The study was conducted to identify differences in bilateral coordination between boys and girls in the first grade of primary school. The sample consisted of 30 first grade students, at the age of 7 years ± 6 months. The children were divided into two groups consisting of boys (14) and girls (16). The level of the children’s bilateral coordination was assessed using the Bilateral Coordination (7 variables) subtests that are part of the BOT-2 - the Bruininks-Oseretsky Motor Proficiency Test Battery. The tests are used as a standardized measure of the level of motor skills in children. The obtained data was processed in the SPSS 19 statistical programme, and the non-parametric Mann-Whitney U test was used to determine differences in bilateral coordination. After processing the data, it was noted that there are differences in bilateral coordination between boys and girls, but that they were not statistically significant. For more reliable results and, therefore, more conclusive results and conclusions, it is necessary to perform tests on a larger sample of children.

The purpose of this systematic review is to point out the application of the Fitlight trainer system in sports. The following electronic databases were used to search the literature: Google Scholar, PubMed, Medline, Mendeley, 2014-2020. Following the selection procedure according to the relevant criteria, 22 studies were selected as fit to needs of this systematic review. A review of researches that used the Fitlight training system in sports has revealed a rather heterogeneous selection of topics. The results obtained are classified according to the sports in which the research was conducted. The results summary provided us with the insight into the multiple uses of the Fitlight system in sports as a training and measurement tool. It can be used in both individual and team sports. It can test sensory-cognitive and various motor skills: reaction speed, speed of single movement, speed of running, agility. Based on all of the information provided, the possibility of multiple applications of the Fitlight system in sports was highlighted.

The aim of this research was to examine physicochemical parameters of finished and semi-finished meat products: minced meat (beef, pork and mixed meat) and raw formed meat products (ćevapi, patties/burgers and sujuk sausages), and to determine quality parameters of these products on the Bosnia and Herzegovina market. Examined parameters included: protein content, fat, collagen in meat proteins and salt (NaCl). We analysed 282 samples of these products and found deviations with regard to the applicable Ordinance on minced meat and semi-finished and finished meat products of “The Law on Food of Bosnia and Herzegovina” in all parameters, except salt (NaCl). The majority of deviations referred to the content of collagen in meat proteins, with as much as 21.98 % of examined samples in violation of the applicable legislation.

Abstract Simultaneous adsorption of heavy metals in complex multi metal system is insufficiently explored. This research gives results of key process parameters optimization for simultaneous removal of Cd(II), Co(II), Cr(III), Cu(II), Mn(II), Ni(II) and Pb(II) from aqueous solution (batch system). New lemon peel-based biomaterial was prepared and characterized by infrared spectroscopy with Fourier transformation (FTIR), scanning electron microscopy (SEM), electron dispersive spectroscopy (EDS), while the quantification of metals was made by atomic absorption spectrometry (AAS). Simultaneous removal of seven metals ions was favorable at pH 5 with 300 mg/50 mL solid-liquid phase ratio, within 60 min at room temperature with total obtained adsorption capacity of 46.77 mg g−1. Kinetic modeling showed that pseudo-second order kinetic and Weber-Morris diffusion models best describe the adsorption mechanism of all seven heavy metals onto lemon peel.

Intelligent manufacturing plays an important role in Industry 4.0. Key technologies such as artificial intelligence (AI), big data analytics (BDA), the internet of things (IoT), cyber-physical systems (CPSs), and cloud computing enable intelligent manufacturing systems (IMS). Artificial intelligence (AI) plays an essential role in IMS by providing typical features such as learning, reasoning, acting, modeling, intelligent interconnecting, and intelligent decision making. Artificial intelligence's impact on manufacturing is involved in Industry 4.0 through big data analytics, predictive maintenance, data-driven system modeling, control and optimization, human-robot collaboration, and smart machine communication. The recent advances in machine and deep learning algorithms combined with powerful computational hardware have opened new possibilities for technological progress in manufacturing, which led to improving and optimizing any business model.

Improvements in energy efficiency in Industry 4.0 is the main imperative in manufacturing. An important challenge in many fields of complex industrial processes is energy-efficient optimization. The basic idea of the energy efficiency optimization algorithm is to find the optimal assigned value of process parameters to achieve the lowest energy cost and the best working conditions. Applying the multi-objective approach for solving real industry optimization problems is a challenging task. Therefore, this chapter provides an overview of the most significant issues in multi-objective optimization problem. Improving energy efficiency with the multi-objective optimization has opened new opportunities for technological progress in Industry 4.0.