The Albian Carbonate-clastic succession in the present study is represented by the Mauddud and Nahr Umr formations were deposited during the Albian stage within the Wasia Group More than 200 thin sections of cores and cuttings in addition to well logs data for Nahr Umr and Mauddud formations from 4 boreholes within two oil fields (Ba-4, Ba-8, Ns-2 and Ns-4) were used to interpret the different associations facies as well as the facies architectures to describe the sedimentary framework of the basin and development the petrophysical properties. Seven major microfacies were diagnosed in the carbonate succession of the Mauddud Formation, while the Nar Umr Formation includes five lithofacies; their grain types characteristic and deposit

The significant addition of immersive technologies, Virtual Reality (VR), Augmented Reality(AR) and Mixed Reality(MR) are transforming the domain of design education. Still, finding an equilibrium between these new tools alongside with traditional methods of teaching is a menace which educational institutes needs to solve. This paper proposes a structure that would help the ease with which to include immersive technologies within design education, keeping in mind the solid points of more conventional pedagogical methods. Based on a survey of interior design programs, this research highlights the potential for VR, AR and MR in student engagement, creativity skills and professional practices. The results suggest that adoption of an im

This study examines the impact of Digital Transformation (DT) on the Financial Reporting Quality (FRQ), taking into account the moderating role of the Trust Services Framework (TSF), in the context of rapid developments in the digital business environment and the resulting challenges and opportunities for accounting and financial systems. To achieve the study objectives, a descriptive–analytical approach was adopted, and a questionnaire was used as the primary data collection instrument. The study sample comprised 87 professionals working in accounting and financial functions. DT was measured through four dimensions: cloud computing, automation, data analytics, and systems integration. FRQ was assessed using the dimensions of accuracy and

The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and l

Time series is an important statistical method adopted in the analysis of phenomena, practices, and events in all areas during specific time periods and predict future values ​​contribute to give a rough estimate of the status of the study, so the study aimed to adopt the ARIMA models to forecast the volume of cargo handled and achieved in four ports (Umm Qasr Port, Khor Al Zubair Port, Abu Flus Port, and Maqal Port(, Monthly data on the volume of cargo handled for the years (2006-2018) were collected (156) observations. The study found that the most efficient model is ARIMA (1,1,1).

The volume of go

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty