Composition—property correlations in some lubricating oil base stocks

The dependence of properties on the composition of lubricating oil base stocks (LOBS) available from Indian refineries has been studied. It has been observed that pour point is greatly influenced by the saturated component of the base stocks, whereas viscosity and viscosity index are greatly influenced by the aromatic content. The effect of various components on the viscosity/shear stress versus temperature behaviour of LOBS samples has also been investigated.     

Fractional analysis of radiative blood transport through a porous channel containing multishaped cobalt nanoparticles: An application to hemodynamics

In this work, impacts of dispersing nonspherical shaped cobalt nanoparticles in the blood are analyzed for magnetohydrodynamic radiative transport of blood inside a vertical porous channel. An Oldroyd-B model is used to feature flow characteristics of blood along with Fourier's principle of heat transmission for the mathematical modeling of the problem. A fractional system is constructed by employing the idea of the Caputo–Fabrizio derivative on subsequent differential equations. The Laplace transform method is adopted to solve the fractional flow and energy equations subject to generalized boundary conditions, which involve time-dependent functions $h\left(\tau \right)$ and $g\left(\tau \right)$, respectively. Instead of promoting the analytic velocity and energy expressions, Zakian's numerical algorithm is operated to achieve the reverse transformation purpose of Laplace domain functions. To certify the obtained solutions, two additional numerical algorithms named Stehfest's algorithm and Durbin's algorithm are inculcated in this study, and comparative illustrations are drawn. For the extensive investigation of shear stress and heat transfer phenomenon, numerical simulations for the coefficient of skin friction and Nusselt number are performed, and outcomes are communicated through various tables. The impacts of shape-dependent viscosity and other significant parameters on flow patterns are investigated through graphs for multiple motion types of the left channel wall. Meanwhile, the thermal performance of nanofluid is examined for platelet, brick, cylinder, and blade shape nanoparticles, along with other thermal parameters. In addition, some recently reported results and flow profiles for Maxwell, second-grade, and viscous fluids are deduced graphically as special cases of the current study.     

Electrical resistivity and flexural strength of mullite-bonded porous SiC: Effect of transition metal additives

The influence of various transition metal (Mo, Co, Ni, and Fe) additives on the electrical and mechanical properties of mullite-strengthened SiC sintered in the range of 1100–1500°C under an Ar atmosphere was investigated. Kaolin was added as a low-temperature sintering aid in the designed formulations. The addition of metal additives reduced the electrical resistivity, enhanced the mechanical strength, and produced samples with an approximately 40% porosity. The decrease in electrical resistivity was attributed to the in situ formation of electrically conductive metal silicides, whereas the conversion of kaolin to mullite enhanced the mechanical properties. The comparative study shows that at the same metal-additive content (15 wt%), Fe was the most effective element to reduce the electrical resistivity and increase the flexural strength, which were recorded as 8.0 × 10⁰ Ω cm and 20 ± 2 MPa, respectively, of the samples sintered at merely 1300°C.     

A novel hybrid requirement’s prioritization approach based on critical software project factors

Cognition, Technology & Work - Requirements prioritization is essential for development of quality software products. Requirements prioritization helps focus on the most important requirements...     

Comparison of the Microstructures and Mechanical Properties in the Overlapping Region of Low Carbon Steel Additive Bead Fabricated by WAAM and FSP

Metallurgical and Materials Transactions A - The periodic structural characteristics of the grains in the overlapping region of the WAAM deposited additive bead translate into spatially dependent...     

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed. In this technique, the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data. Next is the training of a balanced dataset using SVM. Since this algorithm requires an iterative process to search for the best penalty parameter during training, an improved SA algorithm was proposed for this task. In this proposed improvement, a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process. Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM. Registering at an average of 89.65% of accuracy for the binary class classification has demonstrated the good performance of the proposed works.     

Towards efficient human–machine interaction for home energy management with seasonal scheduling using deep fuzzy neural optimizer

Cognition, Technology & Work - Maintaining the records of domestic consumers’ electricity consumption patterns is very complex task for the utilities, especially for extracting the...     

Impact of salicylic acid and calcium chloride on quality attributes of peach stored at refrigeration temperature

Food Science and Biotechnology - Salicylic acid (SA) in different concentration were used to assess its individual effect as well as combined impact with 3% calcium chloride (CaCl2) on Swat No. 8...     

Age and Gender Classification Using Backpropagation andBaggingAlgorithms

Voice classification is important in creating more intelligent systems that help with student exams, identifying criminals, and security systems. The main aim of the research is to develop a system able to predicate and classify gender, age, and accent. So, a new system called Classifying Voice Gender, Age, and Accent (CVGAA) is proposed. Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories. It has high precision compared to other algorithms used in this problem, as the adaptive backpropagation algorithm had an accuracy of 98% and the Bagging algorithm had an accuracy of 98.10% in the gender identification data. Bagging has the best accuracy among all algorithms, with 55.39% accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.     

Aspect Level Songs Rating Based Upon Reviews in English

With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms—Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.