Lie group analysis for the effects of chemical reaction on MHD stagnation-point flow of heat and mass transfer towards a heated porous stretching sheet with suction or injection

An analysis is carried out to study two dimensional stagnation-point flow of heat and mass transfer of an incompressible, electrically conducting fluid towards a heated porous stretching sheet embedded in a porous medium in the presence of chemical reaction, heat generation/absorption and suction or injection effects. A scaling group of transformations is applied to the governing equations. After finding three absolute invariants a third order ordinary differential equation corresponding to the momentum equation and two second order ordinary differential equation corresponding to energy and diffusion equations are derived. Furthermore the similarity equations are solved numerically by using shooting technique with fourth-order Runge–Kutta integration scheme. A comparison with known results is excellent. The phenomenon of stagnation-point flow towards a heated porous stretching sheet in the presence of chemical reaction, suction or injection with heat generation/absorption effects play an important role on MHD heat and mass transfer boundary layer. The results thus obtained are presented graphically and discussed

Similarity Solutions for a Steady MHD Falkner-Skan Flow and Heat Transfer over a Wedge Considering the Effects of Variable Viscosity and Thermal Conductivity

An analysis is carried out to study the Falkner–Skan flow and heat transfer of an incompressible, electrically conducting fluid over a wedge in the presence of variable viscosity and thermal conductivity effects. The similarity solutions are obtained using scaling group of transformations. Furthermore the similarity equations are solved numerically by employing Kellr-Box method. Numerical results of the local skin friction coefficient and the local Nusselt number as well as the velocity and the temperature profiles are presented for different physical parameters

Sol–Gel Synthesis of Iron-Doped Sepiolite as a Novel Humidity-Sensing Material

Nowadays, humidity sensors are attracting a great deal of attention, and there are many studies focusing on enhancing their performances. Nevertheless, their fabrication through facile methods at reasonable cost is a significant factor. In this article, a new magnesium silicate nanopowder was successfully synthesized using a simple and low-cost sol–gel method. Subsequently, modified sepiolite was achieved by the substitution of iron ions in the synthesized nanopowders. The specimens were then characterized by X-ray diffraction, field emission–scanning electron microscopy, X-ray photoelectron spectroscopy, thermogravimetric–differential thermal analysis, infrared spectroscopy, and nitrogen adsorption. Furthermore, humidity sensors were manufactured by screen printing the prepared powders on alumina substrates with interdigitated Pt electrodes. The results showed that the fabricated sensors with modified sepiolite exhibited interesting characteristics for humidity detection

Data mining: a tool for detecting cyclical disturbances in supply networks.

Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause

Data augmentation to improve the soundscape ranking index prediction

Predicting the sound quality of an environment represents an important task especially in urban parks where the coexistence of sources of anthropic and biophonic nature produces complex sound patterns. To this end, an index has been defined by us, denoted as soundscape ranking index (SRI), which assigns a positive weight to natural sounds (biophony) and a negative one to anthropogenic sounds. A numerical strategy to optimize the weight values has been implemented by training two machine learning algorithms, the random forest (RF) and the perceptron (PPN), over an augmented data-set. Due to the availability of a relatively small fraction of labelled recorded sounds, we employed Monte Carlo simulations to mimic the distribution of the original data-set while keeping the original balance among the classes. The results show an increase in the classification performance. We discuss the issues that special care needs to be addressed when the augmented data are based on a too small original data-set

The Extended Log-Logistic Distribution: Properties, Inference, and Applications in Medicine and Geology

In this paper, a new flexible extension of the log-logistic model called the extended odd Weibull log-logistic (EOWLL) distribution is studied. The EOWLL density can be expressed as a mixture of Dagum densities. The EOWLL distribution provides decreasing, increasing, upside-down bathtub, and reversed-J-shaped hazard rates, and right-skewed, symmetrical, and left-skewed densities. Its mathematical properties are derived. The EOWLL parameters are estimated via eight classical methods of estimation. Additionally, extensive simulations are obtained to explore the performance of the proposed methods for small and large samples. Two real-life sets of data from medicine and geology are analyzed, showing the flexibility of the EOWLL distribution as compared to other log-logistic extensions. The results show that the EOWLL distribution is more appropriate as compared to the Kumaraswamy transmuted log-logistic, alpha-power transformed log-logistic, and additive Weibull log-logistic distributions, among others