Soran University

Zeebaree A.Y., Sultan S.J., Sami A.S., Shareef B.A., Sharaf S.Y., Rashid R.F., Zebari O.I., Fatah K.K.

Natural clay is considered one of the most attractive substances due to its broad applications and environmental benignity. In this work, Kurdistan montmorillonite clay (KMC) has been easily separated from the soil without any chemical treatment. It is employed as an efficient adsorbent for removing the cationic toxic dyes from the influent. Different methods, such as BET, FESEM, TEM, UV–VIS, XRD, XRF, XPS, and Zeta potential, have been applied to study how well clay works. In an effort to match the isothermal data, Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich equations were used. The experimental findings have shown that a Langmuir isotherm equation provides a good fit for the equilibrium data (R2 = 0.999). The rate parameters were assessed using pseudo-first-order, pseudo-second-order, intra-particle diffusion, and liquid film diffusion equations and were consistent with the pseudo-second-order kinetic model (R2 = 0.999). Additionally, the results revealed that the clay exhibited a high adsorption capacity (2.45 mg/g) and removal for methylene blue (MB) dye (98%) in one minute. The outcomes show that KMC effectively adsorbs MB dye and may be used as a low-cost substitute in wastewater treatment to get rid of cationic dyes.

Aziz S.B., Mohammed S.S., Qader I.N., Ibrahim P.A., Babakr K.A., Omer R.A., Hamid D.A., Salih I.L., Rasul H.H., Rahman A.A., Aspoukeh P., Hussein S.M., Mahmood P.H., Muhammed A.W., Omar S.Y.

In this study, a solid polymer electrolyte (SPE) was synthesized using the solution casting method. Lithium nitrate (LiNO3) as the ion source and glycerol as a plasticizer were added in varying concentrations (9, 18, 27, and 36% by weight) to methylcellulose (MC) as the host polymer. X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and electrical impedance spectroscopy (EIS) were used to investigate the effect of glycerol on the morphology, chemical structure, and ionic conductivity of the polymer electrolytes. According to the XRD results, the addition of glycerol, acting as a plasticizer, reduced the hump characteristic of the amorphous structure. Furthermore, glycerol enhanced ionic conductivity by increasing polymer chain mobility, improving ion dissociation, and potentially creating transient pathways for ion transport. FTIR spectroscopy confirmed the interaction between the polymer and dissolved salt, indicating the formation of polymer-salt complexes and showing an increase in the intensity of peaks associated with the hydroxyl (–OH) group as the glycerol concentration increased. EIS analysis demonstrated that DC conductivity rose from 2.9 µS/cm to 7.28 µS/cm with increasing glycerol content. Additionally, the frequency-dependent dielectric parameters, including dielectric constant (ε′) and dielectric loss (ε″), showed higher values at low frequencies, with both increasing as glycerol concentration increased, indicating that glycerol enhances ion conductivity and polarization in the polymer electrolyte. The electrical modulus analysis revealed that polarization relaxation decreased with higher glycerol concentrations, while conductivity increased at high frequencies. Glycerol significantly enhances the flexibility, amorphous nature, and ion mobility of MC-based polymer electrolytes, making them suitable for advanced applications in electrochemical devices.

Jamal M., Mohammed A., Ali J.

ABSTRACTThis study evaluates the impact of cement chemical composition on the compressive strength (CS) of cement slurries, utilizing silica fume (SF) and fly ash (FA) as additional materials. A comprehensive analysis was conducted on 317 datasets from the literature, focusing on factors including silicon dioxide (SiO₂), aluminum oxide (Al₂O₃), calcium oxide (CaO), iron oxide (Fe₂O₃), water‐to‐binder (w/b) ratio, and SF and FA content, as well as curing time and temperature. The research presents three geochemical moduli, namely, silicate modulus (SM), aluminate modulus (AM), and hydraulic modulus (HM), to assess and forecast CS. The investigation utilizing full quadratic (FQ) and cubic (CUB) models underscores the precision of prediction models corroborated by statistical metrics, such as scatter index (SI), root mean squared error (RMSE), and correlation coefficient (R2). Univariate, bivariate, and multivariate evaluations indicate that SM, AM, and HM significantly decrease input parameters while preserving or enhancing model accuracy. The ideal replacement percentages for SF and FA to maximize strength were determined to be 14.6% and 11.6%, respectively. The optimal values for SM, AM, and HM were 2.62, 1.38, and 2.21, respectively. The results establish a solid framework for optimizing cement formulations, presenting sustainable alternatives for improved mechanical performance and decreased material consumption in oil well cementing and building applications.

Jalil P.J., Mhamedsharif R.M., Shnawa B.H., Hamad S.M., Aspoukeh P., Ahmed M.H.

