Misan Journal of Engineering Sciences

A Predictive Modeling Framework for Pressure Drop Estimation and Optimal Design of Pressure Boosting Stations in Oil and Gas Transmission Pipelines

Ammar Alkhafaji — Sun, 28 Jun 2026 00:00:00 +0000

Accurate estimation of pressure drops is a critical component of efficient operation of transmission pipelines as well as pipeline design of oil and gas pipelines. Although they are effective in some situations, traditional techniques are not always effective and they lack flexibility and precision in multi-flow regime and structure of pipelines that are large in dimensions. According to the given research paper, a generalized predictive modeling framework can be proposed to be used to supplement the traditional hydraulic correlations with the assistance of analytically obtained, as well as computationally efficient expressions. The model is a combination of the physical variables flow rate, pipe diameter, roughness, fluid properties and length to provide a more accurate prediction of pressure losses. Besides estimation, the model is also employed in determining as well as optimization of the design and space layout of pressure boosting stations that is a key aspect of ensuring sustainability of operational pressure and energy savings through employing long distance pipelines. The sensitivity tests and the comparative analysis confirm the applicability of the framework to a broad range of operation situations. Results have shown that the enhanced model has high advantages in terms of predictive power and power efficiency and is a scalable, versatile solution to engineering issues in oil and gas infrastructures. The research will help to make the operation of the pipeline more sustainable and cost-efficient and make a wise choice in the conditions of the real life.

Behaviour of Concrete-Filled Aluminium Columns with Curved Sections

Rawa Al-Jarosh — Sun, 28 Jun 2026 00:00:00 +0000

The structural performances of concrete-filled aluminium tubular (CFAT) stub columns with curved sections subjected to axial compression are studied using finite element modelling in the present investigation. The nonlinear properties of concrete and aluminium materials, along with the interactions among their components, are all taken into consideration while generating the finite element models. For concrete-filled aluminium tube columns with circular and rectangular sections, Zhou and Young (2008, 2009) obtained experimental ultimate strengths and load-axial strain relationships that is compared to the results obtained by finite element model. Various parametric analyses on essential materials and geometric features have been performed employing the validated finite element models. The mode of failure, load axial displacement relationship, and ultimate strength are investigated. The parametric investigation specimens are divided into four groups that present the influence of different parameters on the behaviour of a concert filled with short aluminium columns, which are cross-section shapes, aluminium proof stress, concrete cylinder strength, and tube thickness. To analyse the effect of cross-section parameter, these three sections—circular, elliptical, and round-ended—have been examined through first group. In the second group normal and high strength aluminium alloy are used in the tubes. Normal and high strength concrete effect are analysed in the third group. 1.9, 3, 5 mm thickness are investigated in the fourth group. To prevent overall column buckling, the lengths of the columns were selected to ensure that the ratio of length to diameter was consistently maintained at an approximate value of 3. Due to the outcomes, the circular cross-section had the greatest ultimate strength and ductility, therefore being the most desirable cross-section. A clear enhancement in ultimate load with increasing the aluminium proof stress, concrete cylinder strength, and aluminium wall thickness.

Forecasting Potential Wheat Cultivation Zones In Zakho District, Northwest Iraq Using Remote Sensing Data And Machine Learning Techniques

Kaifi Chomani, Masood Musheer Gori — Sun, 28 Jun 2026 00:00:00 +0000

This research highlights the significant challenges of accurate wheat area mapping in the Zakho district of the Kurdistan region of Iraq, through the development of an accurate wheat classification and mapping system by using the integration of optical and radar imagery from Sentinel 1 and Sentinel 2 datasets, geographic information system (GIS), and machine learning algorithms. The Support Vector Machine (SVM) were used in this research to classify wheat and non-wheat areas utilizing multi-source satellite imagery, with combination of normalized difference vegetation index (NDVI) and Ratio Vegetation Index (RVI) of optical imagery, along with VV and VH polarization bands in radar data. 54 wheat farm data collected as a sample to be used as a training data for SVM method and validation to create wheat map for the study area. The results indicated that Zakho district are covered by 126,486.07 donums of wheat which is 9.2% of total area of the district in 2023. The accuracy of the classification was validated using field-collected ground truth samples, achieving an overall accuracy of 95%, and further cross-validated by comparison with official agricultural directory records. The research addressed the significance of using multiple combinations of optical and radar datasets with GIS, remote sensing and machine learning techniques to precisely map crop and agricultural farms in complex geopolitical regions. The developed maps provide accurate evidence to support the rights of farmers, promoting informed decision-making and equitable resource distribution. The outcome of this research can be used in future agricultural assessments, resulting in economic stability and sustainable development

AI Proactive Agent of IoT-MANET for QoS Provision

Karrar Majeed Soaieb, Basim Khalaf Jarullah Al-Shammari — Sun, 28 Jun 2026 00:00:00 +0000

Mobile ad hoc networks (MANETs) is one of vast array of wireless technologies in the internet of things IoT. The merging of IoT and ad hoc networking offers a strong boost to applications within smart cities, healthcare monitoring, as well as disaster management and industrial automation. With improvements in IoT data rates, network capacity, latency, routing protocols, power efficiency along with Quality of Service (QoS), Next Generation Networks (NGNs) and IoT-based Mobile Ad Hoc Networks (IoT-MANETs) are envisioned as end-to-end solutions to existing networking challenges that will yield required Network QoS (NQoS). The rise of using IoT-MANETs for many applications, leads to the QoS supporting. In this study, we proposed network cross-layers design of IoT-MANET QoS management and AI proactive agent machine model accordingly aims to achieve QoS levels and management by meet the NQoS. The results of the system model is that the QoS levels can be changed dynamically between QoS ranking points depending on the input NQoS parameters of the IoT-MANET.

