Computational studies on physico-chemical properties in the quality analysis of corn and peanut oil

Oils are commonly used in cooking as a frying medium which has been constantly subjected to different levels of heating. In this work, we have considered the most commonly used oils namely peanut oil and corn oil. Quality analyses of corn and peanut oils were made by relating macroscopic properties (ultrasonic velocity, viscosity, and density) to microscopic parameters (intermolecular free length, adiabatic compressibility etc.,) by subjecting them to six cycles of heating (190 ˚C). Variation in the mentioned property indexes, the degree of degradation and reusability for the next heating cycle that could be used in the food industry and processing were monitored. Using Newton-Laplace and Wood’s equation, the adiabatic compressibility, acoustic impedance, and intermolecular free length of the oil were estimated from the experimental data. Ultrasonic velocity was observed linearly as related to viscosity with the dependency factor (R2 = 0.932). With the aid of experiential data, the physical thermodynamic parameters, particularly particle size, packing factor, chemical potential, and L-J potential were computed. A high correlation factor was observed by fitting ultrasonic velocity, viscosity, and density to Parthasarathy and Bakshi, and Rodenbush equations. In the study, ultrasonic velocity, a macroscopic parameter, could be decoded to determine the microscopic variations in oil subjected to different temperatures in an industrial application.

Design of Portfolio using Multivariate Analysis

Stock markets are considered a barometer of the respective country’s economy around the world. Modern portfolio theory advocates diversification for risk management, which helps maintain returns as long as indices around the world are not perfectly correlated. The relationship exists across markets; as a result, co-movement has drawn the attention of individual investors and portfolio managers for the construction of their portfolios to maximize returns for a given level of risk. The study of co-movements provides inputs for portfolio construction and facilitates the identification of markets where indices may move in the same direction or the opposite direction and the country’s stock markets that are not correlated. A review of the literature revealed that statistical tools like Correlation, Factor analysis, and Granger causality test, etc., are some of the tools that can be used to understand co-movements of markets. Alan harper et al. (2012) study used principle component analysis and inferred that Indian stock returns are aligned with its trading partners and concluded that maximizing the investors’ returns by reducing the risk. Tak Kee Hui concluded that factor analysis provides inputs for selecting foreign markets for risk diversification. This study examines the potential for diversification using 22 world stock market indices using multivariate analysis.

The New Ion-Selective Electrodes Developed for Ferric Cations Determination, Modified with Synthesized Al and Fe−Based Nanoparticles

The solid-state ion-selective electrodes presented here are based on the FePO4:Ag2S:polytetrafluoroethylene (PTFE) = 1:1:2 with an addition of (0.25–1)% microwave-synthesized hematite (α-Fe2O3), magnetite (Fe3O4), boehmite [γ-AlO(OH)], and alumina (Al2O3) nanoparticles (NPs) in order to establish ideal membrane composition for iron(III) cations determination. Synthesized NPs are characterized with Fourier-Transform Infrared (FTIR) spectroscopy, Powder X-Ray Diffraction (PXRD), and Scanning Electron Microscopy (SEM) with Energy Dispersive Spectroscopy (EDS). The iron oxides NPs, more specifically, magnetite and hematite, showed a more positive effect on the sensing properties than boehmite and alumina NPs. The hematite NPs had the most significant effect on the linear range for the determination of ferric cations. The membrane containing 0.25% hematite NPs showed a slope of −19.75 mV per decade in the linear range from 1.2∙10−6 to 10−2 mol L−1, with a correlation factor of 0.9925. The recoveries for the determination of ferric cations in standard solutions were 99.4, 106.7, 93.6, and 101.1% for different concentrations.

