This scientific paper examined the relation between conventional and unconventional monetary policy, with an anticipation to provide a comprehensive assessment of how they behave with the goal of mitigating financial distress, at the state level, influencing global economy. The sampling frame involved five variables related to the US Federal Reserve and five variables related to the European Central Bank, observed during the period 2015-2020 (data for January, April, July and October of each year, regarding every research variable were collected). These variables included: Euro Area Inflation Rate, ECB Bonds Yields, Euro Area Broad Money Supply (M2), Euro Area Unemployment Rate, Debt to GDP; and US Inflation Rate, US Treasury Yields, US Broad Money Supply (M2), US Unemployment Rate, Debt to GDP. Accordingly, two adequate research models were created. Research methodology focused on examining the accuracy of the hypotheses using SmartPLS 3 as a tool for conducting mediation analysis. Research implications suggest that Quantitative Easing caused a significant increase in the Federal Reserve’s and European Central Bank’s balance sheet, especially during the global financial crisis (2007-2008) and during the post-crisis, recovery period. In terms of policy recommendations, monetary authorities need to have policy sets ready in place, in order to know how to behave during and post an economic crisis. This scientific paper will serve as an accurate source of information to future researchers in the field of conventional and unconventional monetary measures, because the work is well systematically organized, clear for interpretation and provides an extensive insight into the Fed’s and ECB’s transmission mechanisms of monetary policy.

Sensory integration is the organization of the senses for their use. It is a neuro-biological activity that allows the reception and processing of sensory information, which in large quantities from the senses come to the brain, at all times. The aim of the study is to examine the prevalence of sensory integration difficulties of the tactile sensory system of children with developmental disabilities, and to establish the statistical significance of differences in relation to the type of difficulty. The total sample of respondents (N=60) consisted of four subsamples of 15 respondents, namely; children with autism, children with Down syndrome, children with hearing impairment and children with sight impairment. The Mann-Whitney U test and the Wilcoxon W test at the level of statistical significance of p<0.05 were used to test the statistical significance of the differences between the subsamples of the subjects. The results of the study show that 86.67% of children with autism, 66.67% of children with Down syndrome, 20% of children with hearing impairment and 40% of children with sight impairment have difficulties with sensory integration of the tactile system. Children with hearing impairment (Rank M 43.97), children with sight impairment (Rank M 35.03), children with Down syndrome, and children with autism (Rank M 14.93) show the least difficulty in sensory integration of the tactile sensory system. There is a statistically significant difference in the difficulties of sensory integration of the tactile sensory system between children with autism, children with Down syndrome, children with hearing impairment and children with sight impairment at the level of statistical significance of p<0.05.

In this paper, we compared the models for selecting the optimal portfolio based on different risk measures to identify the periods in which some of the risk measures dominated over others. For decades, the best known return-risk model has been Markowitz’s mean-variance model. Based on the criticism of the classical Markowitz model, a whole series of risk measures and models for selecting the optimal portfolio have been developed, which are divided into two groups: symmetrical and downside risk measures. Based on the tools provided by game theory, we presented a minimax model for selecting the optimal portfolio based on the maximum loss as a measure of risk. Recent research has shown the adequacy of the application of this risk measure and its dominance concerning variance in certain circumstances. Theoretically, the model based on maximum loss as a measure of risk relies on a much smaller number of assumptions that must be satisfied. In the empirical part of the paper, we analyzed the real return performance, structure, correlation, stability, and predictive efficiency of the model based on maximum loss return as a measure of risk and compared it with the other famous models to determine whether the maximum loss-based risk measure model is more suitable for use in certain circumstances than conventional return-risk models. We compared portfolios created based on different models over the period of 2000–2020 from a selected sample of stocks that are components of the STOXX Europe 600 index, which covers 90% of the free market capitalization in the European capital market. The observed period included 3 bear market periods, including the period of market decline during the COVID-19 crisis. Our analysis showed that there was no significant difference in portfolio returns depending on the selected model using the “buy-and-hold” strategy, but there were crisis periods. The results showed a significantly higher stability of portfolios selected on the criterion of minimizing the maximum loss than others. In periods of market decline, this portfolio achieved the best performance and had a shorter recovery period than others. This allowed superior use of the minimax model at least for investors with a pronounced risk aversion.

ABSTRACT Background Fractures following trauma affect physical and mental health for working-age persons, and the International Classification of Functioning, Disability, and Health (ICF) can help therapists understand the fractures’ impact on daily lives. Purpose To examine self-reported functioning and outcomes six months after upper and lower body fractures and compare limitations using the ICF. Methods Data were collected from 160 patients with fractures as part of a prospective cohort study. The primary outcome measure was the Short Musculoskeletal Function Assessment questionnaire that covers all domains of the ICF. Moreover, sick leave, sense of coherence, and physical activity were reported. Results Six months after the injury, function had improved significantly, but patients reported problems on all domains in the ICF with few differences between the upper and lower body groups. Leisure activities caused problems for 63 (38.8%) of the patients and bothered 86 (53.8%). Problems performing work bothered 63 (39.4%) with no significant difference between the groups, although a significantly higher proportion in the upper body group had returned to work within two months (p < .001). Conclusion Six months after fractures, adults reported problems on all ICF domains, especially on the participation dimension, which therapists should address in the rehabilitation process.

This paper aims to examine the links between university-industry collaboration (UIC) predictors (inputs) and the results of UIC cooperation (outputs). The focus of the research is UIC within the European Union member states and the Western Balkan countries. The analysis was conducted using the partial least squares structural equation modeling (PLS-SEM). This method enabled examining the links between variables that are not directly observable. The authors used data for the period of three years, 2015–2018. The results prove that countries investing in UIC predictors (inputs) have better UIC performance (outputs). Based on the statistical analysis, the authors identified the investments in knowledge, networking, and research and development (R&D), in general, as the most significant that impact UIC performance.

Abstract Background Crimean–Congo haemorrhagic fever (CCHF) is a widespread tick‐borne zoonosis with reported detection of virus and/or virus‐specific antibodies from over 57 countries across Africa, Asia, Europe and the Middle East and is endemic in the Balkans. Detection of Crimean–Congo Haemorrhagic Fever Virus (CCHFV) antibodies in domestic ruminants has been important in providing initial evidence of virus circulation and in localising CCHFV high‐risk spots for human infection. Objectives The present study investigated the possible exposure of sheep to CCHFV in Bosnia and Herzegovina (B&H). Methods To investigate the presence of anti‐CCHFV antibodies in sheep, all sera (n = 176) were tested using multi‐species double antigen enzyme‐linked immunosorbent assay (ELISA). Reactive sera were further complementary tested by adapted commercial indirect immunofluorescence assay (IFA) using FITC‐conjugated protein G instead of anti‐human immunoglobulins. Results CCHFV specific antibodies were detected in 17 (9.66%) animals using ELISA test. All negative sera were determined as negative by both tests, while 13 out of 17 ELISA‐positive reactors were also determined as unambiguously positive by IFA test. The age group with the highest proportion of seropositive rectors were the oldest animals. Conclusions This is the first report of anti‐CCHFV antibodies in sheep from B&H providing the evidence of CCHFV circulation in the country's sheep population. So far, these findings indicate the circulation of the virus in the westernmost region of the Balkans and point to the potential CCHFV spread further out of this endemic area.

The differential ionization rate for strong-field ionization by tailored laser fields of atomic systems averaged over the magnetic quantum number satisfies particular inversion and reflection symmetries. The symmetries of the elliptic-dichroism parameter, which is related to the change of sign of the ellipticity of the laser field, are considered in detail, with particular emphasis on high-order above-threshold ionization. The general results are illustrated by the examples of an elliptically polarized laser field and a bi-elliptical orthogonally polarized two-color (BEOTC) field. For the BEOTC field the differential ionization rate and the elliptic-dichroism parameter are investigated for the ω-2ω and ω-3ω field combinations and for various relative phases between the laser-field components. The inversion and reflection symmetries of the photoelectron momentum distribution in the polarization plane of the field depend on the parities of r and s in the rω--sω BEOTC field combination and on the relative phase between the field components. We suggest that, by analyzing the symmetry properties of the measured momentum distribution of the elliptic-dichroism parameter, one can identify the mechanism of strong-field ionization. If the rescattering mechanism is dominant one can use these distributions to obtain information about the atomic and molecular structure and dynamics.

B‐cell depletion induced by anti‐cluster of differentiation 20 (CD20) monoclonal antibody (mAb) therapy of patients with lymphoma is expected to impair humoral responses to severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) vaccination, but effects on CD8 T‐cell responses are unknown. Here, we investigated humoral and CD8 T‐cell responses following two vaccinations in patients with lymphoma undergoing anti‐CD20‐mAb therapy as single agent or in combination with chemotherapy or other anti‐neoplastic agents during the last 9 months prior to inclusion, and in healthy age‐matched blood donors. Antibody measurements showed that seven of 110 patients had antibodies to the receptor‐binding domain of the SARS‐CoV‐2 Spike protein 3–6 weeks after the second dose of vaccination. Peripheral blood CD8 T‐cell responses against prevalent human leucocyte antigen (HLA) class I SARS‐CoV‐2 epitopes were determined by peptide‐HLA multimer analysis. Strong CD8 T‐cell responses were observed in samples from 20/29 patients (69%) and 12/16 (75%) controls, with similar median response magnitudes in the groups and some of the strongest responses observed in patients. We conclude that despite the absence of humoral immune responses in fully SARS‐CoV‐2‐vaccinated, anti‐CD20‐treated patients with lymphoma, their CD8 T‐cell responses reach similar frequencies and magnitudes as for controls. Patients with lymphoma on B‐cell depleting therapies are thus likely to benefit from current coronavirus disease 2019 (COVID‐19) vaccines, and development of vaccines aimed at eliciting T‐cell responses to non‐Spike epitopes might provide improved protection.

The computing power demands to run artificial neural networks (ANNs) are increasing at rates much greater than improvements made with current CMOS-based technologies. The demand has contributed to a need for novel paradigms, including memristor-based accelerators. This work explores two algorithmic approaches to mitigate non-idealities inherent in most memristor-based systems. The first is to apply a concept of committee machines during inference, and the second is nonideality-aware training of memristor-based ANNs.

Donald Trump's term as President of the United States was marked by, among others: nationalism, populism, rejection of democracy, political arbitrariness, exclusivity towards racial and national minorities, xenophobia expressed towards immigrants, but also close (Mexicans) and distant peoples (Chinese), forcing divisions in American society, misogyny, spreading misinformation in all spheres and especially during the pandemic, belittling the attitude towards intellectuals, and especially constantlycalling out and insulting journalists. Injustice towards media houses and journalists did not stop during the entire term of Donald Trump, who called them 'fake news media' and 'enemy of the people, thus turning the public against them and creating big problems for them, which resulted in open violence during police protests. the assassination of African-American George Floyd, but also in the attack on Capitol Hill. All of these terms, along with some other features of his rule in domestic and foreign policy, are encompassed by a common denominator called ‘trumpism’. Although he was defeated in 2020 presidential election, his legacy remained significantly present in American society, but also outside it - on the American and Asian continents, but also in Europe, and especially in the Balkans. Using qualitative content analysis and comparative analysis of Donald Trump's communication model, especially in his relationship with political opponents, media houses and journalists, and the communication model of Balkan politicians, this paper deals with information and communication matrices that have been very successfully accepted and perfected by some politicians. Balkans through a special review of the spread of 'information disorder' in its three manifestations - misinformation, misinformation, and misinformation.

Abstract: The paper argues that the narrative of the independent Republic of Bosnia and Herzegovina and of its capital city Sarajevo under siege (1992-1995) was built on the trope of Sarajevo’s European, Western-oriented, cosmopolitan cultural identity, based on the image initially nurtured by Socialist Yugoslavia. In the new context of the implosion of the Socialist Federal Republic of Yugoslavia (1945 -1991) the siege of Sarajevo and the war in one of the Yugoslav republics, Bosnia and Herzegovina, Yugoslav socialism was replaced by the multi-ethnic and cosmopolitan character of the young Republic of Bosnia and Herzegovina. I argue that the image of Sarajevo during the siege, as a by-product of foreign attention to the plight of the country and its citizens, was built on the pre-existing premises that promoted Socialist Yugoslavia as Western oriented and therefore progressive, in contrast to other communist countries beyond the Iron Curtain.

Concepts developed in the field of political economy offer a useful framework for explaining, from a western perspective, the phenomenon of Trumpism as an expression of anti-system politics emerging rich democracies in response to the effects of neoliberal growth models and the cartel forms of political parties. While insightful, this theory does not account for the role of media in presumed relationships. The article aims to offer, through exploratory analysis, the theoretical basis for conceptualization of the links between media, anti-system politics, and growth models. Trumpism developed, we argue, under the conditions of destabilization of traditional institutional arrangements of the news business, which enabled and limited the functions of journalism important for the democratization of the „Western“society. The paper contributes to the development of an alternative theoretical approach to the study of the media aspect of Trumpism concerning the prevailing corpus of ideas about "post-truth", "false news" and "echo chambers".

When lexemes are borrowed from a foreign language they go through different phases of integration, typically divided into four types: phonetic, orthographic, morphological, and semantic. The question of which gender to assign to a loanword and which gender assignment criteria to apply is still ambiguous in linguistics. Therefore, the aim of this paper is to determine the regularities of gender assignment to lexicalized and non-lexicalized anglicisms in German within linguistics and glottodidactics. In addition, the present study also investigates the question of whether deviations in gender assignment, in the case of lexicalized anglicisms from our corpus, can be explained by their semantic differences. The corpus for our research is composed of a total of 194 scientific articles, containing a wide range of linguistic topics and issues in the field of glottodidactics. The analysis showed that different criteria are used to assign different genders. Thus, in the case of feminine and neutral nouns, the suffixal analogy prevails, while in the case of masculine nouns, semantic analogy and monosyllabicity play a greater role.

Analysis of reports published by the leading national centers for monitoring wildfires and other emergencies revealed that the devastation caused by wildfires has increased by 2.96-fold when compared to a decade earlier. The reports show that the total number of wildfires is declining; however, their impact on the wildlife appears to be more devastating. In recent years, deep neural network models have demonstrated state-of-the-art accuracy on many computer vision tasks. In this paper, we describe the design and implementation of a lightweight wildfire image classification model (LW-FIRE) based on convolutional neural networks. We explore different ways of using the existing dataset to efficiently train a deep convolutional neural network. We also propose a new method for dataset transformation to increase the number of samples in the dataset and improve the accuracy and generalization of the deep learning model. Experimental results show that the proposed model outperforms the state-of-the-art methods, and is suitable for real-time classification of wildfire images.