Decoupling MIMO of Planar Array of Circular Microstrip Antenna for Wireless Applications

rusul laftah — Sun, 28 Jun 2026 00:00:00 +0000

This paper describes the creation and simulation of a circular patch antenna array designed for 5.8 GHz operation. The array incorporates three inset-fed microstrip circular patch antennas for beamforming, which helps to reduce the substantial path loss that exists in high-data-rate mm-5G mobile station environments. The configuration consists of three circular patch antenna arrays with separate strip line feeds. In the proposed design, a 7.2 mm-diameter circular patch is placed. To model the antenna array, a FR-4 substrate with a relative permittivity (εr) of 4.4 was employed. Strip line feeding techniques were applied, and the substrate's dimensions are 27 x 67 x 1.6 mm³. The acquired bandwidth is excellent for WLAN applications because it spans frequencies between 5.15 GHz and 6.56 GHz. Over the stipulated bandwidth, the measured gain reaches 4.1 dB with an input impedance of around 50 Ω. Strong agreement was established between the simulated results and experimental data. The design and simulation method made use of the Finite Element Method (FEM)-based High Frequency Structural Simulation (HFSTM) tool.

Artificial Intelligence and Machine Learning in Water Resources and Environmental Engineering: A Systematic Review and Future Research Agenda

Sarah Mohammed — Sun, 28 Jun 2026 00:00:00 +0000

Artificial Intelligence (AI) and Machine Learning (ML) are now leading-edge technologies being used to develop new ways to manage water resources and environmental engineering that outperform traditional statistical models by leveraging their capacity to manage nonlinear dynamics and large-scale multivariate datasets. The purpose of this systematic review is to determine the current status of AI/ML applications in the context of each method using a structured approach to synthesizing available evidence from primary databases such as Scopus and Web of Science. The methodologies used in this review are divided into a central taxonomy: predictive modeling using supervised learning; discovering patterns through unsupervised learning; making adaptive decisions using RL; and example hybrid models which use both analytical and data-driven algorithms.
There are many insights to be gained from this research study artificial intelligence(AI) has shown a high degree of accuracy in making flood predictions across various locations worldwide by means of Long Short-Term Memory neural networks (LSTM), with some reported values being nearly 99 % Nash-Sutcliffe Efficiency for specific locations in the tropics [1]; however, these findings must be interpreted carefully since they lacked adequate evaluation and were based only on one region where there was sufficient historical data, whereas this may not hold true in an area where little or no historical data exist and because these different types of locations of evaluating AI’s use. Along with AI, Internet of Things (IoT) devices have enabled real-time monitoring of water quality with close to .99 precision. AI is being used in the field of environmental engineering for automation of sorting waste with accuracy’s above 95% in the transition toward a circular economy. A significant hole remains in our knowledge regarding the advancement of AI/ML technology in water resources and environmental engineering. The vast majority of studies devoted to studying AI/ML in the water resources and environmental engineering industries utilize various AI/ML models to perform predictions of future conditions; however, few of these studies look at real-life applications for these AI/ML technologies or how they will be implemented at scale in countries that have limited access to quality data, such as Middle Eastern and dry developing countries. This gap was the motivation behind conducting this review; to consolidate evidence related to generalizability of model outputs, the need for open access data, and the movement from purely predictive models to decision-support systems. Nevertheless, critical challenges continue to exists such as reproducibility problems that arise from lack of freely available datasets/ code and lack of interpretability caused by complexity of deep neural net models. To resolve these drawbacks, the present review recommends: (i) To adopt PIML for improved accuracy through enforcing prior laws of physics within a ML model as well as increased clarity regarding the underlying mechanism of an ML prediction. (ii) Create a standardized data-sharing protocol to enable the use of benchmark datasets that are identified across the community. (iii) Use XAI techniques (SHAP and LIME) to better align an ML model’s predictions with howstakeholders are using those predictions for their decisions. (iv) Increase research on transfer learning strategies to improve the applicability of ML models in areas where there is limited data climate variation. All four of these approaches are the directions of future research proposed in this article.

Analysis of Power Quality in a Hybrid Power System

Mohammed sahib — Sun, 28 Jun 2026 00:00:00 +0000

Renewable energy sources (RES) play a significant role to tack a climate change, reduce an energy cost, improve a power reliability. However, they will be effect on the power quality (PQ) of production energy when it is connected with utilized grid because of their randomness generations, resulting in, voltage instability, fluctuations in frequency, harmonic distortion and low power factor. In this paper, PQ issues and their improvement methods, including; filters, Flexible Alternate Current transmission system devices, and classifying the control techniques; traditional, intelligent, and hybrid, are reviewed. In addition, a preliminary investigation on a Dynamic Static Compensator (DSTATCOM) device with a Proportional-Integral (PI), is conducted using a 25kV distribution networks of households and verified its capacity to correct PQ issues and provide voltage stabilization. The motivation of this review article was to allow research customers to understand PQ issues and solutions but also a starting part whereby comparison can be made on whether or not PI control was beneficial in the DSTATCOM, and sets a regulatory limit to conduct more intense and feasible solutions based on intelligent and hybrid systems.