Green synthesis is chosen for its environmental friendliness, as it eliminates the need for toxic chemicals by using natural compounds as reducing and stabilising agents. This research investigates the synthesis of silver nanoparticles (Ag-NPs) through green methods, utilising Ziziphus spina-christi leaf extract, and compares them with commercially available silver nanoparticles. The study compares the structural, morphological, physicochemical, toxicity, anti-inflammatory, and catalytic properties of green-synthesized Ag-NPs (GS-Ag-NPs) with commercial Ag-NPs (CS-Ag-NPs). Various techniques, including UV–Vis spectroscopy, Fourier transform infrared spectroscopy, Scanning electron microscopy, X-ray diffraction, transmission electron microscopy, dynamic light scattering, energy-dispersive X-ray spectroscopy, zeta potential analysis, and thermal gravimetric analysis were employed to characterise the synthesised Ag-NPs. In vitro toxicity assessments, anti-inflammatory activity assays, and catalytic activity studies were conducted to evaluate the biocompatibility, anti-inflammatory, and catalytic properties of the synthesised Ag-NPs. The results indicate that GS-Ag-NPs exhibit a more uniform size distribution (20.23 nm) and spherical morphology than commercial Ag-NPs, which have a larger size (38.2 nm). In vitro toxicity assessments show that GS-Ag-NPs are more biocompatible, with minimal haemolysis (3.2 ± 0.1%) compared to commercial Ag-NPs (19 ± 0.48%). Additionally, GS-Ag-NPs demonstrate enhanced anti-inflammatory activity (19.04%) and slightly higher catalytic activity in dye degradation processes at lower dye concentrations. This comparative analysis highlights the advantages of green synthesis methods in producing biocompatible, stable, and functionally superior Ag-NPs. The findings suggest the potential of GS-Ag-NPs for applications in various fields, including biomedicine, catalysis, and environmental remediation.

Jahanjooy S., Hashemi H., Bagheri M., Karam D.B.

This research introduces a method for seismic-well tie using a modified Dynamic Time Warping, DTW algorithm with fuzzy features. The Seismic-Fuzzy DTW technique aligns synthetic seismograms to seismic traces by considering waveform similarity and geological features. It uses acoustic impedance models and membership results from fuzzy model-based inversion. Traditional seismic-well tie methods frequently prioritize amplitude matching above geological consistency. The proposed approach rethinks the well tie target by stressing the high correlation of fuzzy acoustic impedance features. The results show improvements over the traditional DTW-based technique. The result’s correctness, however, depends on the accuracy of the fuzzy seismic inversion data. It is proposed that more research be conducted into potential mismatches, noise effects, and complex geological structures. The algorithm’s effectiveness could be improved by incorporating more data types and optimizing its behavior under different geological settings. Overall, this unique approach yields promising results by combining seismic and well data to improve seismic interpretation outcomes.

Sherwani A.F., Abdulrahman P.I., Younis K.H., Mohammed A.S., Zrar Y.J.

This study addresses the urgent need for sustainable alternatives in the construction industry, which significantly contributes to global carbon emissions, particularly from cement production. The research focuses on assessing the impact of crumb rubber (CR) on the compressive strength (CS) of self-compacting concrete when combined with eco-friendly materials like fly ash and ground granulated blast furnace slag. Utilizing a dataset of 439 data points, the study employs four predictive models—Nonlinear Regression, Interaction, Pure Quadratic, and Artificial Neural Networks (ANN)—to evaluate CS under various mix ratios and curing periods. The findings highlight the ANN model as the most accurate for predicting CS, with CR content being a critical factor. Additionally, the study develops a power function model for predicting splitting-tensile strength, offering a valuable tool for future research and industry applications.

Balaky S.M., Sh. Asaad I., Tamar‐Agha M.Y., Radwan A.E.

ABSTRACTThe Mirga Mir Formation (Lower Triassic) was studied for its sedimentology and lithostratigraphy from two outcrops (Nazdur and Beduhe areas) c. 20 km apart, in the Northern Iraq‐Kurdistan Region. The collected samples were investigated by standard polarised microscope, X‐ray diffraction and scanning electron microscopy to infer their mineralogical characteristics, provenance and paleodepositional environments. The Mirga Mir Formation is a mixed carbonate‐siliciclastic succession consisting of thin‐, medium‐, occasionally thick‐bedded, and yellowish‐grey limestones, sandy dolomitic and argillaceous limestones, alternating with thin‐ to thick‐bedded grey shale/marl. Thin‐laminated siltstones and fine‐ to coarse‐grained sandstones are seen in the lower and middle parts of the formation. The petrographic study of carbonates and clastics (sandstones) showed that most limestones are carbonate mud (micrite). The skeletal grains consist principally of thin‐shelled pelagic bivalves (Posidonia), serpulid worm tubes (Spirorbis), microgastropods, ammonoids, calcispheres, brachiopods, dasyclad green algae, miliolid foraminifers and ostracods. Non‐skeletal grains include ooids, peloids, intraclasts and extraclasts. Depending on field observations, five different lithologic units were recognised. These are in ascending order: oolitic limestone unit, sandy limestone‐shale unit, shale‐limestone unit, argillaceous limestone‐shale unit and brecciated‐slumped limestone unit. Sandstones occur as thin beds within the lower and middle parts of the studied formation. In addition, thick coarse‐grained sandstone beds occur in the middle part of the Nazdur section only. Petrographically, thin sandstone beds consist of fine‐grained quartz. The thick sandstone bed is an immature, litharenite and consists of rock fragments (mostly sedimentary), quartz and feldspars. The X‐ray diffraction analysis and scanning electron microscope micrographs of shales revealed that the abundant clay minerals in the Mirga Mir Formation are illite, followed by kaolinite and chlorite. In addition to illite‐chlorite mixed layers, non‐clay minerals include calcite and quartz, with low feldspars. Based on detailed microfacies analysis of the limestones, 12 microfacies types from the carbonates and mixed detrital carbonates are distinguished. These facies were subdivided, according to their environmental interpretation, into five facies associations: Offshoal/basinal, Foreshoal/slope, Shoal, Back Shoal/Lagoon. The study introduces a good example of a gently sloping epeiric carbonate ramp that shows a gradual shallowing of the basin from the offshoal/basinal sediments to the foreshoal/slope and backshoal/lagoonal environments.

Ahmed N.M., Soleymani F., Saeed R.K.

In this paper, a new Milstein-type derivative-free numerical method is proposed for Itô stochastic differential equations (SDEs). The mean-square stability of the method is thoroughly analyzed, and its error estimation is rigorously examined. The method is subsequently extended twice using the concepts of balanced methods and drift-split-step schemes to achieve the highest possible mean-square stability while preserving the derivative-free structure. Detailed numerical simulations are provided to support the theoretical discussions.

Farid Hama Ali H., Mohammed A.S.

Bas Y.J., Kakrasul J.I., Ismail K.S., Hamad S.M.

Concrete is one of the most versatile composite materials used in construction on a large scale. A by-product of concrete production is recycled aggregate. Recycling aggregate helps reduce the consumption of natural resources and minimizes the amount of waste sent to landfills, contributing to environmental sustainability. Recycled coarse aggregate (RCA) concrete has been used as a partial or full replacement for gravel, aiming to maintain or improve key mechanical properties, such as compressive strength (CS). Compressive strength becoming increasingly critical and is prioritized early in the construction design process to ensure structural integrity. A mathematical model is essential for accurately predicting the compressive strength of recycled aggregate concrete. This study evaluates predictive models for estimating the compressive strength of recycled aggregate concrete (RAC) based on 708 experimental results from the literature. The considered models are the interaction model, full quadratic model (FQ), artificial neural network (ANN), and M5P-tree. The modeling process was focused on the key factors affecting the compressive strength of concrete when RCA was used as a substitute. These variables include the water-to-cement ratio from 0.25 to 0.97, cement content from 185 to 864 kg/m3, gravel content from 0 to 1436 kg/m3, gravel size from 4.75 to 37.5 mm, sand content from 363 to 1105 kg/m3, Superplasticizer from 0 to 4.28%, curing time from 1 to 365 days, and recycled coarse aggregates from 0 to 1393.31 kg/m3. According to statistical analysis, the ANN model achieved the highest predictive accuracy with an R2 of 0.965 and a low root mean square error (RMSE) of 4.32 MPa, significantly outperforming other models like the interaction model and M5P-tree.

Saberikia M.R., Beitollai H., Jader R., Farbeh H.

Most real-time systems are embedded in portable, battery-powered devices that have strict limitations on power consumption. Safety-critical embedded systems, in particular, demand a high level of reliability. To effectively enhance both reliability and power consumption, it is crucial to consider both criteria with an accurate and stable model. Existing research on power and reliability models for embedded systems often lacks the accuracy required for safety-critical applications and fails to account for all hardware and software components. This paper proposes a machine learning-based optimization model designed to improve the accuracy and stability of reliability and power consumption assessments. The proposed model demonstrates a significant enhancement in accuracy compared to previous randomization models, showing a [Formula: see text] improvement in reliability and a [Formula: see text] improvement in power consumption relative to existing state-of-the-art models.

Mashayekhi B., Mohammed Y.

