The effective application of the method of measuring and evaluating performance according to the Balanced  Scorecard the need for an information system a comprehensive and integrated for internal and external environment, Which requires the need to develop accounting information system in general and cost management information systems to suit the particular requirements of the environment in terms of the development of modern methods of measurement to include the use of some methods that have proven effective in measuring and evaluating performance.

The research problem in need of management to develop methods of measuring and evaluating performance through the use of both financial measures and non

Acinetobacter baumannii received attention for its multi-drug resistant associated with many severe infections and outbreaks in clinical environment. The aims of the study are to investigate the antibiotic susceptibility profile of clinically isolated A. baumannii, biofilm production, and the efficiency of Low Frequency Ultrasound (LFU) and honey to attenuate biofilm production. A total of 100 samples were taken from different sources from Baghdad hospitals. The susceptibility patterns revealed the percentage of pan drug resistant (PDR) isolates were 1.5 %, 72.7 % were extended drug resistant (XDR), 16.7 % were multidrug resistant (MDR), and 9.1 % were non MDR and sensitive to most antibiotics used. The ability to form

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

   Nanoparticles (NPs) have unique capabilities that make them an eye-opener opportunity for the upstream oil industry. Their nano-size allows them to flow within reservoir rocks without the fear of retention between micro-sized pores. Incorporating NPs with drilling and completion fluids has proved to be an effective additive that improves various properties such as mud rheology, filtration, thermal conductivity, and wellbore stability. However, the biodegradability of drilling fluid chemicals is becoming a global issue as the discharged wetted cuttings raise toxicity concerns and environmental hazards. Therefore, it is urged to utilize chemicals that tend to break down and susceptible to biodegradation. This research presents the pra

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To