Mixed convection heat transfer in a vertical concentric annulus packed with a metallic porous media and heated at a constant heat flux is experimentally investigated with water as the working fluid. A series of experiments have been carried out with a Rayleigh number range from Ra=122418.92 to 372579.31 and Reynolds number that based on the particles diameter of Re_d=14.62, 19.48 and 24.36. Under steady state condition, the measured data were collected and analyzed. Results show that the wall surface temperatures are affected by the imposed heat flux variation and Reynolds number variation. The variation of the local heat transfer coefficient and the mean Nusselt number are presented and analyzed. An empirical

This work evaluates the influence of combining twisted fins in a triple-tube heat exchanger utilised for latent heat thermal energy storage (LHTES) in three-dimensional numerical simulation and comparing the outcome with the cases of the straight fins and no fins. The phase change material (PCM) is in the annulus between the inner and the outer tube, these tubes include a cold fluid that flows in the counter current path, to solidify the PCM and release the heat storage energy. The performance of the unit was assessed based on the liquid fraction and temperature profiles as well as solidification and the energy storage rate. This study aims to find suitable and efficient fins number and the optimum values of the Re and the inlet tem

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

    The current research aims to identify the Impact of  strategy of modeling in the of deductive thinking and the attitvde towards mathematics among students in the high school stage

 through check the following hypotheses:       

1.There is no difference statistically significant at the level (0.05) between the scores  mean of the experimental group students who have studied according to the modeling strategy and scores of control group students who have studied according to ordinary method in deductive thinking. 

2.

Focal adhesion kinase (FAK), ephrin receptor type A4 (EphA4), and adiponectin (ADPN) are important indicators in inflammation, tumor growth, migration, and angiogenesis in some cancers. The predictive impact of their concentrations in acute myeloid leukemia (AML) patients to be identified remains. The research sought to explore the effect of FAK, EphA4, and ADPN as prognostic biomarkers, and their influence on patient survival, and to look for any potential correlation between their levels with hematological parameters in AML patients.

This research deals with the poetic image of poets of the eighth century poetic, where they embodied the features of the religious life in which they live, and their impact on the Koranic text in the reflection of the image on their poems, where it becomes clear the ability of the poet at that stage to clarify the aesthetic components of the poetic text; Investigations, singled out the first topic: the analogy, and the second metaphorical picture, and the third: the picture.

This paper assesses the impact of changes and fluctuations in bank deposits on the money supply in Iraq. Employing  the research constructs an Error Correction Model (ECM) using  monthly time series data from 2010 to 2015. The analysis begins with  the Phillips-Perron unit root test to ascertain the stationarity of the  time series and the Engle and Granger cointegration test to examine  the existence of a long-term relationship. Nonparametric regression functions are estimated using two methods: Smoothing Spline and M-smoothing. The results indicate that the  M-smoothing approach is the most effective, achieving the shortest adjustment period and the highest adjustment ratio for short-term disturbances, thereby facilitating a return