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Effect of some nucleating agents on thermal expansion behaviour of Li2O-BaO-Al2O3-SiO2 glasses and glass-ceramics
The thermal expansion behaviour of some glasses and glass-ceramics within the system spodumence (LiAlSi2O6)-celsian (BaAl2Si2O8) containing LiF, TiO2 and Cr2O3 as nucleation catalysts was described. LiF and TiO2 were found to increase the thermal expansion of the glasses investigated, whereas Cr2O3 slightly lowered the expansion coefficient. The dilatometric transition and softening points of the glasses showed the reverse behaviour. The thermal expansion of the glass-ceramics was a function of type and amount of nucleating agent and heat treatment which greatly affected the mineralogical constitution of the materials
Predicting and Controlling Fertility Using Family Planning Methods
Without a real reduction in population fertility rates, developing societies will push for more spending on their infrastructure and more demand for basic services for new-born, and more dependency and crowding, and the attendant ills and various social, economic and cultural problems, which will push these countries towards Directing a large part (if not most) of development revenues to meet the growing population. In general, the importance of this study lies in how to predict fertility rates using the rates of family planning methods (practice rates, years of protection) and to identify the method of neural networks and its accuracy in dealing with fertility data in particular. The study concluded that the prevalence of family planning methods (PR) and protection rate (CYP) are used to estimate and predict the total fertility rate (TFR) very efficiently, and artificial neu6ral networks have reached a high rate and high accuracy in estimating and predicting the total fertility rate (TFR) is highly and reliable (99.6%)
Development of High Thermal Stability Geopolymer Composites Enhanced by Nano Metakaolin
This paper deals with study of thermal stability of geopolymer composites enhanced by nano metakaolin materials (NMK) and exposed to high firing temperature up to 1000 °C. The main geopolymer made up of water cooled slag having various kaolin ratios. The activators used are Na2SiO3 and NaOH in the ratio of 3:3. The thermo-physical, micro-structural and mechanical properties of the geopolymers before and after the exposure to elevated temperatures of 300, 500, 600 800 and 1000 °C have been investigated. The fire shrinkage of the geopolymer specimens increased by increasing temperature up to 1000 oC. Also, the fire shrinkage increased slowly up to 500 °C. The mechanical strength of geopolymer specimens increased with temperature up to 500 oC. The good thermo-physical and mechanical properties for these geopolymer composites increase the possibility of vast application of these eco-friendly materials in construction sectors
On the stationary vibrations of a rectangular plate subjected to stress prescribed partially at the circumference
The stationary periodical problem of a vibrating rectangular plate, stressed at a segment
while fixed elsewhere at one of its edges, is considered. Using the finite Fourier transformation, the problem
is converted to a singular integral equation that in turn can be reduced to an infinite system of algebraic
equations. The truncation of the algebraic system is justified
Parametrically controlling solitary wave dynamics in modified Kortweg-de Vries equation
We demonstrate the control of solitary wave dynamics of modified Kortweg-de
Vries (MKdV) equation through the temporal variations of the distributed
coefficients. This is explicated through exact cnoidal wave and localized
soliton solutions of the MKdV equation with variable coefficients. The solitons
can be accelerated and their propagation can be manipulated by suitable
variations of the above parameters. In sharp contrast with nonlinear
Schr\"{o}dinger equation, the soliton amplitude and widths are time
independent.Comment: 4 pages, 5 eps figure
Development of High Thermal Stability Geopolymer Composites Enhanced by Nano Metakaolin
This paper deals with study of thermal stability of geopolymer composites enhanced by nano metakaolin materials (NMK) and exposed to high firing temperature up to 1000 °C. The main geopolymer made up of water cooled slag having various kaolin ratios. The activators used are Na2SiO3 and NaOH in the ratio of 3:3. The thermo-physical, micro-structural and mechanical properties of the geopolymers before and after the exposure to elevated temperatures of 300, 500, 600 800 and 1000 °C have been investigated. The fire shrinkage of the geopolymer specimens increased by increasing temperature up to 1000 oC. Also, the fire shrinkage increased slowly up to 500 °C. The mechanical strength of geopolymer specimens increased with temperature up to 500 oC. The good thermo-physical and mechanical properties for these geopolymer composites increase the possibility of vast application of these eco-friendly materials in construction sectors
Obstructed D-Branes in Landau-Ginzburg Orbifolds
We study deformations of Landau-Ginzburg D-branes corresponding to obstructed
rational curves on Calabi-Yau threefolds. We determine D-brane moduli spaces
and D-brane superpotentials by evaluating higher products up to homotopy in the
Landau-Ginzburg orbifold category. For concreteness we work out the details for
lines on a perturbed Fermat quintic. In this case we show that our results
reproduce the local analytic structure of the Hilbert scheme of curves on the
threefold.Comment: 44 pages; v3: typos correcte
On Some Classes of mKdV Periodic Solutions
We obtain exact periodic solutions of the positive and negative modified
Kortweg-de Vries (mKdV) equations. We examine the dynamical stability of these
solitary wave lattices through direct numerical simulations. While the positive
mKdV breather lattice solutions are found to be unstable, the two-soliton
lattice solution of the same equation is found to be stable. Similarly, a
negative mKdV lattice solution is found to be stable. We also touch upon the
implications of these results for the KdV equation.Comment: 8 pages, 3 figures, to appear in J. Phys.
Skin cancer classification using explainable artificial intelligence on pre-extracted image features
Skin cancer is the most common type of cancer worldwide, affecting a large population recently. To date, various machine learning techniques exploiting skin images have been applied directly to skin cancer classification, showing promising results in improving diagnostic accuracy. This study aims to develop a machine learning-based model capable of accurately classifying skin cancer by utilizing extracted features from preprocessed images in the publicly available PH² dataset. Preprocessed features are known to provide more significant information than raw image data, as they capture specific characteristics of the images that are relevant to the classification task. The proposed model of this study can identify the most pertinent information in the images more accurately, thereby improving the performance and interpretability of the machine learning classification. Our simulation results illustrate that employing XG-boost yields an accuracy of 94% and an area under the curve value of 0.9947, further indicating that the proposed technique effectively distinguishes between non-melanoma and melanoma skin cancer. Explainable artificial intelligence provides some explanations by leveraging model-agnostic methods such as partial dependence plot, permutation importance, and SHAP. Moreover, the explainable artificial intelligence results show that asymmetry and pigment network features are the most important feature in the classification of skin cancer. These specific characteristics emerge as the most influential factors in distinguishing between different types of skin cancer
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