Urban land uses of all kinds are the constituent elements of the urban spatial structure. Because of the influence of economic and social factors, cities in general are characterized by the dynamic state of their elements over time. Urban functions occur in a certain way with different spatial patterns. Hence, urban planners and the relevant urban management teams should understand the future spatial pattern of these changes by resorting to quantitative models in spatial planning. This is to ensure that future predictions are made with a high level of accuracy so that appropriate strategies can be used to address the problems arising from such changes. The Markov chain method is one of the quantitative models used in spatial planning to ana

Experimental activity coefficients at infinite dilution are particularly useful for calculating the parameters needed in an expression for the excess Gibbs energy. If reliable values of γ∞1 and γ∞2 are available, either from direct experiment or from a correlation, it is possible to predict the composition of the azeotrope and vapor-liquid equilibrium over the entire range of composition. These can be used to evaluate two adjustable constants in any desired expression for G E. In this study MOSCED model and SPACE model are two different methods were used to calculate γ∞1 and γ∞2

Objectives: Hyperglycosylated human chorionic gonadotropin (hCG) is a variant of hCG. In addition, it has a different oligosaccharide structure compared to the regular hCG and promotes the invasion and differentiation of peripheral cytotrophoblast. This study aimed to measure hyperglycosylated hCG as a predictor in the diagnosis of placenta accreta. Materials and Methods: In general, 90 pregnant women were involved in this case-control study among which, 30 ladies (control group) were pregnant within the gestational age of ≥36 weeks with at least one previous caesarean section and a normal sited placenta in transabdominal ultrasound (TAU). The other 60 pregnant women (case

The choice of gate dielectric materials is fundamental for organic field effect transistors (OFET), integrated circuits, and several electronic applications. The operation of the OFET depends on two essential parameters: the insulation between the semiconductor layer and the gate electrode and the capacitance of the insulator. In this work, the electrical behavior of a pentacene-based OFET with a top contact / bottom gate was studied. Organic polyvinyl alcohol (PVA) and inorganic hafnium oxide (HfO₂) were chosen as gate dielectric materials to lower the operation voltage to achieve the next generation of electronic applications. In this study, the performance of the OFET was studied using monolayer and bilayer gate insulators.

The choice of gate dielectric materials is fundamental for organic field effect transistors (OFET), integrated circuits, and several electronic applications. The operation of the OFET depends on two essential parameters: the insulation between the semiconductor layer and the gate electrode and the capacitance of the insulator. In this work, the electrical behavior of a pentacene-based OFET with a top contact / bottom gate was studied. Organic polyvinyl alcohol (PVA) and inorganic hafnium oxide (HfO2) were chosen as gate dielectric materials to lower the operation voltage to achieve the next generation of electronic applications. In this study, the performance of the OFET was studied using monolayer and bilayer gate insulators. To mo

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c