Bringing deep learning techniques to electromagnetic imaging is of interest considering its great success in various fields. Deep neural nets however are known for being data hungry machines, and in many practical cases, such as electromagnetic medical imaging, there is not enough to feed them. Scarcity of data necessitates reliance on simulations to generate a sufficiently large dataset for deep learning to perform any complicated task. Simulations however, can not perfectly represent real environments and therefore, any neural net trained on simulation data will invariably fail when evaluated on real data. This work customizes a deep domain adaptation technique for matching distributions of complex-valued electromagnetic data. We demonstrate the advantage of using complex-valued models over regular ones. An operational neural network trained on simulation data and adapted to practical data to perform brain injury localization is presented.

We analyzed the efficiency of the insurance industry in Bosnia and Herzegovina (BiH) in the period from 2015 to 2019 in order to identify good and bad practices, sources of inefficiency and to propose guidelines for the necessary efficiency improvements based on the results. Efficiency measurement was performed using the nonparametric Data Envelopment Analysis (DEA) technique as the most commonly used tool for efficiency analysis in finance. We used one output and two input variables according to the input-oriented approach assuming a variable return to scale (VRS). Empirical research was conducted on all insurance companies from BiH, which are grouped according to the size of assets, type of insurance, and headquarters in order to determine whether there are differences in the efficiency of insurance companies in terms of their size, type of insurance, or depending on whether it operates in the Federation of Bosnia and Herzegovina (FBiH) or Republic of Srpska (RS). The results of the analysis indicate significant inefficiencies in the insurance sector in BiH, but also differences among the observed groups. The insurance sector is more efficient in FBiH compared to RS, and insurance companies in the composite insurance market are significantly more efficient than companies in the non-life insurance market. Finally, the research has showed a relatively high level of positive correlation between the size of an insurance company and its efficiency. According to all efficiency indicators, there is significant potential for efficiency improvement. Based on the analysis, the main causes of inefficiency were identified and guidelines for improving efficiency were proposed.

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.

INTRODUCTION Gastric acidity plays an important role in the protection of infants against various pathogens from the environment. The histamine-2 receptor blockers (H2-blockers) are off-labeled drugs that are frequently prescribed in preterm neonates to prevent stress ulcers. The impact of the H2-blockers on the development of the necrotizing enterocolitis (NEC) in preterm infants is still controversial, particularly in the developing world. MATERIALS AND METHODS One hundred twenty-two preterm infants were enrolled in the study. The multivariate logistic regression model was used to identify potential postnatal risk factors associated with NEC. RESULTS Preterm infants (n = 51) with total NEC, medical NEC, and surgical NEC had the highest rate of receiving ranitidine compared with controls (n = 71) (39.2%, 19.6%, and 47.6%, p < 0.05). Logistic regression analysis revealed that ranitidine use and nosocomial infections were significantly associated with NEC development (odds ratios 1.55 and 3.3). CONCLUSIONS We confirm that ranitidine administration was associated with an increased risk of NEC in preterm infants. H2-blockers use should be only administered in very strictly selected cases after careful consideration of the risk-benefit ratio.

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.

Autophagy is a dynamic process, conserved in all eukaryotes. It is responsible for the degradation of cytoplasmic content. Autophagy is crucial in cell survival and cell death. It plays a significant role in the cell response to stress, nutrient deficiencies, embryonic development, tumor suppression, response to pathogens and aging. The process of autophagy is also involved in the pathology of human diseases, such as cancer, diabetes, cardiomyopathy, and neurodegenerative diseases such as Alzheimer's and Parkinson's disease. Autophagy is a mechanism that involves degradation of cells, proteins, damaged organelles and pathogens through the lysosomal mechanisms, thus autophagy supports cell survival during starvation, hypoxia and metabolic stress. However, if extensive and/or excessive, autophagy can promote apoptosis (type I) or function as an alternative cell-death pathway, called autophagic cell death (type II). Autophagy can either promote cancer cell death, or serve as a survival mechanism against apoptosis or necrosis induced by various anticancer treatments. Given the contradictory role of autophagy during tumor initiation and progression, the use of autophagy in therapy depends on the context and must be approached individually