The study included isolate and diagnose fungus Fusarium solani of the local soil and purified and development in the PDB medium and the filtrate extracted using a solvent (Ethyl acetate) to obtain the fungal secondary metabolites extract. This extract has shown bioactivity against both reference isolates (E.coli (ATCC25922) and S.aureus(NCTC6571)) and pathogenic isolates S.pyogenes, K. pneumonia and S.typhimurium using agar disk diffusion technique , The diameters of the inhibition zones of fungal secondary metabolites24.0 mm against E.coli and 31.5 mm against S.aureus,and 34.0 mm against K.pneumoniae and 18.0 mm against S.pyogenes and 33.5mm against S.typhimurium. The test revealed the minimum inhibitory concentration (MIC) of the fungal

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

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

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

BACKGROUND: Clavicle fractures are common injuries in young active individuals, the mid third of the clavicle is most commonly fractured part(80% of clavicle fracture) OBJECTIVE: To compare the outcomes of operative and non operative management of displaced and or comminuted closed fracture of the mid third of clavicle in young adults PATIENTS AND METHODS: This prospective observational study of 24 patients of fracture of the mid third of the clavicle was conducted in Alkindy teaching hospital from July 2015 to January 2017 and divided into two groups; one managed by operative treatment with plate and screws and the other by non operative sling immobilization after taking the consent and the patients were seen at 2, 4, 6 weeks,3, 6, and 9 m

Silver nanoparticles synthesized by different species