Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan

Blend solid polymer electrolytes (BSPEs) comprising PVA/HPMC/CuSO4 were prepared using a solution casting approach. BSPEs were synthesized with varying weight percentages of CuSO4 (0, 10, 20, and 30 wt.%). A variety of experimental methods, including X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and linear sweep voltammetry (LSV), were used to characterize these BSPE systems. The polymer blend matrix underwent structural alterations, according to the XRD data. The standard data from JCPDS card numbers for copper sulfate matches well with the observed strong peaks of PVA/HPMC+20 wt.% of CuSO4 BSPE. The complex

The human gastrointestinal system is a complex ecosystem crucial for well-being. During sepsis-induced gut injury, the integrity of the intestinal barrier can be compromised. Lipopolysaccharide (LPS), an endotoxin from Gram-negative bacteria, disrupts the intestinal barrier, contributing to inflammation and various dysfunctions. The current study explores the protective effects of limonene, a natural compound with diverse biological properties, against LPS-induced jejunal injury in mice. Oral administration of limonene at dosages of 100 and 200 mg/kg was used in the LPS mouse model. The Murine Sepsis Score (MSS) was utilized to evaluate the severity of sepsis, while serum levels of urea and creatinine served as indicators of renal f

A modified water injection technique has organized by this study to improve oil recovery of the Mishrif reservoirs using polymerized alkaline surfactant water (PAS-Water) injection. It is planned to modify the existing water injection technology, first to control and balance the hazardous troublemaker reservoir facies of fifty-micron pore sizes with over 500 millidarcies permeability, along with the non-troublemaker types of less than twenty micron pore sizes with 45 to 100 millidarcies permeability. Second to control Mishrif reservoirs rock-wettability. Special core analysis under reservoir conditions of 2250 psi and 90 °C has carried out on tens of standard core plugs with heterogeneous buildup, using the proposed renewal water f

Regular sampling for six months from January to July 2012 were taken in small, shallow, perennial, standing ponds near the Greater Zab River, Gwer district, Erbil. A variety of physicochemical parameters were determined. Air and water temperature were falling between 15.2 - 34.7 ? C and 15.5 and 26.5 ?C. The waters are neutral (pH 7.38-8.27), hard, alkaline, salty, high in TDS and EC (892-966?S/cm, and rich in nutrients (NO3: 2.1-4.1mg/l, PO4: 0.33-0.62 mg / l , SO4: 24.7-80.2 mg / l ). The attention fixed on a filamentous blue- green algae Glaucospira Lagerheim, 1982) which is new to Iraqi flora. It is a filament (trichome), solitary, pale or yellowish blue – green, without sheath, Screw like coiled, motile, some of them are activ

This study proposes a pioneering Ethical Artificial Intelligence (EAI) framework for advancing sustainable development in Iraq by integrating eight multidimensional sustainability indicators—administrative, technological, economic, environmental, social, legal, security, and governance. Utilizing data from 60 completed development projects, the framework combines SPSS statistical analysis, the SMART-AI model, and Artificial Neural Networks (ANN) to identify key determinants of project success and failure. Results reveal a 37% project failure rate, with administrative and technological deficiencies emerging as the most influential predictors. The SMART-AI model achieved an accuracy of 91.3% using stratified k-fold cross-validation. A bilin