Low grade crude palm oil (LGCPO) presents as an attractive option as feedstock for biodiesel production due to its low cost and non-competition with food resources. Typically, LGCPO contains high contents of free fatty acids (FFA), rendering it impossible in direct trans-esterification processes due to the saponification reaction. Esterification is the typical pre-treatment process to reduce the FFA content and to produce fatty acid methyl ester (FAME). The pre-treatment of LGCPO using two different acid catalysts, such as titanium oxysulphate sulphuric acid complex hydrate (TiOSH) and 5-sulfosalicylic acid dihydrate (5-SOCAH) was investigated for the first time in this study. The optimum conditions for the homogenous catalyst (5-SOCAH) wer

This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining

The present research aims to design an electronic system based on cloud computing to develop electronic tasks for students of the University of Mosul. Achieving this goal required designing an electronic system that includes all theoretical information, applied procedures, instructions, orders for computer programs, and identifying its effectiveness in developing Electronic tasks for students of the University of Mosul. Accordingly, the researchers formulated three hypotheses related to the cognitive and performance aspects of the electronic tasks. To verify the research hypotheses, a sample of (91) students is intentionally chosen from the research community, represented by the students of the college of education for humanities and col

The primary objective of the present  research is to diagnose the most mportant imensions of quality of work life in a sample of individuals working in the Northern Gas Filling Company in Nynawa. The study sample consisted of 140 employees of the Northern Region Filling Company in even ,Through a questionnaire prepared for this purpose. The study concluded that there are five main factors that are considered to be measure of the quality of working life and are based on their importance: Empowerment, organizational health, internal work environment, motivation, development and training of workers. The study recommended that the organization should payattentio

      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences