The Suez Canal University is an Egyptian university serving the Suez Canal region. Its faculties are located in the three governorates of the Suez Canal Region (Port Said, Ismailia & Suez). It was established in 1974. It is well-known for its non-traditional research. It has 48 faculties (16 in Ismaïlia, 13 in Port Said, 10 in Suez and 9 in Arish) with a total number of students reaching 21,325.
This paper proposes an adaptive Overcurrent (OC) relay protection scheme that combines load forecasting and clustering-based operating state identification to dynamically adjust relay settings. A Long Short-Term Memory (LSTM) model predicts feeder current, allowing the system to proactively select appropriate relay setting groups for different load conditions. The method is validated on the IEEE 33-bus distribution network. Results show that the forecasting model achieves an Mean Absolute Percentage Error (MAPE) of 2.94 %, while the relay settings derived from predicted loads closely match those obtained from actual data. The difference in operating time is negligible between forecast-based and actual coordination and a safety protection mechanism is employed to deal with the forecast or classification errors by switching to a safe backup protection group. These results demonstrate the effectiveness of the proposed approach in maintaining reliable protection performance under changing operating conditions through SCADA, and are activated using IEC 61,850 protocols and GOOSE-based group switching mechanisms. The time-domain analysis shows a worst-case timing deviation below 41 ms, well within the 300 ms Coordination Time Interval (CTI) requirement.
This study investigates thermal plasma-assisted pyro-gasification as an intensified allothermal route for converting biomass-derived bio-oil and representative oxygenated compounds into H2- and CO-rich syngas. An integrated experimental-numerical framework combined bio-oil fractionation, model-oil formulation, gasification tests, optical shadowgraphy, and supporting plasma-droplet calculations. Gas-phase tests using methane, acetic acid, and ethanol showed that H2 and CO dominated the permanent gas and that conversion increased with plasma specific enthalpy and the dimensionless specific-enthalpy ratio. Shadowgraphy identified rapid combtype primary jet breakup followed by stripping or tear-off secondary fragmentation. The numerical framework is defined as a steady, two-dimensional axisymmetric Eulerian-Lagrangian model employing temperaturedependent properties, RNG k-epsilon turbulence, an optically thin LTE plasma representation, and Kelvin-Helmholtz WAVE breakup. Quantitative comparisons gave experimental/simulated Sauter mean diameters of 110/121 mu m after optical-cutoff correction and thermocouple temperature-rise deviations of 7.4-18.5%. The combined evidence indicates that intrinsic kinetics were not the sole limitation under the tested gas-phase conditions; plasma-reactant contact, energy transfer, effective exposure time, injection geometry, and liquid heat/mass transfer were also decisive.
This paper examines how frontline hotel employees appraise generative artificial intelligence (GenAI) apps as challenge or hindrance technostressors and how such appraisals affect service sabotage behaviors. Further, this paper investigates the mediating roles of ego depletion and psychological detachment. Data was gathered from 1276 front-desk employees working in three-star chain and independent hotels across four Middle East and North Africa countries. Results indicated that hindrance appraisals of GenAI apps increased service sabotage through heightened ego depletion, whereas challenge appraisals reduced service sabotage by enhancing psychological detachment. In chain hotels, challenge appraisals more promote psychological detachment, whereas hindrance appraisals lead to affected ego depletion in independent hotels. This paper advances hospitality research by integrating challenge-hindrance technostress appraisals to explain service sabotage in AI-enabled service contexts.
Ultrafine particles (UFPs) generated by wheel-rail interaction can accumulate in subway tunnels and are strongly affected by train-induced unsteady airflow. This study proposes a floor-mounted static two-stage electrostatic precipitation system for tunnel-scale particle control by utilizing piston wind during subway operation. A fullscale numerical model coupling train-induced airflow, UFPs transport, and electrostatic removal is developed to compare piston-wind-driven and fan-driven configurations and to optimize key layout parameters, including module spacing, inlet velocity, and ESP height. The results show that UFPs preferentially accumulate in the nearground region beneath the train, supporting the feasibility of floor-mounted particle interception. The pistonwind-driven system provides passive purification without additional ventilation demand, whereas the fandriven system improves removal only when the imposed inlet velocity exceeds the local piston-wind velocity of approximately 6 m/s. Reducing module spacing and increasing ESP height improve tunnel-scale removal by expanding spatial coverage and increasing the inlet airflow rate. The optimized configuration reduces the total amount of UFPs in the tunnel by up to 37.38 %. These findings provide practical guidance for tunnel-scale deployment of passive electrostatic purification systems in subway tunnel environments.
Purpose. Despite the high valuable and marketability of pomegranate, its cultivation in arid lands faces different stresses such as drought, poor soil, and salinity. These conditions lead to poor productivity and fruit quality, along with reduced commercial appeal. Cultivating proper cultivars while providing an effective nutrients combination could address various issues of arid climates. Methods. The study focused on assessing the potential improvements in yield and quality of the pomegranate cultivar named ‘Wonderful’ grown in saline-sandy soil as a result of lpotassium (K) and boron (B) fertilization. Spraying of potassium sulfate (K2SO4) and potassium nitrate (KNO3) at rates of 0.5 and 1.0
