AS Salman, SK Hameed…, Karbala Journal of Physical Education Sciences, 2020

The present work describes numerical and experimental investigation of the heat transfer characteristics in a plate-fin, having built-in piezoelectric actuator mounted on the base plate (substrate). The geometrical configuration considered in the present work is representative of a single element of the plate-fin and triple fins. Air is taken as the working fluid. A performance data for a single rectangular fin and triple fins are provided for different frequency levels (5, 30 and
50HZ) , different input power (5,10,20,30,40 and 50W) and different inlet velocity (0.5, 1, 2, 3, 4, 5 and 6m/s) for the single rectangular fin and triple fins with and without oscillation. The investigation was also performed with different geometrical fin

Background: Data on the impact of neonatal and total pediatric admissions volume on neonatal mortality are sparse. Objectives: This study is done to estimate the neonatal mortality in relation to neonatal admissions and to total hospital admissions in Al-Alwyia Pediatric Teaching Hospital through years 2005-2012 Type of the study: A retrospective study.Methods: statistical records of all cases admitted to APTH were studied during 2005-2012.Results: Neonatal mortality decreased to the nadir at last year of study period (2012) and reached 6.1% of neonatal admissions compared to 2005 level which was 9.7 %. Mortality rate among premature and low birth weight (LBW) infants decreased also. The study also reveals that neonatal mortality constit

To use quality indicators to study the management of ST-segment elevation myocardial infarction (STEMI) in different regions.

The aim of the research is to know the effect of financial leverage on the market value of the stock by applying it to a sample of private Iraqi commercial banks listed in the Iraqi Stock Exchange for the period (2010-2019) and to show the extent of that effect, based on the bank’s annual reports for the mentioned period through the use of financial leverage ratios represented b (Equity multiplier, cash balance ratio), its discussion, analysis, description, inferential description, and testing of research hypotheses,

A set of conclusions has been reached, the most important of which are: The research sample banks depend in their financial structures on borrowed funds in a greater proportion than their r

This paper performance for preparation and identification of six new complexes of a number of transition metals Cr (lII), Mn (I1), Fe (l), Co (II), Ni (I1), Cu (Il) with: N - (3,4,5-Trimethoxy phenyl-N - benzoyl Thiourea (TMPBT) as a bidentet ligand. The prepared complexes have been characterized, identified on the basis of elemental analysis (C.H.N), atomic absorption, molar conductivity, molar-ratio ,pH effect study, I. Rand UV spectra studies. The complexes have the structural formula ML₂X₃ for Cr (III), Fe (III), and ML₂X₂ for Mn (II), Ni (II), and MLX₂ for Co (Il) , Cu (Il).

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

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