The significance of internal auditing and its quality cannot be overstated, making it essential to investigate the factors influencing this quality. This study, employing a cross-sectional analysis, aims to assess how the characteristics of external auditors affect the perceived quality of internal audits in Iranian and Iraqi banks. In 2024, data regarding the attributes of external auditors and the perceived quality of internal audits were collected through a questionnaire distributed to external auditors from various banks in Iran and Iraq. The data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study reveals a positive relationship between external auditors’ competence and independence and the perceived quality of internal audits, while it shows a negative impact of external audit methodologies on this perceived quality. These findings highlight the importance of external auditors’ independence as a key determinant of perceived internal audit quality.

Jalil P.J., Mhamedsharif R.M., Shnawa B.H., Hamad S.M., Aspoukeh P., Wsu K.W., Muhammedsharif S.M., Ahmed M.H.

The green synthesis of nanoparticles represents an eco-friendly and sustainable alternative to conventional chemical and physical synthesis methods. This approach minimizes the use of hazardous chemicals and leverages biological resources, aligning with the principles of green chemistry. This study aimed to characterise the green synthesised ZnONPs and evaluate their antimicrobial and anti-inflammatory activities. ZnONPs were synthesised using Washingtonia filifera seed extract and characterised using Scanning Electron Microscopy (SEM), UV–Vis spectroscopy, Fourier Transform Infrared (FT-IR) spectroscopy, energy-dispersive spectroscopy (EDX), and X-ray diffraction (XRD). Their antimicrobial activity against bacteria and fungi, as well as their anti-inflammatory potency, were assessed. SEM data revealed that the ZnONPs, fabricated with palm seed extract metabolites, were spherical with an average size of 50 nm. FT-IR analysis identified varied absorption peaks related to the functional groups of the plant extract and nanoparticles. The antimicrobial activity was dose-dependent, with Staphylococcus aureus and Escherichia coli showing inhibition zones of 8.5 ± 0.7 mm and 11.8 ± 0.3 mm, respectively, at 500 µg/mL. Pseudomonas aeruginosa exhibited a notable inhibition zone of 20.4 ± 0.7 mm. The ZnONPs also inhibited fungal mycelium growth. The in vitro anti-inflammatory activity of ZnONPs showed a concentration-dependent increase, with an 89.15% inhibition of RBC haemolysis at 110 µg/mL. The green synthesised ZnONPs demonstrated significant antimicrobial activity against clinical pathogens and potent anti-inflammatory effects, suggesting that this eco-friendly method could be a promising strategy for developing versatile biomedical products.

Ali H.F., Mohammed A.S.

Unconfined compressive strength (UCS) and California bearing ratio (CBR) are key indicators of soil strength, particularly in fine-grained soils that often fail to meet project standards for roads and embankments. This study investigates the effects of fly ash on UCS and CBR, demonstrating an increase in both, though not symmetrically, due to varying percentages of chemical oxides in the soil-fly ash matrix. The relationships between UCS, curing time, chemical oxides (silica, alumina, calcium, magnesia, ferric), maximum dry density, and optimum moisture content (OMC) were analyzed. Three mathematical models, pure quadratic (PQ), interaction (IA), and full quadratic (FQ), were used to model UCS for 111 fly ash-treated and 49 untreated soils. While FQ and IA offered excellent predictions, their complexity led to applying geochemical indices like the hydraulic index (HI) and lime modulus (LM) to simplify the equations, with FQ remaining the most accurate. Sensitivity analysis showed that curing time was the most influential factor on UCS, followed by calcium oxide (CaO). When geochemical indices were applied, the hydraulic index (HI) emerged as the most significant factor. These findings underscore the importance of grouped chemical oxides, particularly SiO2, Al2O3, and Fe2O3, in enhancing soil properties, providing valuable insights for geotechnical engineering applications.

Asfha D.T., Gebretsadik H.T., Latiff A.H., Rahmani O.

Accurate prediction of pore pressure (PP) is crucial for optimizing drilling and reservoir management operations. This study investigates PP prediction in the West Baram Delta, Offshore Sarawak, a mature oil and gas field. The field exhibits varying depths of top overpressure zones, emphasizing the need for reliable PP predictions before the commencement of drilling operations. Sonic transit time, gamma-ray, and density logs are used to establish shale compaction trends and predict PP using Eaton’s and Miller’s methods. The predicted PP at the wellbore location was validated using a repeat formation tester data from two wells. Results showed that the Eaton DT method underestimates PP while the Miller method provides more accurate predictions at deeper overpressure zones. In shallow overpressure zones, where disequilibrium compaction is the dominant mechanism, the Eaton DT and Miller methods yield acceptable PP predictions. A 3D PP model demonstrated a better agreement with Eaton DT predictions at wellbore locations. The onset of overpressure was identified at approximately 2150 m depth. This study's out come provides valuable insights for PP evaluation in the West Baram Delta and in other similar geological settings prior to the drilling operation.