Objectives: This study aims to evaluate the role of social media in promoting awareness of green university initiatives and assess the effectiveness of sustainability reports in engaging students at Baghdad University. In alignment with Sustainable Development Goal 12 (Responsible Consumption and Production),It seeks to provide recommendations for enhancing digital platforms for sustainability communication.   Theoretical Framework: The study is grounded in the Green University Model, Social Media Engagement Theory, and the Sustainability Reporting Framework, which emphasize integrating sustainable practices in education, using digital platforms for community engagement, and leveraging sustainability reports for transparency and

A fully automatic electrothermal atomic emission spectrometry (ETA-AES) is described. This system is based on an echelle monochromator modified for wave¬length modulation which is completely controlled by microcomputer . The advantages of the system in atomic spectrometry have been discussed . Aspects of the analytical performances such as calibration ? dection limit, precision , and recovery for copper are considered . This system is applied for routine determination of copper in commercial powdered mill? by slurr>' atomization versus aqueous atomization techniques.

In the present work is the deposition of copper oxide using the pulsed laser deposition technique using Reactive Pulsed Laser as a Deposition technique (RPLD), 1.064μm, 7 nsec Q-switch Nd-YAG laser with 400 mJ/cm2 laser energy’s has been used to ablated high purity cupper target and deposited on the porous silicon substrates recorded and study the effect of rapid thermal annealing on the structural characteristics, morphological, electrical characteristics and properties of the solar cell. Results of AFM likelihood of improved absorption, thereby reducing the reflection compared with crystalline silicon surface. The results showed the characteristics of the solar cell and a clear improvement in the efficiency of the solar cell in the

The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est

An experimental analysis was included to study and investigate the mass transport behavior of cupric ions reduction as the main reaction in the presence of 0.5M H₂SO₄ by weight difference technique (WDT). The experiments were carried out by electrochemical cell with a rotating cylinder electrode as cathode. The impacts of different operating conditions on mass transfer coefficient were analyzed such as rotation speeds 100-500 rpm, electrolyte temperatures 30-60 , and cupric ions concentration 250-750 ppm. The order of copper reduction reaction was investigated and it shows a first order reaction behavior. The mass transfer coefficient for the described system was correlated with the aid of dimensionless groups as fo

The buildup factor of cylindrical samples (shields) for Brass, Copper & lead (Brass, Cu, Pb (was studied, where buildup factor were calculated with thickness between (0-12) m.f.p. for Co60 and Cs137sources with activities (30) & (41) MBq respectively , using scintillation detector NaI(T?) with (3"×3")volume .The results shows increases of buildup factor for low atomic number(Z) samples where the energy of radiation source was constant, also shows increases of buildup factor with decreases the energy of radiation source. An empirical equation was obtained using Matlab7 program this equation have agreements with most obtained data for 96%.

     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

This work was carried to study the capability of activated alumina from bauxite compared with activated carbon adsorption capability to reduce the color content from Al-Hilla Textile Company wastewater. Six dyes were studied from two types(reactive and dispersed) namely (blue, red, yellow) from wastewater and aqueous solutions.
Forty eight experiments were carried out to study the effect of various initial conditions (bed height, flow rate, initial concentration, pH value, temperature, and competitive adsorption) on adsorption process.
The results showed that the adsorption process using activated carbon insured a good degree of color reduction reaching (99.7%) and was better than activated bauxite which reached (95%).