems are not suitable for IoT because they depend on known attack signatures, require high computational power, and many false alarms. These limitations make them difficult to deploy on resource-constrained edge devices. The aim of this study is to develop a lightweight and accurate intrusion detection framework specifically designed for IoT environments. We introduce IoT Network Vigilant, a hybrid framework improves intrusion detection performance while remaining efficient enough for real-time use. The framework consists of three key parts. First, we design 27 new IoT-specific features that capture device behavior, traffic asymmetry, and temporal patterns. Second, we apply a two-stage Flower Pollination Algorithm (FPA) to select the most useful features. The first stage ranks features using mutual information, and the second stage removes redundant features using correlation analysis. This process reduces the dataset size by about one-third. Third, we employ Binarized Neural Networks (BNNs), which use binary weights and activations, allowing fast and low-power classification. The model is tested on the IoTID20 dataset, and class imbalance is handled using SMOTE. The results show strong performance, with 98.43% accuracy, 99.03% precision, and 97.32% recall. These scores represent a 4.5% improvement in accuracy compared with existing methods. Overall, this framework offers a robust, efficient, and deployable intrusion detection solution for modern IoT networks.

This study presents the design and performance evaluation of a hybrid optical communication system capable of supporting high-rate data transmission across both terrestrial and underwater environments. Conventional free-space optical (FSO) and underwater optical wireless communication (UOWC) systems have typically been investigated in isolation, limiting their applicability in scenarios requiring seamless air–water connectivity. To overcome this limitation, we propose and analyze an integrated hybrid FSO–UOWC system using OptiSystem simulations. The system incorporates an Erbium-Doped Fiber Amplifier (EDFA) to compensate for atmospheric attenuation and improve the received optical signal. Simulation results demonstrate that the proposed design significantly enhances link quality, with the received signal power improving from –29.5 dBm (without EDFA) to –5.48 dBm (with EDFA). Correspondingly, the quality factor increased from 16.18 to 60.05, ensuring reliable transmission with a bit error rate (BER) maintainedbelow 10⁻⁹. The hybrid link also supports reliable operation over water depths of up to 30 m, extending its practical applicability for marine monitoring, defense, and offshore communication networks. Compared to conventional single-medium solutions, the proposed system provides higher capacity, longer range, and improved robustness against alignment and attenuation challenges at the air–water interface. Overall, this work highlights a novel hybrid optical framework that effectively bridges terrestrial andunderwater channels, offering a promising solution for next-generation broadband optical communication systems.

The fifth-generation (5G) New Radio (NR) introduces stringent delay and reliability requirements to support diverse services, including enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). A major challenge is the efficient coexistence of these heterogeneous services, as eMBB demands high throughput while URLLC requires extreme reliability and minimal latency. This study investigates the coexistence of eMBB and URLLC under time-varying channel conditions and formulates a many-to-many URLLC resource allocation problem. To address this, we propose an opportunistic resource allocation scheme based on a genetic algorithm (GA) that dynamically optimizes resource block (RB) allocation for both services. The GA employs a heuristic fitness function designed to maximize eMBB fairness and throughput while ensuring URLLC reliability. Simulation results demonstrate that the proposed approach significantly improves overall system performance: eMBB fairness exceeds 95%, and the average eMBB data rate increases by approximately 500 Kbps compared to random allocation. Moreover, URLLC users maintain a consistent data rate of 600 Kbps, outperforming benchmark methods while satisfying the 99.999% reliability requirement. The results confirm thatthe proposed GA-based approach effectively balances throughput, fairness, and reliability, making it a promising solution for future 5G networks with mixed traffic demands.

The parameters of an electrical power system have wide variations during the operation of electrical devices; these changes are required to be measured using an accurate meter. A cost-effective Digital Sampling Multi-meter (DSMM) using a microcontroller and two sensors was proposed to overcome the complexity of traditional power meters. Voltage and current signals from sensors were discretely time-sampled and then analysed based on the Instantaneous Power Calculation Algorithm (IPCA) using an Arduino to measure six of the electrical quantities. It Root Mean Square (RMS) terminal voltage and load current, real, reactive, and apparent power, and finally Power Factor (PF), then these were displayed on both the LCD and the PC serial monitor. Also, the instantaneous waveforms of the voltage, current, and power are easily analysed and plotted with any type of load. The IPCA algorithm was adopted because of its real-time measurement, high accuracy, ease of implementation, and low cost.Measurements are performed forlinear loads (resistive, inductive, and capacitive) and non-linear loads (rectifier circuits). The proposed meter's accuracy was found to have acceptable relative percentage errors for most measured parameters, especially at linear loads (maximum relative error for Vrms0.7% at inductive load, 5% for Irmsat capacitive load, and 5% for PF at capacitive load), calculated based on the FLUKE Power Quality Meter, which was considered a high-quality measurement instrument.

This study proposes a novel liquefied-natural-gas-assisted combined cooling, heating, and power (CCHP) plant in which the cryogenic energy released during regasification is cascaded through a closed nitrogen Brayton cold-box, medium-temperature heat is coupled to a toluene organic Rankine cycle (ORC), and a lithium-bromide/water absorption refrigeration cycle (ARC) provides cooling while domestic hot water at 60 °C is recovered. A comprehensive steady-state model was developed in Engineering Equation Solver(EES) and benchmarked against three published data sets; the largest deviations were 2.8% for the gas-turbine block and 0.3% for the chiller, confirming the model’s reliability. Energy and exergy balances were solved simultaneously for every component, enabling consistent comparison of power-only, CHP, and CCHP modes for a 160-MW-class plant. Under nominal conditions the system delivers 168.8 MW net power, 11.7 MW cooling, and 143.7 kg/s hot water, with energy and exergy efficiencies of 93.2% and 51%, respectively. Parametric sweeps of combustor outlet temperature, compressor pressure ratio, LNG regasification pressure, and heat exchanger pinch points expose design trade-offs, pinpoint a practical 6–7 MPa regasification-pressure window, and versus a recent benchmark lift exergy efficiency from 41% to 51% (~24% gain) while cutting specific fuel consumption by 18% at equal net power. These improvements are achieved without exotic working fluids or deepcryogenic stages, making the concept deployable with current hardware. The integrated methodology and data set form a transferable benchmark for future LNG-based energy hubs and highlight clear upgrade paths most notably staged combustion and advanced heat-exchanger networks to push performance even further.

Generative design is a technique that effectively produces lightweight yet structurally robust components. However, the final result is highly dependent upon manufacturing constraints during the design phase. This work investigates how machining, 3D printing, and die casting constraints shape a reengineered car brake pedal. Each option was constructed within Autodesk Fusion 360 using identical loads and fixations and material characteristics. Variations in performance were evaluated by FEA with respect to weight, stress pattern, stiffness, movement under load, and safety margin. In contrast, the greatest mass reduction was attained by the AM-based design; the mass dropped from 1.36 kg to 0.58 kg, a 41.3% drop, while still meeting the minimum required safety margin of at least 2.0. Meanwhile, the average mass for the die-cast design reached 0.70 kg, based on proper taper and thickness rules of the part while maintaining strength. On the contrary, the machined-limited part had the heaviest mass value of 2.77 kg. Nevertheless, it portrayed the highest stiffness; besides that, it attained the highest safety value, FoS = 5.84, highlighting how subtractive manufacturing methods sacrifice mass for durability. Manufacturing limits impact geometric design results by limiting feasible forms while varying mechanical component functionalities. Accordingly, this paper presents an investigation into different manufacturing techniques and their impacts on design solutions, using examples of different techniques to highlight efficiency. Ways of proposing lighter brake pedal designs that are easier to produce involve adjusting material usage while easing assembly steps to enhance feasibility without compromising performance.