Optimization of Voltage Profile and Power Loss Reduction in Iraqi Distribution Networks under High Load Conditions with Renewable Energy Integration

ALAA Kadhim — Sun, 28 Jun 2026 00:00:00 +0000

Electrical distribution networks in developing countries, including Iraq, suffer from significant power losses and voltage instability, especially under high-load conditions during peak summer demand. Conventional approaches such as capacitor placement, distributed generation (DG), and network reconfiguration have been widely applied; however, these methods are typically implemented independently and often fail to provide sufficient performance improvement under realistic operating conditions.To overcome these limitations, this study proposes a hybrid optimization framework that integrates solar photovoltaic (PV) generation, optimal capacitor placement, and network reconfiguration within a unified model. A modified IEEE 33-bus system is used to simulate Iraqi network conditions with a 40% load increase. The optimization problem is formulated as a multi-objective function and solved using Particle Swarm Optimization (PSO).The results demonstrate that PV integration alone reduces power losses by approximately 24%, while the proposed hybrid approach achieves up to 42% reduction. Furthermore, the minimum bus voltage is improved from 0.88 pu to 0.975 pu, ensuring compliance with standard operational limits.These findings demonstrate that the proposed approach provides a more effective and practical solution for improving the performance and reliability of distribution networks under high-load conditions.

A Comprehensive Review of Path Loss Prediction Methods and Models of Vehicular to Infrastructure (V2I) Communication

Zeinab Mohammad Kazim — Sun, 28 Jun 2026 00:00:00 +0000

Vehicle-to-Infrastructure communication is a key enabler of Intelligent Transportation Systems, supporting traffic efficiency and road safety. However, accurate path-loss prediction remains challenging due to dynamic urban propagation conditions, frequent non-line-of-sight situations, mobility, and heterogeneous roadside deployments. This paper presents a comprehensive review of V2I path-loss prediction methods, encompassing classical empirical and semi-deterministic models, as well as modern artificial intelligence approaches, including machine learning and deep learning. We summarize the main modeling assumptions, required inputs, strengths, and limitations across different environments and frequency bands, and we highlight recent trends such as hybrid physics-guided learning, data-driven feature engineering, and interpretability tools. Finally, we discuss open research challenges and future directions toward robust, generalizable, and practical V2I path-loss modeling.

In-situ synthesis of Medium-Entropy (HfZrTi)B2 Ceramic Composite from Initial Oxide Powders

Ekhlas Khalaf — Sun, 28 Jun 2026 00:00:00 +0000

In the current work, the B2 medium-entropy, ceramic composite (HfZrTi)B2 was in situ synthesized through reactive spark plasma sintering (RSPS) at 2000 °C and at two loadings of B4C powder, 10 minutes. The obtained specimens were described based on the microstructure, relative density, hardness, and fracture toughness. The data show that the increase in the B4C content significantly enhances the reduction reaction of the oxide precursors, therefore, leading to the formation of the (HfZrTi)B2 phase to a higher degree. The lattice-parameter calculations provided a value of 14.7668 A of the stoichiometric composition and a value of 3.14985 A of the sample with the increased B4C content, which has validated the insertion of a larger lattice distortion in the sample with the higher B4C content. Additionally, the specimen with higher B4C also obtained a lower porosity of 12.9% and a hardness value of 10.6 Gpa, as compared to the other specimen with lower B4C loading, which had a porosity of 17.7% and a hardness of 9.4 Gpa. The fracture toughness could not be measured due to relatively high porosity.

Effect of Nano-Zirconia Dip Coating on the Surface Properties of PEEK for Dental Implants

Baydaa Rahi — Sun, 28 Jun 2026 00:00:00 +0000

Dental implants that are biofunctionally coated are placed in a strategic location in bone where they assume an integral function in augmenting the process of healing and promoting bone growth in the implantation site. The purpose of this present study was to evaluate the impact of two different nano-zirconia (ZrO₂) coating thicknesses on dip-coated polyetheretherketone (PEEK) implants by using zirconia nanoparticles with a particle size of 40–50 nm and coating layer thicknesses ranging from 18.25–35.14 μm and 58.67–65.34 μm. Both an uncoated control group of PEEK discs and two groups of PEEK discs coated with varying thicknesses were considered. Surface roughness was determined using a profilometer, and field-emission scanning electron microscopy (FE-SEM) was used to study coating morphology, nanoparticle distribution, and coating thickness by image analysis. X-ray diffraction (XRD) was used to identify crystalline phases of the coating. Water contact angle measurements were also performed to analyze wettability; the thickest coating had a contact angle of 34.17°, indicating a significant increase in hydrophilicity. Adhesion tests indicated good interfacial adhesion for the thinner coating (5B, ASTM D3359). The thicker layer received a slightly lower rating (4B) due to some coating removal along the cut edges. A decrease in adhesion strength with increasing thickness was attributed to the accumulation of internal residual stresses and decreased interfacial cohesion. Overall, nano-ZrO₂ dip coatings improved surface morphology and interfacial performance, suggesting their potential as a reliable surface modification strategy for dental implants

