Abstract The interest of raising thermal conductivity and optimizing industrial based materials for an enhanced efficiency and productivity has propels various studies on nanomaterials and non-Newtonian fluids. The uses of nanotechnology in technological advancement and industrial promotion could not be overstressed. Thus, the aim of this study is to numerically investigate the entropy formation, heat propagation and conduction of hybridized Casson ferrofluid with dissipation and radiation in a thin needle. The inherent heat transfer capability of magneto-hybridized Fe2O3 and Al2O3 solid nanoparticles in H2O based solvent motivated the study. The streaming nanofluid is controlled by magnetic field, heat generation and continuous stretching velocity. A partial derivative mathematical model is developed to describe the flow dimensions, and via similarity quantities, an invariant transformation of the model is obtained. A numerical and localized spectral linearization scheme is adopted for the thermodynamic and parameters sensitivities analyzes of the thermal fluid. As observed, the heat transfer comparison rate establishes that the thermal propagation of Fe2O3-Al2O3/water is 8.28% higher than Al2O3/water at 0.01% of volume fraction. The entropy formation is raised with rising nanoparticle size, and radiation and dissipation terms boosted temperature distribution.

Intrinsic protein disorder can predict a whole host of neurodegenerative diseases like Alzheimer's. Predicting protein disorder itself is best undertaken using computational methods. In this paper, a novel approach to predicting protein disorder using a Convolutional Neural Network (CNN) algorithm. The algorithm had found a 92% auc-roc score, which indicates the performance of a binary classification model. To extract features, the model had used OneHotEncoding, a technique that converts the sequential data into numerical values that is fed into the model. The data was also gathered from a variety of sources including the Protein Data Bank (PDB), The Disordered Protein Database (Disprot), and the Swiss protein database (Swissprot) [1]-[3]. This paper has a unique approach in the model built and distinguished itself from prior models based on the structure of the model and feature extraction that was performed. Using a tensorflow framework, the model used multiple convolutional layers of varying filter length and a final dense layer to enable the model to learn the features and predict associated outputs. Furthermore, the output from the initial stages was fed into a binary cross entropy classifier that gave the resulting judgement of order or disorder.

In this paper, we explore the use of supervised machine learning models to predict the employability of high school students with local businesses for part-time jobs. We further compare the performance of trained models used in this analysis to one another. Empirical results show that it is possible to predict the employability of high school students with local businesses with high-predictive accuracies. The trained predictive models perform better with larger dataset, with up to 93% accuracy.

This letter is in response to the news of a new Virginia Academy of Science, Engineering, and Medicine (VASEM), which has recently been formed in the Commonwealth of Virginia (see Physics Today, January 2014, page 22). That new academy is not affiliated with the Virginia Academy of Science (VAS), an organization that has been in place for the past 92 years. The membership of VAS is open to all scientists and people interested in science in Virginia, from faculty and research scientists to postdocs and graduate and undergraduate students, as well as business professionals and others who support our mission. We also welcome middle school and high school students and their teachers as members of our Virginia Junior Academy of Science.The mission of VAS is to promote research and education in science, technology, engineering, math, and health (STEMH) in Virginia through grants, publications, and meetings. We welcome VASEM as a new academy in Virginia and look forward to working with them to increase STEMH activities in the state.© 2014 American Institute of Physics.

AbstractThe reactions of some ortho‐substituted anilines with various α,β‐acetylenic ketones were investigated as a route to 4‐alkyl‐, 4‐aryl‐, 4‐hydroxy‐, and 4‐amino‐3‐quinolyl ketones. The anilines examined were 2‐aminoacetophenone (1), 2‐aminobenzophenone (2), anthranilonitrile (3), methyl anthranilate (4), and ethyl anthranilate (5). The acetylenic ketones used were 1,4‐diphenyl‐2‐butyne‐1,4‐dione (6), 3‐butyn‐2‐one (7), 1,3‐diphenyl‐2‐propyn‐1‐one (8), and 4‐phenyl‐3‐butyn‐2‐one (9). The acetylenic ketones typically reacted with the anilines to give enamines; however, exceptions were found. Acetylene 6 reacts with 3 to give the enamine (13) along with a small amount of 2,3‐dibenzoyl‐4‐quinolamine (14). The reactions of 1 or 2 with 6 give the respective quinoline derivatives directly. Acetylene 8 reacts with 2 to give 3‐benzoyl‐2,4‐diphenylquinoline (22) directly, whereas no reaction occurs between 8 and 1 or 3. Acetylene 9 does not react with 1, 2, or 3. The enamines exist as the intramolecularly hydrogen bonded isomers and usually undergo cyclization with 5 molar equivalents of methanolic sodium methoxide to give quinoline derivatives. The 4‐quinolinols exist predominantly as the 4‐quinolinone tautomer.