Deployable lightweight structures are studied in the disciplines of architecture, civil engineering, aerospace engineering, mechanical engineering and other fields of their application. The research into these structures in individual fields resulted in a large amount of data sorted out by numerous classifications. The previously made classifications proposed by different authors are presented in this paper by the usage of reasonably unified tables that enabled a direct insight into the essential characteristics of these structures, their analysis and mutual comparison. One of the results of these analyses is the proposal for the unified classification given in a separate table in this paper. The results of interdisciplinary studies have been collected into a unified classification which could be applied for the research in different scientific fields, presenting the basic types of these structures, including individual elements and details with their characteristic features. The proposal of the unified classification of deployable structures is made according to the application of the basic elements used for structure forming. The suggested classification, with the review of the results of the present research, is a significant starting point for the scientists in different disciplines and it provides a detailed insight into the studied characteristics of these structures.

In this paper, a novel method for electric field intensity and magnetic flux density estimation in the vicinity of the high voltage overhead transmission lines is proposed. The proposed method is based on two fully connected feed-forward neural networks to independently estimate electric field intensity and magnetic flux density. The artificial neural networks are trained using the scaled conjugate gradient algorithm. Training datasets corresponds to different overhead transmission line configurations that are generated using an algorithm that is especially developed for this purpose. The target values for the electric field intensity and magnetic flux density datasets are calculated using the charge simulation method and Biot-Savart law based method, respectively. This data is generated for fixed applied voltage and current intensity values. In instances when the applied voltage and current intensity values differ from those used in the artificial neural network training, the electric field intensity and magnetic flux density results are appropriately scaled. In order to verify the validity of the proposed method, a comparative analysis of the proposed method with the charge simulation method for electric field intensity calculation and Biot-Savart law-based method for magnetic flux density calculation is presented. Furthermore, the results of the proposed method are compared to measurement results obtained in the vicinity of two 400 kV transmission lines. The performance analysis results showed that proposed method can produce accurate electric field intensity and magnetic flux density estimation results for different overhead transmission line configurations.

The aim of this chapter is to enable marketing managers to gain basic knowledge of the capabilities of the latest data management technology, big data, which has the potential of digitally storing huge amounts of data, processing and utilizing the results of processing different types of data, as well as data of different formats in real-time. Due to the enormous potential of implementing the big data, there are also tremendous expectations in terms of the direct financial benefits of its implementation. Realizing all these expectations is a very complex task, which is set to marketing and other managers. The knowledge and skills of managers acquired by education will greatly help to understand the benefits of faster adoption and implementation of new data management paradigms. This chapter emphasizes the differences between the big data concept and conventional data processing technologies, as well as the benefits and potentials that this concept offers, especially when it comes to the process of making quick marketing decisions or making decisions in a reasonably short time.

Growth and development are indicators of good health, as well as a mirror of quality nutrition and quality of life. By monitoring growth and development, the relationship between motor abilities and morphological characteristics is recognized. The period of younger school age is characterized by strong growth and development of all anthropological dimensions, especially morphological and motor ones, which differ significantly in children, concerning adults. This transversal research aimed to determine the differences in morphological characteristics and motor abilities between lower primary school students. The research was conducted on a sample of 1233 girls, I - IV grades of primary schools from the area of the City of Tuzla. The sample of variables consisted of a set of 10 tests to assess certain anthropological dimensions (5 morphological and 5 motors), appropriate to the age of the study population. Univariate analysis of variance with multiple comparisons was used to determine the differences in the studied spaces between students of different grades, with the applied post hock Bonferroni test. The obtained results showed that there are statistically significant differences in all applied variables (morphological and motor). between treated groups at the level of significance (p≤0.05). The results of the research draw attention to the importance of early stimulation of morphological-motor development and a healthy lifestyle in female students, which are characterized by regular physical activity and a healthy diet. Optimal levels of motor skills, well-developed fundamental motor skills and a healthy body structure are of great importance for health, quality of life-related to health and overall well-being of younger school-age students.

1Medicinski fakultet, Katedra porodične medicine, Univerzitet u Banjaluci, 2Medicinski fakultet, Katedra porodične medicine, Univerzitet u Sarajevu, 3Medicinski fakultet, Foča, Katedra porodične medicine, Univerzitet u Istočnom Sarajevu, 4Dom zdravlja, Banjaluka, Bosna i Hercegovina Obrazac propisivanja benzodiazepina ambulantnim pacijentima koji nemaju dijagnozu mentalnih bolesti Retrospektivna studija

The idea for establishing “EFMI Inside” Newsletter was born in Lyon in August 2019, during “MEDINFO 2019” Conference and EFMI Council meeting, when Catherine Chronaki, Izet Masic, and some other EFMI Council members discussed and concluded to start with magazine in which we can record important and prompt facts and information about past, current and future activities of European Federation for Medical Informatics (EFMI). EFMI has other types of spreading information about important facts of its activities, as “Reports”, but closely for official Council members, as reports of national and other representatives in EFMI. This publication will be an important and useful resource of EFMI and its activities for everybody who wants to be familiar with Medical informatics development and achievements in all areas of this academic and scientific discipline in European countries, but also, worldwide. In the first issue of of “EFMI Inside” readers can find contributions of influential medical informatics persons, former or current EFMI Council members - Presidents or Chairs of Working Groups, Honorary Fellows, and other EFMI members who were actively involved in the development of Medical informatics in their countries, but also worldwide. A lot of facts “inside” of the ”newsletter” was not available and visible on the way like presented in this issue.