Hash Function-Based Multi-Layer AES Key Expansion Framework

Bushra Mohammed Jawad — Sun, 28 Jun 2026 00:00:00 +0000

The advanced encryption standard (AES) is one of the most commonly used encryption algorithms because of its effective encrypted capabilities and strong crypto-security. Due to the growing demand for stronger cryptographic protection, researchers have been finding alternative enhanced key management and key expansion methods that will improve randomness, nonlinearities, and statistical security properties within the framework of AES.In this paper, we proposed a hybrid SHA3−AES framework that utilizes three complementary techniques for strengthening AES. SHA3 is utilized to preprocess the master key to produce a hardened key with higher entropy; dynamic external key evolution employs iterative hashing with SHA3 to produce round dependent keys, thus reducing the number of deterministic relationships that exist between round keys; and SHA3 is also incorporated into AES's internal key expansion process to provide increased non-linearity and complexity for generating round keys. The objective of the proposed framework is to enhance diffusion characteristics of the process and increase randomness of the encryption process while preserving the original AES structure. Security analysis indicates that integrating SHA3 improves key unpredictability and strengthens random behavior through dynamic key dependency and continuous key evolution. Furthermore, experimental observations show that the proposed framework provides substantial improvements to the statistical and security metrics (i.e., entropy, avalanche effect, and randomness distribution) when compared to the traditional implementations of AES. Results indicate that the proposed SHA3-AES model provides a second layer of security for applications with the need for a higher level of security against advanced analytical attacks.

Review of Fault Detection Methods in Three-Phase Induction Motors Employed Techniques

Jaafar ALbehadili, Ahmed raisan — Sun, 28 Jun 2026 00:00:00 +0000

In this article, we have examined the major causes of failure that occur within three-phase induction motors. The factors contributing to this loss of efficiency and increased downtime are both electrical (i.e. stator winding short circuits, broken rotor bars) and mechanical (i.e., bearing problems, misalignment). We have compared existing methods of detecting faults, including insulation resistance testing, current and vibration analysis, acoustic monitoring and thermal monitoring, magnetic flux analysis based upon their ability to give accurate results regarding costs associated with establishing accurate early detection of faults. The limitations of conventional methods for identifying electrical and mechanical faults have been identified within this study as well as an indication that utilizing intelligent systems (e.g., artificial intelligence and machine learning) can result in faster diagnosis of faults and better predictions for the maintenance of these motors. Finally, paper suggested using a hybrid approach to diagnostics by combining traditional techniques and intelligent approaches to enhance performance at detecting faults and improving the reliability of motors.

A Review on Local Buckling Behavior of Concrete-Filled Steel Tubes Considering Diameter-to-Thickness Ratio and Stiffeners

Abdulazeez Marwan — Sun, 28 Jun 2026 00:00:00 +0000

This paper outlines the systematic review carried out with regards to the structural response and the strengthening action of Concrete-Filled Steel Tubular (CFST) columns with various methods of stiffening. This review performs a critical synthesis of 25 recent experimental studies with regards to the effects of five major parameters: stiffener configuration, diameter-to-thickness ratio (D/t) slenderness ratio (L/t) material properties of steel and concrete (fy, fcu) and the geometry of the column. From this research, it can be noted that different types of stiffening, such as longitudinal ribs, internal lining tubes, and reinforcing bars, assist in minimizing the chances of local buckling and also help improve the bonding between the steel tube and concrete. Additionally, it has been noted that stiffeners play an important role in thin-walled columns because they increase the ductility and load-carrying capacity.

Effect of Surface Geometry on Pool Boiling Heat Transfer of Water: A Study by ANSYS

Yaqoob K. Fanyan — Sun, 28 Jun 2026 00:00:00 +0000

This study explores the impact of the type of heating surface on heat transfer in pool boiling of water, especially the impact of geometric shape on heat transfer efficiency. Through a numerical simulation using CFD techniques in the ANSYS Fluent program. In this study, a two-dimensional model was built to simulate the pool boiling of water in several cases to compare the impact of the geometric shape of the heating surface. Where the first simulation was implemented using a smooth copper surface with constant heat flux (30????????????2⁄) and then the simulation was repeated several times, changing only the geometric shape of the heating surface without changing the material or dimensions. With the preservation of all parameters of the first simulation, including thermal flux and mesh settings, to maintain the accuracy of the results and obtain a correct comparison. Where the first simulation was amended by the geometrical shape of the heating surface by providing it with different geometric shapes (semicircular, square, trapezoidal), with the preservation of the groove area in the three cases. By comparing the results of different shapes and extracted from ANSYS (heat transfer coefficient, overall heat transfer performance, thermal resistance and Nusselt number), it shows that semicircular grooves increase heat transfer coefficient (HTC) by 42.1%, overall heat transfer performance by 42% and Nusselt number by 35%, and decrease the thermal resistance by 42.4% compared to smooth surface. The heating surface with square grooves increase heat transfer coefficient (HTC) by 66.24%, overall heat transfer performance by 65% and Nusselt number by 50.2%, and decrease the thermal resistance by 66.3% compared to a smooth surface. The heating surface with trapezoidal grooves increases heat transfer coefficient (HTC) by 96.92%, overall heat transfer performance by 95% and Nusselt number by 90.62%, and decrease the thermal resistance by 96.7% compared to a smooth surface.
Therefore, by comparing the results of each geometric shape of the heating surface in each case to the smooth surface, and then compare the results of these different shapes with each other, it was concluded that the best geometric shape of the grooves was a trapezoidal geometry. Which achieves the highest value of heat transfer coefficient (HTC), overall heat transfer performance and Nusselt number, and the lowest value of thermal resistance between the difference in geometric shapes of grooves.

