Problem koji se istražio odnosi se na ispitivanje stavova vaspitača o saradnji i partnerstvu porodice i predškolske ustanove kao različitim konceptima. Istraživanjem se došlo do podataka i rezultata istraživanja o tome da li je zastupljeno partnerstvo između porodice i predškolske ustanove. Istraživanjem je obuhvaćen uzorak od 120 ispitanika. U sitraživanju su učestvovali ispitanici obje polne strukture, u starosnoj dobi od 22 do 60 godina. Kao instrument u istraživanju korištena je skala stavova petostepenog Likertovog tipa za mjerenje afirmativnosti stavova vaspitača prema partnerstvu u odnosu na saradnju između porodice i predškolske ustanove. Kvantitativna analiza empirijskog istraživanja pokazala je, za uzorak u cjelini, da se u populaciji vaspitača pouzdano može očekivati preferiranje partnerstva u odnosu na saradnju porodice i predškolske ustanove.

PROBLEM The aim of this study was to quantify and compare the distribution of regulatory CD25+ FOXP3+ and activated CD4+ CD25+ T cells in decidua basalis and parietalis of severe and mild pre-eclampsia (PE) to normal healthy pregnancies. METHOD OF STUDY Decidual tissue (decidua basalis and parietalis) of 13 women with mild PE, 15 women with severe PE and 19 women with healthy term pregnancies were analyzed by immunohistochemistry and double immunofluorescence. RESULTS The total number of CD25+ FOXP3+ cells/mm2 in decidua basalis was decreased in the severe and mild PE versus normal pregnancy group. The total number of CD4+ CD25+ cells/mm2 in decidua basalis was decreased in the severe PE versus normal pregnancy group. The number of CD25+ FOXP3+ and CD4+ CD25+ cells in decidua parietalis was decreased in both PE groups. CONCLUSION Our data suggest that immunological changes of PE reflect on decidua basalis and parietalis and emphasize the importance of characterizing T cells in both decidual departments.

U radu autor prikazuje rezultate istražibanja provedenog na uzorku654 učenika iz 7 osnovnih škola grada Tuzle, uzrasta od 13 do 14 godina. Unutar provedenoh istražianja željelo se putem originalnog Testa muzičke kreativnosti – MASKA (muzučka skala kreativnosti), konstruisanog samo za ovu priliku, ispitati i tražiti odgovore da li učenici primijete koliko ih nastava muzičke kulture motiviše za kreativan rad. Pregled rezultata dobijenih multiplom regresijom, ukazuju da na osnovu samo četiri parametra muzičke kreativnosti moguće pojasniti kvalitet nastave muzičke kulture u osnovnoj školi. Dobijeni nalazi pokazuju da je statistički značajna razlika, s obzirom na muzičku kreativnost učenika, samo u odnosu na pol djece, u korist učenika osnovne škole ženskog pola. Ocjena iz muzičke kulture najznačajniji je prediktor motivacije učenika za uživanje u muzici. Značajan je i nalaz koji nas upućuje da dječaci slabije ispoljavaju emocije u odnosu na djevojčice.

Although results of animal research show that interactions between stress and sex hormones are implicated in the development of affective disorders in women, translation of these findings to patients has been scarce. As a basic step toward advancing this field of research, we analyzed findings of studies which reported circulating cortisol levels in healthy women in the follicular vs. luteal phase of the menstrual cycle. We deemed this analysis critical not only to advance our understanding of basic physiology, but also as an important contrast to the findings of future studies evaluating stress and sex hormones in women with affective disorders. We hypothesized that cortisol levels would be lower in the follicular phase based on the proposition that changes in levels of potent GABAergic neurosteroids, including allopregnanolone, during the menstrual cycle dynamically change in the opposite direction relative to cortisol levels. Implementing strict inclusion criteria, we compiled results of high-quality studies involving 778 study participants to derive a standardized mean difference between circulating cortisol levels in the follicular vs. luteal phase of the menstrual cycle. In line with our hypothesis, our meta-analysis found that women in the follicular phase had higher cortisol levels than women in the luteal phase, with an overall Hedges' g of 0.13 (p < 0.01) for the random effects model. No significant between-study difference was detected, with the level of heterogeneity in the small range. Furthermore, there was no evidence of publication bias. As cortisol regulation is a delicate process, we review some of the basic mechanisms by which progesterone, its potent metabolites, and estradiol regulate cortisol output and circulation to contribute to the net effect of higher cortisol in the follicular phase.

