Topology and its applications occupy the interest of many researching centers in the advanced world. From this point of view and because the near open sets play a very important role in general topology and they are now the research topics of many topologists worldwide and its sets doesn’t enter in fibrewise topology yet. Therefore, we use some of the near open sets to be model for introduce results and new spaces in fibrewise topological spaces. Also, there is a very important role of closure operators in constructing a topological spaces, so we introduce a new closure operators on the power set of vertices on graphs and conclusion theorems and new spaces from it. Furthermore, we discuss the relationships of connectedness between some types of graphs and new spaces by using graph closure operators and we give some definitions of near open subgraphs using the new closure operators on graphs. The boundary regions in approximation spaces are considered as uncertainty regions. There are a lot of information which result from many experiments that may make the boundary regions to be all elements of the society under study or to be all elements of the society except a small number of elements, which leads to the failure of several results and decisions which could be reached in such cases. In the context of this thesis, we tried to introduce some solution to such dilemmas, through the division of the boundary regions into several levels. This leaves us to get to the mechanism for decreasing the boundary regions and making it small as possible. We also offer some theories of uncertainty through the topological spaces which result from new closure operator of graphs on the approximation spaces. Finally, we study some related applications.
Recent research has examined the improvement of physical and dielectric properties of BaTiO3 ceramic material by small addition of excess TiO2 or BaCO3. The prepared samples sintered at different temperatures and varying soaking time. The results show that increasing the sintering temperature within 1350°C and soaking time of 10 hrs give better electrical and physical properties, which indicate the reaction is complete at higher temperature and period.
An optical spectroscopic study is reported in this article to study the correlation between the supermassive black hole (SMBH) and the star formation rate (SFR) for a sample of Seyfert galaxies type (I and II). The study focused on 45 galaxy of Seyfert 1, in addition to 45 galaxy of Seyfert 2, where these samples have been selected form different survey of Salon Digital Sky Survey (SDSS). The redshift (z) of these objects were between (0.02 – 0.26). The results of Seyfert 1 galaxies shows that there good correlation between the SMBH and the SFR depending on statistical analysis parameter named Spearman’s Rank Correlation in a factor of (ρ=0.609), as well as the Seyfert 2 galaxies results show a good correlation between the SMBH
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Sorghum cultivation is often accompanied by low field emergence rates and weak seedlings, which may be due to genetic or environmental stress. A factorial experiment was conducted in the spring and fall seasons of 2022 using a randomized complete block design with split-plot arrangement and four replications. Planting dates (spring season: Feb. 15th, Mar. 1st, 15th, and Apr. 1st, 15th; fall season: Jun. 15th, Jul. 1st, 15th, and Aug. 1st, 15th) were allocated to the main plots. Seeds stimulation treatments (35% banana peel extract + 100 mg L-1 citric acid and distilled water soaking treatment only) were allocated to the subplots. The interaction treatment (banana peel extract + citric acid) with the planting date of April 15 showed the high
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Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
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This research deals with the design and simulation of a solar power system consisting of a KC200GT solar panel, a closed loop boost converter and a three phase inverter by using Matlab / Simulink. The mathematical equations of the solar panel design are presented. The electrical characteristics of the panel are tested at the values of 1000 for light radiation and 25 °C for temperature environment. The Proportional Integral (PI) controller is connected as feedback with the Boost converter to obtain a stable output voltage by reducing the oscillations in the voltage to charge a battery connected to the output of the converter. Two methods (Particle Swarm Optimization (PSO) and Zeigler- Nichols) are used for tuning
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The paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms
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Mixed-effects conditional logistic regression is evidently more effective in the study of qualitative differences in longitudinal pollution data as well as their implications on heterogeneous subgroups. This study seeks that conditional logistic regression is a robust evaluation method for environmental studies, thru the analysis of environment pollution as a function of oil production and environmental factors. Consequently, it has been established theoretically that the primary objective of model selection in this research is to identify the candidate model that is optimal for the conditional design. The candidate model should achieve generalizability, goodness-of-fit, parsimony and establish equilibrium between bias and variab
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