Reliable Propagation Model for Satellite Communication over lraqi Regions

Taghreed Noori, Hasanain A. H. Al-Behadili — Sun, 28 Jun 2026 00:00:00 +0000

Satellite communication systems are an important part of the worldwide telecommunication systems especially in areas where terrestrial infrastructure is limited or unreliable. Their performance is highly dependent on the accuracy of models for the propagation of radio waves, which have to account for the effects of the atmosphere such as rain, humidity, airborne dust. This paper shows a comprehensive review and analytical analysis of the satellite signal propagation over Iraqi regions by taking into consideration the unique climatic conditions of this country that are associated with the existence of frequent dust storms, high humidity for a long time in southern regions and seasonal rainfalls in northern areas. The results of the review demonstrate that the accuracy of widely used international propagation models such as ITU-R P.618, Crane and Longley-Rice can be reduced when applied directly to the dust dominant and spatially heterogeneous environment of Iraq mainly because of the lack of local calibration and the representation of the attenuation related to aerosols. Global, regional and Iraqi studies are systematically analysed to assess physical, empirical and hybrid strategies of the propagation to reveal critical gaps of long-term measurements, spatial coverage and standardized modelling practices. Based on these findings, a conceptual hybrid framework which is called the AI-Based Iraqi Propagation Model (AI-IPM) is proposed. The framework combines the use of ITU-R physical formulations with machine learning techniques to adaptively predict the attenuation based on the indicators of meteorological conditions and the link quality. While enhanced robustness is a feature that is anticipated for dust- and humidity-dominated conditions, large-scale validation for across the country scale necessitates the use of standardised long-term propagation data sets. The paper concludes with recommendations of the need for a national Iraqi propagation database to aid reliable planning of satellite communications, especially where Ka-band and Q/V-band systems are concerned.

A Machine Learning Framework for Heart Disease Prediction Using Swarm Intelligence

YOUSIF A SAADOON — Sun, 28 Jun 2026 00:00:00 +0000

Cardiovascular disease continues to be one of the main causes of death around the globe, highlighting the importance of developing precise and dependable predictive models which can facilitate the diagnosis at an early stage and help in making clinical decisions. The last few years have seen the usage of machine learning and deep learning techniques to predict heart disease from structured clinical data with a strong potential; however, obtaining consistently high predictive performance along with model robustness has remained a challenge. The paper presents a new framework for heart disease prediction that is based on the swarm intelligence and ensemble learning concepts and uses tabular clinical data. The method we suggest couples the particle swarm optimization-based feature selection with a swarm-optimized ensemble weighting strategy which allows for the adaptive combination of several complementary classifiers, such as the gradient-boosted decision trees, randomized tree ensembles, support vector machines, and logistic regression models. The robust framework will automatically find the informative feature subsets and will also adjust the ensemble contributions for the maximum predictive accuracy. The proposed model was put to the test on a prominent heart disease dataset that contains 1,025 clinical records along with 13 input features. The results obtained from the experiments indicate that the newly developed swarm-optimized ensemble has 100% classification accuracy, AUC of 1.00, sensitivity of 100%, and specificity of 100%, which are all the same or better than the results reported for the best machine learning and deep learning techniques in the literature. Furthermore, the suggested method is still choosing small feature subsets consistently while getting almost no decrease in predictive performance. This means that the use of swarm intelligence as an optimizer is very effective when it comes to the area of predictive modeling of heart disease with an ensemble approach

Seismic Response Assessment of Al-Adhaim Earth Dam Under Earthquake Loading

Ahmed M Mahdi, hasan Obaid Abass, SAAD SH. SAMMEN — Sun, 28 Jun 2026 00:00:00 +0000

This paper investigates the seismic response of the Al-Adhaim earth dam using the finite element GeoStudio program. The analysis focuses on pore water pressure, horizontal and vertical effective stresses, displacements, accelerations, and liquefaction potential under a 30-second earthquake record. Four instrumented points were selected on the body of dam: two at the crest (A and B) and two near the core foundation (C and D). Results show that pore water pressure variations remain within safe limits, crest points exhibit higher dynamic response than deep points, and the foundation demonstrates strong damping capacity. Liquefaction zones are limited to upstream saturated sandy layers, while the clay core remains stable. Overall, the dam demonstrates satisfactory seismic performance for the studied scenario of earthquake

