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The present work was done to investigate the possible effects of Nigella sativa oil (NSO) on gastric secretion and ethanol-induced ulcer in rats. Thirty two adult male rats were used in this study (four groups) and several parameters were determined to assess any degree of protection. It was found that the administration of NSO in rats produced a significant increase in mucin content and glutathione level and a significant decrease in mucosal histamine content. Ethanol administration produced a 100% ulcer induction with an ulcer score of 12.62+/-1.35 (mean+/-S.E., n=8). It caused a significant reduction in free acidity and glutathione level while it produced a significant increase in mucosal histamine content. When animals were pretreated with NSO before induction of ulcer, there was a significant increase in glutathione level, mucin content and free acidity and a significant decrease in gastric mucosal histamine content with a protection ratio of 53.56% as compared to the ethanol group. It can be concluded that NSO imparted a protective action against ethanol induced ulcer in rats.

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.

This paper discusses the effect of various climatic conditions that pertain to passive design measurements and their relationships with building configurations to improve indoor thermal comfort based on the different climate zones in Egypt to support Egypt’s sustainability agenda 2030. We find the most appropriate design settings that can increase the indoor thermal comfort, such as building orientation and shape. These settings can be modeled using DesignBuilder software combined with Egyptian meteorological data. This software is used accompanied by computational fluid dynamics to numerically assess the outcomes of different changes, by simulating indoor climate condition factors such as wind speed and temperature. Natural ventilation simulations were performed for four different shapes to create comprehensive dataset scenarios covering a general range of shapes and orientations. Seven scenarios were optimized to put forward a series of building bioclimatic design approaches for the different characteristic regions. The results indicated that the temperature decreased by about 3.2%, and the air velocity increased within the study domain by 200% in the best and the worst cases, respectively, of the four different shapes. The results of the study gave evidence that the configuration of buildings, direction, and wind speed are very important factors for defining the natural ventilation within these domains to support the green building concept and the sustainable design for a better lifestyle.

Abstract To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability.

The environmental control and management of municipal solid waste (MSW) dumping sites is considered one of the sensitive challenges faced by executive municipalities. This is especially true in Makkah due to the gradual increase in urban population and visitors, with an overall MSW generation of about one million tons per year. Consequently, the geo-environmental evaluation of the Kaakia dumping site shed light on the potential environmental threats, in terms of ambient air quality levels and meteorological parameters, in addition to geophysical inspection. An air quality survey discussed the major trends of ambient air pollutants (SO2, NO2, CO, O3, CH4, and PM10) downwind from the Kaakia dumping site. It indicated the presence of a significant increase in sporadic plumes of Methane concentration. The maximum hourly averages ranged between 22.9–26.6 µg/m3 for SO2, 44.4–64.0 µg/m3 for NO2, 0.86–1.38 mg/m3 for CO, 150.2–158.8 µg/m3 for O3, 5.09–5.9 ppm for CH4, and 955–994 µg/m3 for PM10. The ground penetrating radar (GPR) geophysical survey indicated the subsurface sequence of three geological layers, as confirmed by nearby bores of the investigated site: (1) a surface layer formed of alluvial sediments of sand, which were 2.5–3.1 m thick; (2) a second layer represented by sand and gravel, with a thickness of 4.6–6.5 m; and (3) a third layer equivalent to saturated alluvial sediments mixed with rock fragments that extended to a depth of 13 m. The signals of the GPR were attenuated at the base of the conducted profiles due to the percolation of generated leachate to the subsurface sequence and contaminated groundwater aquifer.

Abstract The main purpose of salinity gradient solar ponds (SGSPs) is to store the maximum possible solar thermal energy. A well-established salinity and temperature gradients are the main points to achieved optimum storage efficiency. In this work, a high-fidelity model is developed using computational fluid dynamics (CFD) to simulate the SGSP behavior under hot climate regions. The model is able to simulate the double convective effect by solving Navier-Stokes and energy equations, simultaneously. Brines with different salinities (i.e. 10%,15, and 25%) are used to investigate their role on the developed salinity/temperature gradients. Simulation results show the successful establishment of the three zones (i.e. upper convective, non-convective, and lower convective) with relatively stable salinity and temperature gradients. However, injecting the lower convective zone (or storage zone) with 10% saline brine results in preserving the highest storage temperature of around 79.2°C after flow time of six hours.

The transportation impact on pollution and global climate change, has forced the automotive sector to search for more ecological solutions. Owing to the different properties of Fuel Cell (FC), real potential for reducing vehicles’ emissions has been witnessed. The optimization of FC integration within Electric Vehicles (EVs) is one of the original solutions. This paper presents an innovating solution of multi-stack Fuel Cell Electrical Vehicle (FCEV) in terms of efficiency, durability and ecological impact on environment. The main objective is to illustrate the interest of using the multi-stack FC system on the global autonomy, cycling, and efficiency enhancement, besides optimizing its operation performance.

AbstractThe static charges and induced voltages from extra-high-voltage alternating current transmission lines (EHVACTLs) on parallel oil pipelines (POPLs) raise the risk levels for people and animals. Thus, the objective of this paper was to reduce and/or mitigate the electric field which is concentrated on POPLs by using grounded shield wires under EHVACTLs. Three techniques are employed to reduce the electric field effects on POPLs of two distinct types of transmission lines (TLs), 500 kV and 220 kV. The first technique involves raising the tower’s height to improve the clearance space between the POPLs and the TL conductors. The second technique is increasing the horizontal distance between the POPLs and the nearest stressed conductors of the TL. The third technique involves placing shield wires beneath the stressed conductors of the EHVACTLs. The electric field under the EHVACTLs is calculated with and without the grounded shield wires using charge simulation method. The results of the first technique revealed that with increasing the tower height from 10 m to 15, 20, 25, and 30 m, the electric field decreased by 43.75%, 62.5%, 68.75%, and 75%, respectively. Herein, employing the second technique, the electric field intensity is reduced by 20% and 21% depending on the POPL placed at a distance from the right stressed conductor equal to the horizontal clearance between conductors of 500 kV and 220 kV, respectively. Besides, the results of the third technique proved that the shield wires under the EHVACTLs reduced the electric field intensity on the POPLs by 17.65% and 24.71% for 500-kV and 220-kV TLs, respectively.