Center of Gravity Coordinates Estimation Based on an Overall Brightness Average Determined from the 3D Vision System

In advanced manufacturing technologies (including complex automated processes) and their branches of industry, perception and evaluation of the object parameters are the most critical factors. Many production machines and workplaces are currently equipped as standard with high-quality special sensing devices based on vision systems to detect these parameters. This article focuses on designing a reachable and fully functional vision system based on two standard CCD cameras usage, while the emphasis is on the RS 232C communication interface between two sites (vision and robotic systems). To this, we combine principles of the 1D photogrammetric calibration method from two known points at a stable point field and the available packages inside the processing unit of the vision system (as filtering, enhancing and extracting edges, weak and robust smoothing, etc.). A correlation factor at camera system (for reliable recognition of the sensed object) was set from 84 to 100%. Then, the pilot communication between both systems was proposed and then tested through CREAD/CWRITE commands according to protocol 3964R (used for the data transfer). Moreover, the system was proven by successful transition of the data into the robotic system. Since research gaps in this field still exist and many vision systems are based on PC processing or intelligent cameras, our potential research topic tries to provide the price–performance ratio solution for those who cannot regularly invest in the newest vision technology; however, they could still do so to stay competitive.

Solid Fraction Determination at the Rigidity Point by Advanced Thermal Analysis

The aim of this work is to determine the Solid Fraction (SF) at the rigidity point (FRP) by applying advanced thermal analysis techniques. The variation of the FRP value is important to explain the solidification behavior and the presence or absence of defects in aluminum alloys. As the final alloy composition plays a key role on obtained properties, the influence of major and minor alloying elements on FRP has been studied. A Taguchi design of experiments and a previously developed calculating method, based on the application of high rank derivatives has been employed to determinate first the rigidity point temperature (RPT) and after the corresponding FRP for AlSi10Mg alloys. A correlation factor of r2 of 0.81 was obtained for FRP calculation formula in function of the alloy composition.

Development of haploids in the winter bread wheat anthers

In the modern world, the use of isolated anther cultivation technology is currently an integral part of the wheat breeding process. The development of haploids in the winter bread wheat anthers will allow obtaining new forms of wheat in the shortest possible time and without large areas. The purpose of the current study was to estimate the F3 winter bread wheat hybrids according to the anthers’ sensitivity to androgenesis and plant regeneration in vitro and to identify the factors affecting the yield of haploid production. There has been studied the ability to androgenesis in vitro in the anthers of four winter bread wheat hybrids of intensive and semi-intensive type of the FSBSI “ARC “Donskoy”. There has been assessed the role of the mineral composition of three induction nutrient media N6, W14 and NPB-99. There has been established a correlation between the main stages of development of haploids and a genotype. The highest regeneration rate of green plants was obtained in the sample F3 623 of intensive type (3.3%). The most suitable medium for androgenesis of the winter bread wheat anthers in vitro is NPB-99. Since the genotype F3 623 of intensive type demonstrated high values of haploid production capacity, it could be successfully used in breeding programs for the rapid production of homozygous wheat anther lines in vitro. Using two-way analysis of variance, there has been identified a correlation between the effects of a genotype, nutrient medium and their interaction with the main parameters of haploid formation in winter wheat. The formation of embryogenic structures is mainly associated with the effect of a genotype (46.52%). The proportion of the nutrient composition of the medium was low (1.82%), and the correlation factor was 2.1%. The genotype had the greatest effect on the indicator of the regenerants’ number. The nutrient medium had little effect. Regarding the regeneration of green plants, which is the main indicator of the haploid production, the share of a genotype effect was the largest (47.32%). The contribution of the medium and the correlation of factors were less important, but statistically significant.

Determination of the Thermodynamic Parameters of the Pyrolysis Process of Post-Consumption Thermoplastics by Non-Isothermal Thermogravimetric Analysis

Currently, the pyrolysis process is an important technology for the final treatment of plastic waste worldwide. For this reason, knowing in detail the chemical process and the thermodynamics that accompany cracking reactions is of utmost importance. The present study aims to determine the thermodynamic parameters of the degradation process of conventional thermoplastics (polystyrene (PS), polyethylene terephthalate (PET), high-density polyethylene (HDPE), polypropylene (PP) and polyvinyl chloride (PVC)) from the study of their chemical kinetics by thermogravimetric analysis (TG). Non-isothermal thermogravimetry was performed at three heating rates from room temperature to 550 °C with an inert nitrogen atmosphere with a flow of 20 mL min−1. Once the TG data is obtained, an analysis is carried out with the isoconversional models of Friedman (FR), Kissinger-Akahira-Sunose (KAS), and Flynn-Wall-Ozawa (FWO) in order to determine the one that best fits the experimental data, and with this, the calculation of the activation energy and the pre-exponential factor is performed. The validation of the model was carried out using the correlation factor, determining that the KAS model is the one that best adjusts for the post-consumer thermoplastic degradation process at the three heating rates. With the use of the kinetic parameters, the variation of the Gibbs free energy is determined in each of the cases, where it is necessary that for structures containing aromatic groups a lower energy is presented, which implies a relative ease of degradation compared to the linear structures.