In the Direction of an All-encompassing Comparison for Autism Diagnosis Using Assembles

Ayad Al-Kanani — Sun, 28 Jun 2026 00:00:00 +0000

Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social communication and behavioral patterns. Accurate and early diagnosis of ASD remains an important research challenge in medical data analysis and machine learning. While many previous studies have focused on individual (base) classifiers, the effectiveness of ensemble learning techniques for ASD diagnosis requires further investigation. In this study, a comprehensive comparative analysis of ensemble methods is conducted using the Adult Autism dataset obtained from the UCI Machine Learning Repository. The preprocessing stage includes handling missing values using the KNN imputation method and reducing the impact of outliers to improve data quality. Subsequently, four ensemble techniques, namely Bagging, Boosting, Voting, and Stacking, are applied and evaluated using WEKA and RapidMiner data mining tools. The performance of the proposed framework is assessed using Accuracy, Precision, Recall, and F1-score metrics. Experimental results demonstrate that ensemble learning methods provide highly competitive classification performance and outperform several corresponding base classifiers on the investigated dataset. The findings highlight the effectiveness of combining preprocessing techniques with ensemble learning approaches for improving ASD diagnosis and prediction.

Three-Test Liver Disease Screening Using Machine Learning: A Cost-Effective Approach Through Feature Prioritization

Ahmed Majid — Sun, 28 Jun 2026 00:00:00 +0000

Standard liver disease screening requires testing eight or more laboratory parameters, which increases costs and limits accessibility in resource-limited settings. This study aimed to identify a minimal test combination that maintains diagnostic accuracy while reducing costs. Seven machine learning algorithms—Support Vector Machine, Boosting, Multilayer Perceptron, Bagging, Random Forest, K-Nearest Neighbors, and J48 Decision Tree—were evaluated using the Indian Liver Patient Dataset (583 patients, 10 laboratory parameters). Features were ranked based on their diagnostic value to identify the most informative tests. Algorithm performance was assessed across six scenarios with progressive feature removal, measuring accuracy, Kappa statistic, ROC area, and F-measure. The Kappa statistic revealed classification issues that accuracy metrics alone masked. Feature ranking identified Total Bilirubin, Direct Bilirubin, and SGPT as the three most informative parameters. Random Forest achieved the best performance using only these three tests (accuracy 72.04%, ROC 0.70, Kappa 0.24), matching the accuracy obtained with all eight parameters. This approach reduces the required tests by 62.5% and screening costs by 60%. Support Vector Machine reached 71% accuracy, but its zero Kappa value indicated failure to learn real diagnostic patterns, instead exploiting the dataset's 71.4% disease rate. Bagging achieved the highest ROC (0.73) but showed lower overall balance than Random Forest. The three-parameter Random Forest model enables accurate and cost-effective liver disease screening, correctly identifying 87% of diseased patients. This approach reduces testing from eight parameters to three without compromising diagnostic accuracy. The feature ranking method can be applied to other diseases where comprehensive testing increases healthcare costs.

Enhancing the Thermal Performance of Air‑Conditioning Systems Using Phase Change Materials: A Comprehensive Review

Iqbal Hussein Alwan — Sun, 28 Jun 2026 00:00:00 +0000

With the use of conventional vapor-compression heating, ventilation, and air conditioning (HVAC) systems, the air-conditioning system electrically consumes most of the electricity for the building, particularly due to peak electrical demand and greenhouse gas emissions associated with air-conditioning systems. This review describes how using thermo-responsive phase change materials (PCM) can help to enhance the thermal performance of air-conditioning (AC), as well as reduce energy consumption. The types of PCM characterised include: organic, inorganic, and composite/eutectic materials; the importance of the characteristics (phase change temperature, heat of fusion, thermal conductivity, cycling stability, and HVAC compatibility) of PCM to any HVAC application is discussed with a particular emphasis on the methods of improving heat transfer and controlling leakages through the use of nano-enhanced and encapsulated composites. Additionally, this paper covers the three strategies of integrating PCM within an AC system, PCM-enhanced heat exchangers; PCM-based thermal energy storage (TES) devices; and PCM-embedded building materials. The results from experimental, field, and numerical studies indicate that PCM integration in AC systems can substantially reduce indoor temperature fluctuations (up to 46%), reduce heating/cooling loads (up to 31%), improves the coefficient of performance (COP) for air-conditioning by 7-88%, shift as much as 89% of the cooling load to off-peak times and produce annual cooling-energy savings of 7-32% - depending on the specific climate and/or configuration types and controls. The thermophysical enhancement techniques (nanoparticle additives, porous supports/metal foams, encapsulation) have been summarized in this review along with significant reductions in CO₂ emissions e.g. in operational terms due to life cycle studies of these techniques but the payback times are dependent on weather and costs. This paper has evaluated the major technical barriers (long term stability, supercooling, phase separation, flammability and corrosion), manufacturing & scalability issues and integration with existing infrastructure along with the various economic and market barriers. Finally, we have identified emerging opportunities for the PCM ‑enhanced cooling systems, such as solid to solid PCMs, bio-based PCMs, multi-functional nano-composites, advanced control or optimisation (including AI-based) and coupling with renewable energy systems, and highlighted a number of research gaps that must be closed for the large scale and cost-effective deployment of PCM-enhanced cooling to be realized

