Researchers often equate database accounting models in general and the Resources-Events-Agents (REA) accounting model in particular with events accounting as proposed by Sorter (1969). In fact, REA accounting, database accounting, and events accounting are very different. Because REA accounting has become a popular topic in AIS research, it is important to agree on exactly what is meant by certain ideas, both in concept and in historical origin. This article clarifies the analyzing framework of REA accounting model and highlights the differences between the terms events accounting, database accounting, semantically-modeled accounting, and REA accounting. It als

This study investigates the impact of agricultural investment policy—represented by agricultural loans and investment allocations—on rice crop production in Iraq over the period 2003–2023, employing the Autoregressive Distributed Lag (ARDL) model. Using time-series econometric analysis, the study confirms a short-term positive and statistically significant effect of financial support on rice output, while revealing statistically insignificant long-term effects. The presence of a cointegration relationship suggests long-term equilibrium between agricultural policy variables and rice production. However, the absence of causality in the Yamamoto-Toda test implies that structural and institutional inefficiencies may dilute the long-term i

In this research was conducted to provide a product to analyze the performance sensor fiber optic used to measure and feel the intensity of the electric field results showed obtained that use sensor long gives reactive high electric field strength and a high value for allergic sensor, but that is at the expense of reducing the intensity of the electric field that is detected

Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

Different frequency distributions models were fitted to the monthly data of raw water Turbidity at water treatment plants (WTPs) along Tigris River in Baghdad. Eight water treatment plants in Baghdad were selected, with raw water turbidity data for the period (2008-2014). The frequency distribution models used in this study are the Normal, Log-normal, Weibull, Exponential and two parameters Gamma type. The Kolmogorov-Smirnov test was used to evaluate the goodness of fit.  The data for years (2008-2011) were used for building the models. The best fitted distributions were Log-Normal (LN) for Al-Karkh, Al-Wathbah, Al-Qadisiya, Al-Dawrah and, Al-Rashid WTPs. Gamma distribution fitted well for East Tigris and Al-Karamah

Different frequency distributions models were fitted to the monthly data of raw water Turbidity at water treatment plants (WTPs) along Tigris River in Baghdad. Eight water treatment plants in Baghdad were selected, with raw water turbidity data for the period (2008-2014). The frequency distribution models used in this study are the Normal, Log-normal, Weibull, Exponential and two parameters Gamma type. The Kolmogorov-Smirnov test was used to evaluate the goodness of fit. The data for years (2008-2011) were used for building the models. The best fitted distributions were Log-Normal (LN) for Al-Karkh, Al-Wathbah, Al-Qadisiya, Al- Dawrah and, Al-Rashid WTPs. Gamma distribution fitted well for East Tigris and Al-Karamah WTPs. As for Al-

This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Reg