Considering a growing demand for medicinal/cosmetic products with natural actives, this study focuses on the low-energy nanoemulsions (LE-NEs) prepared via the Phase inversion composition (PIC) method at room temperature as potential carriers for natural oil. Four different red raspberry seed oils (ROs) were tested, as follows: cold-pressed vs. CO 2extracted, organic vs. non-organic, refined vs. unrefined. The oil phase was optimized with Tocopheryl acetate and Isostearyl isostearate, while water phase was adjusted with either glycerol or an antioxidant hydro-glycolic extract. This study has used a combined approach to formulation development, employing both conventional methods (pseudo-ternary phase diagram − PTPD, electrical conductivity, particle size measurements, microscopical analysis, and rheological measurements) and the methods novel to this area, such as textural analysis and Raman spectroscopy. Raman spectroscopy has detected fine differences in chemical composition among ROs, and it detected the interactions within nanoemulsions. It was shown that the cold-pressed, unrefined, organic grade oil (RO2) with 6.62% saturated fatty acids and 92.25% unsaturated fatty acids, was optimal for the LE-NEs. Textural analysis confirmed the existence of cubic gel-like phase as a crucial step in the formation of stable RO2-loaded LE-NEs, with droplets in the narrow nano-range (125 to 135 nm; PDI � 0.1). The DPPH test in methanol and ABTS in aqueous medium have revealed a synergistic free radical scavenging effect between lipophilic and hydrophilic antioxidants in LE-NEs. The nanoemulsion carrier has improved the biological effect of raw materials on HeLa cervical adenocarcinoma cells, while exhibiting good safety profile, as confirmed on MRC-5 normal

The analysis of influence of factors that depend on construction characteristics of the vibrosieves with circular vibrations on screening efficiency is presented in this paper. The dependence of the screening efficiency on the aperture size, length and inclination of the screen, as well as on vibration amplitude, is considered. Based on obtained results, one can see that the screening efficiency increases with vibration amplitude and the screen length increase. Further, increases of the screen inclination and aperture size are causing an initial increase of the screening efficiency, which is later decreasing.

Summary Fear of needles can significantly limit professional and social functioning of a person, and is highly prevalent in general population (4%). The aim of our study was to reveal risk factors that are associated with fear of needles among healthy university students of medicine and pharmacy. The study was of a cross-sectional type. In total, 301 students of medicine or pharmacy (82% female and 18% male) attending from 1st to 5th year of study were surveyed at the Faculty of Medical Sciences, University of Kragujevac, Serbia. The students were surveyed using a questionnaires (scales) for assessing the fear of needless, a visual analog scale for self-assessment intensity of the fear of needless, and a general questionnaire with questions about socio-demographic characteristics of the participants. Using a score on the scales as out-come variables, multiple regressions were employed to reveal factors that may influence the fear of needles. Average values of Blood/Injection Fear Scale, Injection Phobia Scale-Anxiety and Medical Avoidance Survey scores were 7.89 ± 9.48, 4.46 ± 5.18 and 89.95 ± 12.73, respectively. The following factors affected significantly the score of the scales: course of study, chronic disease in the family, fear of a dentist, smell of the room phobia, sound phobia, score on the Beck’s anxiety scale and fear of a situation when medical staff give an injection. The presence of chronic disease in the family was a protective factor, while the other six factors were contributing to the fear of needles. Fear of needles is more prevalent among the students of pharmacy than among the students of medicine. It is less frequent among students with chronic disease in their family, while fear of dentist, smell of the room phobia, sound phobia, general anxiety and fear from the situation when medical staff give an injection are all factors that predispose students of medicine or pharmacy to develop fear of needles.

To design a residential or commercial building with high energy performance that would be economical at the same time, an analysis was performed that relates these two aspects of the problem. The first aspect is focused on evaluation of the thermal performance of a multi-layered wall in order to achieve the lowest energy consumption for heating and cooling. The second aspect of the analysis covered the choice of materials (type, thickness and price) so that the building has the lowest possible construction costs, but the best achieved thermal comfort. The three types of external walls with the same structure were analyzed in this paper. The lowest and highest values of the layer thickness offered by the manufacturer were chosen and their dynamic characteristics for the heat transfer were calculated. The following step was to perform optimization of the objective function, which was defined by the unit price of the material per mass of the material, that is, the economical aspect was provided. The genetic algorithm method was used to obtain the optimal thickness of the external wall layers that provided the best dynamic characteristics for the heat transfer in the defined conditions.

Artificial neural networks (ANN) are a powerful tool in the decision-making process, especially in solving the complex problems with a large number of input data. The possibility to predict the work-related injuries in the underground coal mines, based on application of the neural networks, is analyzed in this work. the input data for the network were obtained based on a survey of 1300 respondents. After analyzing the input data influence on the network output, 14 most influential inputs were selected, with help of which the network correctly predicted whether the worker would suffer the work-related injury or not, with 80% precision. The two models were developed, based on the multilayer perceptron (MLP) and radial basis function (RBF) networks. The two models’ results were compared to each other. The sensitivity analysis was used to select the most influential parameters, like mine, age of miners, as well as their work experience. The parameters were further analyzed by use of the descriptive statistics. The selected parameters are direct indicators of problems that can cause injuries. The obtained results point to the fact that the work-related injuries can be successfully predicted by application of the artificial neural networks. The proposed models’ importance is reflected in the clear indicators for enforcing the stricter occupational safety and organizational measures in order to reduce the number of work-related injuries in underground mines.