This research was aimed to study the exposure of Razzazah Lake  to major hydrological changes in recent years as a result of natural climatic changes  and  drought, high evaporation in lake due to stop discharge from Habbaniyah Lake  by Al- majera channel. During 2019, we collected surface water samples at three locations, and three samples from groundwater, in addition one samples from each location Imam Ali Drop and Sewage water  of Karbala. The Results  show that the heavy isotopes in lake and groundwater well are enriched during the warm period, and depleted during the cold period. Chemically, The dominant cations and anions in Al-Razzaza lake water are mainly of in Order  Ca > Na > Mg and  Cl>SO4 and the water

In this study, thin film of pure zinc oxide (ZnO) and ZnO doped with Ga2O3 films with different concentrations (0, 0.03, 0.05, 0.07 and 0.09)wt% were prepared by pulsed laser deposition. The powder mixture was then sintered in a furnace at 1273 K for 5 h. The resulting powders were thoroughly ground and then pressed using a special press to form discs with a diameter of 1 cm and a thickness of 0.5 cm. The deposition was carried out under a vacuum of 2.5×10−2 mbar on various substrates, including glass for AFM measurements and n-type crystalline silicon wafers for the gas sensor. AFM results revealed a progressive increase in both grain size and roughness with moderate doping and irregularity at high doping levels. Gas-sensing meas

Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to

     The oral cavity has the second major and most diverse microflora after the gut. Since it is the starting point of digestion; the oral microbiota is essential for maintaining oral and systemic health.  Thus, this study aimed to find the bacterial isolates which act as a causative agent of periodontal diseases in Iraqi patients and their relation with some immune oral parameters. Saliva specimens and oral swabs of 91 patients (51) and control individuals (40) were collected in this study. The specimens of patients were collected from Al-Amirya and Almaamon Specialized Dental Centers in Baghdad for a period from November/2021 to February/2022. The microbiological results revealed, that the most prevalent bacterial isolates we

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

Water-based drilling fluids (WBDFs) are the industry-preferred medium for drilling reactive shale formations, yet they remain susceptible to shale swelling, fluid invasion, and cuttings dispersion—all of which compromise wellbore stability. Although oil-based fluids (OBFs) provide effective shale inhibition, their high cost and adverse environmental profile limit their use. Nanoparticles (NPs) derived from waste feedstocks offer a low-cost, environmentally responsible alternative for enhancing WBDF performance, but no prior study has simultaneously evaluated both waste-derived SiO2 and Al2O2 NPs within a single WBDF system. This study synthesised SiO2 NPs from Halfaya bentonite clay waste and Al2O2 NPs from recycled aluminium scrap, char