The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

The most important recommendations of this research are:-

1.The benefit of the other countries experiences about the transparency of the government performance, without depending on the transparency that imitating the other experiences; that may not fit with the Iraqi government units.

2.The Ministry of Finance has to prepare the citizen guidebook about the government performance which is considered an essential document, it should be simple and available for all the parties of relation; and for not specialized citizen for the purpose of simplifying the understanding of government performance details.

3.The benefit of the government units websites of inte

Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing artificial TABU algorithm to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as sport, chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement.

The aim of this research was to estimate the production function to measure returns to scale and distribution efficiency of  resources used in the production of wheat. Cross sectional data used of a random sample of 130 farmers in Dhi Qar Province. The results of the quantitative analysis of estimating production function showed that the double logarithmic form was the best estimated model based on economic and statistical indicators. However, that form suffered from heteroscedasticity and autocorrelation, so the robust regression technique was chosen. Value of returns to scale was 0.89 and this indicates decreasing returns to scale. This means that production function is in the second stage of the function. The results of the dist

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

The  thermal  and  electrical  performance  of  different  designs  of  air  based  hybrid photovoltaic/thermal collectors is investigated experimentally and theoretically. The circulating air is used to cool PV panels and to collect the absorbed energy to improve their performance. Four different collectors have been designed, manufactured and instrumented namely; double PV panels without cooling (model I), single duct double pass collector (model II), double duct single pass (model III), and single duct single pass (model IV) . Each collector consists of: channel duct, glass cover, axial fan to circulate air and two PV panel in parallel connection. The temperature of the upper and