Analysis of Low Cost Inverter (Three Phase – Four Switch) under Abnormal Conditions

myasar younus — Sun, 28 Jun 2026 00:00:00 +0000

This revision explores the principle operation and control of a low-cost four-switch three-phase inverter (FSTPI). Dissimilar the traditional three limb six-switch inverter, the FSTPI topology uses four switches and two DC-link capacitors, thus decreasing circuit components then overall cost. Offering economic advantage, this topology simplifies control difficulty. The inverter’s performance was examined under both balanced and unbalanced load environment via MATLAB simulations. Specific attention was agreed to the consequence of changing load impedances on total harmonic distortion (THD), current and voltage waveforms, and overall efficiency. The results validate that variations in load impedance create only minor variations in THD, which in turn slightly distort the current and voltage signals. Specifically, under the first and second load states, the THD was measured at 2.87% and 5.21. Also, the study reveals an inverse relationship between THD and efficiency: when THD increased from 2.87 the efficiency decreased to 94.6%, whereas at a lower THD value of 5.21 % efficiency improved to 96.6%. Under unbalanced load conditions, the inverter exhibited only a slight increase in distortion of the AC current, while efficiency remained nearly constant compared to balanced operation. These findings confirm the suitability of the FSTPI topology for engineering applications requiring reliable performance under both balanced and unbalanced load conditions..

Utilizing GIS for Estimation Bearing Capacity of Shallow Foundation in Baquba City, Iraq

Abeer Jasim Fehan, hasan Obaid Abass — Sun, 28 Jun 2026 00:00:00 +0000

This paper aims to estimate the allowable bearing capacity of shallow foundations in Baqubah City, Diyala Governorate, Iraq, using Geographic Information System (ArcGIS Pro) together with Terzaghi’s bearing-capacity equation. Three common foundation types were considered in the analysis, such as square foundations (1.5 × 1.5 m), strip foundations (1 × 1 m), and rectangular foundations (2 × 4 m), and the calculations were conducted at different foundation depths across the study area. The analysis was based on geotechnical information obtained from 76 boreholes distributed in different locations within Baqubah City. Soil parameters available in the investigation reports were used to calculate the allowable bearing capacity values. In some boreholes where the undrained shear strength (Cu) was not directly available in the reports, the values were estimated from Standard Penetration Test (SPT) results using empirical correlations commonly reported in geotechnical literature. The calculated results were then integrated with GIS techniques to produce spatial maps showing the differences in allowable bearing capacity in the study area. The Inverse Distance Weighting (IDW) interpolation method was used to produce these maps based on the available borehole data. This method was chosen because it is simple and works well when the number of available data points is limited. The maps prepared in this paper show that the soil bearing capacity is not the same across Baqubah city and that the allowable bearing capacity changes from one place to another depending on soil conditions and foundation depth. The results showed that the allowable bearing capacity ranges from (3.1 to 22.9) t/m² with higher values showed in northern parts due to stronger soil conditions. These results provide useful preliminary information for engineers when selecting suitable foundation types during the early stage of foundation design. Still, detailed geotechnical investigations are required for final design before construction.

Dual-Converter Control Strategies for Electric-Vehicle Battery Charging: A Review

sarah A.Mahdi — Sun, 28 Jun 2026 00:00:00 +0000

Electric vehicle charging systems are a major factor where modern transportation will happen, specifically as there is a growing demand for fast, efficient, and good quality charging stations to be built. The use of advanced power-electronic converters and control systems are critical for both enhancing charging efficiency, improving battery protection and extending battery service life. There is currently a lot of interest in the utilization of dual converter systems based on dual-active-bridge DC-DC systems because of their ability to support both direct current and alternating current power distribution plus their capability to isolate the battery from the electrical supply source.
The intent of this paper is to provide a detailed overview of the charging system control strategies that exist for modern EVs that utilize a dual-converter system. This review will focus on Lithium-Ion battery systems, which are most charged using the constant current/constant voltage charging profile. Traditional control techniques, which include Proportional-Integral-Derivative and Sliding-Mode Control will be discussed along with the more recently introduced intelligent control techniques, such as Fuzzy Logic, Artificial Neural Networks, and Reinforcement Learning. The various control methodologies and their comparative advantages/disadvantages will be evaluated according to several different categories, including their dynamic response capabilities, knowledge robustness, computational complexity, real-time application capability and their impact upon the overall battery performance.
According to this review classical controllers are still suitable for low-cost and real-time applications, intellectual and hybrid control strategies have greater adaptability, efficiency, loaded current stress reduction, and greater electrification and battery lifespan when considering real-world operational constraints. This review provides a structured synthesis of existing literature, including comparative summary tables, and identifies opportunities for research into data-driven and battery-aware control strategies for future EV charging systems.

Numerical Analysis of the Effect of Porous Structure on Pressure Distribution in Heat Exchangers Using CFD

Hayder — Sun, 28 Jun 2026 00:00:00 +0000

This study aims to investigate the effect of the arrangement of porous layers on pressure drop through a numerical simulation using CFD techniques in ANSYS Fluent software. In this study, a two-dimensional model was constructed for a horizontal channel containing three consecutive porous layers with different values of porosity, such that they are arranged transversely within the channel. The simulations were carried out for six different porous layer arrangements, where only the positions of these layers were swapped with each other in these cases without changing the porous values or the thickness of layers. While keeping all parameters of the simulation unchanged, including the outlet pressure and the air inlet velocity. The results comparison showed that by changing the arrangement of the porous layers, the pressure losses can be reduced by up to 34.5% while maintaining a relatively high value of pressure at the outlet of these layers. This, in turn, improves the operational efficiency by choosing the arrangement that achieves the least resistance and therefore the least losses.

Advances in Osteoporosis Diagnosis: A Comprehensive Review of Conventional and Emerging Methods

Ghufran ALmashhadani — Sun, 28 Jun 2026 00:00:00 +0000

Osteoporosis is a common, chronic, and progressive metabolic bone disorder that is characterized by low bone mass and degeneration of the microarchitecture of the skeleton which predisposes individuals to an increased risk of fractures, particularly among postmenopausal women and elderly individuals. In this review, commonly used and newly developed techniques for the diagnosis of osteoporosis are reviewed. Dual-energy X-ray absorptiometry (DXA) is still the clinical application of choice to date for bone mineral density (BMD) measurement in view of its utility and convenience, whereas quantitative computed tomography (QCT) affords a three-dimensional structural assessment and sensitive probes for trabecular degradation. Magnetic Resonance Imaging (MRI) is an excellent modality for assessing bone microarchitecture and marrow composition, while quantitative ultrasound (QUS) offers a low-cost radiation-free screening solution. Biochemical markers (procollagen type I N-terminal propeptide [P1NP], bone-specific alkaline phosphatase [B-ALP], osteocalcin [OC], C-telopeptide of type I collagen [CTX] have also proven to be useful for the evaluation of bone turnover and treatment response. Digital tools, such as Artificial Intelligence (AI) models and mobile health (mHealth) applications based on emerging digital technologies, are changing our diagnostic paths with increased prediction accuracy, patient involvement, and remote surveillance. However, despite these achievements, diagnostic issues remain due to biological variations as well as the lack of consensus and widespread clinical validation. Combining imaging modalities, biochemical markers, and AI-based analytics appears the most hopeful route toward early, personalized, yet affordable osteoporosis detection

Critical Drivers and Barriers to Blockchain Adoption in the Iraqi Construction Supply Chain: A Grey Delphi–DEMATEL Approach

sultan Alkarawi, Firas Jaber, Ali Ezzat Hasan — Sun, 28 Jun 2026 00:00:00 +0000

Blockchain technology (BT) has recently captured the attention of researchers and practitioners in the construction sector, in particular. Researchers widely agree that blockchain is not just a buzzword, but a revolutionary technology that is revolutionizing the world of corporate management, construction companies, and their supply chain operations. Despite the remarkable progress in the use of this technology in the construction sector, blockchain applications related to operations and supply chain management (OSCM) in Iraq remain completely absent. With increasing interoperability and resource constraints, this study presents a list of obstacles to blockchain adoption. In a review of 1,176 recent articles, filtering for articles on the challenges of using blockchain in construction supply chains (CSC) yielded 86 articles. The obstacles revolve around five main themes: (1) Technology, (2) Organization, (3) Environment, (4) Trust, and (5) Acceptance. Using a Grey Delphi–DEMATEL Approach, the results concluded that there are influential obstacles, such as inadequate information sharing, concerning trust management, as well as others that are influenced, including insufficient policies in the organization, absence of technical support. Therefore, this study seeks to extend our understanding of the BT and how the companies create and capture business value with it.

Under Balanced Technique to Enhance Drilling Operations

amel assi — Sun, 28 Jun 2026 00:00:00 +0000

Underbalanced drilling (UBD) is a successful drilling technique that upholds well pressure purposefully lesser than formation pressure. This approach effectively permits reservoir fluids to flow inside the well throgh drilling, diminishing formation damage and meaningfully attractive hydrocarbon recovery. UBD signifies a standard shift from traditional drilling techniqe. It bring into line with the industry’s growing suring on cost-effective, reservoir-friendly, and high-performance techniques. The correlation between Rate of Pentration (ROP) and UBD for several wells by drilling program is demonstrates the progressive development in drilling performance because of accrued experience, optimizing engineering design, and superior formation reaction under reduced bottom hole pressure. UBD obviously heightened ROP across the field likened to offset overbalanced wells, assistant its request in future progress phases. Effective and cost-effective of directional wells requires the execution of best drilling performs and progressive techniques to optimize drilling processes. Disappointment to sufficiently consider drilling risks can result in unproductive drilling processes and Non-Productive Time (NPT). The application of UBD be contingent on the mechanical stability of the drilled formation, among other issues. In over-all, poorly consolidated, depleted formations are not appropriate for that technology. Finally, the results show that underpressure drilling (UBD) is better than overpressure drilling (OBD) in the typical porous shale and limestone formations of the studied wells.