Objective: To explain the global between-countries variance in number of deaths per million citizens (nDpm) and case fatality rate (CFR) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Design: Systematic analysis. Data sources: Worldometer, European Centre for Disease Prevention and Control, United Nations Main outcome measures: The explanators of nDpm and CFR were mathematically hypothesised and tested on publicly-available data from 88 countries with linear regression models on May 1st 2020. The derived explanators - age-adjusted infection fatality rate (IFRadj) and case detection rate (CDR) - were estimated for each country based on a SARS-CoV-2 model of China. The accuracy and agreement of the models with observed data was assessed with R2 and Bland-Altman plots, respectively. Sensitivity analyses involved removal of outliers and testing the models at five retrospective and two prospective time points. Results: Globally, IFRadj estimates varied between countries, ranging from below 0.2% in the youngest nations, to above 1.3% in Portugal, Greece, Italy, and Japan. The median estimated global CDR of SARS-CoV-2 infections on April 16th 2020 was 12.9%, suggesting that most of the countries have a much higher number of cases than reported. At least 93% and up to 99% of the variance in nDpm was explained by reported prevalence expressed as cases per million citizens (nCpm), IFRadj, and CDR. IFRadj and CDR accounted for up to 97% of the variance in CFR, but this model was less reliable than the nDpm model, being sensitive to outliers (R2 as low as 67.5%). Conclusions: The current differences in SARS-CoV-2 mortality between countries are driven mainly by reported prevalence of infections, age distribution, and CDR. The nDpm might be a more stable estimate than CFR in comparing mortality burden between countries.

Neural networks are important method of machine learning that can be used to predict air quality with high accuracy. Using NARX-SP neural network type, several neural network models are developed to predict concentration of air pollutants in Sarajevo for two prediction cases, for 24th and 48th hour ahead, with different combinations of inputs and outputs. The data used in this paper contain hourly values of meteorological parameters (air humidity, pressure and temperature, wind speed and direction) and concentrations of SO2, PM10, NO2, O3 and CO from 2016 to 2018. Optimal models are selected for both prediction cases. It is concluded that the optimal models have very good performances and can be used to predict concentration of pollutants in Sarajevo with great accuracy and contribute to improve quality of life. By adequate application of optimal models, concentration of air pollutants can be predicted for each hour over the next 48 hours.

This paper investigates the relationship of information communication technology (ICT) and virtual reality (VR), and tourism, or specifically its interrelations and links to tourism sustainability. As a consumer technology, VR is still a relatively new concept, although it has been researched and used in the tourism industry for marketing purposes. The aim is to understand the different aspects of VR and ICTs and potentially link them to sustainability and perspectives on mass tourism, as well as to the potential future developments related to the ability of ICT and tourism to meet the tourists’ needs to a greater extent in the future. By use of the systematic mapping methodology, the insights into these concepts and their relations to each other are provided. The study reveals the evolution and links between the investigated concepts, the existing challenges and solutions, and the remaining gaps. The present findings indicate that VR as a trend in the tourism industry still needs significant work and improvement until it is ready to fully immerse itself into the tourism sector and especially involve itself into the issues concerning tourism and the potential of sustainability concept within the industry. Many of the concerns and conflicts still exist, but the potential of its right implementation is enormous.

Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. Quantum computing also has the power to support stronger more resistant technologies. In this paper, we describe a digital cash scheme created by Dmitry Gavinsky, which utilises the capability of quantum computing. We contribute by setting out the methods for implementing this scheme. For both the creation and verification of quantum coins we convert the algebraic steps into computing steps. As part of this, we describe the methods used to convert information stored on classical bits to information stored on quantum bits.

Noninvasive contactless methods for electric power line monitoring based on magnetic field measurement have become an interesting topic for researchers and the electric power industry since introduction of the Smart Grid concept. By measuring and analyzing magnetic field originating from currents in power line conductors, it is possible to detect faults in the network. In medium voltage distribution networks, where a variety of different pole geometries are present, different criteria for fault detection have to be employed for each geometry, which complicates detection and influences accuracy. This paper proposes a novel approach for fault detection in medium voltage distribution networks which is based on processing of signals measured by low cost contactless magnetic field sensors. In order to create a generalized method for fault detection, a sequence of mathematical transformations of the measured magnetic field components is applied. A novel geometric transformation which eliminates influence of pole geometry is introduced, providing signals from which steady-state symmetrical components of the rotating magnetic field are computed. Those components are used as general fault detection criteria. The proposed approach is confirmed to be applicable for different fault types by a set of experiments on three-phase overhead power line model scaled to laboratory conditions.

Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments. We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling -action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019). After training agents with these predictive objectives in a visually-rich, 3D environment with an assortment of objects, colors, shapes, and spatial configurations, we probe their internal state representations with synthetic (English) questions, without backpropagating gradients from the question-answering decoder into the agent. The performance of different agents when probed this way reveals that they learn to encode factual, and seemingly compositional, information about objects, properties and spatial relations from their physical environment. Our approach is intuitive, i.e. humans can easily interpret responses of the model as opposed to inspecting continuous vectors, and model-agnostic, i.e. applicable to any modeling approach. By revealing the implicit knowledge of objects, quantities, properties and relations acquired by agents as they learn, question-conditional agent probing can stimulate the design and development of stronger predictive learning objectives.