Assessment of Creep Properties Using Small Punch Test for a 9%Cr-Mo-Co-B Power Plant Steel

The present study provides a feasible method to evaluate creep properties for a 9%Cr-Mo-Co-B power plant steel by comparing two sets of data obtained from small punch tests and conventional uniaxial creep tests. The method includes three steps: firstly, conduct a series of small punch tests and conventional creep tests in different load and temperature conditions; secondly, convert the load and central deflection data obtained from the small punch test to stress and strain data; thirdly, determinate the best fit correlation factor by comparing the two sets of data in selected creep models. It is found that two sets of data show a similar trend in stress–rupture time relation, stress–minimum strain rate relation and LMP–stress relation. The correlation factor, ksp, can effectively bridge the gap between the load in small punch test and the stress in conventional creep test. For a high-Cr martensitic heat-resistant steel named as CB2, the ksp value 1.4 can make a good prediction for rupture time, while for minimum creep rate and the Larson–Miller parameter, the ksp value 1.4 will lead a conservative prediction in the low-stress range.

Gesture Classification of Surface Electromyography Signals Using Machine Learning Algorithms for Hand Prosthetics

The actions of humans executed by their hands play a remarkable part in controlling and handling variety of objects in their daily life activities. The effect of losing or degradation in the functioning of one hand has a greater influence in bringing down the regular activity. Hence the design of prosthetic hands which assists the individuals to enhance their regular activity seems a better remedy in this new era. This paper puts forward a classification framework using machine learning algorithms for classifying hand gesture signals. The surface electromyography (sEMG) dataset acquired for 9 wrist movements of publicly available database are utilized to identify the potential biomarkers for classification and in evaluating the efficacy of the proposed algorithm. The statistical and time domain features of the sEMG signals from 27 intact subjects and 11 trans-radial amputated subjects are extracted and the optimal features are determined implementing the feature selection approach based on correlation factor. The classifiers performance of machine learning algorithms namely support vector machine (SVM), Naïve bayes (NB) and Ensemble classifier are evaluated. The experimental results highlight that the SVM classifier can yield the maximum accuracy movement classification of 99.6% for intact and 97.56% for trans-amputee subjects. The proposed approach offers better accuracy and sensitivity compared to other approaches that have used the sEMG dataset for movement classification.

Nursing students' attitudes about their profession

Introduction: Positive attitudes toward the nursing profession among nursing students improve the sustainability of the profession. Studying the attitudes of nursing students toward nursing is of great importance, as it can indicate their remaining in the profession. This study aimed to determine the attitudes of nursing students toward the nursing profession.Methods: This was a cross-sectional, descriptive, analytical study. The research was conducted at the Faculty of Health Studies at the University of Sarajevo from May to June 2019. The study involved a convenience sample of 107 currently enrolled Bachelor of Nursing students from the 1st to the 4th (and final) year of full-time study and part-time students in the Baccalaureate of Nursing Care program at the University of Sarajevo. The students voluntarily and anonymously completed a questionnaire consisting of demographic information and the Nursing Image Questionnaire.Results: Overall, 107 students participated in the research. Their mean age was 23 years old (standard deviation, 5 years). Spearman’s correlation factor shows a statistically significant correlation between the scores and the mode of studying (full-time or part-time) (rho = −0.200*, p = 0.039) and whether respondents working in the profession or not (rho = 0.249*, p = 0.010).Conclusion: The attitudes of future Bachelor of Nursing toward the nursing profession were very positive. Full-time students had more positive attitudes toward the profession, compared with part-time students, although the difference was not statistically significant.