Journal of University of Babylon

Depending on fossil fuels such as crude oil is becoming a big environmental and economic dilemma. Fossil fuels release carbon oxides which hurt the environment by forming greenhouse effect which leads to the global warming and climate change. Moreover, countries over the world and especially Iraq use the crude oil and natural gas daily which makes this fuel expensive and threatened to be stopped one day. In this study, a simple review focuses on renewable energy as a clean and cheap energy that they could be recommended to use safely in Iraq. In conclusion, the renewable energies are the optimal solution to avoid the increased cost of the crude oil and reducing the risk of the environment pollution.

Geopolymer is a promising alterative of the ordinary Portland cement (OPC). The aim of this study is to prepared lightweight geopolymer concrete by using crumb rubber as aggregate. Sodium silicate solution and sodium hydroxide were used to prepare the alkali activator solution, kaolin was used as a source of the solid part of the geopolymer paste. Crumb rubber was used as partial replacement (5, 10, 15%) by volume of aggregate to produce of lightweight geopolymer concrete. To enhance the bond strength between crumb rubber and geopolymer paste, the crumb rubber was treated with Maliec Anhydride (MAn).
The alkali solution was subjected to heat treatment, the effect of the SiO2/Al2O3 ratio, and amount of water were investigated, also, the impact of waste rubber on the mechanical characteristics (compressive strength, flexural strength) and physical properties (density, water absorption and porosity) of lightweight geopolymer concrete were investigated.
This study shows that the heat treatment is important to achieve high compressive strength; a strength of 117 MPa was obtained using a ratio of SiO2/Al2O3 of 3.6 and 8 ml of water for geopolymer paste. A compressive strength of 60.6 MPa and flexural strength of 6.076 MPa were obtained for a geopolymer concrete with 554.4 kg and 1293.6 kg fine and course aggregate respectively. Also, the experimental results showed that the strengths and density of lightweight geopolymer concrete were decreased with the increase of waste rubber content. The reduction in weight was about 100 kg per cubic meter for lightweight geopolymer concrete compared with the geopolymer concrete and 300 kg as compared with OPC concrete.

The complex characteristics of the rainfall- runoff mechanism, along with its non-linear attributes and inherent uncertainties, have prompted scholars to explore alternative approaches inspired by natural phenomena. In order to tackle these obstacles, artificial neural networks (ANN) and fuzzy systems (FL) have been utilised as feasible substitutes for conventional physical models. Furthermore, the procurement of comprehensive data is considered essential for precise analysis and modelling. This study's primary objective was to use pertinent climatic data such as; Precipitation (P), Temperature (T), Relative humidity (Rh), and Wind speed (Ws) to predict the runoff coefficient using the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. Different ranges (60:40; 70:30; 80:20) were used for the training and testing phases. The model was employed to predict the runoff coefficient in the Aksu river basin in Antalya province in Turkey. The study conducted a comparative analysis of the results, taking into account various performance indicators of the model, such as mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). Based on the findings presented, the (60:40) range showed the best results as evidenced by its low RMSE and MAE values and its high R2 and NSE values (RMSE:0.056, MAE:1.92, NSE:0.868, R2 :0.996). It was concluded that the ANFIS model magnificently predicts runoff coefficients with an exceptional level of precision, also the study findings indicate that accurate runoff coefficient estimation can be achieved using meteorological data without incorporating more intricate and interrelated data.

Any substance that alters the natural properties of the atmosphere, whether it be chemical, physical, or biological, is considered an air pollutant. Air pollution can causes health, economic and environmental damages, etc. This study aims to evaluate the health impact of long-term exposure to particulate matter (PM2.5), as this pollutant is the one that governs air quality. Using the AirQ software and based on the data obtained from the Babylon Environment Directorate . The dust storms that occurred in the spring and summer of 2022 contributed to an increase in the PM2.5 seasonal concentration to 84 µg/m3. The long-term exposure was evaluated to the pollutant, which causes the following diseases: (Lung cancer , Acute Lower Respiratory Disease , Chronic Obstructive Pulmonary, Ischemic Heart Disease, and stroke). The results showed that the relative risk increased to 1.376 for lung cancer, 1.534 for ALRI, 1.412 for COPD, 1.399 for IHD, and 1,367 for stroke, the highest risk was recorded for ALRI because children are more susceptible to disease than others. The air quality was moderate at a rate of 45% to unhealthy for sensitive groups at a rate of 31% in 2022 and good at a rate of 28% to moderate at a rate of 45% in 2021.

Non-Orthogonal Multiple Access (NOMA) is a promising technology for the fifth and future generations of wireless communication networks, which increases spectral efficiency and reduces latency. However, NOMA performance can be affected by imperfect successive interference cancellation (SIC). Deep learning techniques have been proposed to aid in signal detection and channel estimation in NOMA systems. In this study, we propose a new approach using one-dimensional convolutional neural networks (1D CNN) to address the limitations of current deep learning methods. Unlike other deep learning methods that rely on time dependencies for data classification, 1D CNN uses a 1-dimensional convolution layer for feature extraction, resulting in high reliability. Simulation results demonstrate that our proposed method outperforms existing deep learning techniques in terms of sample error rate (SER) by 7dB. Moreover, reducing the cyclic prefix (CP) parameter increases inter-sample interference (ISI), but our method still achieves a 6 dB improvement over approaches in [11,13] and traditional channel estimation techniques like maximum likelihood (ML) at low signal-to-noise ratios (SNR).

The elimination of heavy metallic ions from wastewater the use of fava beans changed into investigated. The sorption behavior of metallic ions on the adsorbent relying on touch time, pH, resin dosage, and initial steel attention became studied in batch approach. The adsorption system, which is pH dependent, indicates maximum elimination of metal ions at pH of 4 for preliminary metal ion awareness of 5-25 mg/l, through a dammar dosage of 0.1-1 g and the symmetry became touched in 1.30 hr. The results showed that the optimum clearance was 95% when the initial concentration was 25 mg/L, the time was 120 minutes, the dose was 1 g, and the pH was 4. The final result implies that fava beans may be used as a performance adsorbent material for the removal of heavy metallic ions from waste water ions. amongst these kinds of techniques, the adsorption is economically favorable and technically smooth to separate. as opposed to the use of business substances researchers have labored on cheaper materials including natural and agricultural products. numerous works involved the removal of heavy metals however the gift paintings make a speciality of the sorbents based totally on biomass and their performance in elimination of heavy metals from waste water.